diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index dfe0770..0000000 --- a/.gitattributes +++ /dev/null @@ -1,2 +0,0 @@ -# Auto detect text files and perform LF normalization -* text=auto diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 0eabc7b..0000000 --- a/.gitignore +++ /dev/null @@ -1,137 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ -test_logs/ -train_logs/ -*.png -*.PNG -*.jpg -*.JPG - -env/ -GUI/ - -wandb/ -train_logs/ -test_logs/ -arcface_ckpt/ -insightface_func/ -parsing_model/ - -*.rar -*.zip -*.tar -*.pth \ No newline at end of file diff --git a/GUI.bat b/GUI.bat deleted file mode 100644 index ad48787..0000000 --- a/GUI.bat +++ /dev/null @@ -1 +0,0 @@ -python GUI.py \ No newline at end of file diff --git a/GUI.py b/GUI.py deleted file mode 100644 index a4ffb86..0000000 --- a/GUI.py +++ /dev/null @@ -1,1125 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: GUI copy 2.py -# Created Date: Wednesday December 22nd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 22nd April 2022 11:23:17 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -from glob import glob -from ipaddress import ip_address -import os -import re -import sys -import time -import json -import tkinter -try: - import paramiko -except: - from pip._internal import main - main(['install', 'paramiko']) - import paramiko - -try: - import pyperclip -except: - from pip._internal import main - main(['install', 'pyperclip']) - import pyperclip - -import threading -import tkinter as tk -import tkinter.ttk as ttk - -import subprocess -from pathlib import Path -from tkinter.filedialog import askopenfilename - - - - -############################################################# -# Predefined functions -############################################################# - -def read_config(path): - with open(path,'r') as cf: - nodelocaltionstr = cf.read() - nodelocaltioninf = json.loads(nodelocaltionstr) - if isinstance(nodelocaltioninf,str): - nodelocaltioninf = json.loads(nodelocaltioninf) - return nodelocaltioninf - -def write_config(path, info): - with open(path, 'w') as cf: - configjson = json.dumps(info, indent=4) - cf.writelines(configjson) - -class fileUploaderClass(object): - def __init__(self,serverIp,userName,passWd,port=22): - self.__ip__ = serverIp - self.__userName__ = userName - self.__passWd__ = passWd - self.__port__ = port - self.__ssh__ = paramiko.SSHClient() - self.__ssh__.set_missing_host_key_policy(paramiko.AutoAddPolicy()) - - def sshScpPut(self,localFile,remoteFile): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - remoteDir = remoteFile.split("/") - if remoteFile[0]=='/': - sftp.chdir('/') - - for item in remoteDir[0:-1]: - if item == "": - continue - try: - sftp.chdir(item) - except: - sftp.mkdir(item) - sftp.chdir(item) - sftp.put(localFile,remoteDir[-1]) - sftp.close() - self.__ssh__.close() - print("[To %s]:%s remotefile:%s success"%(self.__ip__,localFile,remoteFile)) - - def sshScpPuts(self,localFiles,remoteFiles): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - for i_dir in range(len(remoteFiles)): - remoteDir = remoteFiles[i_dir].split("/") - if remoteFiles[i_dir][0]=='/': - sftp.chdir('/') - for item in remoteDir[0:-1]: - if item == "": - continue - try: - sftp.chdir(item) - except: - sftp.mkdir(item) - sftp.chdir(item) - sftp.put(localFiles[i_dir],remoteDir[-1]) - print("[To %s]:%s remotefile:%s success"%(self.__ip__,localFiles[i_dir],remoteFiles[i_dir])) - sftp.close() - self.__ssh__.close() - - def sshExec(self, cmd): - try: - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - _, stdout, _ = self.__ssh__.exec_command(cmd) - results = stdout.read().strip().decode('utf-8') - self.__ssh__.close() - return results - except Exception as e: - print(e) - finally: - self.__ssh__.close() - - def sshScpGetNames(self,remoteDir): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - wocao = sftp.listdir(remoteDir) - # print(wocao.st_mtime) - roots = {} - for item in wocao: - wocao = sftp.stat(remoteDir+"/"+item) - roots[item] = { - "t":wocao.st_mtime, - "p":remoteDir+"/"+item - } - # temp= remoteDir+ "/"+item - # child_dirs = sftp.listdir(temp) - # child_dirs = ["save\\" +item + "\\" + i for i in child_dirs] - # list_name += child_dirs - sftp.close() - self.__ssh__.close() - return roots - - def sshScpGetRNames(self,remoteDir): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - wocao = sftp.listdir(remoteDir) - # print(wocao.st_mtime) - roots = {} - for item in wocao: - wocao = sftp.stat(remoteDir+"/"+item) - roots[item] = { - "t":wocao.st_mtime, - "p":remoteDir+"/"+item - } - # temp= remoteDir+ "/"+item - # child_dirs = sftp.listdir(temp) - # child_dirs = ["save\\" +item + "\\" + i for i in child_dirs] - # list_name += child_dirs - sftp.close() - self.__ssh__.close() - return roots - - def sshScpGetRNamesBySuffix(self, remoteDir, suffix): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - wocao = sftp.listdir(remoteDir) - # print(wocao.st_mtime) - roots = {} - for item in wocao: - wocao = sftp.stat(remoteDir+"/"+item) - roots[item] = { - "t":wocao.st_mtime, - "p":remoteDir+"/"+item - } - # temp= remoteDir+ "/"+item - # child_dirs = sftp.listdir(temp) - # child_dirs = ["save\\" +item + "\\" + i for i in child_dirs] - # list_name += child_dirs - sftp.close() - self.__ssh__.close() - return roots - - def sshScpGet(self, remoteFile, localFile, showProgress=False): - self.__ssh__.connect(self.__ip__, self.__port__, self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - if showProgress: - sftp.get(remoteFile, localFile,callback=self.__putCallBack__) - else: - sftp.get(remoteFile, localFile) - sftp.close() - self.__ssh__.close() - - def sshScpGetFiles(self, remoteFiles, localFiles, showProgress=False): - self.__ssh__.connect(self.__ip__, self.__port__, self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - for i in range(len(remoteFiles)): - if showProgress: - sftp.get(remoteFiles[i], localFiles[i],callback=self.__putCallBack__) - else: - sftp.get(remoteFiles[i], localFiles[i]) - print("Get %s success!"%(remoteFiles[i])) - sftp.close() - self.__ssh__.close() - - def sshScpGetDir(self, remoteDir, localDir, showProgress=False): - self.__ssh__.connect(self.__ip__, self.__port__, self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - files = sftp.listdir(remoteDir) - for i_f in files: - i_remote_file = Path(remoteDir,i_f).as_posix() - local_file = Path(localDir,i_f) - if showProgress: - sftp.get(i_remote_file, local_file,callback=self.__putCallBack__) - else: - sftp.get(i_remote_file, local_file) - sftp.close() - self.__ssh__.close() - - def __putCallBack__(self,transferred,total): - print("current transferred %.1f percent"%(transferred/total*100)) - - def sshScpRename(self, oldpath, newpath): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - sftp.rename(oldpath,newpath) - sftp.close() - self.__ssh__.close() - print("ssh oldpath:%s newpath:%s success"%(oldpath,newpath)) - - def sshScpDelete(self,path): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - sftp.remove(path) - sftp.close() - self.__ssh__.close() - print("ssh delete:%s success"%(path)) - - def sshScpDeleteDir(self,path): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - self.__rm__(sftp,path) - sftp.close() - self.__ssh__.close() - - def __rm__(self,sftp,path): - try: - files = sftp.listdir(path=path) - print(files) - for f in files: - filepath = os.path.join(path, f).replace('\\','/') - self.__rm__(sftp,filepath) - sftp.rmdir(path) - print("ssh delete:%s success"%(path)) - except: - print(path) - sftp.remove(path) - print("ssh delete:%s success"%(path)) - -class TextRedirector(object): - def __init__(self, widget, tag="stdout"): - self.widget = widget - self.tag = tag - - def write(self, str): - self.widget.configure(state="normal") - self.widget.insert("end", str, (self.tag,)) - self.widget.configure(state="disabled") - self.widget.see(tk.END) - - def flush(self): - pass - -############################################################# -# Main Class -############################################################# - -class Application(tk.Frame): - - tab_info = [] - tab_body = None - current_index = 0 - gui_root = "GUI/" - machine_json = gui_root + "machines.json" - filesynlogroot = "file_sync/" - filesynlogroot = gui_root + filesynlogroot - ignore_json = gui_root + "guiignore.json" - machine_list = [] - machine_dict = {} - - ignore_text={ - "white_list":{ - "extension":["py", - "yaml" - ], - "file":[], - "path":[] - }, - "black_list":{ - "extension":[ - "png", - "yaml" - ], - "file":[], - "path":["save/", "GUI/",] - } - } - env_text={ - "train_log_root":"./train_logs", - "test_log_root":"./test_logs", - "systemLog":"./system/system_log.log", - "dataset_paths":{ - "train_dataset_root":"", - "val_dataset_root":"", - "test_dataset_root":"" - }, - "train_config_path":"./train_yamls", - "train_scripts_path":"./train_scripts", - "test_scripts_path":"./test_scripts", - "config_json_name":"model_config.json" - } - machine_text = { - "ip": "localhost", - "user": "username", - "port": 22, - "passwd": "12345678", - "path": ".", - "ckp_path":"save", - "logfilename": "filestate_machine_localhost.json" - } - current_log = {} - current_ckpt = {} - - def __init__(self, master=None): - tk.Frame.__init__(self, master,bg='black') - # self.font_size = 16 - self.font_list = ("Times New Roman",14) - self.padx = 5 - self.pady = 5 - if not Path(self.gui_root).exists(): - Path(self.gui_root).mkdir(parents=True) - self.window_init() - - def __label_text__(self, usr, root): - return "User Name: %s\nWorkspace: %s"%(usr, root) - - def window_init(self): - cwd = os.getcwd() - self.master.title('File Synchronize - %s'%cwd) - # self.master.iconbitmap('./utilities/_logo.ico') - self.master.geometry("{}x{}".format(640, 800)) - font_list = self.font_list - try: - self.machines = read_config(self.machine_json) - except: - self.machine_list = [self.machine_text,] - write_config(self.machine_json,self.machine_list) - # subprocess.call("start %s"%self.machine_json, shell=True) - ################################################################################################# - list_frame = tk.Frame(self.master) - list_frame.pack(fill="both", padx=5,pady=5) - list_frame.columnconfigure(0, weight=1) - list_frame.columnconfigure(1, weight=1) - list_frame.columnconfigure(2, weight=1) - - self.mac_var = tkinter.StringVar() - - self.list_com = ttk.Combobox(list_frame, textvariable=self.mac_var) - self.list_com.grid(row=0,column=0,sticky=tk.EW) - - - open_button = tk.Button(list_frame, text = "Update", - font=font_list, command = self.Machines_Update, bg='#F4A460', fg='#F5F5F5') - open_button.grid(row=0,column=1,sticky=tk.EW) - - open_button = tk.Button(list_frame, text = "Machines", - font=font_list, command = self.MachineConfig, bg='#F4A460', fg='#F5F5F5') - open_button.grid(row=0,column=2,sticky=tk.EW) - - ################################################################################################# - self.mac_text = tk.StringVar() - mac_label = tk.Label(self.master, textvariable=self.mac_text,font=self.font_list,justify="left") - mac_label.pack(fill="both", padx=5,pady=5) - self.mac_text.set(self.list_com.get()) - self.machines_update() - def xFunc(event): - ip = self.list_com.get() - cur_mac = self.machine_dict[ip] - str_temp= self.__label_text__(cur_mac["user"],cur_mac["path"]) - self.mac_text.set(str_temp) - self.update_log_task() - self.update_ckpt_task() - self.list_com.bind("<>",xFunc) - ################################################################################################# - run_frame = tk.Frame(self.master) - run_frame.pack(fill="both", padx=5,pady=5) - run_frame.columnconfigure(0, weight=1) - run_frame.columnconfigure(1, weight=1) - run_test_button = tk.Button(run_frame, text = "Synch Files", - font=font_list, command = self.Synchronize, bg='#006400', fg='#FF0000') - run_test_button.grid(row=0,column=0,sticky=tk.EW) - - open_button = tk.Button(run_frame, text = "Synch To All", - font=font_list, command = self.SynchronizeAll, bg='#F4A460', fg='#F5F5F5') - open_button.grid(row=0,column=1,sticky=tk.EW) - - ################################################################################################# - ssh_frame = tk.Frame(self.master) - ssh_frame.pack(fill="both", padx=5,pady=5) - ssh_frame.columnconfigure(0, weight=1) - ssh_frame.columnconfigure(1, weight=1) - ssh_frame.columnconfigure(2, weight=1) - ssh_button = tk.Button(ssh_frame, text = "Open SSH", - font=font_list, command = self.OpenSSH, bg='#990033', fg='#F5F5F5') - ssh_button.grid(row=0,column=0,sticky=tk.EW) - - ssh_button = tk.Button(ssh_frame, text = "Copy Passwd", - font=font_list, command = self.CopyPasswd, bg='#990033', fg='#F5F5F5') - ssh_button.grid(row=0,column=1,sticky=tk.EW) - - ssh_button = tk.Button(ssh_frame, text = "Pull Log", - font=font_list, command = self.PullLog, bg='#990033', fg='#F5F5F5') - ssh_button.grid(row=0,column=2,sticky=tk.EW) - - ################################################################################################# - config_frame = tk.Frame(self.master) - config_frame.pack(fill="both", padx=5,pady=5) - config_frame.columnconfigure(0, weight=1) - config_frame.columnconfigure(1, weight=1) - config_frame.columnconfigure(2, weight=1) - - cmd_btn = tk.Button(config_frame, text = "Open CMD", - font=font_list, command = self.OpenCMD, bg='#0033FF', fg='#F5F5F5') - cmd_btn.grid(row=0,column=0,sticky=tk.EW) - - cwd_btn = tk.Button(config_frame, text = "CWD", - font=font_list, command = self.CWD, bg='#0033FF', fg='#F5F5F5') - cwd_btn.grid(row=0,column=1,sticky=tk.EW) - - gpu_btn = tk.Button(config_frame, text = "GPU Usage", - font=font_list, command = self.GPUUsage, bg='#0033FF', fg='#F5F5F5') - gpu_btn.grid(row=0,column=2,sticky=tk.EW) - - ################################################################################################ - - config_frame = tk.Frame(self.master) - config_frame.pack(fill="both", padx=5,pady=5) - config_frame.columnconfigure(0, weight=1) - config_frame.columnconfigure(1, weight=1) - config_frame.columnconfigure(2, weight=1) - - machine_btn = tk.Button(config_frame, text = "Ignore Conf", - font=font_list, command = self.IgnoreConfig, bg='#660099', fg='#F5F5F5') - machine_btn.grid(row=0,column=0,sticky=tk.EW) - - machine_btn2 = tk.Button(config_frame, text = "Env Conf", - font=font_list, command = self.EnvConfig, bg='#660099', fg='#F5F5F5') - machine_btn2.grid(row=0,column=1,sticky=tk.EW) - - machine_btn2 = tk.Button(config_frame, text = "Test Conf", - font=font_list, command = self.TestConfig, bg='#660099', fg='#F5F5F5') - machine_btn2.grid(row=0,column=2,sticky=tk.EW) - - ################################################################################################# - log_frame = tk.Frame(self.master) - log_frame.pack(fill="both", padx=5,pady=5) - log_frame.columnconfigure(0, weight=1) - log_frame.columnconfigure(1, weight=1) - log_frame.columnconfigure(2, weight=1) - log_frame.columnconfigure(3, weight=1) - - self.log_var = tkinter.StringVar() - - self.log_com = ttk.Combobox(log_frame, textvariable=self.log_var) - self.log_com.grid(row=0,column=0,sticky=tk.EW) - def select_log(event): - self.update_ckpt_task() - self.log_com.bind("<>",select_log) - - self.test_var = tkinter.StringVar() - - self.test_com = ttk.Combobox(log_frame, textvariable=self.test_var) - self.test_com.grid(row=0,column=1,sticky=tk.EW) - - log_update_button = tk.Button(log_frame, text = "Fresh", - font=font_list, command = self.UpdateLog, bg='#F4A460', fg='#F5F5F5') - log_update_button.grid(row=0,column=2,sticky=tk.EW) - - # log_update_button = tk.Button(log_frame, text = "Pull Log", - # font=font_list, command = self.PullLog, bg='#F4A460', fg='#F5F5F5') - # log_update_button.grid(row=0,column=2,sticky=tk.EW) - - log_update_button = tk.Button(log_frame, text = "Fresh CKPT", - font=font_list, command = self.UpdateCKPT, bg='#F4A460', fg='#F5F5F5') - log_update_button.grid(row=0,column=3,sticky=tk.EW) - - ################################################################################################# - test_frame = tk.Frame(self.master) - test_frame.pack(fill="both", padx=5,pady=5) - test_frame.columnconfigure(0, weight=1) - test_frame.columnconfigure(1, weight=1) - test_frame.columnconfigure(2, weight=1) - # test_frame.columnconfigure(3, weight=1) - - self.testscript_var = tkinter.StringVar() - - self.testscript_com = ttk.Combobox(test_frame, textvariable=self.testscript_var) - self.testscript_com.grid(row=0,column=0,sticky=tk.EW) - - testscript_files = Path("./test_scripts").glob("*.py") - testscript_list = [] - for item in testscript_files: - basename = item.name - basename = os.path.splitext(basename)[0] - testscript_list.append(basename) - self.testscript_com["value"] = testscript_list - - # test_update_button = tk.Button(test_frame, text = "Fresh CKPT", - # font=font_list, command = self.UpdateCKPT, bg='#F4A460', fg='#F5F5F5') - # test_update_button.grid(row=0,column=1,sticky=tk.EW) - - test_update_button = tk.Button(test_frame, text = "Test", - font=font_list, command = self.Test, bg='#F4A460', fg='#F5F5F5') - test_update_button.grid(row=0,column=1,sticky=tk.EW) - - # test_update_button = tk.Button(test_frame, text = "Test Config", - # font=font_list, command = self.TestConfig, bg='#660099', fg='#F5F5F5') - # test_update_button.grid(row=0,column=2,sticky=tk.EW) - - test_update_button = tk.Button(test_frame, text = "Sample", - font=font_list, command = self.OpenSample, bg='#0033FF', fg='#F5F5F5') - test_update_button.grid(row=0,column=2,sticky=tk.EW) - - # ################################################################################################# - - select_frame = tk.Frame(self.master) - select_frame.pack(fill="both", padx=5,pady=5) - - select_frame.columnconfigure(0, weight=2) - select_frame.columnconfigure(1, weight=5) - select_frame.columnconfigure(2, weight=1) - select_frame.columnconfigure(3, weight=2) - select_frame.columnconfigure(4, weight=5) - select_frame.columnconfigure(5, weight=1) - select_frame.columnconfigure(6, weight=3) - - self.preprocess_var = tkinter.StringVar() - - self.preprocess_com = ttk.Combobox(select_frame, textvariable=self.preprocess_var) - self.preprocess_com.grid(row=0,column=6,sticky=tk.EW) - self.preprocess_com["value"] = ["True", "False"] - self.preprocess_com.current(1) - self.ID_path = tkinter.StringVar() - self.ID_path.set("...") - tk.Label(select_frame, text="ID:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - - tk.Entry(select_frame, textvariable= self.ID_path, font=font_list)\ - .grid(row=0,column=1,sticky=tk.EW) - - tk.Button(select_frame, text = "...", font=font_list, - command = self.Select_ID_path, bg='#F4A460', fg='#F5F5F5')\ - .grid(row=0,column=2,sticky=tk.EW) - - - self.Attr_path = tkinter.StringVar() - self.Attr_path.set("...") - tk.Label(select_frame, text="Attr:",font=font_list,justify="left")\ - .grid(row=0,column=3,sticky=tk.EW) - - tk.Entry(select_frame, textvariable= self.Attr_path, font=font_list)\ - .grid(row=0,column=4,sticky=tk.EW) - - tk.Button(select_frame, text = "...", font=font_list, - command = self.Select_Attr_path, bg='#F4A460', fg='#F5F5F5')\ - .grid(row=0,column=5,sticky=tk.EW) - - - # ################################################################################################# - # tbtext_frame = tk.Frame(self.master) - # tbtext_frame.pack(fill="both", padx=5,pady=5) - # tbtext_frame.columnconfigure(0, weight=5) - - # self.tensorlog_str = tk.StringVar() - # tb_text = tk.Entry(tbtext_frame,font=font_list, textvariable=self.tensorlog_str ) - # tb_text.grid(row=0,column=0,sticky=tk.EW) - # config_tb = tk.Button(tbtext_frame, text = "Config", - # font=font_list, command = self.OpenConfig, bg='#003472', fg='#F5F5F5') - # config_tb.grid(row=0,column=1,sticky=tk.EW) - # ################################################################################################# - - - # ################################################################################################# - # tb_frame = tk.Frame(self.master) - # tb_frame.pack(fill="both", padx=5,pady=5) - # tb_frame.columnconfigure(1, weight=1) - # tb_frame.columnconfigure(0, weight=1) - # open_tb = tk.Button(tb_frame, text = "Open Tensorboard", - # font=font_list, command = self.OpenTensorboard, bg='#003472', fg='#F5F5F5') - # open_tb.grid(row=0,column=0,sticky=tk.EW) - # download_tb = tk.Button(tb_frame, text = "Update Tensorboard Logs", - # font=font_list, command = self.DownloadTBLogs, bg='#003472', fg='#F5F5F5') - # download_tb.grid(row=0,column=1,sticky=tk.EW) - - # ################################################################################################# - - text = tk.Text(self.master, wrap="word") - text.pack(fill="both",expand="yes", padx=5,pady=5) - - - sys.stdout = TextRedirector(text, "stdout") - - self.master.protocol("WM_DELETE_WINDOW", self.on_closing) - - - def Select_ID_path(self): - thread_update = threading.Thread(target=self.select_ID_task) - thread_update.start() - - def select_ID_task(self): - path = askopenfilename() - print("Selected ID: %s"%path) - self.ID_path.set(path) - - def Select_Attr_path(self): - thread_update = threading.Thread(target=self.select_Attr_task) - thread_update.start() - - def select_Attr_task(self): - path = askopenfilename() - print("Selected Attibutes: %s"%path) - self.Attr_path.set(path) - - def UpdateCKPT(self): - thread_update = threading.Thread(target=self.update_ckpt_task) - thread_update.start() - - def update_ckpt_task(self): - print("Loading checkpoints..........................") - remotemachine, mac = self.connection() - log = self.log_com.get() - remote_path = os.path.join(mac["path"],mac["ckp_path"], log, "checkpoints").replace("\\", "/") - - if remotemachine == []: - files = Path(remote_path).glob("*/") - first_level = {} - for one_file in files: - first_level[one_file.name] = { - "t":"", - "p":"" - } - else: - first_level = remotemachine.sshScpGetNames(remote_path) - - if len(first_level) == 0: - self.test_com["value"] = [""] - self.test_com.current(0) - print("No checkpoint found!") - return - logs = [] - for k,v in first_level.items(): - logs.append([k,v["t"]]) - # logs = sorted(logs) - logs = sorted(logs, key= lambda logs : logs[1],reverse=True) - self.test_com["value"] =[item[0] for item in logs] - - self.test_com.current(0) - self.current_ckpt = first_level - print("Checkpoints list update success!") - - def CopyPasswd(self): - def copy(): - ip = self.list_com.get() - cur_mac = self.machine_dict[ip] - passwd = cur_mac["passwd"] - pyperclip.copy(passwd) - - thread_update = threading.Thread(target=copy) - thread_update.start() - - def Test(self): - def test_task(): - ip = self.list_com.get() - log = self.log_com.get() - ckpt = self.test_com.get() - script_name = self.testscript_com.get() - id_path = self.ID_path.get() - attr_path = self.Attr_path.get() - preprocess = self.preprocess_com.get() - # if preprocess == "Preprocess-Off": - # preprocess = "off" - # else: - # preprocess = "on" - ckpt = re.sub("\D", "", ckpt) - cwd = os.getcwd() - # files = str(Path(log, ckpt)) - # print(files) - print("start cmd /k \"cd /d %s && conda activate base \ - && python test.py -v %s -s %s -t %s -n %s -i %s -a %s --preprocess %s \""%(cwd, log, ckpt, script_name, ip, id_path, attr_path, preprocess)) - subprocess.check_call("start cmd /k \"cd /d %s && conda activate base \ - && python test.py -v %s -s %s -t %s -n %s --preprocess %s -i %s -a %s\""%(cwd, log, ckpt, script_name, ip, preprocess , id_path, attr_path), shell=True) - thread_update = threading.Thread(target=test_task) - thread_update.start() - - def Machines_Update(self): - # self.update_log_task() - thread_update = threading.Thread(target=self.machines_update) - thread_update.start() - - def machines_update(self): - self.machine_list = read_config(self.machine_json) - print(self.machine_list) - ip_list = [] - for item in self.machine_list: - self.machine_dict[item["ip"]] = item - ip_list.append(item["ip"]) - print(ip_list) - self.list_com["value"] = ip_list - self.list_com.current(0) - ip = self.list_com.get() - cur_mac = self.machine_dict[ip] - str_temp= self.__label_text__(cur_mac["user"],cur_mac["path"]) - self.mac_text.set(str_temp) - print("Machine list update success!") - - def connection(self): - ip = self.list_com.get() - cur_mac = self.machine_dict[ip] - ssh_ip = cur_mac["ip"] - ssh_username = cur_mac["user"] - ssh_passwd = cur_mac["passwd"] - ssh_port = int(cur_mac["port"]) - print("Processing IP: %s."%ssh_ip) - if ip.lower() == "local" or ip.lower() == "localhost": - print("localhost no need to connect!") - return [], cur_mac - remotemachine = fileUploaderClass(ssh_ip,ssh_username,ssh_passwd,ssh_port) - return remotemachine, cur_mac - - def __decode_filestr__(self, filestr): - cells = filestr.split("\n") - print(cells) - - def update_log_task(self): - print("Processing! Do not touch!") - remotemachine,mac = self.connection() - remote_path = os.path.join(mac["path"],mac["ckp_path"]).replace("\\", "/") - if remotemachine == []: - files = Path(remote_path).glob("*/") - first_level = {} - for one_file in files: - first_level[one_file.name] = { - "t":"", - "p":"" - } - # elif remotemachine == "localhost": - - else: - first_level = remotemachine.sshScpGetNames(remote_path) - if len(first_level) == 0: - print("No training log found!") - return - logs = [] - for k,v in first_level.items(): - logs.append([k,v["t"]]) - # logs = sorted(logs) - logs = sorted(logs, key= lambda logs : logs[1],reverse=True) - self.log_com["value"] = [item[0] for item in logs] - self.log_com.current(0) - self.current_log = first_level - self.update_ckpt_task() - print("Done!") - - def UpdateLog(self): - thread_update = threading.Thread(target=self.update_log_task) - thread_update.start() - - def PullLog(self): - def pull_log_task(): - remotemachine,mac = self.connection() - log = self.log_com.get() - remote_path = self.current_log[log]["p"] - if remotemachine == []: - return - all_level = remotemachine.sshScpGetRNames(remote_path) - file_need_download = [] - local_position = [] - local_dir = Path("./",mac["ckp_path"],log) - if not local_dir.exists(): - local_dir.mkdir() - - for k,v in all_level.items(): - local_file = Path("./",mac["ckp_path"],log,k) - if local_file.exists(): - if int(local_file.stat().st_mtime) < v["t"]: - file_need_download.append(v["p"]) - local_position.append(str(local_file)) - # print(int(local_file.stat().st_mtime)) - # print(v["t"]) - else: - file_need_download.append(v["p"]) - local_position.append(str(local_file)) - if len(file_need_download) > 0 : - remotemachine.sshScpGetFiles(file_need_download, local_position) - - else: - print("No file need to pull......") - self.update_ckpt_task() - - thread_update = threading.Thread(target=pull_log_task) - thread_update.start() - - def TestConfig(self): - def test_config_task(): - subprocess.call("start %s"%"test.py", shell=True) - thread_update = threading.Thread(target=test_config_task) - thread_update.start() - - def OpenSample(self): - def open_cmd_task(): - log = self.log_com.get() - cwd = os.getcwd() - sample = os.path.join(cwd,"test_logs",log,"samples") - subprocess.call("explorer "+sample, shell=False) - thread_update = threading.Thread(target=open_cmd_task) - thread_update.start() - - def OpenCMD(self): - def open_cmd_task(): - subprocess.call("start cmd", shell=True) - thread_update = threading.Thread(target=open_cmd_task) - thread_update.start() - - def CWD(self): - def open_cmd_task(): - cwd = os.getcwd() - subprocess.call("explorer "+cwd, shell=False) - thread_update = threading.Thread(target=open_cmd_task) - thread_update.start() - - def OpenSSH(self): - def open_ssh_task(): - ip = self.list_com.get() - if ip.lower() == "local" or ip.lower() == "localhost": - print("localhost no need to connect!") - return - cur_mac = self.machine_dict[ip] - ssh_ip = cur_mac["ip"] - ssh_username = cur_mac["user"] - ssh_passwd = cur_mac["passwd"] - ssh_port = cur_mac["port"] - # subprocess.call("start cmd", shell=True) - subprocess.call("start cmd /k ssh %s@%s -p %s"%(ssh_username, ssh_ip, ssh_port), shell=True) - # subprocess.call("start echo %s"%(ssh_passwd), shell=True) - # p = Popen("cp -rf a/* b/", shell=True, stdout=PIPE, stderr=PIPE) - # proc = subprocess.Popen("ssh %s@%s -p %s"%(ssh_username, ssh_ip, ssh_port), - # stdin=subprocess.PIPE, stdout=subprocess.PIPE, - # stderr=subprocess.PIPE,creationflags =subprocess.CREATE_NEW_CONSOLE) - # # out, err = proc.communicate(ssh_passwd.encode("utf-8")) - # proc.stdin.write(ssh_passwd.encode('utf-8')) - # print(out.decode('utf-8')) - - thread_update = threading.Thread(target=open_ssh_task) - thread_update.start() - - def GPUUsage(self): - def gpu_usage_task(): - remotemachine,_ = self.connection() - if remotemachine == "local" or remotemachine == "localhost": - print("localhost no need to connect!") - return - - results = remotemachine.sshExec("nvidia-smi") - print(results) - - thread_update = threading.Thread(target=gpu_usage_task) - thread_update.start() - - def IgnoreConfig(self): - def ignore_config_task(): - if not os.path.exists(self.ignore_json): - print("guiignore.json file does not exist...") - - if not os.path.exists(self.gui_root): - os.makedirs(self.gui_root) - write_config(self.ignore_json,self.ignore_text) - subprocess.call("start %s"%self.ignore_json, shell=True) - thread_update = threading.Thread(target=ignore_config_task) - thread_update.start() - - def EnvConfig(self): - def env_config_task(): - root_dir = os.getcwd() - logs_dir = os.path.join(root_dir,"env","env.json") - if not os.path.exists(logs_dir): - print("env.json file does not exist...") - - if not os.path.exists(os.path.join(root_dir,"env")): - os.makedirs(os.path.join(root_dir,"env")) - write_config(logs_dir,self.env_text) - subprocess.call("start env/env.json", shell=True) - - thread_update = threading.Thread(target=env_config_task) - thread_update.start() - - def MachineConfig(self): - def machine_config_task(): - subprocess.call("start %s"%self.machine_json, shell=True) - thread_update = threading.Thread(target=machine_config_task) - thread_update.start() - - def OpenConfig(self): - def open_config_task(): - root_dir = os.getcwd() - logs_dir = os.path.join(root_dir,"env","logs_position.json") - if not os.path.exists(logs_dir): - print("logs configuration file does not exist...") - positions={ - "template":{ - "root_path":"./", - "machine_name":"localhost", - } - } - if not os.path.exists(os.path.join(root_dir,"env")): - os.makedirs(os.path.join(root_dir,"env")) - write_config(logs_dir,positions) - subprocess.call("start env/logs_position.json", shell=True) - # time.sleep(5) - # subprocess.call("start http://localhost:6006/", shell=True) - thread_update = threading.Thread(target=open_config_task) - thread_update.start() - - def OpenTensorboard(self): - thread_update = threading.Thread(target=self.open_tensorboard_task) - thread_update.start() - - def open_tensorboard_task(self): - self.download_tblogs() - root_dir = os.getcwd() - logs_dir = os.path.join(root_dir,"train_logs") - subprocess.call("start cmd /k tensorboard --logdir=\"%s\""%(logs_dir), shell=True) - time.sleep(5) - subprocess.call("start http://localhost:6006/", shell=True) - - def DownloadTBLogs(self): - thread_update = threading.Thread(target=self.download_tblogs) - thread_update.start() - - def download_tblogs(self): - tb_monitor_logs = self.tensorlog_str.get() - tb_monitor_logs = tb_monitor_logs.split(";") - root_dir = os.getcwd() - mach_dir = os.path.join(root_dir,"env","machine_config.json") - machines = read_config(mach_dir) - logs_dir = os.path.join(root_dir,"env","logs_position.json") - tb_logs = read_config(logs_dir) - - for i_logs in tb_monitor_logs: - try: - mac_name = tb_logs[i_logs]["machine_name"] - i_mac = machines[mac_name] - i_mac["log_name"] = i_logs - # mac_list.append(i_mac) - remotemachine = fileUploaderClass(i_mac["ip"],i_mac["usrname"],i_mac["passwd"],i_mac["port"]) - path_temp = Path(i_mac["root"],"train_logs",i_logs,"summary").as_posix() - local_dir = Path(root_dir,"train_logs",i_logs,"summary") - if not Path(local_dir).exists(): - Path(local_dir).mkdir(parents=True) - remotemachine.sshScpGetDir(path_temp,local_dir) - print("%s log files download successful!"%i_logs) - except Exception as e: - print(e) - - def Synchronize(self): - def update(): - self.update_action() - thread_update = threading.Thread(target=update) - thread_update.start() - - def SynchronizeAll(self): - def update_all(): - for i_mach in range(len(self.tab_info["configs"])): - self.update_action(i_mach) - thread_update = threading.Thread(target=update_all) - thread_update.start() - - def update_action(self): - last_state = {} - changed_files = [] - - ip = self.list_com.get() - cur_mac = self.machine_dict[ip] - if ip.lower() == "local" or ip.lower() == "localhost": - print("localhost no need to update!") - return - ssh_ip = cur_mac["ip"] - ssh_username = cur_mac["user"] - ssh_passwd = cur_mac["passwd"] - ssh_port = cur_mac["port"] - root_path = cur_mac["path"] - - log_path = os.path.join(self.filesynlogroot,cur_mac["logfilename"]) - - if not Path(self.filesynlogroot).exists(): - Path(self.filesynlogroot).mkdir(parents=True) - else: - if Path(log_path).exists(): - with open(log_path,'r') as cf: - nodelocaltionstr = cf.read() - last_state = json.loads(nodelocaltionstr) - - all_files = [] - # scan files - file_filter = read_config("./GUI/guiignore.json") - - white_list = file_filter["white_list"] - - black_list = file_filter["black_list"] - - white_ext = white_list["extension"] - - black_path = black_list["path"] - - black_file = black_list["file"] - - for item in white_ext: - if item=="": - print("something error in the white list") - continue - files = Path('.').rglob('*.%s'%item) # ./* - for one_file in files: - all_files.append(one_file) - for i_dir in black_path: - files = Path('.', i_dir).rglob('*.%s'%item) - for one_file in files: - # print(one_file) - all_files.remove(one_file) - for item in black_file: - try: - all_files.remove(Path('.', item)) - except: - print("%s does not exist!"%item) - - # check updated files - for item in all_files: - temp = item.stat().st_mtime - if item._str in last_state: - last_mtime = last_state[item._str] - if last_mtime != temp: - changed_files.append(item._str) - last_state[item._str] = temp - else: - changed_files.append(item._str) - last_state[item._str] = temp - - print("[To %s]"%ssh_ip,changed_files) - - localfiles = [] - remotefiles = [] - - for item in changed_files: - localfiles.append(item) - remotefiles.append(Path(root_path,item).as_posix()) - - try: - remotemachine = fileUploaderClass(ssh_ip,ssh_username,ssh_passwd,ssh_port) - remotemachine.sshScpPuts(localfiles,remotefiles) - with open(log_path, 'w') as cf: - configjson = json.dumps(last_state, indent=4) - cf.writelines(configjson) - except Exception as e: - print(e) - print("File Synchronize Failed!") - - # def __save_config__(self): - - # previous_info = read_config(self.log_path) - - # for i in range(len(self.tab_info["names"])): - - # databind = self.tab_info["databind"][i] - - # data_aquire = { - # "name": self.tab_info["names"][i], - # "remote_ip": databind["remote_ip"].get(), - # "remote_user": databind["remote_user"].get(), - # "remote_port": databind["remote_port"].get(), - # "remote_passwd":databind["remote_passwd"].get(), - # "remote_path": databind["remote_path"].get(), - # "logfilename": "filestate_%s.json"%self.tab_info["names"][i] - # } - # if self.tab_info["names"][i] in previous_info["names"]: - # location = previous_info["names"].index(self.tab_info["names"][i]) - # previous_info["configs"][location] = data_aquire - - # else: - # previous_info["names"].append(self.tab_info["names"][i]) - # previous_info["configs"].append(data_aquire) - - # previous_info["databind"] = [] - # write_config(self.log_path,previous_info) - - def on_closing(self): - - # self.__save_config__() - self.master.destroy() - - - -if __name__ == "__main__": - app = Application() - app.mainloop() \ No newline at end of file diff --git a/GUI/file_sync/filestate_machine0.json b/GUI/file_sync/filestate_machine0.json deleted file mode 100644 index cba068f..0000000 --- a/GUI/file_sync/filestate_machine0.json +++ /dev/null @@ -1,307 +0,0 @@ -{ - 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"test_logs/", - "GUI/" - ] - } -} \ No newline at end of file diff --git a/GUI/machines.json b/GUI/machines.json deleted file mode 100644 index 313a9a2..0000000 --- a/GUI/machines.json +++ /dev/null @@ -1,39 +0,0 @@ -[ - { - "ip": "101.33.242.26", - "user": "ubuntu", - "port": 22, - "passwd": "zpKlOW0sMlyt!xhE", - "path": "/home/ubuntu/CXH/simswap_plus", - "ckp_path": "train_logs", - "logfilename": "filestate_machine0.json" - }, - { - "ip": "119.29.91.52", - "user": "ubuntu", - "port": 22, - "passwd": "zpKlOW0sMlyt!xhE", - "path": "/home/ubuntu/CXH/simswap_plus", - "ckp_path": "train_logs", - "logfilename": "filestate_machine3.json" - }, - { - "ip": "2001:da8:8000:6880:f284:d61c:3c76:f9cb", - "user": "ps", - "port": 22, - "passwd": "glass123456", - "path": "/data1/cxh/simswap_plus", - "ckp_path": "train_logs", - "logfilename": "filestate_machine1.json" - } - , - { - "ip": "192.168.4.120", - "user": "gdp", - "port": 22, - "passwd": "glass123456", - "path": "/home/gdp/harddisk/Data2/simswap_plus", - "ckp_path": "train_logs", - "logfilename": "filestate_machine2.json" - } -] \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 2a4748d..0000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 chenxuanhong - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/README.md b/README.md deleted file mode 100644 index 52d0739..0000000 --- a/README.md +++ /dev/null @@ -1,33 +0,0 @@ -# Simswap++ - -## Dependencies -- moviepy -- python >= 3.7 -- yaml (pip install pyyaml) -- paramiko (For ssh file transportation) -- pytorch >= 1.9 -- pillow -- torchvision -- opencv -- matplotlib -- timm -- cupy (for involution) you need to create a new env in anaconda (conda install pytorch==1.10.1 cudatoolkit==10.2.89 cupy==10.1.0 -c pytorch -c conda-forge) - -## logger - -- wandb (pip install wandb) - -***OR*** - -- Do not need to install tensorboard and tensorboardX any more. - -***Logger is an option setting, which can be adjust with train.py --logger [wandb, tensorbaord, None]*** - -## Usage -- To configure the project in the ```main.py```. - - -## Acknowledgement - -## Related Projects -Learn about our other projects [[RainNet]](https://neuralchen.github.io/RainNet), [[Sketch Generation]](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale), [[CooGAN]](https://github.com/neuralchen/CooGAN), [[Knowledge Style Transfer]](https://github.com/AceSix/Knowledge_Transfer), [[Youtube downloader]](https://github.com/AIARTSJTU/YoutubeDataCollector). \ No newline at end of file diff --git a/annotation.py b/annotation.py deleted file mode 100644 index 81b0c9d..0000000 --- a/annotation.py +++ /dev/null @@ -1,132 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: annotation.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 30th March 2022 11:36:20 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import cv2 -import os -import glob -import json - -import argparse - -keytable={ - "left":2424832, - "right":2555904, - "up":2490368, - "down":2621440, - "esc":27, - "space":32 -} - -def str2bool(v): - return v.lower() in ('true') - -def getParameters(): - parser = argparse.ArgumentParser() - # general - parser.add_argument('--image_dir', type=str, default="G:/VGGFace2-HQ/VGGface2_None_norm_512_true_bygfpgan") - parser.add_argument('--savetxt', type=str, default="./check_list.txt") - parser.add_argument('--winWidth', type=int, default=512) - parser.add_argument('--winHeight', type=int, default=512) - return parser.parse_args() - -if __name__ == "__main__": - config = getParameters() - savePath = config.savetxt - - log_txt = "./breakpoint.json" - - - temp_path = os.path.join(config.image_dir,'*/') - pathes = glob.glob(temp_path) - dataset = [] - for dir_item in pathes: - join_path = glob.glob(os.path.join(dir_item,'*.jpg')) - print("processing %s"%dir_item,end='\r') - temp_list = [] - for item in join_path: - temp_list.append(item) - dataset.append(temp_list) - cv2.namedWindow("Annatation",0) - cv2.resizeWindow("Annatation", config.winWidth, config.winHeight) - - font = cv2.FONT_HERSHEY_SIMPLEX - try: - with open(log_txt,'r') as cf: - breakpoint_json = cf.read() - breakpoint_json = json.loads(breakpoint_json) - if isinstance(breakpoint_json,str): - breakpoint_json = json.loads(breakpoint_json) - except: - breakpoint_json = {"breakpoint":[0,0]} - save_point = breakpoint_json["breakpoint"] - - - total_dir = len(dataset) - dir_pointer = save_point[0] - indir_pointer = save_point[1] - - - while(1): - try: - img = cv2.imread(os.path.join(config.image_dir,dataset[dir_pointer][indir_pointer])) - imgshow = cv2.putText(img, "[%d]/[%d]-[%d]/[%d]"%(dir_pointer+1,total_dir, - indir_pointer+1,len(dataset[dir_pointer])), (0, 30), font, 0.4, (255, 255, 255), 1) - imgshow = cv2.putText(imgshow, "%s"%(dataset[dir_pointer][indir_pointer][-19:]), (0, 60), font, 0.4, (255, 255, 255), 1) - cv2.imshow('Annatation',imgshow) - waitkey_num = cv2.waitKeyEx(20) - # if waitkey_num != -1: - # print(waitkey_num) - if waitkey_num == keytable["left"]: - # print("Left") - indir_pointer -= 1 - if indir_pointer<0: - indir_pointer = 0 - - if waitkey_num == keytable["right"]: - # print("Right") - indir_pointer += 1 - if indir_pointer>= len(dataset[dir_pointer]): - indir_pointer = len(dataset[dir_pointer])-1 - - if waitkey_num == keytable["up"]: - # print("Left") - dir_pointer -= 1 - indir_pointer = 0 - if dir_pointer<0: - dir_pointer = 0 - - if waitkey_num == keytable["down"]: - # print("Right") - dir_pointer += 1 - indir_pointer = 0 - if dir_pointer>= total_dir: - dir_pointer = total_dir-1 - - if waitkey_num == keytable["space"]: - image_name_cur = dataset[dir_pointer][indir_pointer][-19:] - print("Save image name %s"%image_name_cur) - with open(savePath,'a+') as logf: - logf.writelines("%s\n"%(image_name_cur)) - if waitkey_num == keytable["esc"]: - breakpoint_json = {"breakpoint":[dir_pointer,indir_pointer]} - with open(log_txt, 'w') as cf: - configjson = json.dumps(breakpoint_json, indent=4) - cf.writelines(configjson) - break - except KeyboardInterrupt: - breakpoint_json = {"breakpoint":[dir_pointer,indir_pointer]} - with open(log_txt, 'w') as cf: - configjson = json.dumps(breakpoint_json, indent=4) - cf.writelines(configjson) - break - cv2.destroyAllWindows() \ No newline at end of file diff --git a/arcface_torch/.gitignore b/arcface_torch/.gitignore deleted file mode 100644 index e3b9c68..0000000 --- a/arcface_torch/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -**__pycache__/ -.vscode -bak*/ -work_dirs/ -models/ \ No newline at end of file diff --git a/arcface_torch/README.md b/arcface_torch/README.md deleted file mode 100644 index efe5c21..0000000 --- a/arcface_torch/README.md +++ /dev/null @@ -1,136 +0,0 @@ -# Distributed Arcface Training in Pytorch - -This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millions -identity on a single server. - -## Requirements - -- Install [PyTorch](http://pytorch.org) (torch>=1.6.0), our doc for [install.md](docs/install.md). -- (Optional) Install [DALI](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/), our doc for [install_dali.md](docs/install_dali.md). -- `pip install -r requirement.txt`. - -## How to Training - -To train a model, run `train.py` with the path to the configs. -The example commands below show how to run -distributed training. - -### 1. To run on a machine with 8 GPUs: - -```shell -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12581 train.py configs/ms1mv3_r50_lr02 -``` - -### 2. To run on 2 machines with 8 GPUs each: - -Node 0: - -```shell -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=12581 train.py configs/webface42m_r100_lr01_pfc02_bs4k_16gpus -``` - -Node 1: - -```shell -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=12581 train.py configs/webface42m_r100_lr01_pfc02_bs4k_16gpus -``` - -## Download Datasets or Prepare Datasets - -- [MS1MV3](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-retinaface) (93k IDs, 5.2M images) -- [Glint360K](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc#4-download) (360k IDs, 17.1M images) -- [WebFace42M](docs/prepare_webface42m.md) (2M IDs, 42.5M images) - -## Model Zoo - -- The models are available for non-commercial research purposes only. -- All models can be found in here. -- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g): e8pw -- [OneDrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d) - -### Performance on IJB-C and [**ICCV2021-MFR**](https://github.com/deepinsight/insightface/blob/master/challenges/mfr/README.md) - -ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face -recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities. -As the result, we can evaluate the FAIR performance for different algorithms. - -For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The -globalised multi-racial testset contains 242,143 identities and 1,624,305 images. - - - -| Datasets | Backbone | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | Training Throughout | log | -|:-------------------------|:-----------|:------------|:------------|:------------|:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| MS1MV3 | mobileface | 65.76 | 94.44 | 91.85 | ~13000 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_mobileface_lr02/training.log)\|[config](configs/ms1mv3_mobileface_lr02.py) | -| Glint360K | mobileface | 69.83 | 95.17 | 92.58 | -11000 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_mobileface_lr02_bs4k/training.log)\|[config](configs/glint360k_mobileface_lr02_bs4k.py) | -| WebFace42M-PartialFC-0.2 | mobileface | 73.80 | 95.40 | 92.64 | (16GPUs)~18583 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_mobilefacenet_pfc02_bs8k_16gpus/training.log)\|[config](configs/webface42m_mobilefacenet_pfc02_bs8k_16gpus.py) | -| MS1MV3 | r100 | 83.23 | 96.88 | 95.31 | ~3400 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_r100_lr02/training.log)\|[config](configs/ms1mv3_r100_lr02.py) | -| Glint360K | r100 | 90.86 | 97.53 | 96.43 | ~5000 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_r100_lr02_bs4k_16gpus/training.log)\|[config](configs/glint360k_r100_lr02_bs4k_16gpus.py) | -| WebFace42M-PartialFC-0.2 | r50(bs4k) | 93.83 | 97.53 | 96.16 | (8 GPUs)~5900 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_bs4k_pfc02/training.log)\|[config](configs/webface42m_r50_lr01_pfc02_bs4k_8gpus.py) | -| WebFace42M-PartialFC-0.2 | r50(bs8k) | 93.96 | 97.46 | 96.12 | (16GPUs)~11000 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_lr01_pfc02_bs8k_16gpus/training.log)\|[config](configs/webface42m_r50_lr01_pfc02_bs8k_16gpus.py) | -| WebFace42M-PartialFC-0.2 | r50(bs4k) | 94.04 | 97.48 | 95.94 | (32GPUs)~17000 | log\|[config](configs/webface42m_r50_lr01_pfc02_bs4k_32gpus.py) | -| WebFace42M-PartialFC-0.2 | r100(bs4k) | 96.69 | 97.85 | 96.63 | (16GPUs)~5200 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r100_bs4k_pfc02/training.log)\|[config](configs/webface42m_r100_lr01_pfc02_bs4k_16gpus.py) | -| WebFace42M-PartialFC-0.2 | r200 | - | - | - | - | log\|config | - -`PartialFC-0.2` means negivate class centers sample rate is 0.2. - - -## Speed Benchmark - -`arcface_torch` can train large-scale face recognition training set efficiently and quickly. When the number of -classes in training sets is greater than 1 Million, partial fc sampling strategy will get same -accuracy with several times faster training performance and smaller GPU memory. -Partial FC is a sparse variant of the model parallel architecture for large sacle face recognition. Partial FC use a -sparse softmax, where each batch dynamicly sample a subset of class centers for training. In each iteration, only a -sparse part of the parameters will be updated, which can reduce a lot of GPU memory and calculations. With Partial FC, -we can scale trainset of 29 millions identities, the largest to date. Partial FC also supports multi-machine distributed -training and mixed precision training. - -![Image text](https://github.com/anxiangsir/insightface_arcface_log/blob/master/partial_fc_v2.png) - -More details see -[speed_benchmark.md](docs/speed_benchmark.md) in docs. - -### 1. Training speed of different parallel methods (samples / second), Tesla V100 32GB * 8. (Larger is better) - -`-` means training failed because of gpu memory limitations. - -| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 | -|:--------------------------------|:--------------|:---------------|:---------------| -| 125000 | 4681 | 4824 | 5004 | -| 1400000 | **1672** | 3043 | 4738 | -| 5500000 | **-** | **1389** | 3975 | -| 8000000 | **-** | **-** | 3565 | -| 16000000 | **-** | **-** | 2679 | -| 29000000 | **-** | **-** | **1855** | - -### 2. GPU memory cost of different parallel methods (MB per GPU), Tesla V100 32GB * 8. (Smaller is better) - -| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 | -|:--------------------------------|:--------------|:---------------|:---------------| -| 125000 | 7358 | 5306 | 4868 | -| 1400000 | 32252 | 11178 | 6056 | -| 5500000 | **-** | 32188 | 9854 | -| 8000000 | **-** | **-** | 12310 | -| 16000000 | **-** | **-** | 19950 | -| 29000000 | **-** | **-** | 32324 | - - -## Citations - -``` -@inproceedings{deng2019arcface, - title={Arcface: Additive angular margin loss for deep face recognition}, - author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos}, - booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, - pages={4690--4699}, - year={2019} -} -@inproceedings{an2020partical_fc, - title={Partial FC: Training 10 Million Identities on a Single Machine}, - author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and - Zhang, Debing and Fu Ying}, - booktitle={Arxiv 2010.05222}, - year={2020} -} -``` diff --git a/arcface_torch/backbones/__init__.py b/arcface_torch/backbones/__init__.py deleted file mode 100644 index 55bd4c5..0000000 --- a/arcface_torch/backbones/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -from .iresnet import iresnet18, iresnet34, iresnet50, iresnet100, iresnet200 -from .mobilefacenet import get_mbf - - -def get_model(name, **kwargs): - # resnet - if name == "r18": - return iresnet18(False, **kwargs) - elif name == "r34": - return iresnet34(False, **kwargs) - elif name == "r50": - return iresnet50(False, **kwargs) - elif name == "r100": - return iresnet100(False, **kwargs) - elif name == "r200": - return iresnet200(False, **kwargs) - elif name == "r2060": - from .iresnet2060 import iresnet2060 - return iresnet2060(False, **kwargs) - elif name == "mbf": - fp16 = kwargs.get("fp16", False) - num_features = kwargs.get("num_features", 512) - return get_mbf(fp16=fp16, num_features=num_features) - else: - raise ValueError() \ No newline at end of file diff --git a/arcface_torch/backbones/iresnet.py b/arcface_torch/backbones/iresnet.py deleted file mode 100644 index ebd6075..0000000 --- a/arcface_torch/backbones/iresnet.py +++ /dev/null @@ -1,186 +0,0 @@ -import torch -from torch import nn - -__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200'] - - -def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, - out_planes, - kernel_size=3, - stride=stride, - padding=dilation, - groups=groups, - bias=False, - dilation=dilation) - - -def conv1x1(in_planes, out_planes, stride=1): - """1x1 convolution""" - return nn.Conv2d(in_planes, - out_planes, - kernel_size=1, - stride=stride, - bias=False) - - -class IBasicBlock(nn.Module): - expansion = 1 - def __init__(self, inplanes, planes, stride=1, downsample=None, - groups=1, base_width=64, dilation=1): - super(IBasicBlock, self).__init__() - if groups != 1 or base_width != 64: - raise ValueError('BasicBlock only supports groups=1 and base_width=64') - if dilation > 1: - raise NotImplementedError("Dilation > 1 not supported in BasicBlock") - self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,) - self.conv1 = conv3x3(inplanes, planes) - self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,) - self.prelu = nn.PReLU(planes) - self.conv2 = conv3x3(planes, planes, stride) - self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - identity = x - out = self.bn1(x) - out = self.conv1(out) - out = self.bn2(out) - out = self.prelu(out) - out = self.conv2(out) - out = self.bn3(out) - if self.downsample is not None: - identity = self.downsample(x) - out += identity - return out - - -class IResNet(nn.Module): - fc_scale = 7 * 7 - def __init__(self, - block, layers, dropout=0, num_features=512, zero_init_residual=False, - groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False): - super(IResNet, self).__init__() - self.fp16 = fp16 - self.inplanes = 64 - self.dilation = 1 - if replace_stride_with_dilation is None: - replace_stride_with_dilation = [False, False, False] - if len(replace_stride_with_dilation) != 3: - raise ValueError("replace_stride_with_dilation should be None " - "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) - self.groups = groups - self.base_width = width_per_group - self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05) - self.prelu = nn.PReLU(self.inplanes) - self.layer1 = self._make_layer(block, 64, layers[0], stride=2) - self.layer2 = self._make_layer(block, - 128, - layers[1], - stride=2, - dilate=replace_stride_with_dilation[0]) - self.layer3 = self._make_layer(block, - 256, - layers[2], - stride=2, - dilate=replace_stride_with_dilation[1]) - self.layer4 = self._make_layer(block, - 512, - layers[3], - stride=2, - dilate=replace_stride_with_dilation[2]) - self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,) - self.dropout = nn.Dropout(p=dropout, inplace=True) - self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features) - self.features = nn.BatchNorm1d(num_features, eps=1e-05) - nn.init.constant_(self.features.weight, 1.0) - self.features.weight.requires_grad = False - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.normal_(m.weight, 0, 0.1) - elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - - if zero_init_residual: - for m in self.modules(): - if isinstance(m, IBasicBlock): - nn.init.constant_(m.bn2.weight, 0) - - def _make_layer(self, block, planes, blocks, stride=1, dilate=False): - downsample = None - previous_dilation = self.dilation - if dilate: - self.dilation *= stride - stride = 1 - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - conv1x1(self.inplanes, planes * block.expansion, stride), - nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ), - ) - layers = [] - layers.append( - block(self.inplanes, planes, stride, downsample, self.groups, - self.base_width, previous_dilation)) - self.inplanes = planes * block.expansion - for _ in range(1, blocks): - layers.append( - block(self.inplanes, - planes, - groups=self.groups, - base_width=self.base_width, - dilation=self.dilation)) - - return nn.Sequential(*layers) - - def forward(self, x): - with torch.cuda.amp.autocast(self.fp16): - x = self.conv1(x) - x = self.bn1(x) - x = self.prelu(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.bn2(x) - x = torch.flatten(x, 1) - x = self.dropout(x) - x = self.fc(x.float() if self.fp16 else x) - x = self.features(x) - return x - - -def _iresnet(arch, block, layers, pretrained, progress, **kwargs): - model = IResNet(block, layers, **kwargs) - if pretrained: - raise ValueError() - return model - - -def iresnet18(pretrained=False, progress=True, **kwargs): - return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained, - progress, **kwargs) - - -def iresnet34(pretrained=False, progress=True, **kwargs): - return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained, - progress, **kwargs) - - -def iresnet50(pretrained=False, progress=True, **kwargs): - return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained, - progress, **kwargs) - - -def iresnet100(pretrained=False, progress=True, **kwargs): - return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained, - progress, **kwargs) - - -def iresnet200(pretrained=False, progress=True, **kwargs): - return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained, - progress, **kwargs) diff --git a/arcface_torch/backbones/iresnet2060.py b/arcface_torch/backbones/iresnet2060.py deleted file mode 100644 index 21d1122..0000000 --- a/arcface_torch/backbones/iresnet2060.py +++ /dev/null @@ -1,176 +0,0 @@ -import torch -from torch import nn - -assert torch.__version__ >= "1.8.1" -from torch.utils.checkpoint import checkpoint_sequential - -__all__ = ['iresnet2060'] - - -def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, - out_planes, - kernel_size=3, - stride=stride, - padding=dilation, - groups=groups, - bias=False, - dilation=dilation) - - -def conv1x1(in_planes, out_planes, stride=1): - """1x1 convolution""" - return nn.Conv2d(in_planes, - out_planes, - kernel_size=1, - stride=stride, - bias=False) - - -class IBasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None, - groups=1, base_width=64, dilation=1): - super(IBasicBlock, self).__init__() - if groups != 1 or base_width != 64: - raise ValueError('BasicBlock only supports groups=1 and base_width=64') - if dilation > 1: - raise NotImplementedError("Dilation > 1 not supported in BasicBlock") - self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05, ) - self.conv1 = conv3x3(inplanes, planes) - self.bn2 = nn.BatchNorm2d(planes, eps=1e-05, ) - self.prelu = nn.PReLU(planes) - self.conv2 = conv3x3(planes, planes, stride) - self.bn3 = nn.BatchNorm2d(planes, eps=1e-05, ) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - identity = x - out = self.bn1(x) - out = self.conv1(out) - out = self.bn2(out) - out = self.prelu(out) - out = self.conv2(out) - out = self.bn3(out) - if self.downsample is not None: - identity = self.downsample(x) - out += identity - return out - - -class IResNet(nn.Module): - fc_scale = 7 * 7 - - def __init__(self, - block, layers, dropout=0, num_features=512, zero_init_residual=False, - groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False): - super(IResNet, self).__init__() - self.fp16 = fp16 - self.inplanes = 64 - self.dilation = 1 - if replace_stride_with_dilation is None: - replace_stride_with_dilation = [False, False, False] - if len(replace_stride_with_dilation) != 3: - raise ValueError("replace_stride_with_dilation should be None " - "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) - self.groups = groups - self.base_width = width_per_group - self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05) - self.prelu = nn.PReLU(self.inplanes) - self.layer1 = self._make_layer(block, 64, layers[0], stride=2) - self.layer2 = self._make_layer(block, - 128, - layers[1], - stride=2, - dilate=replace_stride_with_dilation[0]) - self.layer3 = self._make_layer(block, - 256, - layers[2], - stride=2, - dilate=replace_stride_with_dilation[1]) - self.layer4 = self._make_layer(block, - 512, - layers[3], - stride=2, - dilate=replace_stride_with_dilation[2]) - self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05, ) - self.dropout = nn.Dropout(p=dropout, inplace=True) - self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features) - self.features = nn.BatchNorm1d(num_features, eps=1e-05) - nn.init.constant_(self.features.weight, 1.0) - self.features.weight.requires_grad = False - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.normal_(m.weight, 0, 0.1) - elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - - if zero_init_residual: - for m in self.modules(): - if isinstance(m, IBasicBlock): - nn.init.constant_(m.bn2.weight, 0) - - def _make_layer(self, block, planes, blocks, stride=1, dilate=False): - downsample = None - previous_dilation = self.dilation - if dilate: - self.dilation *= stride - stride = 1 - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - conv1x1(self.inplanes, planes * block.expansion, stride), - nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ), - ) - layers = [] - layers.append( - block(self.inplanes, planes, stride, downsample, self.groups, - self.base_width, previous_dilation)) - self.inplanes = planes * block.expansion - for _ in range(1, blocks): - layers.append( - block(self.inplanes, - planes, - groups=self.groups, - base_width=self.base_width, - dilation=self.dilation)) - - return nn.Sequential(*layers) - - def checkpoint(self, func, num_seg, x): - if self.training: - return checkpoint_sequential(func, num_seg, x) - else: - return func(x) - - def forward(self, x): - with torch.cuda.amp.autocast(self.fp16): - x = self.conv1(x) - x = self.bn1(x) - x = self.prelu(x) - x = self.layer1(x) - x = self.checkpoint(self.layer2, 20, x) - x = self.checkpoint(self.layer3, 100, x) - x = self.layer4(x) - x = self.bn2(x) - x = torch.flatten(x, 1) - x = self.dropout(x) - x = self.fc(x.float() if self.fp16 else x) - x = self.features(x) - return x - - -def _iresnet(arch, block, layers, pretrained, progress, **kwargs): - model = IResNet(block, layers, **kwargs) - if pretrained: - raise ValueError() - return model - - -def iresnet2060(pretrained=False, progress=True, **kwargs): - return _iresnet('iresnet2060', IBasicBlock, [3, 128, 1024 - 128, 3], pretrained, progress, **kwargs) diff --git a/arcface_torch/backbones/mobilefacenet.py b/arcface_torch/backbones/mobilefacenet.py deleted file mode 100644 index 8773149..0000000 --- a/arcface_torch/backbones/mobilefacenet.py +++ /dev/null @@ -1,130 +0,0 @@ -''' -Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py -Original author cavalleria -''' - -import torch.nn as nn -from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module -import torch - - -class Flatten(Module): - def forward(self, x): - return x.view(x.size(0), -1) - - -class ConvBlock(Module): - def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): - super(ConvBlock, self).__init__() - self.layers = nn.Sequential( - Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False), - BatchNorm2d(num_features=out_c), - PReLU(num_parameters=out_c) - ) - - def forward(self, x): - return self.layers(x) - - -class LinearBlock(Module): - def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): - super(LinearBlock, self).__init__() - self.layers = nn.Sequential( - Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False), - BatchNorm2d(num_features=out_c) - ) - - def forward(self, x): - return self.layers(x) - - -class DepthWise(Module): - def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1): - super(DepthWise, self).__init__() - self.residual = residual - self.layers = nn.Sequential( - ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)), - ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride), - LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1)) - ) - - def forward(self, x): - short_cut = None - if self.residual: - short_cut = x - x = self.layers(x) - if self.residual: - output = short_cut + x - else: - output = x - return output - - -class Residual(Module): - def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)): - super(Residual, self).__init__() - modules = [] - for _ in range(num_block): - modules.append(DepthWise(c, c, True, kernel, stride, padding, groups)) - self.layers = Sequential(*modules) - - def forward(self, x): - return self.layers(x) - - -class GDC(Module): - def __init__(self, embedding_size): - super(GDC, self).__init__() - self.layers = nn.Sequential( - LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)), - Flatten(), - Linear(512, embedding_size, bias=False), - BatchNorm1d(embedding_size)) - - def forward(self, x): - return self.layers(x) - - -class MobileFaceNet(Module): - def __init__(self, fp16=False, num_features=512): - super(MobileFaceNet, self).__init__() - scale = 2 - self.fp16 = fp16 - self.layers = nn.Sequential( - ConvBlock(3, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1)), - ConvBlock(64 * scale, 64 * scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64), - DepthWise(64 * scale, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128), - Residual(64 * scale, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), - DepthWise(64 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256), - Residual(128 * scale, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), - DepthWise(128 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512), - Residual(128 * scale, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), - ) - self.conv_sep = ConvBlock(128 * scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0)) - self.features = GDC(num_features) - self._initialize_weights() - - def _initialize_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - if m.bias is not None: - m.bias.data.zero_() - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1) - m.bias.data.zero_() - elif isinstance(m, nn.Linear): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x): - with torch.cuda.amp.autocast(self.fp16): - x = self.layers(x) - x = self.conv_sep(x.float() if self.fp16 else x) - x = self.features(x) - return x - - -def get_mbf(fp16, num_features): - return MobileFaceNet(fp16, num_features) \ No newline at end of file diff --git a/arcface_torch/configs/3millions.py b/arcface_torch/configs/3millions.py deleted file mode 100644 index 559ebe3..0000000 --- a/arcface_torch/configs/3millions.py +++ /dev/null @@ -1,22 +0,0 @@ -from easydict import EasyDict as edict - -# configs for test speed - -config = edict() -config.loss = "cosface" -config.network = "r50" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 0.99 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 64 # total_batch_size = batch_size * num_gpus -config.lr = 0.1 # batch size is 512 - -config.rec = "synthetic" -config.num_classes = 300 * 10000 -config.num_epoch = 30 -config.warmup_epoch = -1 -config.val_targets = [] diff --git a/arcface_torch/configs/__init__.py b/arcface_torch/configs/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/arcface_torch/configs/base.py b/arcface_torch/configs/base.py deleted file mode 100644 index 5c96d42..0000000 --- a/arcface_torch/configs/base.py +++ /dev/null @@ -1,47 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "arcface" -config.network = "r50" -config.resume = False -config.output = "ms1mv3_arcface_r50" - -config.embedding_size = 512 -config.sample_rate = 1 -config.fp16 = False -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 128 -config.lr = 0.1 # batch size is 512 -config.dali = False -config.verbose = 2000 -config.frequent = 10 -config.score = None - -# if config.dataset == "emore": -# config.rec = "/train_tmp/faces_emore" -# config.num_classes = 85742 -# config.num_image = 5822653 -# config.num_epoch = 16 -# config.warmup_epoch = -1 -# config.val_targets = ["lfw", ] - -# elif config.dataset == "ms1m-retinaface-t1": -# config.rec = "/train_tmp/ms1m-retinaface-t1" -# config.num_classes = 93431 -# config.num_image = 5179510 -# config.num_epoch = 25 -# config.warmup_epoch = -1 -# config.val_targets = ["lfw", "cfp_fp", "agedb_30"] - -# elif config.dataset == "glint360k": -# config.rec = "/train_tmp/glint360k" -# config.num_classes = 360232 -# config.num_image = 17091657 -# config.num_epoch = 20 -# config.warmup_epoch = -1 -# config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/arcface_torch/configs/glint360k_mobileface_lr02_bs4k.py b/arcface_torch/configs/glint360k_mobileface_lr02_bs4k.py deleted file mode 100644 index 485e31f..0000000 --- a/arcface_torch/configs/glint360k_mobileface_lr02_bs4k.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "mbf" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 1e-4 -config.batch_size = 512 -config.lr = 0.4 -config.verbose = 5000 -config.dali = False - -config.rec = "/train_tmp/glint360k" -config.num_classes = 360232 -config.num_image = 17091657 -config.num_epoch = 20 -config.warmup_epoch = 2 -config.val_targets = ['lfw', 'cfp_fp', "agedb_30"] diff --git a/arcface_torch/configs/glint360k_r100_lr02_bs4k_16gpus.py b/arcface_torch/configs/glint360k_r100_lr02_bs4k_16gpus.py deleted file mode 100644 index 0e87c8f..0000000 --- a/arcface_torch/configs/glint360k_r100_lr02_bs4k_16gpus.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "r100" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 256 -config.lr = 0.4 -config.verbose = 5000 -config.dali = False - -config.rec = "/train_tmp/glint360k" -config.num_classes = 360232 -config.num_image = 17091657 -config.num_epoch = 20 -config.warmup_epoch = 2 -config.val_targets = ['lfw', 'cfp_fp', "agedb_30"] diff --git a/arcface_torch/configs/ms1mv3_mobileface_lr02.py b/arcface_torch/configs/ms1mv3_mobileface_lr02.py deleted file mode 100644 index f5dcaa1..0000000 --- a/arcface_torch/configs/ms1mv3_mobileface_lr02.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "arcface" -config.network = "mbf" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 1e-4 -config.batch_size = 256 -config.lr = 0.2 -config.verbose = 5000 -config.dali = False - -config.rec = "/train_tmp/ms1m-retinaface-t1" -config.num_classes = 93431 -config.num_image = 5179510 -config.num_epoch = 40 -config.warmup_epoch = 2 -config.val_targets = ['lfw', 'cfp_fp', "agedb_30"] diff --git a/arcface_torch/configs/ms1mv3_r100_lr02.py b/arcface_torch/configs/ms1mv3_r100_lr02.py deleted file mode 100644 index ec4caef..0000000 --- a/arcface_torch/configs/ms1mv3_r100_lr02.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "arcface" -config.network = "r100" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 128 -config.lr = 0.2 -config.verbose = 2000 -config.dali = False - -config.rec = "/train_tmp/ms1m-retinaface-t1" -config.num_classes = 93431 -config.num_image = 5179510 -config.num_epoch = 25 -config.warmup_epoch = 0 -config.val_targets = ['lfw', 'cfp_fp', "agedb_30"] diff --git a/arcface_torch/configs/ms1mv3_r50_lr02.py b/arcface_torch/configs/ms1mv3_r50_lr02.py deleted file mode 100644 index 2eefde4..0000000 --- a/arcface_torch/configs/ms1mv3_r50_lr02.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "arcface" -config.network = "r50" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 128 -config.lr = 0.2 -config.verbose = 2000 -config.dali = False - -config.rec = "/train_tmp/ms1m-retinaface-t1" -config.num_classes = 93431 -config.num_image = 5179510 -config.num_epoch = 25 -config.warmup_epoch = 2 -config.val_targets = ['lfw', 'cfp_fp', "agedb_30"] diff --git a/arcface_torch/configs/webface42m_mobilefacenet_pfc02_bs8k_16gpus.py b/arcface_torch/configs/webface42m_mobilefacenet_pfc02_bs8k_16gpus.py deleted file mode 100644 index 5cd522f..0000000 --- a/arcface_torch/configs/webface42m_mobilefacenet_pfc02_bs8k_16gpus.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "mbf" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 0.2 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 1e-4 -config.batch_size = 512 -config.lr = 0.4 -config.verbose = 10000 -config.dali = False - -config.rec = "/train_tmp/WebFace42M" -config.num_classes = 2059906 -config.num_image = 42474557 -config.num_epoch = 20 -config.warmup_epoch = 2 -config.val_targets = [] diff --git a/arcface_torch/configs/webface42m_r100_lr01_pfc02_bs4k_16gpus.py b/arcface_torch/configs/webface42m_r100_lr01_pfc02_bs4k_16gpus.py deleted file mode 100644 index e46f4e2..0000000 --- a/arcface_torch/configs/webface42m_r100_lr01_pfc02_bs4k_16gpus.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "r100" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 0.2 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 256 -config.lr = 0.3 -config.verbose = 2000 -config.dali = False - -config.rec = "/train_tmp/WebFace42M" -config.num_classes = 2059906 -config.num_image = 42474557 -config.num_epoch = 20 -config.warmup_epoch = 1 -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs4k_32gpus.py b/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs4k_32gpus.py deleted file mode 100644 index b5eb8bc..0000000 --- a/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs4k_32gpus.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "r50" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 0.2 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 128 -config.lr = 0.4 -config.verbose = 10000 -config.dali = False - -config.rec = "/train_tmp/WebFace42M" -config.num_classes = 2059906 -config.num_image = 42474557 -config.num_epoch = 20 -config.warmup_epoch = 2 -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs4k_8gpus.py b/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs4k_8gpus.py deleted file mode 100644 index 6b63b7d..0000000 --- a/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs4k_8gpus.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "r50" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 0.2 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 512 -config.lr = 0.4 -config.verbose = 10000 -config.dali = False - -config.rec = "/train_tmp/WebFace42M" -config.num_classes = 2059906 -config.num_image = 42474557 -config.num_epoch = 20 -config.warmup_epoch = 2 -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs8k_16gpus.py b/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs8k_16gpus.py deleted file mode 100644 index 699d4a8..0000000 --- a/arcface_torch/configs/webface42m_r50_lr01_pfc02_bs8k_16gpus.py +++ /dev/null @@ -1,27 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "r50" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 0.2 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 512 -config.lr = 0.6 -config.verbose = 10000 -config.dali = False - -config.rec = "/train_tmp/WebFace42M" -config.num_classes = 2059906 -config.num_image = 42474557 -config.num_epoch = 20 -config.warmup_epoch = 4 -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/arcface_torch/dataset.py b/arcface_torch/dataset.py deleted file mode 100644 index 80d562e..0000000 --- a/arcface_torch/dataset.py +++ /dev/null @@ -1,209 +0,0 @@ -import numbers -import os -import queue as Queue -import threading -from typing import Iterable - -import mxnet as mx -import numpy as np -import torch -from torch import distributed -from torch.utils.data import DataLoader, Dataset -from torchvision import transforms - -def get_dataloader( - root_dir: str, - local_rank: int, - batch_size: int, - dali = False) -> Iterable: - if dali and root_dir != "synthetic": - rec = os.path.join(root_dir, 'train.rec') - idx = os.path.join(root_dir, 'train.idx') - return dali_data_iter( - batch_size=batch_size, rec_file=rec, - idx_file=idx, num_threads=2, local_rank=local_rank) - else: - if root_dir == "synthetic": - train_set = SyntheticDataset() - else: - train_set = MXFaceDataset(root_dir=root_dir, local_rank=local_rank) - train_sampler = torch.utils.data.distributed.DistributedSampler(train_set, shuffle=True) - train_loader = DataLoaderX( - local_rank=local_rank, - dataset=train_set, - batch_size=batch_size, - sampler=train_sampler, - num_workers=2, - pin_memory=True, - drop_last=True, - ) - return train_loader - -class BackgroundGenerator(threading.Thread): - def __init__(self, generator, local_rank, max_prefetch=6): - super(BackgroundGenerator, self).__init__() - self.queue = Queue.Queue(max_prefetch) - self.generator = generator - self.local_rank = local_rank - self.daemon = True - self.start() - - def run(self): - torch.cuda.set_device(self.local_rank) - for item in self.generator: - self.queue.put(item) - self.queue.put(None) - - def next(self): - next_item = self.queue.get() - if next_item is None: - raise StopIteration - return next_item - - def __next__(self): - return self.next() - - def __iter__(self): - return self - - -class DataLoaderX(DataLoader): - - def __init__(self, local_rank, **kwargs): - super(DataLoaderX, self).__init__(**kwargs) - self.stream = torch.cuda.Stream(local_rank) - self.local_rank = local_rank - - def __iter__(self): - self.iter = super(DataLoaderX, self).__iter__() - self.iter = BackgroundGenerator(self.iter, self.local_rank) - self.preload() - return self - - def preload(self): - self.batch = next(self.iter, None) - if self.batch is None: - return None - with torch.cuda.stream(self.stream): - for k in range(len(self.batch)): - self.batch[k] = self.batch[k].to(device=self.local_rank, non_blocking=True) - - def __next__(self): - torch.cuda.current_stream().wait_stream(self.stream) - batch = self.batch - if batch is None: - raise StopIteration - self.preload() - return batch - - -class MXFaceDataset(Dataset): - def __init__(self, root_dir, local_rank): - super(MXFaceDataset, self).__init__() - self.transform = transforms.Compose( - [transforms.ToPILImage(), - transforms.RandomHorizontalFlip(), - transforms.ToTensor(), - transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), - ]) - self.root_dir = root_dir - self.local_rank = local_rank - path_imgrec = os.path.join(root_dir, 'train.rec') - path_imgidx = os.path.join(root_dir, 'train.idx') - self.imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') - s = self.imgrec.read_idx(0) - header, _ = mx.recordio.unpack(s) - if header.flag > 0: - self.header0 = (int(header.label[0]), int(header.label[1])) - self.imgidx = np.array(range(1, int(header.label[0]))) - else: - self.imgidx = np.array(list(self.imgrec.keys)) - - def __getitem__(self, index): - idx = self.imgidx[index] - s = self.imgrec.read_idx(idx) - header, img = mx.recordio.unpack(s) - label = header.label - if not isinstance(label, numbers.Number): - label = label[0] - label = torch.tensor(label, dtype=torch.long) - sample = mx.image.imdecode(img).asnumpy() - if self.transform is not None: - sample = self.transform(sample) - return sample, label - - def __len__(self): - return len(self.imgidx) - - -class SyntheticDataset(Dataset): - def __init__(self): - super(SyntheticDataset, self).__init__() - img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32) - img = np.transpose(img, (2, 0, 1)) - img = torch.from_numpy(img).squeeze(0).float() - img = ((img / 255) - 0.5) / 0.5 - self.img = img - self.label = 1 - - def __getitem__(self, index): - return self.img, self.label - - def __len__(self): - return 1000000 - - -def dali_data_iter( - batch_size: int, rec_file: str, idx_file: str, num_threads: int, - initial_fill=32768, random_shuffle=True, - prefetch_queue_depth=1, local_rank=0, name="reader", - mean=(127.5, 127.5, 127.5), - std=(127.5, 127.5, 127.5)): - """ - Parameters: - ---------- - initial_fill: int - Size of the buffer that is used for shuffling. If random_shuffle is False, this parameter is ignored. - - """ - rank: int = distributed.get_rank() - world_size: int = distributed.get_world_size() - import nvidia.dali.fn as fn - import nvidia.dali.types as types - from nvidia.dali.pipeline import Pipeline - from nvidia.dali.plugin.pytorch import DALIClassificationIterator - - pipe = Pipeline( - batch_size=batch_size, num_threads=num_threads, - device_id=local_rank, prefetch_queue_depth=prefetch_queue_depth, ) - condition_flip = fn.random.coin_flip(probability=0.5) - with pipe: - jpegs, labels = fn.readers.mxnet( - path=rec_file, index_path=idx_file, initial_fill=initial_fill, - num_shards=world_size, shard_id=rank, - random_shuffle=random_shuffle, pad_last_batch=False, name=name) - images = fn.decoders.image(jpegs, device="mixed", output_type=types.RGB) - images = fn.crop_mirror_normalize( - images, dtype=types.FLOAT, mean=mean, std=std, mirror=condition_flip) - pipe.set_outputs(images, labels) - pipe.build() - return DALIWarper(DALIClassificationIterator(pipelines=[pipe], reader_name=name, )) - - -@torch.no_grad() -class DALIWarper(object): - def __init__(self, dali_iter): - self.iter = dali_iter - - def __next__(self): - data_dict = self.iter.__next__()[0] - tensor_data = data_dict['data'].cuda() - tensor_label: torch.Tensor = data_dict['label'].cuda().long() - tensor_label.squeeze_() - return tensor_data, tensor_label - - def __iter__(self): - return self - - def reset(self): - self.iter.reset() diff --git a/arcface_torch/docs/eval.md b/arcface_torch/docs/eval.md deleted file mode 100644 index dd1d9e2..0000000 --- a/arcface_torch/docs/eval.md +++ /dev/null @@ -1,31 +0,0 @@ -## Eval on ICCV2021-MFR - -coming soon. - - -## Eval IJBC -You can eval ijbc with pytorch or onnx. - - -1. Eval IJBC With Onnx -```shell -CUDA_VISIBLE_DEVICES=0 python onnx_ijbc.py --model-root ms1mv3_arcface_r50 --image-path IJB_release/IJBC --result-dir ms1mv3_arcface_r50 -``` - -2. Eval IJBC With Pytorch -```shell -CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \ ---model-prefix ms1mv3_arcface_r50/backbone.pth \ ---image-path IJB_release/IJBC \ ---result-dir ms1mv3_arcface_r50 \ ---batch-size 128 \ ---job ms1mv3_arcface_r50 \ ---target IJBC \ ---network iresnet50 -``` - -## Inference - -```shell -python inference.py --weight ms1mv3_arcface_r50/backbone.pth --network r50 -``` diff --git a/arcface_torch/docs/install.md b/arcface_torch/docs/install.md deleted file mode 100644 index 6314a40..0000000 --- a/arcface_torch/docs/install.md +++ /dev/null @@ -1,51 +0,0 @@ -## v1.8.0 -### Linux and Windows -```shell -# CUDA 11.0 -pip --default-timeout=100 install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html - -# CUDA 10.2 -pip --default-timeout=100 install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 - -# CPU only -pip --default-timeout=100 install torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html - -``` - - -## v1.7.1 -### Linux and Windows -```shell -# CUDA 11.0 -pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html - -# CUDA 10.2 -pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 - -# CUDA 10.1 -pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html - -# CUDA 9.2 -pip install torch==1.7.1+cu92 torchvision==0.8.2+cu92 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html - -# CPU only -pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html -``` - - -## v1.6.0 - -### Linux and Windows -```shell -# CUDA 10.2 -pip install torch==1.6.0 torchvision==0.7.0 - -# CUDA 10.1 -pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html - -# CUDA 9.2 -pip install torch==1.6.0+cu92 torchvision==0.7.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html - -# CPU only -pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html -``` \ No newline at end of file diff --git a/arcface_torch/docs/install_dali.md b/arcface_torch/docs/install_dali.md deleted file mode 100644 index 1333ed7..0000000 --- a/arcface_torch/docs/install_dali.md +++ /dev/null @@ -1 +0,0 @@ -TODO diff --git a/arcface_torch/docs/modelzoo.md b/arcface_torch/docs/modelzoo.md deleted file mode 100644 index e69de29..0000000 diff --git a/arcface_torch/docs/prepare_webface42m.md b/arcface_torch/docs/prepare_webface42m.md deleted file mode 100644 index 1675edb..0000000 --- a/arcface_torch/docs/prepare_webface42m.md +++ /dev/null @@ -1,22 +0,0 @@ - - - -## 1. Download Datasets and Unzip - -Download WebFace42M from [https://www.face-benchmark.org/download.html](https://www.face-benchmark.org/download.html). - - -## 2. Create **Pre-shuffle** Rec File for DALI - -Note: preshuffled rec is very important to DALI, and rec without preshuffled can cause performance degradation, origin insightface style rec file -do not support Nvidia DALI, you must follow this command [mxnet.tools.im2rec](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) to generate a pre-shuffle rec file. - -```shell -# 1) create train.lst using follow command -python -m mxnet.tools.im2rec --list --recursive train "Your WebFace42M Root" - -# 2) create train.rec and train.idx using train.lst using following command -python -m mxnet.tools.im2rec --num-thread 16 --quality 100 train "Your WebFace42M Root" -``` - -Finally, you will get three files: `train.lst`, `train.rec`, `train.idx`. which `train.idx`, `train.rec` are using for training. diff --git a/arcface_torch/docs/speed_benchmark.md b/arcface_torch/docs/speed_benchmark.md deleted file mode 100644 index 055aee0..0000000 --- a/arcface_torch/docs/speed_benchmark.md +++ /dev/null @@ -1,93 +0,0 @@ -## Test Training Speed - -- Test Commands - -You need to use the following two commands to test the Partial FC training performance. -The number of identites is **3 millions** (synthetic data), turn mixed precision training on, backbone is resnet50, -batch size is 1024. -```shell -# Model Parallel -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions -# Partial FC 0.1 -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions_pfc -``` - -- GPU Memory - -``` -# (Model Parallel) gpustat -i -[0] Tesla V100-SXM2-32GB | 64'C, 94 % | 30338 / 32510 MB -[1] Tesla V100-SXM2-32GB | 60'C, 99 % | 28876 / 32510 MB -[2] Tesla V100-SXM2-32GB | 60'C, 99 % | 28872 / 32510 MB -[3] Tesla V100-SXM2-32GB | 69'C, 99 % | 28872 / 32510 MB -[4] Tesla V100-SXM2-32GB | 66'C, 99 % | 28888 / 32510 MB -[5] Tesla V100-SXM2-32GB | 60'C, 99 % | 28932 / 32510 MB -[6] Tesla V100-SXM2-32GB | 68'C, 100 % | 28916 / 32510 MB -[7] Tesla V100-SXM2-32GB | 65'C, 99 % | 28860 / 32510 MB - -# (Partial FC 0.1) gpustat -i -[0] Tesla V100-SXM2-32GB | 60'C, 95 % | 10488 / 32510 MB │······················· -[1] Tesla V100-SXM2-32GB | 60'C, 97 % | 10344 / 32510 MB │······················· -[2] Tesla V100-SXM2-32GB | 61'C, 95 % | 10340 / 32510 MB │······················· -[3] Tesla V100-SXM2-32GB | 66'C, 95 % | 10340 / 32510 MB │······················· -[4] Tesla V100-SXM2-32GB | 65'C, 94 % | 10356 / 32510 MB │······················· -[5] Tesla V100-SXM2-32GB | 61'C, 95 % | 10400 / 32510 MB │······················· -[6] Tesla V100-SXM2-32GB | 68'C, 96 % | 10384 / 32510 MB │······················· -[7] Tesla V100-SXM2-32GB | 64'C, 95 % | 10328 / 32510 MB │······················· -``` - -- Training Speed - -```python -# (Model Parallel) trainging.log -Training: Speed 2271.33 samples/sec Loss 1.1624 LearningRate 0.2000 Epoch: 0 Global Step: 100 -Training: Speed 2269.94 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 150 -Training: Speed 2272.67 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 200 -Training: Speed 2266.55 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 250 -Training: Speed 2272.54 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 300 - -# (Partial FC 0.1) trainging.log -Training: Speed 5299.56 samples/sec Loss 1.0965 LearningRate 0.2000 Epoch: 0 Global Step: 100 -Training: Speed 5296.37 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 150 -Training: Speed 5304.37 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 200 -Training: Speed 5274.43 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 250 -Training: Speed 5300.10 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 300 -``` - -In this test case, Partial FC 0.1 only use1 1/3 of the GPU memory of the model parallel, -and the training speed is 2.5 times faster than the model parallel. - - -## Speed Benchmark - -1. Training speed of different parallel methods (samples/second), Tesla V100 32GB * 8. (Larger is better) - -| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 | -| :--- | :--- | :--- | :--- | -|125000 | 4681 | 4824 | 5004 | -|250000 | 4047 | 4521 | 4976 | -|500000 | 3087 | 4013 | 4900 | -|1000000 | 2090 | 3449 | 4803 | -|1400000 | 1672 | 3043 | 4738 | -|2000000 | - | 2593 | 4626 | -|4000000 | - | 1748 | 4208 | -|5500000 | - | 1389 | 3975 | -|8000000 | - | - | 3565 | -|16000000 | - | - | 2679 | -|29000000 | - | - | 1855 | - -2. GPU memory cost of different parallel methods (GB per GPU), Tesla V100 32GB * 8. (Smaller is better) - -| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 | -| :--- | :--- | :--- | :--- | -|125000 | 7358 | 5306 | 4868 | -|250000 | 9940 | 5826 | 5004 | -|500000 | 14220 | 7114 | 5202 | -|1000000 | 23708 | 9966 | 5620 | -|1400000 | 32252 | 11178 | 6056 | -|2000000 | - | 13978 | 6472 | -|4000000 | - | 23238 | 8284 | -|5500000 | - | 32188 | 9854 | -|8000000 | - | - | 12310 | -|16000000 | - | - | 19950 | -|29000000 | - | - | 32324 | diff --git a/arcface_torch/eval/__init__.py b/arcface_torch/eval/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/arcface_torch/eval/verification.py b/arcface_torch/eval/verification.py deleted file mode 100644 index edacf8d..0000000 --- a/arcface_torch/eval/verification.py +++ /dev/null @@ -1,409 +0,0 @@ -"""Helper for evaluation on the Labeled Faces in the Wild dataset -""" - -# MIT License -# -# Copyright (c) 2016 David Sandberg -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - -import datetime -import os -import pickle - -import mxnet as mx -import numpy as np -import sklearn -import torch -from mxnet import ndarray as nd -from scipy import interpolate -from sklearn.decomposition import PCA -from sklearn.model_selection import KFold - - -class LFold: - def __init__(self, n_splits=2, shuffle=False): - self.n_splits = n_splits - if self.n_splits > 1: - self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle) - - def split(self, indices): - if self.n_splits > 1: - return self.k_fold.split(indices) - else: - return [(indices, indices)] - - -def calculate_roc(thresholds, - embeddings1, - embeddings2, - actual_issame, - nrof_folds=10, - pca=0): - assert (embeddings1.shape[0] == embeddings2.shape[0]) - assert (embeddings1.shape[1] == embeddings2.shape[1]) - nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) - nrof_thresholds = len(thresholds) - k_fold = LFold(n_splits=nrof_folds, shuffle=False) - - tprs = np.zeros((nrof_folds, nrof_thresholds)) - fprs = np.zeros((nrof_folds, nrof_thresholds)) - accuracy = np.zeros((nrof_folds)) - indices = np.arange(nrof_pairs) - - if pca == 0: - diff = np.subtract(embeddings1, embeddings2) - dist = np.sum(np.square(diff), 1) - - for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): - if pca > 0: - print('doing pca on', fold_idx) - embed1_train = embeddings1[train_set] - embed2_train = embeddings2[train_set] - _embed_train = np.concatenate((embed1_train, embed2_train), axis=0) - pca_model = PCA(n_components=pca) - pca_model.fit(_embed_train) - embed1 = pca_model.transform(embeddings1) - embed2 = pca_model.transform(embeddings2) - embed1 = sklearn.preprocessing.normalize(embed1) - embed2 = sklearn.preprocessing.normalize(embed2) - diff = np.subtract(embed1, embed2) - dist = np.sum(np.square(diff), 1) - - # Find the best threshold for the fold - acc_train = np.zeros((nrof_thresholds)) - for threshold_idx, threshold in enumerate(thresholds): - _, _, acc_train[threshold_idx] = calculate_accuracy( - threshold, dist[train_set], actual_issame[train_set]) - best_threshold_index = np.argmax(acc_train) - for threshold_idx, threshold in enumerate(thresholds): - tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy( - threshold, dist[test_set], - actual_issame[test_set]) - _, _, accuracy[fold_idx] = calculate_accuracy( - thresholds[best_threshold_index], dist[test_set], - actual_issame[test_set]) - - tpr = np.mean(tprs, 0) - fpr = np.mean(fprs, 0) - return tpr, fpr, accuracy - - -def calculate_accuracy(threshold, dist, actual_issame): - predict_issame = np.less(dist, threshold) - tp = np.sum(np.logical_and(predict_issame, actual_issame)) - fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) - tn = np.sum( - np.logical_and(np.logical_not(predict_issame), - np.logical_not(actual_issame))) - fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) - - tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn) - fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn) - acc = float(tp + tn) / dist.size - return tpr, fpr, acc - - -def calculate_val(thresholds, - embeddings1, - embeddings2, - actual_issame, - far_target, - nrof_folds=10): - assert (embeddings1.shape[0] == embeddings2.shape[0]) - assert (embeddings1.shape[1] == embeddings2.shape[1]) - nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) - nrof_thresholds = len(thresholds) - k_fold = LFold(n_splits=nrof_folds, shuffle=False) - - val = np.zeros(nrof_folds) - far = np.zeros(nrof_folds) - - diff = np.subtract(embeddings1, embeddings2) - dist = np.sum(np.square(diff), 1) - indices = np.arange(nrof_pairs) - - for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): - - # Find the threshold that gives FAR = far_target - far_train = np.zeros(nrof_thresholds) - for threshold_idx, threshold in enumerate(thresholds): - _, far_train[threshold_idx] = calculate_val_far( - threshold, dist[train_set], actual_issame[train_set]) - if np.max(far_train) >= far_target: - f = interpolate.interp1d(far_train, thresholds, kind='slinear') - threshold = f(far_target) - else: - threshold = 0.0 - - val[fold_idx], far[fold_idx] = calculate_val_far( - threshold, dist[test_set], actual_issame[test_set]) - - val_mean = np.mean(val) - far_mean = np.mean(far) - val_std = np.std(val) - return val_mean, val_std, far_mean - - -def calculate_val_far(threshold, dist, actual_issame): - predict_issame = np.less(dist, threshold) - true_accept = np.sum(np.logical_and(predict_issame, actual_issame)) - false_accept = np.sum( - np.logical_and(predict_issame, np.logical_not(actual_issame))) - n_same = np.sum(actual_issame) - n_diff = np.sum(np.logical_not(actual_issame)) - # print(true_accept, false_accept) - # print(n_same, n_diff) - val = float(true_accept) / float(n_same) - far = float(false_accept) / float(n_diff) - return val, far - - -def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0): - # Calculate evaluation metrics - thresholds = np.arange(0, 4, 0.01) - embeddings1 = embeddings[0::2] - embeddings2 = embeddings[1::2] - tpr, fpr, accuracy = calculate_roc(thresholds, - embeddings1, - embeddings2, - np.asarray(actual_issame), - nrof_folds=nrof_folds, - pca=pca) - thresholds = np.arange(0, 4, 0.001) - val, val_std, far = calculate_val(thresholds, - embeddings1, - embeddings2, - np.asarray(actual_issame), - 1e-3, - nrof_folds=nrof_folds) - return tpr, fpr, accuracy, val, val_std, far - -@torch.no_grad() -def load_bin(path, image_size): - try: - with open(path, 'rb') as f: - bins, issame_list = pickle.load(f) # py2 - except UnicodeDecodeError as e: - with open(path, 'rb') as f: - bins, issame_list = pickle.load(f, encoding='bytes') # py3 - data_list = [] - for flip in [0, 1]: - data = torch.empty((len(issame_list) * 2, 3, image_size[0], image_size[1])) - data_list.append(data) - for idx in range(len(issame_list) * 2): - _bin = bins[idx] - img = mx.image.imdecode(_bin) - if img.shape[1] != image_size[0]: - img = mx.image.resize_short(img, image_size[0]) - img = nd.transpose(img, axes=(2, 0, 1)) - for flip in [0, 1]: - if flip == 1: - img = mx.ndarray.flip(data=img, axis=2) - data_list[flip][idx][:] = torch.from_numpy(img.asnumpy()) - if idx % 1000 == 0: - print('loading bin', idx) - print(data_list[0].shape) - return data_list, issame_list - -@torch.no_grad() -def test(data_set, backbone, batch_size, nfolds=10): - print('testing verification..') - data_list = data_set[0] - issame_list = data_set[1] - embeddings_list = [] - time_consumed = 0.0 - for i in range(len(data_list)): - data = data_list[i] - embeddings = None - ba = 0 - while ba < data.shape[0]: - bb = min(ba + batch_size, data.shape[0]) - count = bb - ba - _data = data[bb - batch_size: bb] - time0 = datetime.datetime.now() - img = ((_data / 255) - 0.5) / 0.5 - net_out: torch.Tensor = backbone(img) - _embeddings = net_out.detach().cpu().numpy() - time_now = datetime.datetime.now() - diff = time_now - time0 - time_consumed += diff.total_seconds() - if embeddings is None: - embeddings = np.zeros((data.shape[0], _embeddings.shape[1])) - embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :] - ba = bb - embeddings_list.append(embeddings) - - _xnorm = 0.0 - _xnorm_cnt = 0 - for embed in embeddings_list: - for i in range(embed.shape[0]): - _em = embed[i] - _norm = np.linalg.norm(_em) - _xnorm += _norm - _xnorm_cnt += 1 - _xnorm /= _xnorm_cnt - - embeddings = embeddings_list[0].copy() - embeddings = sklearn.preprocessing.normalize(embeddings) - acc1 = 0.0 - std1 = 0.0 - embeddings = embeddings_list[0] + embeddings_list[1] - embeddings = sklearn.preprocessing.normalize(embeddings) - print(embeddings.shape) - print('infer time', time_consumed) - _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds) - acc2, std2 = np.mean(accuracy), np.std(accuracy) - return acc1, std1, acc2, std2, _xnorm, embeddings_list - - -def dumpR(data_set, - backbone, - batch_size, - name='', - data_extra=None, - label_shape=None): - print('dump verification embedding..') - data_list = data_set[0] - issame_list = data_set[1] - embeddings_list = [] - time_consumed = 0.0 - for i in range(len(data_list)): - data = data_list[i] - embeddings = None - ba = 0 - while ba < data.shape[0]: - bb = min(ba + batch_size, data.shape[0]) - count = bb - ba - - _data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb) - time0 = datetime.datetime.now() - if data_extra is None: - db = mx.io.DataBatch(data=(_data,), label=(_label,)) - else: - db = mx.io.DataBatch(data=(_data, _data_extra), - label=(_label,)) - model.forward(db, is_train=False) - net_out = model.get_outputs() - _embeddings = net_out[0].asnumpy() - time_now = datetime.datetime.now() - diff = time_now - time0 - time_consumed += diff.total_seconds() - if embeddings is None: - embeddings = np.zeros((data.shape[0], _embeddings.shape[1])) - embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :] - ba = bb - embeddings_list.append(embeddings) - embeddings = embeddings_list[0] + embeddings_list[1] - embeddings = sklearn.preprocessing.normalize(embeddings) - actual_issame = np.asarray(issame_list) - outname = os.path.join('temp.bin') - with open(outname, 'wb') as f: - pickle.dump((embeddings, issame_list), - f, - protocol=pickle.HIGHEST_PROTOCOL) - - -# if __name__ == '__main__': -# -# parser = argparse.ArgumentParser(description='do verification') -# # general -# parser.add_argument('--data-dir', default='', help='') -# parser.add_argument('--model', -# default='../model/softmax,50', -# help='path to load model.') -# parser.add_argument('--target', -# default='lfw,cfp_ff,cfp_fp,agedb_30', -# help='test targets.') -# parser.add_argument('--gpu', default=0, type=int, help='gpu id') -# parser.add_argument('--batch-size', default=32, type=int, help='') -# parser.add_argument('--max', default='', type=str, help='') -# parser.add_argument('--mode', default=0, type=int, help='') -# parser.add_argument('--nfolds', default=10, type=int, help='') -# args = parser.parse_args() -# image_size = [112, 112] -# print('image_size', image_size) -# ctx = mx.gpu(args.gpu) -# nets = [] -# vec = args.model.split(',') -# prefix = args.model.split(',')[0] -# epochs = [] -# if len(vec) == 1: -# pdir = os.path.dirname(prefix) -# for fname in os.listdir(pdir): -# if not fname.endswith('.params'): -# continue -# _file = os.path.join(pdir, fname) -# if _file.startswith(prefix): -# epoch = int(fname.split('.')[0].split('-')[1]) -# epochs.append(epoch) -# epochs = sorted(epochs, reverse=True) -# if len(args.max) > 0: -# _max = [int(x) for x in args.max.split(',')] -# assert len(_max) == 2 -# if len(epochs) > _max[1]: -# epochs = epochs[_max[0]:_max[1]] -# -# else: -# epochs = [int(x) for x in vec[1].split('|')] -# print('model number', len(epochs)) -# time0 = datetime.datetime.now() -# for epoch in epochs: -# print('loading', prefix, epoch) -# sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) -# # arg_params, aux_params = ch_dev(arg_params, aux_params, ctx) -# all_layers = sym.get_internals() -# sym = all_layers['fc1_output'] -# model = mx.mod.Module(symbol=sym, context=ctx, label_names=None) -# # model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))]) -# model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], -# image_size[1]))]) -# model.set_params(arg_params, aux_params) -# nets.append(model) -# time_now = datetime.datetime.now() -# diff = time_now - time0 -# print('model loading time', diff.total_seconds()) -# -# ver_list = [] -# ver_name_list = [] -# for name in args.target.split(','): -# path = os.path.join(args.data_dir, name + ".bin") -# if os.path.exists(path): -# print('loading.. ', name) -# data_set = load_bin(path, image_size) -# ver_list.append(data_set) -# ver_name_list.append(name) -# -# if args.mode == 0: -# for i in range(len(ver_list)): -# results = [] -# for model in nets: -# acc1, std1, acc2, std2, xnorm, embeddings_list = test( -# ver_list[i], model, args.batch_size, args.nfolds) -# print('[%s]XNorm: %f' % (ver_name_list[i], xnorm)) -# print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1)) -# print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2)) -# results.append(acc2) -# print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results))) -# elif args.mode == 1: -# raise ValueError -# else: -# model = nets[0] -# dumpR(ver_list[0], model, args.batch_size, args.target) diff --git a/arcface_torch/eval_ijbc.py b/arcface_torch/eval_ijbc.py deleted file mode 100644 index 9c5a650..0000000 --- a/arcface_torch/eval_ijbc.py +++ /dev/null @@ -1,483 +0,0 @@ -# coding: utf-8 - -import os -import pickle - -import matplotlib -import pandas as pd - -matplotlib.use('Agg') -import matplotlib.pyplot as plt -import timeit -import sklearn -import argparse -import cv2 -import numpy as np -import torch -from skimage import transform as trans -from backbones import get_model -from sklearn.metrics import roc_curve, auc - -from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap -from prettytable import PrettyTable -from pathlib import Path - -import sys -import warnings - -sys.path.insert(0, "../") -warnings.filterwarnings("ignore") - -parser = argparse.ArgumentParser(description='do ijb test') -# general -parser.add_argument('--model-prefix', default='', help='path to load model.') -parser.add_argument('--image-path', default='', type=str, help='') -parser.add_argument('--result-dir', default='.', type=str, help='') -parser.add_argument('--batch-size', default=128, type=int, help='') -parser.add_argument('--network', default='iresnet50', type=str, help='') -parser.add_argument('--job', default='insightface', type=str, help='job name') -parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB') -args = parser.parse_args() - -target = args.target -model_path = args.model_prefix -image_path = args.image_path -result_dir = args.result_dir -gpu_id = None -use_norm_score = True # if Ture, TestMode(N1) -use_detector_score = True # if Ture, TestMode(D1) -use_flip_test = True # if Ture, TestMode(F1) -job = args.job -batch_size = args.batch_size - - -class Embedding(object): - def __init__(self, prefix, data_shape, batch_size=1): - image_size = (112, 112) - self.image_size = image_size - weight = torch.load(prefix) - resnet = get_model(args.network, dropout=0, fp16=False).cuda() - resnet.load_state_dict(weight) - model = torch.nn.DataParallel(resnet) - self.model = model - self.model.eval() - src = np.array([ - [30.2946, 51.6963], - [65.5318, 51.5014], - [48.0252, 71.7366], - [33.5493, 92.3655], - [62.7299, 92.2041]], dtype=np.float32) - src[:, 0] += 8.0 - self.src = src - self.batch_size = batch_size - self.data_shape = data_shape - - def get(self, rimg, landmark): - - assert landmark.shape[0] == 68 or landmark.shape[0] == 5 - assert landmark.shape[1] == 2 - if landmark.shape[0] == 68: - landmark5 = np.zeros((5, 2), dtype=np.float32) - landmark5[0] = (landmark[36] + landmark[39]) / 2 - landmark5[1] = (landmark[42] + landmark[45]) / 2 - landmark5[2] = landmark[30] - landmark5[3] = landmark[48] - landmark5[4] = landmark[54] - else: - landmark5 = landmark - tform = trans.SimilarityTransform() - tform.estimate(landmark5, self.src) - M = tform.params[0:2, :] - img = cv2.warpAffine(rimg, - M, (self.image_size[1], self.image_size[0]), - borderValue=0.0) - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - img_flip = np.fliplr(img) - img = np.transpose(img, (2, 0, 1)) # 3*112*112, RGB - img_flip = np.transpose(img_flip, (2, 0, 1)) - input_blob = np.zeros((2, 3, self.image_size[1], self.image_size[0]), dtype=np.uint8) - input_blob[0] = img - input_blob[1] = img_flip - return input_blob - - @torch.no_grad() - def forward_db(self, batch_data): - imgs = torch.Tensor(batch_data).cuda() - imgs.div_(255).sub_(0.5).div_(0.5) - feat = self.model(imgs) - feat = feat.reshape([self.batch_size, 2 * feat.shape[1]]) - return feat.cpu().numpy() - - -# 将一个listå°½é‡å‡åˆ†æˆn份，é™åˆ¶len(list)==n，份数大于原list内元素个数则分é…空list[] -def divideIntoNstrand(listTemp, n): - twoList = [[] for i in range(n)] - for i, e in enumerate(listTemp): - twoList[i % n].append(e) - return twoList - - -def read_template_media_list(path): - # ijb_meta = np.loadtxt(path, dtype=str) - ijb_meta = pd.read_csv(path, sep=' ', header=None).values - templates = ijb_meta[:, 1].astype(np.int) - medias = ijb_meta[:, 2].astype(np.int) - return templates, medias - - -# In[ ]: - - -def read_template_pair_list(path): - # pairs = np.loadtxt(path, dtype=str) - pairs = pd.read_csv(path, sep=' ', header=None).values - # print(pairs.shape) - # print(pairs[:, 0].astype(np.int)) - t1 = pairs[:, 0].astype(np.int) - t2 = pairs[:, 1].astype(np.int) - label = pairs[:, 2].astype(np.int) - return t1, t2, label - - -# In[ ]: - - -def read_image_feature(path): - with open(path, 'rb') as fid: - img_feats = pickle.load(fid) - return img_feats - - -# In[ ]: - - -def get_image_feature(img_path, files_list, model_path, epoch, gpu_id): - batch_size = args.batch_size - data_shape = (3, 112, 112) - - files = files_list - print('files:', len(files)) - rare_size = len(files) % batch_size - faceness_scores = [] - batch = 0 - img_feats = np.empty((len(files), 1024), dtype=np.float32) - - batch_data = np.empty((2 * batch_size, 3, 112, 112)) - embedding = Embedding(model_path, data_shape, batch_size) - for img_index, each_line in enumerate(files[:len(files) - rare_size]): - name_lmk_score = each_line.strip().split(' ') - img_name = os.path.join(img_path, name_lmk_score[0]) - img = cv2.imread(img_name) - lmk = np.array([float(x) for x in name_lmk_score[1:-1]], - dtype=np.float32) - lmk = lmk.reshape((5, 2)) - input_blob = embedding.get(img, lmk) - - batch_data[2 * (img_index - batch * batch_size)][:] = input_blob[0] - batch_data[2 * (img_index - batch * batch_size) + 1][:] = input_blob[1] - if (img_index + 1) % batch_size == 0: - print('batch', batch) - img_feats[batch * batch_size:batch * batch_size + - batch_size][:] = embedding.forward_db(batch_data) - batch += 1 - faceness_scores.append(name_lmk_score[-1]) - - batch_data = np.empty((2 * rare_size, 3, 112, 112)) - embedding = Embedding(model_path, data_shape, rare_size) - for img_index, each_line in enumerate(files[len(files) - rare_size:]): - name_lmk_score = each_line.strip().split(' ') - img_name = os.path.join(img_path, name_lmk_score[0]) - img = cv2.imread(img_name) - lmk = np.array([float(x) for x in name_lmk_score[1:-1]], - dtype=np.float32) - lmk = lmk.reshape((5, 2)) - input_blob = embedding.get(img, lmk) - batch_data[2 * img_index][:] = input_blob[0] - batch_data[2 * img_index + 1][:] = input_blob[1] - if (img_index + 1) % rare_size == 0: - print('batch', batch) - img_feats[len(files) - - rare_size:][:] = embedding.forward_db(batch_data) - batch += 1 - faceness_scores.append(name_lmk_score[-1]) - faceness_scores = np.array(faceness_scores).astype(np.float32) - # img_feats = np.ones( (len(files), 1024), dtype=np.float32) * 0.01 - # faceness_scores = np.ones( (len(files), ), dtype=np.float32 ) - return img_feats, faceness_scores - - -# In[ ]: - - -def image2template_feature(img_feats=None, templates=None, medias=None): - # ========================================================== - # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim] - # 2. compute media feature. - # 3. compute template feature. - # ========================================================== - unique_templates = np.unique(templates) - template_feats = np.zeros((len(unique_templates), img_feats.shape[1])) - - for count_template, uqt in enumerate(unique_templates): - - (ind_t,) = np.where(templates == uqt) - face_norm_feats = img_feats[ind_t] - face_medias = medias[ind_t] - unique_medias, unique_media_counts = np.unique(face_medias, - return_counts=True) - media_norm_feats = [] - for u, ct in zip(unique_medias, unique_media_counts): - (ind_m,) = np.where(face_medias == u) - if ct == 1: - media_norm_feats += [face_norm_feats[ind_m]] - else: # image features from the same video will be aggregated into one feature - media_norm_feats += [ - np.mean(face_norm_feats[ind_m], axis=0, keepdims=True) - ] - media_norm_feats = np.array(media_norm_feats) - # media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True)) - template_feats[count_template] = np.sum(media_norm_feats, axis=0) - if count_template % 2000 == 0: - print('Finish Calculating {} template features.'.format( - count_template)) - # template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True)) - template_norm_feats = sklearn.preprocessing.normalize(template_feats) - # print(template_norm_feats.shape) - return template_norm_feats, unique_templates - - -# In[ ]: - - -def verification(template_norm_feats=None, - unique_templates=None, - p1=None, - p2=None): - # ========================================================== - # Compute set-to-set Similarity Score. - # ========================================================== - template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int) - for count_template, uqt in enumerate(unique_templates): - template2id[uqt] = count_template - - score = np.zeros((len(p1),)) # save cosine distance between pairs - - total_pairs = np.array(range(len(p1))) - batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation - sublists = [ - total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize) - ] - total_sublists = len(sublists) - for c, s in enumerate(sublists): - feat1 = template_norm_feats[template2id[p1[s]]] - feat2 = template_norm_feats[template2id[p2[s]]] - similarity_score = np.sum(feat1 * feat2, -1) - score[s] = similarity_score.flatten() - if c % 10 == 0: - print('Finish {}/{} pairs.'.format(c, total_sublists)) - return score - - -# In[ ]: -def verification2(template_norm_feats=None, - unique_templates=None, - p1=None, - p2=None): - template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int) - for count_template, uqt in enumerate(unique_templates): - template2id[uqt] = count_template - score = np.zeros((len(p1),)) # save cosine distance between pairs - total_pairs = np.array(range(len(p1))) - batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation - sublists = [ - total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize) - ] - total_sublists = len(sublists) - for c, s in enumerate(sublists): - feat1 = template_norm_feats[template2id[p1[s]]] - feat2 = template_norm_feats[template2id[p2[s]]] - similarity_score = np.sum(feat1 * feat2, -1) - score[s] = similarity_score.flatten() - if c % 10 == 0: - print('Finish {}/{} pairs.'.format(c, total_sublists)) - return score - - -def read_score(path): - with open(path, 'rb') as fid: - img_feats = pickle.load(fid) - return img_feats - - -# # Step1: Load Meta Data - -# In[ ]: - -assert target == 'IJBC' or target == 'IJBB' - -# ============================================================= -# load image and template relationships for template feature embedding -# tid --> template id, mid --> media id -# format: -# image_name tid mid -# ============================================================= -start = timeit.default_timer() -templates, medias = read_template_media_list( - os.path.join('%s/meta' % image_path, - '%s_face_tid_mid.txt' % target.lower())) -stop = timeit.default_timer() -print('Time: %.2f s. ' % (stop - start)) - -# In[ ]: - -# ============================================================= -# load template pairs for template-to-template verification -# tid : template id, label : 1/0 -# format: -# tid_1 tid_2 label -# ============================================================= -start = timeit.default_timer() -p1, p2, label = read_template_pair_list( - os.path.join('%s/meta' % image_path, - '%s_template_pair_label.txt' % target.lower())) -stop = timeit.default_timer() -print('Time: %.2f s. ' % (stop - start)) - -# # Step 2: Get Image Features - -# In[ ]: - -# ============================================================= -# load image features -# format: -# img_feats: [image_num x feats_dim] (227630, 512) -# ============================================================= -start = timeit.default_timer() -img_path = '%s/loose_crop' % image_path -img_list_path = '%s/meta/%s_name_5pts_score.txt' % (image_path, target.lower()) -img_list = open(img_list_path) -files = img_list.readlines() -# files_list = divideIntoNstrand(files, rank_size) -files_list = files - -# img_feats -# for i in range(rank_size): -img_feats, faceness_scores = get_image_feature(img_path, files_list, - model_path, 0, gpu_id) -stop = timeit.default_timer() -print('Time: %.2f s. ' % (stop - start)) -print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], - img_feats.shape[1])) - -# # Step3: Get Template Features - -# In[ ]: - -# ============================================================= -# compute template features from image features. -# ============================================================= -start = timeit.default_timer() -# ========================================================== -# Norm feature before aggregation into template feature? -# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face). -# ========================================================== -# 1. FaceScore （Feature Norm） -# 2. FaceScore （Detector） - -if use_flip_test: - # concat --- F1 - # img_input_feats = img_feats - # add --- F2 - img_input_feats = img_feats[:, 0:img_feats.shape[1] // - 2] + img_feats[:, img_feats.shape[1] // 2:] -else: - img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] - -if use_norm_score: - img_input_feats = img_input_feats -else: - # normalise features to remove norm information - img_input_feats = img_input_feats / np.sqrt( - np.sum(img_input_feats ** 2, -1, keepdims=True)) - -if use_detector_score: - print(img_input_feats.shape, faceness_scores.shape) - img_input_feats = img_input_feats * faceness_scores[:, np.newaxis] -else: - img_input_feats = img_input_feats - -template_norm_feats, unique_templates = image2template_feature( - img_input_feats, templates, medias) -stop = timeit.default_timer() -print('Time: %.2f s. ' % (stop - start)) - -# # Step 4: Get Template Similarity Scores - -# In[ ]: - -# ============================================================= -# compute verification scores between template pairs. -# ============================================================= -start = timeit.default_timer() -score = verification(template_norm_feats, unique_templates, p1, p2) -stop = timeit.default_timer() -print('Time: %.2f s. ' % (stop - start)) - -# In[ ]: -save_path = os.path.join(result_dir, args.job) -# save_path = result_dir + '/%s_result' % target - -if not os.path.exists(save_path): - os.makedirs(save_path) - -score_save_file = os.path.join(save_path, "%s.npy" % target.lower()) -np.save(score_save_file, score) - -# # Step 5: Get ROC Curves and TPR@FPR Table - -# In[ ]: - -files = [score_save_file] -methods = [] -scores = [] -for file in files: - methods.append(Path(file).stem) - scores.append(np.load(file)) - -methods = np.array(methods) -scores = dict(zip(methods, scores)) -colours = dict( - zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2'))) -x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1] -tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels]) -fig = plt.figure() -for method in methods: - fpr, tpr, _ = roc_curve(label, scores[method]) - roc_auc = auc(fpr, tpr) - fpr = np.flipud(fpr) - tpr = np.flipud(tpr) # select largest tpr at same fpr - plt.plot(fpr, - tpr, - color=colours[method], - lw=1, - label=('[%s (AUC = %0.4f %%)]' % - (method.split('-')[-1], roc_auc * 100))) - tpr_fpr_row = [] - tpr_fpr_row.append("%s-%s" % (method, target)) - for fpr_iter in np.arange(len(x_labels)): - _, min_index = min( - list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr))))) - tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100)) - tpr_fpr_table.add_row(tpr_fpr_row) -plt.xlim([10 ** -6, 0.1]) -plt.ylim([0.3, 1.0]) -plt.grid(linestyle='--', linewidth=1) -plt.xticks(x_labels) -plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True)) -plt.xscale('log') -plt.xlabel('False Positive Rate') -plt.ylabel('True Positive Rate') -plt.title('ROC on IJB') -plt.legend(loc="lower right") -fig.savefig(os.path.join(save_path, '%s.pdf' % target.lower())) -print(tpr_fpr_table) diff --git a/arcface_torch/inference.py b/arcface_torch/inference.py deleted file mode 100644 index 3e5156e..0000000 --- a/arcface_torch/inference.py +++ /dev/null @@ -1,35 +0,0 @@ -import argparse - -import cv2 -import numpy as np -import torch - -from backbones import get_model - - -@torch.no_grad() -def inference(weight, name, img): - if img is None: - img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.uint8) - else: - img = cv2.imread(img) - img = cv2.resize(img, (112, 112)) - - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - img = np.transpose(img, (2, 0, 1)) - img = torch.from_numpy(img).unsqueeze(0).float() - img.div_(255).sub_(0.5).div_(0.5) - net = get_model(name, fp16=False) - net.load_state_dict(torch.load(weight)) - net.eval() - feat = net(img).numpy() - print(feat) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='PyTorch ArcFace Training') - parser.add_argument('--network', type=str, default='r50', help='backbone network') - parser.add_argument('--weight', type=str, default='') - parser.add_argument('--img', type=str, default=None) - args = parser.parse_args() - inference(args.weight, args.network, args.img) diff --git a/arcface_torch/losses.py b/arcface_torch/losses.py deleted file mode 100644 index 697b4d8..0000000 --- a/arcface_torch/losses.py +++ /dev/null @@ -1,47 +0,0 @@ -import torch -import math - -class ArcFace(torch.nn.Module): - """ ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf): - """ - def __init__(self, s=64.0, margin=0.5): - super(ArcFace, self).__init__() - self.scale = s - self.cos_m = math.cos(margin) - self.sin_m = math.sin(margin) - self.theta = math.cos(math.pi - margin) - self.sinmm = math.sin(math.pi - margin) * margin - self.easy_margin = False - - - def forward(self, logits: torch.Tensor, labels: torch.Tensor): - index = torch.where(labels != -1)[0] - target_logit = logits[index, labels[index].view(-1)] - - sin_theta = torch.sqrt(1.0 - torch.pow(target_logit, 2)) - cos_theta_m = target_logit * self.cos_m - sin_theta * self.sin_m # cos(target+margin) - if self.easy_margin: - final_target_logit = torch.where( - target_logit > 0, cos_theta_m, target_logit) - else: - final_target_logit = torch.where( - target_logit > self.theta, cos_theta_m, target_logit - self.sinmm) - - logits[index, labels[index].view(-1)] = final_target_logit - logits = logits * self.scale - return logits - - -class CosFace(torch.nn.Module): - def __init__(self, s=64.0, m=0.40): - super(CosFace, self).__init__() - self.s = s - self.m = m - - def forward(self, logits: torch.Tensor, labels: torch.Tensor): - index = torch.where(labels != -1)[0] - target_logit = logits[index, labels[index].view(-1)] - final_target_logit = target_logit - self.m - logits[index, labels[index].view(-1)] = final_target_logit - logits = logits * self.s - return logits diff --git a/arcface_torch/lr_scheduler.py b/arcface_torch/lr_scheduler.py deleted file mode 100644 index 4248964..0000000 --- a/arcface_torch/lr_scheduler.py +++ /dev/null @@ -1,29 +0,0 @@ -from torch.optim.lr_scheduler import _LRScheduler - - -class PolyScheduler(_LRScheduler): - def __init__(self, optimizer, base_lr, max_steps, warmup_steps, last_epoch=-1): - self.base_lr = base_lr - self.warmup_lr_init = 0.0001 - self.max_steps: int = max_steps - self.warmup_steps: int = warmup_steps - self.power = 2 - super(PolyScheduler, self).__init__(optimizer, last_epoch, False) - - def get_warmup_lr(self): - alpha = float(self.last_epoch) / float(self.warmup_steps) - return [self.base_lr * alpha for _ in self.optimizer.param_groups] - - def get_lr(self): - if self.last_epoch == -1: - return [self.warmup_lr_init for _ in self.optimizer.param_groups] - if self.last_epoch < self.warmup_steps: - return self.get_warmup_lr() - else: - alpha = pow( - 1 - - float(self.last_epoch - self.warmup_steps) - / float(self.max_steps - self.warmup_steps), - self.power, - ) - return [self.base_lr * alpha for _ in self.optimizer.param_groups] diff --git a/arcface_torch/onnx_helper.py b/arcface_torch/onnx_helper.py deleted file mode 100644 index ca922ca..0000000 --- a/arcface_torch/onnx_helper.py +++ /dev/null @@ -1,250 +0,0 @@ -from __future__ import division -import datetime -import os -import os.path as osp -import glob -import numpy as np -import cv2 -import sys -import onnxruntime -import onnx -import argparse -from onnx import numpy_helper -from insightface.data import get_image - -class ArcFaceORT: - def __init__(self, model_path, cpu=False): - self.model_path = model_path - # providers = None will use available provider, for onnxruntime-gpu it will be "CUDAExecutionProvider" - self.providers = ['CPUExecutionProvider'] if cpu else None - - #input_size is (w,h), return error message, return None if success - def check(self, track='cfat', test_img = None): - #default is cfat - max_model_size_mb=1024 - max_feat_dim=512 - max_time_cost=15 - if track.startswith('ms1m'): - max_model_size_mb=1024 - max_feat_dim=512 - max_time_cost=10 - elif track.startswith('glint'): - max_model_size_mb=1024 - max_feat_dim=1024 - max_time_cost=20 - elif track.startswith('cfat'): - max_model_size_mb = 1024 - max_feat_dim = 512 - max_time_cost = 15 - elif track.startswith('unconstrained'): - max_model_size_mb=1024 - max_feat_dim=1024 - max_time_cost=30 - else: - return "track not found" - - if not os.path.exists(self.model_path): - return "model_path not exists" - if not os.path.isdir(self.model_path): - return "model_path should be directory" - onnx_files = [] - for _file in os.listdir(self.model_path): - if _file.endswith('.onnx'): - onnx_files.append(osp.join(self.model_path, _file)) - if len(onnx_files)==0: - return "do not have onnx files" - self.model_file = sorted(onnx_files)[-1] - print('use onnx-model:', self.model_file) - try: - session = onnxruntime.InferenceSession(self.model_file, providers=self.providers) - except: - return "load onnx failed" - input_cfg = session.get_inputs()[0] - input_shape = input_cfg.shape - print('input-shape:', input_shape) - if len(input_shape)!=4: - return "length of input_shape should be 4" - if not isinstance(input_shape[0], str): - #return "input_shape[0] should be str to support batch-inference" - print('reset input-shape[0] to None') - model = onnx.load(self.model_file) - model.graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None' - new_model_file = osp.join(self.model_path, 'zzzzrefined.onnx') - onnx.save(model, new_model_file) - self.model_file = new_model_file - print('use new onnx-model:', self.model_file) - try: - session = onnxruntime.InferenceSession(self.model_file, providers=self.providers) - except: - return "load onnx failed" - input_cfg = session.get_inputs()[0] - input_shape = input_cfg.shape - print('new-input-shape:', input_shape) - - self.image_size = tuple(input_shape[2:4][::-1]) - #print('image_size:', self.image_size) - input_name = input_cfg.name - outputs = session.get_outputs() - output_names = [] - for o in outputs: - output_names.append(o.name) - #print(o.name, o.shape) - if len(output_names)!=1: - return "number of output nodes should be 1" - self.session = session - self.input_name = input_name - self.output_names = output_names - #print(self.output_names) - model = onnx.load(self.model_file) - graph = model.graph - if len(graph.node)<8: - return "too small onnx graph" - - input_size = (112,112) - self.crop = None - if track=='cfat': - crop_file = osp.join(self.model_path, 'crop.txt') - if osp.exists(crop_file): - lines = open(crop_file,'r').readlines() - if len(lines)!=6: - return "crop.txt should contain 6 lines" - lines = [int(x) for x in lines] - self.crop = lines[:4] - input_size = tuple(lines[4:6]) - if input_size!=self.image_size: - return "input-size is inconsistant with onnx model input, %s vs %s"%(input_size, self.image_size) - - self.model_size_mb = os.path.getsize(self.model_file) / float(1024*1024) - if self.model_size_mb > max_model_size_mb: - return "max model size exceed, given %.3f-MB"%self.model_size_mb - - input_mean = None - input_std = None - if track=='cfat': - pn_file = osp.join(self.model_path, 'pixel_norm.txt') - if osp.exists(pn_file): - lines = open(pn_file,'r').readlines() - if len(lines)!=2: - return "pixel_norm.txt should contain 2 lines" - input_mean = float(lines[0]) - input_std = float(lines[1]) - if input_mean is not None or input_std is not None: - if input_mean is None or input_std is None: - return "please set input_mean and input_std simultaneously" - else: - find_sub = False - find_mul = False - for nid, node in enumerate(graph.node[:8]): - print(nid, node.name) - if node.name.startswith('Sub') or node.name.startswith('_minus'): - find_sub = True - if node.name.startswith('Mul') or node.name.startswith('_mul') or node.name.startswith('Div'): - find_mul = True - if find_sub and find_mul: - print("find sub and mul") - #mxnet arcface model - input_mean = 0.0 - input_std = 1.0 - else: - input_mean = 127.5 - input_std = 127.5 - self.input_mean = input_mean - self.input_std = input_std - for initn in graph.initializer: - weight_array = numpy_helper.to_array(initn) - dt = weight_array.dtype - if dt.itemsize<4: - return 'invalid weight type - (%s:%s)' % (initn.name, dt.name) - if test_img is None: - test_img = get_image('Tom_Hanks_54745') - test_img = cv2.resize(test_img, self.image_size) - else: - test_img = cv2.resize(test_img, self.image_size) - feat, cost = self.benchmark(test_img) - batch_result = self.check_batch(test_img) - batch_result_sum = float(np.sum(batch_result)) - if batch_result_sum in [float('inf'), -float('inf')] or batch_result_sum != batch_result_sum: - print(batch_result) - print(batch_result_sum) - return "batch result output contains NaN!" - - if len(feat.shape) < 2: - return "the shape of the feature must be two, but get {}".format(str(feat.shape)) - - if feat.shape[1] > max_feat_dim: - return "max feat dim exceed, given %d"%feat.shape[1] - self.feat_dim = feat.shape[1] - cost_ms = cost*1000 - if cost_ms>max_time_cost: - return "max time cost exceed, given %.4f"%cost_ms - self.cost_ms = cost_ms - print('check stat:, model-size-mb: %.4f, feat-dim: %d, time-cost-ms: %.4f, input-mean: %.3f, input-std: %.3f'%(self.model_size_mb, self.feat_dim, self.cost_ms, self.input_mean, self.input_std)) - return None - - def check_batch(self, img): - if not isinstance(img, list): - imgs = [img, ] * 32 - if self.crop is not None: - nimgs = [] - for img in imgs: - nimg = img[self.crop[1]:self.crop[3], self.crop[0]:self.crop[2], :] - if nimg.shape[0] != self.image_size[1] or nimg.shape[1] != self.image_size[0]: - nimg = cv2.resize(nimg, self.image_size) - nimgs.append(nimg) - imgs = nimgs - blob = cv2.dnn.blobFromImages( - images=imgs, scalefactor=1.0 / self.input_std, size=self.image_size, - mean=(self.input_mean, self.input_mean, self.input_mean), swapRB=True) - net_out = self.session.run(self.output_names, {self.input_name: blob})[0] - return net_out - - - def meta_info(self): - return {'model-size-mb':self.model_size_mb, 'feature-dim':self.feat_dim, 'infer': self.cost_ms} - - - def forward(self, imgs): - if not isinstance(imgs, list): - imgs = [imgs] - input_size = self.image_size - if self.crop is not None: - nimgs = [] - for img in imgs: - nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:] - if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]: - nimg = cv2.resize(nimg, input_size) - nimgs.append(nimg) - imgs = nimgs - blob = cv2.dnn.blobFromImages(imgs, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True) - net_out = self.session.run(self.output_names, {self.input_name : blob})[0] - return net_out - - def benchmark(self, img): - input_size = self.image_size - if self.crop is not None: - nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:] - if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]: - nimg = cv2.resize(nimg, input_size) - img = nimg - blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True) - costs = [] - for _ in range(50): - ta = datetime.datetime.now() - net_out = self.session.run(self.output_names, {self.input_name : blob})[0] - tb = datetime.datetime.now() - cost = (tb-ta).total_seconds() - costs.append(cost) - costs = sorted(costs) - cost = costs[5] - return net_out, cost - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='') - # general - parser.add_argument('workdir', help='submitted work dir', type=str) - parser.add_argument('--track', help='track name, for different challenge', type=str, default='cfat') - args = parser.parse_args() - handler = ArcFaceORT(args.workdir) - err = handler.check(args.track) - print('err:', err) diff --git a/arcface_torch/onnx_ijbc.py b/arcface_torch/onnx_ijbc.py deleted file mode 100644 index 31c491b..0000000 --- a/arcface_torch/onnx_ijbc.py +++ /dev/null @@ -1,269 +0,0 @@ -import argparse -import os -import pickle -import timeit - -import cv2 -import mxnet as mx -import numpy as np -import pandas as pd -import prettytable -import skimage.transform -import torch -from sklearn.metrics import roc_curve -from sklearn.preprocessing import normalize -from torch.utils.data import DataLoader -from onnx_helper import ArcFaceORT - -SRC = np.array( - [ - [30.2946, 51.6963], - [65.5318, 51.5014], - [48.0252, 71.7366], - [33.5493, 92.3655], - [62.7299, 92.2041]] - , dtype=np.float32) -SRC[:, 0] += 8.0 - - -@torch.no_grad() -class AlignedDataSet(mx.gluon.data.Dataset): - def __init__(self, root, lines, align=True): - self.lines = lines - self.root = root - self.align = align - - def __len__(self): - return len(self.lines) - - def __getitem__(self, idx): - each_line = self.lines[idx] - name_lmk_score = each_line.strip().split(' ') - name = os.path.join(self.root, name_lmk_score[0]) - img = cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB) - landmark5 = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32).reshape((5, 2)) - st = skimage.transform.SimilarityTransform() - st.estimate(landmark5, SRC) - img = cv2.warpAffine(img, st.params[0:2, :], (112, 112), borderValue=0.0) - img_1 = np.expand_dims(img, 0) - img_2 = np.expand_dims(np.fliplr(img), 0) - output = np.concatenate((img_1, img_2), axis=0).astype(np.float32) - output = np.transpose(output, (0, 3, 1, 2)) - return torch.from_numpy(output) - - -@torch.no_grad() -def extract(model_root, dataset): - model = ArcFaceORT(model_path=model_root) - model.check() - feat_mat = np.zeros(shape=(len(dataset), 2 * model.feat_dim)) - - def collate_fn(data): - return torch.cat(data, dim=0) - - data_loader = DataLoader( - dataset, batch_size=128, drop_last=False, num_workers=4, collate_fn=collate_fn, ) - num_iter = 0 - for batch in data_loader: - batch = batch.numpy() - batch = (batch - model.input_mean) / model.input_std - feat = model.session.run(model.output_names, {model.input_name: batch})[0] - feat = np.reshape(feat, (-1, model.feat_dim * 2)) - feat_mat[128 * num_iter: 128 * num_iter + feat.shape[0], :] = feat - num_iter += 1 - if num_iter % 50 == 0: - print(num_iter) - return feat_mat - - -def read_template_media_list(path): - ijb_meta = pd.read_csv(path, sep=' ', header=None).values - templates = ijb_meta[:, 1].astype(np.int) - medias = ijb_meta[:, 2].astype(np.int) - return templates, medias - - -def read_template_pair_list(path): - pairs = pd.read_csv(path, sep=' ', header=None).values - t1 = pairs[:, 0].astype(np.int) - t2 = pairs[:, 1].astype(np.int) - label = pairs[:, 2].astype(np.int) - return t1, t2, label - - -def read_image_feature(path): - with open(path, 'rb') as fid: - img_feats = pickle.load(fid) - return img_feats - - -def image2template_feature(img_feats=None, - templates=None, - medias=None): - unique_templates = np.unique(templates) - template_feats = np.zeros((len(unique_templates), img_feats.shape[1])) - for count_template, uqt in enumerate(unique_templates): - (ind_t,) = np.where(templates == uqt) - face_norm_feats = img_feats[ind_t] - face_medias = medias[ind_t] - unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True) - media_norm_feats = [] - for u, ct in zip(unique_medias, unique_media_counts): - (ind_m,) = np.where(face_medias == u) - if ct == 1: - media_norm_feats += [face_norm_feats[ind_m]] - else: # image features from the same video will be aggregated into one feature - media_norm_feats += [np.mean(face_norm_feats[ind_m], axis=0, keepdims=True), ] - media_norm_feats = np.array(media_norm_feats) - template_feats[count_template] = np.sum(media_norm_feats, axis=0) - if count_template % 2000 == 0: - print('Finish Calculating {} template features.'.format( - count_template)) - template_norm_feats = normalize(template_feats) - return template_norm_feats, unique_templates - - -def verification(template_norm_feats=None, - unique_templates=None, - p1=None, - p2=None): - template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int) - for count_template, uqt in enumerate(unique_templates): - template2id[uqt] = count_template - score = np.zeros((len(p1),)) - total_pairs = np.array(range(len(p1))) - batchsize = 100000 - sublists = [total_pairs[i: i + batchsize] for i in range(0, len(p1), batchsize)] - total_sublists = len(sublists) - for c, s in enumerate(sublists): - feat1 = template_norm_feats[template2id[p1[s]]] - feat2 = template_norm_feats[template2id[p2[s]]] - similarity_score = np.sum(feat1 * feat2, -1) - score[s] = similarity_score.flatten() - if c % 10 == 0: - print('Finish {}/{} pairs.'.format(c, total_sublists)) - return score - - -def verification2(template_norm_feats=None, - unique_templates=None, - p1=None, - p2=None): - template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int) - for count_template, uqt in enumerate(unique_templates): - template2id[uqt] = count_template - score = np.zeros((len(p1),)) # save cosine distance between pairs - total_pairs = np.array(range(len(p1))) - batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation - sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)] - total_sublists = len(sublists) - for c, s in enumerate(sublists): - feat1 = template_norm_feats[template2id[p1[s]]] - feat2 = template_norm_feats[template2id[p2[s]]] - similarity_score = np.sum(feat1 * feat2, -1) - score[s] = similarity_score.flatten() - if c % 10 == 0: - print('Finish {}/{} pairs.'.format(c, total_sublists)) - return score - - -def main(args): - use_norm_score = True # if Ture, TestMode(N1) - use_detector_score = True # if Ture, TestMode(D1) - use_flip_test = True # if Ture, TestMode(F1) - assert args.target == 'IJBC' or args.target == 'IJBB' - - start = timeit.default_timer() - templates, medias = read_template_media_list( - os.path.join('%s/meta' % args.image_path, '%s_face_tid_mid.txt' % args.target.lower())) - stop = timeit.default_timer() - print('Time: %.2f s. ' % (stop - start)) - - start = timeit.default_timer() - p1, p2, label = read_template_pair_list( - os.path.join('%s/meta' % args.image_path, - '%s_template_pair_label.txt' % args.target.lower())) - stop = timeit.default_timer() - print('Time: %.2f s. ' % (stop - start)) - - start = timeit.default_timer() - img_path = '%s/loose_crop' % args.image_path - img_list_path = '%s/meta/%s_name_5pts_score.txt' % (args.image_path, args.target.lower()) - img_list = open(img_list_path) - files = img_list.readlines() - dataset = AlignedDataSet(root=img_path, lines=files, align=True) - img_feats = extract(args.model_root, dataset) - - faceness_scores = [] - for each_line in files: - name_lmk_score = each_line.split() - faceness_scores.append(name_lmk_score[-1]) - faceness_scores = np.array(faceness_scores).astype(np.float32) - stop = timeit.default_timer() - print('Time: %.2f s. ' % (stop - start)) - print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1])) - start = timeit.default_timer() - - if use_flip_test: - img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] + img_feats[:, img_feats.shape[1] // 2:] - else: - img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] - - if use_norm_score: - img_input_feats = img_input_feats - else: - img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True)) - - if use_detector_score: - print(img_input_feats.shape, faceness_scores.shape) - img_input_feats = img_input_feats * faceness_scores[:, np.newaxis] - else: - img_input_feats = img_input_feats - - template_norm_feats, unique_templates = image2template_feature( - img_input_feats, templates, medias) - stop = timeit.default_timer() - print('Time: %.2f s. ' % (stop - start)) - - start = timeit.default_timer() - score = verification(template_norm_feats, unique_templates, p1, p2) - stop = timeit.default_timer() - print('Time: %.2f s. ' % (stop - start)) - result_dir = args.model_root - - save_path = os.path.join(result_dir, "{}_result".format(args.target)) - if not os.path.exists(save_path): - os.makedirs(save_path) - score_save_file = os.path.join(save_path, "{}.npy".format(args.target)) - np.save(score_save_file, score) - files = [score_save_file] - methods = [] - scores = [] - for file in files: - methods.append(os.path.basename(file)) - scores.append(np.load(file)) - methods = np.array(methods) - scores = dict(zip(methods, scores)) - x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1] - tpr_fpr_table = prettytable.PrettyTable(['Methods'] + [str(x) for x in x_labels]) - for method in methods: - fpr, tpr, _ = roc_curve(label, scores[method]) - fpr = np.flipud(fpr) - tpr = np.flipud(tpr) - tpr_fpr_row = [] - tpr_fpr_row.append("%s-%s" % (method, args.target)) - for fpr_iter in np.arange(len(x_labels)): - _, min_index = min( - list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr))))) - tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100)) - tpr_fpr_table.add_row(tpr_fpr_row) - print(tpr_fpr_table) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='do ijb test') - # general - parser.add_argument('--model-root', default='', help='path to load model.') - parser.add_argument('--image-path', default='/train_tmp/IJB_release/IJBC', type=str, help='') - parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB') - main(parser.parse_args()) diff --git a/arcface_torch/partial_fc.py b/arcface_torch/partial_fc.py deleted file mode 100644 index d4f7e68..0000000 --- a/arcface_torch/partial_fc.py +++ /dev/null @@ -1,330 +0,0 @@ -import collections -from typing import Callable - -import torch -from torch import distributed -from torch.nn.functional import linear, normalize - - -class PartialFC(torch.nn.Module): - """ - https://arxiv.org/abs/2010.05222 - A distributed sparsely updating variant of the FC layer, named Partial FC (PFC). - - When sample rate less than 1, in each iteration, positive class centers and a random subset of - negative class centers are selected to compute the margin-based softmax loss, all class - centers are still maintained throughout the whole training process, but only a subset is - selected and updated in each iteration. - - .. note:: - When sample rate equal to 1, Partial FC is equal to model parallelism(default sample rate is 1). - - Example: - -------- - >>> module_pfc = PartialFC(embedding_size=512, num_classes=8000000, sample_rate=0.2) - >>> for img, labels in data_loader: - >>> embeddings = net(img) - >>> loss = module_pfc(embeddings, labels, optimizer) - >>> loss.backward() - >>> optimizer.step() - """ - _version = 1 - def __init__( - self, - margin_loss: Callable, - embedding_size: int, - num_classes: int, - sample_rate: float = 1.0, - fp16: bool = False, - ): - """ - Paramenters: - ----------- - embedding_size: int - The dimension of embedding, required - num_classes: int - Total number of classes, required - sample_rate: float - The rate of negative centers participating in the calculation, default is 1.0. - """ - super(PartialFC, self).__init__() - assert ( - distributed.is_initialized() - ), "must initialize distributed before create this" - self.rank = distributed.get_rank() - self.world_size = distributed.get_world_size() - - self.dist_cross_entropy = DistCrossEntropy() - self.embedding_size = embedding_size - self.sample_rate: float = sample_rate - self.fp16 = fp16 - self.num_local: int = num_classes // self.world_size + int( - self.rank < num_classes % self.world_size - ) - self.class_start: int = num_classes // self.world_size * self.rank + min( - self.rank, num_classes % self.world_size - ) - self.num_sample: int = int(self.sample_rate * self.num_local) - self.last_batch_size: int = 0 - self.weight: torch.Tensor - self.weight_mom: torch.Tensor - self.weight_activated: torch.nn.Parameter - self.weight_activated_mom: torch.Tensor - self.is_updated: bool = True - self.init_weight_update: bool = True - - if self.sample_rate < 1: - self.register_buffer("weight", - tensor=torch.normal(0, 0.01, (self.num_local, embedding_size))) - self.register_buffer("weight_mom", - tensor=torch.zeros_like(self.weight)) - self.register_parameter("weight_activated", - param=torch.nn.Parameter(torch.empty(0, 0))) - self.register_buffer("weight_activated_mom", - tensor=torch.empty(0, 0)) - self.register_buffer("weight_index", - tensor=torch.empty(0, 0)) - else: - self.weight_activated = torch.nn.Parameter(torch.normal(0, 0.01, (self.num_local, embedding_size))) - - # margin_loss - if isinstance(margin_loss, Callable): - self.margin_softmax = margin_loss - else: - raise - - @torch.no_grad() - def sample(self, - labels: torch.Tensor, - index_positive: torch.Tensor, - optimizer: torch.optim.Optimizer): - """ - This functions will change the value of labels - - Parameters: - ----------- - labels: torch.Tensor - pass - index_positive: torch.Tensor - pass - optimizer: torch.optim.Optimizer - pass - """ - positive = torch.unique(labels[index_positive], sorted=True).cuda() - if self.num_sample - positive.size(0) >= 0: - perm = torch.rand(size=[self.num_local]).cuda() - perm[positive] = 2.0 - index = torch.topk(perm, k=self.num_sample)[1].cuda() - index = index.sort()[0].cuda() - else: - index = positive - self.weight_index = index - - labels[index_positive] = torch.searchsorted(index, labels[index_positive]) - - self.weight_activated = torch.nn.Parameter(self.weight[self.weight_index]) - self.weight_activated_mom = self.weight_mom[self.weight_index] - - if isinstance(optimizer, torch.optim.SGD): - # TODO the params of partial fc must be last in the params list - optimizer.state.pop(optimizer.param_groups[-1]["params"][0], None) - optimizer.param_groups[-1]["params"][0] = self.weight_activated - optimizer.state[self.weight_activated][ - "momentum_buffer" - ] = self.weight_activated_mom - else: - raise - - @torch.no_grad() - def update(self): - """ partial weight to global - """ - if self.init_weight_update: - self.init_weight_update = False - return - - if self.sample_rate < 1: - self.weight[self.weight_index] = self.weight_activated - self.weight_mom[self.weight_index] = self.weight_activated_mom - - - def forward( - self, - local_embeddings: torch.Tensor, - local_labels: torch.Tensor, - optimizer: torch.optim.Optimizer, - ): - """ - Parameters: - ---------- - local_embeddings: torch.Tensor - feature embeddings on each GPU(Rank). - local_labels: torch.Tensor - labels on each GPU(Rank). - - Returns: - ------- - loss: torch.Tensor - pass - """ - local_labels.squeeze_() - local_labels = local_labels.long() - self.update() - - batch_size = local_embeddings.size(0) - if self.last_batch_size == 0: - self.last_batch_size = batch_size - assert self.last_batch_size == batch_size, ( - "last batch size do not equal current batch size: {} vs {}".format( - self.last_batch_size, batch_size)) - - _gather_embeddings = [ - torch.zeros((batch_size, self.embedding_size)).cuda() - for _ in range(self.world_size) - ] - _gather_labels = [ - torch.zeros(batch_size).long().cuda() for _ in range(self.world_size) - ] - _list_embeddings = AllGather(local_embeddings, *_gather_embeddings) - distributed.all_gather(_gather_labels, local_labels) - - embeddings = torch.cat(_list_embeddings) - labels = torch.cat(_gather_labels) - - labels = labels.view(-1, 1) - index_positive = (self.class_start <= labels) & ( - labels < self.class_start + self.num_local - ) - labels[~index_positive] = -1 - labels[index_positive] -= self.class_start - - if self.sample_rate < 1: - self.sample(labels, index_positive, optimizer) - - with torch.cuda.amp.autocast(self.fp16): - norm_embeddings = normalize(embeddings) - norm_weight_activated = normalize(self.weight_activated) - logits = linear(norm_embeddings, norm_weight_activated) - if self.fp16: - logits = logits.float() - logits = logits.clamp(-1, 1) - - logits = self.margin_softmax(logits, labels) - loss = self.dist_cross_entropy(logits, labels) - return loss - - def state_dict(self, destination=None, prefix="", keep_vars=False): - if destination is None: - destination = collections.OrderedDict() - destination._metadata = collections.OrderedDict() - - for name, module in self._modules.items(): - if module is not None: - module.state_dict(destination, prefix + name + ".", keep_vars=keep_vars) - if self.sample_rate < 1: - destination["weight"] = self.weight.detach() - else: - destination["weight"] = self.weight_activated.data.detach() - return destination - - def load_state_dict(self, state_dict, strict: bool = True): - if self.sample_rate < 1: - self.weight = state_dict["weight"].to(self.weight.device) - self.weight_mom.zero_() - self.weight_activated.data.zero_() - self.weight_activated_mom.zero_() - self.weight_index.zero_() - else: - self.weight_activated.data = state_dict["weight"].to(self.weight_activated.data.device) - -class DistCrossEntropyFunc(torch.autograd.Function): - """ - CrossEntropy loss is calculated in parallel, allreduce denominator into single gpu and calculate softmax. - Implemented of ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf): - """ - - @staticmethod - def forward(ctx, logits: torch.Tensor, label: torch.Tensor): - """ """ - batch_size = logits.size(0) - # for numerical stability - max_logits, _ = torch.max(logits, dim=1, keepdim=True) - # local to global - distributed.all_reduce(max_logits, distributed.ReduceOp.MAX) - logits.sub_(max_logits) - logits.exp_() - sum_logits_exp = torch.sum(logits, dim=1, keepdim=True) - # local to global - distributed.all_reduce(sum_logits_exp, distributed.ReduceOp.SUM) - logits.div_(sum_logits_exp) - index = torch.where(label != -1)[0] - # loss - loss = torch.zeros(batch_size, 1, device=logits.device) - loss[index] = logits[index].gather(1, label[index]) - distributed.all_reduce(loss, distributed.ReduceOp.SUM) - ctx.save_for_backward(index, logits, label) - return loss.clamp_min_(1e-30).log_().mean() * (-1) - - @staticmethod - def backward(ctx, loss_gradient): - """ - Args: - loss_grad (torch.Tensor): gradient backward by last layer - Returns: - gradients for each input in forward function - `None` gradients for one-hot label - """ - ( - index, - logits, - label, - ) = ctx.saved_tensors - batch_size = logits.size(0) - one_hot = torch.zeros( - size=[index.size(0), logits.size(1)], device=logits.device - ) - one_hot.scatter_(1, label[index], 1) - logits[index] -= one_hot - logits.div_(batch_size) - return logits * loss_gradient.item(), None - - -class DistCrossEntropy(torch.nn.Module): - def __init__(self): - super(DistCrossEntropy, self).__init__() - - def forward(self, logit_part, label_part): - return DistCrossEntropyFunc.apply(logit_part, label_part) - - -class AllGatherFunc(torch.autograd.Function): - """AllGather op with gradient backward""" - - @staticmethod - def forward(ctx, tensor, *gather_list): - gather_list = list(gather_list) - distributed.all_gather(gather_list, tensor) - return tuple(gather_list) - - @staticmethod - def backward(ctx, *grads): - grad_list = list(grads) - rank = distributed.get_rank() - grad_out = grad_list[rank] - - dist_ops = [ - distributed.reduce(grad_out, rank, distributed.ReduceOp.SUM, async_op=True) - if i == rank - else distributed.reduce( - grad_list[i], i, distributed.ReduceOp.SUM, async_op=True - ) - for i in range(distributed.get_world_size()) - ] - for _op in dist_ops: - _op.wait() - - grad_out *= len(grad_list) # cooperate with distributed loss function - return (grad_out, *[None for _ in range(len(grad_list))]) - - -AllGather = AllGatherFunc.apply diff --git a/arcface_torch/requirement.txt b/arcface_torch/requirement.txt deleted file mode 100644 index f72c1b3..0000000 --- a/arcface_torch/requirement.txt +++ /dev/null @@ -1,5 +0,0 @@ -tensorboard -easydict -mxnet -onnx -sklearn diff --git a/arcface_torch/run.sh b/arcface_torch/run.sh deleted file mode 100644 index 4069075..0000000 --- a/arcface_torch/run.sh +++ /dev/null @@ -1,9 +0,0 @@ - -CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch \ ---nproc_per_node=8 \ ---nnodes=1 \ ---node_rank=0 \ ---master_addr="127.0.0.1" \ ---master_port=12345 train.py $@ - -ps -ef | grep "train" | grep -v grep | awk '{print "kill -9 "$2}' | sh diff --git a/arcface_torch/torch2onnx.py b/arcface_torch/torch2onnx.py deleted file mode 100644 index 63ce2c5..0000000 --- a/arcface_torch/torch2onnx.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np -import onnx -import torch - - -def convert_onnx(net, path_module, output, opset=11, simplify=False): - assert isinstance(net, torch.nn.Module) - img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32) - img = img.astype(np.float) - img = (img / 255. - 0.5) / 0.5 # torch style norm - img = img.transpose((2, 0, 1)) - img = torch.from_numpy(img).unsqueeze(0).float() - - weight = torch.load(path_module) - net.load_state_dict(weight, strict=True) - net.eval() - torch.onnx.export(net, img, output, keep_initializers_as_inputs=False, verbose=False, opset_version=opset) - model = onnx.load(output) - graph = model.graph - graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None' - if simplify: - from onnxsim import simplify - model, check = simplify(model) - assert check, "Simplified ONNX model could not be validated" - onnx.save(model, output) - - -if __name__ == '__main__': - import os - import argparse - from backbones import get_model - - parser = argparse.ArgumentParser(description='ArcFace PyTorch to onnx') - parser.add_argument('input', type=str, help='input backbone.pth file or path') - parser.add_argument('--output', type=str, default=None, help='output onnx path') - parser.add_argument('--network', type=str, default=None, help='backbone network') - parser.add_argument('--simplify', type=bool, default=False, help='onnx simplify') - args = parser.parse_args() - input_file = args.input - if os.path.isdir(input_file): - input_file = os.path.join(input_file, "model.pt") - assert os.path.exists(input_file) - # model_name = os.path.basename(os.path.dirname(input_file)).lower() - # params = model_name.split("_") - # if len(params) >= 3 and params[1] in ('arcface', 'cosface'): - # if args.network is None: - # args.network = params[2] - assert args.network is not None - print(args) - backbone_onnx = get_model(args.network, dropout=0) - if args.output is None: - args.output = os.path.join(os.path.dirname(args.input), "model.onnx") - convert_onnx(backbone_onnx, input_file, args.output, simplify=args.simplify) diff --git a/arcface_torch/train.py b/arcface_torch/train.py deleted file mode 100644 index 9e27e8c..0000000 --- a/arcface_torch/train.py +++ /dev/null @@ -1,161 +0,0 @@ -import argparse -import logging -import os - -import torch -from torch import distributed -from torch.utils.tensorboard import SummaryWriter - -from backbones import get_model -from dataset import get_dataloader -from torch.utils.data import DataLoader -from lr_scheduler import PolyScheduler -from losses import CosFace, ArcFace -from partial_fc import PartialFC -from utils.utils_callbacks import CallBackLogging, CallBackVerification -from utils.utils_config import get_config -from utils.utils_logging import AverageMeter, init_logging - - -try: - world_size = int(os.environ["WORLD_SIZE"]) - rank = int(os.environ["RANK"]) - distributed.init_process_group("nccl") -except KeyError: - world_size = 1 - rank = 0 - distributed.init_process_group( - backend="nccl", - init_method="tcp://127.0.0.1:12584", - rank=rank, - world_size=world_size, - ) - - -def main(args): - torch.cuda.set_device(args.local_rank) - cfg = get_config(args.config) - - os.makedirs(cfg.output, exist_ok=True) - init_logging(rank, cfg.output) - summary_writer = ( - SummaryWriter(log_dir=os.path.join(cfg.output, "tensorboard")) - if rank == 0 - else None - ) - train_loader = get_dataloader( - cfg.rec, local_rank=args.local_rank, batch_size=cfg.batch_size, dali=cfg.dali) - backbone = get_model( - cfg.network, dropout=0.0, fp16=cfg.fp16, num_features=cfg.embedding_size - ).cuda() - - backbone = torch.nn.parallel.DistributedDataParallel( - module=backbone, broadcast_buffers=False, device_ids=[args.local_rank]) - backbone.train() - - if cfg.loss == "arcface": - margin_loss = ArcFace() - elif cfg.loss == "cosface": - margin_loss = CosFace() - else: - raise - - module_partial_fc = PartialFC( - margin_loss, - cfg.embedding_size, - cfg.num_classes, - cfg.sample_rate, - cfg.fp16 - ) - module_partial_fc.train().cuda() - - # TODO the params of partial fc must be last in the params list - opt = torch.optim.SGD( - params=[ - {"params": backbone.parameters(), }, - {"params": module_partial_fc.parameters(), }, - ], - lr=cfg.lr, - momentum=0.9, - weight_decay=cfg.weight_decay - ) - total_batch_size = cfg.batch_size * world_size - cfg.warmup_step = cfg.num_image // total_batch_size * cfg.warmup_epoch - cfg.total_step = cfg.num_image // total_batch_size * cfg.num_epoch - lr_scheduler = PolyScheduler( - optimizer=opt, - base_lr=cfg.lr, - max_steps=cfg.total_step, - warmup_steps=cfg.warmup_step - ) - - for key, value in cfg.items(): - num_space = 25 - len(key) - logging.info(": " + key + " " * num_space + str(value)) - - callback_verification = CallBackVerification( - val_targets=cfg.val_targets, rec_prefix=cfg.rec, summary_writer=summary_writer - ) - callback_logging = CallBackLogging( - frequent=cfg.frequent, - total_step=cfg.total_step, - batch_size=cfg.batch_size, - writer=summary_writer - ) - - loss_am = AverageMeter() - start_epoch = 0 - global_step = 0 - amp = torch.cuda.amp.grad_scaler.GradScaler(growth_interval=100) - - for epoch in range(start_epoch, cfg.num_epoch): - - if isinstance(train_loader, DataLoader): - train_loader.sampler.set_epoch(epoch) - for _, (img, local_labels) in enumerate(train_loader): - global_step += 1 - local_embeddings = backbone(img) - loss: torch.Tensor = module_partial_fc(local_embeddings, local_labels, opt) - - if cfg.fp16: - amp.scale(loss).backward() - amp.unscale_(opt) - torch.nn.utils.clip_grad_norm_(backbone.parameters(), 5) - amp.step(opt) - amp.update() - else: - loss.backward() - torch.nn.utils.clip_grad_norm_(backbone.parameters(), 5) - opt.step() - - opt.zero_grad() - lr_scheduler.step() - - with torch.no_grad(): - loss_am.update(loss.item(), 1) - callback_logging(global_step, loss_am, epoch, cfg.fp16, lr_scheduler.get_last_lr()[0], amp) - - if global_step % cfg.verbose == 0 and global_step > 200: - callback_verification(global_step, backbone) - - path_pfc = os.path.join(cfg.output, "softmax_fc_gpu_{}.pt".format(rank)) - torch.save(module_partial_fc.state_dict(), path_pfc) - if rank == 0: - path_module = os.path.join(cfg.output, "model.pt") - torch.save(backbone.module.state_dict(), path_module) - - if cfg.dali: - train_loader.reset() - - if rank == 0: - path_module = os.path.join(cfg.output, "model.pt") - torch.save(backbone.module.state_dict(), path_module) - distributed.destroy_process_group() - - -if __name__ == "__main__": - torch.backends.cudnn.benchmark = True - parser = argparse.ArgumentParser(description="Distributed Arcface Training in Pytorch") - parser.add_argument("config", type=str, help="py config file") - parser.add_argument("--local_rank", type=int, default=0, help="local_rank") - main(parser.parse_args()) diff --git a/arcface_torch/utils/__init__.py b/arcface_torch/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/arcface_torch/utils/plot.py b/arcface_torch/utils/plot.py deleted file mode 100644 index 7f1d39d..0000000 --- a/arcface_torch/utils/plot.py +++ /dev/null @@ -1,71 +0,0 @@ -import os -import sys - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap -from prettytable import PrettyTable -from sklearn.metrics import roc_curve, auc - -with open(sys.argv[1], "r") as f: - files = f.readlines() - -files = [x.strip() for x in files] -image_path = "/train_tmp/IJB_release/IJBC" - - -def read_template_pair_list(path): - pairs = pd.read_csv(path, sep=' ', header=None).values - t1 = pairs[:, 0].astype(np.int) - t2 = pairs[:, 1].astype(np.int) - label = pairs[:, 2].astype(np.int) - return t1, t2, label - - -p1, p2, label = read_template_pair_list( - os.path.join('%s/meta' % image_path, - '%s_template_pair_label.txt' % 'ijbc')) - -methods = [] -scores = [] -for file in files: - methods.append(file) - scores.append(np.load(file)) - -methods = np.array(methods) -scores = dict(zip(methods, scores)) -colours = dict( - zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2'))) -x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1] -tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels]) -fig = plt.figure() -for method in methods: - fpr, tpr, _ = roc_curve(label, scores[method]) - roc_auc = auc(fpr, tpr) - fpr = np.flipud(fpr) - tpr = np.flipud(tpr) # select largest tpr at same fpr - plt.plot(fpr, - tpr, - color=colours[method], - lw=1, - label=('[%s (AUC = %0.4f %%)]' % - (method.split('-')[-1], roc_auc * 100))) - tpr_fpr_row = [] - tpr_fpr_row.append(method) - for fpr_iter in np.arange(len(x_labels)): - _, min_index = min( - list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr))))) - tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100)) - tpr_fpr_table.add_row(tpr_fpr_row) -plt.xlim([10 ** -6, 0.1]) -plt.ylim([0.3, 1.0]) -plt.grid(linestyle='--', linewidth=1) -plt.xticks(x_labels) -plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True)) -plt.xscale('log') -plt.xlabel('False Positive Rate') -plt.ylabel('True Positive Rate') -plt.title('ROC on IJB') -plt.legend(loc="lower right") -print(tpr_fpr_table) diff --git a/arcface_torch/utils/utils_callbacks.py b/arcface_torch/utils/utils_callbacks.py deleted file mode 100644 index 97fe403..0000000 --- a/arcface_torch/utils/utils_callbacks.py +++ /dev/null @@ -1,110 +0,0 @@ -import logging -import os -import time -from typing import List - -import torch - -from eval import verification -from utils.utils_logging import AverageMeter -from torch.utils.tensorboard import SummaryWriter -from torch import distributed - - -class CallBackVerification(object): - - def __init__(self, val_targets, rec_prefix, summary_writer=None, image_size=(112, 112)): - self.rank: int = distributed.get_rank() - self.highest_acc: float = 0.0 - self.highest_acc_list: List[float] = [0.0] * len(val_targets) - self.ver_list: List[object] = [] - self.ver_name_list: List[str] = [] - if self.rank is 0: - self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size) - - self.summary_writer = summary_writer - - def ver_test(self, backbone: torch.nn.Module, global_step: int): - results = [] - for i in range(len(self.ver_list)): - acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test( - self.ver_list[i], backbone, 10, 10) - logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm)) - logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2)) - - self.summary_writer: SummaryWriter - self.summary_writer.add_scalar(tag=self.ver_name_list[i], scalar_value=acc2, global_step=global_step, ) - - if acc2 > self.highest_acc_list[i]: - self.highest_acc_list[i] = acc2 - logging.info( - '[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i])) - results.append(acc2) - - def init_dataset(self, val_targets, data_dir, image_size): - for name in val_targets: - path = os.path.join(data_dir, name + ".bin") - if os.path.exists(path): - data_set = verification.load_bin(path, image_size) - self.ver_list.append(data_set) - self.ver_name_list.append(name) - - def __call__(self, num_update, backbone: torch.nn.Module): - if self.rank is 0 and num_update > 0: - backbone.eval() - self.ver_test(backbone, num_update) - backbone.train() - - -class CallBackLogging(object): - def __init__(self, frequent, total_step, batch_size, writer=None): - self.frequent: int = frequent - self.rank: int = distributed.get_rank() - self.world_size: int = distributed.get_world_size() - self.time_start = time.time() - self.total_step: int = total_step - self.batch_size: int = batch_size - self.writer = writer - - self.init = False - self.tic = 0 - - def __call__(self, - global_step: int, - loss: AverageMeter, - epoch: int, - fp16: bool, - learning_rate: float, - grad_scaler: torch.cuda.amp.GradScaler): - if self.rank == 0 and global_step > 0 and global_step % self.frequent == 0: - if self.init: - try: - speed: float = self.frequent * self.batch_size / (time.time() - self.tic) - speed_total = speed * self.world_size - except ZeroDivisionError: - speed_total = float('inf') - - time_now = (time.time() - self.time_start) / 3600 - time_total = time_now / ((global_step + 1) / self.total_step) - time_for_end = time_total - time_now - if self.writer is not None: - self.writer.add_scalar('time_for_end', time_for_end, global_step) - self.writer.add_scalar('learning_rate', learning_rate, global_step) - self.writer.add_scalar('loss', loss.avg, global_step) - if fp16: - msg = "Speed %.2f samples/sec Loss %.4f LearningRate %.4f Epoch: %d Global Step: %d " \ - "Fp16 Grad Scale: %2.f Required: %1.f hours" % ( - speed_total, loss.avg, learning_rate, epoch, global_step, - grad_scaler.get_scale(), time_for_end - ) - else: - msg = "Speed %.2f samples/sec Loss %.4f LearningRate %.4f Epoch: %d Global Step: %d " \ - "Required: %1.f hours" % ( - speed_total, loss.avg, learning_rate, epoch, global_step, time_for_end - ) - logging.info(msg) - loss.reset() - self.tic = time.time() - else: - self.init = True - self.tic = time.time() diff --git a/arcface_torch/utils/utils_config.py b/arcface_torch/utils/utils_config.py deleted file mode 100644 index 0c02eaf..0000000 --- a/arcface_torch/utils/utils_config.py +++ /dev/null @@ -1,16 +0,0 @@ -import importlib -import os.path as osp - - -def get_config(config_file): - assert config_file.startswith('configs/'), 'config file setting must start with configs/' - temp_config_name = osp.basename(config_file) - temp_module_name = osp.splitext(temp_config_name)[0] - config = importlib.import_module("configs.base") - cfg = config.config - config = importlib.import_module("configs.%s" % temp_module_name) - job_cfg = config.config - cfg.update(job_cfg) - if cfg.output is None: - cfg.output = osp.join('work_dirs', temp_module_name) - return cfg \ No newline at end of file diff --git a/arcface_torch/utils/utils_logging.py b/arcface_torch/utils/utils_logging.py deleted file mode 100644 index c787b6a..0000000 --- a/arcface_torch/utils/utils_logging.py +++ /dev/null @@ -1,41 +0,0 @@ -import logging -import os -import sys - - -class AverageMeter(object): - """Computes and stores the average and current value - """ - - def __init__(self): - self.val = None - self.avg = None - self.sum = None - self.count = None - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - -def init_logging(rank, models_root): - if rank == 0: - log_root = logging.getLogger() - log_root.setLevel(logging.INFO) - formatter = logging.Formatter("Training: %(asctime)s-%(message)s") - handler_file = logging.FileHandler(os.path.join(models_root, "training.log")) - handler_stream = logging.StreamHandler(sys.stdout) - handler_file.setFormatter(formatter) - handler_stream.setFormatter(formatter) - log_root.addHandler(handler_file) - log_root.addHandler(handler_stream) - log_root.info('rank_id: %d' % rank) diff --git a/breakpoint.json b/breakpoint.json deleted file mode 100644 index 6726984..0000000 --- a/breakpoint.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "breakpoint": [ - 54, - 101 - ] -} \ No newline at end of file diff --git a/check_list.txt b/check_list.txt deleted file mode 100644 index 54858f9..0000000 --- a/check_list.txt +++ /dev/null @@ -1,8264 +0,0 @@ -n001001\0015_03.jpg -n001001\0043_02.jpg -n001001\0053_01.jpg -n001001\0094_01.jpg -n001001\0171_02.jpg -n001001\0233_02.jpg -n001001\0278_02.jpg -n001001\0281_03.jpg -n001001\0335_02.jpg -n001001\0356_05.jpg -n001001\0421_01.jpg -n001001\0455_02.jpg -n001002\0012_02.jpg -n001002\0091_02.jpg -n001002\0094_01.jpg -n001002\0106_02.jpg -n001002\0142_02.jpg -n001002\0263_01.jpg -n001002\0280_01.jpg -n001002\0357_01.jpg -n001002\0398_04.jpg -n001003\0007_02.jpg -n001003\0029_01.jpg -n001003\0124_01.jpg -n001003\0139_03.jpg -n001003\0145_03.jpg -n001003\0174_01.jpg -n001003\0230_02.jpg -n001003\0327_02.jpg -n001003\0329_01.jpg -n001003\0338_01.jpg -n001004\0168_01.jpg -n001005\0003_01.jpg -n001005\0018_03.jpg -n001005\0060_01.jpg -n001005\0122_02.jpg -n001005\0131_03.jpg -n001005\0144_01.jpg 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-n000059\0222_01.jpg diff --git a/clear_dataset.py b/clear_dataset.py deleted file mode 100644 index fb6da04..0000000 --- a/clear_dataset.py +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: clear_dataset.py -# Created Date: Thursday March 24th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 3rd April 2022 1:20:44 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import json -from utilities.json_config import readConfig, writeConfig - -if __name__ == "__main__": - savePath = "./vggface2hq_failed.txt" - env_config = readConfig('env/env.json') - env_config = env_config["path"] - dataset_root = env_config["dataset_paths"]["vggface2_hq"]["images"] - # dataset_root = "G:/VGGFace2-HQ/newversion" - print(dataset_root) - - with open(savePath,'r') as logf: - for line in logf: - img_path = os.path.join(dataset_root,line.replace("\n","")).replace("\\","/") - try: - os.rename(img_path,img_path+".deleted") - except Exception as e: - print(e) \ No newline at end of file diff --git a/components/DeConv.py b/components/DeConv.py deleted file mode 100644 index e5d7af9..0000000 --- a/components/DeConv.py +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: DeConv.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 19th February 2022 5:35:38 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - -from torch import nn - -class DeConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero"): - super().__init__() - self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - padding_size = int((kernel_size -1)/2) - if padding.lower() == "reflect": - self.conv = nn.Sequential( - nn.ReflectionPad2d(padding_size), - nn.Conv2d(in_channels = in_channels, - out_channels = out_channels, kernel_size= kernel_size, bias= False)) - # for layer in self.conv: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - elif padding.lower() == "zero": - self.conv = nn.Conv2d(in_channels = in_channels, padding = 1, - out_channels = out_channels, kernel_size= kernel_size, bias= False) - # nn.init.xavier_uniform_(self.conv.weight) - # self.__weights_init__() - - # def __weights_init__(self): - # nn.init.xavier_uniform_(self.conv.weight) - - def forward(self, input): - h = self.upsampling(input) - h = self.conv(h) - return h \ No newline at end of file diff --git a/components/DeConv_Depthwise.py b/components/DeConv_Depthwise.py deleted file mode 100644 index 9835b9a..0000000 --- a/components/DeConv_Depthwise.py +++ /dev/null @@ -1,35 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: DeConv copy.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 17th February 2022 10:20:46 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# -from tokenize import group -from torch import nn - -class DeConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero", up_mode = "bilinear"): - super().__init__() - if up_mode.lower() == "bilinear": - self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - elif up_mode.lower() == "nearest": - self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale) - padding_size = int((kernel_size -1)/2) - self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1) - self.conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=padding_size, bias=False, groups=out_channels) - # nn.init.xavier_uniform_(self.conv.weight) - # self.__weights_init__() - - # def __weights_init__(self): - # nn.init.xavier_uniform_(self.conv.weight) - - def forward(self, input): - h = self.conv1x1(input) - h = self.upsampling(h) - h = self.conv(h) - return h \ No newline at end of file diff --git a/components/DeConv_Depthwise1.py b/components/DeConv_Depthwise1.py deleted file mode 100644 index fd7df1c..0000000 --- a/components/DeConv_Depthwise1.py +++ /dev/null @@ -1,33 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: DeConv copy.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 16th February 2022 1:42:49 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# -from audioop import bias -from tokenize import group -from torch import nn - -class DeConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero"): - super().__init__() - self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - padding_size = int((kernel_size -1)/2) - self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1, bias = False) - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=padding_size, groups=in_channels) - # nn.init.xavier_uniform_(self.conv.weight) - # self.__weights_init__() - - # def __weights_init__(self): - # nn.init.xavier_uniform_(self.conv.weight) - - def forward(self, input): - h = self.upsampling(input) - h = self.conv(h) - h = self.conv1x1(h) - return h \ No newline at end of file diff --git a/components/DeConv_Depthwise_ECA.py b/components/DeConv_Depthwise_ECA.py deleted file mode 100644 index c06755b..0000000 --- a/components/DeConv_Depthwise_ECA.py +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: DeConv copy.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 19th February 2022 6:16:08 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# -from tokenize import group -from torch import nn -import math - -class DeConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero", up_mode = "bilinear"): - super().__init__() - if up_mode.lower() == "bilinear": - self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - elif up_mode.lower() == "nearest": - self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale) - b = 1 - gamma = 2 - k_size = int(abs(math.log(out_channels,2)+b)/gamma) - k_size = k_size if k_size % 2 else k_size+1 - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.se = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) - self.sigmoid = nn.Sigmoid() - - padding_size = int((kernel_size -1)/2) - self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1) - self.conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=padding_size, bias=False, groups=out_channels) - # nn.init.xavier_uniform_(self.conv.weight) - # self.__weights_init__() - - # def __weights_init__(self): - # nn.init.xavier_uniform_(self.conv.weight) - - def forward(self, input): - h = self.conv1x1(input) - h = self.upsampling(h) - y = self.avg_pool(h) - y = self.se(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) - y = self.sigmoid(y) - - h = self.conv(h) - return h * y.expand_as(h) \ No newline at end of file diff --git a/components/DeConv_ECA_Invo.py b/components/DeConv_ECA_Invo.py deleted file mode 100644 index 652ca51..0000000 --- a/components/DeConv_ECA_Invo.py +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: DeConv copy.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 26th February 2022 5:55:46 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -from torch import nn -from components.misc.Involution_ECA import involution - - -class DeConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero", up_mode = "bilinear"): - super().__init__() - if up_mode.lower() == "bilinear": - self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - elif up_mode.lower() == "nearest": - self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale) - # self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - padding_size = int((kernel_size -1)/2) - self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1) - # self.same_padding = nn.ReflectionPad2d(padding_size) - if padding.lower() == "reflect": - - self.conv = involution(out_channels,kernel_size,1) - # self.conv = nn.Sequential( - # nn.ReflectionPad2d(padding_size), - # nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= kernel_size, bias= False)) - # for layer in self.conv: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - elif padding.lower() == "zero": - self.conv = involution(out_channels,kernel_size,1) - # nn.init.xavier_uniform_(self.conv.weight) - # self.__weights_init__() - - # def __weights_init__(self): - # nn.init.xavier_uniform_(self.conv.weight) - - def forward(self, input): - h = self.conv1x1(input) - h = self.upsampling(h) - h = self.conv(h) - return h \ No newline at end of file diff --git a/components/DeConv_Invo.py b/components/DeConv_Invo.py deleted file mode 100644 index 03ae788..0000000 --- a/components/DeConv_Invo.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: DeConv copy.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 10th February 2022 1:10:04 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - -from components.misc.Involution import involution -from torch import nn - -class DeConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero"): - super().__init__() - self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - padding_size = int((kernel_size -1)/2) - self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1) - # self.same_padding = nn.ReflectionPad2d(padding_size) - if padding.lower() == "reflect": - - self.conv = involution(out_channels,kernel_size,1) - # self.conv = nn.Sequential( - # nn.ReflectionPad2d(padding_size), - # nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= kernel_size, bias= False)) - # for layer in self.conv: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - elif padding.lower() == "zero": - self.conv = involution(out_channels,kernel_size,1) - # nn.init.xavier_uniform_(self.conv.weight) - # self.__weights_init__() - - # def __weights_init__(self): - # nn.init.xavier_uniform_(self.conv.weight) - - def forward(self, input): - h = self.conv1x1(input) - h = self.upsampling(h) - h = self.conv(h) - return h \ No newline at end of file diff --git a/components/DeConv_Invobn.py b/components/DeConv_Invobn.py deleted file mode 100644 index 22258cf..0000000 --- a/components/DeConv_Invobn.py +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: DeConv copy.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 26th February 2022 4:07:55 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -from torch import nn -from components.misc.Involution_BN import involution - - -class DeConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero", up_mode = "bilinear"): - super().__init__() - if up_mode.lower() == "bilinear": - self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - elif up_mode.lower() == "nearest": - self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale) - # self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale) - padding_size = int((kernel_size -1)/2) - self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1) - # self.same_padding = nn.ReflectionPad2d(padding_size) - if padding.lower() == "reflect": - - self.conv = involution(out_channels,kernel_size,1) - # self.conv = nn.Sequential( - # nn.ReflectionPad2d(padding_size), - # nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= kernel_size, bias= False)) - # for layer in self.conv: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - elif padding.lower() == "zero": - self.conv = involution(out_channels,kernel_size,1) - # nn.init.xavier_uniform_(self.conv.weight) - # self.__weights_init__() - - # def __weights_init__(self): - # nn.init.xavier_uniform_(self.conv.weight) - - def forward(self, input): - h = self.conv1x1(input) - h = self.upsampling(h) - h = self.conv(h) - return h \ No newline at end of file diff --git a/components/ECA.py b/components/ECA.py deleted file mode 100644 index 595e730..0000000 --- a/components/ECA.py +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: ECA.py -# Created Date: Tuesday February 23rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 23rd February 2021 9:14:28 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -import math -import torch -from torch import nn -from torch.nn.parameter import Parameter - -class eca_layer(nn.Module): - """Constructs a ECA module. - Args: - channel: Number of channels of the input feature map - k_size: Adaptive selection of kernel size - """ - def __init__(self, channel): - super(eca_layer, self).__init__() - - b = 1 - gamma = 2 - k_size = int(abs(math.log(channel,2)+b)/gamma) - k_size = k_size if k_size % 2 else k_size+1 - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) - self.sigmoid = nn.Sigmoid() - - def forward(self, x): - # x: input features with shape [b, c, h, w] - # b, c, h, w = x.size() - - # feature descriptor on the global spatial information - y = self.avg_pool(x) - - # Two different branches of ECA module - y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) - - # Multi-scale information fusion - y = self.sigmoid(y) - - return x * y.expand_as(x) \ No newline at end of file diff --git a/components/ECA_Depthwise_Conv.py b/components/ECA_Depthwise_Conv.py deleted file mode 100644 index b9c21e3..0000000 --- a/components/ECA_Depthwise_Conv.py +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: DeConv copy.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 19th February 2022 6:15:53 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -from torch import nn -import math - -class ECADW(nn.Module): - def __init__(self, in_channels, kernel_size = 3, stride = 2, padding="zero"): - super().__init__() - b = 1 - gamma = 2 - k_size = int(abs(math.log(in_channels,2)+b)/gamma) - k_size = k_size if k_size % 2 else k_size+1 - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.se = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) - self.sigmoid = nn.Sigmoid() - - padding_size = int((kernel_size -1)/2) - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, - padding=padding_size, bias=False, groups=in_channels, stride=stride) - # nn.init.xavier_uniform_(self.conv.weight) - # self.__weights_init__() - - # def __weights_init__(self): - # nn.init.xavier_uniform_(self.conv.weight) - - def forward(self, input): - y = self.avg_pool(input) - y = self.se(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) - y = self.sigmoid(y) - h = self.conv(input) - return h * y.expand_as(h) \ No newline at end of file diff --git a/components/Generator.py b/components/Generator.py deleted file mode 100644 index 083404a..0000000 --- a/components/Generator.py +++ /dev/null @@ -1,186 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 13th February 2022 2:03:21 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn -from torch.nn import init -from torch.nn import functional as F - -class InstanceNorm(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(InstanceNorm, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - x = x - torch.mean(x, (2, 3), True) - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class ResnetBlock_Adain(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)): - super(ResnetBlock_Adain, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - chn = kwargs["g_conv_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1), - nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1), - nn.BatchNorm2d(128), activation) - - self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1), - nn.BatchNorm2d(256), activation) - - self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1), - nn.BatchNorm2d(512), activation) - - self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1), - nn.BatchNorm2d(512), activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Adain(512, latent_size=chn, padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(512), activation - ) - - self.up3 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(256), activation - ) - - self.up2 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(128), activation - ) - - self.up1 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(64), activation - ) - - self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - # x = input # 3*224*224 - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_256.py b/components/Generator_256.py deleted file mode 100644 index 9417ac3..0000000 --- a/components/Generator_256.py +++ /dev/null @@ -1,304 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 19th April 2022 7:03:46 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"].lower() - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - norm_mask= nn.InstanceNorm2d - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - norm_mask = nn.BatchNorm2d - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 128 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 64 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 32 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - # self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 1 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 32 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 64 - - # self.maskhead = nn.Sequential( - # nn.Conv2d(in_channel*2, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - # norm_mask, # 64 - # activation, - # nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid()) - self.maskhead_lr = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel, affine=True), # 32 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel, in_channel//4, kernel_size=3, stride=1, padding=1, bias=False), - norm_mask(in_channel//4, affine=True), # 64 - activation - ) - self.maskhead_hr = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//4, in_channel//16, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel//16, affine=True), # 128 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//16, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() # 256 - ) - self.maskhead_out = nn.Sequential(nn.Conv2d(in_channel//4, 1, kernel_size=1, stride=1), - nn.Sigmoid()) - - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - self.up2 = ResUpBlk(in_channel*2, in_channel, normalize=norm) - - # self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - self.up1 = ResUpBlk(in_channel, in_channel, normalize=norm) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - mask_feat= self.maskhead_lr(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up4(res,id) - res = self.up3(res,id) - mask_lr= self.maskhead_out(mask_feat) - # res = (1-mask) * self.sigma(skip) + mask * res - res = (1-mask_lr) * skip + mask_lr * res - res = self.up2(res) # + skip - res = self.up1(res) - res = self.to_rgb(res) - mask_hr=self.maskhead_hr(mask_feat) - res = (1-mask_hr) * img + mask_hr * res - return res, mask_lr, mask_hr \ No newline at end of file diff --git a/components/Generator_2mask.py b/components/Generator_2mask.py deleted file mode 100644 index 9ea274b..0000000 --- a/components/Generator_2mask.py +++ /dev/null @@ -1,312 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 15th April 2022 12:30:27 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"].lower() - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - norm_mask= nn.InstanceNorm2d - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - norm_mask = nn.BatchNorm2d - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - # self.maskhead = nn.Sequential( - # nn.Conv2d(in_channel*2, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - # norm_mask, # 64 - # activation, - # nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid()) - self.maskhead_lr = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel, affine=True), # 32 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel, in_channel//4, kernel_size=3, stride=1, padding=1, bias=False), - norm_mask(in_channel//4, affine=True), # 64 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//4, in_channel//8, kernel_size=3, stride=1, padding=1), - norm_mask(in_channel//8, affine=True), # 128 - activation, - ) - self.maskhead_hr = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//8, in_channel//16, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel//16, affine=True), # 256 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//16, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() # 512 - ) - self.maskhead_out = nn.Sequential(nn.Conv2d(in_channel//8, 1, kernel_size=1, stride=1), - nn.Sigmoid()) - - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - self.up2 = ResUpBlk(in_channel*2, in_channel, normalize=norm) - - # self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - self.up1 = ResUpBlk(in_channel, in_channel, normalize=norm) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - mask_feat= self.maskhead_lr(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - mask_lr= self.maskhead_out(mask_feat) - # res = (1-mask) * self.sigma(skip) + mask * res - res = (1-mask_lr) * skip + mask_lr * res - res = self.up2(res) # + skip - res = self.up1(res) - res = self.to_rgb(res) - mask_hr=self.maskhead_hr(mask_feat) - res = (1-mask_hr) * img + mask_hr * res - return res, mask_lr, mask_hr \ No newline at end of file diff --git a/components/Generator_2mask2.py b/components/Generator_2mask2.py deleted file mode 100644 index 7040df7..0000000 --- a/components/Generator_2mask2.py +++ /dev/null @@ -1,312 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 19th April 2022 12:45:55 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"].lower() - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - norm_mask= nn.InstanceNorm2d - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - norm_mask = nn.BatchNorm2d - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - # self.maskhead = nn.Sequential( - # nn.Conv2d(in_channel*2, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - # norm_mask, # 64 - # activation, - # nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid()) - self.maskhead_lr = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel, affine=True), # 32 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel, in_channel//4, kernel_size=3, stride=1, padding=1, bias=False), - norm_mask(in_channel//4, affine=True), # 64 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//4, in_channel//8, kernel_size=3, stride=1, padding=1), - norm_mask(in_channel//8, affine=True), # 128 - activation, - ) - self.maskhead_hr = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//8, in_channel//16, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel//16, affine=True), # 256 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//16, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() # 512 - ) - self.maskhead_out = nn.Sequential(nn.Conv2d(in_channel//8, 1, kernel_size=1, stride=1), - nn.Sigmoid()) - - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # self.up2 = ResUpBlk(in_channel*2, in_channel, normalize=norm) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # self.up1 = ResUpBlk(in_channel, in_channel, normalize=norm) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - mask_feat= self.maskhead_lr(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - mask_lr= self.maskhead_out(mask_feat) - # res = (1-mask) * self.sigma(skip) + mask * res - res = (1-mask_lr) * skip + mask_lr * res - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - mask_hr=self.maskhead_hr(mask_feat) - res = (1-mask_hr) * img + mask_hr * res - return res, mask_lr, mask_hr \ No newline at end of file diff --git a/components/Generator_2mask_DWConv.py b/components/Generator_2mask_DWConv.py deleted file mode 100644 index 8d5c743..0000000 --- a/components/Generator_2mask_DWConv.py +++ /dev/null @@ -1,453 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 18th April 2022 10:20:12 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import math - -import torch -from torch import nn -import torch.nn.functional as F - -from components.ModulatedDWConv import ModulatedDWConv2d - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Sequential( - nn.Conv2d(dim_in, dim_in, 3, 1, 1, groups=dim_in), - nn.Conv2d(dim_in, dim_in, 1, 1) - ) - - self.conv2 = nn.Sequential( - nn.Conv2d(dim_in, dim_in, 3, 1, 1, groups=dim_in), - nn.Conv2d(dim_in, dim_out, 1, 1) - ) - # self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - # self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -# class ResUpBlk(nn.Module): -# def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): -# super().__init__() -# self.actv = actv -# self.normalize = normalize -# self.learned_sc = dim_in != dim_out -# self.equal_var = math.sqrt(2) -# self._build_weights(dim_in, dim_out) - -# def _build_weights(self, dim_in, dim_out): -# self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) -# self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) -# if self.normalize.lower() == "in": -# self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) -# self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) -# elif self.normalize.lower() == "bn": -# self.norm1 = nn.BatchNorm2d(dim_in) -# self.norm2 = nn.BatchNorm2d(dim_out) -# if self.learned_sc: -# self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - -# def _shortcut(self, x): -# x = F.interpolate(x, scale_factor=2, mode='nearest') -# if self.learned_sc: -# x = self.conv1x1(x) -# return x - -# def _residual(self, x): -# x = self.norm1(x) -# x = self.actv(x) -# x = F.interpolate(x, scale_factor=2, mode='nearest') -# x = self.conv1(x) -# x = self.norm2(x) -# x = self.actv(x) -# x = self.conv2(x) -# return x - -# def forward(self, x): -# out = self._residual(x) -# out = (out + self._shortcut(x)) / self.equal_var -# return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Sequential( - nn.Conv2d(dim_in, dim_in, 3, 1, 1,groups=dim_in), - nn.Conv2d(dim_in, dim_out, 1, 1) - ) - - self.conv2 = nn.Sequential( - nn.Conv2d(dim_out, dim_out, 3, 1, 1,groups=dim_out), - nn.Conv2d(dim_out, dim_out, 1) - ) - - # self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - # self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class ModulatedResBlk(nn.Module): - def __init__(self, - dim_in, - dim_out, - style_dim=512, - actv=nn.LeakyReLU(0.2), - upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = ModulatedDWConv2d(dim_in, dim_out, style_dim, 3) - self.conv2 = ModulatedDWConv2d(dim_out, dim_out, style_dim, 3) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x,s) - x = self.actv(x) - x = self.conv2(x,s) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"].lower() - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - norm_mask= nn.InstanceNorm2d - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - norm_mask = nn.BatchNorm2d - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - # self.maskhead = nn.Sequential( - # nn.Conv2d(in_channel*2, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - # norm_mask, # 64 - # activation, - # nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid()) - self.maskhead_lr = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel, affine=True), # 32 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel, in_channel//4, kernel_size=3, stride=1, padding=1, bias=False), - norm_mask(in_channel//4, affine=True), # 64 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//4, in_channel//8, kernel_size=3, stride=1, padding=1), - norm_mask(in_channel//8, affine=True), # 128 - activation, - ) - - self.maskhead_hr = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//8, in_channel//16, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel//16, affine=True), # 256 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//16, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() # 512 - ) - - self.maskhead_out = nn.Sequential(nn.Conv2d(in_channel//8, 1, kernel_size=1, stride=1), - nn.Sigmoid()) - - # self.maskhead_lr = nn.Sequential( - # nn.UpsamplingNearest2d(scale_factor = 2), - # nn.Conv2d(in_channel*8, in_channel*8, 3, 1, 1,groups=in_channel*8), - # nn.Conv2d(in_channel*8, in_channel, 1, bias=False), - # # nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - # norm_mask(in_channel, affine=True), # 32 - # activation, - # nn.UpsamplingNearest2d(scale_factor = 2), - # nn.Conv2d(in_channel, in_channel, 3, 1, 1,groups=in_channel), - # nn.Conv2d(in_channel, in_channel//4, 1, bias=False), - # # nn.Conv2d(in_channel, in_channel//4, kernel_size=3, stride=1, padding=1, bias=False), - # norm_mask(in_channel//4, affine=True), # 64 - # activation, - # nn.UpsamplingNearest2d(scale_factor = 2), - # # nn.Conv2d(in_channel//4, in_channel//8, kernel_size=3, stride=1, padding=1), - # nn.Conv2d(in_channel//4, in_channel//4, 3, 1, 1,groups=in_channel//4), - # nn.Conv2d(in_channel//4, in_channel//8, 1, bias=False), - # norm_mask(in_channel//8, affine=True), # 128 - # activation, - # ) - # self.maskhead_hr = nn.Sequential( - # nn.UpsamplingNearest2d(scale_factor = 2), - # # nn.Conv2d(in_channel//8, in_channel//16, kernel_size=3, stride=1, padding=1,bias=False), - # nn.Conv2d(in_channel//8, in_channel//8, 3, 1, 1,groups=in_channel//8), - # nn.Conv2d(in_channel//8, in_channel//16, 1, bias=False), - # norm_mask(in_channel//16, affine=True), # 256 - # activation, - # nn.UpsamplingNearest2d(scale_factor = 2), - # nn.Conv2d(in_channel//16, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid() # 512 - # ) - # self.maskhead_out = nn.Sequential(nn.Conv2d(in_channel//8, 1, kernel_size=1, stride=1), - # nn.Sigmoid()) - - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel*2, in_channel, normalize=norm) - - # self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel, in_channel, normalize=norm) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - mask_feat= self.maskhead_lr(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - mask_lr= self.maskhead_out(mask_feat) - # res = (1-mask) * self.sigma(skip) + mask * res - res = (1-mask_lr) * skip + mask_lr * res - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - mask_hr=self.maskhead_hr(mask_feat) - res = (1-mask_hr) * img + mask_hr * res - return res, mask_lr, mask_hr \ No newline at end of file diff --git a/components/Generator_2maskhead_config copy.py b/components/Generator_2maskhead_config copy.py deleted file mode 100644 index f44ac61..0000000 --- a/components/Generator_2maskhead_config copy.py +++ /dev/null @@ -1,293 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 13th April 2022 10:22:52 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="bn", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - norm = norm.lower() - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - # self.sigma = ResBlk(in_channel*2,in_channel*2) - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.maskhead_lr = nn.Sequential( - nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel), # 64 - activation, - nn.Conv2d(in_channel, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() - ) - - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - self.up2 = ResUpBlk(in_channel*2, in_channel, normalize=norm) - - # self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - self.up1 = ResUpBlk(in_channel, in_channel, normalize=norm) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - self.maskhead_hr = nn.Sequential( - nn.Conv2d(in_channel, in_channel//8, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel//8), # 64 - activation, - nn.Conv2d(in_channel//8, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() - ) - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - mask= self.maskhead_lr(res) - # res = (1-mask) * self.sigma(skip) + mask * res - res = (1-mask) * skip + mask * res - res = self.up2(res) # + skip - res = self.up1(res) - mask_hr = self.maskhead_hr(res) - res = self.to_rgb(res) - res = (1-mask_hr)*img + mask_hr*res - return res, mask, mask_hr \ No newline at end of file diff --git a/components/Generator_2maskhead_config.py b/components/Generator_2maskhead_config.py deleted file mode 100644 index f44ac61..0000000 --- a/components/Generator_2maskhead_config.py +++ /dev/null @@ -1,293 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 13th April 2022 10:22:52 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="bn", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - norm = norm.lower() - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - # self.sigma = ResBlk(in_channel*2,in_channel*2) - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.maskhead_lr = nn.Sequential( - nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel), # 64 - activation, - nn.Conv2d(in_channel, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() - ) - - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - self.up2 = ResUpBlk(in_channel*2, in_channel, normalize=norm) - - # self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - self.up1 = ResUpBlk(in_channel, in_channel, normalize=norm) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - self.maskhead_hr = nn.Sequential( - nn.Conv2d(in_channel, in_channel//8, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel//8), # 64 - activation, - nn.Conv2d(in_channel//8, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() - ) - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - mask= self.maskhead_lr(res) - # res = (1-mask) * self.sigma(skip) + mask * res - res = (1-mask) * skip + mask * res - res = self.up2(res) # + skip - res = self.up1(res) - mask_hr = self.maskhead_hr(res) - res = self.to_rgb(res) - res = (1-mask_hr)*img + mask_hr*res - return res, mask, mask_hr \ No newline at end of file diff --git a/components/Generator_Invobn_config.py b/components/Generator_Invobn_config.py deleted file mode 100644 index 9e3e65d..0000000 --- a/components/Generator_Invobn_config.py +++ /dev/null @@ -1,273 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 27th February 2022 10:36:58 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn - -# from components.DeConv_Invo import DeConv - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - elif res_mode.lower() == "depthwise": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "invo": - from components.misc.Involution_BN import involution - conv1 += [involution(dim,3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "eca_invo": - from components.misc.Involution_ECA import involution - conv1 += [involution(dim, 3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - elif res_mode.lower() == "depthwise": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "invo": - from components.misc.Involution_BN import involution - conv2 += [involution(dim, 3, 1,0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "eca_invo": - from components.misc.Involution_ECA import involution - conv2 += [involution(dim, 3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.style1(x, dlatents_in_slice) - y = self.conv1(y) - - y = self.act1(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - - out = x + y - return out - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - - aggregator = kwargs["aggregator"] - res_mode = aggregator - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - if aggregator == "invo": - from components.misc.Involution_BN import involution - from components.DeConv_Invobn import DeConv - - elif aggregator == "eca_invo": - from components.misc.Involution_ECA import involution - from components.DeConv_ECA_Invo import DeConv - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), - activation) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential( - involution(in_channel,3,2), - nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*2), - activation) - - self.down2 = nn.Sequential( - involution(in_channel*2,3,2), - nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*4), - activation) - - self.down3 = nn.Sequential( - involution(in_channel*4,3,2), - nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - self.down4 = nn.Sequential( - involution(in_channel*8,3,2), - nn.Conv2d(in_channel*8, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, - padding_type=padding_type, activation=activation, res_mode=res_mode)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - DeConv(in_channel*8,in_channel*8,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*8), - activation - ) - - self.up3 = nn.Sequential( - DeConv(in_channel*8,in_channel*4,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*4), - activation - ) - - self.up2 = nn.Sequential( - DeConv(in_channel*4,in_channel*2,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*2), - activation - ) - - self.up1 = nn.Sequential( - DeConv(in_channel*2,in_channel,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel), - activation - ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res \ No newline at end of file diff --git a/components/Generator_Invobn_config1.py b/components/Generator_Invobn_config1.py deleted file mode 100644 index c1baa70..0000000 --- a/components/Generator_Invobn_config1.py +++ /dev/null @@ -1,244 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 26th February 2022 4:04:55 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn -from components.DeConv_Invobn import DeConv -from components.misc.Involution_BN import involution -# from components.DeConv_Invo import DeConv - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - elif res_mode.lower() == "depthwise": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - elif res_mode.lower() == "depthwise": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.style1(x, dlatents_in_slice) - y = self.conv1(y) - - y = self.act1(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - - out = x + y - return out - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), - activation) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential( - involution(in_channel,3,2), - nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*2), - activation) - - self.down2 = nn.Sequential( - involution(in_channel*2,3,2), - nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*4), - activation) - - self.down3 = nn.Sequential( - involution(in_channel*4,3,2), - nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - self.down4 = nn.Sequential( - involution(in_channel*8,3,2), - nn.Conv2d(in_channel*8, in_channel*16, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*16), - activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(in_channel*16, latent_size=id_dim, - padding_type=padding_type, activation=activation, res_mode=res_mode)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - DeConv(in_channel*16,in_channel*8,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*8), - activation - ) - - self.up3 = nn.Sequential( - DeConv(in_channel*8,in_channel*4,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*4), - activation - ) - - self.up2 = nn.Sequential( - DeConv(in_channel*4,in_channel*2,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*2), - activation - ) - - self.up1 = nn.Sequential( - DeConv(in_channel*2,in_channel,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel), - activation - ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res \ No newline at end of file diff --git a/components/Generator_Invobn_config2.py b/components/Generator_Invobn_config2.py deleted file mode 100644 index cc14b55..0000000 --- a/components/Generator_Invobn_config2.py +++ /dev/null @@ -1,274 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 27th February 2022 7:50:18 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn - -# from components.DeConv_Invo import DeConv - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - elif res_mode.lower() == "depthwise": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "invo": - from components.misc.Involution_BN import involution - conv1 += [involution(dim,3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "eca_invo": - from components.misc.Involution_ECA import involution - conv1 += [involution(dim, 3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - res_mode = "conv" - if res_mode.lower() == "conv": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - elif res_mode.lower() == "depthwise": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "invo": - from components.misc.Involution_BN import involution - conv2 += [involution(dim, 3, 1,0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "eca_invo": - from components.misc.Involution_ECA import involution - conv2 += [involution(dim, 3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.style1(x, dlatents_in_slice) - y = self.conv1(y) - - y = self.act1(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - - out = x + y - return out - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - - aggregator = kwargs["aggregator"] - res_mode = aggregator - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - if aggregator == "invo": - from components.misc.Involution_BN import involution - from components.DeConv_Invobn import DeConv - - elif aggregator == "eca_invo": - from components.misc.Involution_ECA import involution - from components.DeConv_ECA_Invo import DeConv - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), - activation) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential( - involution(in_channel,3,2), - nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*2), - activation) - - self.down2 = nn.Sequential( - involution(in_channel*2,3,2), - nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*4), - activation) - - self.down3 = nn.Sequential( - involution(in_channel*4,3,2), - nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - self.down4 = nn.Sequential( - involution(in_channel*8,3,2), - nn.Conv2d(in_channel*8, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, - padding_type=padding_type, activation=activation, res_mode=res_mode)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - DeConv(in_channel*8,in_channel*8,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*8), - activation - ) - - self.up3 = nn.Sequential( - DeConv(in_channel*8,in_channel*4,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*4), - activation - ) - - self.up2 = nn.Sequential( - DeConv(in_channel*4,in_channel*2,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*2), - activation - ) - - self.up1 = nn.Sequential( - DeConv(in_channel*2,in_channel,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel), - activation - ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res \ No newline at end of file diff --git a/components/Generator_Invobn_config3.py b/components/Generator_Invobn_config3.py deleted file mode 100644 index 007e0ed..0000000 --- a/components/Generator_Invobn_config3.py +++ /dev/null @@ -1,274 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 3rd March 2022 6:16:01 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn - -# from components.DeConv_Invo import DeConv - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - elif res_mode.lower() == "depthwise": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "invo": - from components.misc.Involution_BN import involution - conv1 += [involution(dim,3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "eca_invo": - from components.misc.Involution_ECA import involution - conv1 += [involution(dim, 3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - # res_mode = "conv" - if res_mode.lower() == "conv": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - elif res_mode.lower() == "depthwise": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "invo": - from components.misc.Involution_BN import involution - conv2 += [involution(dim, 3, 1,0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "eca_invo": - from components.misc.Involution_ECA import involution - conv2 += [involution(dim, 3, 1, 0), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.style1(x, dlatents_in_slice) - y = self.conv1(y) - - y = self.act1(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - - out = x + y - return out - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - # from components.misc.Involution_BN import involution - if aggregator == "invo": - from components.misc.Involution_BN import involution - from components.DeConv_Invobn import DeConv - - elif aggregator == "eca_invo": - from components.misc.Involution_ECA import involution - from components.DeConv_ECA_Invo import DeConv - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), - activation) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential( - involution(in_channel,3,2), - nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*2), - activation) - - self.down2 = nn.Sequential( - involution(in_channel*2,3,2), - nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*4), - activation) - - self.down3 = nn.Sequential( - involution(in_channel*4,3,2), - nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - self.down4 = nn.Sequential( - involution(in_channel*8,3,2), - nn.Conv2d(in_channel*8, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, - padding_type=padding_type, activation=activation, res_mode=res_mode)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - DeConv(in_channel*8,in_channel*8,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*8), - activation - ) - - self.up3 = nn.Sequential( - DeConv(in_channel*8,in_channel*4,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*4), - activation - ) - - self.up2 = nn.Sequential( - DeConv(in_channel*4,in_channel*2,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*2), - activation - ) - - self.up1 = nn.Sequential( - DeConv(in_channel*2,in_channel,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel), - activation - ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res \ No newline at end of file diff --git a/components/Generator_LSTU_config.py b/components/Generator_LSTU_config.py deleted file mode 100644 index a151d24..0000000 --- a/components/Generator_LSTU_config.py +++ /dev/null @@ -1,242 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 27th February 2022 7:50:18 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn -from components.LSTU import LSTU - -# from components.DeConv_Invo import DeConv -class InstanceNorm(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(InstanceNorm, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - x = x - torch.mean(x, (2, 3), True) - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class ResnetBlock_Adain(nn.Module): - def __init__(self, - dim, - latent_size, - padding_type, - activation=nn.ReLU(True), - res_mode="depthwise"): - super(ResnetBlock_Adain, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()] - elif res_mode.lower() == "depthwise": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - InstanceNorm()] - elif res_mode.lower() == "depthwise_eca": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - InstanceNorm()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()] - elif res_mode.lower() == "depthwise": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - InstanceNorm()] - elif res_mode.lower() == "depthwise_eca": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - InstanceNorm()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - from components.DeConv_Depthwise import DeConv - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), - activation) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential( - nn.Conv2d(in_channel, in_channel*2, stride=2, kernel_size=3, padding=1, bias=False), - nn.BatchNorm2d(in_channel*2), - activation) - - self.down2 = nn.Sequential( - nn.Conv2d(in_channel*2, in_channel*4, stride=2, kernel_size=3, padding=1, bias=False), - nn.BatchNorm2d(in_channel*4), - activation) - - # self.lstu = LSTU(in_channel*4,in_channel*4,in_channel*8,4) - - self.down3 = nn.Sequential( - nn.Conv2d(in_channel*4, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - self.down4 = nn.Sequential( - nn.Conv2d(in_channel*8, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Adain(in_channel*8, latent_size=id_dim, - padding_type=padding_type, activation=activation, res_mode=res_mode)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation - ) - - self.up3 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel*4), - activation - ) - - self.up2 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel*2), - activation - ) - - self.up1 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel), - activation - ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - # skip = self.lstu(res1, res) - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) # + skip - res = self.up1(res) - res = self.last_layer(res) - - return res \ No newline at end of file diff --git a/components/Generator_ResSkip_config.py b/components/Generator_ResSkip_config.py deleted file mode 100644 index 7255a59..0000000 --- a/components/Generator_ResSkip_config.py +++ /dev/null @@ -1,276 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 29th March 2022 12:02:53 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn -import torch.nn.functional as F -import math -from components.LSTU import LSTU - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.lstu = LSTU(in_channel*2,norm) - - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel*2, in_channel, normalize="in") # 256 - - # self.lstu = nn.Sequential(nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel), - # activation, - # nn.Conv2d(in_channel, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid() - # ) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - # res = self.down6(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - # res = self.up6(res,id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - res,mask = self.lstu(skip, res) - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - return res,mask \ No newline at end of file diff --git a/components/Generator_ResSkip_config1.py b/components/Generator_ResSkip_config1.py deleted file mode 100644 index 3fb0783..0000000 --- a/components/Generator_ResSkip_config1.py +++ /dev/null @@ -1,285 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 29th March 2022 1:08:05 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - - lstu_script = kwargs["lstu_script"] - lstu_class = kwargs["lstu_class"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - script_name = "components." + lstu_script - package = __import__(script_name, fromlist=True) - lstu_class = getattr(package, lstu_class) - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.lstu = lstu_class(in_channel*2,norm) - - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel*2, in_channel, normalize="in") # 256 - - # self.lstu = nn.Sequential(nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel), - # activation, - # nn.Conv2d(in_channel, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid() - # ) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - # res = self.down6(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - # res = self.up6(res,id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - res,mask = self.lstu(skip, res) - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - return res \ No newline at end of file diff --git a/components/Generator_Res_config.py b/components/Generator_Res_config.py deleted file mode 100644 index fa41c4a..0000000 --- a/components/Generator_Res_config.py +++ /dev/null @@ -1,372 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 27th February 2022 7:50:18 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn -from components.LSTU import LSTU - -# from components.DeConv_Invo import DeConv -class InstanceNorm(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(InstanceNorm, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - x = x - torch.mean(x, (2, 3), True) - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class ResnetBlock_Adain(nn.Module): - def __init__(self, - dim, - latent_size, - padding_type, - activation=nn.ReLU(True), - res_mode="depthwise"): - super(ResnetBlock_Adain, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()] - elif res_mode.lower() == "depthwise": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - InstanceNorm()] - elif res_mode.lower() == "depthwise_eca": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - InstanceNorm()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()] - elif res_mode.lower() == "depthwise": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - InstanceNorm()] - elif res_mode.lower() == "depthwise_eca": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - InstanceNorm()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - -class ResUpSampleBlock(nn.Module): - def __init__(self, - in_dim, - out_dim, - latent_size, - activation=nn.LeakyReLU(0.2), - res_mode="depthwise"): - super(ResUpSampleBlock, self).__init__() - conv1 = [] - self.in1 = InstanceNorm() - self.in2 = InstanceNorm() - if res_mode.lower() == "conv": - - conv1 += [activation, - nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv1 += [activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=in_dim, bias=False), - nn.Conv2d(in_dim, out_dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv1 += [activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=in_dim, bias=False), - nn.Conv2d(in_dim, out_dim, kernel_size=1)] - - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, in_dim) - - conv2 = [] - if res_mode.lower() == "conv": - conv2 += [activation, - nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv2 += [activation, - nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1,groups=out_dim, bias=False), - nn.Conv2d(out_dim, out_dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv2 += [activation, - nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1,groups=out_dim, bias=False), - nn.Conv2d(out_dim, out_dim, kernel_size=1)] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, out_dim) - - self.reshape1_1 = nn.Conv2d(in_dim, out_dim, kernel_size=1) - self.resampling = nn.UpsamplingBilinear2d(scale_factor=2) - - - def forward(self, x, dlatents_in_slice): - y = self.in1(x) - y = self.style1(y, dlatents_in_slice) - y = self.conv1(y) - y = self.resampling(y) - y = self.in2(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - res = self.reshape1_1(x) - res = self.resampling(res) - out = res + y - return out - - -class ResDownSampleBlock(nn.Module): - def __init__(self, - in_dim, - out_dim, - activation=nn.LeakyReLU(0.2), - res_mode="depthwise"): - super(ResDownSampleBlock, self).__init__() - conv1 = [] - if res_mode.lower() == "conv": - - conv1 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv1 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=in_dim, bias=False), - nn.Conv2d(in_dim, in_dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv1 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=in_dim, bias=False), - nn.Conv2d(in_dim, in_dim, kernel_size=1)] - - self.conv1 = nn.Sequential(*conv1) - - conv2 = [] - if res_mode.lower() == "conv": - conv2 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv2 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=out_dim, bias=False), - nn.Conv2d(in_dim, out_dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv2 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=out_dim, bias=False), - nn.Conv2d(in_dim, out_dim, kernel_size=1)] - self.conv2 = nn.Sequential(*conv2) - - self.reshape1_1 = nn.Conv2d(in_dim, out_dim, kernel_size=1) - self.resampling = nn.AvgPool2d(3,2,1) - - - def forward(self, x): - y = self.conv1(x) - y = self.resampling(y) - y = self.conv2(y) - res = self.reshape1_1(x) - res = self.resampling(res) - out = res + y - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.LeakyReLU(0.2) - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, stride=2, padding=0, bias=False), - nn.BatchNorm2d(in_channel), - activation) # 256 - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResDownSampleBlock(in_channel, in_channel*2,res_mode=res_mode) - # nn.Sequential( - # nn.Conv2d(in_channel, in_channel*2, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*2), - # activation) # 128 - - self.down2 = ResDownSampleBlock(in_channel*2, in_channel*4,res_mode=res_mode) - # nn.Sequential( - # nn.Conv2d(in_channel*2, in_channel*4, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*4), - # activation) # 64 - - # self.lstu = LSTU(in_channel*4,in_channel*4,in_channel*8,4) - - self.down3 = ResDownSampleBlock(in_channel*4, in_channel*8,res_mode=res_mode) - # nn.Sequential( - # nn.Conv2d(in_channel*4, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation) # 32 - - # self.down4 = nn.Sequential( - # nn.Conv2d(in_channel*8, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation) - - - - ### resnet blocks - # BN = [] - # for i in range(res_num): - # BN += [ - # ResnetBlock_Adain(in_channel*8, latent_size=id_dim, - # padding_type=padding_type, activation=activation, res_mode=res_mode)] - # self.BottleNeck = nn.Sequential(*BN) - - self.up4 = ResUpSampleBlock(in_channel*8,in_channel*8,id_dim,res_mode=res_mode) # 64 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation - # ) - - self.up3 = ResUpSampleBlock(in_channel*8,in_channel*4,id_dim,res_mode=res_mode) # 128 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*4), - # activation - # ) - - self.up2 = ResUpSampleBlock(in_channel*4,in_channel*2,id_dim,res_mode=res_mode) # 256 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*2), - # activation - # ) - - self.up1 = ResUpSampleBlock(in_channel*2,in_channel,id_dim,res_mode=res_mode) # 512 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel), - # activation - # ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.up4(res,id) - res = self.up3(res,id) - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.last_layer(res) - - return res \ No newline at end of file diff --git a/components/Generator_Res_config1.py b/components/Generator_Res_config1.py deleted file mode 100644 index 762a0c5..0000000 --- a/components/Generator_Res_config1.py +++ /dev/null @@ -1,370 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 24th March 2022 11:24:26 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn -from components.LSTU import LSTU - -# from components.DeConv_Invo import DeConv -class InstanceNorm(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(InstanceNorm, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - x = x - torch.mean(x, (2, 3), True) - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class ResnetBlock_Adain(nn.Module): - def __init__(self, - dim, - latent_size, - activation=nn.LeakyReLU(0.2), - res_mode="depthwise"): - super(ResnetBlock_Adain, self).__init__() - - conv1 = [] - self.in1 = InstanceNorm() - self.in2 = InstanceNorm() - if res_mode.lower() == "conv": - - conv1 += [activation, - nn.Conv2d(dim, dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv1 += [activation, - nn.Conv2d(dim, dim, kernel_size=3, padding=1,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv1 += [activation, - nn.Conv2d(dim, dim, kernel_size=3, padding=1,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1)] - - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, dim) - - conv2 = [] - if res_mode.lower() == "conv": - conv2 += [activation, - nn.Conv2d(dim, dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv2 += [activation, - nn.Conv2d(dim, dim, kernel_size=3, padding=1,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv2 += [activation, - nn.Conv2d(dim, dim, kernel_size=3, padding=1,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1)] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.in1(x) - y = self.style1(y, dlatents_in_slice) - y = self.conv1(y) - y = self.in2(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - - out = x + y - return out - -class ResUpSampleBlock(nn.Module): - def __init__(self, - in_dim, - out_dim, - latent_size, - activation=nn.LeakyReLU(0.2), - res_mode="depthwise"): - super(ResUpSampleBlock, self).__init__() - conv1 = [] - self.in1 = InstanceNorm() - self.in2 = InstanceNorm() - if res_mode.lower() == "conv": - - conv1 += [activation, - nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv1 += [activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=in_dim, bias=False), - nn.Conv2d(in_dim, out_dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv1 += [activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=in_dim, bias=False), - nn.Conv2d(in_dim, out_dim, kernel_size=1)] - - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, in_dim) - - conv2 = [] - if res_mode.lower() == "conv": - conv2 += [activation, - nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv2 += [activation, - nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1,groups=out_dim, bias=False), - nn.Conv2d(out_dim, out_dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv2 += [activation, - nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1,groups=out_dim, bias=False), - nn.Conv2d(out_dim, out_dim, kernel_size=1)] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, out_dim) - - self.reshape1_1 = nn.Conv2d(in_dim, out_dim, kernel_size=1) - self.resampling = nn.UpsamplingBilinear2d(scale_factor=2) - - - def forward(self, x, dlatents_in_slice): - y = self.in1(x) - y = self.style1(y, dlatents_in_slice) - y = self.conv1(y) - y = self.resampling(y) - y = self.in2(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - res = self.reshape1_1(x) - res = self.resampling(res) - out = res + y - return out - - -class ResDownSampleBlock(nn.Module): - def __init__(self, - in_dim, - out_dim, - activation=nn.LeakyReLU(0.2), - res_mode="depthwise"): - super(ResDownSampleBlock, self).__init__() - conv1 = [] - if res_mode.lower() == "conv": - - conv1 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv1 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=in_dim, bias=False), - nn.Conv2d(in_dim, in_dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv1 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=in_dim, bias=False), - nn.Conv2d(in_dim, in_dim, kernel_size=1)] - - self.conv1 = nn.Sequential(*conv1) - - conv2 = [] - if res_mode.lower() == "conv": - conv2 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1, bias=False)] - - elif res_mode.lower() == "depthwise": - conv2 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=out_dim, bias=False), - nn.Conv2d(in_dim, out_dim, kernel_size=1)] - - elif res_mode.lower() == "depthwise_eca": - conv2 += [ - nn.BatchNorm2d(in_dim), - activation, - nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1,groups=out_dim, bias=False), - nn.Conv2d(in_dim, out_dim, kernel_size=1)] - self.conv2 = nn.Sequential(*conv2) - - self.reshape1_1 = nn.Conv2d(in_dim, out_dim, kernel_size=1) - self.resampling = nn.AvgPool2d(3,2,1) - - - def forward(self, x): - y = self.conv1(x) - y = self.resampling(y) - y = self.conv2(y) - res = self.reshape1_1(x) - res = self.resampling(res) - out = res + y - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - # activation = nn.LeakyReLU(0.2) - activation = nn.ReLU() - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, stride=2, padding=0, bias=False), - nn.BatchNorm2d(in_channel), - activation) # 256 - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResDownSampleBlock(in_channel, in_channel*2, activation=activation, res_mode=res_mode) # 128 - # nn.Sequential( - # nn.Conv2d(in_channel, in_channel*2, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*2), - # activation) # 128 - - self.down2 = ResDownSampleBlock(in_channel*2, in_channel*4, activation=activation, res_mode=res_mode) # 64 - # nn.Sequential( - # nn.Conv2d(in_channel*2, in_channel*4, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*4), - # activation) # 64 - - # self.lstu = LSTU(in_channel*4,in_channel*4,in_channel*8,4) - - self.down3 = ResDownSampleBlock(in_channel*4, in_channel*8, activation=activation, res_mode=res_mode) # 32 - - self.down4 = ResDownSampleBlock(in_channel*8, in_channel*8, activation=activation, res_mode=res_mode) # 16 - # nn.Sequential( - # nn.Conv2d(in_channel*4, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation) # 32 - - # self.down4 = nn.Sequential( - # nn.Conv2d(in_channel*8, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation) - - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Adain(in_channel*8, latent_size=id_dim, activation=activation, res_mode=res_mode)] - self.BottleNeck = nn.Sequential(*BN) - - self.up5 = ResUpSampleBlock(in_channel*8, in_channel*8, id_dim, activation=activation, res_mode=res_mode) # 32 - - self.up4 = ResUpSampleBlock(in_channel*8, in_channel*4, id_dim, activation=activation, res_mode=res_mode) # 64 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation - # ) - - self.up3 = ResUpSampleBlock(in_channel*4, in_channel*2, id_dim, activation=activation, res_mode=res_mode) # 128 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*4), - # activation - # ) - - self.up2 = ResUpSampleBlock(in_channel*2, in_channel, id_dim, activation=activation, res_mode=res_mode) # 256 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*2), - # activation - # ) - - self.up1 = ResUpSampleBlock(in_channel, in_channel , id_dim, activation=activation, res_mode=res_mode) # 512 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel), - # activation - # ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up4(res,id) - res = self.up3(res,id) - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.last_layer(res) - - return res \ No newline at end of file diff --git a/components/Generator_Res_config2.py b/components/Generator_Res_config2.py deleted file mode 100644 index c030512..0000000 --- a/components/Generator_Res_config2.py +++ /dev/null @@ -1,284 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 24th March 2022 2:38:05 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -from torch import nn -import torch.nn.functional as F -import math -from components.LSTU import LSTU - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize=False, downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize: - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class HighPass(nn.Module): - def __init__(self, w_hpf, device): - super(HighPass, self).__init__() - self.register_buffer('filter', - torch.tensor([[-1, -1, -1], - [-1, 8., -1], - [-1, -1, -1]]) / w_hpf) - - def forward(self, x): - filter = self.filter.unsqueeze(0).unsqueeze(1).repeat(x.size(1), 1, 1, 1) - return F.conv2d(x, filter, padding=1, groups=x.size(1)) - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - # self.first_layer = nn.Sequential( - # nn.Conv2d(3, in_channel, kernel_size=1, padding=0, bias=False), - # # nn.BatchNorm2d(in_channel), - # nn.InstanceNorm2d(in_channel, affine=True), - # activation) # 256 - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=True, downsample=True)# 128 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=True, downsample=True)# 128 - # ResDownSampleBlock(in_channel, in_channel*2, activation=activation, res_mode=res_mode) # 128 - # nn.Sequential( - # nn.Conv2d(in_channel, in_channel*2, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*2), - # activation) # 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=True, downsample=True)# 64 - # ResDownSampleBlock(in_channel*2, in_channel*4, activation=activation, res_mode=res_mode) # 64 - # nn.Sequential( - # nn.Conv2d(in_channel*2, in_channel*4, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*4), - # activation) # 64 - - # self.lstu = LSTU(in_channel*4,in_channel*4,in_channel*8,4) - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=True, downsample=True)# 32 - # ResDownSampleBlock(in_channel*4, in_channel*8, activation=activation, res_mode=res_mode) # 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 16 - # ResDownSampleBlock(in_channel*8, in_channel*8, activation=activation, res_mode=res_mode) # 16 - # nn.Sequential( - # nn.Conv2d(in_channel*4, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation) # 32 - - # self.down4 = nn.Sequential( - # nn.Conv2d(in_channel*8, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation) - - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), - # activation - # ) - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - # ResUpSampleBlock(in_channel*4, in_channel*2, id_dim, activation=activation, res_mode=res_mode) # 128 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*4), - # activation - # ) - - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) # 256 - # ResUpSampleBlock(in_channel*2, in_channel, id_dim, activation=activation, res_mode=res_mode) # 256 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*2), - # activation - # ) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) # 512 - # ResUpSampleBlock(in_channel, in_channel , id_dim, activation=activation, res_mode=res_mode) # 512 - # nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel), - # activation - # ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - # nn.Conv2d(3, 3, kernel_size=3, padding=0)) - - self.to_rgb = nn.Sequential( - nn.InstanceNorm2d(in_channel, affine=True), - nn.LeakyReLU(0.2), - nn.Conv2d(in_channel, 3, 1, 1, 0)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - res = self.down5(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - - return res \ No newline at end of file diff --git a/components/Generator_Res_config3.py b/components/Generator_Res_config3.py deleted file mode 100644 index 41690bf..0000000 --- a/components/Generator_Res_config3.py +++ /dev/null @@ -1,285 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 30th March 2022 4:14:27 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - - lstu_script = kwargs["lstu_script"] - lstu_class = kwargs["lstu_class"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - script_name = "components." + lstu_script - package = __import__(script_name, fromlist=True) - lstu_class = getattr(package, lstu_class) - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.lstu = lstu_class(in_channel*2,norm) - - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel*2, in_channel, normalize="in") # 256 - - # self.lstu = nn.Sequential(nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel), - # activation, - # nn.Conv2d(in_channel, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid() - # ) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - # res = self.down6(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - # res = self.up6(res,id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - # res,mask = self.lstu(skip, res) - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - return res \ No newline at end of file diff --git a/components/Generator_VGGStyle_maskhead_config.py b/components/Generator_VGGStyle_maskhead_config.py deleted file mode 100644 index 6fffd06..0000000 --- a/components/Generator_VGGStyle_maskhead_config.py +++ /dev/null @@ -1,204 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 6th April 2022 12:55:51 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainUpBlock(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2)): - super().__init__() - self.actv = actv - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.norm = AdaIN(style_dim, dim_out) - - def forward(self, x, s): - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv(x) - x = self.norm(x, s) - x = self.actv(x) - return x - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(in_channel, in_channel, kernel_size=3, stride=2, padding=1, bias=False), # 256 - nn.BatchNorm2d(in_channel), activation) - - self.down2 = nn.Sequential(nn.Conv2d(in_channel, in_channel*2, kernel_size=3, stride=2, padding=1, bias=False), # 128 - nn.BatchNorm2d(in_channel*2), activation) - - self.down3 = nn.Sequential(nn.Conv2d(in_channel*2, in_channel*4, kernel_size=3, stride=2, padding=1, bias=False), # 64 - nn.BatchNorm2d(in_channel*4), activation) - - self.down4 = nn.Sequential(nn.Conv2d(in_channel*4, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False), # 32 - nn.BatchNorm2d(in_channel*8), activation) - - self.down5 = nn.Sequential(nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False), # 32 - nn.BatchNorm2d(in_channel*8), activation) - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - self.maskhead = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - nn.BatchNorm2d(in_channel), # 32 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel//2), # 64 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1, bias=False), - nn.Sigmoid() - ) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainUpBlock(in_channel*8, in_channel*8, style_dim=id_dim) # 32 - - self.up4 = AdainUpBlock(in_channel*8, in_channel*4, style_dim=id_dim) # 64 - - self.up3 = AdainUpBlock(in_channel*4, in_channel*2, style_dim=id_dim) # 128 - - self.up2 = AdainUpBlock(in_channel*2, in_channel, style_dim=id_dim) - - self.up1 = AdainUpBlock(in_channel, in_channel, style_dim=id_dim) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - - self.to_rgb = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - mask= self.maskhead(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - res = (1-mask) * skip + mask * res - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - return res, mask \ No newline at end of file diff --git a/components/Generator_eca_depthwise.py b/components/Generator_eca_depthwise.py deleted file mode 100644 index b922c2a..0000000 --- a/components/Generator_eca_depthwise.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 19th February 2022 6:25:38 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn - -# from components.DeConv_Invo import DeConv - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - elif res_mode.lower() == "depthwise": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - from components.ECA_Depthwise_Conv import ECADW - conv1 += [ECADW(dim, kernel_size=3, padding=p, stride=2), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - elif res_mode.lower() == "depthwise": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - from components.ECA_Depthwise_Conv import ECADW - conv2 += [ECADW(dim, kernel_size=3, padding=p, stride=2), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.style1(x, dlatents_in_slice) - y = self.conv1(y) - - y = self.act1(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - - out = x + y - return out - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - res_mode = kwargs["res_mode"] - conv_mode = kwargs["conv_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - from components.ECA_Depthwise_Conv import ECADW - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), activation) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential(ECADW(in_channel,kernel_size=3, padding=1, stride=2), - nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*2), activation) - - self.down2 = nn.Sequential(ECADW(in_channel*2, kernel_size=3, padding=1, stride=2), - nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*4), activation) - - self.down3 = nn.Sequential(ECADW(in_channel*4, kernel_size=3, padding=1, stride=2), - nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), activation) - - self.down4 = nn.Sequential(ECADW(in_channel*8, kernel_size=3, padding=1, stride=2), - nn.Conv2d(in_channel*8, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - if conv_mode.lower() == "conv": - from components.DeConv import DeConv - Deconv = DeConv - elif conv_mode.lower() == "depthwise": - from components.DeConv_Depthwise import DeConv - Deconv = DeConv - elif conv_mode.lower() == "depthwise_eca": - from components.DeConv_Depthwise_ECA import DeConv - Deconv = DeConv - - self.up4 = nn.Sequential( - DeConv(in_channel*8,in_channel*8,3), - nn.BatchNorm2d(in_channel*8), activation - ) - - self.up3 = nn.Sequential( - DeConv(in_channel*8,in_channel*4,3), - nn.BatchNorm2d(in_channel*4), activation - ) - - self.up2 = nn.Sequential( - DeConv(in_channel*4,in_channel*2,3), - nn.BatchNorm2d(in_channel*2), activation - ) - - self.up1 = nn.Sequential( - DeConv(in_channel*2,in_channel,3), - nn.BatchNorm2d(in_channel), activation - ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_featout_config.py b/components/Generator_featout_config.py deleted file mode 100644 index ae475a6..0000000 --- a/components/Generator_featout_config.py +++ /dev/null @@ -1,298 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 2nd April 2022 1:27:23 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - - lstu_script = kwargs["lstu_script"] - lstu_class = kwargs["lstu_class"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - script_name = "components." + lstu_script - package = __import__(script_name, fromlist=True) - lstu_class = getattr(package, lstu_class) - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - self.maskhead = nn.Sequential( - nn.ConvTranspose2d(in_channel*8, in_channel, kernel_size=4, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel), # 32 - activation, - nn.ConvTranspose2d(in_channel, in_channel//2, kernel_size=4, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel), # 64 - activation, - nn.ConvTranspose2d(in_channel//2, 1, kernel_size=4, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel), # 128 - nn.Sigmoid() - ) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.lstu = lstu_class(in_channel*2,norm) - - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel*2, in_channel, normalize="in") # 256 - - # self.lstu = nn.Sequential(nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel), - # activation, - # nn.Conv2d(in_channel, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid() - # ) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id, feat_out=False): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - if feat_out: - return res - # res = self.down6(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - # res = self.up6(res,id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - res,mask = self.lstu(skip, res) - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - return res \ No newline at end of file diff --git a/components/Generator_involution_maskhead_config.py b/components/Generator_involution_maskhead_config.py deleted file mode 100644 index 4acf04d..0000000 --- a/components/Generator_involution_maskhead_config.py +++ /dev/null @@ -1,280 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 3rd April 2022 1:06:31 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - self.maskhead = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - nn.BatchNorm2d(in_channel), # 32 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel//2), # 64 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1, bias=False), - nn.Sigmoid() - ) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - mask= self.maskhead(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - res = (1-mask) * skip + mask * res - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - return res, mask \ No newline at end of file diff --git a/components/Generator_maskhead_config.py b/components/Generator_maskhead_config.py deleted file mode 100644 index 4acf04d..0000000 --- a/components/Generator_maskhead_config.py +++ /dev/null @@ -1,280 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 3rd April 2022 1:06:31 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - self.maskhead = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - nn.BatchNorm2d(in_channel), # 32 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel//2), # 64 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1, bias=False), - nn.Sigmoid() - ) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - mask= self.maskhead(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - res = (1-mask) * skip + mask * res - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - return res, mask \ No newline at end of file diff --git a/components/Generator_maskhead_config1.py b/components/Generator_maskhead_config1.py deleted file mode 100644 index 197fc33..0000000 --- a/components/Generator_maskhead_config1.py +++ /dev/null @@ -1,279 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 6th April 2022 8:38:50 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="bn", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"] - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.sigma = ResBlk(in_channel*2,in_channel*2) - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.maskhead = nn.Sequential( - nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel), # 64 - activation, - nn.Conv2d(in_channel, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() - ) - - self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - - self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - mask= self.maskhead(res) - res = (1-mask) * self.sigma(skip) + mask * res - res = self.up2(res,id) # + skip - res = self.up1(res,id) - res = self.to_rgb(res) - return res, mask \ No newline at end of file diff --git a/components/Generator_maskhead_config2.py b/components/Generator_maskhead_config2.py deleted file mode 100644 index c1609ad..0000000 --- a/components/Generator_maskhead_config2.py +++ /dev/null @@ -1,283 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 13th April 2022 3:12:53 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="bn", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"].lower() - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - # self.sigma = ResBlk(in_channel*2,in_channel*2) - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - self.maskhead = nn.Sequential( - nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel), # 64 - activation, - nn.Conv2d(in_channel, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() - ) - - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - self.up2 = ResUpBlk(in_channel*2, in_channel, normalize="bn") - - # self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - self.up1 = ResUpBlk(in_channel, in_channel, normalize="bn") - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - mask= self.maskhead(res) - # res = (1-mask) * self.sigma(skip) + mask * res - res = (1-mask) * skip + mask * res - res = self.up2(res) # + skip - res = self.up1(res) - res = self.to_rgb(res) - return res, mask \ No newline at end of file diff --git a/components/Generator_modulation_depthwise_config.py b/components/Generator_modulation_depthwise_config.py deleted file mode 100644 index 2e03dc5..0000000 --- a/components/Generator_modulation_depthwise_config.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 19th February 2022 5:16:02 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -from audioop import bias -import torch -from torch import nn -from components.DeConv_Depthwise import DeConv -# from components.DeConv_Invo import DeConv - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - elif res_mode.lower() == "depthwise": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - if res_mode.lower() == "conv": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - elif res_mode.lower() == "depthwise": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - elif res_mode.lower() == "depthwise_eca": - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False), - nn.Conv2d(dim, dim, kernel_size=1), - Demodule()] - - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.style1(x, dlatents_in_slice) - y = self.conv1(y) - - y = self.act1(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - - out = x + y - return out - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), - activation) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(in_channel, in_channel, kernel_size=3, groups=in_channel, padding=1, stride=2), - nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*2), - activation) - - self.down2 = nn.Sequential(nn.Conv2d(in_channel*2, in_channel*2, kernel_size=3, groups=in_channel*2, padding=1, stride=2), - nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*4), - activation) - - self.down3 = nn.Sequential(nn.Conv2d(in_channel*4, in_channel*4, kernel_size=3, groups=in_channel*4, padding=1, stride=2), - nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - self.down4 = nn.Sequential(nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, groups=in_channel*8, padding=1, stride=2), - nn.Conv2d(in_channel*8, in_channel*8, kernel_size=1, bias=False), - nn.BatchNorm2d(in_channel*8), - activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, - padding_type=padding_type, activation=activation, res_mode=res_mode)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - DeConv(in_channel*8,in_channel*8,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*8), - activation - ) - - self.up3 = nn.Sequential( - DeConv(in_channel*8,in_channel*4,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*4), - activation - ) - - self.up2 = nn.Sequential( - DeConv(in_channel*4,in_channel*2,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel*2), - activation - ) - - self.up1 = nn.Sequential( - DeConv(in_channel*2,in_channel,3,up_mode=up_mode), - nn.BatchNorm2d(in_channel), - activation - ) - # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_modulation_up.py b/components/Generator_modulation_up.py deleted file mode 100644 index 6d5e273..0000000 --- a/components/Generator_modulation_up.py +++ /dev/null @@ -1,194 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 16th February 2022 1:34:58 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn -from components.DeConv_Invo import DeConv - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - chn = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(128), activation) - - self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(256), activation) - - self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(512, latent_size=chn, padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - DeConv(512,512,3), - nn.BatchNorm2d(512), activation - ) - - self.up3 = nn.Sequential( - DeConv(512,256,3), - nn.BatchNorm2d(256), activation - ) - - self.up2 = nn.Sequential( - DeConv(256,128,3), - nn.BatchNorm2d(128), activation - ) - - self.up1 = nn.Sequential( - DeConv(128,64,3), - nn.BatchNorm2d(64), activation - ) - self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_ori.py b/components/Generator_ori.py deleted file mode 100644 index d48ab2b..0000000 --- a/components/Generator_ori.py +++ /dev/null @@ -1,186 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 13th February 2022 2:06:14 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -from audioop import bias -import torch -from torch import nn -from torch.nn import init -from torch.nn import functional as F - -class InstanceNorm(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(InstanceNorm, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - x = x - torch.mean(x, (2, 3), True) - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class ResnetBlock_Adain(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)): - super(ResnetBlock_Adain, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - chn = kwargs["g_conv_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), - nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(128), activation) - - self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(256), activation) - - self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Adain(512, latent_size=chn, padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(512), activation - ) - - self.up3 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(256), activation - ) - - self.up2 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(128), activation - ) - - self.up1 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(64), activation - ) - - self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_ori_config.py b/components/Generator_ori_config.py deleted file mode 100644 index e06b002..0000000 --- a/components/Generator_ori_config.py +++ /dev/null @@ -1,188 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 4th March 2022 1:41:59 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn -from components.DeConv_Invo import DeConv - -class InstanceNorm(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(InstanceNorm, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - x = x - torch.mean(x, (2, 3), True) - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class ResnetBlock_Adain(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)): - super(ResnetBlock_Adain, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(in_channel, in_channel*2, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel*2), activation) - - self.down2 = nn.Sequential(nn.Conv2d(in_channel*2, in_channel*4, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel*4), activation) - - self.down3 = nn.Sequential(nn.Conv2d(in_channel*4, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel*8), activation) - - # self.down4 = nn.Sequential(nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), activation) - - ### resnet blocks - BN = [] - for _ in range(res_num): - BN += [ - ResnetBlock_Adain(in_channel*8, latent_size=id_dim, - padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up4 = nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), activation - # ) - - self.up3 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel*4), activation - ) - - self.up2 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel*2), activation - ) - - self.up1 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel), activation - ) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - # x = input # 3*224*224 - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - # res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - # res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_ori_modulation_config.py b/components/Generator_ori_modulation_config.py deleted file mode 100644 index fc4113f..0000000 --- a/components/Generator_ori_modulation_config.py +++ /dev/null @@ -1,203 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 4th March 2022 1:47:16 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - # res_mode = "conv" - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.style1(x, dlatents_in_slice) - y = self.conv1(y) - - y = self.act1(y) - y = self.style2(y, dlatents_in_slice) - y = self.conv2(y) - - out = x + y - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(in_channel), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(in_channel, in_channel*2, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel*2), activation) - - self.down2 = nn.Sequential(nn.Conv2d(in_channel*2, in_channel*4, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel*4), activation) - - self.down3 = nn.Sequential(nn.Conv2d(in_channel*4, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(in_channel*8), activation) - - # self.down4 = nn.Sequential(nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), activation) - - ### resnet blocks - BN = [] - for _ in range(res_num): - BN += [ - ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, - padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up4 = nn.Sequential( - # nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False), - # nn.BatchNorm2d(in_channel*8), activation - # ) - - self.up3 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel*4), activation - ) - - self.up2 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel*2), activation - ) - - self.up1 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel), activation - ) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), - nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - # x = input # 3*224*224 - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - # res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - # res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_oriae_modulation.py b/components/Generator_oriae_modulation.py deleted file mode 100644 index feb4057..0000000 --- a/components/Generator_oriae_modulation.py +++ /dev/null @@ -1,198 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 15th February 2022 12:03:17 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -from audioop import bias -import torch -from torch import nn -from torch.nn import init -from torch.nn import functional as F - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - chn = kwargs["g_conv_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), nn.Conv2d(3, 64, kernel_size=3, padding=0, bias=False), - nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(128), activation) - - self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(256), activation) - - self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(512, latent_size=chn, padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(512), activation - ) - - self.up3 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(256), activation - ) - - self.up2 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(128), activation - ) - - self.up1 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(64), activation - ) - - self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), nn.Conv2d(64, 3, kernel_size=3, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_reduce.py b/components/Generator_reduce.py deleted file mode 100644 index 457a95a..0000000 --- a/components/Generator_reduce.py +++ /dev/null @@ -1,197 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 16th February 2022 10:15:11 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn -from torch.nn import init -from torch.nn import functional as F - -class Demodule(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(Demodule, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class Modulation(nn.Module): - def __init__(self, latent_size, channels): - super(Modulation, self).__init__() - self.linear = nn.Linear(latent_size, channels) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * style - return x - -class ResnetBlock_Modulation(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)): - super(ResnetBlock_Modulation, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = Modulation(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = Modulation(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - chn = kwargs["g_conv_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - nn.BatchNorm2d(64), activation) - - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(128), activation) - - self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(256), activation) - - self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Modulation(512, latent_size=chn, padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(512), activation - ) - - self.up3 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(256), activation - ) - - self.up2 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(128), activation - ) - - self.up1 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(64), activation - ) - - self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/Generator_starganv2.py b/components/Generator_starganv2.py deleted file mode 100644 index 728f71b..0000000 --- a/components/Generator_starganv2.py +++ /dev/null @@ -1,297 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator_Invobn_config1.py -# Created Date: Saturday February 26th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 13th April 2022 6:30:26 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os - -import torch -from torch import nn -import torch.nn.functional as F -import math - - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class AdaIN(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm2d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1, 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class ResUpBlk(nn.Module): - def __init__(self, dim_in, dim_out,actv=nn.LeakyReLU(0.2),normalize="in"): - super().__init__() - self.actv = actv - self.normalize = normalize - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x): - x = self.norm1(x) - x = self.actv(x) - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - out = self._residual(x) - out = (out + self._shortcut(x)) / self.equal_var - return out - -class AdainResBlk(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=512, - actv=nn.LeakyReLU(0.2), upsample=False): - super().__init__() - self.actv = actv - self.upsample = upsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out, style_dim) - - def _build_weights(self, dim_in, dim_out, style_dim=64): - self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1) - self.norm1 = AdaIN(style_dim, dim_in) - self.norm2 = AdaIN(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - if self.upsample: - x = F.interpolate(x, scale_factor=2, mode='nearest') - x = self.conv1(x) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / self.equal_var - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - id_dim = kwargs["id_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - in_channel = kwargs["in_channel"] - up_mode = kwargs["up_mode"] - norm = kwargs["norm"].lower() - - aggregator = kwargs["aggregator"] - res_mode = kwargs["res_mode"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - if norm.lower() == "in": - norm_out = nn.InstanceNorm2d(in_channel, affine=True) - norm_mask= nn.InstanceNorm2d - elif norm.lower() == "bn": - norm_out = nn.BatchNorm2d(in_channel) - norm_mask = nn.BatchNorm2d - - - activation = nn.LeakyReLU(0.2) - # activation = nn.ReLU() - - self.from_rgb = nn.Conv2d(3, in_channel, 1, 1, 0) - # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = ResBlk(in_channel, in_channel, normalize=norm, downsample=True)# 256 - - self.down2 = ResBlk(in_channel, in_channel*2, normalize=norm, downsample=True)# 128 - - self.down3 = ResBlk(in_channel*2, in_channel*4,normalize=norm, downsample=True)# 64 - - self.down4 = ResBlk(in_channel*4, in_channel*8, normalize=norm, downsample=True)# 32 - - self.down5 = ResBlk(in_channel*8, in_channel*8, normalize=norm, downsample=True)# 16 - - # self.down6 = ResBlk(in_channel*8, in_channel*8, normalize=True, downsample=True)# 8 - - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=False)] - self.BottleNeck = nn.Sequential(*BN) - - # self.up6 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 16 - - self.up5 = AdainResBlk(in_channel*8, in_channel*8, style_dim=id_dim, upsample=True) # 32 - - self.up4 = AdainResBlk(in_channel*8, in_channel*4, style_dim=id_dim, upsample=True) # 64 - - self.up3 = AdainResBlk(in_channel*4, in_channel*2, style_dim=id_dim, upsample=True) # 128 - - # self.maskhead = nn.Sequential( - # nn.Conv2d(in_channel*2, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - # norm_mask, # 64 - # activation, - # nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1), - # nn.Sigmoid()) - self.maskhead = nn.Sequential( - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel*8, in_channel, kernel_size=3, stride=1, padding=1,bias=False), - norm_mask(in_channel, affine=True), # 32 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel, in_channel//4, kernel_size=3, stride=1, padding=1, bias=False), - norm_mask(in_channel//4, affine=True), # 64 - activation, - nn.UpsamplingNearest2d(scale_factor = 2), - nn.Conv2d(in_channel//4, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() - ) - - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - # self.up2 = AdainResBlk(in_channel*2, in_channel, style_dim=id_dim, upsample=True) - self.up2 = ResUpBlk(in_channel*2, in_channel, normalize=norm) - - # self.up1 = AdainResBlk(in_channel, in_channel, style_dim=id_dim, upsample=True) - self.up1 = ResUpBlk(in_channel, in_channel, normalize=norm) - # ResUpBlk(in_channel, in_channel, normalize="in") # 512 - - - - self.to_rgb = nn.Sequential( - norm_out, - activation, - nn.Conv2d(in_channel, 3, 3, 1, 1)) - - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - # nn.Conv2d(64, 3, kernel_size=7, padding=0)) - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.from_rgb(img) - res = self.down1(res) - skip = self.down2(res) - res = self.down3(skip) - res = self.down4(res) - res = self.down5(res) - mask= self.maskhead(res) - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - res = self.up5(res,id) - res = self.up4(res,id) - res = self.up3(res,id) - - # res = (1-mask) * self.sigma(skip) + mask * res - res = (1-mask) * skip + mask * res - res = self.up2(res) # + skip - res = self.up1(res) - res = self.to_rgb(res) - return res, mask \ No newline at end of file diff --git a/components/Generator_upsample.py b/components/Generator_upsample.py deleted file mode 100644 index d0ccfbc..0000000 --- a/components/Generator_upsample.py +++ /dev/null @@ -1,183 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 13th February 2022 2:15:23 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn -from components.DeConv_Invo import DeConv - -class InstanceNorm(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(InstanceNorm, self).__init__() - self.epsilon = epsilon - - def forward(self, x): - x = x - torch.mean(x, (2, 3), True) - tmp = torch.mul(x, x) # or x ** 2 - tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) - return x * tmp - -class ApplyStyle(nn.Module): - """ - @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb - """ - def __init__(self, latent_size, channels): - super(ApplyStyle, self).__init__() - self.linear = nn.Linear(latent_size, channels * 2) - - def forward(self, x, latent): - style = self.linear(latent) # style => [batch_size, n_channels*2] - shape = [-1, 2, x.size(1), 1, 1] - style = style.view(shape) # [batch_size, 2, n_channels, ...] - #x = x * (style[:, 0] + 1.) + style[:, 1] - x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 - return x - -class ResnetBlock_Adain(nn.Module): - def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)): - super(ResnetBlock_Adain, self).__init__() - - p = 0 - conv1 = [] - if padding_type == 'reflect': - conv1 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv1 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()] - self.conv1 = nn.Sequential(*conv1) - self.style1 = ApplyStyle(latent_size, dim) - self.act1 = activation - - p = 0 - conv2 = [] - if padding_type == 'reflect': - conv2 += [nn.ReflectionPad2d(1)] - elif padding_type == 'replicate': - conv2 += [nn.ReplicationPad2d(1)] - elif padding_type == 'zero': - p = 1 - else: - raise NotImplementedError('padding [%s] is not implemented' % padding_type) - conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()] - self.conv2 = nn.Sequential(*conv2) - self.style2 = ApplyStyle(latent_size, dim) - - - def forward(self, x, dlatents_in_slice): - y = self.conv1(x) - y = self.style1(y, dlatents_in_slice) - y = self.act1(y) - y = self.conv2(y) - y = self.style2(y, dlatents_in_slice) - out = x + y - return out - - -class Generator(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - chn = kwargs["g_conv_dim"] - k_size = kwargs["g_kernel_size"] - res_num = kwargs["res_num"] - - padding_size= int((k_size -1)/2) - padding_type= 'reflect' - - activation = nn.ReLU(True) - - self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), - nn.BatchNorm2d(64), activation) - # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=3, padding=0, bias=False), - # nn.BatchNorm2d(64), activation) - ### downsample - self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(128), activation) - - self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(256), activation) - - self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1, bias=False), - nn.BatchNorm2d(512), activation) - - ### resnet blocks - BN = [] - for i in range(res_num): - BN += [ - ResnetBlock_Adain(512, latent_size=chn, padding_type=padding_type, activation=activation)] - self.BottleNeck = nn.Sequential(*BN) - - self.up4 = nn.Sequential( - DeConv(512,512,3), - nn.BatchNorm2d(512), activation - ) - - self.up3 = nn.Sequential( - DeConv(512,256,3), - nn.BatchNorm2d(256), activation - ) - - self.up2 = nn.Sequential( - DeConv(256,128,3), - nn.BatchNorm2d(128), activation - ) - - self.up1 = nn.Sequential( - DeConv(128,64,3), - nn.BatchNorm2d(64), activation - ) - self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, 3, kernel_size=3, padding=0)) - - - - # self.__weights_init__() - - # def __weights_init__(self): - # for layer in self.encoder: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - # for layer in self.encoder2: - # if isinstance(layer,nn.Conv2d): - # nn.init.xavier_uniform_(layer.weight) - - def forward(self, img, id): - res = self.first_layer(img) - res = self.down1(res) - res = self.down2(res) - res = self.down3(res) - res = self.down4(res) - - for i in range(len(self.BottleNeck)): - res = self.BottleNeck[i](res, id) - - res = self.up4(res) - res = self.up3(res) - res = self.up2(res) - res = self.up1(res) - res = self.last_layer(res) - - return res diff --git a/components/LSTU.py b/components/LSTU.py deleted file mode 100644 index 240fb21..0000000 --- a/components/LSTU.py +++ /dev/null @@ -1,119 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 28th March 2022 11:47:55 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import math -import torch -from torch import nn - -import torch.nn.functional as F - -# class LSTU(nn.Module): -# def __init__( -# self, -# in_channel, -# out_channel, -# latent_channel, -# scale = 4 -# ): -# super().__init__() -# sig = nn.Sigmoid() -# self.relu = nn.ReLU(True) - -# self.up_sample = nn.Sequential(nn.Conv2d(latent_channel, out_channel/4, kernel_size=3, stride=1, padding=1, bias=False), -# nn.BatchNorm2d(out_channel/4), -# self.relu, -# nn.Conv2d(latent_channel/4, out_channel, kernel_size=3, stride=1, padding=1), -# ) - -# self.forget_gate = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False), -# nn.BatchNorm2d(out_channel), sig) - -# self.reset_gate = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False), -# nn.BatchNorm2d(out_channel), sig) - -# self.conv11 = nn.Sequential(nn.Conv2d(out_channel, out_channel, kernel_size=1, bias=True)) - -# def forward(self, encoder_in, bottleneck_in): -# h_hat_l_1 = self.up_sample(bottleneck_in) # upsample and make `channel` identical to `out_channel` -# h_bar_l = self.conv11(h_hat_l_1) -# f_l = self.forget_gate(h_hat_l_1) -# r_l = self.reset_gate (h_hat_l_1) -# h_hat_l = (1-f_l)*h_bar_l + f_l* encoder_in -# x_hat_l = r_l* self.relu(h_hat_l) + (1-r_l)* h_hat_l_1 -# return x_hat_l - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class LSTU(nn.Module): - def __init__( - self, - in_channel, - norm - ): - super().__init__() - self.sig = nn.Sigmoid() - - self.mask_head = ResBlk(in_channel, 1, normalize=norm) - # self.forget_gate = ResBlk(in_channel,in_channel, normalize=norm) - - def forward(self, encoder_in, decoder_in): - mask = self.sig(self.mask_head(decoder_in)) # upsample and make `channel` identical to `out_channel` - # enc_feat= self.forget_gate(encoder_in) - out = (1-mask)*encoder_in + mask * decoder_in - return out, mask \ No newline at end of file diff --git a/components/LSTU_Config.py b/components/LSTU_Config.py deleted file mode 100644 index 05dff5c..0000000 --- a/components/LSTU_Config.py +++ /dev/null @@ -1,124 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Generator.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 29th March 2022 12:20:26 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import math -import torch -from torch import nn - -import torch.nn.functional as F - -# class LSTU(nn.Module): -# def __init__( -# self, -# in_channel, -# out_channel, -# latent_channel, -# scale = 4 -# ): -# super().__init__() -# sig = nn.Sigmoid() -# self.relu = nn.ReLU(True) - -# self.up_sample = nn.Sequential(nn.Conv2d(latent_channel, out_channel/4, kernel_size=3, stride=1, padding=1, bias=False), -# nn.BatchNorm2d(out_channel/4), -# self.relu, -# nn.Conv2d(latent_channel/4, out_channel, kernel_size=3, stride=1, padding=1), -# ) - -# self.forget_gate = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False), -# nn.BatchNorm2d(out_channel), sig) - -# self.reset_gate = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False), -# nn.BatchNorm2d(out_channel), sig) - -# self.conv11 = nn.Sequential(nn.Conv2d(out_channel, out_channel, kernel_size=1, bias=True)) - -# def forward(self, encoder_in, bottleneck_in): -# h_hat_l_1 = self.up_sample(bottleneck_in) # upsample and make `channel` identical to `out_channel` -# h_bar_l = self.conv11(h_hat_l_1) -# f_l = self.forget_gate(h_hat_l_1) -# r_l = self.reset_gate (h_hat_l_1) -# h_hat_l = (1-f_l)*h_bar_l + f_l* encoder_in -# x_hat_l = r_l* self.relu(h_hat_l) + (1-r_l)* h_hat_l_1 -# return x_hat_l - - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.equal_var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x /self.equal_var # unit variance - -class LSTU(nn.Module): - def __init__( - self, - in_channel, - norm - ): - super().__init__() - - # self.mask_head = ResBlk(in_channel, 1, normalize=norm) - self.mask_head = nn.Sequential(nn.Conv2d(in_channel, in_channel//2, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(in_channel//2), - nn.LeakyReLU(0.2), - nn.Conv2d(in_channel//2, 1, kernel_size=3, stride=1, padding=1), - nn.Sigmoid() - ) - # self.forget_gate = ResBlk(in_channel,in_channel, normalize=norm) - - def forward(self, encoder_in, decoder_in): - mask = self.mask_head(decoder_in) # upsample and make `channel` identical to `out_channel` - # enc_feat= self.forget_gate(encoder_in) - out = (1-mask)*encoder_in + mask * decoder_in - return out, mask \ No newline at end of file diff --git a/components/ModulatedDWConv.py b/components/ModulatedDWConv.py deleted file mode 100644 index 9461cba..0000000 --- a/components/ModulatedDWConv.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: ModulatedDWConv.py -# Created Date: Monday April 18th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 18th April 2022 10:33:48 am -# Modified By: Chen Xuanhong -# Modified from: https://github.com/bes-dev/MobileStyleGAN.pytorch -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import math -import torch -import torch.nn as nn -import torch.nn.functional as F - -class ModulatedDWConv2d(nn.Module): - def __init__( - self, - channels_in, - channels_out, - style_dim, - kernel_size, - demodulate=True - ): - super().__init__() - # create conv - self.weight_dw = nn.Parameter( - torch.randn(channels_in, 1, kernel_size, kernel_size) - ) - self.weight_permute = nn.Parameter( - torch.randn(channels_out, channels_in, 1, 1) - ) - # create modulation network - self.modulation = nn.Linear(style_dim, channels_in, bias=True) - self.modulation.bias.data.fill_(1.0) - # create demodulation parameters - self.demodulate = demodulate - if self.demodulate: - self.register_buffer("style_inv", torch.randn(1, 1, channels_in, 1, 1)) - # some service staff - self.scale = 1.0 / math.sqrt(channels_in * kernel_size ** 2) - self.padding = kernel_size // 2 - - def forward(self, x, style): - modulation = self.get_modulation(style) - x = modulation * x - x = F.conv2d(x, self.weight_dw, padding=self.padding, groups=x.size(1)) - x = F.conv2d(x, self.weight_permute) - if self.demodulate: - demodulation = self.get_demodulation(style) - x = demodulation * x - return x - - def get_modulation(self, style): - style = self.modulation(style).view(style.size(0), -1, 1, 1) - modulation = self.scale * style - return modulation - - def get_demodulation(self, style): - w = (self.weight_dw.transpose(0, 1) * self.weight_permute).unsqueeze(0) - norm = torch.rsqrt((self.scale * self.style_inv * w).pow(2).sum([2, 3, 4]) + 1e-8) - demodulation = norm - return demodulation.view(*demodulation.size(), 1, 1) \ No newline at end of file diff --git a/components/Nonstau_Discriminator.py b/components/Nonstau_Discriminator.py deleted file mode 100644 index 638d3dd..0000000 --- a/components/Nonstau_Discriminator.py +++ /dev/null @@ -1,96 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Nonstau_Discriminator.py -# Created Date: Monday March 28th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 28th March 2022 10:03:56 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# -import math -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - elif self.normalize.lower() == "none": - self.normalize = False - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x / self.var # unit variance - -class Discriminator(torch.nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - img_size = kwargs["img_size"] - num_domains = 1 - max_conv_dim = kwargs["max_conv_dim"] - norm = kwargs["norm"] - dim_in = 2**14 // img_size - blocks = [] - blocks += [nn.Conv2d(3, dim_in, 3, 1, 1)] - - repeat_num = int(np.log2(img_size)) - 2 - for _ in range(repeat_num): - dim_out = min(dim_in*2, max_conv_dim) - blocks += [ResBlk(dim_in, dim_out, normalize=norm, downsample=True)] - dim_in = dim_out - - blocks += [nn.LeakyReLU(0.2)] - blocks += [nn.Conv2d(dim_out, dim_out, 4, 1, 0)] - blocks += [nn.LeakyReLU(0.2)] - blocks += [nn.Conv2d(dim_out, num_domains, 1, 1, 0)] - self.main = nn.Sequential(*blocks) - - def forward(self, x): - out = self.main(x) - out = out.view(out.size(0), -1) # (batch, num_domains) - return out \ No newline at end of file diff --git a/components/Nonstau_Discriminator_FM.py b/components/Nonstau_Discriminator_FM.py deleted file mode 100644 index 8a49c00..0000000 --- a/components/Nonstau_Discriminator_FM.py +++ /dev/null @@ -1,107 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Nonstau_Discriminator.py -# Created Date: Monday March 28th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 13th April 2022 3:11:40 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# -import math -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -class ResBlk(nn.Module): - def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), - normalize="in", downsample=False): - super().__init__() - self.actv = actv - self.normalize = normalize - self.downsample = downsample - self.learned_sc = dim_in != dim_out - self.var = math.sqrt(2) - self._build_weights(dim_in, dim_out) - - def _build_weights(self, dim_in, dim_out): - self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) - self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) - if self.normalize.lower() == "in": - self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) - self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) - elif self.normalize.lower() == "bn": - self.norm1 = nn.BatchNorm2d(dim_in) - self.norm2 = nn.BatchNorm2d(dim_in) - elif self.normalize.lower() == "none": - self.normalize = False - if self.learned_sc: - self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) - - def _shortcut(self, x): - if self.learned_sc: - x = self.conv1x1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - return x - - def _residual(self, x): - if self.normalize: - x = self.norm1(x) - x = self.actv(x) - x = self.conv1(x) - if self.downsample: - x = F.avg_pool2d(x, 2) - if self.normalize: - x = self.norm2(x) - x = self.actv(x) - x = self.conv2(x) - return x - - def forward(self, x): - x = self._shortcut(x) + self._residual(x) - return x / self.var # unit variance - -class Discriminator(torch.nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - img_size = kwargs["img_size"] - num_domains = 1 - max_conv_dim = kwargs["max_conv_dim"] - norm = kwargs["norm"].lower() - dim_in = 2**14 // img_size - blocks = [] - blocks += [nn.Conv2d(3, dim_in, 3, 1, 1)] - - repeat_num = int(np.log2(img_size)) - 2 - for _ in range(repeat_num-2): - dim_out = min(dim_in*2, max_conv_dim) - blocks += [ResBlk(dim_in, dim_out, normalize=norm, downsample=True)] - dim_in = dim_out - blocks1 = [] - for _ in range(2): # 16 - dim_out = min(dim_in*2, max_conv_dim) - blocks1 += [ResBlk(dim_in, dim_out, normalize=norm, downsample=True)] - dim_in = dim_out - - blocks1 += [nn.LeakyReLU(0.2)] - blocks1 += [nn.Conv2d(dim_out, dim_out, 4, 1, 0)] - blocks1 += [nn.LeakyReLU(0.2)] - blocks1 += [nn.Conv2d(dim_out, num_domains, 1, 1, 0)] - self.main = nn.Sequential(*blocks) - self.tail = nn.Sequential(*blocks1) - - def get_feature(self,x): - mid = self.main(x) - return mid - - def forward(self, x): - mid = self.main(x) - out = self.tail(mid) - out = out.view(out.size(0), -1) # (batch, num_domains) - return out,mid diff --git a/components/arcface_decoder.py b/components/arcface_decoder.py deleted file mode 100644 index 311a927..0000000 --- a/components/arcface_decoder.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: arcface_decoder.py -# Created Date: Saturday January 29th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 29th January 2022 2:55:39 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn -from torch.nn import init -from torch.nn import functional as F - -class Decoder(nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - - activation = nn.ReLU(True) - - self.fc = nn.Linear(512, 7*7*512) - - self.up4 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(512), activation - ) - - self.up3 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(256), activation - ) - - self.up2 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(128), activation - ) - - self.up1 = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear'), - nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(64), activation - ) - - self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) - def forward(self, input): - x = input # - x = self.fc(x) - x = x.view(x.size(0),512,7,7) - x = self.up4(x) - x = self.up3(x) - x = self.up2(x) - x = self.up1(x) - x = self.last_layer(x) - - return x \ No newline at end of file diff --git a/components/misc/Involution.py b/components/misc/Involution.py deleted file mode 100644 index 79a62cf..0000000 --- a/components/misc/Involution.py +++ /dev/null @@ -1,302 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Involution.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 11th February 2022 12:08:41 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import torch -import torch.nn as nn -from torch.nn.modules.utils import _pair -from torch.autograd import Function - -import cupy -from string import Template -from collections import namedtuple - - - -Stream = namedtuple('Stream', ['ptr']) - - -def Dtype(t): - if isinstance(t, torch.cuda.FloatTensor): - return 'float' - elif isinstance(t, torch.cuda.DoubleTensor): - return 'double' - - -@cupy._util.memoize(for_each_device=True) -def load_kernel(kernel_name, code, **kwargs): - code = Template(code).substitute(**kwargs) - kernel_code = cupy.cuda.compile_with_cache(code) - return kernel_code.get_function(kernel_name) - - -CUDA_NUM_THREADS = 1024 - -kernel_loop = ''' -#define CUDA_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ - i < (n); \ - i += blockDim.x * gridDim.x) -''' - - -def GET_BLOCKS(N): - return (N + CUDA_NUM_THREADS - 1) // CUDA_NUM_THREADS - - -_involution_kernel = kernel_loop + ''' -extern "C" -__global__ void involution_forward_kernel( -const ${Dtype}* bottom_data, const ${Dtype}* weight_data, ${Dtype}* top_data) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int n = index / ${channels} / ${top_height} / ${top_width}; - const int c = (index / ${top_height} / ${top_width}) % ${channels}; - const int h = (index / ${top_width}) % ${top_height}; - const int w = index % ${top_width}; - const int g = c / (${channels} / ${groups}); - ${Dtype} value = 0; - #pragma unroll - for (int kh = 0; kh < ${kernel_h}; ++kh) { - #pragma unroll - for (int kw = 0; kw < ${kernel_w}; ++kw) { - const int h_in = -${pad_h} + h * ${stride_h} + kh * ${dilation_h}; - const int w_in = -${pad_w} + w * ${stride_w} + kw * ${dilation_w}; - if ((h_in >= 0) && (h_in < ${bottom_height}) - && (w_in >= 0) && (w_in < ${bottom_width})) { - const int offset = ((n * ${channels} + c) * ${bottom_height} + h_in) - * ${bottom_width} + w_in; - const int offset_weight = ((((n * ${groups} + g) * ${kernel_h} + kh) * ${kernel_w} + kw) * ${top_height} + h) - * ${top_width} + w; - value += weight_data[offset_weight] * bottom_data[offset]; - } - } - } - top_data[index] = value; - } -} -''' - - -_involution_kernel_backward_grad_input = kernel_loop + ''' -extern "C" -__global__ void involution_backward_grad_input_kernel( - const ${Dtype}* const top_diff, const ${Dtype}* const weight_data, ${Dtype}* const bottom_diff) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int n = index / ${channels} / ${bottom_height} / ${bottom_width}; - const int c = (index / ${bottom_height} / ${bottom_width}) % ${channels}; - const int h = (index / ${bottom_width}) % ${bottom_height}; - const int w = index % ${bottom_width}; - const int g = c / (${channels} / ${groups}); - ${Dtype} value = 0; - #pragma unroll - for (int kh = 0; kh < ${kernel_h}; ++kh) { - #pragma unroll - for (int kw = 0; kw < ${kernel_w}; ++kw) { - const int h_out_s = h + ${pad_h} - kh * ${dilation_h}; - const int w_out_s = w + ${pad_w} - kw * ${dilation_w}; - if (((h_out_s % ${stride_h}) == 0) && ((w_out_s % ${stride_w}) == 0)) { - const int h_out = h_out_s / ${stride_h}; - const int w_out = w_out_s / ${stride_w}; - if ((h_out >= 0) && (h_out < ${top_height}) - && (w_out >= 0) && (w_out < ${top_width})) { - const int offset = ((n * ${channels} + c) * ${top_height} + h_out) - * ${top_width} + w_out; - const int offset_weight = ((((n * ${groups} + g) * ${kernel_h} + kh) * ${kernel_w} + kw) * ${top_height} + h_out) - * ${top_width} + w_out; - value += weight_data[offset_weight] * top_diff[offset]; - } - } - } - } - bottom_diff[index] = value; - } -} -''' - - -_involution_kernel_backward_grad_weight = kernel_loop + ''' -extern "C" -__global__ void involution_backward_grad_weight_kernel( - const ${Dtype}* const top_diff, const ${Dtype}* const bottom_data, ${Dtype}* const buffer_data) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int h = (index / ${top_width}) % ${top_height}; - const int w = index % ${top_width}; - const int kh = (index / ${kernel_w} / ${top_height} / ${top_width}) - % ${kernel_h}; - const int kw = (index / ${top_height} / ${top_width}) % ${kernel_w}; - const int h_in = -${pad_h} + h * ${stride_h} + kh * ${dilation_h}; - const int w_in = -${pad_w} + w * ${stride_w} + kw * ${dilation_w}; - if ((h_in >= 0) && (h_in < ${bottom_height}) - && (w_in >= 0) && (w_in < ${bottom_width})) { - const int g = (index / ${kernel_h} / ${kernel_w} / ${top_height} / ${top_width}) % ${groups}; - const int n = (index / ${groups} / ${kernel_h} / ${kernel_w} / ${top_height} / ${top_width}) % ${num}; - ${Dtype} value = 0; - #pragma unroll - for (int c = g * (${channels} / ${groups}); c < (g + 1) * (${channels} / ${groups}); ++c) { - const int top_offset = ((n * ${channels} + c) * ${top_height} + h) - * ${top_width} + w; - const int bottom_offset = ((n * ${channels} + c) * ${bottom_height} + h_in) - * ${bottom_width} + w_in; - value += top_diff[top_offset] * bottom_data[bottom_offset]; - } - buffer_data[index] = value; - } else { - buffer_data[index] = 0; - } - } -} -''' - - -class _involution(Function): - @staticmethod - def forward(ctx, input, weight, stride, padding, dilation): - assert input.dim() == 4 and input.is_cuda - assert weight.dim() == 6 and weight.is_cuda - batch_size, channels, height, width = input.size() - kernel_h, kernel_w = weight.size()[2:4] - output_h = int((height + 2 * padding[0] - (dilation[0] * (kernel_h - 1) + 1)) / stride[0] + 1) - output_w = int((width + 2 * padding[1] - (dilation[1] * (kernel_w - 1) + 1)) / stride[1] + 1) - - output = input.new(batch_size, channels, output_h, output_w) - n = output.numel() - - with torch.cuda.device_of(input): - f = load_kernel('involution_forward_kernel', _involution_kernel, Dtype=Dtype(input), nthreads=n, - num=batch_size, channels=channels, groups=weight.size()[1], - bottom_height=height, bottom_width=width, - top_height=output_h, top_width=output_w, - kernel_h=kernel_h, kernel_w=kernel_w, - stride_h=stride[0], stride_w=stride[1], - dilation_h=dilation[0], dilation_w=dilation[1], - pad_h=padding[0], pad_w=padding[1]) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[input.data_ptr(), weight.data_ptr(), output.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - ctx.save_for_backward(input, weight) - ctx.stride, ctx.padding, ctx.dilation = stride, padding, dilation - return output - - @staticmethod - def backward(ctx, grad_output): - assert grad_output.is_cuda and grad_output.is_contiguous() - input, weight = ctx.saved_tensors - stride, padding, dilation = ctx.stride, ctx.padding, ctx.dilation - - batch_size, channels, height, width = input.size() - kernel_h, kernel_w = weight.size()[2:4] - output_h, output_w = grad_output.size()[2:] - - grad_input, grad_weight = None, None - - opt = dict(Dtype=Dtype(grad_output), - num=batch_size, channels=channels, groups=weight.size()[1], - bottom_height=height, bottom_width=width, - top_height=output_h, top_width=output_w, - kernel_h=kernel_h, kernel_w=kernel_w, - stride_h=stride[0], stride_w=stride[1], - dilation_h=dilation[0], dilation_w=dilation[1], - pad_h=padding[0], pad_w=padding[1]) - - with torch.cuda.device_of(input): - if ctx.needs_input_grad[0]: - grad_input = input.new(input.size()) - - n = grad_input.numel() - opt['nthreads'] = n - - f = load_kernel('involution_backward_grad_input_kernel', - _involution_kernel_backward_grad_input, **opt) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[grad_output.data_ptr(), weight.data_ptr(), grad_input.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - if ctx.needs_input_grad[1]: - grad_weight = weight.new(weight.size()) - - n = grad_weight.numel() - opt['nthreads'] = n - - f = load_kernel('involution_backward_grad_weight_kernel', - _involution_kernel_backward_grad_weight, **opt) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[grad_output.data_ptr(), input.data_ptr(), grad_weight.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - return grad_input, grad_weight, None, None, None - - -def _involution_cuda(input, weight, bias=None, stride=1, padding=0, dilation=1): - """ involution kernel - """ - assert input.size(0) == weight.size(0) - assert input.size(-2)//stride == weight.size(-2) - assert input.size(-1)//stride == weight.size(-1) - if input.is_cuda: - out = _involution.apply(input, weight, _pair(stride), _pair(padding), _pair(dilation)) - if bias is not None: - out += bias.view(1,-1,1,1) - else: - raise NotImplementedError - return out - - -class involution(nn.Module): - - def __init__(self, - channels, - kernel_size, - stride): - super(involution, self).__init__() - self.kernel_size = kernel_size - self.stride = stride - self.channels = channels - reduction_ratio = 4 - self.group_channels = 8 - self.groups = self.channels // self.group_channels - self.seblock = nn.Sequential( - nn.Conv2d(in_channels = channels, out_channels = channels // reduction_ratio, kernel_size= 1), - # nn.BatchNorm2d(channels // reduction_ratio), - nn.ReLU(), - nn.Conv2d(in_channels = channels // reduction_ratio, out_channels = kernel_size**2 * self.groups, kernel_size= 1) - ) - - # self.conv1 = ConvModule( - # in_channels=channels, - # out_channels=channels // reduction_ratio, - # kernel_size=1, - # conv_cfg=None, - # norm_cfg=dict(type='BN'), - # act_cfg=dict(type='ReLU')) - # self.conv2 = ConvModule( - # in_channels=channels // reduction_ratio, - # out_channels=kernel_size**2 * self.groups, - # kernel_size=1, - # stride=1, - # conv_cfg=None, - # norm_cfg=None, - # act_cfg=None) - if stride > 1: - self.avgpool = nn.AvgPool2d(stride, stride) - - def forward(self, x): - # weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x))) - weight = self.seblock(x) - b, c, h, w = weight.shape - weight = weight.view(b, self.groups, self.kernel_size, self.kernel_size, h, w) - out = _involution_cuda(x, weight, stride=self.stride, padding=(self.kernel_size-1)//2) - return out diff --git a/components/misc/Involution_BN.py b/components/misc/Involution_BN.py deleted file mode 100644 index 28caa99..0000000 --- a/components/misc/Involution_BN.py +++ /dev/null @@ -1,303 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Involution.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 26th February 2022 5:19:56 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import torch -import torch.nn as nn -from torch.nn.modules.utils import _pair -from torch.autograd import Function - -import cupy -from string import Template -from collections import namedtuple - - - -Stream = namedtuple('Stream', ['ptr']) - - -def Dtype(t): - if isinstance(t, torch.cuda.FloatTensor): - return 'float' - elif isinstance(t, torch.cuda.DoubleTensor): - return 'double' - - -@cupy._util.memoize(for_each_device=True) -def load_kernel(kernel_name, code, **kwargs): - code = Template(code).substitute(**kwargs) - kernel_code = cupy.cuda.compile_with_cache(code) - return kernel_code.get_function(kernel_name) - - -CUDA_NUM_THREADS = 1024 - -kernel_loop = ''' -#define CUDA_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ - i < (n); \ - i += blockDim.x * gridDim.x) -''' - - -def GET_BLOCKS(N): - return (N + CUDA_NUM_THREADS - 1) // CUDA_NUM_THREADS - - -_involution_kernel = kernel_loop + ''' -extern "C" -__global__ void involution_forward_kernel( -const ${Dtype}* bottom_data, const ${Dtype}* weight_data, ${Dtype}* top_data) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int n = index / ${channels} / ${top_height} / ${top_width}; - const int c = (index / ${top_height} / ${top_width}) % ${channels}; - const int h = (index / ${top_width}) % ${top_height}; - const int w = index % ${top_width}; - const int g = c / (${channels} / ${groups}); - ${Dtype} value = 0; - #pragma unroll - for (int kh = 0; kh < ${kernel_h}; ++kh) { - #pragma unroll - for (int kw = 0; kw < ${kernel_w}; ++kw) { - const int h_in = -${pad_h} + h * ${stride_h} + kh * ${dilation_h}; - const int w_in = -${pad_w} + w * ${stride_w} + kw * ${dilation_w}; - if ((h_in >= 0) && (h_in < ${bottom_height}) - && (w_in >= 0) && (w_in < ${bottom_width})) { - const int offset = ((n * ${channels} + c) * ${bottom_height} + h_in) - * ${bottom_width} + w_in; - const int offset_weight = ((((n * ${groups} + g) * ${kernel_h} + kh) * ${kernel_w} + kw) * ${top_height} + h) - * ${top_width} + w; - value += weight_data[offset_weight] * bottom_data[offset]; - } - } - } - top_data[index] = value; - } -} -''' - - -_involution_kernel_backward_grad_input = kernel_loop + ''' -extern "C" -__global__ void involution_backward_grad_input_kernel( - const ${Dtype}* const top_diff, const ${Dtype}* const weight_data, ${Dtype}* const bottom_diff) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int n = index / ${channels} / ${bottom_height} / ${bottom_width}; - const int c = (index / ${bottom_height} / ${bottom_width}) % ${channels}; - const int h = (index / ${bottom_width}) % ${bottom_height}; - const int w = index % ${bottom_width}; - const int g = c / (${channels} / ${groups}); - ${Dtype} value = 0; - #pragma unroll - for (int kh = 0; kh < ${kernel_h}; ++kh) { - #pragma unroll - for (int kw = 0; kw < ${kernel_w}; ++kw) { - const int h_out_s = h + ${pad_h} - kh * ${dilation_h}; - const int w_out_s = w + ${pad_w} - kw * ${dilation_w}; - if (((h_out_s % ${stride_h}) == 0) && ((w_out_s % ${stride_w}) == 0)) { - const int h_out = h_out_s / ${stride_h}; - const int w_out = w_out_s / ${stride_w}; - if ((h_out >= 0) && (h_out < ${top_height}) - && (w_out >= 0) && (w_out < ${top_width})) { - const int offset = ((n * ${channels} + c) * ${top_height} + h_out) - * ${top_width} + w_out; - const int offset_weight = ((((n * ${groups} + g) * ${kernel_h} + kh) * ${kernel_w} + kw) * ${top_height} + h_out) - * ${top_width} + w_out; - value += weight_data[offset_weight] * top_diff[offset]; - } - } - } - } - bottom_diff[index] = value; - } -} -''' - - -_involution_kernel_backward_grad_weight = kernel_loop + ''' -extern "C" -__global__ void involution_backward_grad_weight_kernel( - const ${Dtype}* const top_diff, const ${Dtype}* const bottom_data, ${Dtype}* const buffer_data) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int h = (index / ${top_width}) % ${top_height}; - const int w = index % ${top_width}; - const int kh = (index / ${kernel_w} / ${top_height} / ${top_width}) - % ${kernel_h}; - const int kw = (index / ${top_height} / ${top_width}) % ${kernel_w}; - const int h_in = -${pad_h} + h * ${stride_h} + kh * ${dilation_h}; - const int w_in = -${pad_w} + w * ${stride_w} + kw * ${dilation_w}; - if ((h_in >= 0) && (h_in < ${bottom_height}) - && (w_in >= 0) && (w_in < ${bottom_width})) { - const int g = (index / ${kernel_h} / ${kernel_w} / ${top_height} / ${top_width}) % ${groups}; - const int n = (index / ${groups} / ${kernel_h} / ${kernel_w} / ${top_height} / ${top_width}) % ${num}; - ${Dtype} value = 0; - #pragma unroll - for (int c = g * (${channels} / ${groups}); c < (g + 1) * (${channels} / ${groups}); ++c) { - const int top_offset = ((n * ${channels} + c) * ${top_height} + h) - * ${top_width} + w; - const int bottom_offset = ((n * ${channels} + c) * ${bottom_height} + h_in) - * ${bottom_width} + w_in; - value += top_diff[top_offset] * bottom_data[bottom_offset]; - } - buffer_data[index] = value; - } else { - buffer_data[index] = 0; - } - } -} -''' - - -class _involution(Function): - @staticmethod - def forward(ctx, input, weight, stride, padding, dilation): - assert input.dim() == 4 and input.is_cuda - assert weight.dim() == 6 and weight.is_cuda - batch_size, channels, height, width = input.size() - kernel_h, kernel_w = weight.size()[2:4] - output_h = int((height + 2 * padding[0] - (dilation[0] * (kernel_h - 1) + 1)) / stride[0] + 1) - output_w = int((width + 2 * padding[1] - (dilation[1] * (kernel_w - 1) + 1)) / stride[1] + 1) - - output = input.new(batch_size, channels, output_h, output_w) - n = output.numel() - - with torch.cuda.device_of(input): - f = load_kernel('involution_forward_kernel', _involution_kernel, Dtype=Dtype(input), nthreads=n, - num=batch_size, channels=channels, groups=weight.size()[1], - bottom_height=height, bottom_width=width, - top_height=output_h, top_width=output_w, - kernel_h=kernel_h, kernel_w=kernel_w, - stride_h=stride[0], stride_w=stride[1], - dilation_h=dilation[0], dilation_w=dilation[1], - pad_h=padding[0], pad_w=padding[1]) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[input.data_ptr(), weight.data_ptr(), output.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - ctx.save_for_backward(input, weight) - ctx.stride, ctx.padding, ctx.dilation = stride, padding, dilation - return output - - @staticmethod - def backward(ctx, grad_output): - assert grad_output.is_cuda and grad_output.is_contiguous() - input, weight = ctx.saved_tensors - stride, padding, dilation = ctx.stride, ctx.padding, ctx.dilation - - batch_size, channels, height, width = input.size() - kernel_h, kernel_w = weight.size()[2:4] - output_h, output_w = grad_output.size()[2:] - - grad_input, grad_weight = None, None - - opt = dict(Dtype=Dtype(grad_output), - num=batch_size, channels=channels, groups=weight.size()[1], - bottom_height=height, bottom_width=width, - top_height=output_h, top_width=output_w, - kernel_h=kernel_h, kernel_w=kernel_w, - stride_h=stride[0], stride_w=stride[1], - dilation_h=dilation[0], dilation_w=dilation[1], - pad_h=padding[0], pad_w=padding[1]) - - with torch.cuda.device_of(input): - if ctx.needs_input_grad[0]: - grad_input = input.new(input.size()) - - n = grad_input.numel() - opt['nthreads'] = n - - f = load_kernel('involution_backward_grad_input_kernel', - _involution_kernel_backward_grad_input, **opt) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[grad_output.data_ptr(), weight.data_ptr(), grad_input.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - if ctx.needs_input_grad[1]: - grad_weight = weight.new(weight.size()) - - n = grad_weight.numel() - opt['nthreads'] = n - - f = load_kernel('involution_backward_grad_weight_kernel', - _involution_kernel_backward_grad_weight, **opt) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[grad_output.data_ptr(), input.data_ptr(), grad_weight.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - return grad_input, grad_weight, None, None, None - - -def _involution_cuda(input, weight, bias=None, stride=1, padding=0, dilation=1): - """ involution kernel - """ - assert input.size(0) == weight.size(0) - assert input.size(-2)//stride == weight.size(-2) - assert input.size(-1)//stride == weight.size(-1) - if input.is_cuda: - out = _involution.apply(input, weight, _pair(stride), _pair(padding), _pair(dilation)) - if bias is not None: - out += bias.view(1,-1,1,1) - else: - raise NotImplementedError - return out - - -class involution(nn.Module): - - def __init__(self, - channels, - kernel_size, - stride, - padding=1): - super(involution, self).__init__() - self.kernel_size = kernel_size - self.stride = stride - self.channels = channels - reduction_ratio = 4 - self.group_channels = 8 - self.groups = self.channels // self.group_channels - self.seblock = nn.Sequential( - nn.Conv2d(in_channels = channels, out_channels = channels // reduction_ratio, kernel_size= 1), - nn.BatchNorm2d(channels // reduction_ratio), - nn.ReLU(), - nn.Conv2d(in_channels = channels // reduction_ratio, out_channels = kernel_size**2 * self.groups, kernel_size= 1) - ) - self.padding = padding - # self.conv1 = ConvModule( - # in_channels=channels, - # out_channels=channels // reduction_ratio, - # kernel_size=1, - # conv_cfg=None, - # norm_cfg=dict(type='BN'), - # act_cfg=dict(type='ReLU')) - # self.conv2 = ConvModule( - # in_channels=channels // reduction_ratio, - # out_channels=kernel_size**2 * self.groups, - # kernel_size=1, - # stride=1, - # conv_cfg=None, - # norm_cfg=None, - # act_cfg=None) - if stride > 1: - self.avgpool = nn.AvgPool2d(stride, stride) - - def forward(self, x): - # weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x))) - weight = self.seblock(x if self.stride == 1 else self.avgpool(x)) - b, c, h, w = weight.shape - weight = weight.view(b, self.groups, self.kernel_size, self.kernel_size, h, w) - out = _involution_cuda(x, weight, stride=self.stride, padding=self.padding) - return out diff --git a/components/misc/Involution_ECA.py b/components/misc/Involution_ECA.py deleted file mode 100644 index 905793d..0000000 --- a/components/misc/Involution_ECA.py +++ /dev/null @@ -1,308 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Involution.py -# Created Date: Tuesday July 20th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 26th February 2022 5:50:12 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import torch -import torch.nn as nn -from torch.nn.modules.utils import _pair -from torch.autograd import Function - -import cupy -import math - -from string import Template -from collections import namedtuple - - - -Stream = namedtuple('Stream', ['ptr']) - - -def Dtype(t): - if isinstance(t, torch.cuda.FloatTensor): - return 'float' - elif isinstance(t, torch.cuda.DoubleTensor): - return 'double' - - -@cupy._util.memoize(for_each_device=True) -def load_kernel(kernel_name, code, **kwargs): - code = Template(code).substitute(**kwargs) - kernel_code = cupy.cuda.compile_with_cache(code) - return kernel_code.get_function(kernel_name) - - -CUDA_NUM_THREADS = 1024 - -kernel_loop = ''' -#define CUDA_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ - i < (n); \ - i += blockDim.x * gridDim.x) -''' - - -def GET_BLOCKS(N): - return (N + CUDA_NUM_THREADS - 1) // CUDA_NUM_THREADS - - -_involution_kernel = kernel_loop + ''' -extern "C" -__global__ void involution_forward_kernel( -const ${Dtype}* bottom_data, const ${Dtype}* weight_data, ${Dtype}* top_data) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int n = index / ${channels} / ${top_height} / ${top_width}; - const int c = (index / ${top_height} / ${top_width}) % ${channels}; - const int h = (index / ${top_width}) % ${top_height}; - const int w = index % ${top_width}; - const int g = c / (${channels} / ${groups}); - ${Dtype} value = 0; - #pragma unroll - for (int kh = 0; kh < ${kernel_h}; ++kh) { - #pragma unroll - for (int kw = 0; kw < ${kernel_w}; ++kw) { - const int h_in = -${pad_h} + h * ${stride_h} + kh * ${dilation_h}; - const int w_in = -${pad_w} + w * ${stride_w} + kw * ${dilation_w}; - if ((h_in >= 0) && (h_in < ${bottom_height}) - && (w_in >= 0) && (w_in < ${bottom_width})) { - const int offset = ((n * ${channels} + c) * ${bottom_height} + h_in) - * ${bottom_width} + w_in; - const int offset_weight = ((((n * ${groups} + g) * ${kernel_h} + kh) * ${kernel_w} + kw) * ${top_height} + h) - * ${top_width} + w; - value += weight_data[offset_weight] * bottom_data[offset]; - } - } - } - top_data[index] = value; - } -} -''' - - -_involution_kernel_backward_grad_input = kernel_loop + ''' -extern "C" -__global__ void involution_backward_grad_input_kernel( - const ${Dtype}* const top_diff, const ${Dtype}* const weight_data, ${Dtype}* const bottom_diff) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int n = index / ${channels} / ${bottom_height} / ${bottom_width}; - const int c = (index / ${bottom_height} / ${bottom_width}) % ${channels}; - const int h = (index / ${bottom_width}) % ${bottom_height}; - const int w = index % ${bottom_width}; - const int g = c / (${channels} / ${groups}); - ${Dtype} value = 0; - #pragma unroll - for (int kh = 0; kh < ${kernel_h}; ++kh) { - #pragma unroll - for (int kw = 0; kw < ${kernel_w}; ++kw) { - const int h_out_s = h + ${pad_h} - kh * ${dilation_h}; - const int w_out_s = w + ${pad_w} - kw * ${dilation_w}; - if (((h_out_s % ${stride_h}) == 0) && ((w_out_s % ${stride_w}) == 0)) { - const int h_out = h_out_s / ${stride_h}; - const int w_out = w_out_s / ${stride_w}; - if ((h_out >= 0) && (h_out < ${top_height}) - && (w_out >= 0) && (w_out < ${top_width})) { - const int offset = ((n * ${channels} + c) * ${top_height} + h_out) - * ${top_width} + w_out; - const int offset_weight = ((((n * ${groups} + g) * ${kernel_h} + kh) * ${kernel_w} + kw) * ${top_height} + h_out) - * ${top_width} + w_out; - value += weight_data[offset_weight] * top_diff[offset]; - } - } - } - } - bottom_diff[index] = value; - } -} -''' - - -_involution_kernel_backward_grad_weight = kernel_loop + ''' -extern "C" -__global__ void involution_backward_grad_weight_kernel( - const ${Dtype}* const top_diff, const ${Dtype}* const bottom_data, ${Dtype}* const buffer_data) { - CUDA_KERNEL_LOOP(index, ${nthreads}) { - const int h = (index / ${top_width}) % ${top_height}; - const int w = index % ${top_width}; - const int kh = (index / ${kernel_w} / ${top_height} / ${top_width}) - % ${kernel_h}; - const int kw = (index / ${top_height} / ${top_width}) % ${kernel_w}; - const int h_in = -${pad_h} + h * ${stride_h} + kh * ${dilation_h}; - const int w_in = -${pad_w} + w * ${stride_w} + kw * ${dilation_w}; - if ((h_in >= 0) && (h_in < ${bottom_height}) - && (w_in >= 0) && (w_in < ${bottom_width})) { - const int g = (index / ${kernel_h} / ${kernel_w} / ${top_height} / ${top_width}) % ${groups}; - const int n = (index / ${groups} / ${kernel_h} / ${kernel_w} / ${top_height} / ${top_width}) % ${num}; - ${Dtype} value = 0; - #pragma unroll - for (int c = g * (${channels} / ${groups}); c < (g + 1) * (${channels} / ${groups}); ++c) { - const int top_offset = ((n * ${channels} + c) * ${top_height} + h) - * ${top_width} + w; - const int bottom_offset = ((n * ${channels} + c) * ${bottom_height} + h_in) - * ${bottom_width} + w_in; - value += top_diff[top_offset] * bottom_data[bottom_offset]; - } - buffer_data[index] = value; - } else { - buffer_data[index] = 0; - } - } -} -''' - - -class _involution(Function): - @staticmethod - def forward(ctx, input, weight, stride, padding, dilation): - assert input.dim() == 4 and input.is_cuda - assert weight.dim() == 6 and weight.is_cuda - batch_size, channels, height, width = input.size() - kernel_h, kernel_w = weight.size()[2:4] - output_h = int((height + 2 * padding[0] - (dilation[0] * (kernel_h - 1) + 1)) / stride[0] + 1) - output_w = int((width + 2 * padding[1] - (dilation[1] * (kernel_w - 1) + 1)) / stride[1] + 1) - - output = input.new(batch_size, channels, output_h, output_w) - n = output.numel() - - with torch.cuda.device_of(input): - f = load_kernel('involution_forward_kernel', _involution_kernel, Dtype=Dtype(input), nthreads=n, - num=batch_size, channels=channels, groups=weight.size()[1], - bottom_height=height, bottom_width=width, - top_height=output_h, top_width=output_w, - kernel_h=kernel_h, kernel_w=kernel_w, - stride_h=stride[0], stride_w=stride[1], - dilation_h=dilation[0], dilation_w=dilation[1], - pad_h=padding[0], pad_w=padding[1]) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[input.data_ptr(), weight.data_ptr(), output.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - ctx.save_for_backward(input, weight) - ctx.stride, ctx.padding, ctx.dilation = stride, padding, dilation - return output - - @staticmethod - def backward(ctx, grad_output): - assert grad_output.is_cuda and grad_output.is_contiguous() - input, weight = ctx.saved_tensors - stride, padding, dilation = ctx.stride, ctx.padding, ctx.dilation - - batch_size, channels, height, width = input.size() - kernel_h, kernel_w = weight.size()[2:4] - output_h, output_w = grad_output.size()[2:] - - grad_input, grad_weight = None, None - - opt = dict(Dtype=Dtype(grad_output), - num=batch_size, channels=channels, groups=weight.size()[1], - bottom_height=height, bottom_width=width, - top_height=output_h, top_width=output_w, - kernel_h=kernel_h, kernel_w=kernel_w, - stride_h=stride[0], stride_w=stride[1], - dilation_h=dilation[0], dilation_w=dilation[1], - pad_h=padding[0], pad_w=padding[1]) - - with torch.cuda.device_of(input): - if ctx.needs_input_grad[0]: - grad_input = input.new(input.size()) - - n = grad_input.numel() - opt['nthreads'] = n - - f = load_kernel('involution_backward_grad_input_kernel', - _involution_kernel_backward_grad_input, **opt) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[grad_output.data_ptr(), weight.data_ptr(), grad_input.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - if ctx.needs_input_grad[1]: - grad_weight = weight.new(weight.size()) - - n = grad_weight.numel() - opt['nthreads'] = n - - f = load_kernel('involution_backward_grad_weight_kernel', - _involution_kernel_backward_grad_weight, **opt) - f(block=(CUDA_NUM_THREADS,1,1), - grid=(GET_BLOCKS(n),1,1), - args=[grad_output.data_ptr(), input.data_ptr(), grad_weight.data_ptr()], - stream=Stream(ptr=torch.cuda.current_stream().cuda_stream)) - - return grad_input, grad_weight, None, None, None - - -def _involution_cuda(input, weight, bias=None, stride=1, padding=0, dilation=1): - """ involution kernel - """ - assert input.size(0) == weight.size(0) - assert input.size(-2)//stride == weight.size(-2) - assert input.size(-1)//stride == weight.size(-1) - if input.is_cuda: - out = _involution.apply(input, weight, _pair(stride), _pair(padding), _pair(dilation)) - if bias is not None: - out += bias.view(1,-1,1,1) - else: - raise NotImplementedError - return out - - -class involution(nn.Module): - - def __init__(self, - channels, - kernel_size, - stride, - padding=1): - super(involution, self).__init__() - self.kernel_size = kernel_size - self.stride = stride - self.channels = channels - reduction_ratio = 4 - self.group_channels = 8 - self.groups = self.channels // self.group_channels - self.seblock = nn.Sequential( - nn.Conv2d(in_channels = channels, out_channels = channels // reduction_ratio, kernel_size= 1), - nn.BatchNorm2d(channels // reduction_ratio), - nn.ReLU(), - nn.Conv2d(in_channels = channels // reduction_ratio, out_channels = kernel_size**2 * self.groups, kernel_size= 1) - ) - self.padding = padding - - b = 1 - gamma = 2 - k_size = int(abs(math.log(channels,2)+b)/gamma) - k_size = k_size if k_size % 2 else k_size+1 - self.eca_pool = nn.AdaptiveAvgPool2d(1) - self.eca_conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) - self.sigmoid = nn.Sigmoid() - - if stride > 1: - self.avgpool = nn.AvgPool2d(stride, stride) - - def forward(self, x): - - y = self.eca_pool(x) - - # Two different branches of ECA module - y = self.eca_conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) - - # Multi-scale information fusion - y = self.sigmoid(y) - - # weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x))) - weight = self.seblock(x if self.stride == 1 else self.avgpool(x)) - b, c, h, w = weight.shape - weight = weight.view(b, self.groups, self.kernel_size, self.kernel_size, h, w) - out = _involution_cuda(x, weight, stride=self.stride, padding=self.padding) - return out * y.expand_as(out) diff --git a/components/pg_modules/blocks.py b/components/pg_modules/blocks.py deleted file mode 100644 index 78bd113..0000000 --- a/components/pg_modules/blocks.py +++ /dev/null @@ -1,325 +0,0 @@ -import functools -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils import spectral_norm - - -### single layers - - -def conv2d(*args, **kwargs): - return spectral_norm(nn.Conv2d(*args, **kwargs)) - - -def convTranspose2d(*args, **kwargs): - return spectral_norm(nn.ConvTranspose2d(*args, **kwargs)) - - -def embedding(*args, **kwargs): - return spectral_norm(nn.Embedding(*args, **kwargs)) - - -def linear(*args, **kwargs): - return spectral_norm(nn.Linear(*args, **kwargs)) - - -def NormLayer(c, mode='batch'): - if mode == 'group': - return nn.GroupNorm(c//2, c) - elif mode == 'batch': - return nn.BatchNorm2d(c) - - -### Activations - - -class GLU(nn.Module): - def forward(self, x): - nc = x.size(1) - assert nc % 2 == 0, 'channels dont divide 2!' - nc = int(nc/2) - return x[:, :nc] * torch.sigmoid(x[:, nc:]) - - -class Swish(nn.Module): - def forward(self, feat): - return feat * torch.sigmoid(feat) - - -### Upblocks - - -class InitLayer(nn.Module): - def __init__(self, nz, channel, sz=4): - super().__init__() - - self.init = nn.Sequential( - convTranspose2d(nz, channel*2, sz, 1, 0, bias=False), - NormLayer(channel*2), - GLU(), - ) - - def forward(self, noise): - noise = noise.view(noise.shape[0], -1, 1, 1) - return self.init(noise) - - -def UpBlockSmall(in_planes, out_planes): - block = nn.Sequential( - nn.Upsample(scale_factor=2, mode='nearest'), - conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False), - NormLayer(out_planes*2), GLU()) - return block - - -class UpBlockSmallCond(nn.Module): - def __init__(self, in_planes, out_planes, z_dim): - super().__init__() - self.in_planes = in_planes - self.out_planes = out_planes - self.up = nn.Upsample(scale_factor=2, mode='nearest') - self.conv = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False) - - which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim) - self.bn = which_bn(2*out_planes) - self.act = GLU() - - def forward(self, x, c): - x = self.up(x) - x = self.conv(x) - x = self.bn(x, c) - x = self.act(x) - return x - - -def UpBlockBig(in_planes, out_planes): - block = nn.Sequential( - nn.Upsample(scale_factor=2, mode='nearest'), - conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False), - NoiseInjection(), - NormLayer(out_planes*2), GLU(), - conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False), - NoiseInjection(), - NormLayer(out_planes*2), GLU() - ) - return block - - -class UpBlockBigCond(nn.Module): - def __init__(self, in_planes, out_planes, z_dim): - super().__init__() - self.in_planes = in_planes - self.out_planes = out_planes - self.up = nn.Upsample(scale_factor=2, mode='nearest') - self.conv1 = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False) - self.conv2 = conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False) - - which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim) - self.bn1 = which_bn(2*out_planes) - self.bn2 = which_bn(2*out_planes) - self.act = GLU() - self.noise = NoiseInjection() - - def forward(self, x, c): - # block 1 - x = self.up(x) - x = self.conv1(x) - x = self.noise(x) - x = self.bn1(x, c) - x = self.act(x) - - # block 2 - x = self.conv2(x) - x = self.noise(x) - x = self.bn2(x, c) - x = self.act(x) - - return x - - -class SEBlock(nn.Module): - def __init__(self, ch_in, ch_out): - super().__init__() - self.main = nn.Sequential( - nn.AdaptiveAvgPool2d(4), - conv2d(ch_in, ch_out, 4, 1, 0, bias=False), - Swish(), - conv2d(ch_out, ch_out, 1, 1, 0, bias=False), - nn.Sigmoid(), - ) - - def forward(self, feat_small, feat_big): - return feat_big * self.main(feat_small) - - -### Downblocks - - -class SeparableConv2d(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, bias=False): - super(SeparableConv2d, self).__init__() - self.depthwise = conv2d(in_channels, in_channels, kernel_size=kernel_size, - groups=in_channels, bias=bias, padding=1) - self.pointwise = conv2d(in_channels, out_channels, - kernel_size=1, bias=bias) - - def forward(self, x): - out = self.depthwise(x) - out = self.pointwise(out) - return out - - -class DownBlock(nn.Module): - def __init__(self, in_planes, out_planes, separable=False): - super().__init__() - if not separable: - self.main = nn.Sequential( - conv2d(in_planes, out_planes, 4, 2, 1), - NormLayer(out_planes), - nn.LeakyReLU(0.2, inplace=True), - ) - else: - self.main = nn.Sequential( - SeparableConv2d(in_planes, out_planes, 3), - NormLayer(out_planes), - nn.LeakyReLU(0.2, inplace=True), - nn.AvgPool2d(2, 2), - ) - - def forward(self, feat): - return self.main(feat) - - -class DownBlockPatch(nn.Module): - def __init__(self, in_planes, out_planes, separable=False): - super().__init__() - self.main = nn.Sequential( - DownBlock(in_planes, out_planes, separable), - conv2d(out_planes, out_planes, 1, 1, 0, bias=False), - NormLayer(out_planes), - nn.LeakyReLU(0.2, inplace=True), - ) - - def forward(self, feat): - return self.main(feat) - - -### CSM - - -class ResidualConvUnit(nn.Module): - def __init__(self, cin, activation, bn): - super().__init__() - self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1, bias=True) - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, x): - return self.skip_add.add(self.conv(x), x) - - -class FeatureFusionBlock(nn.Module): - def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, lowest=False): - super().__init__() - - self.deconv = deconv - self.align_corners = align_corners - - self.expand = expand - out_features = features - if self.expand==True: - out_features = features//2 - - self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, *xs): - output = xs[0] - - if len(xs) == 2: - output = self.skip_add.add(output, xs[1]) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=self.align_corners - ) - - output = self.out_conv(output) - - return output - - -### Misc - - -class NoiseInjection(nn.Module): - def __init__(self): - super().__init__() - self.weight = nn.Parameter(torch.zeros(1), requires_grad=True) - - def forward(self, feat, noise=None): - if noise is None: - batch, _, height, width = feat.shape - noise = torch.randn(batch, 1, height, width).to(feat.device) - - return feat + self.weight * noise - - -class CCBN(nn.Module): - ''' conditional batchnorm ''' - def __init__(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1): - super().__init__() - self.output_size, self.input_size = output_size, input_size - - # Prepare gain and bias layers - self.gain = which_linear(input_size, output_size) - self.bias = which_linear(input_size, output_size) - - # epsilon to avoid dividing by 0 - self.eps = eps - # Momentum - self.momentum = momentum - - self.register_buffer('stored_mean', torch.zeros(output_size)) - self.register_buffer('stored_var', torch.ones(output_size)) - - def forward(self, x, y): - # Calculate class-conditional gains and biases - gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1) - bias = self.bias(y).view(y.size(0), -1, 1, 1) - out = F.batch_norm(x, self.stored_mean, self.stored_var, None, None, - self.training, 0.1, self.eps) - return out * gain + bias - - -class Interpolate(nn.Module): - """Interpolation module.""" - - def __init__(self, size, mode='bilinear', align_corners=False): - """Init. - Args: - scale_factor (float): scaling - mode (str): interpolation mode - """ - super(Interpolate, self).__init__() - - self.interp = nn.functional.interpolate - self.size = size - self.mode = mode - self.align_corners = align_corners - - def forward(self, x): - """Forward pass. - Args: - x (tensor): input - Returns: - tensor: interpolated data - """ - - x = self.interp( - x, - size=self.size, - mode=self.mode, - align_corners=self.align_corners, - ) - - return x diff --git a/components/pg_modules/diffaug.py b/components/pg_modules/diffaug.py deleted file mode 100644 index 54020be..0000000 --- a/components/pg_modules/diffaug.py +++ /dev/null @@ -1,76 +0,0 @@ -# Differentiable Augmentation for Data-Efficient GAN Training -# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han -# https://arxiv.org/pdf/2006.10738 - -import torch -import torch.nn.functional as F - - -def DiffAugment(x, policy='', channels_first=True): - if policy: - if not channels_first: - x = x.permute(0, 3, 1, 2) - for p in policy.split(','): - for f in AUGMENT_FNS[p]: - x = f(x) - if not channels_first: - x = x.permute(0, 2, 3, 1) - x = x.contiguous() - return x - - -def rand_brightness(x): - x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5) - return x - - -def rand_saturation(x): - x_mean = x.mean(dim=1, keepdim=True) - x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean - return x - - -def rand_contrast(x): - x_mean = x.mean(dim=[1, 2, 3], keepdim=True) - x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean - return x - - -def rand_translation(x, ratio=0.125): - shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) - translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device) - translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device) - grid_batch, grid_x, grid_y = torch.meshgrid( - torch.arange(x.size(0), dtype=torch.long, device=x.device), - torch.arange(x.size(2), dtype=torch.long, device=x.device), - torch.arange(x.size(3), dtype=torch.long, device=x.device), - ) - grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1) - grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1) - x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0]) - x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2) - return x - - -def rand_cutout(x, ratio=0.2): - cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) - offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device) - offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device) - grid_batch, grid_x, grid_y = torch.meshgrid( - torch.arange(x.size(0), dtype=torch.long, device=x.device), - torch.arange(cutout_size[0], dtype=torch.long, device=x.device), - torch.arange(cutout_size[1], dtype=torch.long, device=x.device), - ) - grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1) - grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1) - mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device) - mask[grid_batch, grid_x, grid_y] = 0 - x = x * mask.unsqueeze(1) - return x - - -AUGMENT_FNS = { - 'color': [rand_brightness, rand_saturation, rand_contrast], - 'translation': [rand_translation], - 'cutout': [rand_cutout], -} diff --git a/components/pg_modules/discriminator.py b/components/pg_modules/discriminator.py deleted file mode 100644 index 02728e9..0000000 --- a/components/pg_modules/discriminator.py +++ /dev/null @@ -1,186 +0,0 @@ -from functools import partial -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from components.pg_modules.blocks import DownBlock, DownBlockPatch, conv2d -from components.pg_modules.projector import F_RandomProj -from components.pg_modules.diffaug import DiffAugment - - -class SingleDisc(nn.Module): - def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False): - super().__init__() - channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64, - 256: 32, 512: 16, 1024: 8} - - # interpolate for start sz that are not powers of two - if start_sz not in channel_dict.keys(): - sizes = np.array(list(channel_dict.keys())) - start_sz = sizes[np.argmin(abs(sizes - start_sz))] - self.start_sz = start_sz - - # if given ndf, allocate all layers with the same ndf - if ndf is None: - nfc = channel_dict - else: - nfc = {k: ndf for k, v in channel_dict.items()} - - # for feature map discriminators with nfc not in channel_dict - # this is the case for the pretrained backbone (midas.pretrained) - if nc is not None and head is None: - nfc[start_sz] = nc - - layers = [] - - # Head if the initial input is the full modality - if head: - layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False), - nn.LeakyReLU(0.2, inplace=True)] - - # Down Blocks - DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable) - while start_sz > end_sz: - layers.append(DB(nfc[start_sz], nfc[start_sz//2])) - start_sz = start_sz // 2 - - layers.append(conv2d(nfc[end_sz], 1, 4, 1, 0, bias=False)) - self.main = nn.Sequential(*layers) - - def forward(self, x, c): - return self.main(x) - - -class SingleDiscCond(nn.Module): - def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_dim=64, embedding_dim=128): - super().__init__() - self.cmap_dim = cmap_dim - - # midas channels - channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64, - 256: 32, 512: 16, 1024: 8} - - # interpolate for start sz that are not powers of two - if start_sz not in channel_dict.keys(): - sizes = np.array(list(channel_dict.keys())) - start_sz = sizes[np.argmin(abs(sizes - start_sz))] - self.start_sz = start_sz - - # if given ndf, allocate all layers with the same ndf - if ndf is None: - nfc = channel_dict - else: - nfc = {k: ndf for k, v in channel_dict.items()} - - # for feature map discriminators with nfc not in channel_dict - # this is the case for the pretrained backbone (midas.pretrained) - if nc is not None and head is None: - nfc[start_sz] = nc - - layers = [] - - # Head if the initial input is the full modality - if head: - layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False), - nn.LeakyReLU(0.2, inplace=True)] - - # Down Blocks - DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable) - while start_sz > end_sz: - layers.append(DB(nfc[start_sz], nfc[start_sz//2])) - start_sz = start_sz // 2 - self.main = nn.Sequential(*layers) - - # additions for conditioning on class information - self.cls = conv2d(nfc[end_sz], self.cmap_dim, 4, 1, 0, bias=False) - self.embed = nn.Embedding(num_embeddings=c_dim, embedding_dim=embedding_dim) - self.embed_proj = nn.Sequential( - nn.Linear(self.embed.embedding_dim, self.cmap_dim), - nn.LeakyReLU(0.2, inplace=True), - ) - - def forward(self, x, c): - h = self.main(x) - out = self.cls(h) - - # conditioning via projection - cmap = self.embed_proj(self.embed(c.argmax(1))).unsqueeze(-1).unsqueeze(-1) - out = (out * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) - - return out - - -class MultiScaleD(nn.Module): - def __init__( - self, - channels, - resolutions, - num_discs=1, - proj_type=2, # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing - cond=0, - separable=False, - patch=False, - **kwargs, - ): - super().__init__() - - assert num_discs in [1, 2, 3, 4] - - # the first disc is on the lowest level of the backbone - self.disc_in_channels = channels[:num_discs] - self.disc_in_res = resolutions[:num_discs] - Disc = SingleDiscCond if cond else SingleDisc - - mini_discs = [] - for i, (cin, res) in enumerate(zip(self.disc_in_channels, self.disc_in_res)): - start_sz = res if not patch else 16 - mini_discs += [str(i), Disc(nc=cin, start_sz=start_sz, end_sz=8, separable=separable, patch=patch)], - self.mini_discs = nn.ModuleDict(mini_discs) - - def forward(self, features, c): - all_logits = [] - for k, disc in self.mini_discs.items(): - all_logits.append(disc(features[k], c).view(features[k].size(0), -1)) - - all_logits = torch.cat(all_logits, dim=1) - return all_logits - - -class ProjectedDiscriminator(torch.nn.Module): - def __init__( - self, - diffaug=True, - interp224=True, - backbone_kwargs={}, - **kwargs - ): - super().__init__() - self.diffaug = diffaug - self.interp224 = interp224 - self.feature_network = F_RandomProj(**backbone_kwargs) - self.discriminator = MultiScaleD( - channels=self.feature_network.CHANNELS, - resolutions=self.feature_network.RESOLUTIONS, - **backbone_kwargs, - ) - - def train(self, mode=True): - self.feature_network = self.feature_network.train(False) - self.discriminator = self.discriminator.train(mode) - return self - - def eval(self): - return self.train(False) - - def forward(self, x, c): - if self.diffaug: - x = DiffAugment(x, policy='color,translation,cutout') - - if self.interp224: - x = F.interpolate(x, 224, mode='bilinear', align_corners=False) - - features = self.feature_network(x) - logits = self.discriminator(features, c) - - return logits diff --git a/components/pg_modules/networks_fastgan.py b/components/pg_modules/networks_fastgan.py deleted file mode 100644 index 1a32056..0000000 --- a/components/pg_modules/networks_fastgan.py +++ /dev/null @@ -1,178 +0,0 @@ -# original implementation: https://github.com/odegeasslbc/FastGAN-pytorch/blob/main/models.py -# -# modified by Axel Sauer for "Projected GANs Converge Faster" -# -import torch.nn as nn -from pg_modules.blocks import (InitLayer, UpBlockBig, UpBlockBigCond, UpBlockSmall, UpBlockSmallCond, SEBlock, conv2d) - - -def normalize_second_moment(x, dim=1, eps=1e-8): - return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() - - -class DummyMapping(nn.Module): - def __init__(self): - super().__init__() - - def forward(self, z, c, **kwargs): - return z.unsqueeze(1) # to fit the StyleGAN API - - -class FastganSynthesis(nn.Module): - def __init__(self, ngf=128, z_dim=256, nc=3, img_resolution=256, lite=False): - super().__init__() - self.img_resolution = img_resolution - self.z_dim = z_dim - - # channel multiplier - nfc_multi = {2: 16, 4:16, 8:8, 16:4, 32:2, 64:2, 128:1, 256:0.5, - 512:0.25, 1024:0.125} - nfc = {} - for k, v in nfc_multi.items(): - nfc[k] = int(v*ngf) - - # layers - self.init = InitLayer(z_dim, channel=nfc[2], sz=4) - - UpBlock = UpBlockSmall if lite else UpBlockBig - - self.feat_8 = UpBlock(nfc[4], nfc[8]) - self.feat_16 = UpBlock(nfc[8], nfc[16]) - self.feat_32 = UpBlock(nfc[16], nfc[32]) - self.feat_64 = UpBlock(nfc[32], nfc[64]) - self.feat_128 = UpBlock(nfc[64], nfc[128]) - self.feat_256 = UpBlock(nfc[128], nfc[256]) - - self.se_64 = SEBlock(nfc[4], nfc[64]) - self.se_128 = SEBlock(nfc[8], nfc[128]) - self.se_256 = SEBlock(nfc[16], nfc[256]) - - self.to_big = conv2d(nfc[img_resolution], nc, 3, 1, 1, bias=True) - - if img_resolution > 256: - self.feat_512 = UpBlock(nfc[256], nfc[512]) - self.se_512 = SEBlock(nfc[32], nfc[512]) - if img_resolution > 512: - self.feat_1024 = UpBlock(nfc[512], nfc[1024]) - - def forward(self, input, c, **kwargs): - # map noise to hypersphere as in "Progressive Growing of GANS" - input = normalize_second_moment(input[:, 0]) - - feat_4 = self.init(input) - feat_8 = self.feat_8(feat_4) - feat_16 = self.feat_16(feat_8) - feat_32 = self.feat_32(feat_16) - feat_64 = self.se_64(feat_4, self.feat_64(feat_32)) - feat_128 = self.se_128(feat_8, self.feat_128(feat_64)) - - if self.img_resolution >= 128: - feat_last = feat_128 - - if self.img_resolution >= 256: - feat_last = self.se_256(feat_16, self.feat_256(feat_last)) - - if self.img_resolution >= 512: - feat_last = self.se_512(feat_32, self.feat_512(feat_last)) - - if self.img_resolution >= 1024: - feat_last = self.feat_1024(feat_last) - - return self.to_big(feat_last) - - -class FastganSynthesisCond(nn.Module): - def __init__(self, ngf=64, z_dim=256, nc=3, img_resolution=256, num_classes=1000, lite=False): - super().__init__() - - self.z_dim = z_dim - nfc_multi = {2: 16, 4:16, 8:8, 16:4, 32:2, 64:2, 128:1, 256:0.5, - 512:0.25, 1024:0.125, 2048:0.125} - nfc = {} - for k, v in nfc_multi.items(): - nfc[k] = int(v*ngf) - - self.img_resolution = img_resolution - - self.init = InitLayer(z_dim, channel=nfc[2], sz=4) - - UpBlock = UpBlockSmallCond if lite else UpBlockBigCond - - self.feat_8 = UpBlock(nfc[4], nfc[8], z_dim) - self.feat_16 = UpBlock(nfc[8], nfc[16], z_dim) - self.feat_32 = UpBlock(nfc[16], nfc[32], z_dim) - self.feat_64 = UpBlock(nfc[32], nfc[64], z_dim) - self.feat_128 = UpBlock(nfc[64], nfc[128], z_dim) - self.feat_256 = UpBlock(nfc[128], nfc[256], z_dim) - - self.se_64 = SEBlock(nfc[4], nfc[64]) - self.se_128 = SEBlock(nfc[8], nfc[128]) - self.se_256 = SEBlock(nfc[16], nfc[256]) - - self.to_big = conv2d(nfc[img_resolution], nc, 3, 1, 1, bias=True) - - if img_resolution > 256: - self.feat_512 = UpBlock(nfc[256], nfc[512]) - self.se_512 = SEBlock(nfc[32], nfc[512]) - if img_resolution > 512: - self.feat_1024 = UpBlock(nfc[512], nfc[1024]) - - self.embed = nn.Embedding(num_classes, z_dim) - - def forward(self, input, c, update_emas=False): - c = self.embed(c.argmax(1)) - - # map noise to hypersphere as in "Progressive Growing of GANS" - input = normalize_second_moment(input[:, 0]) - - feat_4 = self.init(input) - feat_8 = self.feat_8(feat_4, c) - feat_16 = self.feat_16(feat_8, c) - feat_32 = self.feat_32(feat_16, c) - feat_64 = self.se_64(feat_4, self.feat_64(feat_32, c)) - feat_128 = self.se_128(feat_8, self.feat_128(feat_64, c)) - - if self.img_resolution >= 128: - feat_last = feat_128 - - if self.img_resolution >= 256: - feat_last = self.se_256(feat_16, self.feat_256(feat_last, c)) - - if self.img_resolution >= 512: - feat_last = self.se_512(feat_32, self.feat_512(feat_last, c)) - - if self.img_resolution >= 1024: - feat_last = self.feat_1024(feat_last, c) - - return self.to_big(feat_last) - - -class Generator(nn.Module): - def __init__( - self, - z_dim=256, - c_dim=0, - w_dim=0, - img_resolution=256, - img_channels=3, - ngf=128, - cond=0, - mapping_kwargs={}, - synthesis_kwargs={} - ): - super().__init__() - self.z_dim = z_dim - self.c_dim = c_dim - self.w_dim = w_dim - self.img_resolution = img_resolution - self.img_channels = img_channels - - # Mapping and Synthesis Networks - self.mapping = DummyMapping() # to fit the StyleGAN API - Synthesis = FastganSynthesisCond if cond else FastganSynthesis - self.synthesis = Synthesis(ngf=ngf, z_dim=z_dim, nc=img_channels, img_resolution=img_resolution, **synthesis_kwargs) - - def forward(self, z, c, **kwargs): - w = self.mapping(z, c) - img = self.synthesis(w, c) - return img diff --git a/components/pg_modules/networks_stylegan2.py b/components/pg_modules/networks_stylegan2.py deleted file mode 100644 index c554a2f..0000000 --- a/components/pg_modules/networks_stylegan2.py +++ /dev/null @@ -1,537 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. -# -# modified by Axel Sauer for "Projected GANs Converge Faster" -# -import numpy as np -import torch -from torch_utils import misc -from torch_utils import persistence -from torch_utils.ops import conv2d_resample -from torch_utils.ops import upfirdn2d -from torch_utils.ops import bias_act -from torch_utils.ops import fma - - -@misc.profiled_function -def normalize_2nd_moment(x, dim=1, eps=1e-8): - return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() - - -@misc.profiled_function -def modulated_conv2d( - x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. - weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. - styles, # Modulation coefficients of shape [batch_size, in_channels]. - noise = None, # Optional noise tensor to add to the output activations. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - padding = 0, # Padding with respect to the upsampled image. - resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). - demodulate = True, # Apply weight demodulation? - flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). - fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation? -): - batch_size = x.shape[0] - out_channels, in_channels, kh, kw = weight.shape - misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(styles, [batch_size, in_channels]) # [NI] - - # Pre-normalize inputs to avoid FP16 overflow. - if x.dtype == torch.float16 and demodulate: - weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk - styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I - - # Calculate per-sample weights and demodulation coefficients. - w = None - dcoefs = None - if demodulate or fused_modconv: - w = weight.unsqueeze(0) # [NOIkk] - w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] - if demodulate: - dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] - if demodulate and fused_modconv: - w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] - - # Execute by scaling the activations before and after the convolution. - if not fused_modconv: - x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) - x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) - if demodulate and noise is not None: - x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)) - elif demodulate: - x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) - elif noise is not None: - x = x.add_(noise.to(x.dtype)) - return x - - # Execute as one fused op using grouped convolution. - with misc.suppress_tracer_warnings(): # this value will be treated as a constant - batch_size = int(batch_size) - misc.assert_shape(x, [batch_size, in_channels, None, None]) - x = x.reshape(1, -1, *x.shape[2:]) - w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) - x = x.reshape(batch_size, -1, *x.shape[2:]) - if noise is not None: - x = x.add_(noise) - return x - - -@persistence.persistent_class -class FullyConnectedLayer(torch.nn.Module): - def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 1, # Learning rate multiplier. - bias_init = 0, # Initial value for the additive bias. - ): - super().__init__() - self.in_features = in_features - self.out_features = out_features - self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) - self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None - self.weight_gain = lr_multiplier / np.sqrt(in_features) - self.bias_gain = lr_multiplier - - def forward(self, x): - w = self.weight.to(x.dtype) * self.weight_gain - b = self.bias - if b is not None: - b = b.to(x.dtype) - if self.bias_gain != 1: - b = b * self.bias_gain - - if self.activation == 'linear' and b is not None: - x = torch.addmm(b.unsqueeze(0), x, w.t()) - else: - x = x.matmul(w.t()) - x = bias_act.bias_act(x, b, act=self.activation) - return x - - def extra_repr(self): - return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' - - -@persistence.persistent_class -class Conv2dLayer(torch.nn.Module): - def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - kernel_size, # Width and height of the convolution kernel. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output to +-X, None = disable clamping. - channels_last = False, # Expect the input to have memory_format=channels_last? - trainable = True, # Update the weights of this layer during training? - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.activation = activation - self.up = up - self.down = down - self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) - self.padding = kernel_size // 2 - self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) - self.act_gain = bias_act.activation_funcs[activation].def_gain - - memory_format = torch.channels_last if channels_last else torch.contiguous_format - weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format) - bias = torch.zeros([out_channels]) if bias else None - if trainable: - self.weight = torch.nn.Parameter(weight) - self.bias = torch.nn.Parameter(bias) if bias is not None else None - else: - self.register_buffer('weight', weight) - if bias is not None: - self.register_buffer('bias', bias) - else: - self.bias = None - - def forward(self, x, gain=1): - w = self.weight * self.weight_gain - b = self.bias.to(x.dtype) if self.bias is not None else None - flip_weight = (self.up == 1) # slightly faster - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) - - act_gain = self.act_gain * gain - act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) - return x - - def extra_repr(self): - return ' '.join([ - f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},', - f'up={self.up}, down={self.down}']) - - -@persistence.persistent_class -class MappingNetwork(torch.nn.Module): - def __init__(self, - z_dim, # Input latent (Z) dimensionality, 0 = no latent. - c_dim, # Conditioning label (C) dimensionality, 0 = no label. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output, None = do not broadcast. - num_layers = 8, # Number of mapping layers. - embed_features = None, # Label embedding dimensionality, None = same as w_dim. - layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track. - ): - super().__init__() - self.z_dim = z_dim - self.c_dim = c_dim - self.w_dim = w_dim - self.num_ws = num_ws - self.num_layers = num_layers - self.w_avg_beta = w_avg_beta - - if embed_features is None: - embed_features = w_dim - if c_dim == 0: - embed_features = 0 - if layer_features is None: - layer_features = w_dim - features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] - - if c_dim > 0: - self.embed = FullyConnectedLayer(c_dim, embed_features) - for idx in range(num_layers): - in_features = features_list[idx] - out_features = features_list[idx + 1] - layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) - setattr(self, f'fc{idx}', layer) - - if num_ws is not None and w_avg_beta is not None: - self.register_buffer('w_avg', torch.zeros([w_dim])) - - def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False): - # Embed, normalize, and concat inputs. - x = None - with torch.autograd.profiler.record_function('input'): - if self.z_dim > 0: - misc.assert_shape(z, [None, self.z_dim]) - x = normalize_2nd_moment(z.to(torch.float32)) - if self.c_dim > 0: - misc.assert_shape(c, [None, self.c_dim]) - y = normalize_2nd_moment(self.embed(c.to(torch.float32))) - x = torch.cat([x, y], dim=1) if x is not None else y - - # Main layers. - for idx in range(self.num_layers): - layer = getattr(self, f'fc{idx}') - x = layer(x) - - # Update moving average of W. - if update_emas and self.w_avg_beta is not None: - with torch.autograd.profiler.record_function('update_w_avg'): - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) - - # Broadcast. - if self.num_ws is not None: - with torch.autograd.profiler.record_function('broadcast'): - x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) - - # Apply truncation. - if truncation_psi != 1: - with torch.autograd.profiler.record_function('truncate'): - assert self.w_avg_beta is not None - if self.num_ws is None or truncation_cutoff is None: - x = self.w_avg.lerp(x, truncation_psi) - else: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) - return x - - def extra_repr(self): - return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' - - -@persistence.persistent_class -class SynthesisLayer(torch.nn.Module): - def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this layer. - kernel_size = 3, # Convolution kernel size. - up = 1, # Integer upsampling factor. - use_noise = True, # Enable noise input? - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - channels_last = False, # Use channels_last format for the weights? - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.w_dim = w_dim - self.resolution = resolution - self.up = up - self.use_noise = use_noise - self.activation = activation - self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) - self.padding = kernel_size // 2 - self.act_gain = bias_act.activation_funcs[activation].def_gain - - self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) - memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) - if use_noise: - self.register_buffer('noise_const', torch.randn([resolution, resolution])) - self.noise_strength = torch.nn.Parameter(torch.zeros([])) - self.bias = torch.nn.Parameter(torch.zeros([out_channels])) - - def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1): - assert noise_mode in ['random', 'const', 'none'] - in_resolution = self.resolution // self.up - misc.assert_shape(x, [None, self.in_channels, in_resolution, in_resolution]) - styles = self.affine(w) - - noise = None - if self.use_noise and noise_mode == 'random': - noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength - if self.use_noise and noise_mode == 'const': - noise = self.noise_const * self.noise_strength - - flip_weight = (self.up == 1) # slightly faster - x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, - padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) - - act_gain = self.act_gain * gain - act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) - return x - - def extra_repr(self): - return ' '.join([ - f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},', - f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}']) - - -@persistence.persistent_class -class ToRGBLayer(torch.nn.Module): - def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.w_dim = w_dim - self.conv_clamp = conv_clamp - self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) - memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) - self.bias = torch.nn.Parameter(torch.zeros([out_channels])) - self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) - - def forward(self, x, w, fused_modconv=True): - styles = self.affine(w) * self.weight_gain - x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv) - x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) - return x - - def extra_repr(self): - return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}' - - -@persistence.persistent_class -class SynthesisBlock(torch.nn.Module): - def __init__(self, - in_channels, # Number of input channels, 0 = first block. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this block. - img_channels, # Number of output color channels. - is_last, # Is this the last block? - architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training. - **layer_kwargs, # Arguments for SynthesisLayer. - ): - assert architecture in ['orig', 'skip', 'resnet'] - super().__init__() - self.in_channels = in_channels - self.w_dim = w_dim - self.resolution = resolution - self.img_channels = img_channels - self.is_last = is_last - self.architecture = architecture - self.use_fp16 = use_fp16 - self.channels_last = (use_fp16 and fp16_channels_last) - self.fused_modconv_default = fused_modconv_default - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) - self.num_conv = 0 - self.num_torgb = 0 - - if in_channels == 0: - self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) - - if in_channels != 0: - self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, - resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) - self.num_conv += 1 - - self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution, - conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) - self.num_conv += 1 - - if is_last or architecture == 'skip': - self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim, - conv_clamp=conv_clamp, channels_last=self.channels_last) - self.num_torgb += 1 - - if in_channels != 0 and architecture == 'resnet': - self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2, - resample_filter=resample_filter, channels_last=self.channels_last) - - def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs): - _ = update_emas # unused - misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) - w_iter = iter(ws.unbind(dim=1)) - if ws.device.type != 'cuda': - force_fp32 = True - dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 - memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format - if fused_modconv is None: - fused_modconv = self.fused_modconv_default - if fused_modconv == 'inference_only': - fused_modconv = (not self.training) - - # Input. - if self.in_channels == 0: - x = self.const.to(dtype=dtype, memory_format=memory_format) - x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) - else: - misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) - x = x.to(dtype=dtype, memory_format=memory_format) - - # Main layers. - if self.in_channels == 0: - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - elif self.architecture == 'resnet': - y = self.skip(x, gain=np.sqrt(0.5)) - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) - x = y.add_(x) - else: - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - - # ToRGB. - if img is not None: - misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) - img = upfirdn2d.upsample2d(img, self.resample_filter) - if self.is_last or self.architecture == 'skip': - y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) - y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) - img = img.add_(y) if img is not None else y - - assert x.dtype == dtype - assert img is None or img.dtype == torch.float32 - return x, img - - def extra_repr(self): - return f'resolution={self.resolution:d}, architecture={self.architecture:s}' - - -@persistence.persistent_class -class SynthesisNetwork(torch.nn.Module): - def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - **block_kwargs, # Arguments for SynthesisBlock. - ): - assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 - super().__init__() - self.w_dim = w_dim - self.img_resolution = img_resolution - self.img_resolution_log2 = int(np.log2(img_resolution)) - self.img_channels = img_channels - self.num_fp16_res = num_fp16_res - self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) - - self.num_ws = 0 - for res in self.block_resolutions: - in_channels = channels_dict[res // 2] if res > 4 else 0 - out_channels = channels_dict[res] - use_fp16 = (res >= fp16_resolution) - is_last = (res == self.img_resolution) - block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res, - img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs) - self.num_ws += block.num_conv - if is_last: - self.num_ws += block.num_torgb - setattr(self, f'b{res}', block) - - def forward(self, ws, c=None, **block_kwargs): - block_ws = [] - with torch.autograd.profiler.record_function('split_ws'): - misc.assert_shape(ws, [None, self.num_ws, self.w_dim]) - ws = ws.to(torch.float32) - w_idx = 0 - for res in self.block_resolutions: - block = getattr(self, f'b{res}') - block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) - w_idx += block.num_conv - - x = img = None - for res, cur_ws in zip(self.block_resolutions, block_ws): - block = getattr(self, f'b{res}') - x, img = block(x, img, cur_ws, **block_kwargs) - return img - - def extra_repr(self): - return ' '.join([ - f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},', - f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},', - f'num_fp16_res={self.num_fp16_res:d}']) - - -@persistence.persistent_class -class Generator(torch.nn.Module): - def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality. - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output resolution. - img_channels, # Number of output color channels. - mapping_kwargs = {}, # Arguments for MappingNetwork. - **synthesis_kwargs, # Arguments for SynthesisNetwork. - ): - super().__init__() - self.z_dim = z_dim - self.c_dim = c_dim - self.w_dim = w_dim - self.img_resolution = img_resolution - self.img_channels = img_channels - self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) - self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) - - def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): - ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) - img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs) - return img diff --git a/components/pg_modules/projector.py b/components/pg_modules/projector.py deleted file mode 100644 index 7ca03c1..0000000 --- a/components/pg_modules/projector.py +++ /dev/null @@ -1,158 +0,0 @@ -import torch -import torch.nn as nn -import timm -from components.pg_modules.blocks import FeatureFusionBlock - - -def _make_scratch_ccm(scratch, in_channels, cout, expand=False): - # shapes - out_channels = [cout, cout*2, cout*4, cout*8] if expand else [cout]*4 - - scratch.layer0_ccm = nn.Conv2d(in_channels[0], out_channels[0], kernel_size=1, stride=1, padding=0, bias=True) - scratch.layer1_ccm = nn.Conv2d(in_channels[1], out_channels[1], kernel_size=1, stride=1, padding=0, bias=True) - scratch.layer2_ccm = nn.Conv2d(in_channels[2], out_channels[2], kernel_size=1, stride=1, padding=0, bias=True) - scratch.layer3_ccm = nn.Conv2d(in_channels[3], out_channels[3], kernel_size=1, stride=1, padding=0, bias=True) - - scratch.CHANNELS = out_channels - - return scratch - - -def _make_scratch_csm(scratch, in_channels, cout, expand): - scratch.layer3_csm = FeatureFusionBlock(in_channels[3], nn.ReLU(False), expand=expand, lowest=True) - scratch.layer2_csm = FeatureFusionBlock(in_channels[2], nn.ReLU(False), expand=expand) - scratch.layer1_csm = FeatureFusionBlock(in_channels[1], nn.ReLU(False), expand=expand) - scratch.layer0_csm = FeatureFusionBlock(in_channels[0], nn.ReLU(False)) - - # last refinenet does not expand to save channels in higher dimensions - scratch.CHANNELS = [cout, cout, cout*2, cout*4] if expand else [cout]*4 - - return scratch - - -def _make_efficientnet(model): - pretrained = nn.Module() - pretrained.layer0 = nn.Sequential(model.conv_stem, model.bn1, model.act1, *model.blocks[0:2]) - pretrained.layer1 = nn.Sequential(*model.blocks[2:3]) - pretrained.layer2 = nn.Sequential(*model.blocks[3:5]) - pretrained.layer3 = nn.Sequential(*model.blocks[5:9]) - return pretrained - - -def calc_channels(pretrained, inp_res=224): - channels = [] - tmp = torch.zeros(1, 3, inp_res, inp_res) - - # forward pass - tmp = pretrained.layer0(tmp) - channels.append(tmp.shape[1]) - tmp = pretrained.layer1(tmp) - channels.append(tmp.shape[1]) - tmp = pretrained.layer2(tmp) - channels.append(tmp.shape[1]) - tmp = pretrained.layer3(tmp) - channels.append(tmp.shape[1]) - - return channels - - -def _make_projector(im_res, cout, proj_type, expand=False): - assert proj_type in [0, 1, 2], "Invalid projection type" - - ### Build pretrained feature network - model = timm.create_model('tf_efficientnet_lite0', pretrained=True) - pretrained = _make_efficientnet(model) - - # determine resolution of feature maps, this is later used to calculate the number - # of down blocks in the discriminators. Interestingly, the best results are achieved - # by fixing this to 256, ie., we use the same number of down blocks per discriminator - # independent of the dataset resolution - im_res = 256 - pretrained.RESOLUTIONS = [im_res//4, im_res//8, im_res//16, im_res//32] - pretrained.CHANNELS = calc_channels(pretrained) - - if proj_type == 0: return pretrained, None - - ### Build CCM - scratch = nn.Module() - scratch = _make_scratch_ccm(scratch, in_channels=pretrained.CHANNELS, cout=cout, expand=expand) - pretrained.CHANNELS = scratch.CHANNELS - - if proj_type == 1: return pretrained, scratch - - ### build CSM - scratch = _make_scratch_csm(scratch, in_channels=scratch.CHANNELS, cout=cout, expand=expand) - - # CSM upsamples x2 so the feature map resolution doubles - pretrained.RESOLUTIONS = [res*2 for res in pretrained.RESOLUTIONS] - pretrained.CHANNELS = scratch.CHANNELS - - return pretrained, scratch - - -class F_RandomProj(nn.Module): - def __init__( - self, - im_res=256, - cout=64, - expand=True, - proj_type=2, # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing - **kwargs, - ): - super().__init__() - self.proj_type = proj_type - self.cout = cout - self.expand = expand - - # build pretrained feature network and random decoder (scratch) - self.pretrained, self.scratch = _make_projector(im_res=im_res, cout=self.cout, proj_type=self.proj_type, expand=self.expand) - self.CHANNELS = self.pretrained.CHANNELS - self.RESOLUTIONS = self.pretrained.RESOLUTIONS - - def forward(self, x, get_features=False): - # predict feature maps - out0 = self.pretrained.layer0(x) - out1 = self.pretrained.layer1(out0) - out2 = self.pretrained.layer2(out1) - out3 = self.pretrained.layer3(out2) - - # start enumerating at the lowest layer (this is where we put the first discriminator) - backbone_features = { - '0': out0, - '1': out1, - '2': out2, - '3': out3, - } - if get_features: - return backbone_features - - if self.proj_type == 0: return backbone_features - - out0_channel_mixed = self.scratch.layer0_ccm(backbone_features['0']) - out1_channel_mixed = self.scratch.layer1_ccm(backbone_features['1']) - out2_channel_mixed = self.scratch.layer2_ccm(backbone_features['2']) - out3_channel_mixed = self.scratch.layer3_ccm(backbone_features['3']) - - out = { - '0': out0_channel_mixed, - '1': out1_channel_mixed, - '2': out2_channel_mixed, - '3': out3_channel_mixed, - } - - if self.proj_type == 1: return out - - # from bottom to top - out3_scale_mixed = self.scratch.layer3_csm(out3_channel_mixed) - out2_scale_mixed = self.scratch.layer2_csm(out3_scale_mixed, out2_channel_mixed) - out1_scale_mixed = self.scratch.layer1_csm(out2_scale_mixed, out1_channel_mixed) - out0_scale_mixed = self.scratch.layer0_csm(out1_scale_mixed, out0_channel_mixed) - - out = { - '0': out0_scale_mixed, - '1': out1_scale_mixed, - '2': out2_scale_mixed, - '3': out3_scale_mixed, - } - - return out, backbone_features diff --git a/components/projected_discriminator.py b/components/projected_discriminator.py deleted file mode 100644 index 3f2f23d..0000000 --- a/components/projected_discriminator.py +++ /dev/null @@ -1,194 +0,0 @@ - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from functools import partial - -from components.pg_modules.blocks import DownBlock, DownBlockPatch, conv2d -from components.pg_modules.projector import F_RandomProj -# from components.pg_modules.diffaug import DiffAugment - - -class SingleDisc(nn.Module): - def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False): - super().__init__() - channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64, - 256: 32, 512: 16, 1024: 8} - - # interpolate for start sz that are not powers of two - if start_sz not in channel_dict.keys(): - sizes = np.array(list(channel_dict.keys())) - start_sz = sizes[np.argmin(abs(sizes - start_sz))] - self.start_sz = start_sz - - # if given ndf, allocate all layers with the same ndf - if ndf is None: - nfc = channel_dict - else: - nfc = {k: ndf for k, v in channel_dict.items()} - - # for feature map discriminators with nfc not in channel_dict - # this is the case for the pretrained backbone (midas.pretrained) - if nc is not None and head is None: - nfc[start_sz] = nc - - layers = [] - - # Head if the initial input is the full modality - if head: - layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False), - nn.LeakyReLU(0.2, inplace=True)] - - # Down Blocks - DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable) - while start_sz > end_sz: - layers.append(DB(nfc[start_sz], nfc[start_sz//2])) - start_sz = start_sz // 2 - - layers.append(conv2d(nfc[end_sz], 1, 4, 1, 0, bias=False)) - self.main = nn.Sequential(*layers) - - def forward(self, x, c): - return self.main(x) - - -class SingleDiscCond(nn.Module): - def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_dim=64, embedding_dim=128): - super().__init__() - self.cmap_dim = cmap_dim - - # midas channels - channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64, - 256: 32, 512: 16, 1024: 8} - - # interpolate for start sz that are not powers of two - if start_sz not in channel_dict.keys(): - sizes = np.array(list(channel_dict.keys())) - start_sz = sizes[np.argmin(abs(sizes - start_sz))] - self.start_sz = start_sz - - # if given ndf, allocate all layers with the same ndf - if ndf is None: - nfc = channel_dict - else: - nfc = {k: ndf for k, v in channel_dict.items()} - - # for feature map discriminators with nfc not in channel_dict - # this is the case for the pretrained backbone (midas.pretrained) - if nc is not None and head is None: - nfc[start_sz] = nc - - layers = [] - - # Head if the initial input is the full modality - if head: - layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False), - nn.LeakyReLU(0.2, inplace=True)] - - # Down Blocks - DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable) - while start_sz > end_sz: - layers.append(DB(nfc[start_sz], nfc[start_sz//2])) - start_sz = start_sz // 2 - self.main = nn.Sequential(*layers) - - # additions for conditioning on class information - self.cls = conv2d(nfc[end_sz], self.cmap_dim, 4, 1, 0, bias=False) - self.embed = nn.Embedding(num_embeddings=c_dim, embedding_dim=embedding_dim) - self.embed_proj = nn.Sequential( - nn.Linear(self.embed.embedding_dim, self.cmap_dim), - nn.LeakyReLU(0.2, inplace=True), - ) - - def forward(self, x, c): - h = self.main(x) - out = self.cls(h) - - # conditioning via projection - cmap = self.embed_proj(self.embed(c.argmax(1))).unsqueeze(-1).unsqueeze(-1) - out = (out * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) - - return out - - -class MultiScaleD(nn.Module): - def __init__( - self, - channels, - resolutions, - num_discs=4, - proj_type=2, # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing - cond=0, - separable=False, - patch=False, - **kwargs, - ): - super().__init__() - - assert num_discs in [1, 2, 3, 4] - - # the first disc is on the lowest level of the backbone - self.disc_in_channels = channels[:num_discs] - self.disc_in_res = resolutions[:num_discs] - Disc = SingleDiscCond if cond else SingleDisc - - mini_discs = [] - for i, (cin, res) in enumerate(zip(self.disc_in_channels, self.disc_in_res)): - start_sz = res if not patch else 16 - mini_discs += [str(i), Disc(nc=cin, start_sz=start_sz, end_sz=8, separable=separable, patch=patch)], - self.mini_discs = nn.ModuleDict(mini_discs) - - def forward(self, features, c): - all_logits = [] - for k, disc in self.mini_discs.items(): - res = disc(features[k], c).view(features[k].size(0), -1) - all_logits.append(res) - - all_logits = torch.cat(all_logits, dim=1) - return all_logits - - -class ProjectedDiscriminator(torch.nn.Module): - def __init__( - self, - **kwargs - ): - super().__init__() - self.diffaug = kwargs["diffaug"] - self.interp224 = kwargs["interp224"] - backbone_kwargs = kwargs["backbone_kwargs"] - - self.interp224 = False - self.feature_network = F_RandomProj(**backbone_kwargs) - self.discriminator = MultiScaleD( - channels=self.feature_network.CHANNELS, - resolutions=self.feature_network.RESOLUTIONS, - **backbone_kwargs, - ) - - def train(self, mode=True): - self.feature_network = self.feature_network.train(False) - self.discriminator = self.discriminator.train(mode) - return self - - def eval(self): - return self.train(False) - - def get_feature(self, x): - features = self.feature_network(x, get_features=True) - return features - - def forward(self, x, c): - # if self.diffaug: - # x = DiffAugment(x, policy='color,translation,cutout') - - # if self.interp224: - # x = F.interpolate(x, 224, mode='bilinear', align_corners=False) - - features,backbone_features = self.feature_network(x) - logits = self.discriminator(features, c) - - return logits,backbone_features - diff --git a/data_tools/StyleResize.py b/data_tools/StyleResize.py deleted file mode 100644 index 01c8d09..0000000 --- a/data_tools/StyleResize.py +++ /dev/null @@ -1,36 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: StyleResize.py -# Created Date: Friday April 17th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 18th April 2020 1:39:53 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# -from PIL import Image -import torchvision.transforms.functional as F - -class StyleResize(object): - - def __call__(self, images): - th, tw = images.size # target height, width - if max(th,tw) > 1800: - alpha = 1800. / float(min(th,tw)) - h = int(th*alpha) - w = int(tw*alpha) - images = F.resize(images, (h, w)) - if max(th,tw) < 800: - # Resize the smallest side of the image to 800px - alpha = 800. / float(min(th,tw)) - if alpha < 4.: - h = int(th*alpha) - w = int(tw*alpha) - images = F.resize(images, (h, w)) - else: - images = F.resize(images, (800, 800)) - return images - - def __repr__(self): - return self.__class__.__name__ + '()' \ No newline at end of file diff --git a/data_tools/data_loader.py b/data_tools/data_loader.py deleted file mode 100644 index 4010d41..0000000 --- a/data_tools/data_loader.py +++ /dev/null @@ -1,269 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: data_loader.py -# Created Date: Saturday April 4th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 5th January 2021 2:12:29 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - -import os -import glob -import torch -import random -from PIL import Image -from pathlib import Path -from torch.utils import data -import torchvision.datasets as dsets -from torchvision import transforms as T -import torchvision.transforms.functional as F - - -class StyleResize(object): - def __call__(self, images): - th, tw = images.size # target height, width - if max(th,tw) > 1800: - alpha = 1800. / float(min(th,tw)) - h = int(th*alpha) - w = int(tw*alpha) - images = F.resize(images, (h, w)) - if max(th,tw) < 800: - # Resize the smallest side of the image to 800px - alpha = 800. / float(min(th,tw)) - if alpha < 4.: - h = int(th*alpha) - w = int(tw*alpha) - images = F.resize(images, (h, w)) - else: - images = F.resize(images, (800, 800)) - return images - - def __repr__(self): - return self.__class__.__name__ + '()' - -class DataPrefetcher(): - def __init__(self, loader): - self.loader = loader - self.dataiter = iter(loader) - self.stream = torch.cuda.Stream() - # self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1) - # self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.mean = self.mean.half() - # self.std = self.std.half() - self.__preload__() - - def __preload__(self): - try: - self.content, self.style, self.label = next(self.dataiter) - except StopIteration: - self.dataiter = iter(self.loader) - self.content, self.style, self.label = next(self.dataiter) - - with torch.cuda.stream(self.stream): - self.content= self.content.cuda(non_blocking=True) - self.style = self.style.cuda(non_blocking=True) - self.label = self.label.cuda(non_blocking=True) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.next_input = self.next_input.half() - # else: - # self.next_input = self.next_input.float() - # self.next_input = self.next_input.sub_(self.mean).div_(self.std) - - def next(self): - torch.cuda.current_stream().wait_stream(self.stream) - content = self.content - style = self.style - label = self.label - self.__preload__() - return content, style, label - - def __len__(self): - """Return the number of images.""" - return len(self.loader) - -class TotalDataset(data.Dataset): - """Dataset class for the Artworks dataset and content dataset.""" - def __init__(self, content_image_dir,style_image_dir, - selectedContent,selectedStyle, - content_transform,style_transform, - subffix='jpg', random_seed=1234): - """Initialize and preprocess the CelebA dataset.""" - self.content_image_dir= content_image_dir - self.style_image_dir = style_image_dir - self.content_transform= content_transform - self.style_transform = style_transform - self.selectedContent = selectedContent - self.selectedStyle = selectedStyle - self.subffix = subffix - self.content_dataset = [] - self.art_dataset = [] - self.random_seed= random_seed - self.__preprocess__() - self.num_images = len(self.content_dataset) - self.art_num = len(self.art_dataset) - - def __preprocess__(self): - """Preprocess the Artworks dataset.""" - print("processing content images...") - for dir_item in self.selectedContent: - join_path = Path(self.content_image_dir,dir_item)#.replace('/','_')) - if join_path.exists(): - print("processing %s"%dir_item) - images = join_path.glob('*.%s'%(self.subffix)) - for item in images: - self.content_dataset.append(item) - else: - print("%s dir does not exist!"%dir_item) - label_index = 0 - print("processing style images...") - for class_item in self.selectedStyle: - images = Path(self.style_image_dir).glob('%s/*.%s'%(class_item, self.subffix)) - for item in images: - self.art_dataset.append([item, label_index]) - label_index += 1 - random.seed(self.random_seed) - random.shuffle(self.content_dataset) - random.shuffle(self.art_dataset) - # self.dataset = images - print('Finished preprocessing the Art Works dataset, total image number: %d...'%len(self.art_dataset)) - print('Finished preprocessing the Content dataset, total image number: %d...'%len(self.content_dataset)) - - def __getitem__(self, index): - """Return one image and its corresponding attribute label.""" - filename = self.content_dataset[index] - image = Image.open(filename) - content = self.content_transform(image) - art_index = random.randint(0,self.art_num-1) - filename,label = self.art_dataset[art_index] - image = Image.open(filename) - style = self.style_transform(image) - return content,style,label - - def __len__(self): - """Return the number of images.""" - return self.num_images - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) - -def GetLoader(s_image_dir,c_image_dir, - style_selected_dir, content_selected_dir, - crop_size=178, batch_size=16, num_workers=8, - colorJitterEnable=True, colorConfig={"brightness":0.05,"contrast":0.05,"saturation":0.05,"hue":0.05}): - """Build and return a data loader.""" - - s_transforms = [] - c_transforms = [] - - s_transforms.append(T.Resize(768)) - # s_transforms.append(T.Resize(900)) - c_transforms.append(T.Resize(768)) - - s_transforms.append(T.RandomCrop(crop_size,pad_if_needed=True,padding_mode='reflect')) - c_transforms.append(T.RandomCrop(crop_size)) - - s_transforms.append(T.RandomHorizontalFlip()) - c_transforms.append(T.RandomHorizontalFlip()) - - s_transforms.append(T.RandomVerticalFlip()) - c_transforms.append(T.RandomVerticalFlip()) - - if colorJitterEnable: - if colorConfig is not None: - print("Enable color jitter!") - colorBrightness = colorConfig["brightness"] - colorContrast = colorConfig["contrast"] - colorSaturation = colorConfig["saturation"] - colorHue = (-colorConfig["hue"],colorConfig["hue"]) - s_transforms.append(T.ColorJitter(brightness=colorBrightness,\ - contrast=colorContrast,saturation=colorSaturation, hue=colorHue)) - c_transforms.append(T.ColorJitter(brightness=colorBrightness,\ - contrast=colorContrast,saturation=colorSaturation, hue=colorHue)) - s_transforms.append(T.ToTensor()) - c_transforms.append(T.ToTensor()) - - s_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) - c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) - - s_transforms = T.Compose(s_transforms) - c_transforms = T.Compose(c_transforms) - - content_dataset = TotalDataset(c_image_dir,s_image_dir, content_selected_dir, style_selected_dir - , c_transforms,s_transforms) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True) - prefetcher = DataPrefetcher(content_data_loader) - return prefetcher - -def GetValiDataTensors( - image_dir=None, - selected_imgs=[], - crop_size=178, - mean = (0.5, 0.5, 0.5), - std=(0.5, 0.5, 0.5) - ): - - transforms = [] - - transforms.append(T.Resize(768)) - - transforms.append(T.RandomCrop(crop_size,pad_if_needed=True,padding_mode='reflect')) - - transforms.append(T.ToTensor()) - - transforms.append(T.Normalize(mean=mean, std=std)) - - transforms = T.Compose(transforms) - - result_img = [] - print("Start to read validation data......") - if len(selected_imgs) != 0: - for s_img in selected_imgs: - if image_dir == None: - temp_img = s_img - else: - temp_img = os.path.join(image_dir, s_img) - temp_img = Image.open(temp_img) - temp_img = transforms(temp_img).cuda().unsqueeze(0) - result_img.append(temp_img) - else: - s_imgs = glob.glob(os.path.join(image_dir, '*.jpg')) - s_imgs = s_imgs + glob.glob(os.path.join(image_dir, '*.png')) - for s_img in s_imgs: - temp_img = os.path.join(image_dir, s_img) - temp_img = Image.open(temp_img) - temp_img = transforms(temp_img).cuda().unsqueeze(0) - result_img.append(temp_img) - print("Finish to read validation data......") - print("Total validation images: %d"%len(result_img)) - return result_img - -def ScanAbnormalImg(image_dir, selected_imgs): - """Scan the dataset, this function is designed to exclude or remove the non-RGB images.""" - print("processing images...") - subffix = "jpg" - for dir_item in selected_imgs: - join_path = Path(image_dir,dir_item)#.replace('/','_')) - if join_path.exists(): - print("processing %s"%dir_item) - images = join_path.glob('*.%s'%(subffix)) - for item in images: - # print(str(item.name)[0:6]) - # temp = cv2.imread(str(item)) - temp = Image.open(item) - # exclude the abnormal images - if temp.mode!="RGB": - print(temp.mode) - print("Found one abnormal image!") - print(item) - os.remove(str(item)) - - else: - print("%s dir does not exist!"%dir_item) \ No newline at end of file diff --git a/data_tools/data_loader_FFHQ_multigpu.py b/data_tools/data_loader_FFHQ_multigpu.py deleted file mode 100644 index d8e4570..0000000 --- a/data_tools/data_loader_FFHQ_multigpu.py +++ /dev/null @@ -1,185 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: data_loader_VGGFace2HQ copy.py -# Created Date: Sunday February 6th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 15th February 2022 1:50:19 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import glob -import torch -import random -import numpy as np -from PIL import Image -from torch.utils import data -from torchvision import transforms as T -# from StyleResize import StyleResize - -class InfiniteSampler(torch.utils.data.Sampler): - def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): - assert len(dataset) > 0 - assert num_replicas > 0 - assert 0 <= rank < num_replicas - assert 0 <= window_size <= 1 - super().__init__(dataset) - self.dataset = dataset - self.rank = rank - self.num_replicas = num_replicas - self.shuffle = shuffle - self.seed = seed - self.window_size = window_size - - def __iter__(self): - order = np.arange(len(self.dataset)) - rnd = None - window = 0 - if self.shuffle: - rnd = np.random.RandomState(self.seed) - rnd.shuffle(order) - window = int(np.rint(order.size * self.window_size)) - - idx = 0 - while True: - i = idx % order.size - if idx % self.num_replicas == self.rank: - yield order[i] - if window >= 2: - j = (i - rnd.randint(window)) % order.size - order[i], order[j] = order[j], order[i] - idx += 1 - -class data_prefetcher(): - def __init__(self, loader, cur_gpu): - torch.cuda.set_device(cur_gpu) # must add this line to avoid excessive use of GPU 0 by the prefetcher - self.loader = loader - self.dataiter = iter(loader) - self.stream = torch.cuda.Stream(device=cur_gpu) - self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda(device=cur_gpu).view(1,3,1,1) - self.std = torch.tensor([0.229, 0.224, 0.225]).cuda(device=cur_gpu).view(1,3,1,1) - self.cur_gpu = cur_gpu - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.mean = self.mean.half() - # self.std = self.std.half() - # self.num_images = loader.__len__() - self.preload() - - def preload(self): - # try: - self.src_image1, self.src_image2 = next(self.dataiter) - # except StopIteration: - # self.dataiter = iter(self.loader) - # self.src_image1, self.src_image2 = next(self.dataiter) - - with torch.cuda.stream(self.stream): - self.src_image1 = self.src_image1.cuda(device= self.cur_gpu, non_blocking=True) - self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std) - self.src_image2 = self.src_image2.cuda(device= self.cur_gpu, non_blocking=True) - self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.next_input = self.next_input.half() - # else: - # self.next_input = self.next_input.float() - # self.next_input = self.next_input.sub_(self.mean).div_(self.std) - def next(self): - torch.cuda.current_stream(device= self.cur_gpu,).wait_stream(self.stream) - src_image1 = self.src_image1 - src_image2 = self.src_image2 - self.preload() - return src_image1, src_image2 - - # def __len__(self): - # """Return the number of images.""" - # return self.num_images - -class VGGFace2HQDataset(data.Dataset): - """Dataset class for the Artworks dataset and content dataset.""" - - def __init__(self, - image_dir, - img_transform, - subffix='jpg', - random_seed=1234): - """Initialize and preprocess the VGGFace2 HQ dataset.""" - self.image_dir = image_dir - self.img_transform = img_transform - self.subffix = subffix - self.dataset = [] - self.random_seed = random_seed - self.preprocess() - self.num_images = len(self.dataset) - - def preprocess(self): - """Preprocess the VGGFace2 HQ dataset.""" - print("processing VGGFace2 HQ dataset images...") - - temp_path = os.path.join(self.image_dir,'*/') - pathes = glob.glob(temp_path) - self.dataset = [] - for dir_item in pathes: - join_path = glob.glob(os.path.join(dir_item,'*.jpg')) - print("processing %s"%dir_item,end='\r') - temp_list = [] - for item in join_path: - temp_list.append(item) - self.dataset.append(temp_list) - random.seed(self.random_seed) - random.shuffle(self.dataset) - print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset)) - - def __getitem__(self, index): - """Return two src domain images and two dst domain images.""" - dir_tmp1 = self.dataset[index] - dir_tmp1_len = len(dir_tmp1) - - filename1 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - filename2 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - image1 = self.img_transform(Image.open(filename1)) - image2 = self.img_transform(Image.open(filename2)) - return image1, image2 - - def __len__(self): - """Return the number of images.""" - return self.num_images - -def GetLoader( dataset_roots, - rank, - num_gpus, - batch_size=16, - **kwargs - ): - """Build and return a data loader.""" - - data_root = dataset_roots - random_seed = kwargs["random_seed"] - num_workers = kwargs["dataloader_workers"] - - c_transforms = [] - - c_transforms.append(T.ToTensor()) - c_transforms = T.Compose(c_transforms) - - content_dataset = VGGFace2HQDataset( - data_root, - c_transforms, - "jpg", - random_seed) - device = torch.device('cuda', rank) - sampler = InfiniteSampler(dataset=content_dataset, rank=rank, num_replicas=num_gpus, seed=random_seed) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True, sampler=sampler) - # content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - # drop_last=False,shuffle=True,num_workers=num_workers,pin_memory=True) - prefetcher = data_prefetcher(content_data_loader,device) - return prefetcher - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) \ No newline at end of file diff --git a/data_tools/data_loader_VGGFace2HQ.py b/data_tools/data_loader_VGGFace2HQ.py deleted file mode 100644 index f14892b..0000000 --- a/data_tools/data_loader_VGGFace2HQ.py +++ /dev/null @@ -1,196 +0,0 @@ -import os -import glob -import torch -import random -from PIL import Image -from pathlib import Path -from torch.utils import data -from torchvision import transforms as T -# from StyleResize import StyleResize - - -class data_prefetcher(): - def __init__(self, loader, cur_gpu): - self.loader = loader - self.dataiter = iter(loader) - self.stream = torch.cuda.Stream(device=cur_gpu) - self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda(device=cur_gpu).view(1,3,1,1) - self.std = torch.tensor([0.229, 0.224, 0.225]).cuda(device=cur_gpu).view(1,3,1,1) - self.cur_gpu = cur_gpu - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.mean = self.mean.half() - # self.std = self.std.half() - self.num_images = len(loader) - self.preload() - - def preload(self): - try: - self.src_image1, self.src_image2 = next(self.dataiter) - except StopIteration: - self.dataiter = iter(self.loader) - self.src_image1, self.src_image2 = next(self.dataiter) - - with torch.cuda.stream(self.stream): - self.src_image1 = self.src_image1.cuda(device= self.cur_gpu, non_blocking=True) - self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std) - self.src_image2 = self.src_image2.cuda(device= self.cur_gpu, non_blocking=True) - self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.next_input = self.next_input.half() - # else: - # self.next_input = self.next_input.float() - # self.next_input = self.next_input.sub_(self.mean).div_(self.std) - def next(self): - torch.cuda.current_stream(device= self.cur_gpu,).wait_stream(self.stream) - src_image1 = self.src_image1 - src_image2 = self.src_image2 - self.preload() - return src_image1, src_image2 - - def __len__(self): - """Return the number of images.""" - return self.num_images - -class VGGFace2HQDataset(data.Dataset): - """Dataset class for the Artworks dataset and content dataset.""" - - def __init__(self, - image_dir, - img_transform, - subffix='jpg', - random_seed=1234): - """Initialize and preprocess the VGGFace2 HQ dataset.""" - self.image_dir = image_dir - self.img_transform = img_transform - self.subffix = subffix - self.dataset = [] - self.random_seed = random_seed - self.preprocess() - self.num_images = len(self.dataset) - - def preprocess(self): - """Preprocess the VGGFace2 HQ dataset.""" - print("processing VGGFace2 HQ dataset images...") - - temp_path = os.path.join(self.image_dir,'*/') - pathes = glob.glob(temp_path) - self.dataset = [] - for dir_item in pathes: - join_path = glob.glob(os.path.join(dir_item,'*.jpg')) - print("processing %s"%dir_item,end='\r') - temp_list = [] - for item in join_path: - temp_list.append(item) - self.dataset.append(temp_list) - random.seed(self.random_seed) - random.shuffle(self.dataset) - print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset)) - - def __getitem__(self, index): - """Return two src domain images and two dst domain images.""" - dir_tmp1 = self.dataset[index] - dir_tmp1_len = len(dir_tmp1) - - filename1 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - filename2 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - image1 = self.img_transform(Image.open(filename1)) - image2 = self.img_transform(Image.open(filename2)) - return image1, image2 - - def __len__(self): - """Return the number of images.""" - return self.num_images - -def GetLoader( dataset_roots, - cur_gpu, - batch_size=16, - **kwargs - ): - """Build and return a data loader.""" - - data_root = dataset_roots - random_seed = kwargs["random_seed"] - num_workers = kwargs["dataloader_workers"] - - c_transforms = [] - - c_transforms.append(T.ToTensor()) - c_transforms = T.Compose(c_transforms) - - content_dataset = VGGFace2HQDataset( - data_root, - c_transforms, - "jpg", - random_seed) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True) - prefetcher = data_prefetcher(content_data_loader,cur_gpu) - return prefetcher - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) - -if __name__ == "__main__": - from torchvision.utils import save_image - style_class = ["vangogh","picasso","samuel"] - categories_names = \ - ['a/abbey', 'a/arch', 'a/amphitheater', 'a/aqueduct', 'a/arena/rodeo', 'a/athletic_field/outdoor', - 'b/badlands', 'b/balcony/exterior', 'b/bamboo_forest', 'b/barn', 'b/barndoor', 'b/baseball_field', - 'b/basilica', 'b/bayou', 'b/beach', 'b/beach_house', 'b/beer_garden', 'b/boardwalk', 'b/boathouse', - 'b/botanical_garden', 'b/bullring', 'b/butte', 'c/cabin/outdoor', 'c/campsite', 'c/campus', - 'c/canal/natural', 'c/canal/urban', 'c/canyon', 'c/castle', 'c/church/outdoor', 'c/chalet', - 'c/cliff', 'c/coast', 'c/corn_field', 'c/corral', 'c/cottage', 'c/courtyard', 'c/crevasse', - 'd/dam', 'd/desert/vegetation', 'd/desert_road', 'd/doorway/outdoor', 'f/farm', 'f/fairway', - 'f/field/cultivated', 'f/field/wild', 'f/field_road', 'f/fishpond', 'f/florist_shop/indoor', - 'f/forest/broadleaf', 'f/forest_path', 'f/forest_road', 'f/formal_garden', 'g/gazebo/exterior', - 'g/glacier', 'g/golf_course', 'g/greenhouse/indoor', 'g/greenhouse/outdoor', 'g/grotto', 'g/gorge', - 'h/hayfield', 'h/herb_garden', 'h/hot_spring', 'h/house', 'h/hunting_lodge/outdoor', 'i/ice_floe', - 'i/ice_shelf', 'i/iceberg', 'i/inn/outdoor', 'i/islet', 'j/japanese_garden', 'k/kasbah', - 'k/kennel/outdoor', 'l/lagoon', 'l/lake/natural', 'l/lawn', 'l/library/outdoor', 'l/lighthouse', - 'm/mansion', 'm/marsh', 'm/mausoleum', 'm/moat/water', 'm/mosque/outdoor', 'm/mountain', - 'm/mountain_path', 'm/mountain_snowy', 'o/oast_house', 'o/ocean', 'o/orchard', 'p/park', - 'p/pasture', 'p/pavilion', 'p/picnic_area', 'p/pier', 'p/pond', 'r/raft', 'r/railroad_track', - 'r/rainforest', 'r/rice_paddy', 'r/river', 'r/rock_arch', 'r/roof_garden', 'r/rope_bridge', - 'r/ruin', 's/schoolhouse', 's/sky', 's/snowfield', 's/swamp', 's/swimming_hole', - 's/synagogue/outdoor', 't/temple/asia', 't/topiary_garden', 't/tree_farm', 't/tree_house', - 'u/underwater/ocean_deep', 'u/utility_room', 'v/valley', 'v/vegetable_garden', 'v/viaduct', - 'v/village', 'v/vineyard', 'v/volcano', 'w/waterfall', 'w/watering_hole', 'w/wave', - 'w/wheat_field', 'z/zen_garden', 'a/alcove', 'a/apartment-building/outdoor', 'a/artists_loft', - 'b/building_facade', 'c/cemetery'] - - s_datapath = "D:\\F_Disk\\data_set\\Art_Data\\data_art_backup" - c_datapath = "D:\\Downloads\\data_large" - savepath = "D:\\PatchFace\\PleaseWork\\multi-style-gan\\StyleTransfer\\dataloader_test" - - imsize = 512 - s_datasetloader= getLoader(s_datapath,c_datapath, - style_class, categories_names, - crop_size=imsize, batch_size=16, num_workers=4) - wocao = iter(s_datasetloader) - for i in range(500): - print("new batch") - s_image,c_image,label = next(wocao) - print(label) - # print(label) - # saved_image1 = torch.cat([denorm(image.data),denorm(hahh.data)],3) - # save_image(denorm(image), "%s\\%d-label-%d.jpg"%(savepath,i), nrow=1, padding=1) - pass - # import cv2 - # import os - # for dir_item in categories_names: - # join_path = Path(contentdatapath,dir_item) - # if join_path.exists(): - # print("processing %s"%dir_item,end='\r') - # images = join_path.glob('*.%s'%("jpg")) - # for item in images: - # temp_path = str(item) - # # temp = cv2.imread(temp_path) - # temp = Image.open(temp_path) - # if temp.layers<3: - # print("remove broken image...") - # print("image name:%s"%temp_path) - # del temp - # os.remove(item) \ No newline at end of file diff --git a/data_tools/data_loader_VGGFace2HQ_Rec.py b/data_tools/data_loader_VGGFace2HQ_Rec.py deleted file mode 100644 index e0e1d5a..0000000 --- a/data_tools/data_loader_VGGFace2HQ_Rec.py +++ /dev/null @@ -1,195 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: data_loader_VGGFace2HQ copy.py -# Created Date: Saturday January 29th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 29th January 2022 3:39:14 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import glob -import torch -import random -from PIL import Image -from pathlib import Path -from torch.utils import data -from torchvision import transforms as T -# from StyleResize import StyleResize - -class data_prefetcher(): - def __init__(self, loader): - self.loader = loader - self.dataiter = iter(loader) - self.stream = torch.cuda.Stream() - self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(1,3,1,1) - self.std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(1,3,1,1) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.mean = self.mean.half() - # self.std = self.std.half() - self.num_images = len(loader) - self.preload() - - def preload(self): - try: - self.src_image1 = next(self.dataiter) - except StopIteration: - self.dataiter = iter(self.loader) - self.src_image1 = next(self.dataiter) - - with torch.cuda.stream(self.stream): - self.src_image1 = self.src_image1.cuda(non_blocking=True) - self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.next_input = self.next_input.half() - # else: - # self.next_input = self.next_input.float() - # self.next_input = self.next_input.sub_(self.mean).div_(self.std) - def next(self): - torch.cuda.current_stream().wait_stream(self.stream) - src_image1 = self.src_image1 - self.preload() - return src_image1 - - def __len__(self): - """Return the number of images.""" - return self.num_images - -class VGGFace2HQDataset(data.Dataset): - """Dataset class for the Artworks dataset and content dataset.""" - - def __init__(self, - image_dir, - img_transform, - subffix='jpg', - random_seed=1234): - """Initialize and preprocess the VGGFace2 HQ dataset.""" - self.image_dir = image_dir - self.img_transform = img_transform - self.subffix = subffix - self.dataset = [] - self.random_seed = random_seed - self.preprocess() - self.num_images = len(self.dataset) - - def preprocess(self): - """Preprocess the VGGFace2 HQ dataset.""" - print("processing VGGFace2 HQ dataset images...") - - temp_path = os.path.join(self.image_dir,'*/*') - pathes = glob.glob(temp_path) - self.dataset = [] - for dir_item in pathes: - print("processing %s"%dir_item,end='\r') - self.dataset.append(dir_item) - random.seed(self.random_seed) - random.shuffle(self.dataset) - print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset)) - - def __getitem__(self, index): - """Return two src domain images and two dst domain images.""" - dir_tmp1 = self.dataset[index] - - image1 = self.img_transform(Image.open(dir_tmp1)) - return image1 - - def __len__(self): - """Return the number of images.""" - return self.num_images - -def GetLoader( dataset_roots, - batch_size=16, - **kwargs - ): - """Build and return a data loader.""" - - data_root = dataset_roots - random_seed = kwargs["random_seed"] - num_workers = kwargs["dataloader_workers"] - - c_transforms = [] - c_transforms.append(T.Resize((112,112))) - c_transforms.append(T.ToTensor()) - c_transforms = T.Compose(c_transforms) - - content_dataset = VGGFace2HQDataset( - data_root, - c_transforms, - "jpg", - random_seed) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True) - prefetcher = data_prefetcher(content_data_loader) - return prefetcher - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) - -if __name__ == "__main__": - from torchvision.utils import save_image - style_class = ["vangogh","picasso","samuel"] - categories_names = \ - ['a/abbey', 'a/arch', 'a/amphitheater', 'a/aqueduct', 'a/arena/rodeo', 'a/athletic_field/outdoor', - 'b/badlands', 'b/balcony/exterior', 'b/bamboo_forest', 'b/barn', 'b/barndoor', 'b/baseball_field', - 'b/basilica', 'b/bayou', 'b/beach', 'b/beach_house', 'b/beer_garden', 'b/boardwalk', 'b/boathouse', - 'b/botanical_garden', 'b/bullring', 'b/butte', 'c/cabin/outdoor', 'c/campsite', 'c/campus', - 'c/canal/natural', 'c/canal/urban', 'c/canyon', 'c/castle', 'c/church/outdoor', 'c/chalet', - 'c/cliff', 'c/coast', 'c/corn_field', 'c/corral', 'c/cottage', 'c/courtyard', 'c/crevasse', - 'd/dam', 'd/desert/vegetation', 'd/desert_road', 'd/doorway/outdoor', 'f/farm', 'f/fairway', - 'f/field/cultivated', 'f/field/wild', 'f/field_road', 'f/fishpond', 'f/florist_shop/indoor', - 'f/forest/broadleaf', 'f/forest_path', 'f/forest_road', 'f/formal_garden', 'g/gazebo/exterior', - 'g/glacier', 'g/golf_course', 'g/greenhouse/indoor', 'g/greenhouse/outdoor', 'g/grotto', 'g/gorge', - 'h/hayfield', 'h/herb_garden', 'h/hot_spring', 'h/house', 'h/hunting_lodge/outdoor', 'i/ice_floe', - 'i/ice_shelf', 'i/iceberg', 'i/inn/outdoor', 'i/islet', 'j/japanese_garden', 'k/kasbah', - 'k/kennel/outdoor', 'l/lagoon', 'l/lake/natural', 'l/lawn', 'l/library/outdoor', 'l/lighthouse', - 'm/mansion', 'm/marsh', 'm/mausoleum', 'm/moat/water', 'm/mosque/outdoor', 'm/mountain', - 'm/mountain_path', 'm/mountain_snowy', 'o/oast_house', 'o/ocean', 'o/orchard', 'p/park', - 'p/pasture', 'p/pavilion', 'p/picnic_area', 'p/pier', 'p/pond', 'r/raft', 'r/railroad_track', - 'r/rainforest', 'r/rice_paddy', 'r/river', 'r/rock_arch', 'r/roof_garden', 'r/rope_bridge', - 'r/ruin', 's/schoolhouse', 's/sky', 's/snowfield', 's/swamp', 's/swimming_hole', - 's/synagogue/outdoor', 't/temple/asia', 't/topiary_garden', 't/tree_farm', 't/tree_house', - 'u/underwater/ocean_deep', 'u/utility_room', 'v/valley', 'v/vegetable_garden', 'v/viaduct', - 'v/village', 'v/vineyard', 'v/volcano', 'w/waterfall', 'w/watering_hole', 'w/wave', - 'w/wheat_field', 'z/zen_garden', 'a/alcove', 'a/apartment-building/outdoor', 'a/artists_loft', - 'b/building_facade', 'c/cemetery'] - - s_datapath = "D:\\F_Disk\\data_set\\Art_Data\\data_art_backup" - c_datapath = "D:\\Downloads\\data_large" - savepath = "D:\\PatchFace\\PleaseWork\\multi-style-gan\\StyleTransfer\\dataloader_test" - - imsize = 512 - s_datasetloader= getLoader(s_datapath,c_datapath, - style_class, categories_names, - crop_size=imsize, batch_size=16, num_workers=4) - wocao = iter(s_datasetloader) - for i in range(500): - print("new batch") - s_image,c_image,label = next(wocao) - print(label) - # print(label) - # saved_image1 = torch.cat([denorm(image.data),denorm(hahh.data)],3) - # save_image(denorm(image), "%s\\%d-label-%d.jpg"%(savepath,i), nrow=1, padding=1) - pass - # import cv2 - # import os - # for dir_item in categories_names: - # join_path = Path(contentdatapath,dir_item) - # if join_path.exists(): - # print("processing %s"%dir_item,end='\r') - # images = join_path.glob('*.%s'%("jpg")) - # for item in images: - # temp_path = str(item) - # # temp = cv2.imread(temp_path) - # temp = Image.open(temp_path) - # if temp.layers<3: - # print("remove broken image...") - # print("image name:%s"%temp_path) - # del temp - # os.remove(item) \ No newline at end of file diff --git a/data_tools/data_loader_VGGFace2HQ_multigpu.py b/data_tools/data_loader_VGGFace2HQ_multigpu.py deleted file mode 100644 index 6dcb269..0000000 --- a/data_tools/data_loader_VGGFace2HQ_multigpu.py +++ /dev/null @@ -1,185 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: data_loader_VGGFace2HQ copy.py -# Created Date: Sunday February 6th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 6th April 2022 12:53:53 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import glob -import torch -import random -import numpy as np -from PIL import Image -from torch.utils import data -from torchvision import transforms as T -# from StyleResize import StyleResize - -class InfiniteSampler(torch.utils.data.Sampler): - def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): - assert len(dataset) > 0 - assert num_replicas > 0 - assert 0 <= rank < num_replicas - assert 0 <= window_size <= 1 - super().__init__(dataset) - self.dataset = dataset - self.rank = rank - self.num_replicas = num_replicas - self.shuffle = shuffle - self.seed = seed - self.window_size = window_size - - def __iter__(self): - order = np.arange(len(self.dataset)) - rnd = None - window = 0 - if self.shuffle: - rnd = np.random.RandomState(self.seed) - rnd.shuffle(order) - window = int(np.rint(order.size * self.window_size)) - - idx = 0 - while True: - i = idx % order.size - if idx % self.num_replicas == self.rank: - yield order[i] - if window >= 2: - j = (i - rnd.randint(window)) % order.size - order[i], order[j] = order[j], order[i] - idx += 1 - -class data_prefetcher(): - def __init__(self, loader, cur_gpu): - torch.cuda.set_device(cur_gpu) # must add this line to avoid excessive use of GPU 0 by the prefetcher - self.loader = loader - self.dataiter = iter(loader) - self.stream = torch.cuda.Stream(device=cur_gpu) - self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda(device=cur_gpu).view(1,3,1,1) - self.std = torch.tensor([0.229, 0.224, 0.225]).cuda(device=cur_gpu).view(1,3,1,1) - self.cur_gpu = cur_gpu - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.mean = self.mean.half() - # self.std = self.std.half() - # self.num_images = loader.__len__() - self.preload() - - def preload(self): - # try: - self.src_image1, self.src_image2 = next(self.dataiter) - # except StopIteration: - # self.dataiter = iter(self.loader) - # self.src_image1, self.src_image2 = next(self.dataiter) - - with torch.cuda.stream(self.stream): - self.src_image1 = self.src_image1.cuda(device= self.cur_gpu, non_blocking=True) - self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std) - self.src_image2 = self.src_image2.cuda(device= self.cur_gpu, non_blocking=True) - self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.next_input = self.next_input.half() - # else: - # self.next_input = self.next_input.float() - # self.next_input = self.next_input.sub_(self.mean).div_(self.std) - def next(self): - torch.cuda.current_stream(device= self.cur_gpu,).wait_stream(self.stream) - src_image1 = self.src_image1 - src_image2 = self.src_image2 - self.preload() - return src_image1, src_image2 - - # def __len__(self): - # """Return the number of images.""" - # return self.num_images - -class VGGFace2HQDataset(data.Dataset): - """Dataset class for the Artworks dataset and content dataset.""" - - def __init__(self, - image_dir, - img_transform, - subffix='jpg', - random_seed=1234): - """Initialize and preprocess the VGGFace2 HQ dataset.""" - self.image_dir = image_dir["images"] - self.img_transform = img_transform - self.subffix = subffix - self.dataset = [] - self.random_seed = random_seed - self.preprocess() - self.num_images = len(self.dataset) - - def preprocess(self): - """Preprocess the VGGFace2 HQ dataset.""" - print("processing VGGFace2 HQ dataset images...") - - temp_path = os.path.join(self.image_dir,'*/') - pathes = glob.glob(temp_path) - self.dataset = [] - for dir_item in pathes: - join_path = glob.glob(os.path.join(dir_item,'*.jpg')) - print("processing %s"%dir_item,end='\r') - temp_list = [] - for item in join_path: - temp_list.append(item) - self.dataset.append(temp_list) - random.seed(self.random_seed) - random.shuffle(self.dataset) - print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset)) - - def __getitem__(self, index): - """Return two src domain images and two dst domain images.""" - dir_tmp1 = self.dataset[index] - dir_tmp1_len = len(dir_tmp1) - - filename1 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - filename2 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - image1 = self.img_transform(Image.open(filename1)) - image2 = self.img_transform(Image.open(filename2)) - return image1, image2 - - def __len__(self): - """Return the number of images.""" - return self.num_images - -def GetLoader( dataset_roots, - rank, - num_gpus, - batch_size=16, - **kwargs - ): - """Build and return a data loader.""" - - data_root = dataset_roots - random_seed = kwargs["random_seed"] - num_workers = kwargs["dataloader_workers"] - - c_transforms = [] - - c_transforms.append(T.ToTensor()) - c_transforms = T.Compose(c_transforms) - - content_dataset = VGGFace2HQDataset( - data_root, - c_transforms, - "jpg", - random_seed) - device = torch.device('cuda', rank) - sampler = InfiniteSampler(dataset=content_dataset, rank=rank, num_replicas=num_gpus, seed=random_seed) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True, sampler=sampler) - # content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - # drop_last=False,shuffle=True,num_workers=num_workers,pin_memory=True) - prefetcher = data_prefetcher(content_data_loader,device) - return prefetcher - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) \ No newline at end of file diff --git a/data_tools/data_loader_VGGFace2HQ_multigpu1.py b/data_tools/data_loader_VGGFace2HQ_multigpu1.py deleted file mode 100644 index a98199a..0000000 --- a/data_tools/data_loader_VGGFace2HQ_multigpu1.py +++ /dev/null @@ -1,247 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: data_loader_VGGFace2HQ copy.py -# Created Date: Sunday February 6th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 15th February 2022 1:35:06 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import glob -import torch -import random -import numpy as np -from PIL import Image -from torch.utils import data -from torchvision import transforms as T -# from StyleResize import StyleResize - -class InfiniteSampler(torch.utils.data.Sampler): - def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): - assert len(dataset) > 0 - assert num_replicas > 0 - assert 0 <= rank < num_replicas - assert 0 <= window_size <= 1 - super().__init__(dataset) - self.dataset = dataset - self.rank = rank - self.num_replicas = num_replicas - self.shuffle = shuffle - self.seed = seed - self.window_size = window_size - - def __iter__(self): - order = np.arange(len(self.dataset)) - rnd = None - window = 0 - if self.shuffle: - rnd = np.random.RandomState(self.seed) - rnd.shuffle(order) - window = int(np.rint(order.size * self.window_size)) - - idx = 0 - while True: - i = idx % order.size - if idx % self.num_replicas == self.rank: - yield order[i] - if window >= 2: - j = (i - rnd.randint(window)) % order.size - order[i], order[j] = order[j], order[i] - idx += 1 - -class data_prefetcher(): - def __init__(self, loader, cur_gpu): - torch.cuda.set_device(cur_gpu) # must add this line to avoid excessive use of GPU 0 by the prefetcher - self.loader = loader - self.dataiter = iter(loader) - self.stream = torch.cuda.Stream(device=cur_gpu) - self.mean = torch.tensor([0.485, 0.456, 0.406]).to(cur_gpu).view(1,3,1,1) - self.std = torch.tensor([0.229, 0.224, 0.225]).to(cur_gpu).view(1,3,1,1) - self.cur_gpu = cur_gpu - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.mean = self.mean.half() - # self.std = self.std.half() - # self.num_images = loader.__len__() - self.preload() - - def preload(self): - # try: - self.src_image1, self.src_image2 = next(self.dataiter) - # except StopIteration: - # self.dataiter = iter(self.loader) - # self.src_image1, self.src_image2 = next(self.dataiter) - - with torch.cuda.stream(self.stream): - self.src_image1 = self.src_image1.to(self.cur_gpu, non_blocking=True) - self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std) - self.src_image2 = self.src_image2.to(self.cur_gpu, non_blocking=True) - self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.next_input = self.next_input.half() - # else: - # self.next_input = self.next_input.float() - # self.next_input = self.next_input.sub_(self.mean).div_(self.std) - def next(self): - torch.cuda.current_stream(device= self.cur_gpu,).wait_stream(self.stream) - src_image1 = self.src_image1 - src_image2 = self.src_image2 - self.preload() - return src_image1, src_image2 - - # def __len__(self): - # """Return the number of images.""" - # return self.num_images - -class VGGFace2HQDataset(data.Dataset): - """Dataset class for the Artworks dataset and content dataset.""" - - def __init__(self, - image_dir, - img_transform, - subffix='jpg', - random_seed=1234): - """Initialize and preprocess the VGGFace2 HQ dataset.""" - self.image_dir = image_dir - self.img_transform = img_transform - self.subffix = subffix - self.dataset = [] - self.random_seed = random_seed - self.preprocess() - self.num_images = len(self.dataset) - - def preprocess(self): - """Preprocess the VGGFace2 HQ dataset.""" - print("processing VGGFace2 HQ dataset images...") - - temp_path = os.path.join(self.image_dir,'*/') - pathes = glob.glob(temp_path) - self.dataset = [] - for dir_item in pathes: - join_path = glob.glob(os.path.join(dir_item,'*.jpg')) - print("processing %s"%dir_item,end='\r') - temp_list = [] - for item in join_path: - temp_list.append(item) - self.dataset.append(temp_list) - random.seed(self.random_seed) - random.shuffle(self.dataset) - print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset)) - - def __getitem__(self, index): - """Return two src domain images and two dst domain images.""" - dir_tmp1 = self.dataset[index] - dir_tmp1_len = len(dir_tmp1) - - filename1 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - filename2 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - image1 = self.img_transform(Image.open(filename1)) - image2 = self.img_transform(Image.open(filename2)) - return image1, image2 - - def __len__(self): - """Return the number of images.""" - return self.num_images - -def GetLoader( dataset_roots, - rank, - num_gpus, - batch_size=16, - **kwargs - ): - """Build and return a data loader.""" - - data_root = dataset_roots - random_seed = kwargs["random_seed"] - num_workers = kwargs["dataloader_workers"] - - c_transforms = [] - - c_transforms.append(T.ToTensor()) - c_transforms = T.Compose(c_transforms) - - content_dataset = VGGFace2HQDataset( - data_root, - c_transforms, - "jpg", - random_seed) - device = torch.device('cuda', rank) - sampler = InfiniteSampler(dataset=content_dataset, rank=rank, num_replicas=num_gpus, seed=random_seed) - # content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - # drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True, sampler=sampler) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True, sampler=sampler) - prefetcher = data_prefetcher(content_data_loader,device) - return prefetcher - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) - -if __name__ == "__main__": - from torchvision.utils import save_image - style_class = ["vangogh","picasso","samuel"] - categories_names = \ - ['a/abbey', 'a/arch', 'a/amphitheater', 'a/aqueduct', 'a/arena/rodeo', 'a/athletic_field/outdoor', - 'b/badlands', 'b/balcony/exterior', 'b/bamboo_forest', 'b/barn', 'b/barndoor', 'b/baseball_field', - 'b/basilica', 'b/bayou', 'b/beach', 'b/beach_house', 'b/beer_garden', 'b/boardwalk', 'b/boathouse', - 'b/botanical_garden', 'b/bullring', 'b/butte', 'c/cabin/outdoor', 'c/campsite', 'c/campus', - 'c/canal/natural', 'c/canal/urban', 'c/canyon', 'c/castle', 'c/church/outdoor', 'c/chalet', - 'c/cliff', 'c/coast', 'c/corn_field', 'c/corral', 'c/cottage', 'c/courtyard', 'c/crevasse', - 'd/dam', 'd/desert/vegetation', 'd/desert_road', 'd/doorway/outdoor', 'f/farm', 'f/fairway', - 'f/field/cultivated', 'f/field/wild', 'f/field_road', 'f/fishpond', 'f/florist_shop/indoor', - 'f/forest/broadleaf', 'f/forest_path', 'f/forest_road', 'f/formal_garden', 'g/gazebo/exterior', - 'g/glacier', 'g/golf_course', 'g/greenhouse/indoor', 'g/greenhouse/outdoor', 'g/grotto', 'g/gorge', - 'h/hayfield', 'h/herb_garden', 'h/hot_spring', 'h/house', 'h/hunting_lodge/outdoor', 'i/ice_floe', - 'i/ice_shelf', 'i/iceberg', 'i/inn/outdoor', 'i/islet', 'j/japanese_garden', 'k/kasbah', - 'k/kennel/outdoor', 'l/lagoon', 'l/lake/natural', 'l/lawn', 'l/library/outdoor', 'l/lighthouse', - 'm/mansion', 'm/marsh', 'm/mausoleum', 'm/moat/water', 'm/mosque/outdoor', 'm/mountain', - 'm/mountain_path', 'm/mountain_snowy', 'o/oast_house', 'o/ocean', 'o/orchard', 'p/park', - 'p/pasture', 'p/pavilion', 'p/picnic_area', 'p/pier', 'p/pond', 'r/raft', 'r/railroad_track', - 'r/rainforest', 'r/rice_paddy', 'r/river', 'r/rock_arch', 'r/roof_garden', 'r/rope_bridge', - 'r/ruin', 's/schoolhouse', 's/sky', 's/snowfield', 's/swamp', 's/swimming_hole', - 's/synagogue/outdoor', 't/temple/asia', 't/topiary_garden', 't/tree_farm', 't/tree_house', - 'u/underwater/ocean_deep', 'u/utility_room', 'v/valley', 'v/vegetable_garden', 'v/viaduct', - 'v/village', 'v/vineyard', 'v/volcano', 'w/waterfall', 'w/watering_hole', 'w/wave', - 'w/wheat_field', 'z/zen_garden', 'a/alcove', 'a/apartment-building/outdoor', 'a/artists_loft', - 'b/building_facade', 'c/cemetery'] - - s_datapath = "D:\\F_Disk\\data_set\\Art_Data\\data_art_backup" - c_datapath = "D:\\Downloads\\data_large" - savepath = "D:\\PatchFace\\PleaseWork\\multi-style-gan\\StyleTransfer\\dataloader_test" - - imsize = 512 - s_datasetloader= getLoader(s_datapath,c_datapath, - style_class, categories_names, - crop_size=imsize, batch_size=16, num_workers=4) - wocao = iter(s_datasetloader) - for i in range(500): - print("new batch") - s_image,c_image,label = next(wocao) - print(label) - # print(label) - # saved_image1 = torch.cat([denorm(image.data),denorm(hahh.data)],3) - # save_image(denorm(image), "%s\\%d-label-%d.jpg"%(savepath,i), nrow=1, padding=1) - pass - # import cv2 - # import os - # for dir_item in categories_names: - # join_path = Path(contentdatapath,dir_item) - # if join_path.exists(): - # print("processing %s"%dir_item,end='\r') - # images = join_path.glob('*.%s'%("jpg")) - # for item in images: - # temp_path = str(item) - # # temp = cv2.imread(temp_path) - # temp = Image.open(temp_path) - # if temp.layers<3: - # print("remove broken image...") - # print("image name:%s"%temp_path) - # del temp - # os.remove(item) \ No newline at end of file diff --git a/data_tools/data_loader_VGGFace2HQ_multigpu_w_mask.py b/data_tools/data_loader_VGGFace2HQ_multigpu_w_mask.py deleted file mode 100644 index af1ce98..0000000 --- a/data_tools/data_loader_VGGFace2HQ_multigpu_w_mask.py +++ /dev/null @@ -1,202 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: data_loader_VGGFace2HQ copy.py -# Created Date: Sunday February 6th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 3rd April 2022 9:48:23 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import glob -import torch -import random -import numpy as np -from PIL import Image -from torch.utils import data -from torchvision import transforms as T -import cv2 -# from StyleResize import StyleResize - -class InfiniteSampler(torch.utils.data.Sampler): - def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): - assert len(dataset) > 0 - assert num_replicas > 0 - assert 0 <= rank < num_replicas - assert 0 <= window_size <= 1 - super().__init__(dataset) - self.dataset = dataset - self.rank = rank - self.num_replicas = num_replicas - self.shuffle = shuffle - self.seed = seed - self.window_size = window_size - - def __iter__(self): - order = np.arange(len(self.dataset)) - rnd = None - window = 0 - if self.shuffle: - rnd = np.random.RandomState(self.seed) - rnd.shuffle(order) - window = int(np.rint(order.size * self.window_size)) - - idx = 0 - while True: - i = idx % order.size - if idx % self.num_replicas == self.rank: - yield order[i] - if window >= 2: - j = (i - rnd.randint(window)) % order.size - order[i], order[j] = order[j], order[i] - idx += 1 - -class data_prefetcher(): - def __init__(self, loader, cur_gpu): - torch.cuda.set_device(cur_gpu) # must add this line to avoid excessive use of GPU 0 by the prefetcher - self.loader = loader - self.dataiter = iter(loader) - self.stream = torch.cuda.Stream(device=cur_gpu) - self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda(device=cur_gpu).view(1,3,1,1) - self.std = torch.tensor([0.229, 0.224, 0.225]).cuda(device=cur_gpu).view(1,3,1,1) - self.cur_gpu = cur_gpu - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.mean = self.mean.half() - # self.std = self.std.half() - # self.num_images = loader.__len__() - self.preload() - - def preload(self): - # try: - self.src_image1, self.src_image2, self.mask = next(self.dataiter) - # except StopIteration: - # self.dataiter = iter(self.loader) - # self.src_image1, self.src_image2 = next(self.dataiter) - - with torch.cuda.stream(self.stream): - self.src_image1 = self.src_image1.cuda(device= self.cur_gpu, non_blocking=True) - self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std) - self.src_image2 = self.src_image2.cuda(device= self.cur_gpu, non_blocking=True) - self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std) - self.mask = self.mask.cuda(device= self.cur_gpu, non_blocking=True) - self.mask = self.mask/255.0 - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.next_input = self.next_input.half() - # else: - # self.next_input = self.next_input.float() - # self.next_input = self.next_input.sub_(self.mean).div_(self.std) - def next(self): - torch.cuda.current_stream(device= self.cur_gpu,).wait_stream(self.stream) - src_image1 = self.src_image1 - src_image2 = self.src_image2 - mask = self.mask - self.preload() - return src_image1, src_image2, mask - - # def __len__(self): - # """Return the number of images.""" - # return self.num_images - -class VGGFace2HQDataset(data.Dataset): - """Dataset class for the Artworks dataset and content dataset.""" - - def __init__(self, - image_dir, - mask_dir, - img_transform, - subffix='jpg', - random_seed=1234): - """Initialize and preprocess the VGGFace2 HQ dataset.""" - self.image_dir = image_dir - self.mask_dir = mask_dir - self.img_transform = img_transform - self.subffix = subffix - self.dataset = [] - self.random_seed = random_seed - self.preprocess() - self.num_images = len(self.dataset) - - def preprocess(self): - """Preprocess the VGGFace2 HQ dataset.""" - print("processing VGGFace2 HQ dataset images...") - - temp_path = os.path.join(self.image_dir,'*/') - pathes = glob.glob(temp_path) - self.dataset = [] - for dir_item in pathes: - join_path = glob.glob(os.path.join(dir_item,'*.jpg')) - print("processing %s"%dir_item,end='\r') - dir_path = os.path.dirname(join_path[1]) - dir_name = os.path.join(self.mask_dir, os.path.basename(dir_path)) - # print(dir_name) - temp_list = [] - for item in join_path: - img_name = os.path.basename(item) - img_name, _ = os.path.splitext(img_name) - temp_list.append({ - "i":item, - "m":os.path.join(dir_name, img_name + ".png") - }) - self.dataset.append(temp_list) - random.seed(self.random_seed) - random.shuffle(self.dataset) - print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset)) - - def __getitem__(self, index): - """Return two src domain images and two dst domain images.""" - dir_tmp1 = self.dataset[index] - dir_tmp1_len = len(dir_tmp1) - - filename1 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - filename2 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] - image1 = self.img_transform(Image.open(filename1["i"])) - image2 = self.img_transform(Image.open(filename2["i"])) - mask = torch.from_numpy(cv2.imread(filename1["m"],0)).unsqueeze(0) - return image1, image2, mask - - def __len__(self): - """Return the number of images.""" - return self.num_images - -def GetLoader( dataset_roots, - rank, - num_gpus, - batch_size=16, - **kwargs - ): - """Build and return a data loader.""" - - data_root = dataset_roots["images"] - mask_root = dataset_roots["masks"] - random_seed = kwargs["random_seed"] - num_workers = kwargs["dataloader_workers"] - - c_transforms = [] - - c_transforms.append(T.ToTensor()) - c_transforms = T.Compose(c_transforms) - - content_dataset = VGGFace2HQDataset( - data_root, - mask_root, - c_transforms, - "jpg", - random_seed) - device = torch.device('cuda', rank) - sampler = InfiniteSampler(dataset=content_dataset, rank=rank, num_replicas=num_gpus, seed=random_seed) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True, sampler=sampler) - # content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - # drop_last=False,shuffle=True,num_workers=num_workers,pin_memory=True) - prefetcher = data_prefetcher(content_data_loader,device) - return prefetcher - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) \ No newline at end of file diff --git a/data_tools/data_loader_condition.py b/data_tools/data_loader_condition.py deleted file mode 100644 index 15dc356..0000000 --- a/data_tools/data_loader_condition.py +++ /dev/null @@ -1,253 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: data_loader_modify.py -# Created Date: Saturday April 4th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 4th July 2021 11:12:42 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - -import os -import torch -import random -from PIL import Image -from pathlib import Path -from torch.utils import data -import torchvision.datasets as dsets -from torchvision import transforms as T -from data_tools.StyleResize import StyleResize -# from StyleResize import StyleResize - -class data_prefetcher(): - def __init__(self, loader): - self.loader = loader - self.dataiter = iter(loader) - self.stream = torch.cuda.Stream() - # self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1) - # self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.mean = self.mean.half() - # self.std = self.std.half() - self.preload() - - def preload(self): - try: - self.content, self.style, self.label = next(self.dataiter) - except StopIteration: - self.dataiter = iter(self.loader) - self.content, self.style, self.label = next(self.dataiter) - - with torch.cuda.stream(self.stream): - self.content= self.content.cuda(non_blocking=True) - self.style = self.style.cuda(non_blocking=True) - self.label = self.label.cuda(non_blocking=True) - # With Amp, it isn't necessary to manually convert data to half. - # if args.fp16: - # self.next_input = self.next_input.half() - # else: - # self.next_input = self.next_input.float() - # self.next_input = self.next_input.sub_(self.mean).div_(self.std) - def next(self): - torch.cuda.current_stream().wait_stream(self.stream) - content = self.content - style = self.style - label = self.label - self.preload() - return content, style, label - -class TotalDataset(data.Dataset): - """Dataset class for the Artworks dataset and content dataset.""" - - def __init__(self, content_image_dir,style_image_dir, - selectedContent,selectedStyle, - content_transform,style_transform, - subffix='jpg', random_seed=1234): - """Initialize and preprocess the CelebA dataset.""" - self.content_image_dir = content_image_dir - self.style_image_dir = style_image_dir - self.content_transform = content_transform - self.style_transform = style_transform - self.selectedContent = selectedContent - self.selectedStyle = selectedStyle - self.subffix = subffix - self.content_dataset = [] - self.art_dataset = [] - self.random_seed = random_seed - self.preprocess() - self.num_images = len(self.content_dataset) - self.art_num = len(self.art_dataset) - - def preprocess(self): - """Preprocess the Artworks dataset.""" - print("processing content images...") - for dir_item in self.selectedContent: - join_path = Path(self.content_image_dir,dir_item) - if join_path.exists(): - print("processing %s"%dir_item,end='\r') - images = join_path.glob('*.%s'%(self.subffix)) - for item in images: - self.content_dataset.append(item) - else: - print("%s dir does not exist!"%dir_item,end='\r') - label_index = 0 - print("processing style images...") - for class_item in self.selectedStyle: - images = Path(self.style_image_dir).glob('%s/*.%s'%(class_item, self.subffix)) - for item in images: - self.art_dataset.append([item, label_index]) - label_index += 1 - random.seed(self.random_seed) - random.shuffle(self.content_dataset) - random.shuffle(self.art_dataset) - # self.dataset = images - print('Finished preprocessing the Art Works dataset, total image number: %d...'%len(self.art_dataset)) - print('Finished preprocessing the Content dataset, total image number: %d...'%len(self.content_dataset)) - - def __getitem__(self, index): - """Return one image and its corresponding attribute label.""" - filename = self.content_dataset[index] - image = Image.open(filename) - content = self.content_transform(image) - art_index = random.randint(0,self.art_num-1) - filename,label = self.art_dataset[art_index] - image = Image.open(filename) - style = self.style_transform(image) - return content,style,label - - def __len__(self): - """Return the number of images.""" - return self.num_images - -def GetLoader( dataset_roots, - batch_size=16, - crop_size=512, - **kwargs - ): - """Build and return a data loader.""" - if not kwargs: - a = "Input params error!" - raise ValueError(print(a)) - - colorJitterEnable = kwargs["color_jitter"] - colorConfig = kwargs["color_config"] - num_workers = kwargs["dataloader_workers"] - num_workers = kwargs["dataloader_workers"] - place365_root = dataset_roots["Place365_big"] - wikiart_root = dataset_roots["WikiArt"] - selected_c_dir = kwargs["selected_content_dir"] - selected_s_dir = kwargs["selected_style_dir"] - random_seed = kwargs["random_seed"] - - s_transforms = [] - c_transforms = [] - - s_transforms.append(StyleResize()) - # s_transforms.append(T.Resize(900)) - c_transforms.append(T.Resize(900)) - - s_transforms.append(T.RandomCrop(crop_size, pad_if_needed=True, padding_mode='reflect')) - c_transforms.append(T.RandomCrop(crop_size)) - - s_transforms.append(T.RandomHorizontalFlip()) - c_transforms.append(T.RandomHorizontalFlip()) - - s_transforms.append(T.RandomVerticalFlip()) - c_transforms.append(T.RandomVerticalFlip()) - - if colorJitterEnable: - if colorConfig is not None: - print("Enable color jitter!") - colorBrightness = colorConfig["brightness"] - colorContrast = colorConfig["contrast"] - colorSaturation = colorConfig["saturation"] - colorHue = (-colorConfig["hue"],colorConfig["hue"]) - s_transforms.append(T.ColorJitter(brightness=colorBrightness,\ - contrast=colorContrast,saturation=colorSaturation, hue=colorHue)) - c_transforms.append(T.ColorJitter(brightness=colorBrightness,\ - contrast=colorContrast,saturation=colorSaturation, hue=colorHue)) - s_transforms.append(T.ToTensor()) - c_transforms.append(T.ToTensor()) - - s_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) - c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) - - s_transforms = T.Compose(s_transforms) - c_transforms = T.Compose(c_transforms) - - content_dataset = TotalDataset(place365_root,wikiart_root, - selected_c_dir, selected_s_dir, - c_transforms, s_transforms, "jpg", random_seed) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True) - prefetcher = data_prefetcher(content_data_loader) - return prefetcher - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) - -if __name__ == "__main__": - from torchvision.utils import save_image - style_class = ["vangogh","picasso","samuel"] - categories_names = \ - ['a/abbey', 'a/arch', 'a/amphitheater', 'a/aqueduct', 'a/arena/rodeo', 'a/athletic_field/outdoor', - 'b/badlands', 'b/balcony/exterior', 'b/bamboo_forest', 'b/barn', 'b/barndoor', 'b/baseball_field', - 'b/basilica', 'b/bayou', 'b/beach', 'b/beach_house', 'b/beer_garden', 'b/boardwalk', 'b/boathouse', - 'b/botanical_garden', 'b/bullring', 'b/butte', 'c/cabin/outdoor', 'c/campsite', 'c/campus', - 'c/canal/natural', 'c/canal/urban', 'c/canyon', 'c/castle', 'c/church/outdoor', 'c/chalet', - 'c/cliff', 'c/coast', 'c/corn_field', 'c/corral', 'c/cottage', 'c/courtyard', 'c/crevasse', - 'd/dam', 'd/desert/vegetation', 'd/desert_road', 'd/doorway/outdoor', 'f/farm', 'f/fairway', - 'f/field/cultivated', 'f/field/wild', 'f/field_road', 'f/fishpond', 'f/florist_shop/indoor', - 'f/forest/broadleaf', 'f/forest_path', 'f/forest_road', 'f/formal_garden', 'g/gazebo/exterior', - 'g/glacier', 'g/golf_course', 'g/greenhouse/indoor', 'g/greenhouse/outdoor', 'g/grotto', 'g/gorge', - 'h/hayfield', 'h/herb_garden', 'h/hot_spring', 'h/house', 'h/hunting_lodge/outdoor', 'i/ice_floe', - 'i/ice_shelf', 'i/iceberg', 'i/inn/outdoor', 'i/islet', 'j/japanese_garden', 'k/kasbah', - 'k/kennel/outdoor', 'l/lagoon', 'l/lake/natural', 'l/lawn', 'l/library/outdoor', 'l/lighthouse', - 'm/mansion', 'm/marsh', 'm/mausoleum', 'm/moat/water', 'm/mosque/outdoor', 'm/mountain', - 'm/mountain_path', 'm/mountain_snowy', 'o/oast_house', 'o/ocean', 'o/orchard', 'p/park', - 'p/pasture', 'p/pavilion', 'p/picnic_area', 'p/pier', 'p/pond', 'r/raft', 'r/railroad_track', - 'r/rainforest', 'r/rice_paddy', 'r/river', 'r/rock_arch', 'r/roof_garden', 'r/rope_bridge', - 'r/ruin', 's/schoolhouse', 's/sky', 's/snowfield', 's/swamp', 's/swimming_hole', - 's/synagogue/outdoor', 't/temple/asia', 't/topiary_garden', 't/tree_farm', 't/tree_house', - 'u/underwater/ocean_deep', 'u/utility_room', 'v/valley', 'v/vegetable_garden', 'v/viaduct', - 'v/village', 'v/vineyard', 'v/volcano', 'w/waterfall', 'w/watering_hole', 'w/wave', - 'w/wheat_field', 'z/zen_garden', 'a/alcove', 'a/apartment-building/outdoor', 'a/artists_loft', - 'b/building_facade', 'c/cemetery'] - - s_datapath = "D:\\F_Disk\\data_set\\Art_Data\\data_art_backup" - c_datapath = "D:\\Downloads\\data_large" - savepath = "D:\\PatchFace\\PleaseWork\\multi-style-gan\\StyleTransfer\\dataloader_test" - - imsize = 512 - s_datasetloader= getLoader(s_datapath,c_datapath, - style_class, categories_names, - crop_size=imsize, batch_size=16, num_workers=4) - wocao = iter(s_datasetloader) - for i in range(500): - print("new batch") - s_image,c_image,label = next(wocao) - print(label) - # print(label) - # saved_image1 = torch.cat([denorm(image.data),denorm(hahh.data)],3) - # save_image(denorm(image), "%s\\%d-label-%d.jpg"%(savepath,i), nrow=1, padding=1) - pass - # import cv2 - # import os - # for dir_item in categories_names: - # join_path = Path(contentdatapath,dir_item) - # if join_path.exists(): - # print("processing %s"%dir_item,end='\r') - # images = join_path.glob('*.%s'%("jpg")) - # for item in images: - # temp_path = str(item) - # # temp = cv2.imread(temp_path) - # temp = Image.open(temp_path) - # if temp.layers<3: - # print("remove broken image...") - # print("image name:%s"%temp_path) - # del temp - # os.remove(item) \ No newline at end of file diff --git a/data_tools/test_dataloader_dir.py b/data_tools/test_dataloader_dir.py deleted file mode 100644 index 34faac0..0000000 --- a/data_tools/test_dataloader_dir.py +++ /dev/null @@ -1,81 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: eval_dataloader_DIV2K.py -# Created Date: Tuesday January 12th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 12th October 2021 8:29:51 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -import os -import cv2 -import glob -import torch - -class TestDataset: - def __init__( self, - path, - batch_size = 16, - subffix=['png','jpg']): - """Initialize and preprocess the setX dataset.""" - self.path = path - - self.subffix = subffix - self.dataset = [] - self.pointer = 0 - self.batch_size = batch_size - self.__preprocess__() - self.num_images = len(self.dataset) - - def __preprocess__(self): - """Preprocess the SetX dataset.""" - - print("processing content images...") - for i_suf in self.subffix: - temp_path = os.path.join(self.path,'*.%s'%(i_suf)) - images = glob.glob(temp_path) - for item in images: - file_name = os.path.basename(item) - file_name = os.path.splitext(file_name) - file_name = file_name[0] - # lr_name = os.path.join(set5lr_path, file_name) - self.dataset.append([item,file_name]) - # self.dataset = images - print('Finished preprocessing the content dataset, total image number: %d...'%len(self.dataset)) - - def __call__(self): - """Return one batch images.""" - if self.pointer>=self.num_images: - self.pointer = 0 - a = "The end of the story!" - raise StopIteration(print(a)) - elif (self.pointer+self.batch_size) > self.num_images: - end = self.num_images - else: - end = self.pointer+self.batch_size - for i in range(self.pointer, end): - filename = self.dataset[i][0] - hr_img = cv2.imread(filename) - hr_img = cv2.cvtColor(hr_img,cv2.COLOR_BGR2RGB) - hr_img = hr_img.transpose((2,0,1))#.astype(np.float) - - hr_img = torch.from_numpy(hr_img) - hr_img = hr_img/255.0 - hr_img = 2 * (hr_img - 0.5) - if (i-self.pointer) == 0: - hr_ls = hr_img.unsqueeze(0) - nm_ls = [self.dataset[i][1],] - else: - hr_ls = torch.cat((hr_ls,hr_img.unsqueeze(0)),0) - nm_ls += [self.dataset[i][1],] - self.pointer = end - return hr_ls, nm_ls - - def __len__(self): - return self.num_images - - def __repr__(self): - return self.__class__.__name__ + ' (' + self.path + ')' \ No newline at end of file diff --git a/data_tools/vggface2hq_failed.json b/data_tools/vggface2hq_failed.json deleted file mode 100644 index 3b7ed18..0000000 --- a/data_tools/vggface2hq_failed.json +++ /dev/null @@ -1,67621 +0,0 @@ -{ - "n000002": [ - "0054_01.jpg" - ], - "n000004": [ - "0026_01.jpg", - "0084_01.jpg" - ], - "n000005": [ - "0138_01.jpg" - ], - "n000006": [ - "0014_01.jpg", - "0036_02.jpg", - "0091_01.jpg", - "0300_01.jpg", - "0519_01.jpg", - "0351_01.jpg" - ], - "n000007": [ - 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"n009265": [ - "0164_02.jpg" - ], - "n009266": [ - "0045_04.jpg", - "0174_01.jpg", - "0259_03.jpg", - "0259_03.jpg" - ], - "n009267": [ - "0003_03.jpg", - "0016_03.jpg", - "0299_01.jpg", - "0381_01.jpg" - ], - "n009268": [ - "0024_01.jpg", - "0219_01.jpg", - "0434_01.jpg" - ], - "n009269": [ - "0049_01.jpg", - "0355_01.jpg", - "0365_02.jpg" - ], - "n009270": [ - "0056_01.jpg", - "0076_02.jpg", - "0305_01.jpg" - ], - "n009271": [ - "0016_01.jpg", - "0140_01.jpg", - "0478_01.jpg", - "0505_01.jpg" - ], - "n009272": [ - "0176_01.jpg", - "0203_01.jpg" - ], - "n009273": [ - "0126_01.jpg", - "0294_01.jpg", - "0292_01.jpg", - "0262_01.jpg", - "0300_02.jpg", - "0308_01.jpg", - "0341_02.jpg", - "0407_01.jpg" - ], - "n009274": [ - "0047_01.jpg" - ], - "n009275": [ - "0050_02.jpg", - "0073_01.jpg" - ], - "n009278": [ - "0061_01.jpg" - ] -} \ No newline at end of file diff --git a/dataset.check.py b/dataset.check.py deleted file mode 100644 index e69de45..0000000 --- a/dataset.check.py +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: dataset.check.py -# Created Date: Sunday April 3rd 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 3rd April 2022 2:57:48 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import glob -from utilities.json_config import readConfig, writeConfig - -# dataset = "G:/VGGFace2-HQ/VGGface2_None_norm_512_true_bygfpgan" -# mask_dir= "G:/VGGFace2-HQ/VGGface2_HQ_original_aligned_mask" - -savePath = "./vggface2hq_failed.txt" -env_config = readConfig('env/env.json') -env_config = env_config["path"] -dataset = env_config["dataset_paths"]["vggface2_hq"]["images"] -mask_dir = env_config["dataset_paths"]["vggface2_hq"]["masks"] - -temp_path = os.path.join(dataset,'*/') -pathes = glob.glob(temp_path) -for dir_item in pathes: - join_path = glob.glob(os.path.join(dir_item,'*.jpg')) - print("processing %s"%dir_item,end='\r') - dir_path = os.path.dirname(join_path[1]) - dir_name = os.path.join(mask_dir, os.path.basename(dir_path)) - # print(dir_name) - temp_list = [] - for item in join_path: - img_name = os.path.basename(item) - img_name, _ = os.path.splitext(img_name) - mask_name = os.path.join(dir_name, img_name + ".png") - if not os.path.exists(mask_name): - print(mask_name) \ No newline at end of file diff --git a/dataset_readme.txt b/dataset_readme.txt deleted file mode 100644 index c2cc6db..0000000 --- a/dataset_readme.txt +++ /dev/null @@ -1,113 +0,0 @@ -ÕÅÓêç² --> 1 -ÁõÒà·Æ --> 2 -ÖܽÜÂ× --> 3 -¹ØÏþÍ® --> 4 -ÁÖÖ¾Áá --> 13 -103Ðíá°½âѹÃÜÂëcoshunter.top --> 18 -19 --> [YOUMI尤蜜èŸ] 2020.09.03 VOL.521 妲己_Toxic [61P508MB] -½ð³¿ --> 20 -ÈÝ×æ¶ù --> 21 -ÕÅì§ÒÕ --> 22 -ÑîÓ± --> 23 -ÄßÄÝ --> 24 - -G:/4K/90 Ù¡Àöæ« --> H:/face_data/VGGFace2_HQ/27 -273 ÕÔÔÁ --> 28 -G:/4K/291-²»Ö§³ÖÔÚÏß´ò¿ª£¬ÇëÏÂÔغó½âѹ«h¶ù«her --> H:/face_data/VGGFace2_HQ/29 -G:/4K/114 ÕÅ×ÏÄþ --> H:/face_data/VGGFace2_HQ/30 -G:/4K/120 Êæä¿ --> H:/face_data/VGGFace2_HQ/31 -G:/4K/124 ÕÅ×Ó·ã --> H:/face_data/VGGFace2_HQ/32 -G:/4K/173 ÍõÐÄÁè --> H:/face_data/VGGFace2_HQ/33 -G:/4K/191 ֣ˬ --> H:/face_data/VGGFace2_HQ/34 -G:/4K/286ÓÈÄÝË¿b/·ÀºÎзÎļþ¼Ð£¨ÎðÔÚÏß½âѹ£© ͼ°ü 02/·ÀºÎзÎļþ¼Ð£¨ÎðÔÚÏß½âѹ£© ͼ°ü 02 --> H:/face_data/VGGFace2_HQ/35 -G:/4K/ÐÁÜÆÀÙ --> H:/face_data/VGGFace2_HQ/36 -G:/4K/197 µË¼Ò¼Ñ --> H:/face_data/VGGFace2_HQ/37 -G:/4K/195 Ô¬æ©æ© --> H:/face_data/VGGFace2_HQ/38 -G:/4K/249 Ï£ÁÖÄÈÒÀ.¸ß --> H:/face_data/VGGFace2_HQ/39 -G:/4K/ËزĺÏ/ĸÆäÃÖÑÅԭƬËØ²Ä --> H:/face_data/VGGFace2_HQ/40 -G:/4K/ËزĺÏ/Õź¬ÔÏϵÁÐ --> H:/face_data/VGGFace2_HQ/41 -G:/4K/231ºú¾² --> H:/face_data/VGGFace2_HQ/42 -G:/4K/259 ÖܽàÇí --> H:/face_data/VGGFace2_HQ/43 -G:/4K/190 ÀîêÉ --> H:/face_data/VGGFace2_HQ/44 -G:/4K/193 ÕÅÃÊ --> H:/face_data/VGGFace2_HQ/45 -G:/4K/174 ÅËÖ®ÁÕ --> H:/face_data/VGGFace2_HQ/46 -G:/4K/196 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H:/face_data/VGGFace2_HQ/65 -G:/4K/˽¶à¸öÎļþ/ԭƬ22 --> H:/face_data/hg -G:/4K/˽¶à¸öÎļþ/ԭƬ22 --> H:/face_data/hg1 -G:/4K/ÕÅö¦Ó± --> H:/face_data/VGGFace2_HQ/65 -G:/4K/Á¹¸ß¶¨×± --> H:/face_data/VGGFace2_HQ/66 -G:/4K/ÁÖÖ¾Áá-³õÕû --> H:/face_data/VGGFace2_HQ/67 -G:/4K/000δÕûÀí/ÕÅÐ¡ì³ --> H:/face_data/VGGFace2_HQ/68 -G:/4K/000δÕûÀí/Âí˼´¿ÏµÁÐ1 --> H:/face_data/VGGFace2_HQ/69 -G:/4K/000δÕûÀí/ÍõÀöÀ¤ --> H:/face_data/VGGFace2_HQ/70 -G:/4K/000δÕûÀí/ÁõÌÎÈ«²¿ --> H:/face_data/VGGFace2_HQ/71 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/ÕÅ×Ó·ã --> H:/face_data/VGGFace2_HQ/72 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/ÀîÐ¡è´ --> H:/face_data/VGGFace2_HQ/73 -G:/4K/295-²»Ö§³ÖÔÚÏß´ò¿ª£¬ÇëÏÂÔغó½âѹ¾µ½´ --> H:/face_data/VGGFace2_HQ/74 -G:/4K/297-²»Ö§³ÖÔÚÏß´ò¿ª£¬ÇëÏÂÔغó½âѹһֻÔÆÉÕ --> H:/face_data/VGGFace2_HQ/74 -G:/4K/295-²»Ö§³ÖÔÚÏß´ò¿ª£¬ÇëÏÂÔغó½âѹ¾µ½´ --> H:/face_data/VGGFace2_HQ/75 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/º£±¨ÒÕÈË¿ÉÓÃËزÄ/ÀîÊÀÅô¿ÉÓà --> H:/face_data/VGGFace2_HQ/76 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/º£±¨ÒÕÈË¿ÉÓÃËزÄ/Àî·Æ¶ù¿ÉÓà --> H:/face_data/VGGFace2_HQ/77 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/³ÌÑâÇï --> H:/face_data/VGGFace2_HQ/78 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/°×ÖÛÏÄÂÌ --> H:/face_data/VGGFace2_HQ/79 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/¹û¹û°×ÖÛÏÄÂÌ --> H:/face_data/VGGFace2_HQ/80 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/ÌÕÎ÷°²Ú× --> H:/face_data/VGGFace2_HQ/81 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/×æ¶ù×Ï·ã --> H:/face_data/VGGFace2_HQ/82 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/º£±¨±¸·Ý/×æ¶ù×Ï·ã2 --> H:/face_data/VGGFace2_HQ/83 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ0905-0912 --> H:/face_data/VGGFace2_HQ/84 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ0913-0922 --> H:/face_data/VGGFace2_HQ/85 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ0923-1004 --> H:/face_data/VGGFace2_HQ/86 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ1005-1010 --> H:/face_data/VGGFace2_HQ/87 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ1011-1017 --> H:/face_data/VGGFace2_HQ/88 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ1018-1101 --> H:/face_data/VGGFace2_HQ/89 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ1102-1114 --> H:/face_data/VGGFace2_HQ/90 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ1115-1122 --> H:/face_data/VGGFace2_HQ/91 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ1123-1204 --> H:/face_data/VGGFace2_HQ/92 -G:/4K/000δÕûÀí/´ò°üÇø/ÀîСè´ËÎ×æ¶ùÕÅ×Ó·ãµÈ´ò°ü/¾çÕÕ1205-1212 --> H:/face_data/VGGFace2_HQ/93 -G:/4K/000δÕûÀí/»ÆÞÈ1 --> H:/face_data/VGGFace2_HQ/94 -G:/4K/000δÕûÀí/ãÛÇå×Ó --> H:/face_data/VGGFace2_HQ/95 -G:/4K/000δÕûÀí/ºÎËëÈÈ¿ãÀîÐ¡è´µÈ --> H:/face_data/VGGFace2_HQ/96 -G:/4K/000δÕûÀí/¾°ÌðÈÈ¿ãÓ¾³Ø --> H:/face_data/VGGFace2_HQ/97 -H:/face_data/VGGFace2_HQ/77 --> H:/face_data/VGGFace2_HQ/98 -G:/4K/16 ÎâÓ³½à¿Õ½ãÖÆ·þ --> H:/face_data/VGGFace2_HQ/98 -G:/4K/2 --> H:/face_data/VGGFace2_HQ/99 -G:/4K/108 ÕÅÉغ­ --> H:/face_data/VGGFace2_HQ\108 ÕÅÉغ­ -G:/4K/110 ÕÅÐ¡ì³ --> H:/face_data/VGGFace2_HQ\110 ÕÅÐ¡ì³ -G:/4K/111 ÕÅÐ¡ì³ --> H:/face_data/VGGFace2_HQ\111 ÕÅÐ¡ì³ -G:/4K/119 ËÎÜç --> H:/face_data/VGGFace2_HQ\119 ËÎÜç -G:/4K/120 Êæä¿ --> H:/face_data/VGGFace2_HQ\120 Êæä¿ -G:/4K/125 Êæ俺ϼ¯ --> H:/face_data/VGGFace2_HQ\125 Êæ俺ϼ¯ -G:/4K/¸÷µØËØÈËÅÄÉã/н¨Îļþ¼Ð (6) --> H:/face_data/VGGFace2_HQ\н¨Îļþ¼Ð (6) -G:/4K/¸÷µØËØÈËÅÄÉã/ԭƬ(6) --> H:/face_data/VGGFace2_HQ\ԭƬ(6) -G:/4K/¸÷µØËØÈËÅÄÉã/20161113Îè¾çϵÅÄÉãjpg --> H:/face_data/VGGFace2_HQ\20161113Îè¾çϵÅÄÉãjpg -G:/4K/¸÷µØËØÈËÅÄÉã/155 --> H:/face_data/VGGFace2_HQ\155 -G:/4K/¸÷µØËØÈËÅÄÉã/17 --> H:/face_data/VGGFace2_HQ\17 -G:/4K/249Áõ·Éer½âѹÃÜÂëcoshunter.com»òcoshunter.top --> H:/face_data/VGGFace2_HQ\249Áõ·Éer½âѹÃÜÂëcoshunter.com»òcoshunter -H:/face_data/cw --> H:/face_data/VGGFace2_HQ\cw -H:/face_data/cw --> H:/face_data/VGGFace2_HQ\cw -G:/4K/ÁøÑÒÅïÅÄ --> H:/face_data/VGGFace2_HQ\ÁøÑÒÅïÅÄ -G:/4K/216 ¬¾¸æ© --> H:/face_data/VGGFace2_HQ\216 ¬¾¸æ© -G:/4K/217 ¬¾¸æ© --> H:/face_data/VGGFace2_HQ\217 ¬¾¸æ© -G:/4K/°×°ÙºÏ --> H:/face_data/VGGFace2_HQ\°×°ÙºÏ -G:/4K/°×°ÙºÎ --> H:/face_data/VGGFace2_HQ\°×°ÙºÎ -G:/4K/ÕÅÓêç² --> H:/face_data/VGGFace2_HQ\ÕÅÓêç² -G:/4K/179 ·ëн¶ä --> H:/face_data/VGGFace2_HQ\179 ·ëн¶ä diff --git a/detection_test.py b/detection_test.py deleted file mode 100644 index 1d5c2c7..0000000 --- a/detection_test.py +++ /dev/null @@ -1,20 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: detection_test.py -# Created Date: Tuesday February 15th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 15th February 2022 10:31:52 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -from insightface.app import FaceAnalysis - -import insightface - -if __name__ == "__main__": - app = FaceAnalysis(allowed_modules=['detection']) # enable detection model only - app.prepare(ctx_id=0, det_size=(640, 640)) - \ No newline at end of file diff --git a/dnnlib/__init__.py b/dnnlib/__init__.py deleted file mode 100644 index e7423bf..0000000 --- a/dnnlib/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -from .util import EasyDict, make_cache_dir_path diff --git a/dnnlib/util.py b/dnnlib/util.py deleted file mode 100644 index 6bbdf3b..0000000 --- a/dnnlib/util.py +++ /dev/null @@ -1,491 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Miscellaneous utility classes and functions.""" - -import ctypes -import fnmatch -import importlib -import inspect -import numpy as np -import os -import shutil -import sys -import types -import io -import pickle -import re -import requests -import html -import hashlib -import glob -import tempfile -import urllib -import urllib.request -import uuid - -from distutils.util import strtobool -from typing import Any, List, Tuple, Union - - -# Util classes -# ------------------------------------------------------------------------------------------ - - -class EasyDict(dict): - """Convenience class that behaves like a dict but allows access with the attribute syntax.""" - - def __getattr__(self, name: str) -> Any: - try: - return self[name] - except KeyError: - raise AttributeError(name) - - def __setattr__(self, name: str, value: Any) -> None: - self[name] = value - - def __delattr__(self, name: str) -> None: - del self[name] - - -class Logger(object): - """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" - - def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True): - self.file = None - - if file_name is not None: - self.file = open(file_name, file_mode) - - self.should_flush = should_flush - self.stdout = sys.stdout - self.stderr = sys.stderr - - sys.stdout = self - sys.stderr = self - - def __enter__(self) -> "Logger": - return self - - def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: - self.close() - - def write(self, text: Union[str, bytes]) -> None: - """Write text to stdout (and a file) and optionally flush.""" - if isinstance(text, bytes): - text = text.decode() - if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash - return - - if self.file is not None: - self.file.write(text) - - self.stdout.write(text) - - if self.should_flush: - self.flush() - - def flush(self) -> None: - """Flush written text to both stdout and a file, if open.""" - if self.file is not None: - self.file.flush() - - self.stdout.flush() - - def close(self) -> None: - """Flush, close possible files, and remove stdout/stderr mirroring.""" - self.flush() - - # if using multiple loggers, prevent closing in wrong order - if sys.stdout is self: - sys.stdout = self.stdout - if sys.stderr is self: - sys.stderr = self.stderr - - if self.file is not None: - self.file.close() - self.file = None - - -# Cache directories -# ------------------------------------------------------------------------------------------ - -_dnnlib_cache_dir = None - -def set_cache_dir(path: str) -> None: - global _dnnlib_cache_dir - _dnnlib_cache_dir = path - -def make_cache_dir_path(*paths: str) -> str: - if _dnnlib_cache_dir is not None: - return os.path.join(_dnnlib_cache_dir, *paths) - if 'DNNLIB_CACHE_DIR' in os.environ: - return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths) - if 'HOME' in os.environ: - return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths) - if 'USERPROFILE' in os.environ: - return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths) - return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths) - -# Small util functions -# ------------------------------------------------------------------------------------------ - - -def format_time(seconds: Union[int, float]) -> str: - """Convert the seconds to human readable string with days, hours, minutes and seconds.""" - s = int(np.rint(seconds)) - - if s < 60: - return "{0}s".format(s) - elif s < 60 * 60: - return "{0}m {1:02}s".format(s // 60, s % 60) - elif s < 24 * 60 * 60: - return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) - else: - return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60) - - -def format_time_brief(seconds: Union[int, float]) -> str: - """Convert the seconds to human readable string with days, hours, minutes and seconds.""" - s = int(np.rint(seconds)) - - if s < 60: - return "{0}s".format(s) - elif s < 60 * 60: - return "{0}m {1:02}s".format(s // 60, s % 60) - elif s < 24 * 60 * 60: - return "{0}h {1:02}m".format(s // (60 * 60), (s // 60) % 60) - else: - return "{0}d {1:02}h".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24) - - -def ask_yes_no(question: str) -> bool: - """Ask the user the question until the user inputs a valid answer.""" - while True: - try: - print("{0} [y/n]".format(question)) - return strtobool(input().lower()) - except ValueError: - pass - - -def tuple_product(t: Tuple) -> Any: - """Calculate the product of the tuple elements.""" - result = 1 - - for v in t: - result *= v - - return result - - -_str_to_ctype = { - "uint8": ctypes.c_ubyte, - "uint16": ctypes.c_uint16, - "uint32": ctypes.c_uint32, - "uint64": ctypes.c_uint64, - "int8": ctypes.c_byte, - "int16": ctypes.c_int16, - "int32": ctypes.c_int32, - "int64": ctypes.c_int64, - "float32": ctypes.c_float, - "float64": ctypes.c_double -} - - -def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: - """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" - type_str = None - - if isinstance(type_obj, str): - type_str = type_obj - elif hasattr(type_obj, "__name__"): - type_str = type_obj.__name__ - elif hasattr(type_obj, "name"): - type_str = type_obj.name - else: - raise RuntimeError("Cannot infer type name from input") - - assert type_str in _str_to_ctype.keys() - - my_dtype = np.dtype(type_str) - my_ctype = _str_to_ctype[type_str] - - assert my_dtype.itemsize == ctypes.sizeof(my_ctype) - - return my_dtype, my_ctype - - -def is_pickleable(obj: Any) -> bool: - try: - with io.BytesIO() as stream: - pickle.dump(obj, stream) - return True - except: - return False - - -# Functionality to import modules/objects by name, and call functions by name -# ------------------------------------------------------------------------------------------ - -def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: - """Searches for the underlying module behind the name to some python object. - Returns the module and the object name (original name with module part removed).""" - - # allow convenience shorthands, substitute them by full names - obj_name = re.sub("^np.", "numpy.", obj_name) - obj_name = re.sub("^tf.", "tensorflow.", obj_name) - - # list alternatives for (module_name, local_obj_name) - parts = obj_name.split(".") - name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] - - # try each alternative in turn - for module_name, local_obj_name in name_pairs: - try: - module = importlib.import_module(module_name) # may raise ImportError - get_obj_from_module(module, local_obj_name) # may raise AttributeError - return module, local_obj_name - except: - pass - - # maybe some of the modules themselves contain errors? - for module_name, _local_obj_name in name_pairs: - try: - importlib.import_module(module_name) # may raise ImportError - except ImportError: - if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): - raise - - # maybe the requested attribute is missing? - for module_name, local_obj_name in name_pairs: - try: - module = importlib.import_module(module_name) # may raise ImportError - get_obj_from_module(module, local_obj_name) # may raise AttributeError - except ImportError: - pass - - # we are out of luck, but we have no idea why - raise ImportError(obj_name) - - -def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: - """Traverses the object name and returns the last (rightmost) python object.""" - if obj_name == '': - return module - obj = module - for part in obj_name.split("."): - obj = getattr(obj, part) - return obj - - -def get_obj_by_name(name: str) -> Any: - """Finds the python object with the given name.""" - module, obj_name = get_module_from_obj_name(name) - return get_obj_from_module(module, obj_name) - - -def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: - """Finds the python object with the given name and calls it as a function.""" - assert func_name is not None - func_obj = get_obj_by_name(func_name) - assert callable(func_obj) - return func_obj(*args, **kwargs) - - -def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any: - """Finds the python class with the given name and constructs it with the given arguments.""" - return call_func_by_name(*args, func_name=class_name, **kwargs) - - -def get_module_dir_by_obj_name(obj_name: str) -> str: - """Get the directory path of the module containing the given object name.""" - module, _ = get_module_from_obj_name(obj_name) - return os.path.dirname(inspect.getfile(module)) - - -def is_top_level_function(obj: Any) -> bool: - """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" - return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ - - -def get_top_level_function_name(obj: Any) -> str: - """Return the fully-qualified name of a top-level function.""" - assert is_top_level_function(obj) - module = obj.__module__ - if module == '__main__': - module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] - return module + "." + obj.__name__ - - -# File system helpers -# ------------------------------------------------------------------------------------------ - -def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]: - """List all files recursively in a given directory while ignoring given file and directory names. - Returns list of tuples containing both absolute and relative paths.""" - assert os.path.isdir(dir_path) - base_name = os.path.basename(os.path.normpath(dir_path)) - - if ignores is None: - ignores = [] - - result = [] - - for root, dirs, files in os.walk(dir_path, topdown=True): - for ignore_ in ignores: - dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] - - # dirs need to be edited in-place - for d in dirs_to_remove: - dirs.remove(d) - - files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] - - absolute_paths = [os.path.join(root, f) for f in files] - relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] - - if add_base_to_relative: - relative_paths = [os.path.join(base_name, p) for p in relative_paths] - - assert len(absolute_paths) == len(relative_paths) - result += zip(absolute_paths, relative_paths) - - return result - - -def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: - """Takes in a list of tuples of (src, dst) paths and copies files. - Will create all necessary directories.""" - for file in files: - target_dir_name = os.path.dirname(file[1]) - - # will create all intermediate-level directories - if not os.path.exists(target_dir_name): - os.makedirs(target_dir_name) - - shutil.copyfile(file[0], file[1]) - - -# URL helpers -# ------------------------------------------------------------------------------------------ - -def is_url(obj: Any, allow_file_urls: bool = False) -> bool: - """Determine whether the given object is a valid URL string.""" - if not isinstance(obj, str) or not "://" in obj: - return False - if allow_file_urls and obj.startswith('file://'): - return True - try: - res = requests.compat.urlparse(obj) - if not res.scheme or not res.netloc or not "." in res.netloc: - return False - res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) - if not res.scheme or not res.netloc or not "." in res.netloc: - return False - except: - return False - return True - - -def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any: - """Download the given URL and return a binary-mode file object to access the data.""" - assert num_attempts >= 1 - assert not (return_filename and (not cache)) - - # Doesn't look like an URL scheme so interpret it as a local filename. - if not re.match('^[a-z]+://', url): - return url if return_filename else open(url, "rb") - - # Handle file URLs. This code handles unusual file:// patterns that - # arise on Windows: - # - # file:///c:/foo.txt - # - # which would translate to a local '/c:/foo.txt' filename that's - # invalid. Drop the forward slash for such pathnames. - # - # If you touch this code path, you should test it on both Linux and - # Windows. - # - # Some internet resources suggest using urllib.request.url2pathname() but - # but that converts forward slashes to backslashes and this causes - # its own set of problems. - if url.startswith('file://'): - filename = urllib.parse.urlparse(url).path - if re.match(r'^/[a-zA-Z]:', filename): - filename = filename[1:] - return filename if return_filename else open(filename, "rb") - - assert is_url(url) - - # Lookup from cache. - if cache_dir is None: - cache_dir = make_cache_dir_path('downloads') - - url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() - if cache: - cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) - if len(cache_files) == 1: - filename = cache_files[0] - return filename if return_filename else open(filename, "rb") - - # Download. - url_name = None - url_data = None - with requests.Session() as session: - if verbose: - print("Downloading %s ..." % url, end="", flush=True) - for attempts_left in reversed(range(num_attempts)): - try: - with session.get(url) as res: - res.raise_for_status() - if len(res.content) == 0: - raise IOError("No data received") - - if len(res.content) < 8192: - content_str = res.content.decode("utf-8") - if "download_warning" in res.headers.get("Set-Cookie", ""): - links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] - if len(links) == 1: - url = requests.compat.urljoin(url, links[0]) - raise IOError("Google Drive virus checker nag") - if "Google Drive - Quota exceeded" in content_str: - raise IOError("Google Drive download quota exceeded -- please try again later") - - match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) - url_name = match[1] if match else url - url_data = res.content - if verbose: - print(" done") - break - except KeyboardInterrupt: - raise - except: - if not attempts_left: - if verbose: - print(" failed") - raise - if verbose: - print(".", end="", flush=True) - - # Save to cache. - if cache: - safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) - cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) - temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) - os.makedirs(cache_dir, exist_ok=True) - with open(temp_file, "wb") as f: - f.write(url_data) - os.replace(temp_file, cache_file) # atomic - if return_filename: - return cache_file - - # Return data as file object. - assert not return_filename - return io.BytesIO(url_data) diff --git a/env/env.json b/env/env.json deleted file mode 100644 index 7cfd8fe..0000000 --- a/env/env.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "path":{ - "train_log_root":"./train_logs", - "test_log_root":"./test_logs", - "systemLog":"./system/system_log.log", - "dataset_paths": { - "vggface2_hq": "G:/VGGFace2-HQ/VGGface2_None_norm_512_true_bygfpgan", - "val_dataset_root": "", - "test_dataset_root": "", - "id_pose_source_root": "", - "id_pose_target_root": "" - }, - "train_config_path":"./train_yamls", - "train_scripts_path":"./train_scripts", - "test_scripts_path":"./test_scripts", - "config_json_name":"model_config.json", - "machine_config":"./GUI/machines.json" - } -} \ No newline at end of file diff --git a/face_crop.py b/face_crop.py deleted file mode 100644 index eafe538..0000000 --- a/face_crop.py +++ /dev/null @@ -1,306 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: face_crop.py -# Created Date: Tuesday February 1st 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 24th April 2022 2:01:47 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import cv2 -import sys -import glob -import json -import tkinter -from tkinter.filedialog import askdirectory - -import threading -import tkinter as tk -import tkinter.ttk as ttk - -import subprocess -from pathlib import Path - -import numpy as np - -from insightface_func.face_detect_crop_multi import Face_detect_crop - -class TextRedirector(object): - def __init__(self, widget, tag="stdout"): - self.widget = widget - self.tag = tag - - def write(self, str): - self.widget.configure(state="normal") - self.widget.insert("end", str, (self.tag,)) - self.widget.configure(state="disabled") - self.widget.see(tk.END) - - def flush(self): - pass - -############################################################# -# Main Class -############################################################# - -class Application(tk.Frame): - - - def __init__(self, master=None): - tk.Frame.__init__(self, master,bg='black') - # self.font_size = 16 - self.font_list = ("Times New Roman",14) - self.padx = 5 - self.pady = 5 - self.window_init() - - def __label_text__(self, usr, root): - return "User Name: %s\nWorkspace: %s"%(usr, root) - - def window_init(self): - cwd = os.getcwd() - self.master.title('Face Crop - %s'%cwd) - # self.master.iconbitmap('./utilities/_logo.ico') - self.master.geometry("{}x{}".format(640, 600)) - - font_list = self.font_list - - ################################################################################################# - list_frame = tk.Frame(self.master) - list_frame.pack(fill="both", padx=5,pady=5) - list_frame.columnconfigure(0, weight=1) - list_frame.columnconfigure(1, weight=1) - list_frame.columnconfigure(2, weight=1) - - self.img_path = tkinter.StringVar() - - tk.Label(list_frame, text="Image/Video Path:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - - tk.Entry(list_frame, textvariable= self.img_path, font=font_list)\ - .grid(row=0,column=1,sticky=tk.EW) - - - tk.Button(list_frame, text = "Select Path", font=font_list, - command = self.Select, bg='#F4A460', fg='#F5F5F5')\ - .grid(row=0,column=2,sticky=tk.EW) - ################################################################################################# - list_frame1 = tk.Frame(self.master) - list_frame1.pack(fill="both", padx=5,pady=5) - list_frame1.columnconfigure(0, weight=1) - list_frame1.columnconfigure(1, weight=1) - list_frame1.columnconfigure(2, weight=1) - - self.save_path = tkinter.StringVar() - - tk.Label(list_frame1, text="Target Path:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - - tk.Entry(list_frame1, textvariable= self.save_path, font=font_list)\ - .grid(row=0,column=1,sticky=tk.EW) - - - tk.Button(list_frame1, text = "Select Path", font=font_list, - command = self.Select_Target, bg='#F4A460', fg='#F5F5F5')\ - .grid(row=0,column=2,sticky=tk.EW) - - ################################################################################################# - label_frame = tk.Frame(self.master) - label_frame.pack(fill="both", padx=5,pady=5) - label_frame.columnconfigure(0, weight=1) - label_frame.columnconfigure(1, weight=1) - label_frame.columnconfigure(2, weight=1) - label_frame.columnconfigure(3, weight=1) - - tk.Label(label_frame, text="Crop Size:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - - tk.Label(label_frame, text="Align Mode:",font=font_list,justify="left")\ - .grid(row=0,column=1,sticky=tk.EW) - - tk.Label(label_frame, text="Target Format:",font=font_list,justify="left")\ - .grid(row=0,column=2,sticky=tk.EW) - - tk.Label(label_frame, text="Blurry Thredhold:",font=font_list,justify="left")\ - .grid(row=0,column=3,sticky=tk.EW) - - ################################################################################################# - - test_frame = tk.Frame(self.master) - test_frame.pack(fill="both", padx=5,pady=5) - test_frame.columnconfigure(0, weight=1) - test_frame.columnconfigure(1, weight=1) - test_frame.columnconfigure(2, weight=1) - - self.test_var = tkinter.StringVar() - - self.test_com = ttk.Combobox(test_frame, textvariable=self.test_var) - self.test_com.grid(row=0,column=0,sticky=tk.EW) - self.test_com["value"] = [256,512,768,1024] - self.test_com.current(1) - - self.align_var = tkinter.StringVar() - self.align_com = ttk.Combobox(test_frame, textvariable=self.align_var) - self.align_com.grid(row=0,column=1,sticky=tk.EW) - self.align_com["value"] = ["VGGFace","ffhq"] - self.align_com.current(0) - - self.format_var = tkinter.StringVar() - - self.format_com = ttk.Combobox(test_frame, textvariable=self.format_var) - self.format_com.grid(row=0,column=2,sticky=tk.EW) - self.format_com["value"] = ["png","jpg"] - self.format_com.current(0) - - self.thredhold = tkinter.StringVar() - tk.Entry(test_frame, textvariable= self.thredhold, font=font_list)\ - .grid(row=0,column=3,sticky=tk.EW) - self.thredhold.set("70") - ################################################################################################# - scale_frame = tk.Frame(self.master) - scale_frame.pack(fill="both", padx=5,pady=5) - scale_frame.columnconfigure(0, weight=2) - label_frame.columnconfigure(1, weight=1) - # label_frame.columnconfigure(2, weight=1) - - tk.Label(scale_frame, text="Min Size:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - self.min_scale = tkinter.StringVar() - tk.Scale(scale_frame, from_=0.5, to=2.0, length=500, orient=tk.HORIZONTAL, variable= self.min_scale,\ - font=font_list, resolution=0.1).grid(row=0,column=1,sticky=tk.EW) - - ################################################################################################# - test_frame1 = tk.Frame(self.master) - test_frame1.pack(fill="both", padx=5,pady=5) - test_frame1.columnconfigure(0, weight=1) - # test_frame1.columnconfigure(1, weight=1) - - test_update_button = tk.Button(test_frame1, text = "Crop", - font=font_list, command = self.Crop, bg='#F4A460', fg='#F5F5F5') - test_update_button.grid(row=0,column=0,sticky=tk.EW) - - - - ################################################################################################# - - text = tk.Text(self.master, wrap="word") - text.pack(fill="both",expand="yes", padx=5,pady=5) - - - sys.stdout = TextRedirector(text, "stdout") - - self.init_algorithm() - self.master.protocol("WM_DELETE_WINDOW", self.on_closing) - - def init_algorithm(self): - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - - - # def __scaning_logs__(self): - def Select(self): - thread_update = threading.Thread(target=self.select_task) - thread_update.start() - - def select_task(self): - path = askdirectory() - - if os.path.isdir(path): - print("Selected source directory: %s"%path) - self.img_path.set(path) - - def Select_Target(self): - thread_update = threading.Thread(target=self.select_target_task) - thread_update.start() - - def select_target_task(self): - path = askdirectory() - if os.path.isdir(path): - print("Selected target directory: %s"%path) - self.save_path.set(path) - - def Crop(self): - thread_update = threading.Thread(target=self.crop_task) - thread_update.start() - - def crop_task(self): - mode = self.align_com.get() - if mode == "VGGFace": - mode = "None" - crop_size = int(self.test_com.get()) - - path = self.img_path.get() - tg_path = self.save_path.get() - blur_t = self.thredhold.get() - basepath = os.path.splitext(os.path.basename(path))[0] - tg_path = os.path.join("H:/face_data/VGGFace2_HQ",basepath) - print("target path: ",tg_path) - if not os.path.exists(tg_path): - os.makedirs(tg_path) - tg_format = self.format_com.get() - min_scale = float(self.min_scale.get()) - blur_t = float(blur_t) - print("Blurry thredhold %f"%blur_t) - self.detect.prepare(ctx_id = 0, det_thresh=0.6,\ - det_size=(640,640),mode = mode,crop_size=crop_size,ratio=min_scale) - log_file = "./dataset_readme.txt" - with open(log_file,'a+') as logf: # ,encoding='UTF-8' - logf.writelines("%s --> %s\n"%(path,tg_path)) - if path and tg_path: - imgs_list = [] - if os.path.isdir(path): - print("Input a dir....") - # imgs = glob.glob(os.path.join(path,"**")) - for item in glob.iglob(os.path.join(path,"**"),recursive=True): - imgs_list.append(item) - # print(imgs_list) - index = 0 - for img in imgs_list: - print(img) - try: - attr_img_ori = cv2.imdecode(np.fromfile(img, dtype=np.uint8),-1) - except: - print("Illegal file!") - continue - # attr_img_ori= cv2.imread(img) - try: - attr_img_align_crop, _ = self.detect.get(attr_img_ori) - sub_index = 0 - if len(attr_img_align_crop) < 1: - print("Small face") - for face_i in attr_img_align_crop: - imageVar = cv2.Laplacian(face_i, cv2.CV_64F).var() - f_path =os.path.join(tg_path, str(index).zfill(6)+"_%d.%s"%(sub_index,tg_format)) - # print("save path: ",f_path) - if imageVar < blur_t: - print("Over blurry image!") - continue - # face_i = cv2.putText(face_i, '%.1f'%imageVar,(50, 50), font, 0.8, (15, 9, 255), 2) - # cv2.imwrite(f_path,face_i) - cv2.imencode('.png',face_i)[1].tofile(f_path) - sub_index += 1 - index += 1 - except: - print("Detect no face!") - continue - else: - print("Input an image....") - imgs_list.append(path) - print("Process finished!") - else: - print("Pathes are invalid!") - - def on_closing(self): - - # self.__save_config__() - self.master.destroy() - - - -if __name__ == "__main__": - app = Application() - app.mainloop() \ No newline at end of file diff --git a/face_crop_record.py b/face_crop_record.py deleted file mode 100644 index 5e32aaa..0000000 --- a/face_crop_record.py +++ /dev/null @@ -1,279 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: face_crop.py -# Created Date: Tuesday February 1st 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 22nd April 2022 8:43:40 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import cv2 -import sys -import glob -import json -import tkinter -from tkinter.filedialog import askdirectory - -import threading -import tkinter as tk -import tkinter.ttk as ttk - -import subprocess -from pathlib import Path - -from insightface_func.face_detect_crop_multi import Face_detect_crop - -class TextRedirector(object): - def __init__(self, widget, tag="stdout"): - self.widget = widget - self.tag = tag - - def write(self, str): - self.widget.configure(state="normal") - self.widget.insert("end", str, (self.tag,)) - self.widget.configure(state="disabled") - self.widget.see(tk.END) - - def flush(self): - pass - -############################################################# -# Main Class -############################################################# - -class Application(tk.Frame): - - - def __init__(self, master=None): - tk.Frame.__init__(self, master,bg='black') - # self.font_size = 16 - self.font_list = ("Times New Roman",14) - self.padx = 5 - self.pady = 5 - self.window_init() - - def __label_text__(self, usr, root): - return "User Name: %s\nWorkspace: %s"%(usr, root) - - def window_init(self): - cwd = os.getcwd() - self.master.title('Face Crop - %s'%cwd) - # self.master.iconbitmap('./utilities/_logo.ico') - self.master.geometry("{}x{}".format(640, 600)) - - font_list = self.font_list - - ################################################################################################# - list_frame = tk.Frame(self.master) - list_frame.pack(fill="both", padx=5,pady=5) - list_frame.columnconfigure(0, weight=1) - list_frame.columnconfigure(1, weight=1) - list_frame.columnconfigure(2, weight=1) - - self.img_path = tkinter.StringVar() - - tk.Label(list_frame, text="Image/Video Path:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - - tk.Entry(list_frame, textvariable= self.img_path, font=font_list)\ - .grid(row=0,column=1,sticky=tk.EW) - - - tk.Button(list_frame, text = "Select Path", font=font_list, - command = self.Select, bg='#F4A460', fg='#F5F5F5')\ - .grid(row=0,column=2,sticky=tk.EW) - ################################################################################################# - list_frame1 = tk.Frame(self.master) - list_frame1.pack(fill="both", padx=5,pady=5) - list_frame1.columnconfigure(0, weight=1) - list_frame1.columnconfigure(1, weight=1) - list_frame1.columnconfigure(2, weight=1) - - self.save_path = tkinter.StringVar() - - tk.Label(list_frame1, text="Target Path:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - - tk.Entry(list_frame1, textvariable= self.save_path, font=font_list)\ - .grid(row=0,column=1,sticky=tk.EW) - - - tk.Button(list_frame1, text = "Select Path", font=font_list, - command = self.Select_Target, bg='#F4A460', fg='#F5F5F5')\ - .grid(row=0,column=2,sticky=tk.EW) - - ################################################################################################# - label_frame = tk.Frame(self.master) - label_frame.pack(fill="both", padx=5,pady=5) - label_frame.columnconfigure(0, weight=1) - label_frame.columnconfigure(1, weight=1) - label_frame.columnconfigure(2, weight=1) - - tk.Label(label_frame, text="Crop Size:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - - tk.Label(label_frame, text="Align Mode:",font=font_list,justify="left")\ - .grid(row=0,column=1,sticky=tk.EW) - - tk.Label(label_frame, text="Target Format:",font=font_list,justify="left")\ - .grid(row=0,column=2,sticky=tk.EW) - - ################################################################################################# - - test_frame = tk.Frame(self.master) - test_frame.pack(fill="both", padx=5,pady=5) - test_frame.columnconfigure(0, weight=1) - test_frame.columnconfigure(1, weight=1) - test_frame.columnconfigure(2, weight=1) - - self.test_var = tkinter.StringVar() - - self.test_com = ttk.Combobox(test_frame, textvariable=self.test_var) - self.test_com.grid(row=0,column=0,sticky=tk.EW) - self.test_com["value"] = [256,512,768,1024] - self.test_com.current(1) - - self.align_var = tkinter.StringVar() - self.align_com = ttk.Combobox(test_frame, textvariable=self.align_var) - self.align_com.grid(row=0,column=1,sticky=tk.EW) - self.align_com["value"] = ["VGGFace","ffhq"] - self.align_com.current(0) - - self.format_var = tkinter.StringVar() - - self.format_com = ttk.Combobox(test_frame, textvariable=self.format_var) - self.format_com.grid(row=0,column=2,sticky=tk.EW) - self.format_com["value"] = ["png","jpg"] - self.format_com.current(0) - - - - ################################################################################################# - scale_frame = tk.Frame(self.master) - scale_frame.pack(fill="both", padx=5,pady=5) - scale_frame.columnconfigure(0, weight=2) - label_frame.columnconfigure(1, weight=1) - # label_frame.columnconfigure(2, weight=1) - - tk.Label(scale_frame, text="Min Size:",font=font_list,justify="left")\ - .grid(row=0,column=0,sticky=tk.EW) - self.min_scale = tkinter.StringVar() - tk.Scale(scale_frame, from_=0.5, to=2.0, length=500, orient=tk.HORIZONTAL, variable= self.min_scale,\ - font=font_list, resolution=0.1).grid(row=0,column=1,sticky=tk.EW) - - ################################################################################################# - test_frame1 = tk.Frame(self.master) - test_frame1.pack(fill="both", padx=5,pady=5) - test_frame1.columnconfigure(0, weight=1) - # test_frame1.columnconfigure(1, weight=1) - - test_update_button = tk.Button(test_frame1, text = "Crop", - font=font_list, command = self.Crop, bg='#F4A460', fg='#F5F5F5') - test_update_button.grid(row=0,column=0,sticky=tk.EW) - - - - ################################################################################################# - - text = tk.Text(self.master, wrap="word") - text.pack(fill="both",expand="yes", padx=5,pady=5) - - - sys.stdout = TextRedirector(text, "stdout") - - self.init_algorithm() - self.master.protocol("WM_DELETE_WINDOW", self.on_closing) - - def init_algorithm(self): - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - - - # def __scaning_logs__(self): - def Select(self): - thread_update = threading.Thread(target=self.select_task) - thread_update.start() - - def select_task(self): - path = askdirectory() - print("Selected source directory: %s"%path) - self.img_path.set(path) - - def Select_Target(self): - thread_update = threading.Thread(target=self.select_target_task) - thread_update.start() - - def select_target_task(self): - path = askdirectory() - print("Selected target directory: %s"%path) - self.save_path.set(path) - - def Crop(self): - thread_update = threading.Thread(target=self.crop_task) - thread_update.start() - - def crop_task(self): - mode = self.align_com.get() - crop_size = int(self.test_com.get()) - - path = self.img_path.get() - tg_path = self.save_path.get() - if not os.path.exists(tg_path): - os.makedirs(tg_path) - tg_format = self.format_com.get() - min_scale = float(self.min_scale.get()) - blur_t = 10.0 - font = cv2.FONT_HERSHEY_SIMPLEX - self.detect.prepare(ctx_id = 0, det_thresh=0.6,\ - det_size=(640,640),mode = mode,crop_size=crop_size,ratio=min_scale) - if path and tg_path: - imgs_list = [] - if os.path.isdir(path): - print("Input a dir....") - imgs = glob.glob(os.path.join(path,"*")) - for item in imgs: - imgs_list.append(item) - # print(imgs_list) - index = 0 - for img in imgs_list: - print(img) - attr_img_ori= cv2.imread(img) - try: - attr_img_align_crop, _ = self.detect.get(attr_img_ori) - sub_index = 0 - if len(attr_img_align_crop) < 1: - print("Small face") - for face_i in attr_img_align_crop: - imageVar = cv2.Laplacian(face_i, cv2.CV_64F).var() - f_path =os.path.join(tg_path, str(index).zfill(6)+"_%d.%s"%(sub_index,tg_format)) - if imageVar < blur_t: - print("Over blurry image!") - continue - # face_i = cv2.putText(face_i, '%.1f'%imageVar,(50, 50), font, 0.8, (15, 9, 255), 2) - cv2.imwrite(f_path,face_i) - sub_index += 1 - index += 1 - except: - print("Detect no face!") - continue - else: - print("Input an image....") - imgs_list.append(path) - print("Process finished!") - else: - print("Pathes are invalid!") - - def on_closing(self): - - # self.__save_config__() - self.master.destroy() - - - -if __name__ == "__main__": - app = Application() - app.mainloop() \ No newline at end of file diff --git a/face_crop_video.py b/face_crop_video.py deleted file mode 100644 index b358b73..0000000 --- a/face_crop_video.py +++ /dev/null @@ -1,92 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: face_crop.py -# Created Date: Tuesday February 1st 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 15th April 2022 10:07:15 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import cv2 -import sys -import glob -import argparse -from tqdm import tqdm - -from pathlib import Path - -from insightface_func.face_detect_crop_multi import Face_detect_crop - - -def getParameters(): - parser = argparse.ArgumentParser() - parser.add_argument('-p', '--save_path', type=str, default="./output/", - help="The root path for saving cropped images") - parser.add_argument('-v', '--video', type=str, default="G:\\4K\\Faith.Makes.Great.2021\\40.mp4", - help="The path for input video") - parser.add_argument('-c', '--crop_size', type=int, default=512, - help="expected image resolution") - parser.add_argument('-s', '--min_scale', type=float, default=0.7, - help="tolerance range for the size of the captured face image") - parser.add_argument('-m', '--mode', type=str, default="none", - choices=['ffhq', 'none'],help="none:VGG crop, ffhq:FFHQ crop") - parser.add_argument('-f', '--format', type=str, default="png", - choices=['jpg', 'png'],help="target file format") - parser.add_argument('-i', '--interval', type=int, default=20, - help="number of frames interval") - parser.add_argument('-b', '--blur', type=float, default=20.0, - help="blur degree") - return parser.parse_args() - -def main(config): - mode = config.mode - crop_size = config.crop_size - video = config.video - tg_path = config.save_path - tg_format = config.format - min_scale = config.min_scale - blur_t = config.blur - interval = config.interval - font = cv2.FONT_HERSHEY_SIMPLEX - detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - detect.prepare(ctx_id = 0, det_thresh=0.6,\ - det_size=(640,640),mode = mode,crop_size=crop_size,ratio=min_scale) - video_path = os.path.basename(video) - video_basename = os.path.splitext(video_path)[0] - save_path = os.path.join(tg_path,video_basename) - if not os.path.exists(save_path): - os.makedirs(save_path) - cap = cv2.VideoCapture(video) - frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - frame_index = 0 - # for frame_index in tqdm(range(0,frame_count,interval)): - while cap.isOpened(): - ret, frame = cap.read() - if ret==True: - - img_align_crop = detect.get(frame) - if img_align_crop: - img_align_crop = img_align_crop[0] - sub_index = 0 - for face_i in img_align_crop: - imageVar = cv2.Laplacian(face_i, cv2.CV_64F).var() - f_path =os.path.join(save_path, str(frame_index).zfill(6)+"_%d.%s"%(sub_index,tg_format)) - if imageVar < blur_t: - print("Over blurry image!") - continue - # face_i = cv2.putText(face_i, '%.1f'%imageVar,(50, 50), font, 0.8, (15, 9, 255), 2) - cv2.imwrite(f_path,face_i) - sub_index += 1 - # else: - # print("Detect no face!") - frame_index += 1 - cap.release() - -if __name__ == "__main__": - config = getParameters() - main(config) \ No newline at end of file diff --git a/face_enhancer/experiments/pretrained_models/README.md b/face_enhancer/experiments/pretrained_models/README.md deleted file mode 100644 index 3401a5c..0000000 --- a/face_enhancer/experiments/pretrained_models/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Pre-trained Models and Other Data - -Download pre-trained models and other data. Put them in this folder. - -1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth) -1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth) -1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth) diff --git a/face_enhancer/gfpgan/__init__.py b/face_enhancer/gfpgan/__init__.py deleted file mode 100644 index 94daaee..0000000 --- a/face_enhancer/gfpgan/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# flake8: noqa -from .archs import * -from .data import * -from .models import * -from .utils import * - -# from .version import * diff --git a/face_enhancer/gfpgan/archs/__init__.py b/face_enhancer/gfpgan/archs/__init__.py deleted file mode 100644 index bec5f17..0000000 --- a/face_enhancer/gfpgan/archs/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -import importlib -from basicsr.utils import scandir -from os import path as osp - -# automatically scan and import arch modules for registry -# scan all the files that end with '_arch.py' under the archs folder -arch_folder = osp.dirname(osp.abspath(__file__)) -arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')] -# import all the arch modules -_arch_modules = [importlib.import_module(f'gfpgan.archs.{file_name}') for file_name in arch_filenames] diff --git a/face_enhancer/gfpgan/archs/arcface_arch.py b/face_enhancer/gfpgan/archs/arcface_arch.py deleted file mode 100644 index e6d3bd9..0000000 --- a/face_enhancer/gfpgan/archs/arcface_arch.py +++ /dev/null @@ -1,245 +0,0 @@ -import torch.nn as nn -from basicsr.utils.registry import ARCH_REGISTRY - - -def conv3x3(inplanes, outplanes, stride=1): - """A simple wrapper for 3x3 convolution with padding. - - Args: - inplanes (int): Channel number of inputs. - outplanes (int): Channel number of outputs. - stride (int): Stride in convolution. Default: 1. - """ - return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False) - - -class BasicBlock(nn.Module): - """Basic residual block used in the ResNetArcFace architecture. - - Args: - inplanes (int): Channel number of inputs. - planes (int): Channel number of outputs. - stride (int): Stride in convolution. Default: 1. - downsample (nn.Module): The downsample module. Default: None. - """ - expansion = 1 # output channel expansion ratio - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = nn.BatchNorm2d(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = nn.BatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class IRBlock(nn.Module): - """Improved residual block (IR Block) used in the ResNetArcFace architecture. - - Args: - inplanes (int): Channel number of inputs. - planes (int): Channel number of outputs. - stride (int): Stride in convolution. Default: 1. - downsample (nn.Module): The downsample module. Default: None. - use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. - """ - expansion = 1 # output channel expansion ratio - - def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): - super(IRBlock, self).__init__() - self.bn0 = nn.BatchNorm2d(inplanes) - self.conv1 = conv3x3(inplanes, inplanes) - self.bn1 = nn.BatchNorm2d(inplanes) - self.prelu = nn.PReLU() - self.conv2 = conv3x3(inplanes, planes, stride) - self.bn2 = nn.BatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - self.use_se = use_se - if self.use_se: - self.se = SEBlock(planes) - - def forward(self, x): - residual = x - out = self.bn0(x) - out = self.conv1(out) - out = self.bn1(out) - out = self.prelu(out) - - out = self.conv2(out) - out = self.bn2(out) - if self.use_se: - out = self.se(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.prelu(out) - - return out - - -class Bottleneck(nn.Module): - """Bottleneck block used in the ResNetArcFace architecture. - - Args: - inplanes (int): Channel number of inputs. - planes (int): Channel number of outputs. - stride (int): Stride in convolution. Default: 1. - downsample (nn.Module): The downsample module. Default: None. - """ - expansion = 4 # output channel expansion ratio - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class SEBlock(nn.Module): - """The squeeze-and-excitation block (SEBlock) used in the IRBlock. - - Args: - channel (int): Channel number of inputs. - reduction (int): Channel reduction ration. Default: 16. - """ - - def __init__(self, channel, reduction=16): - super(SEBlock, self).__init__() - self.avg_pool = nn.AdaptiveAvgPool2d(1) # pool to 1x1 without spatial information - self.fc = nn.Sequential( - nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel), - nn.Sigmoid()) - - def forward(self, x): - b, c, _, _ = x.size() - y = self.avg_pool(x).view(b, c) - y = self.fc(y).view(b, c, 1, 1) - return x * y - - -@ARCH_REGISTRY.register() -class ResNetArcFace(nn.Module): - """ArcFace with ResNet architectures. - - Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. - - Args: - block (str): Block used in the ArcFace architecture. - layers (tuple(int)): Block numbers in each layer. - use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. - """ - - def __init__(self, block, layers, use_se=True): - if block == 'IRBlock': - block = IRBlock - self.inplanes = 64 - self.use_se = use_se - super(ResNetArcFace, self).__init__() - - self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.prelu = nn.PReLU() - self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) - self.layer1 = self._make_layer(block, 64, layers[0]) - self.layer2 = self._make_layer(block, 128, layers[1], stride=2) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2) - self.bn4 = nn.BatchNorm2d(512) - self.dropout = nn.Dropout() - self.fc5 = nn.Linear(512 * 8 * 8, 512) - self.bn5 = nn.BatchNorm1d(512) - - # initialization - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.xavier_normal_(m.weight) - elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.Linear): - nn.init.xavier_normal_(m.weight) - nn.init.constant_(m.bias, 0) - - def _make_layer(self, block, planes, num_blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(planes * block.expansion), - ) - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se)) - self.inplanes = planes - for _ in range(1, num_blocks): - layers.append(block(self.inplanes, planes, use_se=self.use_se)) - - return nn.Sequential(*layers) - - def forward(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.prelu(x) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.bn4(x) - x = self.dropout(x) - x = x.view(x.size(0), -1) - x = self.fc5(x) - x = self.bn5(x) - - return x diff --git a/face_enhancer/gfpgan/archs/gfpgan_bilinear_arch.py b/face_enhancer/gfpgan/archs/gfpgan_bilinear_arch.py deleted file mode 100644 index 52e0de8..0000000 --- a/face_enhancer/gfpgan/archs/gfpgan_bilinear_arch.py +++ /dev/null @@ -1,312 +0,0 @@ -import math -import random -import torch -from basicsr.utils.registry import ARCH_REGISTRY -from torch import nn - -from .gfpganv1_arch import ResUpBlock -from .stylegan2_bilinear_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, - StyleGAN2GeneratorBilinear) - - -class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear): - """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). - - It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for - deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__(self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - lr_mlp=0.01, - narrow=1, - sft_half=False): - super(StyleGAN2GeneratorBilinearSFT, self).__init__( - out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - lr_mlp=lr_mlp, - narrow=narrow) - self.sft_half = sft_half - - def forward(self, - styles, - conditions, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2GeneratorBilinearSFT. - - Args: - styles (list[Tensor]): Sample codes of styles. - conditions (list[Tensor]): SFT conditions to generators. - input_is_latent (bool): Whether input is latent style. Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - truncation (float): The truncation ratio. Default: 1. - truncation_latent (Tensor | None): The truncation latent tensor. Default: None. - inject_index (int | None): The injection index for mixing noise. Default: None. - return_latents (bool): Whether to return style latents. Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latents with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - - # the conditions may have fewer levels - if i < len(conditions): - # SFT part to combine the conditions - if self.sft_half: # only apply SFT to half of the channels - out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) - out_sft = out_sft * conditions[i - 1] + conditions[i] - out = torch.cat([out_same, out_sft], dim=1) - else: # apply SFT to all the channels - out = out * conditions[i - 1] + conditions[i] - - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -@ARCH_REGISTRY.register() -class GFPGANBilinear(nn.Module): - """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. - - It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for - deployment. It can be easily converted to the clean version: GFPGANv1Clean. - - - Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. - fix_decoder (bool): Whether to fix the decoder. Default: True. - - num_mlp (int): Layer number of MLP style layers. Default: 8. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - input_is_latent (bool): Whether input is latent style. Default: False. - different_w (bool): Whether to use different latent w for different layers. Default: False. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - channel_multiplier=1, - decoder_load_path=None, - fix_decoder=True, - # for stylegan decoder - num_mlp=8, - lr_mlp=0.01, - input_is_latent=False, - different_w=False, - narrow=1, - sft_half=False): - - super(GFPGANBilinear, self).__init__() - self.input_is_latent = input_is_latent - self.different_w = different_w - self.num_style_feat = num_style_feat - - unet_narrow = narrow * 0.5 # by default, use a half of input channels - channels = { - '4': int(512 * unet_narrow), - '8': int(512 * unet_narrow), - '16': int(512 * unet_narrow), - '32': int(512 * unet_narrow), - '64': int(256 * channel_multiplier * unet_narrow), - '128': int(128 * channel_multiplier * unet_narrow), - '256': int(64 * channel_multiplier * unet_narrow), - '512': int(32 * channel_multiplier * unet_narrow), - '1024': int(16 * channel_multiplier * unet_narrow) - } - - self.log_size = int(math.log(out_size, 2)) - first_out_size = 2**(int(math.log(out_size, 2))) - - self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True) - - # downsample - in_channels = channels[f'{first_out_size}'] - self.conv_body_down = nn.ModuleList() - for i in range(self.log_size, 2, -1): - out_channels = channels[f'{2**(i - 1)}'] - self.conv_body_down.append(ResBlock(in_channels, out_channels)) - in_channels = out_channels - - self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True) - - # upsample - in_channels = channels['4'] - self.conv_body_up = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) - in_channels = out_channels - - # to RGB - self.toRGB = nn.ModuleList() - for i in range(3, self.log_size + 1): - self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0)) - - if different_w: - linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat - else: - linear_out_channel = num_style_feat - - self.final_linear = EqualLinear( - channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None) - - # the decoder: stylegan2 generator with SFT modulations - self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT( - out_size=out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - lr_mlp=lr_mlp, - narrow=narrow, - sft_half=sft_half) - - # load pre-trained stylegan2 model if necessary - if decoder_load_path: - self.stylegan_decoder.load_state_dict( - torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) - # fix decoder without updating params - if fix_decoder: - for _, param in self.stylegan_decoder.named_parameters(): - param.requires_grad = False - - # for SFT modulations (scale and shift) - self.condition_scale = nn.ModuleList() - self.condition_shift = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - if sft_half: - sft_out_channels = out_channels - else: - sft_out_channels = out_channels * 2 - self.condition_scale.append( - nn.Sequential( - EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), - ScaledLeakyReLU(0.2), - EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1))) - self.condition_shift.append( - nn.Sequential( - EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), - ScaledLeakyReLU(0.2), - EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0))) - - def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): - """Forward function for GFPGANBilinear. - - Args: - x (Tensor): Input images. - return_latents (bool): Whether to return style latents. Default: False. - return_rgb (bool): Whether return intermediate rgb images. Default: True. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - """ - conditions = [] - unet_skips = [] - out_rgbs = [] - - # encoder - feat = self.conv_body_first(x) - for i in range(self.log_size - 2): - feat = self.conv_body_down[i](feat) - unet_skips.insert(0, feat) - - feat = self.final_conv(feat) - - # style code - style_code = self.final_linear(feat.view(feat.size(0), -1)) - if self.different_w: - style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) - - # decode - for i in range(self.log_size - 2): - # add unet skip - feat = feat + unet_skips[i] - # ResUpLayer - feat = self.conv_body_up[i](feat) - # generate scale and shift for SFT layers - scale = self.condition_scale[i](feat) - conditions.append(scale.clone()) - shift = self.condition_shift[i](feat) - conditions.append(shift.clone()) - # generate rgb images - if return_rgb: - out_rgbs.append(self.toRGB[i](feat)) - - # decoder - image, _ = self.stylegan_decoder([style_code], - conditions, - return_latents=return_latents, - input_is_latent=self.input_is_latent, - randomize_noise=randomize_noise) - - return image, out_rgbs diff --git a/face_enhancer/gfpgan/archs/gfpganv1_arch.py b/face_enhancer/gfpgan/archs/gfpganv1_arch.py deleted file mode 100644 index e092b4f..0000000 --- a/face_enhancer/gfpgan/archs/gfpganv1_arch.py +++ /dev/null @@ -1,439 +0,0 @@ -import math -import random -import torch -from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, - StyleGAN2Generator) -from basicsr.ops.fused_act import FusedLeakyReLU -from basicsr.utils.registry import ARCH_REGISTRY -from torch import nn -from torch.nn import functional as F - - -class StyleGAN2GeneratorSFT(StyleGAN2Generator): - """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be - applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__(self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - resample_kernel=(1, 3, 3, 1), - lr_mlp=0.01, - narrow=1, - sft_half=False): - super(StyleGAN2GeneratorSFT, self).__init__( - out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - resample_kernel=resample_kernel, - lr_mlp=lr_mlp, - narrow=narrow) - self.sft_half = sft_half - - def forward(self, - styles, - conditions, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2GeneratorSFT. - - Args: - styles (list[Tensor]): Sample codes of styles. - conditions (list[Tensor]): SFT conditions to generators. - input_is_latent (bool): Whether input is latent style. Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - truncation (float): The truncation ratio. Default: 1. - truncation_latent (Tensor | None): The truncation latent tensor. Default: None. - inject_index (int | None): The injection index for mixing noise. Default: None. - return_latents (bool): Whether to return style latents. Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latents with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - - # the conditions may have fewer levels - if i < len(conditions): - # SFT part to combine the conditions - if self.sft_half: # only apply SFT to half of the channels - out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) - out_sft = out_sft * conditions[i - 1] + conditions[i] - out = torch.cat([out_same, out_sft], dim=1) - else: # apply SFT to all the channels - out = out * conditions[i - 1] + conditions[i] - - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ConvUpLayer(nn.Module): - """Convolutional upsampling layer. It uses bilinear upsampler + Conv. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - bias=True, - bias_init_val=0, - activate=True): - super(ConvUpLayer, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - # self.scale is used to scale the convolution weights, which is related to the common initializations. - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) - - if bias and not activate: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - # activation - if activate: - if bias: - self.activation = FusedLeakyReLU(out_channels) - else: - self.activation = ScaledLeakyReLU(0.2) - else: - self.activation = None - - def forward(self, x): - # bilinear upsample - out = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) - # conv - out = F.conv2d( - out, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - # activation - if self.activation is not None: - out = self.activation(out) - return out - - -class ResUpBlock(nn.Module): - """Residual block with upsampling. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - """ - - def __init__(self, in_channels, out_channels): - super(ResUpBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvUpLayer(in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True) - self.skip = ConvUpLayer(in_channels, out_channels, 1, bias=False, activate=False) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out - - -@ARCH_REGISTRY.register() -class GFPGANv1(nn.Module): - """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. - - Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be - applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). - decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. - fix_decoder (bool): Whether to fix the decoder. Default: True. - - num_mlp (int): Layer number of MLP style layers. Default: 8. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - input_is_latent (bool): Whether input is latent style. Default: False. - different_w (bool): Whether to use different latent w for different layers. Default: False. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - channel_multiplier=1, - resample_kernel=(1, 3, 3, 1), - decoder_load_path=None, - fix_decoder=True, - # for stylegan decoder - num_mlp=8, - lr_mlp=0.01, - input_is_latent=False, - different_w=False, - narrow=1, - sft_half=False): - - super(GFPGANv1, self).__init__() - self.input_is_latent = input_is_latent - self.different_w = different_w - self.num_style_feat = num_style_feat - - unet_narrow = narrow * 0.5 # by default, use a half of input channels - channels = { - '4': int(512 * unet_narrow), - '8': int(512 * unet_narrow), - '16': int(512 * unet_narrow), - '32': int(512 * unet_narrow), - '64': int(256 * channel_multiplier * unet_narrow), - '128': int(128 * channel_multiplier * unet_narrow), - '256': int(64 * channel_multiplier * unet_narrow), - '512': int(32 * channel_multiplier * unet_narrow), - '1024': int(16 * channel_multiplier * unet_narrow) - } - - self.log_size = int(math.log(out_size, 2)) - first_out_size = 2**(int(math.log(out_size, 2))) - - self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True) - - # downsample - in_channels = channels[f'{first_out_size}'] - self.conv_body_down = nn.ModuleList() - for i in range(self.log_size, 2, -1): - out_channels = channels[f'{2**(i - 1)}'] - self.conv_body_down.append(ResBlock(in_channels, out_channels, resample_kernel)) - in_channels = out_channels - - self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True) - - # upsample - in_channels = channels['4'] - self.conv_body_up = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) - in_channels = out_channels - - # to RGB - self.toRGB = nn.ModuleList() - for i in range(3, self.log_size + 1): - self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0)) - - if different_w: - linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat - else: - linear_out_channel = num_style_feat - - self.final_linear = EqualLinear( - channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None) - - # the decoder: stylegan2 generator with SFT modulations - self.stylegan_decoder = StyleGAN2GeneratorSFT( - out_size=out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - resample_kernel=resample_kernel, - lr_mlp=lr_mlp, - narrow=narrow, - sft_half=sft_half) - - # load pre-trained stylegan2 model if necessary - if decoder_load_path: - self.stylegan_decoder.load_state_dict( - torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) - # fix decoder without updating params - if fix_decoder: - for _, param in self.stylegan_decoder.named_parameters(): - param.requires_grad = False - - # for SFT modulations (scale and shift) - self.condition_scale = nn.ModuleList() - self.condition_shift = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - if sft_half: - sft_out_channels = out_channels - else: - sft_out_channels = out_channels * 2 - self.condition_scale.append( - nn.Sequential( - EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), - ScaledLeakyReLU(0.2), - EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1))) - self.condition_shift.append( - nn.Sequential( - EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), - ScaledLeakyReLU(0.2), - EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0))) - - def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): - """Forward function for GFPGANv1. - - Args: - x (Tensor): Input images. - return_latents (bool): Whether to return style latents. Default: False. - return_rgb (bool): Whether return intermediate rgb images. Default: True. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - """ - conditions = [] - unet_skips = [] - out_rgbs = [] - - # encoder - feat = self.conv_body_first(x) - for i in range(self.log_size - 2): - feat = self.conv_body_down[i](feat) - unet_skips.insert(0, feat) - - feat = self.final_conv(feat) - - # style code - style_code = self.final_linear(feat.view(feat.size(0), -1)) - if self.different_w: - style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) - - # decode - for i in range(self.log_size - 2): - # add unet skip - feat = feat + unet_skips[i] - # ResUpLayer - feat = self.conv_body_up[i](feat) - # generate scale and shift for SFT layers - scale = self.condition_scale[i](feat) - conditions.append(scale.clone()) - shift = self.condition_shift[i](feat) - conditions.append(shift.clone()) - # generate rgb images - if return_rgb: - out_rgbs.append(self.toRGB[i](feat)) - - # decoder - image, _ = self.stylegan_decoder([style_code], - conditions, - return_latents=return_latents, - input_is_latent=self.input_is_latent, - randomize_noise=randomize_noise) - - return image, out_rgbs - - -@ARCH_REGISTRY.register() -class FacialComponentDiscriminator(nn.Module): - """Facial component (eyes, mouth, noise) discriminator used in GFPGAN. - """ - - def __init__(self): - super(FacialComponentDiscriminator, self).__init__() - # It now uses a VGG-style architectrue with fixed model size - self.conv1 = ConvLayer(3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) - self.conv2 = ConvLayer(64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) - self.conv3 = ConvLayer(128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) - self.conv4 = ConvLayer(128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) - self.conv5 = ConvLayer(256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) - self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) - - def forward(self, x, return_feats=False): - """Forward function for FacialComponentDiscriminator. - - Args: - x (Tensor): Input images. - return_feats (bool): Whether to return intermediate features. Default: False. - """ - feat = self.conv1(x) - feat = self.conv3(self.conv2(feat)) - rlt_feats = [] - if return_feats: - rlt_feats.append(feat.clone()) - feat = self.conv5(self.conv4(feat)) - if return_feats: - rlt_feats.append(feat.clone()) - out = self.final_conv(feat) - - if return_feats: - return out, rlt_feats - else: - return out, None diff --git a/face_enhancer/gfpgan/archs/gfpganv1_clean_arch.py b/face_enhancer/gfpgan/archs/gfpganv1_clean_arch.py deleted file mode 100644 index eb2e15d..0000000 --- a/face_enhancer/gfpgan/archs/gfpganv1_clean_arch.py +++ /dev/null @@ -1,324 +0,0 @@ -import math -import random -import torch -from basicsr.utils.registry import ARCH_REGISTRY -from torch import nn -from torch.nn import functional as F - -from .stylegan2_clean_arch import StyleGAN2GeneratorClean - - -class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): - """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). - - It is the clean version without custom compiled CUDA extensions used in StyleGAN2. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1, sft_half=False): - super(StyleGAN2GeneratorCSFT, self).__init__( - out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - narrow=narrow) - self.sft_half = sft_half - - def forward(self, - styles, - conditions, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2GeneratorCSFT. - - Args: - styles (list[Tensor]): Sample codes of styles. - conditions (list[Tensor]): SFT conditions to generators. - input_is_latent (bool): Whether input is latent style. Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - truncation (float): The truncation ratio. Default: 1. - truncation_latent (Tensor | None): The truncation latent tensor. Default: None. - inject_index (int | None): The injection index for mixing noise. Default: None. - return_latents (bool): Whether to return style latents. Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latents with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - - # the conditions may have fewer levels - if i < len(conditions): - # SFT part to combine the conditions - if self.sft_half: # only apply SFT to half of the channels - out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) - out_sft = out_sft * conditions[i - 1] + conditions[i] - out = torch.cat([out_same, out_sft], dim=1) - else: # apply SFT to all the channels - out = out * conditions[i - 1] + conditions[i] - - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ResBlock(nn.Module): - """Residual block with bilinear upsampling/downsampling. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. - """ - - def __init__(self, in_channels, out_channels, mode='down'): - super(ResBlock, self).__init__() - - self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) - self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) - self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) - if mode == 'down': - self.scale_factor = 0.5 - elif mode == 'up': - self.scale_factor = 2 - - def forward(self, x): - out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) - # upsample/downsample - out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) - out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) - # skip - x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) - skip = self.skip(x) - out = out + skip - return out - - -@ARCH_REGISTRY.register() -class GFPGANv1Clean(nn.Module): - """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. - - It is the clean version without custom compiled CUDA extensions used in StyleGAN2. - - Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. - fix_decoder (bool): Whether to fix the decoder. Default: True. - - num_mlp (int): Layer number of MLP style layers. Default: 8. - input_is_latent (bool): Whether input is latent style. Default: False. - different_w (bool): Whether to use different latent w for different layers. Default: False. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - channel_multiplier=1, - decoder_load_path=None, - fix_decoder=True, - # for stylegan decoder - num_mlp=8, - input_is_latent=False, - different_w=False, - narrow=1, - sft_half=False): - - super(GFPGANv1Clean, self).__init__() - self.input_is_latent = input_is_latent - self.different_w = different_w - self.num_style_feat = num_style_feat - - unet_narrow = narrow * 0.5 # by default, use a half of input channels - channels = { - '4': int(512 * unet_narrow), - '8': int(512 * unet_narrow), - '16': int(512 * unet_narrow), - '32': int(512 * unet_narrow), - '64': int(256 * channel_multiplier * unet_narrow), - '128': int(128 * channel_multiplier * unet_narrow), - '256': int(64 * channel_multiplier * unet_narrow), - '512': int(32 * channel_multiplier * unet_narrow), - '1024': int(16 * channel_multiplier * unet_narrow) - } - - self.log_size = int(math.log(out_size, 2)) - first_out_size = 2**(int(math.log(out_size, 2))) - - self.conv_body_first = nn.Conv2d(3, channels[f'{first_out_size}'], 1) - - # downsample - in_channels = channels[f'{first_out_size}'] - self.conv_body_down = nn.ModuleList() - for i in range(self.log_size, 2, -1): - out_channels = channels[f'{2**(i - 1)}'] - self.conv_body_down.append(ResBlock(in_channels, out_channels, mode='down')) - in_channels = out_channels - - self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1) - - # upsample - in_channels = channels['4'] - self.conv_body_up = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.conv_body_up.append(ResBlock(in_channels, out_channels, mode='up')) - in_channels = out_channels - - # to RGB - self.toRGB = nn.ModuleList() - for i in range(3, self.log_size + 1): - self.toRGB.append(nn.Conv2d(channels[f'{2**i}'], 3, 1)) - - if different_w: - linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat - else: - linear_out_channel = num_style_feat - - self.final_linear = nn.Linear(channels['4'] * 4 * 4, linear_out_channel) - - # the decoder: stylegan2 generator with SFT modulations - self.stylegan_decoder = StyleGAN2GeneratorCSFT( - out_size=out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - narrow=narrow, - sft_half=sft_half) - - # load pre-trained stylegan2 model if necessary - if decoder_load_path: - self.stylegan_decoder.load_state_dict( - torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) - # fix decoder without updating params - if fix_decoder: - for _, param in self.stylegan_decoder.named_parameters(): - param.requires_grad = False - - # for SFT modulations (scale and shift) - self.condition_scale = nn.ModuleList() - self.condition_shift = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - if sft_half: - sft_out_channels = out_channels - else: - sft_out_channels = out_channels * 2 - self.condition_scale.append( - nn.Sequential( - nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), - nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1))) - self.condition_shift.append( - nn.Sequential( - nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), - nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1))) - - def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): - """Forward function for GFPGANv1Clean. - - Args: - x (Tensor): Input images. - return_latents (bool): Whether to return style latents. Default: False. - return_rgb (bool): Whether return intermediate rgb images. Default: True. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - """ - conditions = [] - unet_skips = [] - out_rgbs = [] - - # encoder - feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) - for i in range(self.log_size - 2): - feat = self.conv_body_down[i](feat) - unet_skips.insert(0, feat) - feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) - - # style code - style_code = self.final_linear(feat.view(feat.size(0), -1)) - if self.different_w: - style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) - - # decode - for i in range(self.log_size - 2): - # add unet skip - feat = feat + unet_skips[i] - # ResUpLayer - feat = self.conv_body_up[i](feat) - # generate scale and shift for SFT layers - scale = self.condition_scale[i](feat) - conditions.append(scale.clone()) - shift = self.condition_shift[i](feat) - conditions.append(shift.clone()) - # generate rgb images - if return_rgb: - out_rgbs.append(self.toRGB[i](feat)) - - # decoder - image, _ = self.stylegan_decoder([style_code], - conditions, - return_latents=return_latents, - input_is_latent=self.input_is_latent, - randomize_noise=randomize_noise) - - return image, out_rgbs diff --git a/face_enhancer/gfpgan/archs/stylegan2_bilinear_arch.py b/face_enhancer/gfpgan/archs/stylegan2_bilinear_arch.py deleted file mode 100644 index 1342ee3..0000000 --- a/face_enhancer/gfpgan/archs/stylegan2_bilinear_arch.py +++ /dev/null @@ -1,613 +0,0 @@ -import math -import random -import torch -from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu -from basicsr.utils.registry import ARCH_REGISTRY -from torch import nn -from torch.nn import functional as F - - -class NormStyleCode(nn.Module): - - def forward(self, x): - """Normalize the style codes. - - Args: - x (Tensor): Style codes with shape (b, c). - - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -class EqualLinear(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Size of each sample. - out_channels (int): Size of each output sample. - bias (bool): If set to ``False``, the layer will not learn an additive - bias. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - lr_mul (float): Learning rate multiplier. Default: 1. - activation (None | str): The activation after ``linear`` operation. - Supported: 'fused_lrelu', None. Default: None. - """ - - def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): - super(EqualLinear, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.lr_mul = lr_mul - self.activation = activation - if self.activation not in ['fused_lrelu', None]: - raise ValueError(f'Wrong activation value in EqualLinear: {activation}' - "Supported ones are: ['fused_lrelu', None].") - self.scale = (1 / math.sqrt(in_channels)) * lr_mul - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - if self.bias is None: - bias = None - else: - bias = self.bias * self.lr_mul - if self.activation == 'fused_lrelu': - out = F.linear(x, self.weight * self.scale) - out = fused_leaky_relu(out, bias) - else: - out = F.linear(x, self.weight * self.scale, bias=bias) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, bias={self.bias is not None})') - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - - There is no bias in ModulatedConv2d. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. - Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - eps (float): A value added to the denominator for numerical stability. - Default: 1e-8. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - eps=1e-8, - interpolation_mode='bilinear'): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - self.interpolation_mode = interpolation_mode - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - # modulation inside each modulated conv - self.modulation = EqualLinear( - num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None) - - self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) - - if self.sample_mode == 'upsample': - x = F.interpolate(x, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners) - elif self.sample_mode == 'downsample': - x = F.interpolate(x, scale_factor=0.5, mode=self.interpolation_mode, align_corners=self.align_corners) - - b, c, h, w = x.shape - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size}, ' - f'demodulate={self.demodulate}, sample_mode={self.sample_mode})') - - -class StyleConv(nn.Module): - """Style conv. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode='bilinear'): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=demodulate, - sample_mode=sample_mode, - interpolation_mode=interpolation_mode) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.activate = FusedLeakyReLU(out_channels) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # activation (with bias) - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB from features. - - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - """ - - def __init__(self, in_channels, num_style_feat, upsample=True, interpolation_mode='bilinear'): - super(ToRGB, self).__init__() - self.upsample = upsample - self.interpolation_mode = interpolation_mode - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - self.modulated_conv = ModulatedConv2d( - in_channels, - 3, - kernel_size=1, - num_style_feat=num_style_feat, - demodulate=False, - sample_mode=None, - interpolation_mode=interpolation_mode) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = F.interpolate( - skip, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -@ARCH_REGISTRY.register() -class StyleGAN2GeneratorBilinear(nn.Module): - """StyleGAN2 Generator. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__(self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - lr_mlp=0.01, - narrow=1, - interpolation_mode='bilinear'): - super(StyleGAN2GeneratorBilinear, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.append( - EqualLinear( - num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, - activation='fused_lrelu')) - self.style_mlp = nn.Sequential(*style_mlp_layers) - - channels = { - '4': int(512 * narrow), - '8': int(512 * narrow), - '16': int(512 * narrow), - '32': int(512 * narrow), - '64': int(256 * channel_multiplier * narrow), - '128': int(128 * channel_multiplier * narrow), - '256': int(64 * channel_multiplier * narrow), - '512': int(32 * channel_multiplier * narrow), - '1024': int(16 * channel_multiplier * narrow) - } - self.channels = channels - - self.constant_input = ConstantInput(channels['4'], size=4) - self.style_conv1 = StyleConv( - channels['4'], - channels['4'], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode=interpolation_mode) - self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, interpolation_mode=interpolation_mode) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels['4'] - # noise - for layer_idx in range(self.num_layers): - resolution = 2**((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode='upsample', - interpolation_mode=interpolation_mode)) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode=interpolation_mode)) - self.to_rgbs.append( - ToRGB(out_channels, num_style_feat, upsample=True, interpolation_mode=interpolation_mode)) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward(self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2Generator. - - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. - Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is - False. Default: True. - truncation (float): TODO. Default: 1. - truncation_latent (Tensor | None): TODO. Default: None. - inject_index (int | None): The injection index for mixing noise. - Default: None. - return_latents (bool): Whether to return style latents. - Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latent with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ScaledLeakyReLU(nn.Module): - """Scaled LeakyReLU. - - Args: - negative_slope (float): Negative slope. Default: 0.2. - """ - - def __init__(self, negative_slope=0.2): - super(ScaledLeakyReLU, self).__init__() - self.negative_slope = negative_slope - - def forward(self, x): - out = F.leaky_relu(x, negative_slope=self.negative_slope) - return out * math.sqrt(2) - - -class EqualConv2d(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. - Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. - Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - """ - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0): - super(EqualConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - out = F.conv2d( - x, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size},' - f' stride={self.stride}, padding={self.padding}, ' - f'bias={self.bias is not None})') - - -class ConvLayer(nn.Sequential): - """Conv Layer used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Kernel size. - downsample (bool): Whether downsample by a factor of 2. - Default: False. - bias (bool): Whether with bias. Default: True. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - downsample=False, - bias=True, - activate=True, - interpolation_mode='bilinear'): - layers = [] - self.interpolation_mode = interpolation_mode - # downsample - if downsample: - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - - layers.append( - torch.nn.Upsample(scale_factor=0.5, mode=interpolation_mode, align_corners=self.align_corners)) - stride = 1 - self.padding = kernel_size // 2 - # conv - layers.append( - EqualConv2d( - in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias - and not activate)) - # activation - if activate: - if bias: - layers.append(FusedLeakyReLU(out_channels)) - else: - layers.append(ScaledLeakyReLU(0.2)) - - super(ConvLayer, self).__init__(*layers) - - -class ResBlock(nn.Module): - """Residual block used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - """ - - def __init__(self, in_channels, out_channels, interpolation_mode='bilinear'): - super(ResBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvLayer( - in_channels, - out_channels, - 3, - downsample=True, - interpolation_mode=interpolation_mode, - bias=True, - activate=True) - self.skip = ConvLayer( - in_channels, - out_channels, - 1, - downsample=True, - interpolation_mode=interpolation_mode, - bias=False, - activate=False) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out diff --git a/face_enhancer/gfpgan/archs/stylegan2_clean_arch.py b/face_enhancer/gfpgan/archs/stylegan2_clean_arch.py deleted file mode 100644 index 9e2ee94..0000000 --- a/face_enhancer/gfpgan/archs/stylegan2_clean_arch.py +++ /dev/null @@ -1,368 +0,0 @@ -import math -import random -import torch -from basicsr.archs.arch_util import default_init_weights -from basicsr.utils.registry import ARCH_REGISTRY -from torch import nn -from torch.nn import functional as F - - -class NormStyleCode(nn.Module): - - def forward(self, x): - """Normalize the style codes. - - Args: - x (Tensor): Style codes with shape (b, c). - - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - - There is no bias in ModulatedConv2d. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. - eps (float): A value added to the denominator for numerical stability. Default: 1e-8. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - eps=1e-8): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - - # modulation inside each modulated conv - self.modulation = nn.Linear(num_style_feat, in_channels, bias=True) - # initialization - default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear') - - self.weight = nn.Parameter( - torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) / - math.sqrt(in_channels * kernel_size**2)) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) - - # upsample or downsample if necessary - if self.sample_mode == 'upsample': - x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) - elif self.sample_mode == 'downsample': - x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False) - - b, c, h, w = x.shape - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})') - - -class StyleConv(nn.Module): - """Style conv used in StyleGAN2. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. - """ - - def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1)) - self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) * 2**0.5 # for conversion - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # add bias - out = out + self.bias - # activation - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB (image space) from features. - - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - """ - - def __init__(self, in_channels, num_style_feat, upsample=True): - super(ToRGB, self).__init__() - self.upsample = upsample - self.modulated_conv = ModulatedConv2d( - in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -@ARCH_REGISTRY.register() -class StyleGAN2GeneratorClean(nn.Module): - """Clean version of StyleGAN2 Generator. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1): - super(StyleGAN2GeneratorClean, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.extend( - [nn.Linear(num_style_feat, num_style_feat, bias=True), - nn.LeakyReLU(negative_slope=0.2, inplace=True)]) - self.style_mlp = nn.Sequential(*style_mlp_layers) - # initialization - default_init_weights(self.style_mlp, scale=1, bias_fill=0, a=0.2, mode='fan_in', nonlinearity='leaky_relu') - - # channel list - channels = { - '4': int(512 * narrow), - '8': int(512 * narrow), - '16': int(512 * narrow), - '32': int(512 * narrow), - '64': int(256 * channel_multiplier * narrow), - '128': int(128 * channel_multiplier * narrow), - '256': int(64 * channel_multiplier * narrow), - '512': int(32 * channel_multiplier * narrow), - '1024': int(16 * channel_multiplier * narrow) - } - self.channels = channels - - self.constant_input = ConstantInput(channels['4'], size=4) - self.style_conv1 = StyleConv( - channels['4'], - channels['4'], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None) - self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels['4'] - # noise - for layer_idx in range(self.num_layers): - resolution = 2**((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode='upsample')) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None)) - self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True)) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward(self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2GeneratorClean. - - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - truncation (float): The truncation ratio. Default: 1. - truncation_latent (Tensor | None): The truncation latent tensor. Default: None. - inject_index (int | None): The injection index for mixing noise. Default: None. - return_latents (bool): Whether to return style latents. Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latents with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None diff --git a/face_enhancer/gfpgan/data/__init__.py b/face_enhancer/gfpgan/data/__init__.py deleted file mode 100644 index 69fd9f9..0000000 --- a/face_enhancer/gfpgan/data/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -import importlib -from basicsr.utils import scandir -from os import path as osp - -# automatically scan and import dataset modules for registry -# scan all the files that end with '_dataset.py' under the data folder -data_folder = osp.dirname(osp.abspath(__file__)) -dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')] -# import all the dataset modules -_dataset_modules = [importlib.import_module(f'gfpgan.data.{file_name}') for file_name in dataset_filenames] diff --git a/face_enhancer/gfpgan/data/ffhq_degradation_dataset.py b/face_enhancer/gfpgan/data/ffhq_degradation_dataset.py deleted file mode 100644 index 64e5755..0000000 --- a/face_enhancer/gfpgan/data/ffhq_degradation_dataset.py +++ /dev/null @@ -1,230 +0,0 @@ -import cv2 -import math -import numpy as np -import os.path as osp -import torch -import torch.utils.data as data -from basicsr.data import degradations as degradations -from basicsr.data.data_util import paths_from_folder -from basicsr.data.transforms import augment -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY -from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation, - normalize) - - -@DATASET_REGISTRY.register() -class FFHQDegradationDataset(data.Dataset): - """FFHQ dataset for GFPGAN. - - It reads high resolution images, and then generate low-quality (LQ) images on-the-fly. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - io_backend (dict): IO backend type and other kwarg. - mean (list | tuple): Image mean. - std (list | tuple): Image std. - use_hflip (bool): Whether to horizontally flip. - Please see more options in the codes. - """ - - def __init__(self, opt): - super(FFHQDegradationDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - - self.gt_folder = opt['dataroot_gt'] - self.mean = opt['mean'] - self.std = opt['std'] - self.out_size = opt['out_size'] - - self.crop_components = opt.get('crop_components', False) # facial components - self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1) # whether enlarge eye regions - - if self.crop_components: - # load component list from a pre-process pth files - self.components_list = torch.load(opt.get('component_path')) - - # file client (lmdb io backend) - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = self.gt_folder - if not self.gt_folder.endswith('.lmdb'): - raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") - with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: - self.paths = [line.split('.')[0] for line in fin] - else: - # disk backend: scan file list from a folder - self.paths = paths_from_folder(self.gt_folder) - - # degradation configurations - self.blur_kernel_size = opt['blur_kernel_size'] - self.kernel_list = opt['kernel_list'] - self.kernel_prob = opt['kernel_prob'] - self.blur_sigma = opt['blur_sigma'] - self.downsample_range = opt['downsample_range'] - self.noise_range = opt['noise_range'] - self.jpeg_range = opt['jpeg_range'] - - # color jitter - self.color_jitter_prob = opt.get('color_jitter_prob') - self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob') - self.color_jitter_shift = opt.get('color_jitter_shift', 20) - # to gray - self.gray_prob = opt.get('gray_prob') - - logger = get_root_logger() - logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, sigma: [{", ".join(map(str, self.blur_sigma))}]') - logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]') - logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]') - logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]') - - if self.color_jitter_prob is not None: - logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, shift: {self.color_jitter_shift}') - if self.gray_prob is not None: - logger.info(f'Use random gray. Prob: {self.gray_prob}') - self.color_jitter_shift /= 255. - - @staticmethod - def color_jitter(img, shift): - """jitter color: randomly jitter the RGB values, in numpy formats""" - jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32) - img = img + jitter_val - img = np.clip(img, 0, 1) - return img - - @staticmethod - def color_jitter_pt(img, brightness, contrast, saturation, hue): - """jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats""" - fn_idx = torch.randperm(4) - for fn_id in fn_idx: - if fn_id == 0 and brightness is not None: - brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item() - img = adjust_brightness(img, brightness_factor) - - if fn_id == 1 and contrast is not None: - contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item() - img = adjust_contrast(img, contrast_factor) - - if fn_id == 2 and saturation is not None: - saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item() - img = adjust_saturation(img, saturation_factor) - - if fn_id == 3 and hue is not None: - hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item() - img = adjust_hue(img, hue_factor) - return img - - def get_component_coordinates(self, index, status): - """Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file""" - components_bbox = self.components_list[f'{index:08d}'] - if status[0]: # hflip - # exchange right and left eye - tmp = components_bbox['left_eye'] - components_bbox['left_eye'] = components_bbox['right_eye'] - components_bbox['right_eye'] = tmp - # modify the width coordinate - components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0] - components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0] - components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0] - - # get coordinates - locations = [] - for part in ['left_eye', 'right_eye', 'mouth']: - mean = components_bbox[part][0:2] - half_len = components_bbox[part][2] - if 'eye' in part: - half_len *= self.eye_enlarge_ratio - loc = np.hstack((mean - half_len + 1, mean + half_len)) - loc = torch.from_numpy(loc).float() - locations.append(loc) - return locations - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # load gt image - # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. - gt_path = self.paths[index] - img_bytes = self.file_client.get(gt_path) - img_gt = imfrombytes(img_bytes, float32=True) - - # random horizontal flip - img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True) - h, w, _ = img_gt.shape - - # get facial component coordinates - if self.crop_components: - locations = self.get_component_coordinates(index, status) - loc_left_eye, loc_right_eye, loc_mouth = locations - - # ------------------------ generate lq image ------------------------ # - # blur - kernel = degradations.random_mixed_kernels( - self.kernel_list, - self.kernel_prob, - self.blur_kernel_size, - self.blur_sigma, - self.blur_sigma, [-math.pi, math.pi], - noise_range=None) - img_lq = cv2.filter2D(img_gt, -1, kernel) - # downsample - scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1]) - img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR) - # noise - if self.noise_range is not None: - img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range) - # jpeg compression - if self.jpeg_range is not None: - img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range) - - # resize to original size - img_lq = cv2.resize(img_lq, (w, h), interpolation=cv2.INTER_LINEAR) - - # random color jitter (only for lq) - if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob): - img_lq = self.color_jitter(img_lq, self.color_jitter_shift) - # random to gray (only for lq) - if self.gray_prob and np.random.uniform() < self.gray_prob: - img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY) - img_lq = np.tile(img_lq[:, :, None], [1, 1, 3]) - if self.opt.get('gt_gray'): # whether convert GT to gray images - img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY) - img_gt = np.tile(img_gt[:, :, None], [1, 1, 3]) # repeat the color channels - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) - - # random color jitter (pytorch version) (only for lq) - if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob): - brightness = self.opt.get('brightness', (0.5, 1.5)) - contrast = self.opt.get('contrast', (0.5, 1.5)) - saturation = self.opt.get('saturation', (0, 1.5)) - hue = self.opt.get('hue', (-0.1, 0.1)) - img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue) - - # round and clip - img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255. - - # normalize - normalize(img_gt, self.mean, self.std, inplace=True) - normalize(img_lq, self.mean, self.std, inplace=True) - - if self.crop_components: - return_dict = { - 'lq': img_lq, - 'gt': img_gt, - 'gt_path': gt_path, - 'loc_left_eye': loc_left_eye, - 'loc_right_eye': loc_right_eye, - 'loc_mouth': loc_mouth - } - return return_dict - else: - return {'lq': img_lq, 'gt': img_gt, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/face_enhancer/gfpgan/models/__init__.py b/face_enhancer/gfpgan/models/__init__.py deleted file mode 100644 index 6afad57..0000000 --- a/face_enhancer/gfpgan/models/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -import importlib -from basicsr.utils import scandir -from os import path as osp - -# automatically scan and import model modules for registry -# scan all the files that end with '_model.py' under the model folder -model_folder = osp.dirname(osp.abspath(__file__)) -model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')] -# import all the model modules -_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames] diff --git a/face_enhancer/gfpgan/models/gfpgan_model.py b/face_enhancer/gfpgan/models/gfpgan_model.py deleted file mode 100644 index 684fc60..0000000 --- a/face_enhancer/gfpgan/models/gfpgan_model.py +++ /dev/null @@ -1,579 +0,0 @@ -import math -import os.path as osp -import torch -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.losses.losses import r1_penalty -from basicsr.metrics import calculate_metric -from basicsr.models.base_model import BaseModel -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.registry import MODEL_REGISTRY -from collections import OrderedDict -from torch.nn import functional as F -from torchvision.ops import roi_align -from tqdm import tqdm - - -@MODEL_REGISTRY.register() -class GFPGANModel(BaseModel): - """The GFPGAN model for Towards real-world blind face restoratin with generative facial prior""" - - def __init__(self, opt): - super(GFPGANModel, self).__init__(opt) - self.idx = 0 # it is used for saving data for check - - # define network - self.net_g = build_network(opt['network_g']) - self.net_g = self.model_to_device(self.net_g) - self.print_network(self.net_g) - - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_g', 'params') - self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) - - self.log_size = int(math.log(self.opt['network_g']['out_size'], 2)) - - if self.is_train: - self.init_training_settings() - - def init_training_settings(self): - train_opt = self.opt['train'] - - # ----------- define net_d ----------- # - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_d', None) - if load_path is not None: - self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True)) - - # ----------- define net_g with Exponential Moving Average (EMA) ----------- # - # net_g_ema only used for testing on one GPU and saving. There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - - self.net_g.train() - self.net_d.train() - self.net_g_ema.eval() - - # ----------- facial component networks ----------- # - if ('network_d_left_eye' in self.opt and 'network_d_right_eye' in self.opt and 'network_d_mouth' in self.opt): - self.use_facial_disc = True - else: - self.use_facial_disc = False - - if self.use_facial_disc: - # left eye - self.net_d_left_eye = build_network(self.opt['network_d_left_eye']) - self.net_d_left_eye = self.model_to_device(self.net_d_left_eye) - self.print_network(self.net_d_left_eye) - load_path = self.opt['path'].get('pretrain_network_d_left_eye') - if load_path is not None: - self.load_network(self.net_d_left_eye, load_path, True, 'params') - # right eye - self.net_d_right_eye = build_network(self.opt['network_d_right_eye']) - self.net_d_right_eye = self.model_to_device(self.net_d_right_eye) - self.print_network(self.net_d_right_eye) - load_path = self.opt['path'].get('pretrain_network_d_right_eye') - if load_path is not None: - self.load_network(self.net_d_right_eye, load_path, True, 'params') - # mouth - self.net_d_mouth = build_network(self.opt['network_d_mouth']) - self.net_d_mouth = self.model_to_device(self.net_d_mouth) - self.print_network(self.net_d_mouth) - load_path = self.opt['path'].get('pretrain_network_d_mouth') - if load_path is not None: - self.load_network(self.net_d_mouth, load_path, True, 'params') - - self.net_d_left_eye.train() - self.net_d_right_eye.train() - self.net_d_mouth.train() - - # ----------- define facial component gan loss ----------- # - self.cri_component = build_loss(train_opt['gan_component_opt']).to(self.device) - - # ----------- define losses ----------- # - # pixel loss - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - # perceptual loss - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - # L1 loss is used in pyramid loss, component style loss and identity loss - self.cri_l1 = build_loss(train_opt['L1_opt']).to(self.device) - - # gan loss (wgan) - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - - # ----------- define identity loss ----------- # - if 'network_identity' in self.opt: - self.use_identity = True - else: - self.use_identity = False - - if self.use_identity: - # define identity network - self.network_identity = build_network(self.opt['network_identity']) - self.network_identity = self.model_to_device(self.network_identity) - self.print_network(self.network_identity) - load_path = self.opt['path'].get('pretrain_network_identity') - if load_path is not None: - self.load_network(self.network_identity, load_path, True, None) - self.network_identity.eval() - for param in self.network_identity.parameters(): - param.requires_grad = False - - # regularization weights - self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator - self.net_d_iters = train_opt.get('net_d_iters', 1) - self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) - self.net_d_reg_every = train_opt['net_d_reg_every'] - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - - # ----------- optimizer g ----------- # - net_g_reg_ratio = 1 - normal_params = [] - for _, param in self.net_g.named_parameters(): - normal_params.append(param) - optim_params_g = [{ # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }] - optim_type = train_opt['optim_g'].pop('type') - lr = train_opt['optim_g']['lr'] * net_g_reg_ratio - betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio) - self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas) - self.optimizers.append(self.optimizer_g) - - # ----------- optimizer d ----------- # - net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1) - normal_params = [] - for _, param in self.net_d.named_parameters(): - normal_params.append(param) - optim_params_d = [{ # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_d']['lr'] - }] - optim_type = train_opt['optim_d'].pop('type') - lr = train_opt['optim_d']['lr'] * net_d_reg_ratio - betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio) - self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas) - self.optimizers.append(self.optimizer_d) - - # ----------- optimizers for facial component networks ----------- # - if self.use_facial_disc: - # setup optimizers for facial component discriminators - optim_type = train_opt['optim_component'].pop('type') - lr = train_opt['optim_component']['lr'] - # left eye - self.optimizer_d_left_eye = self.get_optimizer( - optim_type, self.net_d_left_eye.parameters(), lr, betas=(0.9, 0.99)) - self.optimizers.append(self.optimizer_d_left_eye) - # right eye - self.optimizer_d_right_eye = self.get_optimizer( - optim_type, self.net_d_right_eye.parameters(), lr, betas=(0.9, 0.99)) - self.optimizers.append(self.optimizer_d_right_eye) - # mouth - self.optimizer_d_mouth = self.get_optimizer( - optim_type, self.net_d_mouth.parameters(), lr, betas=(0.9, 0.99)) - self.optimizers.append(self.optimizer_d_mouth) - - def feed_data(self, data): - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - - if 'loc_left_eye' in data: - # get facial component locations, shape (batch, 4) - self.loc_left_eyes = data['loc_left_eye'] - self.loc_right_eyes = data['loc_right_eye'] - self.loc_mouths = data['loc_mouth'] - - # uncomment to check data - # import torchvision - # if self.opt['rank'] == 0: - # import os - # os.makedirs('tmp/gt', exist_ok=True) - # os.makedirs('tmp/lq', exist_ok=True) - # print(self.idx) - # torchvision.utils.save_image( - # self.gt, f'tmp/gt/gt_{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1)) - # torchvision.utils.save_image( - # self.lq, f'tmp/lq/lq{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1)) - # self.idx = self.idx + 1 - - def construct_img_pyramid(self): - """Construct image pyramid for intermediate restoration loss""" - pyramid_gt = [self.gt] - down_img = self.gt - for _ in range(0, self.log_size - 3): - down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False) - pyramid_gt.insert(0, down_img) - return pyramid_gt - - def get_roi_regions(self, eye_out_size=80, mouth_out_size=120): - face_ratio = int(self.opt['network_g']['out_size'] / 512) - eye_out_size *= face_ratio - mouth_out_size *= face_ratio - - rois_eyes = [] - rois_mouths = [] - for b in range(self.loc_left_eyes.size(0)): # loop for batch size - # left eye and right eye - img_inds = self.loc_left_eyes.new_full((2, 1), b) - bbox = torch.stack([self.loc_left_eyes[b, :], self.loc_right_eyes[b, :]], dim=0) # shape: (2, 4) - rois = torch.cat([img_inds, bbox], dim=-1) # shape: (2, 5) - rois_eyes.append(rois) - # mouse - img_inds = self.loc_left_eyes.new_full((1, 1), b) - rois = torch.cat([img_inds, self.loc_mouths[b:b + 1, :]], dim=-1) # shape: (1, 5) - rois_mouths.append(rois) - - rois_eyes = torch.cat(rois_eyes, 0).to(self.device) - rois_mouths = torch.cat(rois_mouths, 0).to(self.device) - - # real images - all_eyes = roi_align(self.gt, boxes=rois_eyes, output_size=eye_out_size) * face_ratio - self.left_eyes_gt = all_eyes[0::2, :, :, :] - self.right_eyes_gt = all_eyes[1::2, :, :, :] - self.mouths_gt = roi_align(self.gt, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio - # output - all_eyes = roi_align(self.output, boxes=rois_eyes, output_size=eye_out_size) * face_ratio - self.left_eyes = all_eyes[0::2, :, :, :] - self.right_eyes = all_eyes[1::2, :, :, :] - self.mouths = roi_align(self.output, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio - - def _gram_mat(self, x): - """Calculate Gram matrix. - - Args: - x (torch.Tensor): Tensor with shape of (n, c, h, w). - - Returns: - torch.Tensor: Gram matrix. - """ - n, c, h, w = x.size() - features = x.view(n, c, w * h) - features_t = features.transpose(1, 2) - gram = features.bmm(features_t) / (c * h * w) - return gram - - def gray_resize_for_identity(self, out, size=128): - out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :]) - out_gray = out_gray.unsqueeze(1) - out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False) - return out_gray - - def optimize_parameters(self, current_iter): - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - self.optimizer_g.zero_grad() - - # do not update facial component net_d - if self.use_facial_disc: - for p in self.net_d_left_eye.parameters(): - p.requires_grad = False - for p in self.net_d_right_eye.parameters(): - p.requires_grad = False - for p in self.net_d_mouth.parameters(): - p.requires_grad = False - - # image pyramid loss weight - pyramid_loss_weight = self.opt['train'].get('pyramid_loss_weight', 0) - if pyramid_loss_weight > 0 and current_iter > self.opt['train'].get('remove_pyramid_loss', float('inf')): - pyramid_loss_weight = 1e-12 # very small weight to avoid unused param error - if pyramid_loss_weight > 0: - self.output, out_rgbs = self.net_g(self.lq, return_rgb=True) - pyramid_gt = self.construct_img_pyramid() - else: - self.output, out_rgbs = self.net_g(self.lq, return_rgb=False) - - # get roi-align regions - if self.use_facial_disc: - self.get_roi_regions(eye_out_size=80, mouth_out_size=120) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - - # image pyramid loss - if pyramid_loss_weight > 0: - for i in range(0, self.log_size - 2): - l_pyramid = self.cri_l1(out_rgbs[i], pyramid_gt[i]) * pyramid_loss_weight - l_g_total += l_pyramid - loss_dict[f'l_p_{2**(i+3)}'] = l_pyramid - - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - - # gan loss - fake_g_pred = self.net_d(self.output) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - # facial component loss - if self.use_facial_disc: - # left eye - fake_left_eye, fake_left_eye_feats = self.net_d_left_eye(self.left_eyes, return_feats=True) - l_g_gan = self.cri_component(fake_left_eye, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan_left_eye'] = l_g_gan - # right eye - fake_right_eye, fake_right_eye_feats = self.net_d_right_eye(self.right_eyes, return_feats=True) - l_g_gan = self.cri_component(fake_right_eye, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan_right_eye'] = l_g_gan - # mouth - fake_mouth, fake_mouth_feats = self.net_d_mouth(self.mouths, return_feats=True) - l_g_gan = self.cri_component(fake_mouth, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan_mouth'] = l_g_gan - - if self.opt['train'].get('comp_style_weight', 0) > 0: - # get gt feat - _, real_left_eye_feats = self.net_d_left_eye(self.left_eyes_gt, return_feats=True) - _, real_right_eye_feats = self.net_d_right_eye(self.right_eyes_gt, return_feats=True) - _, real_mouth_feats = self.net_d_mouth(self.mouths_gt, return_feats=True) - - def _comp_style(feat, feat_gt, criterion): - return criterion(self._gram_mat(feat[0]), self._gram_mat( - feat_gt[0].detach())) * 0.5 + criterion( - self._gram_mat(feat[1]), self._gram_mat(feat_gt[1].detach())) - - # facial component style loss - comp_style_loss = 0 - comp_style_loss += _comp_style(fake_left_eye_feats, real_left_eye_feats, self.cri_l1) - comp_style_loss += _comp_style(fake_right_eye_feats, real_right_eye_feats, self.cri_l1) - comp_style_loss += _comp_style(fake_mouth_feats, real_mouth_feats, self.cri_l1) - comp_style_loss = comp_style_loss * self.opt['train']['comp_style_weight'] - l_g_total += comp_style_loss - loss_dict['l_g_comp_style_loss'] = comp_style_loss - - # identity loss - if self.use_identity: - identity_weight = self.opt['train']['identity_weight'] - # get gray images and resize - out_gray = self.gray_resize_for_identity(self.output) - gt_gray = self.gray_resize_for_identity(self.gt) - - identity_gt = self.network_identity(gt_gray).detach() - identity_out = self.network_identity(out_gray) - l_identity = self.cri_l1(identity_out, identity_gt) * identity_weight - l_g_total += l_identity - loss_dict['l_identity'] = l_identity - - l_g_total.backward() - self.optimizer_g.step() - - # EMA - self.model_ema(decay=0.5**(32 / (10 * 1000))) - - # ----------- optimize net_d ----------- # - for p in self.net_d.parameters(): - p.requires_grad = True - self.optimizer_d.zero_grad() - if self.use_facial_disc: - for p in self.net_d_left_eye.parameters(): - p.requires_grad = True - for p in self.net_d_right_eye.parameters(): - p.requires_grad = True - for p in self.net_d_mouth.parameters(): - p.requires_grad = True - self.optimizer_d_left_eye.zero_grad() - self.optimizer_d_right_eye.zero_grad() - self.optimizer_d_mouth.zero_grad() - - fake_d_pred = self.net_d(self.output.detach()) - real_d_pred = self.net_d(self.gt) - l_d = self.cri_gan(real_d_pred, True, is_disc=True) + self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d'] = l_d - # In WGAN, real_score should be positive and fake_score should be negative - loss_dict['real_score'] = real_d_pred.detach().mean() - loss_dict['fake_score'] = fake_d_pred.detach().mean() - l_d.backward() - - # regularization loss - if current_iter % self.net_d_reg_every == 0: - self.gt.requires_grad = True - real_pred = self.net_d(self.gt) - l_d_r1 = r1_penalty(real_pred, self.gt) - l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) - loss_dict['l_d_r1'] = l_d_r1.detach().mean() - l_d_r1.backward() - - self.optimizer_d.step() - - # optimize facial component discriminators - if self.use_facial_disc: - # left eye - fake_d_pred, _ = self.net_d_left_eye(self.left_eyes.detach()) - real_d_pred, _ = self.net_d_left_eye(self.left_eyes_gt) - l_d_left_eye = self.cri_component( - real_d_pred, True, is_disc=True) + self.cri_gan( - fake_d_pred, False, is_disc=True) - loss_dict['l_d_left_eye'] = l_d_left_eye - l_d_left_eye.backward() - # right eye - fake_d_pred, _ = self.net_d_right_eye(self.right_eyes.detach()) - real_d_pred, _ = self.net_d_right_eye(self.right_eyes_gt) - l_d_right_eye = self.cri_component( - real_d_pred, True, is_disc=True) + self.cri_gan( - fake_d_pred, False, is_disc=True) - loss_dict['l_d_right_eye'] = l_d_right_eye - l_d_right_eye.backward() - # mouth - fake_d_pred, _ = self.net_d_mouth(self.mouths.detach()) - real_d_pred, _ = self.net_d_mouth(self.mouths_gt) - l_d_mouth = self.cri_component( - real_d_pred, True, is_disc=True) + self.cri_gan( - fake_d_pred, False, is_disc=True) - loss_dict['l_d_mouth'] = l_d_mouth - l_d_mouth.backward() - - self.optimizer_d_left_eye.step() - self.optimizer_d_right_eye.step() - self.optimizer_d_mouth.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - def test(self): - with torch.no_grad(): - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - self.output, _ = self.net_g_ema(self.lq) - else: - logger = get_root_logger() - logger.warning('Do not have self.net_g_ema, use self.net_g.') - self.net_g.eval() - self.output, _ = self.net_g(self.lq) - self.net_g.train() - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - if self.opt['rank'] == 0: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset_name = dataloader.dataset.opt['name'] - with_metrics = self.opt['val'].get('metrics') is not None - use_pbar = self.opt['val'].get('pbar', False) - - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} - # initialize the best metric results for each dataset_name (supporting multiple validation datasets) - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - self.metric_results = {metric: 0 for metric in self.metric_results} - - metric_data = dict() - if use_pbar: - pbar = tqdm(total=len(dataloader), unit='image') - - for idx, val_data in enumerate(dataloader): - img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] - self.feed_data(val_data) - self.test() - - sr_img = tensor2img(self.output.detach().cpu(), min_max=(-1, 1)) - metric_data['img'] = sr_img - if hasattr(self, 'gt'): - gt_img = tensor2img(self.gt.detach().cpu(), min_max=(-1, 1)) - metric_data['img2'] = gt_img - del self.gt - - # tentative for out of GPU memory - del self.lq - del self.output - torch.cuda.empty_cache() - - if save_img: - if self.opt['is_train']: - save_img_path = osp.join(self.opt['path']['visualization'], img_name, - f'{img_name}_{current_iter}.png') - else: - if self.opt['val']['suffix']: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["val"]["suffix"]}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["name"]}.png') - imwrite(sr_img, save_img_path) - - if with_metrics: - # calculate metrics - for name, opt_ in self.opt['val']['metrics'].items(): - self.metric_results[name] += calculate_metric(metric_data, opt_) - if use_pbar: - pbar.update(1) - pbar.set_description(f'Test {img_name}') - if use_pbar: - pbar.close() - - if with_metrics: - for metric in self.metric_results.keys(): - self.metric_results[metric] /= (idx + 1) - # update the best metric result - self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) - - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): - log_str = f'Validation {dataset_name}\n' - for metric, value in self.metric_results.items(): - log_str += f'\t # {metric}: {value:.4f}' - if hasattr(self, 'best_metric_results'): - log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' - f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') - log_str += '\n' - - logger = get_root_logger() - logger.info(log_str) - if tb_logger: - for metric, value in self.metric_results.items(): - tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter) - - def save(self, epoch, current_iter): - # save net_g and net_d - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - self.save_network(self.net_d, 'net_d', current_iter) - # save component discriminators - if self.use_facial_disc: - self.save_network(self.net_d_left_eye, 'net_d_left_eye', current_iter) - self.save_network(self.net_d_right_eye, 'net_d_right_eye', current_iter) - self.save_network(self.net_d_mouth, 'net_d_mouth', current_iter) - # save training state - self.save_training_state(epoch, current_iter) diff --git a/face_enhancer/gfpgan/train.py b/face_enhancer/gfpgan/train.py deleted file mode 100644 index fe5f1f9..0000000 --- a/face_enhancer/gfpgan/train.py +++ /dev/null @@ -1,11 +0,0 @@ -# flake8: noqa -import os.path as osp -from basicsr.train import train_pipeline - -import gfpgan.archs -import gfpgan.data -import gfpgan.models - -if __name__ == '__main__': - root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) - train_pipeline(root_path) diff --git a/face_enhancer/gfpgan/utils.py b/face_enhancer/gfpgan/utils.py deleted file mode 100644 index 1cc104d..0000000 --- a/face_enhancer/gfpgan/utils.py +++ /dev/null @@ -1,143 +0,0 @@ -import cv2 -import os -import torch -from basicsr.utils import img2tensor, tensor2img -from basicsr.utils.download_util import load_file_from_url -from facexlib.utils.face_restoration_helper import FaceRestoreHelper -from torchvision.transforms.functional import normalize - -from gfpgan.archs.gfpgan_bilinear_arch import GFPGANBilinear -from gfpgan.archs.gfpganv1_arch import GFPGANv1 -from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean - -ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - -class GFPGANer(): - """Helper for restoration with GFPGAN. - - It will detect and crop faces, and then resize the faces to 512x512. - GFPGAN is used to restored the resized faces. - The background is upsampled with the bg_upsampler. - Finally, the faces will be pasted back to the upsample background image. - - Args: - model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically). - upscale (float): The upscale of the final output. Default: 2. - arch (str): The GFPGAN architecture. Option: clean | original. Default: clean. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - bg_upsampler (nn.Module): The upsampler for the background. Default: None. - """ - - def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None): - self.upscale = upscale - self.bg_upsampler = bg_upsampler - - # initialize model - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - # initialize the GFP-GAN - if arch == 'clean': - self.gfpgan = GFPGANv1Clean( - out_size=512, - num_style_feat=512, - channel_multiplier=channel_multiplier, - decoder_load_path=None, - fix_decoder=False, - num_mlp=8, - input_is_latent=True, - different_w=True, - narrow=1, - sft_half=True) - elif arch == 'bilinear': - self.gfpgan = GFPGANBilinear( - out_size=512, - num_style_feat=512, - channel_multiplier=channel_multiplier, - decoder_load_path=None, - fix_decoder=False, - num_mlp=8, - input_is_latent=True, - different_w=True, - narrow=1, - sft_half=True) - elif arch == 'original': - self.gfpgan = GFPGANv1( - out_size=512, - num_style_feat=512, - channel_multiplier=channel_multiplier, - decoder_load_path=None, - fix_decoder=True, - num_mlp=8, - input_is_latent=True, - different_w=True, - narrow=1, - sft_half=True) - # initialize face helper - self.face_helper = FaceRestoreHelper( - upscale, - face_size=512, - crop_ratio=(1, 1), - det_model='retinaface_resnet50', - save_ext='png', - device=self.device) - - if model_path.startswith('https://'): - model_path = load_file_from_url( - url=model_path, model_dir=os.path.join(ROOT_DIR, 'gfpgan/weights'), progress=True, file_name=None) - loadnet = torch.load(model_path) - if 'params_ema' in loadnet: - keyname = 'params_ema' - else: - keyname = 'params' - self.gfpgan.load_state_dict(loadnet[keyname], strict=True) - self.gfpgan.eval() - self.gfpgan = self.gfpgan.to(self.device) - - @torch.no_grad() - def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True): - self.face_helper.clean_all() - - if has_aligned: # the inputs are already aligned - img = cv2.resize(img, (512, 512)) - self.face_helper.cropped_faces = [img] - else: - self.face_helper.read_image(img) - # get face landmarks for each face - self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5) - # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels - # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations. - # align and warp each face - self.face_helper.align_warp_face() - - # face restoration - for cropped_face in self.face_helper.cropped_faces: - # prepare data - cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) - normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) - cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device) - - try: - output = self.gfpgan(cropped_face_t, return_rgb=False)[0] - # convert to image - restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)) - except RuntimeError as error: - print(f'\tFailed inference for GFPGAN: {error}.') - restored_face = cropped_face - - restored_face = restored_face.astype('uint8') - self.face_helper.add_restored_face(restored_face) - - if not has_aligned and paste_back: - # upsample the background - if self.bg_upsampler is not None: - # Now only support RealESRGAN for upsampling background - bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0] - else: - bg_img = None - - self.face_helper.get_inverse_affine(None) - # paste each restored face to the input image - restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img) - return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img - else: - return self.face_helper.cropped_faces, self.face_helper.restored_faces, None diff --git a/face_enhancer/gfpgan/version.py b/face_enhancer/gfpgan/version.py deleted file mode 100644 index 3a5a8d7..0000000 --- a/face_enhancer/gfpgan/version.py +++ /dev/null @@ -1,5 +0,0 @@ -# GENERATED VERSION FILE -# TIME: Wed Mar 30 13:34:44 2022 -__version__ = '1.3.2' -__gitsha__ = 'unknown' -version_info = (1, 3, 2) diff --git a/face_enhancer/gfpgan/weights/README.md b/face_enhancer/gfpgan/weights/README.md deleted file mode 100644 index 4d7b7e6..0000000 --- a/face_enhancer/gfpgan/weights/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Weights - -Put the downloaded weights to this folder. diff --git a/face_enhancer/scripts/convert_gfpganv_to_clean.py b/face_enhancer/scripts/convert_gfpganv_to_clean.py deleted file mode 100644 index 8fdccb6..0000000 --- a/face_enhancer/scripts/convert_gfpganv_to_clean.py +++ /dev/null @@ -1,164 +0,0 @@ -import argparse -import math -import torch - -from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean - - -def modify_checkpoint(checkpoint_bilinear, checkpoint_clean): - for ori_k, ori_v in checkpoint_bilinear.items(): - if 'stylegan_decoder' in ori_k: - if 'style_mlp' in ori_k: # style_mlp_layers - lr_mul = 0.01 - prefix, name, idx, var = ori_k.split('.') - idx = (int(idx) * 2) - 1 - crt_k = f'{prefix}.{name}.{idx}.{var}' - if var == 'weight': - _, c_in = ori_v.size() - scale = (1 / math.sqrt(c_in)) * lr_mul - crt_v = ori_v * scale * 2**0.5 - else: - crt_v = ori_v * lr_mul * 2**0.5 - checkpoint_clean[crt_k] = crt_v - elif 'modulation' in ori_k: # modulation in StyleConv - lr_mul = 1 - crt_k = ori_k - var = ori_k.split('.')[-1] - if var == 'weight': - _, c_in = ori_v.size() - scale = (1 / math.sqrt(c_in)) * lr_mul - crt_v = ori_v * scale - else: - crt_v = ori_v * lr_mul - checkpoint_clean[crt_k] = crt_v - elif 'style_conv' in ori_k: - # StyleConv in style_conv1 and style_convs - if 'activate' in ori_k: # FusedLeakyReLU - # eg. style_conv1.activate.bias - # eg. style_convs.13.activate.bias - split_rlt = ori_k.split('.') - if len(split_rlt) == 4: - prefix, name, _, var = split_rlt - crt_k = f'{prefix}.{name}.{var}' - elif len(split_rlt) == 5: - prefix, name, idx, _, var = split_rlt - crt_k = f'{prefix}.{name}.{idx}.{var}' - crt_v = ori_v * 2**0.5 # 2**0.5 used in FusedLeakyReLU - c = crt_v.size(0) - checkpoint_clean[crt_k] = crt_v.view(1, c, 1, 1) - elif 'modulated_conv' in ori_k: - # eg. style_conv1.modulated_conv.weight - # eg. style_convs.13.modulated_conv.weight - _, c_out, c_in, k1, k2 = ori_v.size() - scale = 1 / math.sqrt(c_in * k1 * k2) - crt_k = ori_k - checkpoint_clean[crt_k] = ori_v * scale - elif 'weight' in ori_k: - crt_k = ori_k - checkpoint_clean[crt_k] = ori_v * 2**0.5 - elif 'to_rgb' in ori_k: # StyleConv in to_rgb1 and to_rgbs - if 'modulated_conv' in ori_k: - # eg. to_rgb1.modulated_conv.weight - # eg. to_rgbs.5.modulated_conv.weight - _, c_out, c_in, k1, k2 = ori_v.size() - scale = 1 / math.sqrt(c_in * k1 * k2) - crt_k = ori_k - checkpoint_clean[crt_k] = ori_v * scale - else: - crt_k = ori_k - checkpoint_clean[crt_k] = ori_v - else: - crt_k = ori_k - checkpoint_clean[crt_k] = ori_v - # end of 'stylegan_decoder' - elif 'conv_body_first' in ori_k or 'final_conv' in ori_k: - # key name - name, _, var = ori_k.split('.') - crt_k = f'{name}.{var}' - # weight and bias - if var == 'weight': - c_out, c_in, k1, k2 = ori_v.size() - scale = 1 / math.sqrt(c_in * k1 * k2) - checkpoint_clean[crt_k] = ori_v * scale * 2**0.5 - else: - checkpoint_clean[crt_k] = ori_v * 2**0.5 - elif 'conv_body' in ori_k: - if 'conv_body_up' in ori_k: - ori_k = ori_k.replace('conv2.weight', 'conv2.1.weight') - ori_k = ori_k.replace('skip.weight', 'skip.1.weight') - name1, idx1, name2, _, var = ori_k.split('.') - crt_k = f'{name1}.{idx1}.{name2}.{var}' - if name2 == 'skip': - c_out, c_in, k1, k2 = ori_v.size() - scale = 1 / math.sqrt(c_in * k1 * k2) - checkpoint_clean[crt_k] = ori_v * scale / 2**0.5 - else: - if var == 'weight': - c_out, c_in, k1, k2 = ori_v.size() - scale = 1 / math.sqrt(c_in * k1 * k2) - checkpoint_clean[crt_k] = ori_v * scale - else: - checkpoint_clean[crt_k] = ori_v - if 'conv1' in ori_k: - checkpoint_clean[crt_k] *= 2**0.5 - elif 'toRGB' in ori_k: - crt_k = ori_k - if 'weight' in ori_k: - c_out, c_in, k1, k2 = ori_v.size() - scale = 1 / math.sqrt(c_in * k1 * k2) - checkpoint_clean[crt_k] = ori_v * scale - else: - checkpoint_clean[crt_k] = ori_v - elif 'final_linear' in ori_k: - crt_k = ori_k - if 'weight' in ori_k: - _, c_in = ori_v.size() - scale = 1 / math.sqrt(c_in) - checkpoint_clean[crt_k] = ori_v * scale - else: - checkpoint_clean[crt_k] = ori_v - elif 'condition' in ori_k: - crt_k = ori_k - if '0.weight' in ori_k: - c_out, c_in, k1, k2 = ori_v.size() - scale = 1 / math.sqrt(c_in * k1 * k2) - checkpoint_clean[crt_k] = ori_v * scale * 2**0.5 - elif '0.bias' in ori_k: - checkpoint_clean[crt_k] = ori_v * 2**0.5 - elif '2.weight' in ori_k: - c_out, c_in, k1, k2 = ori_v.size() - scale = 1 / math.sqrt(c_in * k1 * k2) - checkpoint_clean[crt_k] = ori_v * scale - elif '2.bias' in ori_k: - checkpoint_clean[crt_k] = ori_v - - return checkpoint_clean - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--ori_path', type=str, help='Path to the original model') - parser.add_argument('--narrow', type=float, default=1) - parser.add_argument('--channel_multiplier', type=float, default=2) - parser.add_argument('--save_path', type=str) - args = parser.parse_args() - - ori_ckpt = torch.load(args.ori_path)['params_ema'] - - net = GFPGANv1Clean( - 512, - num_style_feat=512, - channel_multiplier=args.channel_multiplier, - decoder_load_path=None, - fix_decoder=False, - # for stylegan decoder - num_mlp=8, - input_is_latent=True, - different_w=True, - narrow=args.narrow, - sft_half=True) - crt_ckpt = net.state_dict() - - crt_ckpt = modify_checkpoint(ori_ckpt, crt_ckpt) - print(f'Save to {args.save_path}.') - torch.save(dict(params_ema=crt_ckpt), args.save_path, _use_new_zipfile_serialization=False) diff --git a/face_enhancer/scripts/parse_landmark.py b/face_enhancer/scripts/parse_landmark.py deleted file mode 100644 index 74e2ff9..0000000 --- a/face_enhancer/scripts/parse_landmark.py +++ /dev/null @@ -1,85 +0,0 @@ -import cv2 -import json -import numpy as np -import os -import torch -from basicsr.utils import FileClient, imfrombytes -from collections import OrderedDict - -# ---------------------------- This script is used to parse facial landmarks ------------------------------------- # -# Configurations -save_img = False -scale = 0.5 # 0.5 for official FFHQ (512x512), 1 for others -enlarge_ratio = 1.4 # only for eyes -json_path = 'ffhq-dataset-v2.json' -face_path = 'datasets/ffhq/ffhq_512.lmdb' -save_path = './FFHQ_eye_mouth_landmarks_512.pth' - -print('Load JSON metadata...') -# use the official json file in FFHQ dataset -with open(json_path, 'rb') as f: - json_data = json.load(f, object_pairs_hook=OrderedDict) - -print('Open LMDB file...') -# read ffhq images -file_client = FileClient('lmdb', db_paths=face_path) -with open(os.path.join(face_path, 'meta_info.txt')) as fin: - paths = [line.split('.')[0] for line in fin] - -save_dict = {} - -for item_idx, item in enumerate(json_data.values()): - print(f'\r{item_idx} / {len(json_data)}, {item["image"]["file_path"]} ', end='', flush=True) - - # parse landmarks - lm = np.array(item['image']['face_landmarks']) - lm = lm * scale - - item_dict = {} - # get image - if save_img: - img_bytes = file_client.get(paths[item_idx]) - img = imfrombytes(img_bytes, float32=True) - - # get landmarks for each component - map_left_eye = list(range(36, 42)) - map_right_eye = list(range(42, 48)) - map_mouth = list(range(48, 68)) - - # eye_left - mean_left_eye = np.mean(lm[map_left_eye], 0) # (x, y) - half_len_left_eye = np.max((np.max(np.max(lm[map_left_eye], 0) - np.min(lm[map_left_eye], 0)) / 2, 16)) - item_dict['left_eye'] = [mean_left_eye[0], mean_left_eye[1], half_len_left_eye] - # mean_left_eye[0] = 512 - mean_left_eye[0] # for testing flip - half_len_left_eye *= enlarge_ratio - loc_left_eye = np.hstack((mean_left_eye - half_len_left_eye + 1, mean_left_eye + half_len_left_eye)).astype(int) - if save_img: - eye_left_img = img[loc_left_eye[1]:loc_left_eye[3], loc_left_eye[0]:loc_left_eye[2], :] - cv2.imwrite(f'tmp/{item_idx:08d}_eye_left.png', eye_left_img * 255) - - # eye_right - mean_right_eye = np.mean(lm[map_right_eye], 0) - half_len_right_eye = np.max((np.max(np.max(lm[map_right_eye], 0) - np.min(lm[map_right_eye], 0)) / 2, 16)) - item_dict['right_eye'] = [mean_right_eye[0], mean_right_eye[1], half_len_right_eye] - # mean_right_eye[0] = 512 - mean_right_eye[0] # # for testing flip - half_len_right_eye *= enlarge_ratio - loc_right_eye = np.hstack( - (mean_right_eye - half_len_right_eye + 1, mean_right_eye + half_len_right_eye)).astype(int) - if save_img: - eye_right_img = img[loc_right_eye[1]:loc_right_eye[3], loc_right_eye[0]:loc_right_eye[2], :] - cv2.imwrite(f'tmp/{item_idx:08d}_eye_right.png', eye_right_img * 255) - - # mouth - mean_mouth = np.mean(lm[map_mouth], 0) - half_len_mouth = np.max((np.max(np.max(lm[map_mouth], 0) - np.min(lm[map_mouth], 0)) / 2, 16)) - item_dict['mouth'] = [mean_mouth[0], mean_mouth[1], half_len_mouth] - # mean_mouth[0] = 512 - mean_mouth[0] # for testing flip - loc_mouth = np.hstack((mean_mouth - half_len_mouth + 1, mean_mouth + half_len_mouth)).astype(int) - if save_img: - mouth_img = img[loc_mouth[1]:loc_mouth[3], loc_mouth[0]:loc_mouth[2], :] - cv2.imwrite(f'tmp/{item_idx:08d}_mouth.png', mouth_img * 255) - - save_dict[f'{item_idx:08d}'] = item_dict - -print('Save...') -torch.save(save_dict, save_path) diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/.gitignore b/face_parse/PSFRGAN-master/PSFRGAN-master/.gitignore deleted file mode 100644 index 0a07a97..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/.gitignore +++ /dev/null @@ -1,110 +0,0 @@ -check_points/ -pretrain_models* -test_dir_enhance_results/ -test_dir_align_results/ -test_unalign_results/ -tmp* -local* -*.pth - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - 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For -the avoidance of doubt, this paragraph does not form part of the -public licenses. - -Creative Commons may be contacted at creativecommons.org. diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/README.md b/face_parse/PSFRGAN-master/PSFRGAN-master/README.md deleted file mode 100644 index dc8e528..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/README.md +++ /dev/null @@ -1,123 +0,0 @@ -# PSFR-GAN in PyTorch - -[Progressive Semantic-Aware Style Transformation for Blind Face Restoration](https://arxiv.org/abs/2009.08709) -[Chaofeng Chen](https://chaofengc.github.io), [Xiaoming Li](https://csxmli2016.github.io/), [Lingbo Yang](https://lotayou.github.io), [Xianhui Lin](https://dblp.org/pid/147/7708.html), [Lei Zhang](https://www4.comp.polyu.edu.hk/~cslzhang/), [Kwan-Yee K. Wong](https://i.cs.hku.hk/~kykwong/) - -![](test_dir/test_hzgg.jpg) -![](test_hzgg_results/hq_final.jpg) - -### Changelog -- **2021.04.26**: Add pytorch vgg19 model to GoogleDrive and remove `--distributed` option which causes training error. -- **2021.03.22**: Update new model at 15 epoch (52.5k iterations). -- **2021.03.19**: Add train codes for PSFRGAN and FPN. - -## Prerequisites and Installation -- Ubuntu 18.04 -- CUDA 10.1 -- Clone this repository - ``` - git clone https://github.com/chaofengc/PSFR-GAN.git - cd PSFR-GAN - ``` -- Python 3.7, install required packages by `pip3 install -r requirements.txt` - -## Quick Test - -### Download Pretrain Models and Dataset -Download the pretrained models from the following link and put them to `./pretrain_models` -- [GoogleDrive](https://drive.google.com/drive/folders/1Ubejhxd2xd4fxGc_M_LWl3Ux6CgQd9rP?usp=sharing) -- [BaiduNetDisk](https://pan.baidu.com/s/1cru3uUASEfGX6p6L0_7gWQ), extract code: `gj2r` - -### Test single image -Run the following script to enhance face(s) in single input -``` -python test_enhance_single_unalign.py --test_img_path ./test_dir/test_hzgg.jpg --results_dir test_hzgg_results --gpus 1 -``` - -This script do the following things: -- Crop and align all the faces from input image, stored at `results_dir/LQ_faces` -- Parse these faces and then enhance them, results stored at `results_dir/ParseMaps` and `results_dir/HQ` -- Paste then enhanced faces back to the original image `results_dir/hq_final.jpg` -- You can use `--gpus` to specify how many GPUs to use, `<=0` means running on CPU. The program will use GPU with the most available memory. Set `CUDA_VISIBLE_DEVICE` to specify the GPU if you do not want automatic GPU selection. - -### Test image folder -To test multiple images, we first crop out all the faces and align them use the following script. -``` -python align_and_crop_dir.py --src_dir test_dir --results_dir test_dir_align_results -``` - -For images (*e.g.* `multiface_test.jpg`) contain multiple faces, the aligned faces will be stored as `multiface_test_{face_index}.jpg` -And then parse the aligned faces and enhance them with -``` -python test_enhance_dir_align.py --src_dir test_dir_align_results --results_dir test_dir_enhance_results -``` -Results will be saved to three folders respectively: `results_dir/lq`, `results_dir/parse`, `results_dir/hq`. - -### Additional test script - -For your convenience, we also provide script to test multiple unaligned images and paste the enhance results back. **Note the paste back operation could be quite slow for large size images containing many faces (dlib takes time to detect faces in large image).** -``` -python test_enhance_dir_unalign.py --src_dir test_dir --results_dir test_unalign_results -``` -This script basically do the same thing as `test_enhance_single_unalign.py` for each image in `src_dir` - -## Train the Model - -### Data Preparation - -- Download [FFHQ](https://github.com/NVlabs/ffhq-dataset) and put the images to `../datasets/FFHQ/imgs1024` -- Download parsing masks (`512x512`) [HERE](https://drive.google.com/file/d/1eQwO8hKcaluyCnxuZAp0eJVOdgMi30uA/view?usp=sharing) generated by the pretrained FPN and put them to `../datasets/FFHQ/masks512`. - -*Note: you may change `../datasets/FFHQ` to your own path. But images and masks must be stored under `your_own_path/imgs1024` and `your_own_path/masks512` respectively.* - -### Train Script for PSFRGAN - -Here is an example train script for PSFRGAN: - -``` -python train.py --gpus 2 --model enhance --name PSFRGAN_v001 \ - --g_lr 0.0001 --d_lr 0.0004 --beta1 0.5 \ - --gan_mode 'hinge' --lambda_pix 10 --lambda_fm 10 --lambda_ss 1000 \ - --Dinput_nc 22 --D_num 3 --n_layers_D 4 \ - --batch_size 2 --dataset ffhq --dataroot ../datasets/FFHQ \ - --visual_freq 100 --print_freq 10 #--continue_train -``` -- Please change the `--name` option for different experiments. Tensorboard records with the same name will be moved to `check_points/log_archive`, and the weight directory will only store weight history of latest experiment with the same name. -- `--gpus` specify number of GPUs used to train. The script will use GPUs with more available memory first. To specify the GPU index, use `export CUDA_VISIBLE_DEVICES=your_gpu_ids` before the script. -- Uncomment `--continue_train` to resume training. *Current codes do not resume the optimizer state.* -- It needs at least **8GB** memory to train with **batch_size=1**. - -### Scripts for FPN - -You may also train your own FPN and generate masks for the HQ images by yourself with the following steps: - -- Download [CelebAHQ-Mask](https://github.com/switchablenorms/CelebAMask-HQ) dataset. Generate `CelebAMask-HQ-mask` and `CelebAMask-HQ-mask-color` with the provided scripts in `CelebAMask-HQ/face_parsing/Data_preprocessing/`. -- Train FPN with the following commmand -``` -python train.py --gpus 1 --model parse --name FPN_v001 \ - --lr 0.0002 --batch_size 8 \ - --dataset celebahqmask --dataroot ../datasets/CelebAMask-HQ \ - --visual_freq 100 --print_freq 10 #--continue_train -``` -- Generate parsing masks with your own FPN using the following command: -``` -python generate_masks.py --save_masks_dir ../datasets/FFHQ/masks512 --batch_size 8 --parse_net_weight path/to/your/own/FPN -``` - -## Citation -``` -@inproceedings{ChenPSFRGAN, - author = {Chen, Chaofeng and Li, Xiaoming and Lingbo, Yang and Lin, Xianhui and Zhang, Lei and Wong, KKY}, - title = {Progressive Semantic-Aware Style Transformation for Blind Face Restoration}, - Journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, - year = {2021} -} -``` - -## License - -Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. - -## Acknowledgement - -This work is inspired by [SPADE](https://github.com/NVlabs/SPADE), and closed related to [DFDNet](https://github.com/csxmli2016/DFDNet) and [HiFaceGAN](https://github.com/Lotayou/Face-Renovation). Our codes largely benefit from [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/align_and_crop_dir.py b/face_parse/PSFRGAN-master/PSFRGAN-master/align_and_crop_dir.py deleted file mode 100644 index dbb41b4..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/align_and_crop_dir.py +++ /dev/null @@ -1,86 +0,0 @@ -import dlib -import os -import cv2 -import numpy as np -from tqdm import tqdm -from skimage import transform as trans -from skimage import io -import argparse - - -def get_points(img, detector, shape_predictor, size_threshold=999): - dets = detector(img, 1) - if len(dets) == 0: - return None - - all_points = [] - for det in dets: - if isinstance(detector, dlib.cnn_face_detection_model_v1): - rec = det.rect # for cnn detector - else: - rec = det - if rec.width() > size_threshold or rec.height() > size_threshold: - break - shape = shape_predictor(img, rec) - single_points = [] - for i in range(5): - single_points.append([shape.part(i).x, shape.part(i).y]) - all_points.append(np.array(single_points)) - if len(all_points) <= 0: - return None - else: - return all_points - -def align_and_save(img, save_path, src_points, template_path, template_scale=1): - out_size = (512, 512) - reference = np.load(template_path) / template_scale - - ext = os.path.splitext(save_path) - for idx, spoint in enumerate(src_points): - tform = trans.SimilarityTransform() - tform.estimate(spoint, reference) - M = tform.params[0:2,:] - - crop_img = cv2.warpAffine(img, M, out_size) - if len(src_points) > 1: - save_path = ext[0] + '_{}'.format(idx) + ext[1] - dlib.save_image(crop_img.astype(np.uint8), save_path) - print('Saving image', save_path) - -def align_and_save_dir(src_dir, save_dir, template_path='./pretrain_models/FFHQ_template.npy', template_scale=2, use_cnn_detector=True): - out_size = (512, 512) - if use_cnn_detector: - detector = dlib.cnn_face_detection_model_v1('./pretrain_models/mmod_human_face_detector.dat') - else: - detector = dlib.get_frontal_face_detector() - sp = dlib.shape_predictor('./pretrain_models/shape_predictor_5_face_landmarks.dat') - - for name in os.listdir(src_dir): - img_path = os.path.join(src_dir, name) - img = dlib.load_rgb_image(img_path) - - points = get_points(img, detector, sp) - if points is not None: - save_path = os.path.join(save_dir, name) - align_and_save(img, save_path, points, template_path, template_scale) - else: - print('No face detected in', img_path) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--src_dir', type=str, help='source directory containing images to crop and align.') - parser.add_argument('--results_dir', type=str, help='results directory to save the aligned faces.') - parser.add_argument('--not_use_cnn_detector', action='store_true', help='do not use cnn face detector in dlib.') - args = parser.parse_args() - - src_dir = args.src_dir - assert os.path.isdir(src_dir), 'Source path should be a directory containing images' - save_dir = args.results_dir - if not os.path.exists(save_dir): os.makedirs(save_dir, exist_ok=True) - align_and_save_dir(src_dir, save_dir, use_cnn_detector=not args.not_use_cnn_detector) - - - - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/data/__init__.py b/face_parse/PSFRGAN-master/PSFRGAN-master/data/__init__.py deleted file mode 100644 index 9d3dfcc..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/data/__init__.py +++ /dev/null @@ -1,94 +0,0 @@ -"""This package includes all the modules related to data loading and preprocessing - - To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset. - You need to implement four functions: - -- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt). - -- <__len__>: return the size of dataset. - -- <__getitem__>: get a data point from data loader. - -- : (optionally) add dataset-specific options and set default options. - -Now you can use the dataset class by specifying flag '--dataset_mode dummy'. -See our template dataset class 'template_dataset.py' for more details. -""" -import importlib -import torch.utils.data -from data.base_dataset import BaseDataset - - -def find_dataset_using_name(dataset_name): - """Import the module "data/[dataset_name]_dataset.py". - - In the file, the class called DatasetNameDataset() will - be instantiated. It has to be a subclass of BaseDataset, - and it is case-insensitive. - """ - dataset_filename = "data." + dataset_name + "_dataset" - datasetlib = importlib.import_module(dataset_filename) - - dataset = None - target_dataset_name = dataset_name.replace('_', '') + 'dataset' - for name, cls in datasetlib.__dict__.items(): - if name.lower() == target_dataset_name.lower() \ - and issubclass(cls, BaseDataset): - dataset = cls - - if dataset is None: - raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name)) - - return dataset - - -def get_option_setter(dataset_name): - """Return the static method of the dataset class.""" - dataset_class = find_dataset_using_name(dataset_name) - return dataset_class.modify_commandline_options - - -def create_dataset(opt): - """Create a dataset given the option. - - This function wraps the class CustomDatasetDataLoader. - This is the main interface between this package and 'train.py'/'test.py' - - Example: - >>> from data import create_dataset - >>> dataset = create_dataset(opt) - """ - data_loader = CustomDatasetDataLoader(opt) - dataset = data_loader.load_data() - return dataset - - -class CustomDatasetDataLoader(): - """Wrapper class of Dataset class that performs multi-threaded data loading""" - - def __init__(self, opt): - """Initialize this class - - Step 1: create a dataset instance given the name [dataset_mode] - Step 2: create a multi-threaded data loader. - """ - self.opt = opt - dataset_class = find_dataset_using_name(opt.dataset_name) - self.dataset = dataset_class(opt) - print("dataset [%s] was created" % type(self.dataset).__name__) - drop_last = True if opt.isTrain else False - self.dataloader = torch.utils.data.DataLoader( - self.dataset, - batch_size=opt.batch_size, - shuffle=not opt.serial_batches, - num_workers=int(opt.num_threads), drop_last=drop_last) - - def load_data(self): - return self - - def __len__(self): - """Return the number of data in the dataset""" - return min(len(self.dataset), self.opt.max_dataset_size) - - def __iter__(self): - """Return a batch of data""" - for i, data in enumerate(self.dataloader): - if i * self.opt.batch_size >= self.opt.max_dataset_size: - break - yield data diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/data/base_dataset.py b/face_parse/PSFRGAN-master/PSFRGAN-master/data/base_dataset.py deleted file mode 100644 index 6b34f5f..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/data/base_dataset.py +++ /dev/null @@ -1,162 +0,0 @@ -"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets. - -It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses. -""" -import random -import numpy as np -import torch.utils.data as data -from PIL import Image -import torchvision.transforms as transforms -from abc import ABC, abstractmethod - -import imgaug as ia -import imgaug.augmenters as iaa - -class BaseDataset(data.Dataset, ABC): - """This class is an abstract base class (ABC) for datasets. - - To create a subclass, you need to implement the following four functions: - -- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt). - -- <__len__>: return the size of dataset. - -- <__getitem__>: get a data point. - -- : (optionally) add dataset-specific options and set default options. - """ - - def __init__(self, opt): - """Initialize the class; save the options in the class - - Parameters: - opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions - """ - self.opt = opt - self.root = opt.dataroot - - @staticmethod - def modify_commandline_options(parser, is_train): - """Add new dataset-specific options, and rewrite default values for existing options. - - Parameters: - parser -- original option parser - is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. - - Returns: - the modified parser. - """ - return parser - - @abstractmethod - def __len__(self): - """Return the total number of images in the dataset.""" - return 0 - - @abstractmethod - def __getitem__(self, index): - """Return a data point and its metadata information. - - Parameters: - index - - a random integer for data indexing - - Returns: - a dictionary of data with their names. It ususally contains the data itself and its metadata information. - """ - pass - - -def get_params(opt, size): - w, h = size - new_h = h - new_w = w - if opt.preprocess == 'resize_and_crop': - new_h = new_w = opt.load_size - elif opt.preprocess == 'scale_width_and_crop': - new_w = opt.load_size - new_h = opt.load_size * h // w - - x = random.randint(0, np.maximum(0, new_w - opt.crop_size)) - y = random.randint(0, np.maximum(0, new_h - opt.crop_size)) - - flip = random.random() > 0.5 - - return {'crop_pos': (x, y), 'flip': flip} - -def get_transform(opt, params=None, grayscale=False, method=Image.BICUBIC, convert=True): - transform_list = [] - if grayscale: - # transform_list.append(transforms.Grayscale(1)) - from util import util - transform_list.append(util.RGBtoY) - if 'resize' in opt.preprocess: - osize = [opt.load_size, opt.load_size] - transform_list.append(transforms.Resize(osize, method)) - elif 'scale_width' in opt.preprocess: - transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, method))) - - if 'crop' in opt.preprocess: - if params is None: - transform_list.append(transforms.RandomCrop(opt.crop_size)) - else: - if 'crop_size' in params: - transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], params['crop_size']))) - else: - transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.crop_size))) - - if opt.preprocess == 'none': - transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base=4, method=method))) - - if not opt.no_flip: - if params is None: - transform_list.append(transforms.RandomHorizontalFlip()) - elif params['flip']: - transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip']))) - - if convert: - transform_list += [transforms.ToTensor()] - if grayscale: - transform_list += [transforms.Normalize((0.5,), (0.5,))] - else: - transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] - return transforms.Compose(transform_list) - -def __make_power_2(img, base, method=Image.BICUBIC): - ow, oh = img.size - h = int(round(oh / base) * base) - w = int(round(ow / base) * base) - if (h == oh) and (w == ow): - return img - - __print_size_warning(ow, oh, w, h) - return img.resize((w, h), method) - - -def __scale_width(img, target_width, method=Image.BICUBIC): - ow, oh = img.size - if (ow == target_width): - return img - w = target_width - h = int(target_width * oh / ow) - return img.resize((w, h), method) - - -def __crop(img, pos, size): - ow, oh = img.size - x1, y1 = pos - tw = th = size - if (ow > tw or oh > th): - return img.crop((x1, y1, x1 + tw, y1 + th)) - return img - - -def __flip(img, flip): - if flip: - return img.transpose(Image.FLIP_LEFT_RIGHT) - return img - - -def __print_size_warning(ow, oh, w, h): - """Print warning information about image size(only print once)""" - if not hasattr(__print_size_warning, 'has_printed'): - print("The image size needs to be a multiple of 4. " - "The loaded image size was (%d, %d), so it was adjusted to " - "(%d, %d). This adjustment will be done to all images " - "whose sizes are not multiples of 4" % (ow, oh, w, h)) - __print_size_warning.has_printed = True diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/data/celebahqmask_dataset.py b/face_parse/PSFRGAN-master/PSFRGAN-master/data/celebahqmask_dataset.py deleted file mode 100644 index 3d48bd8..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/data/celebahqmask_dataset.py +++ /dev/null @@ -1,60 +0,0 @@ -import os -import random -import numpy as np -from PIL import Image -import imgaug as ia -import imgaug.augmenters as iaa - -from data.image_folder import make_dataset - -import torch -from torch.utils.data import Dataset -from torchvision.transforms import transforms - -from data.base_dataset import BaseDataset -from utils.utils import onehot_parse_map - -from data.ffhq_dataset import complex_imgaug, random_gray - -class CelebAHQMaskDataset(BaseDataset): - - def __init__(self, opt): - BaseDataset.__init__(self, opt) - self.img_size = opt.Pimg_size - self.lr_size = opt.Gin_size - self.hr_size = opt.Gout_size - self.shuffle = True if opt.isTrain else False - - self.img_dataset = sorted(make_dataset(os.path.join(opt.dataroot, 'CelebA-HQ-img'))) - self.mask_dataset = sorted(make_dataset(os.path.join(opt.dataroot, 'CelebAMask-HQ-mask'))) - - self.to_tensor = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) - ]) - - def __len__(self,): - return len(self.img_dataset) - - def __getitem__(self, idx): - sample = {} - img_path = self.img_dataset[idx] - mask_path = self.mask_dataset[idx] - hr_img = Image.open(img_path).convert('RGB') - mask_img = Image.open(mask_path) - - hr_img = hr_img.resize((self.hr_size, self.hr_size)) - hr_img = random_gray(hr_img, p=0.3) - scale_size = np.random.randint(32, 256) - lr_img = complex_imgaug(hr_img, self.img_size, scale_size) - - mask_img = mask_img.resize((self.hr_size, self.hr_size)) - mask_label = torch.tensor(np.array(mask_img)).long() - - hr_tensor = self.to_tensor(hr_img) - lr_tensor = self.to_tensor(lr_img) - - return {'HR': hr_tensor, 'LR': lr_tensor, 'HR_paths': img_path, 'Mask': mask_label} - - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/data/ffhq_dataset.py b/face_parse/PSFRGAN-master/PSFRGAN-master/data/ffhq_dataset.py deleted file mode 100644 index f987223..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/data/ffhq_dataset.py +++ /dev/null @@ -1,88 +0,0 @@ -import os -import random -import numpy as np -from PIL import Image -import imgaug as ia -import imgaug.augmenters as iaa - -from data.image_folder import make_dataset - -import torch -from torch.utils.data import Dataset -from torchvision.transforms import transforms - -from data.base_dataset import BaseDataset -from utils.utils import onehot_parse_map - -class FFHQDataset(BaseDataset): - - def __init__(self, opt): - BaseDataset.__init__(self, opt) - self.img_size = opt.Pimg_size - self.lr_size = opt.Gin_size - self.hr_size = opt.Gout_size - self.shuffle = True if opt.isTrain else False - - self.img_dataset = sorted(make_dataset(os.path.join(opt.dataroot, 'imgs1024'))) - self.mask_dataset = sorted(make_dataset(os.path.join(opt.dataroot, 'masks512'))) - - self.to_tensor = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) - ]) - self.random_crop = transforms.RandomCrop(self.hr_size) - - def __len__(self,): - return len(self.img_dataset) - - def __getitem__(self, idx): - sample = {} - img_path = self.img_dataset[idx] - mask_path = self.mask_dataset[idx] - hr_img = Image.open(img_path).convert('RGB') - mask_img = Image.open(mask_path).convert('RGB') - - hr_img = hr_img.resize((self.hr_size, self.hr_size)) - hr_img = random_gray(hr_img, p=0.3) - scale_size = np.random.randint(32, 256) - lr_img = complex_imgaug(hr_img, self.img_size, scale_size) - - mask_img = mask_img.resize((self.hr_size, self.hr_size)) - mask_label = onehot_parse_map(mask_img) - mask_label = torch.tensor(mask_label).float() - - hr_tensor = self.to_tensor(hr_img) - lr_tensor = self.to_tensor(lr_img) - - return {'HR': hr_tensor, 'LR': lr_tensor, 'HR_paths': img_path, 'Mask': mask_label} - - -def complex_imgaug(x, org_size, scale_size): - """input single RGB PIL Image instance""" - x = np.array(x) - x = x[np.newaxis, :, :, :] - aug_seq = iaa.Sequential([ - iaa.Sometimes(0.5, iaa.OneOf([ - iaa.GaussianBlur((3, 15)), - iaa.AverageBlur(k=(3, 15)), - iaa.MedianBlur(k=(3, 15)), - iaa.MotionBlur((5, 25)) - ])), - iaa.Resize(scale_size, interpolation=ia.ALL), - iaa.Sometimes(0.2, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.1*255), per_channel=0.5)), - iaa.Sometimes(0.7, iaa.JpegCompression(compression=(10, 65))), - iaa.Resize(org_size), - ]) - - aug_img = aug_seq(images=x) - return aug_img[0] - - -def random_gray(x, p=0.5): - """input single RGB PIL Image instance""" - x = np.array(x) - x = x[np.newaxis, :, :, :] - aug = iaa.Sometimes(p, iaa.Grayscale(alpha=1.0)) - aug_img = aug(images=x) - return aug_img[0] - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/data/image_folder.py b/face_parse/PSFRGAN-master/PSFRGAN-master/data/image_folder.py deleted file mode 100644 index d0b4b30..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/data/image_folder.py +++ /dev/null @@ -1,67 +0,0 @@ -"""A modified image folder class - -We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py) -so that this class can load images from both current directory and its subdirectories. -""" - -import torch.utils.data as data - -from PIL import Image -import os -import os.path - -IMG_EXTENSIONS = [ - '.jpg', '.JPG', '.jpeg', '.JPEG', - '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', - '.tif', '.TIF', '.tiff', '.TIFF', -] - - -def is_image_file(filename): - return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) - - -def make_dataset(dir, max_dataset_size=float("inf")): - images = [] - assert os.path.isdir(dir), '%s is not a valid directory' % dir - - for root, _, fnames in sorted(os.walk(dir)): - for fname in fnames: - if is_image_file(fname): - path = os.path.join(root, fname) - images.append(path) - return images[:min(max_dataset_size, len(images))] - - -def default_loader(path): - return Image.open(path).convert('RGB') - - -class ImageFolder(data.Dataset): - - def __init__(self, root, transform=None, return_paths=False, - loader=default_loader): - imgs = make_dataset(root) - if len(imgs) == 0: - raise(RuntimeError("Found 0 images in: " + root + "\n" - "Supported image extensions are: " + - ",".join(IMG_EXTENSIONS))) - - self.root = root - self.imgs = imgs - self.transform = transform - self.return_paths = return_paths - self.loader = loader - - def __getitem__(self, index): - path = self.imgs[index] - img = self.loader(path) - if self.transform is not None: - img = self.transform(img) - if self.return_paths: - return img, path - else: - return img - - def __len__(self): - return len(self.imgs) diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/data/single_dataset.py b/face_parse/PSFRGAN-master/PSFRGAN-master/data/single_dataset.py deleted file mode 100644 index 2f6cb2f..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/data/single_dataset.py +++ /dev/null @@ -1,43 +0,0 @@ -from data.base_dataset import BaseDataset, get_transform -from data.image_folder import make_dataset -from PIL import Image -import numpy as np - -class SingleDataset(BaseDataset): - """This dataset class can load a set of images specified by the path --dataroot /path/to/data. - - It can be used for generating CycleGAN results only for one side with the model option '-model test'. - """ - - def __init__(self, opt): - """Initialize this dataset class. - - Parameters: - opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions - """ - BaseDataset.__init__(self, opt) - self.A_paths = sorted(make_dataset(opt.src_dir, opt.max_dataset_size)) - input_nc = self.opt.output_nc - self.transform = get_transform(opt, grayscale=(input_nc == 1)) - self.opt = opt - - def __getitem__(self, index): - """Return a data point and its metadata information. - - Parameters: - index - - a random integer for data indexing - - Returns a dictionary that contains A and A_paths - A(tensor) - - an image in one domain - A_paths(str) - - the path of the image - """ - A_path = self.A_paths[index] - A_img = Image.open(A_path).convert('RGB') - A_img = A_img.resize((512, 512), Image.BICUBIC) - - A = self.transform(A_img) - return {'LR': A, 'LR_paths': A_path} - - def __len__(self): - """Return the total number of images in the dataset.""" - return len(self.A_paths) diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/generate_mask.py b/face_parse/PSFRGAN-master/PSFRGAN-master/generate_mask.py deleted file mode 100644 index 5bd3e52..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/generate_mask.py +++ /dev/null @@ -1,92 +0,0 @@ -import os -from options.test_options import TestOptions -from data import create_dataset -from models import create_model -from utils import utils -from PIL import Image -from tqdm import tqdm -import torch -import time -import numpy as np -import cv2 -import glob -from torchvision.transforms import transforms - -if __name__ == '__main__': - opt = TestOptions() - opt = opt.parse() # get test options - opt.num_threads = 0 # test code only supports num_threads = 1 - opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed. - opt.no_flip = True - - dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options - model = create_model(opt) # create a model given opt.model and other options - model.load_pretrain_models() - - netP = model.netP - model.eval() - - to_tensor = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) - ]) - - image_dir = "G:/VGGFace2-HQ/VGGface2_None_norm_512_true_bygfpgan/" - output_dir = "G:/VGGFace2-HQ/VGGface2_HQ_original_aligned_mask" - - temp_path = os.path.join(image_dir,'*/') - pathes = glob.glob(temp_path) - dataset = [] - for dir_item in pathes: - join_path = glob.glob(os.path.join(dir_item,'*.jpg')) - print("processing %s"%dir_item,end='\r') - temp_list = [] - for item in join_path: - temp_list.append(item) - dataset.append(temp_list) - - # ------------------------ restore ------------------------ - for i_dir in dataset: - path = os.path.dirname(i_dir[0]) - dir_name = os.path.join(output_dir, os.path.basename(path)) - if not os.path.exists(dir_name): - os.makedirs(dir_name) - - for img_path in i_dir: - hr_img = Image.open(img_path).convert('RGB') - inp = to_tensor(hr_img).unsqueeze(0) - with torch.no_grad(): - parse_map, _ = netP(inp) - parse_map_sm = (parse_map == parse_map.max(dim=1, keepdim=True)[0]).float() - ref_parse_img = utils.color_parse_map(parse_map_sm) - img_name = os.path.basename(img_path) - basename, ext = os.path.splitext(img_name) - save_face_name = f'{basename}.png' - # print(save_face_name) - save_path = os.path.join(dir_name, save_face_name) - # os.makedirs(opt.save_masks_dir, exist_ok=True) - img = cv2.cvtColor(ref_parse_img[0],cv2.COLOR_RGB2GRAY) - cv2.imwrite(save_path,img) - - # for i, data in tqdm(enumerate(dataset), total=len(dataset)//opt.batch_size): - # inp = data['LR'] - # with torch.no_grad(): - # parse_map, _ = netP(inp) - # parse_map_sm = (parse_map == parse_map.max(dim=1, keepdim=True)[0]).float() - # img_path = data['LR_paths'] # get image paths - # ref_parse_img = utils.color_parse_map(parse_map_sm) - # for i in range(len(img_path)): - # img_name = os.path.basename(img_path[i]) - # basename, ext = os.path.splitext(img_name) - # save_face_name = f'{basename}.png' - # # print(save_face_name) - # save_path = os.path.join(opt.save_masks_dir, save_face_name) - # os.makedirs(opt.save_masks_dir, exist_ok=True) - # img = cv2.cvtColor(ref_parse_img[i],cv2.COLOR_RGB2GRAY) - # cv2.imwrite(save_path,img) - # save_img = Image.fromarray(ref_parse_img[i]) - # save_img.save(save_path) - - - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/models/__init__.py b/face_parse/PSFRGAN-master/PSFRGAN-master/models/__init__.py deleted file mode 100644 index fc01113..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/models/__init__.py +++ /dev/null @@ -1,67 +0,0 @@ -"""This package contains modules related to objective functions, optimizations, and network architectures. - -To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel. -You need to implement the following five functions: - -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). - -- : unpack data from dataset and apply preprocessing. - -- : produce intermediate results. - -- : calculate loss, gradients, and update network weights. - -- : (optionally) add model-specific options and set default options. - -In the function <__init__>, you need to define four lists: - -- self.loss_names (str list): specify the training losses that you want to plot and save. - -- self.model_names (str list): define networks used in our training. - -- self.visual_names (str list): specify the images that you want to display and save. - -- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage. - -Now you can use the model class by specifying flag '--model dummy'. -See our template model class 'template_model.py' for more details. -""" - -import importlib -from models.base_model import BaseModel - - -def find_model_using_name(model_name): - """Import the module "models/[model_name]_model.py". - - In the file, the class called DatasetNameModel() will - be instantiated. It has to be a subclass of BaseModel, - and it is case-insensitive. - """ - model_filename = "models." + model_name + "_model" - modellib = importlib.import_module(model_filename) - model = None - target_model_name = model_name.replace('_', '') + 'model' - for name, cls in modellib.__dict__.items(): - if name.lower() == target_model_name.lower() \ - and issubclass(cls, BaseModel): - model = cls - - if model is None: - print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name)) - exit(0) - - return model - - -def get_option_setter(model_name): - """Return the static method of the model class.""" - model_class = find_model_using_name(model_name) - return model_class.modify_commandline_options - - -def create_model(opt): - """Create a model given the option. - - This function warps the class CustomDatasetDataLoader. - This is the main interface between this package and 'train.py'/'test.py' - - Example: - >>> from models import create_model - >>> model = create_model(opt) - """ - model = find_model_using_name(opt.model) - instance = model(opt) - print("model [%s] was created" % type(instance).__name__) - return instance diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/models/base_model.py b/face_parse/PSFRGAN-master/PSFRGAN-master/models/base_model.py deleted file mode 100644 index 7cda51b..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/models/base_model.py +++ /dev/null @@ -1,248 +0,0 @@ -import os -import torch -from collections import OrderedDict -from abc import ABC, abstractmethod -from . import networks - -class BaseModel(ABC): - """This class is an abstract base class (ABC) for models. - To create a subclass, you need to implement the following five functions: - -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). - -- : unpack data from dataset and apply preprocessing. - -- : produce intermediate results. - -- : calculate losses, gradients, and update network weights. - -- : (optionally) add model-specific options and set default options. - """ - - def __init__(self, opt): - """Initialize the BaseModel class. - - Parameters: - opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions - - When creating your custom class, you need to implement your own initialization. - In this fucntion, you should first call - Then, you need to define four lists: - -- self.loss_names (str list): specify the training losses that you want to plot and save. - -- self.model_names (str list): specify the images that you want to display and save. - -- self.visual_names (str list): define networks used in our training. - -- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example. - """ - self.opt = opt - self.gpu_ids = opt.gpu_ids - self.isTrain = opt.isTrain - self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir - self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') # get device name: CPU or GPU - - self.loss_names = [] - self.model_names = [] - self.visual_names = [] - self.optimizers = [] - self.image_paths = [] - self.metric = 0 # used for learning rate policy 'plateau' - - @staticmethod - def modify_commandline_options(parser, is_train): - """Add new model-specific options, and rewrite default values for existing options. - - Parameters: - parser -- original option parser - is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. - - Returns: - the modified parser. - """ - return parser - - @abstractmethod - def set_input(self, input): - """Unpack input data from the dataloader and perform necessary pre-processing steps. - - Parameters: - input (dict): includes the data itself and its metadata information. - """ - pass - - @abstractmethod - def forward(self): - """Run forward pass; called by both functions and .""" - pass - - @abstractmethod - def optimize_parameters(self): - """Calculate losses, gradients, and update network weights; called in every training iteration""" - pass - - def setup(self, opt): - """Load and print networks; create schedulers - - Parameters: - opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions - """ - if self.isTrain: - self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] - if not self.isTrain or opt.continue_train: - load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch - self.load_networks(load_suffix) - self.print_networks(opt.verbose) - - def eval(self): - """Make models eval mode during test time""" - for name in self.model_names: - if isinstance(name, str): - net = getattr(self, 'net' + name) - net.eval() - - def test(self): - """Forward function used in test time. - - This function wraps function in no_grad() so we don't save intermediate steps for backprop - It also calls to produce additional visualization results - """ - with torch.no_grad(): - self.forward() - self.compute_visuals() - - def compute_visuals(self): - """Calculate additional output images for visdom and HTML visualization""" - pass - - def get_image_paths(self): - """ Return image paths that are used to load current data""" - return self.image_paths - - def update_learning_rate(self): - """Update learning rates for all the networks; called at the end of every epoch""" - for scheduler in self.schedulers: - if self.opt.lr_policy == 'plateau': - scheduler.step(self.metric) - else: - scheduler.step() - - lr = self.optimizers[0].param_groups[0]['lr'] - print('learning rate = %.7f' % lr) - - def get_lr(self,): - lrs = {} - for idx, p in enumerate(self.optimizers): - lrs['LR{}'.format(idx)] = p.param_groups[0]['lr'] - return lrs - - def get_current_visuals(self): - """Return visualization images. train.py will display these images with visdom, and save the images to a HTML""" - visual_ret = OrderedDict() - for name in self.visual_names: - if isinstance(name, str): - visual_ret[name] = getattr(self, name) - return visual_ret - - def get_current_losses(self): - """Return traning losses / errors. train.py will print out these errors on console, and save them to a file""" - errors_ret = OrderedDict() - for name in self.loss_names: - if isinstance(name, str): - errors_ret['Loss_' + name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number - return errors_ret - - def save_networks(self, epoch, info=None): - """Save all the networks to the disk. - - Parameters: - epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) - """ - for name in self.model_names: - if isinstance(name, str): - save_filename = '%s_net_%s.pth' % (epoch, name) - save_path = os.path.join(self.save_dir, save_filename) - net = getattr(self, 'net' + name) - - if len(self.gpu_ids) > 0 and torch.cuda.is_available(): - torch.save(net.module.cpu().state_dict(), save_path) - print('Model saved in:', save_filename) - net.cuda(self.gpu_ids[0]) - else: - torch.save(net.cpu().state_dict(), save_path) - if info is not None: - torch.save(info, os.path.join(self.save_dir, '%s.info' % epoch)) - - def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): - """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)""" - key = keys[i] - if i + 1 == len(keys): # at the end, pointing to a parameter/buffer - if module.__class__.__name__.startswith('InstanceNorm') and \ - (key == 'running_mean' or key == 'running_var'): - if getattr(module, key) is None: - state_dict.pop('.'.join(keys)) - if module.__class__.__name__.startswith('InstanceNorm') and \ - (key == 'num_batches_tracked'): - state_dict.pop('.'.join(keys)) - else: - self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) - - def load_networks(self, epoch): - """Load all the networks from the disk. - - Parameters: - epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) - """ - for name in self.load_model_names: - if isinstance(name, str): - load_filename = '%s_net_%s.pth' % (epoch, name) - load_path = os.path.join(self.save_dir, load_filename) - net = getattr(self, 'net' + name) - if isinstance(net, torch.nn.DataParallel) or isinstance(net, torch.nn.parallel.DistributedDataParallel): - net = net.module - print('loading the model from %s' % load_path) - # if you are using PyTorch newer than 0.4 (e.g., built from - # GitHub source), you can remove str() on self.device - map_location = str(self.device) - - state_dict = torch.load(load_path, map_location=map_location) - - # patch InstanceNorm checkpoints prior to 0.4 - # for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop - # self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) - # net.load_state_dict(state_dict) - if not self.opt.no_strict_load: - net.load_state_dict(state_dict) - # Load partial weights - else: - model_dict = net.state_dict() - pretrained_dict = {k: v for k, v in state_dict.items() if k in model_dict} - model_dict.update(pretrained_dict) - net.load_state_dict(model_dict, strict=False) - - info_path = os.path.join(self.save_dir, '%s.info' % epoch) - if os.path.exists(info_path): - info_dict = torch.load(info_path) - for k, v in info_dict.items(): - setattr(self.opt, k, v) - - def print_networks(self, verbose): - """Print the total number of parameters in the network and (if verbose) network architecture - - Parameters: - verbose (bool) -- if verbose: print the network architecture - """ - print('---------- Networks initialized -------------') - for name in self.model_names: - if isinstance(name, str): - net = getattr(self, 'net' + name) - num_params = 0 - for param in net.parameters(): - num_params += param.numel() - print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) - print('-----------------------------------------------') - - def set_requires_grad(self, nets, requires_grad=False): - """Set requies_grad=Fasle for all the networks to avoid unnecessary computations - Parameters: - nets (network list) -- a list of networks - requires_grad (bool) -- whether the networks require gradients or not - """ - if not isinstance(nets, list): - nets = [nets] - for net in nets: - if net is not None: - for param in net.parameters(): - param.requires_grad = requires_grad diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/models/blocks.py b/face_parse/PSFRGAN-master/PSFRGAN-master/models/blocks.py deleted file mode 100644 index 3e02fd5..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/models/blocks.py +++ /dev/null @@ -1,131 +0,0 @@ -# -*- coding: utf-8 -*- -import torch -import torch.nn as nn -from torch.nn.parameter import Parameter -from torch.nn import functional as F -import numpy as np - - -class NormLayer(nn.Module): - """Normalization Layers. - ------------ - # Arguments - - channels: input channels, for batch norm and instance norm. - - input_size: input shape without batch size, for layer norm. - """ - def __init__(self, channels, normalize_shape=None, norm_type='bn'): - super(NormLayer, self).__init__() - norm_type = norm_type.lower() - self.norm_type = norm_type - self.channels = channels - if norm_type == 'bn': - self.norm = nn.BatchNorm2d(channels, affine=True) - elif norm_type == 'in': - self.norm = nn.InstanceNorm2d(channels, affine=False) - elif norm_type == 'gn': - self.norm = nn.GroupNorm(32, channels, affine=True) - elif norm_type == 'pixel': - self.norm = lambda x: F.normalize(x, p=2, dim=1) - elif norm_type == 'layer': - self.norm = nn.LayerNorm(normalize_shape) - elif norm_type == 'none': - self.norm = lambda x: x*1.0 - else: - assert 1==0, 'Norm type {} not support.'.format(norm_type) - - def forward(self, x, ref=None): - return self.norm(x) - - -class ReluLayer(nn.Module): - """Relu Layer. - ------------ - # Arguments - - relu type: type of relu layer, candidates are - - ReLU - - LeakyReLU: default relu slope 0.2 - - PRelu - - SELU - - none: direct pass - """ - def __init__(self, channels, relu_type='relu'): - super(ReluLayer, self).__init__() - relu_type = relu_type.lower() - if relu_type == 'relu': - self.func = nn.ReLU(True) - elif relu_type == 'leakyrelu': - self.func = nn.LeakyReLU(0.2, inplace=True) - elif relu_type == 'prelu': - self.func = nn.PReLU(channels) - elif relu_type == 'selu': - self.func = nn.SELU(True) - elif relu_type == 'none': - self.func = lambda x: x*1.0 - else: - assert 1==0, 'Relu type {} not support.'.format(relu_type) - - def forward(self, x): - return self.func(x) - - -class ConvLayer(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size=3, scale='none', norm_type='none', relu_type='none', use_pad=True, bias=True): - super(ConvLayer, self).__init__() - self.use_pad = use_pad - self.norm_type = norm_type - self.in_channels = in_channels - if norm_type in ['bn']: - bias = False - - stride = 2 if scale == 'down' else 1 - self.scale = scale - - self.scale_func = lambda x: x - if scale == 'up': - self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest') - - self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.)/2))) - self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias) - - self.avgpool = nn.AvgPool2d(2, 2) - self.relu = ReluLayer(out_channels, relu_type) - self.norm = NormLayer(out_channels, norm_type=norm_type) - - def forward(self, x): - out = self.scale_func(x) - if self.use_pad: - out = self.reflection_pad(out) - out = self.conv2d(out) - if self.scale == 'down_avg': - out = self.avgpool(out) - out = self.norm(out) - out = self.relu(out) - return out - - -class ResidualBlock(nn.Module): - """ - Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html - """ - def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'): - super(ResidualBlock, self).__init__() - - if scale == 'none' and c_in == c_out: - self.shortcut_func = lambda x: x - else: - self.shortcut_func = ConvLayer(c_in, c_out, 3, scale) - - scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']} - scale_conf = scale_config_dict[scale] - - self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type) - self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none') - - def forward(self, x): - identity = self.shortcut_func(x) - - res = self.conv1(x) - res = self.conv2(res) - return identity + res - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/models/enhance_model.py b/face_parse/PSFRGAN-master/PSFRGAN-master/models/enhance_model.py deleted file mode 100644 index d83dd7e..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/models/enhance_model.py +++ /dev/null @@ -1,157 +0,0 @@ -import os -import numpy as np -import collections - -import torch -import torch.nn as nn -import torch.optim as optim - -from models import loss -from models import networks -from .base_model import BaseModel -from utils import utils - -class EnhanceModel(BaseModel): - - def modify_commandline_options(parser, is_train): - if is_train: - parser.add_argument('--parse_net_weight', type=str, default='./pretrain_models/parse_multi_iter_90000.pth', help='parse model path') - parser.add_argument('--lambda_pix', type=float, default=10.0, help='weight for parsing map') - parser.add_argument('--lambda_pcp', type=float, default=0.0, help='weight for vgg perceptual loss') - parser.add_argument('--lambda_fm', type=float, default=10.0, help='weight for sr') - parser.add_argument('--lambda_g', type=float, default=1.0, help='weight for sr') - parser.add_argument('--lambda_ss', type=float, default=1000., help='weight for global style') - return parser - - def __init__(self, opt): - BaseModel.__init__(self, opt) - - self.netP = networks.define_P(opt, weight_path=opt.parse_net_weight) - self.netG = networks.define_G(opt, use_norm='spectral_norm') - - if self.isTrain: - self.netD = networks.define_D(opt, opt.Dinput_nc, use_norm='spectral_norm') - self.vgg_model = loss.PCPFeat(weight_path='./pretrain_models/vgg19-dcbb9e9d.pth').to(opt.device) - if len(opt.gpu_ids) > 0: - self.vgg_model = torch.nn.DataParallel(self.vgg_model, opt.gpu_ids, output_device=opt.device) - - self.model_names = ['G'] - self.loss_names = ['Pix', 'PCP', 'G', 'FM', 'D', 'SS'] # Generator loss, fm loss, parsing loss, discriminator loss - self.visual_names = ['img_LR', 'img_HR', 'img_SR', 'ref_Parse', 'hr_mask'] - self.fm_weights = [1**x for x in range(opt.D_num)] - - if self.isTrain: - self.model_names = ['G', 'D'] - self.load_model_names = ['G', 'D'] - - self.criterionParse = torch.nn.CrossEntropyLoss().to(opt.device) - self.criterionFM = loss.FMLoss().to(opt.device) - self.criterionGAN = loss.GANLoss(opt.gan_mode).to(opt.device) - self.criterionPCP = loss.PCPLoss(opt) - self.criterionPix= nn.L1Loss() - self.criterionRS = loss.RegionStyleLoss() - - self.optimizer_G = optim.Adam([p for p in self.netG.parameters() if p.requires_grad], lr=opt.g_lr, betas=(opt.beta1, 0.999)) - self.optimizer_D = optim.Adam([p for p in self.netD.parameters() if p.requires_grad], lr=opt.d_lr, betas=(opt.beta1, 0.999)) - self.optimizers = [self.optimizer_G, self.optimizer_D] - - def eval(self): - self.netG.eval() - self.netP.eval() - - def load_pretrain_models(self,): - self.netP.eval() - print('Loading pretrained LQ face parsing network from', self.opt.parse_net_weight) - if len(self.opt.gpu_ids) > 0: - self.netP.module.load_state_dict(torch.load(self.opt.parse_net_weight)) - else: - self.netP.load_state_dict(torch.load(self.opt.parse_net_weight)) - self.netG.eval() - print('Loading pretrained PSFRGAN from', self.opt.psfr_net_weight) - if len(self.opt.gpu_ids) > 0: - self.netG.module.load_state_dict(torch.load(self.opt.psfr_net_weight), strict=False) - else: - self.netG.load_state_dict(torch.load(self.opt.psfr_net_weight), strict=False) - - def set_input(self, input, cur_iters=None): - self.cur_iters = cur_iters - self.img_LR = input['LR'].to(self.opt.device) - self.img_HR = input['HR'].to(self.opt.device) - self.hr_mask = input['Mask'].to(self.opt.device) - if self.opt.debug: - print('SRNet input shape:', self.img_LR.shape, self.img_HR.shape) - - def forward(self): - with torch.no_grad(): - ref_mask, _ = self.netP(self.img_LR) - self.ref_mask_onehot = (ref_mask == ref_mask.max(dim=1, keepdim=True)[0]).float().detach() - - if self.opt.debug: - print('SRNet reference mask shape:', self.ref_mask_onehot.shape) - self.img_SR = self.netG(self.img_LR, self.ref_mask_onehot) - - self.real_D_results = self.netD(torch.cat((self.img_HR, self.hr_mask), dim=1), return_feat=True) - self.fake_D_results = self.netD(torch.cat((self.img_SR.detach(), self.hr_mask), dim=1), return_feat=False) - self.fake_G_results = self.netD(torch.cat((self.img_SR, self.hr_mask), dim=1), return_feat=True) - - self.img_SR_feats = self.vgg_model(self.img_SR) - self.img_HR_feats = self.vgg_model(self.img_HR) - - def backward_G(self): - # Pix Loss - self.loss_Pix = self.criterionPix(self.img_SR, self.img_HR) * self.opt.lambda_pix - - # semantic style loss - self.loss_SS = self.criterionRS(self.img_SR_feats, self.img_HR_feats, self.hr_mask) * self.opt.lambda_ss - - # perceptual loss - self.loss_PCP = self.criterionPCP(self.img_SR_feats, self.img_HR_feats) * self.opt.lambda_pcp - - # Feature matching loss - tmp_loss = 0 - for i, w in zip(range(self.opt.D_num), self.fm_weights): - tmp_loss = tmp_loss + self.criterionFM(self.fake_G_results[i][1], self.real_D_results[i][1]) * w - self.loss_FM = tmp_loss * self.opt.lambda_fm / self.opt.D_num - - # Generator loss - tmp_loss = 0 - for i in range(self.opt.D_num): - tmp_loss = tmp_loss + self.criterionGAN(self.fake_G_results[i][0], True, for_discriminator=False) - self.loss_G = tmp_loss * self.opt.lambda_g / self.opt.D_num - - total_loss = self.loss_Pix + self.loss_PCP + self.loss_FM + self.loss_G + self.loss_SS - total_loss.backward() - - def backward_D(self, ): - self.loss_D = 0 - for i in range(self.opt.D_num): - self.loss_D += 0.5 * (self.criterionGAN(self.fake_D_results[i], False) + self.criterionGAN(self.real_D_results[i][0], True)) - self.loss_D /= self.opt.D_num - self.loss_D.backward() - - def optimize_parameters(self, ): - # ---- Update G ------------ - self.optimizer_G.zero_grad() - self.backward_G() - self.optimizer_G.step() - - # ---- Update D ------------ - self.optimizer_D.zero_grad() - self.backward_D() - self.optimizer_D.step() - - def get_current_visuals(self, size=512): - out = [] - visual_imgs = [] - out.append(utils.tensor_to_numpy(self.img_LR)) - out.append(utils.tensor_to_numpy(self.img_SR)) - out.append(utils.tensor_to_numpy(self.img_HR)) - - out_imgs = [utils.batch_numpy_to_image(x, size) for x in out] - - visual_imgs += out_imgs - visual_imgs.append(utils.color_parse_map(self.ref_mask_onehot, size)) - visual_imgs.append(utils.color_parse_map(self.hr_mask, size)) - - return visual_imgs - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/models/loss.py b/face_parse/PSFRGAN-master/PSFRGAN-master/models/loss.py deleted file mode 100644 index 421f0de..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/models/loss.py +++ /dev/null @@ -1,224 +0,0 @@ -import torch -from torchvision import models -from utils import utils -from torch import nn - - -def tv_loss(x): - """ - Total Variation Loss. - """ - return torch.sum(torch.abs(x[:, :, :, :-1] - x[:, :, :, 1:]) - ) + torch.sum(torch.abs(x[:, :, :-1, :] - x[:, :, 1:, :])) - - -class PCPFeat(torch.nn.Module): - """ - Features used to calculate Perceptual Loss based on ResNet50 features. - Input: (B, C, H, W), RGB, [0, 1] - """ - def __init__(self, weight_path, model='vgg'): - super(PCPFeat, self).__init__() - if model == 'vgg': - self.model = models.vgg19(pretrained=False) - self.build_vgg_layers() - elif model == 'resnet': - self.model = models.resnet50(pretrained=False) - self.build_resnet_layers() - - self.model.load_state_dict(torch.load(weight_path)) - self.model.eval() - for param in self.model.parameters(): - param.requires_grad = False - - def build_resnet_layers(self): - self.layer1 = torch.nn.Sequential( - self.model.conv1, - self.model.bn1, - self.model.relu, - self.model.maxpool, - self.model.layer1 - ) - self.layer2 = self.model.layer2 - self.layer3 = self.model.layer3 - self.layer4 = self.model.layer4 - self.features = torch.nn.ModuleList( - [self.layer1, self.layer2, self.layer3, self.layer4] - ) - - def build_vgg_layers(self): - vgg_pretrained_features = self.model.features - self.features = [] - feature_layers = [0, 3, 8, 17, 26, 35] - for i in range(len(feature_layers)-1): - module_layers = torch.nn.Sequential() - for j in range(feature_layers[i], feature_layers[i+1]): - module_layers.add_module(str(j), vgg_pretrained_features[j]) - self.features.append(module_layers) - self.features = torch.nn.ModuleList(self.features) - - def preprocess(self, x): - x = (x + 1) / 2 - mean = torch.Tensor([0.485, 0.456, 0.406]).to(x) - std = torch.Tensor([0.229, 0.224, 0.225]).to(x) - mean = mean.view(1, 3, 1, 1) - std = std.view(1, 3, 1, 1) - x = (x - mean) / std - if x.shape[3] < 224: - x = torch.nn.functional.interpolate(x, size=(224, 224), mode='bilinear', align_corners=False) - return x - - def forward(self, x): - x = self.preprocess(x) - - features = [] - for m in self.features: - x = m(x) - features.append(x) - return features - - -class PCPLoss(torch.nn.Module): - """Perceptual Loss. - """ - def __init__(self, - opt, - layer=5, - model='vgg', - ): - super(PCPLoss, self).__init__() - - self.mse = torch.nn.MSELoss() - self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0] - - def forward(self, x_feats, y_feats): - loss = 0 - for xf, yf, w in zip(x_feats, y_feats, self.weights): - loss = loss + self.mse(xf, yf.detach()) * w - return loss - - -class FMLoss(nn.Module): - def __init__(self): - super().__init__() - self.mse = torch.nn.MSELoss() - - def forward(self, x_feats, y_feats): - loss = 0 - for xf, yf in zip(x_feats, y_feats): - loss = loss + self.mse(xf, yf.detach()) - return loss - - -class GANLoss(nn.Module): - def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0): - """ Initialize the GANLoss class. - Parameters: - gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp. - target_real_label (bool) - - label for a real image - target_fake_label (bool) - - label of a fake image - Note: Do not use sigmoid as the last layer of Discriminator. - LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss. - """ - super(GANLoss, self).__init__() - self.register_buffer('real_label', torch.tensor(target_real_label)) - self.register_buffer('fake_label', torch.tensor(target_fake_label)) - self.gan_mode = gan_mode - if gan_mode == 'lsgan': - self.loss = nn.MSELoss() - elif gan_mode == 'vanilla': - self.loss = nn.BCEWithLogitsLoss() - elif gan_mode == 'hinge': - pass - elif gan_mode in ['wgangp']: - self.loss = None - else: - raise NotImplementedError('gan mode %s not implemented' % gan_mode) - - def get_target_tensor(self, prediction, target_is_real): - if target_is_real: - target_tensor = self.real_label - else: - target_tensor = self.fake_label - return target_tensor.expand_as(prediction) - - def __call__(self, prediction, target_is_real, for_discriminator=True): - """Calculate loss given Discriminator's output and grount truth labels. - Parameters: - prediction (tensor) - - tpyically the prediction output from a discriminator - target_is_real (bool) - - if the ground truth label is for real images or fake images - Returns: - the calculated loss. - """ - if self.gan_mode in ['lsgan', 'vanilla']: - target_tensor = self.get_target_tensor(prediction, target_is_real) - loss = self.loss(prediction, target_tensor) - elif self.gan_mode == 'hinge': - if for_discriminator: - if target_is_real: - loss = nn.ReLU()(1 - prediction).mean() - else: - loss = nn.ReLU()(1 + prediction).mean() - else: - assert target_is_real, "The generator's hinge loss must be aiming for real" - loss = - prediction.mean() - return loss - - elif self.gan_mode == 'wgangp': - if target_is_real: - loss = -prediction.mean() - else: - loss = prediction.mean() - return loss - - -class RegionStyleLoss(nn.Module): - def __init__(self, reg_num=19, eps=1e-8): - super().__init__() - self.reg_num = reg_num - self.eps = eps - self.mse = nn.MSELoss() - - def __masked_gram_matrix(self, x, m): - b, c, h, w = x.shape - m = m.view(b, -1, h*w) - x = x.view(b, -1, h*w) - total_elements = m.sum(2) + self.eps - - x = x * m - G = torch.bmm(x, x.transpose(1, 2)) - return G / (c * total_elements.view(b, 1, 1)) - - def __layer_gram_matrix(self, x, mask): - b, c, h, w = x.shape - all_gm = [] - for i in range(self.reg_num): - sub_mask = mask[:, i].unsqueeze(1) - gram_matrix = self.__masked_gram_matrix(x, sub_mask) - all_gm.append(gram_matrix) - return torch.stack(all_gm, dim=1) - - def forward(self, x_feats, y_feats, mask): - loss = 0 - for xf, yf in zip(x_feats[2:], y_feats[2:]): - tmp_mask = torch.nn.functional.interpolate(mask, xf.shape[2:]) - xf_gm = self.__layer_gram_matrix(xf, tmp_mask) - yf_gm = self.__layer_gram_matrix(yf, tmp_mask) - tmp_loss = self.mse(xf_gm, yf_gm.detach()) - loss = loss + tmp_loss - return loss - - -if __name__ == '__main__': - x = [ - torch.randn(2, 64, 512, 512), - torch.randn(2, 128, 256, 256), - torch.randn(2, 256, 128, 128), - torch.randn(2, 512, 64, 64), - torch.randn(2, 512, 32, 32), - ] - - y = torch.randint(10, (2, 19, 512, 512)).float() - loss = RegionStyleLoss() - print(loss(x, x, y)) - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/models/networks.py b/face_parse/PSFRGAN-master/PSFRGAN-master/models/networks.py deleted file mode 100644 index 40bcaed..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/models/networks.py +++ /dev/null @@ -1,254 +0,0 @@ -from models.blocks import * -import torch -from torch import nn -from torch.nn import init -from torch.optim import lr_scheduler -from utils import utils -import numpy as np - -from models import psfrnet -import torch.nn.utils as tutils -from models.loss import PCPFeat - - -def apply_norm(net, weight_norm_type): - for m in net.modules(): - if isinstance(m, nn.Conv2d): - if weight_norm_type.lower() == 'spectral_norm': - tutils.spectral_norm(m) - elif weight_norm_type.lower() == 'weight_norm': - tutils.weight_norm(m) - else: - pass - - -def init_weights(net, init_type='normal', init_gain=0.02): - """Initialize network weights. - Parameters: - net (network) -- network to be initialized - init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal - init_gain (float) -- scaling factor for normal, xavier and orthogonal. - We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might - work better for some applications. Feel free to try yourself. - """ - def init_func(m): # define the initialization function - classname = m.__class__.__name__ - if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): - if init_type == 'normal': - init.normal_(m.weight.data, 0.0, init_gain) - elif init_type == 'xavier': - init.xavier_normal_(m.weight.data, gain=init_gain) - elif init_type == 'kaiming': - init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') - elif init_type == 'orthogonal': - init.orthogonal_(m.weight.data, gain=init_gain) - else: - raise NotImplementedError('initialization method [%s] is not implemented' % init_type) - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies. - init.normal_(m.weight.data, 1.0, init_gain) - init.constant_(m.bias.data, 0.0) - - print('initialize network with %s' % init_type) - net.apply(init_func) # apply the initialization function - - -def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]): - """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights - Parameters: - net (network) -- the network to be initialized - init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal - gain (float) -- scaling factor for normal, xavier and orthogonal. - gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 - Return an initialized network. - """ - if len(gpu_ids) > 0: - assert(torch.cuda.is_available()) - net.to(gpu_ids[0]) - net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs - init_weights(net, init_type, init_gain=init_gain) - return net - - -def get_scheduler(optimizer, opt): - """Return a learning rate scheduler - Parameters: - optimizer -- the optimizer of the network - opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　 - opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine - For 'linear', we keep the same learning rate for the first epochs - and linearly decay the rate to zero over the next epochs. - For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers. - See https://pytorch.org/docs/stable/optim.html for more details. - """ - if opt.lr_policy == 'linear': - def lambda_rule(epoch): - lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1) - return lr_l - scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) - elif opt.lr_policy == 'step': - scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) - elif opt.lr_policy == 'plateau': - scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) - elif opt.lr_policy == 'cosine': - scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0) - else: - return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) - return scheduler - - -def define_P(opt, in_size=512, out_size=512, min_feat_size=32, relu_type='LeakyReLU', isTrain=True, weight_path=None): - net = ParseNet(in_size, out_size, min_feat_size, 64, 19, norm_type=opt.Pnorm, relu_type=relu_type, ch_range=[32, 256]) - if not isTrain: - net.eval() - if weight_path is not None: - net.load_state_dict(torch.load(weight_path)) - if len(opt.gpu_ids) > 0: - assert(torch.cuda.is_available()) - net.to(opt.device) - net = torch.nn.DataParallel(net, opt.gpu_ids, output_device=opt.device) - return net - - -def define_G(opt, isTrain=True, use_norm='none', relu_type='LeakyReLU'): - net = psfrnet.PSFRGenerator(3, 3, in_size=opt.Gin_size, out_size=opt.Gout_size, relu_type=relu_type, parse_ch=19, norm_type=opt.Gnorm) - apply_norm(net, use_norm) - if not isTrain: - net.eval() - if len(opt.gpu_ids) > 0: - assert(torch.cuda.is_available()) - net.to(opt.device) - net = torch.nn.DataParallel(net, opt.gpu_ids, output_device=opt.device) - # init_weights(net, init_type='normal', init_gain=0.02) - return net - - -def define_D(opt, in_channel=3, isTrain=True, use_norm='none'): - net = MultiScaleDiscriminator(in_channel, opt.ndf, opt.n_layers_D, opt.Dnorm, num_D=opt.D_num) - apply_norm(net, use_norm) - if not isTrain: - net.eval() - if len(opt.gpu_ids) > 0: - assert(torch.cuda.is_available()) - net.to(opt.device) - net = torch.nn.DataParallel(net, opt.gpu_ids, output_device=opt.device) - init_weights(net, init_type='normal', init_gain=0.02) - return net - - -class ParseNet(nn.Module): - def __init__(self, - in_size=128, - out_size=128, - min_feat_size=32, - base_ch=64, - parsing_ch=19, - res_depth=10, - relu_type='prelu', - norm_type='bn', - ch_range=[32, 512], - ): - super().__init__() - self.res_depth = res_depth - act_args = {'norm_type': norm_type, 'relu_type': relu_type} - min_ch, max_ch = ch_range - - ch_clip = lambda x: max(min_ch, min(x, max_ch)) - min_feat_size = min(in_size, min_feat_size) - - down_steps = int(np.log2(in_size//min_feat_size)) - up_steps = int(np.log2(out_size//min_feat_size)) - - # =============== define encoder-body-decoder ==================== - self.encoder = [] - self.encoder.append(ConvLayer(3, base_ch, 3, 1)) - head_ch = base_ch - for i in range(down_steps): - cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2) - self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args)) - head_ch = head_ch * 2 - - self.body = [] - for i in range(res_depth): - self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args)) - - self.decoder = [] - for i in range(up_steps): - cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2) - self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args)) - head_ch = head_ch // 2 - - self.encoder = nn.Sequential(*self.encoder) - self.body = nn.Sequential(*self.body) - self.decoder = nn.Sequential(*self.decoder) - self.out_img_conv = ConvLayer(ch_clip(head_ch), 3) - self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch) - - def forward(self, x): - feat = self.encoder(x) - x = feat + self.body(feat) - x = self.decoder(x) - out_img = self.out_img_conv(x) - out_mask = self.out_mask_conv(x) - return out_mask, out_img - - -class MultiScaleDiscriminator(nn.Module): - def __init__(self, input_ch, base_ch=64, n_layers=3, norm_type='none', relu_type='LeakyReLU', num_D=4): - super().__init__() - - self.D_pool = nn.ModuleList() - for i in range(num_D): - netD = NLayerDiscriminator(input_ch, base_ch, depth=n_layers, norm_type=norm_type, relu_type=relu_type) - self.D_pool.append(netD) - - self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False) - - def forward(self, input, return_feat=False): - results = [] - for netd in self.D_pool: - output = netd(input, return_feat) - results.append(output) - # Downsample input - input = self.downsample(input) - return results - - -class NLayerDiscriminator(nn.Module): - def __init__(self, - input_ch = 3, - base_ch = 64, - max_ch = 1024, - depth = 4, - norm_type = 'none', - relu_type = 'LeakyReLU', - ): - super().__init__() - - nargs = {'norm_type': norm_type, 'relu_type': relu_type} - self.norm_type = norm_type - self.input_ch = input_ch - - self.model = [] - self.model.append(ConvLayer(input_ch, base_ch, norm_type='none', relu_type=relu_type)) - for i in range(depth): - cin = min(base_ch * 2**(i), max_ch) - cout = min(base_ch * 2**(i+1), max_ch) - self.model.append(ConvLayer(cin, cout, scale='down_avg', **nargs)) - self.model = nn.Sequential(*self.model) - self.score_out = ConvLayer(cout, 1, use_pad=False) - - def forward(self, x, return_feat=False): - ret_feats = [] - for idx, m in enumerate(self.model): - x = m(x) - ret_feats.append(x) - x = self.score_out(x) - if return_feat: - return x, ret_feats - else: - return x - - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/models/parse_model.py b/face_parse/PSFRGAN-master/PSFRGAN-master/models/parse_model.py deleted file mode 100644 index dea5d62..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/models/parse_model.py +++ /dev/null @@ -1,80 +0,0 @@ -import torch -from .base_model import BaseModel -from . import networks -from utils import utils - -class ParseModel(BaseModel): - def modify_commandline_options(parser, is_train): - if is_train: - parser.add_argument('--parse_map', type=float, default=1.0, help='weight for parsing map') - parser.add_argument('--parse_sr', type=float, default=1.0, help='weight for sr') - return parser - - def __init__(self, opt): - """Initialize this model class. - - Parameters: - opt -- training/test options - - A few things can be done here. - - (required) call the initialization function of BaseModel - - define loss function, visualization images, model names, and optimizers - """ - BaseModel.__init__(self, opt) # call the initialization method of BaseModel - self.loss_names = ['P', 'SR'] - self.visual_names = ['img_LR', 'img_HR', 'gt_Parse', 'img_SR', 'pred_Parse'] - - self.model_names = ['P'] - self.netP = networks.define_P(opt) - - if self.isTrain: # only defined during training time - self.criterionParse = torch.nn.CrossEntropyLoss() - self.criterionSR = torch.nn.L1Loss() - self.optimizer = torch.optim.Adam(self.netP.parameters(), lr=opt.lr, betas=(0.9, 0.999)) - self.optimizers = [self.optimizer] - - def set_input(self, input, cur_iters=None): - self.img_LR = input['LR'].to(self.opt.device) - self.img_HR = input['HR'].to(self.opt.device) - self.gt_Parse = input['Mask'].to(self.opt.device) - if self.opt.debug: - print('ParseNet input shape:', self.img_LR.shape, self.img_HR.shape, self.gt_Parse.shape) - - def load_pretrain_models(self,): - self.netP.eval() - print('Loading pretrained LQ face parsing network from', self.opt.parse_net_weight) - self.netP.load_state_dict(torch.load(self.opt.parse_net_weight)) - - def forward(self): - self.pred_Parse, self.img_SR = self.netP(self.img_LR) - if self.opt.debug: - print('ParseNet output shape', self.pred_Parse.shape, self.img_SR.shape) - - def backward(self): - self.loss_P = self.criterionParse(self.pred_Parse, self.gt_Parse) * self.opt.parse_map - self.loss_SR = self.criterionSR(self.img_SR, self.img_HR) * self.opt.parse_sr - - loss = self.loss_P + self.loss_SR - loss.backward() - - def optimize_parameters(self): - self.optimizer.zero_grad() # clear network G's existing gradients - self.backward() # calculate gradients for network G - self.optimizer.step() - - def get_current_visuals(self, size=512): - out = [] - visual_imgs = [] - out.append(utils.tensor_to_numpy(self.img_LR)) - out.append(utils.tensor_to_numpy(self.img_SR)) - out.append(utils.tensor_to_numpy(self.img_HR)) - out_imgs = [utils.batch_numpy_to_image(x, size) for x in out] - - visual_imgs.append(out_imgs[0]) - visual_imgs.append(out_imgs[1]) - visual_imgs.append(utils.color_parse_map(self.pred_Parse)) - visual_imgs.append(utils.color_parse_map(self.gt_Parse.unsqueeze(1))) - visual_imgs.append(out_imgs[2]) - - return visual_imgs - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/models/psfrnet.py b/face_parse/PSFRGAN-master/PSFRGAN-master/models/psfrnet.py deleted file mode 100644 index 7ea2d03..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/models/psfrnet.py +++ /dev/null @@ -1,130 +0,0 @@ -import torch -import torch.nn as nn -from torch.nn import init -import numpy as np -from models.blocks import * - - -class SPADENorm(nn.Module): - def __init__(self, norm_nc, ref_nc, norm_type='spade', ksz=3): - super().__init__() - - self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) - mid_c = 64 - - self.norm_type = norm_type - if norm_type == 'spade': - self.conv1 = nn.Sequential( - nn.Conv2d(ref_nc, mid_c, ksz, 1, ksz//2), - nn.LeakyReLU(0.2, True), - ) - self.gamma_conv = nn.Conv2d(mid_c, norm_nc, ksz, 1, ksz//2) - self.beta_conv = nn.Conv2d(mid_c, norm_nc, ksz, 1, ksz//2) - - def get_gamma_beta(self, x, conv, gamma_conv, beta_conv): - act = conv(x) - gamma = gamma_conv(act) - beta = beta_conv(act) - return gamma, beta - - def forward(self, x, ref): - normalized_input = self.param_free_norm(x) - if x.shape[-1] != ref.shape[-1]: - ref = nn.functional.interpolate(ref, x.shape[2:], mode='bicubic', align_corners=False) - if self.norm_type == 'spade': - gamma, beta = self.get_gamma_beta(ref, self.conv1, self.gamma_conv, self.beta_conv) - return normalized_input * gamma + beta - elif self.norm_type == 'in': - return normalized_input - - -class SPADEResBlock(nn.Module): - def __init__(self, fin, fout, ref_nc, relu_type, norm_type='spade'): - super().__init__() - - fmiddle = min(fin, fout) - self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) - self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) - - # define normalization layers - self.norm_0 = SPADENorm(fmiddle, ref_nc, norm_type) - self.norm_1 = SPADENorm(fmiddle, ref_nc, norm_type) - self.relu = ReluLayer(fmiddle, relu_type) - - def forward(self, x, ref): - res = self.conv_0(self.relu(self.norm_0(x, ref))) - res = self.conv_1(self.relu(self.norm_1(res, ref))) - out = x + res - - return out - - -class PSFRGenerator(nn.Module): - def __init__(self, input_nc, output_nc, in_size=512, out_size=512, min_feat_size=16, ngf=64, n_blocks=9, parse_ch=19, relu_type='relu', - ch_range=[32, 1024], norm_type='spade'): - super().__init__() - - min_ch, max_ch = ch_range - ch_clip = lambda x: max(min_ch, min(x, max_ch)) - get_ch = lambda size: ch_clip(1024*16//size) - - self.const_input = nn.Parameter(torch.randn(1, get_ch(min_feat_size), min_feat_size, min_feat_size)) - up_steps = int(np.log2(out_size//min_feat_size)) - self.up_steps = up_steps - - ref_ch = 19+3 - - head_ch = get_ch(min_feat_size) - head = [ - nn.Conv2d(head_ch, head_ch, kernel_size=3, padding=1), - SPADEResBlock(head_ch, head_ch, ref_ch, relu_type, norm_type), - ] - - body = [] - for i in range(up_steps): - cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2) - body += [ - nn.Sequential( - nn.Upsample(scale_factor=2), - nn.Conv2d(cin, cout, kernel_size=3, padding=1), - SPADEResBlock(cout, cout, ref_ch, relu_type, norm_type) - ) - ] - head_ch = head_ch // 2 - - self.img_out = nn.Conv2d(ch_clip(head_ch), output_nc, kernel_size=3, padding=1) - - self.head = nn.Sequential(*head) - self.body = nn.Sequential(*body) - self.upsample = nn.Upsample(scale_factor=2) - - def forward_spade(self, net, x, ref): - for m in net: - x = self.forward_spade_m(m, x, ref) - return x - - def forward_spade_m(self, m, x, ref): - if isinstance(m, SPADENorm) or isinstance(m, SPADEResBlock): - x = m(x, ref) - else: - x = m(x) - return x - - def forward(self, x, ref): - b, c, h, w = x.shape - const_input = self.const_input.repeat(b, 1, 1, 1) - ref_input = torch.cat((x, ref), dim=1) - - feat = self.forward_spade(self.head, const_input, ref_input) - - for idx, m in enumerate(self.body): - feat = self.forward_spade(m, feat, ref_input) - - out_img = self.img_out(feat) - - return out_img - - -if __name__ == '__main__': - x = torch.randn(2, 16, 567, 234) - nearest_interpolate(x) diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/options/__init__.py b/face_parse/PSFRGAN-master/PSFRGAN-master/options/__init__.py deleted file mode 100644 index e7eedeb..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/options/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""This package options includes option modules: training options, test options, and basic options (used in both training and test).""" diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/options/base_options.py b/face_parse/PSFRGAN-master/PSFRGAN-master/options/base_options.py deleted file mode 100644 index e0c3c1f..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/options/base_options.py +++ /dev/null @@ -1,165 +0,0 @@ -import argparse -import os -import numpy as np -import random -from utils import utils -import torch -import models -import data -from utils import utils - - -class BaseOptions(): - """This class defines options used during both training and test time. - - It also implements several helper functions such as parsing, printing, and saving the options. - It also gathers additional options defined in functions in both dataset class and model class. - """ - - def __init__(self): - """Reset the class; indicates the class hasn't been initailized""" - self.initialized = False - - def initialize(self, parser): - """Define the common options that are used in both training and test.""" - # basic parameters - parser.add_argument('--dataroot', required=False, help='path to images') - parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models') - parser.add_argument('--gpus', type=int, default=1, help='how many gpus to use') - parser.add_argument('--seed', type=int, default=123, help='Random seed for training') - parser.add_argument('--checkpoints_dir', type=str, default='./check_points', help='models are saved here') - # model parameters - parser.add_argument('--model', type=str, default='enhance', help='chooses which model to train [parse|enhance]') - parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB and 1 for grayscale') - parser.add_argument('--Dinput_nc', type=int, default=3, help='# of input image channels: 3 for RGB and 1 for grayscale') - parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB and 1 for grayscale') - parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer') - parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer') - parser.add_argument('--n_layers_D', type=int, default=4, help='downsampling layers in discriminator') - parser.add_argument('--D_num', type=int, default=3, help='numbers of discriminators') - - parser.add_argument('--Pnorm', type=str, default='bn', help='parsing net norm [in | bn| none]') - parser.add_argument('--Gnorm', type=str, default='spade', help='generator norm [in | bn | none]') - parser.add_argument('--Dnorm', type=str, default='in', help='discriminator norm [in | bn | none]') - parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]') - parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.') - # dataset parameters - parser.add_argument('--dataset_name', type=str, default='single', help='dataset name') - parser.add_argument('--Pimg_size', type=int, default='512', help='image size for face parse net') - parser.add_argument('--Gin_size', type=int, default='512', help='image size for face parse net') - parser.add_argument('--Gout_size', type=int, default='512', help='image size for face parse net') - parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') - parser.add_argument('--num_threads', default=8, type=int, help='# threads for loading data') - parser.add_argument('--batch_size', type=int, default=16, help='input batch size') - parser.add_argument('--load_size', type=int, default=512, help='scale images to this size') - parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size') - parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') - parser.add_argument('--preprocess', type=str, default='none', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]') - parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation') - # additional parameters - parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') - parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]') - parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') - parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}') - - parser.add_argument('--debug', action='store_true', help='if specified, set to debug mode') - self.initialized = True - return parser - - def gather_options(self): - """Initialize our parser with basic options(only once). - Add additional model-specific and dataset-specific options. - These options are defined in the function - in model and dataset classes. - """ - if not self.initialized: # check if it has been initialized - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser = self.initialize(parser) - - # get the basic options - opt, _ = parser.parse_known_args() - - # modify model-related parser options - model_name = opt.model - model_option_setter = models.get_option_setter(model_name) - parser = model_option_setter(parser, self.isTrain) - opt, _ = parser.parse_known_args() # parse again with new defaults - - # modify dataset-related parser options - dataset_name = opt.dataset_name - dataset_option_setter = data.get_option_setter(dataset_name) - parser = dataset_option_setter(parser, self.isTrain) - - # save and return the parser - self.parser = parser - return parser.parse_args() - - def print_options(self, opt): - """Print and save options - - It will print both current options and default values(if different). - It will save options into a text file / [checkpoints_dir] / opt.txt - """ - message = '' - message += '----------------- Options ---------------\n' - for k, v in sorted(vars(opt).items()): - comment = '' - default = self.parser.get_default(k) - if v != default: - comment = '\t[default: %s]' % str(default) - message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) - message += '----------------- End -------------------' - print(message) - - # save to the disk - opt.expr_dir = os.path.join(opt.checkpoints_dir, opt.name) - utils.mkdirs(opt.expr_dir) - file_name = os.path.join(opt.expr_dir, '{}_opt.txt'.format(opt.phase)) - with open(file_name, 'wt') as opt_file: - opt_file.write(message) - opt_file.write('\n') - - opt.log_dir = os.path.join(opt.checkpoints_dir, 'log_dir') - utils.mkdirs(opt.log_dir) - opt.log_archive = os.path.join(opt.checkpoints_dir, 'log_archive') - utils.mkdirs(opt.log_archive) - - def parse(self): - """Parse our options, create checkpoints directory suffix, and set up gpu device.""" - opt = self.gather_options() - opt.isTrain = self.isTrain # train or test - - if opt.debug: - opt.name = 'debug' - opt.save_iter_freq = 1 - opt.save_latest_freq = 1 - opt.visual_freq = 1 - opt.print_freq = 1 - - # Find avaliable GPUs automatically - if opt.gpus > 0: - opt.gpu_ids = utils.get_gpu_memory_map()[1][:opt.gpus] - if not isinstance(opt.gpu_ids, list): - opt.gpu_ids = [opt.gpu_ids] - torch.cuda.set_device(opt.gpu_ids[0]) - opt.device = torch.device('cuda:{}'.format(opt.gpu_ids[0 % opt.gpus])) - opt.data_device = torch.device('cuda:{}'.format(opt.gpu_ids[1 % opt.gpus])) - else: - opt.gpu_ids = [] - opt.device = torch.device('cpu') - - # set random seeds to ensure reproducibility - np.random.seed(opt.seed) - random.seed(opt.seed) - torch.manual_seed(opt.seed) - torch.cuda.manual_seed_all(opt.seed) - - # process opt.suffix - if opt.suffix: - suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' - opt.name = opt.name + suffix - - self.print_options(opt) - - self.opt = opt - return self.opt diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/options/test_options.py b/face_parse/PSFRGAN-master/PSFRGAN-master/options/test_options.py deleted file mode 100644 index 0e572e2..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/options/test_options.py +++ /dev/null @@ -1,30 +0,0 @@ -from .base_options import BaseOptions - - -class TestOptions(BaseOptions): - """This class includes test options. - - It also includes shared options defined in BaseOptions. - """ - - def initialize(self, parser): - parser = BaseOptions.initialize(self, parser) # define shared options - parser.add_argument('--src_dir', type=str, default='G:/VGGFace2-HQ/VGGface2_None_norm_512_true_bygfpgan/n000002', help='source directory containing test images') - parser.add_argument('--save_masks_dir', type=str, default='../datasets/FFHQ/masks512', help='path to save parsing masks for FFHQ') - parser.add_argument('--test_img_path', type=str, default='', help='path for single image test') - parser.add_argument('--test_upscale', type=float, default=1, help='upsample scale for single image test') - parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') - parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') - parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') - parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') - # Dropout and Batchnorm has different behavioir during training and test. - parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') - parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') - parser.add_argument('--parse_net_weight', type=str, default='./pretrain_models/parse_multi_iter_90000.pth', help='parse model path') - parser.add_argument('--psfr_net_weight', type=str, default='./pretrain_models/psfrgan_epoch15_net_G.pth', help='parse model path') - # rewrite devalue values - # To avoid cropping, the load_size should be the same as crop_size - parser.set_defaults(load_size=parser.get_default('crop_size')) - self.isTrain = False - - return parser diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/options/train_options.py b/face_parse/PSFRGAN-master/PSFRGAN-master/options/train_options.py deleted file mode 100644 index 7e4fa0a..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/options/train_options.py +++ /dev/null @@ -1,41 +0,0 @@ -from .base_options import BaseOptions - -class TrainOptions(BaseOptions): - """This class includes training options. - - It also includes shared options defined in BaseOptions. - """ - - def initialize(self, parser): - parser = BaseOptions.initialize(self, parser) - # visdom and HTML visualization parameters - parser.add_argument('--visual_freq', type=int, default=400, help='frequency of show training images in tensorboard') - parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console') - # network saving and loading parameters - parser.add_argument('--save_iter_freq', type=int, default=5000, help='frequency of saving the models') - parser.add_argument('--save_latest_freq', type=int, default=500, help='save latest freq') - parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') - parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration') - parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') - parser.add_argument('--no_strict_load', action='store_true', help='set strict load to false') - parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...') - parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') - # training parameters - parser.add_argument('--resume_epoch', type=int, default=0, help='training resume epoch') - parser.add_argument('--resume_iter', type=int, default=0, help='training resume iter') - parser.add_argument('--total_epochs', type=int, default=50, help='# of epochs to train') - parser.add_argument('--n_epochs', type=int, default=100, help='number of epochs with the initial learning rate') - parser.add_argument('--n_epochs_decay', type=int, default=100, help='number of epochs to linearly decay learning rate to zero') - parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') - parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') - parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') - parser.add_argument('--g_lr', type=float, default=0.0001, help='generator learning rate') - parser.add_argument('--d_lr', type=float, default=0.0004, help='discriminator learning rate') - parser.add_argument('--gan_mode', type=str, default='hinge', help='the type of GAN objective. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.') - parser.add_argument('--lr_policy', type=str, default='step', help='learning rate policy. [linear | step | plateau | cosine]') - parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') - parser.add_argument('--lr_decay_gamma', type=float, default=1, help='multiply by a gamma every lr_decay_iters iterations') - - self.isTrain = True - - return parser diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/requirements.txt b/face_parse/PSFRGAN-master/PSFRGAN-master/requirements.txt deleted file mode 100644 index 5ffeefb..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -torch==1.5.1 -torchvision==0.6.1 -tensorflow>=1.15.4 -tensorboard==1.15.0 -tensorboardX==2.1 -opencv-python -dlib -scikit-image==0.17.2 -scipy==1.4.1 -tqdm -imgaug diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_dir_align.py b/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_dir_align.py deleted file mode 100644 index fbccb83..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_dir_align.py +++ /dev/null @@ -1,62 +0,0 @@ -import os -from options.test_options import TestOptions -from data import create_dataset -from models import create_model -from utils import utils -from PIL import Image -from tqdm import tqdm -import torch -import time -import numpy as np - -if __name__ == '__main__': - opt = TestOptions().parse() # get test options - opt.num_threads = 0 # test code only supports num_threads = 1 - opt.batch_size = 4 # test code only supports batch_size = 1 - opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed. - opt.no_flip = True - - dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options - model = create_model(opt) # create a model given opt.model and other options - model.load_pretrain_models() - - save_dir = opt.results_dir - os.makedirs(save_dir, exist_ok=True) - - print('creating result directory', save_dir) - netP = model.netP - netG = model.netG - model.eval() - max_size = 9999 - os.makedirs(os.path.join(save_dir, 'sr'), exist_ok=True) - for i, data in tqdm(enumerate(dataset), total=len(dataset)//opt.batch_size): - inp = data['LR'] - with torch.no_grad(): - parse_map, _ = netP(inp) - parse_map_sm = (parse_map == parse_map.max(dim=1, keepdim=True)[0]).float() - output_SR = netG(inp, parse_map_sm) - img_path = data['LR_paths'] # get image paths - for i in tqdm(range(len(img_path))): - inp_img = utils.batch_tensor_to_img(inp) - output_sr_img = utils.batch_tensor_to_img(output_SR) - ref_parse_img = utils.color_parse_map(parse_map_sm) - - save_path = os.path.join(save_dir, 'lq', os.path.basename(img_path[i])) - os.makedirs(os.path.join(save_dir, 'lq'), exist_ok=True) - save_img = Image.fromarray(inp_img[i]) - save_img.save(save_path) - - save_path = os.path.join(save_dir, 'hq', os.path.basename(img_path[i])) - os.makedirs(os.path.join(save_dir, 'hq'), exist_ok=True) - save_img = Image.fromarray(output_sr_img[i]) - save_img.save(save_path) - - save_path = os.path.join(save_dir, 'parse', os.path.basename(img_path[i])) - os.makedirs(os.path.join(save_dir, 'parse'), exist_ok=True) - save_img = Image.fromarray(ref_parse_img[i]) - save_img.save(save_path) - - if i > max_size: break - - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_dir_unalign.py b/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_dir_unalign.py deleted file mode 100644 index 3156fe9..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_dir_unalign.py +++ /dev/null @@ -1,57 +0,0 @@ -''' -This script enhance images with unaligned faces in a folder and paste it back to the original place. -''' -import dlib -import os -import cv2 -import numpy as np -from tqdm import tqdm -from skimage import transform as trans -from skimage import io - -import torch -from utils import utils -from options.test_options import TestOptions -from models import create_model - -from test_enhance_single_unalign import * - - -if __name__ == '__main__': - opt = TestOptions().parse() - # face_detector = dlib.get_frontal_face_detector() - face_detector = dlib.cnn_face_detection_model_v1('./pretrain_models/mmod_human_face_detector.dat') - lmk_predictor = dlib.shape_predictor('./pretrain_models/shape_predictor_5_face_landmarks.dat') - template_path = './pretrain_models/FFHQ_template.npy' - enhance_model = def_models(opt) - - for img_name in os.listdir(opt.src_dir): - img_path = os.path.join(opt.src_dir, img_name) - save_current_dir = os.path.join(opt.results_dir, os.path.splitext(img_name)[0]) - os.makedirs(save_current_dir, exist_ok=True) - print('======> Loading image', img_path) - img = dlib.load_rgb_image(img_path) - aligned_faces, tform_params = detect_and_align_faces(img, face_detector, lmk_predictor, template_path) - # Save aligned LQ faces - save_lq_dir = os.path.join(save_current_dir, 'LQ_faces') - os.makedirs(save_lq_dir, exist_ok=True) - print('======> Saving aligned LQ faces to', save_lq_dir) - save_imgs(aligned_faces, save_lq_dir) - - hq_faces, lq_parse_maps = enhance_faces(aligned_faces, enhance_model) - # Save LQ parsing maps and enhanced faces - save_parse_dir = os.path.join(save_current_dir, 'ParseMaps') - save_hq_dir = os.path.join(save_current_dir, 'HQ') - os.makedirs(save_parse_dir, exist_ok=True) - os.makedirs(save_hq_dir, exist_ok=True) - print('======> Save parsing map and the enhanced faces.') - save_imgs(lq_parse_maps, save_parse_dir) - save_imgs(hq_faces, save_hq_dir) - - print('======> Paste the enhanced faces back to the original image.') - hq_img = past_faces_back(img, hq_faces, tform_params, upscale=opt.test_upscale) - final_save_path = os.path.join(save_current_dir, 'hq_final.jpg') - print('======> Save final result to', final_save_path) - io.imsave(final_save_path, hq_img) - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_single_unalign.py b/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_single_unalign.py deleted file mode 100644 index 208aac3..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/test_enhance_single_unalign.py +++ /dev/null @@ -1,126 +0,0 @@ -''' -This script enhance all faces in one image with PSFR-GAN and paste it back to the original place. -''' -import dlib -import os -import cv2 -import numpy as np -from tqdm import tqdm -from skimage import transform as trans -from skimage import io - -import torch -from utils import utils -from options.test_options import TestOptions -from models import create_model - - -def detect_and_align_faces(img, face_detector, lmk_predictor, template_path, template_scale=2, size_threshold=999): - align_out_size = (512, 512) - ref_points = np.load(template_path) / template_scale - - # Detect landmark points - face_dets = face_detector(img, 1) - assert len(face_dets) > 0, 'No faces detected' - - aligned_faces = [] - tform_params = [] - for det in face_dets: - if isinstance(face_detector, dlib.cnn_face_detection_model_v1): - rec = det.rect # for cnn detector - else: - rec = det - if rec.width() > size_threshold or rec.height() > size_threshold: - print('Face is too large') - break - landmark_points = lmk_predictor(img, rec) - single_points = [] - for i in range(5): - single_points.append([landmark_points.part(i).x, landmark_points.part(i).y]) - single_points = np.array(single_points) - tform = trans.SimilarityTransform() - tform.estimate(single_points, ref_points) - tmp_face = trans.warp(img, tform.inverse, output_shape=align_out_size, order=3) - aligned_faces.append(tmp_face*255) - tform_params.append(tform) - return [aligned_faces, tform_params] - - -def def_models(opt): - model = create_model(opt) - model.load_pretrain_models() - model.netP.to(opt.device) - model.netG.to(opt.device) - return model - - -def enhance_faces(LQ_faces, model): - hq_faces = [] - lq_parse_maps = [] - for lq_face in tqdm(LQ_faces): - with torch.no_grad(): - lq_tensor = torch.tensor(lq_face.transpose(2, 0, 1)) / 255. * 2 - 1 - lq_tensor = lq_tensor.unsqueeze(0).float().to(model.device) - parse_map, _ = model.netP(lq_tensor) - parse_map_onehot = (parse_map == parse_map.max(dim=1, keepdim=True)[0]).float() - output_SR = model.netG(lq_tensor, parse_map_onehot) - hq_faces.append(utils.tensor_to_img(output_SR)) - lq_parse_maps.append(utils.color_parse_map(parse_map_onehot)[0]) - return hq_faces, lq_parse_maps - - -def past_faces_back(img, hq_faces, tform_params, upscale=1): - h, w = img.shape[:2] - img = cv2.resize(img, (int(w*upscale), int(h*upscale)), interpolation=cv2.INTER_CUBIC) - for hq_img, tform in tqdm(zip(hq_faces, tform_params), total=len(hq_faces)): - tform.params[0:2,0:2] /= upscale - back_img = trans.warp(hq_img/255., tform, output_shape=[int(h*upscale), int(w*upscale)], order=3) * 255 - - # blur mask to avoid border artifacts - mask = (back_img == 0) - mask = cv2.blur(mask.astype(np.float32), (5,5)) - mask = (mask > 0) - img = img * mask + (1 - mask) * back_img - return img.astype(np.uint8) - - -def save_imgs(img_list, save_dir): - for idx, img in enumerate(img_list): - save_path = os.path.join(save_dir, '{:03d}.jpg'.format(idx)) - io.imsave(save_path, img.astype(np.uint8)) - -if __name__ == '__main__': - opt = TestOptions().parse() - # face_detector = dlib.get_frontal_face_detector() - face_detector = dlib.cnn_face_detection_model_v1('./pretrain_models/mmod_human_face_detector.dat') - lmk_predictor = dlib.shape_predictor('./pretrain_models/shape_predictor_5_face_landmarks.dat') - template_path = './pretrain_models/FFHQ_template.npy' - - print('======> Loading images, crop and align faces.') - img_path = opt.test_img_path - img = dlib.load_rgb_image(img_path) - aligned_faces, tform_params = detect_and_align_faces(img, face_detector, lmk_predictor, template_path) - # Save aligned LQ faces - save_lq_dir = os.path.join(opt.results_dir, 'LQ_faces') - os.makedirs(save_lq_dir, exist_ok=True) - print('======> Saving aligned LQ faces to', save_lq_dir) - save_imgs(aligned_faces, save_lq_dir) - - enhance_model = def_models(opt) - hq_faces, lq_parse_maps = enhance_faces(aligned_faces, enhance_model) - # Save LQ parsing maps and enhanced faces - save_parse_dir = os.path.join(opt.results_dir, 'ParseMaps') - save_hq_dir = os.path.join(opt.results_dir, 'HQ') - os.makedirs(save_parse_dir, exist_ok=True) - os.makedirs(save_hq_dir, exist_ok=True) - print('======> Save parsing map and the enhanced faces.') - save_imgs(lq_parse_maps, save_parse_dir) - save_imgs(hq_faces, save_hq_dir) - - print('======> Paste the enhanced faces back to the original image.') - hq_img = past_faces_back(img, hq_faces, tform_params, upscale=opt.test_upscale) - final_save_path = os.path.join(opt.results_dir, 'hq_final.jpg') - print('======> Save final result to', final_save_path) - io.imsave(final_save_path, hq_img) - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/train.py b/face_parse/PSFRGAN-master/PSFRGAN-master/train.py deleted file mode 100644 index 855b07d..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/train.py +++ /dev/null @@ -1,79 +0,0 @@ -from utils.timer import Timer -from utils.logger import Logger -from utils import utils - -from options.train_options import TrainOptions -from data import create_dataset -from models import create_model - -import torch -import os -import torch.multiprocessing as mp - -def train(opt): - - dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options - dataset_size = len(dataset) # get the number of images in the dataset. - print('The number of training images = %d' % dataset_size) - - model = create_model(opt) - model.setup(opt) - - logger = Logger(opt) - timer = Timer() - - single_epoch_iters = (dataset_size // opt.batch_size) - total_iters = opt.total_epochs * single_epoch_iters - cur_iters = opt.resume_iter + opt.resume_epoch * single_epoch_iters - start_iter = opt.resume_iter - print('Start training from epoch: {:05d}; iter: {:07d}'.format(opt.resume_epoch, opt.resume_iter)) - for epoch in range(opt.resume_epoch, opt.total_epochs + 1): - for i, data in enumerate(dataset, start=start_iter): - cur_iters += 1 - logger.set_current_iter(cur_iters) - # =================== load data =============== - model.set_input(data, cur_iters) - timer.update_time('DataTime') - - # =================== model train =============== - model.forward(), timer.update_time('Forward') - model.optimize_parameters() - loss = model.get_current_losses() - loss.update(model.get_lr()) - logger.record_losses(loss) - timer.update_time('Backward') - - # =================== save model and visualize =============== - if cur_iters % opt.print_freq == 0: - print('Model log directory: {}'.format(opt.expr_dir)) - epoch_progress = '{:03d}|{:05d}/{:05d}'.format(epoch, i, single_epoch_iters) - logger.printIterSummary(epoch_progress, cur_iters, total_iters, timer) - - if cur_iters % opt.visual_freq == 0: - visual_imgs = model.get_current_visuals() - logger.record_images(visual_imgs) - - if cur_iters % opt.save_iter_freq == 0: - print('saving current model (epoch %d, iters %d)' % (epoch, cur_iters)) - save_suffix = 'iter_%d' % cur_iters - info = {'resume_epoch': epoch, 'resume_iter': i+1} - model.save_networks(save_suffix, info) - - if cur_iters % opt.save_latest_freq == 0: - print('saving the latest model (epoch %d, iters %d)' % (epoch, cur_iters)) - info = {'resume_epoch': epoch, 'resume_iter': i+1} - model.save_networks('latest', info) - - if i >= single_epoch_iters - 1: - start_iter = 0 - break - - # model.update_learning_rate() - if opt.debug: break - if opt.debug and epoch >= 0: break - logger.close() - -if __name__ == '__main__': - opt = TrainOptions().parse() - train(opt) - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/utils/logger.py b/face_parse/PSFRGAN-master/PSFRGAN-master/utils/logger.py deleted file mode 100644 index afbdfde..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/utils/logger.py +++ /dev/null @@ -1,91 +0,0 @@ -import os -from collections import OrderedDict -import numpy as np -from .utils import mkdirs -from tensorboardX import SummaryWriter -from datetime import datetime -import socket -import shutil - -class Logger(): - def __init__(self, opts): - time_stamp = '_{}'.format(datetime.now().strftime('%Y-%m-%d_%H:%M')) - self.opts = opts - self.log_dir = os.path.join(opts.log_dir, opts.name+time_stamp) - self.phase_keys = ['train', 'val', 'test'] - self.iter_log = [] - self.epoch_log = OrderedDict() - self.set_mode(opts.phase) - - # check if exist previous log belong to the same experiment name - exist_log = None - for log_name in os.listdir(opts.log_dir): - if opts.name in log_name: - exist_log = log_name - if exist_log is not None: - old_dir = os.path.join(opts.log_dir, exist_log) - archive_dir = os.path.join(opts.log_archive, exist_log) - shutil.move(old_dir, archive_dir) - - self.mk_log_file() - - self.writer = SummaryWriter(self.log_dir) - - def mk_log_file(self): - mkdirs(self.log_dir) - self.txt_files = OrderedDict() - for i in self.phase_keys: - self.txt_files[i] = os.path.join(self.log_dir, 'log_{}'.format(i)) - - def set_mode(self, mode): - self.mode = mode - self.epoch_log[mode] = [] - - def set_current_iter(self, cur_iter): - self.cur_iter = cur_iter - - def record_losses(self, items): - """ - iteration log: [iter][{key: value}] - """ - self.iter_log.append(items) - for k, v in items.items(): - if 'loss' in k.lower(): - self.writer.add_scalar('loss/{}'.format(k), v, self.cur_iter) - - def record_scalar(self, items): - """ - Add scalar records. item, {key: value} - """ - for i in items.keys(): - self.writer.add_scalar('{}'.format(i), items[i], self.cur_iter) - - def record_images(self, visuals, nrow=6, tag='ckpt_image'): - imgs = [] - max_len = visuals[0].shape[0] - for i in range(nrow): - if i >= max_len: continue - tmp_imgs = [x[i] for x in visuals] - imgs.append(np.hstack(tmp_imgs)) - imgs = np.vstack(imgs).astype(np.uint8) - self.writer.add_image(tag, imgs, self.cur_iter, dataformats='HWC') - - def record_text(self, tag, text): - self.writer.add_text(tag, text) - - def printIterSummary(self, epoch, cur_iters, total_it, timer): - msg = '{}\nIter: [{}]{:03d}/{:03d}\t\t'.format( - timer.to_string(total_it - cur_iters), epoch, cur_iters, total_it) - for k, v in self.iter_log[-1].items(): - msg += '{}: {:.6f}\t'.format(k, v) - print(msg + '\n') - with open(self.txt_files[self.mode], 'a+') as f: - f.write(msg + '\n') - - def close(self): - self.writer.export_scalars_to_json(os.path.join(self.log_dir, 'all_scalars.json')) - self.writer.close() - - - - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/utils/timer.py b/face_parse/PSFRGAN-master/PSFRGAN-master/utils/timer.py deleted file mode 100644 index 1ec4f74..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/utils/timer.py +++ /dev/null @@ -1,34 +0,0 @@ -import time -import datetime -from collections import OrderedDict - -class Timer(): - def __init__(self): - self.reset_timer() - self.start = time.time() - - def reset_timer(self): - self.before = time.time() - self.timer = OrderedDict() - - def restart(self): - self.before = time.time() - - def update_time(self, key): - self.timer[key] = time.time() - self.before - self.before = time.time() - - def to_string(self, iters_left, short=False): - iter_total = sum(self.timer.values()) - msg = "{:%Y-%m-%d %H:%M:%S}\tElapse: {}\tTimeLeft: {}\t".format( - datetime.datetime.now(), - datetime.timedelta(seconds=round(time.time() - self.start)), - datetime.timedelta(seconds=round(iter_total*iters_left)) - ) - if short: - msg += '{}: {:.2f}s'.format('|'.join(self.timer.keys()), iter_total) - else: - msg += '\tIterTotal: {:.2f}s\t{}: {} '.format(iter_total, - '|'.join(self.timer.keys()), ' '.join('{:.2f}s'.format(x) for x in self.timer.values())) - return msg - diff --git a/face_parse/PSFRGAN-master/PSFRGAN-master/utils/utils.py b/face_parse/PSFRGAN-master/PSFRGAN-master/utils/utils.py deleted file mode 100644 index ed49cc9..0000000 --- a/face_parse/PSFRGAN-master/PSFRGAN-master/utils/utils.py +++ /dev/null @@ -1,169 +0,0 @@ -import torch -import numpy as np -import cv2 as cv -from skimage import io -from PIL import Image -import os -import subprocess - -# MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] -MASK_COLORMAP = [[0, 0, 0], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [0,0, 0], [0, 0, 0], [255, 255, 255], [255, 255, 255], [255, 255, 255], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]] -label_list = ['skin', 'nose', 'eye_g', 'l_eye', 'r_eye', 'l_brow', 'r_brow', 'l_ear', 'r_ear', 'mouth', 'u_lip', 'l_lip', 'hair', 'hat', 'ear_r', 'neck_l', 'neck', 'cloth'] - -def array_to_heatmap(x): - x = (x - x.min()) / (x.max() - x.min()) * 255 - x = x.astype(np.uint8) - return cv.applyColorMap(x.astype(np.uint8), cv.COLORMAP_RAINBOW) - -def img_to_tensor(img_path, device, size=None, mode='rgb'): - """ - Read image from img_path, and convert to (C, H, W) tensor in range [-1, 1] - """ - img = Image.open(img_path).convert('RGB') - img = np.array(img) - if mode=='bgr': - img = img[..., ::-1] - if size: - img = cv.resize(img, size) - img = img / 255 * 2 - 1 - img_tensor = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).to(device) - return img_tensor.float() - -def tensor_to_img(tensor, save_path=None, size=None, mode='RGB', normal=[-1, 1]): - """ - mode: RGB or L (gray image) - Input: tensor with shape (C, H, W) - Output: PIL Image - """ - if isinstance(size, int): - size = (size, size) - img_array = tensor.squeeze().data.cpu().numpy() - if mode == 'RGB': - img_array = img_array.transpose(1, 2, 0) - - if size is not None: - img_array = cv.resize(img_array, size, interpolation=cv.INTER_LINEAR) - - if len(normal): - img_array = (img_array - normal[0]) / (normal[1] - normal[0]) * 255 - img_array = img_array.clip(0, 255) - - img_array = img_array.astype(np.uint8) - if save_path: - img = Image.fromarray(img_array, mode) - img.save(save_path) - - return img_array - -def tensor_to_numpy(tensor): - return tensor.data.cpu().numpy() - -def batch_numpy_to_image(array, size=None): - """ - Input: numpy array (B, C, H, W) in [-1, 1] - """ - if isinstance(size, int): - size = (size, size) - - out_imgs = [] - array = np.clip((array + 1)/2 * 255, 0, 255) - array = np.transpose(array, (0, 2, 3, 1)) - for i in range(array.shape[0]): - if size is not None: - tmp_array = cv.resize(array[i], size) - else: - tmp_array = array[i] - out_imgs.append(tmp_array) - return np.array(out_imgs).astype(np.uint8) - -def batch_tensor_to_img(tensor, size=None): - """ - Input: (B, C, H, W) - Return: RGB image, [0, 255] - """ - arrays = tensor_to_numpy(tensor) - out_imgs = batch_numpy_to_image(arrays, size) - return out_imgs - -def color_parse_map(tensor, size=None): - """ - input: tensor or batch tensor - return: colorized parsing maps - """ - if len(tensor.shape) < 4: - tensor = tensor.unsqueeze(0) - if tensor.shape[1] > 1: - tensor = tensor.argmax(dim=1) - - tensor = tensor.squeeze(1).data.cpu().numpy() - color_maps = [] - for t in tensor: - tmp_img = np.zeros(tensor.shape[1:] + (3,)) - for idx, color in enumerate(MASK_COLORMAP): - tmp_img[t == idx] = color - if size is not None: - tmp_img = cv.resize(tmp_img, (size, size)) - color_maps.append(tmp_img.astype(np.uint8)) - return color_maps - -def onehot_parse_map(img): - """ - input: RGB color parse map - output: one hot encoding of parse map - """ - n_label = len(MASK_COLORMAP) - img = np.array(img, dtype=np.uint8) - h, w = img.shape[:2] - onehot_label = np.zeros((n_label, h, w)) - colormap = np.array(MASK_COLORMAP).reshape(n_label, 1, 1, 3) - colormap = np.tile(colormap, (1, h, w, 1)) - for idx, color in enumerate(MASK_COLORMAP): - tmp_label = colormap[idx] == img - onehot_label[idx] = tmp_label[..., 0] * tmp_label[..., 1] * tmp_label[..., 2] - return onehot_label - - -def mkdirs(paths): - if isinstance(paths, list) and not isinstance(paths, str): - for path in paths: - if not os.path.exists(path): - os.makedirs(path) - else: - if not os.path.exists(paths): - os.makedirs(paths) - - -def get_gpu_memory_map(): - """Get the current gpu usage within visible cuda devices. - - Returns - ------- - Memory Map: dict - Keys are device ids as integers. - Values are memory usage as integers in MB. - Device Ids: gpu ids sorted in descending order according to the available memory. - """ - result = subprocess.check_output( - [ - 'nvidia-smi', '--query-gpu=memory.used', - '--format=csv,nounits,noheader' - ]).decode('utf-8') - # Convert lines into a dictionary - gpu_memory = np.array([int(x) for x in result.strip().split('\n')]) - if 'CUDA_VISIBLE_DEVICES' in os.environ: - visible_devices = sorted([int(x) for x in os.environ['CUDA_VISIBLE_DEVICES'].split(',')]) - else: - visible_devices = range(len(gpu_memory)) - gpu_memory_map = dict(zip(range(len(visible_devices)), gpu_memory[visible_devices])) - return gpu_memory_map, sorted(gpu_memory_map, key=gpu_memory_map.get) - - -if __name__ == '__main__': - hm = torch.randn(32, 68, 128, 128).cuda() - flip(hm, 2) - x = torch.ones(32, 68) - y = torch.ones(32, 68) - print(get_gpu_memory_map()) - - - diff --git a/filter.py b/filter.py deleted file mode 100644 index d927ce0..0000000 --- a/filter.py +++ /dev/null @@ -1,52 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: filter.py -# Created Date: Wednesday April 13th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 13th April 2022 3:49:23 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import cv2 -import torch.nn as nn -import torch -import torch.nn.functional as F - -import numpy as np -from PIL import Image -from torchvision import transforms - -class HighPass(nn.Module): - def __init__(self, w_hpf, device): - super(HighPass, self).__init__() - self.filter = torch.tensor([[-1, -1, -1], - [-1, 8., -1], - [-1, -1, -1]]).to(device) / w_hpf - - def forward(self, x): - filter = self.filter.unsqueeze(0).unsqueeze(1).repeat(x.size(1), 1, 1, 1) - return F.conv2d(x, filter, padding=1, groups=x.size(1)) - -if __name__ == "__main__": - transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - imagenet_std = torch.tensor([0.229, 0.224, 0.225]).view(3,1,1) - imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).view(3,1,1) - - img = "G:/swap_data/ID/2.jpg" - attr = cv2.imread(img) - attr = Image.fromarray(cv2.cvtColor(attr,cv2.COLOR_BGR2RGB)) - attr = transformer_Arcface(attr).unsqueeze(0) - results = HighPass(0.5,torch.device("cpu"))(attr) - - results = results * imagenet_std + imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) * 255 - results = cv2.cvtColor(results,cv2.COLOR_RGB2BGR) - cv2.imwrite("filter_results2.png",results) diff --git a/flops.py b/flops.py deleted file mode 100644 index e1f013a..0000000 --- a/flops.py +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: flops.py -# Created Date: Sunday February 13th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 18th April 2022 10:52:57 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os - -import torch -from thop import profile -from thop import clever_format - - - -if __name__ == '__main__': -# - # script = "Generator_modulation_up" - script = "Generator_2mask" - # script = "Generator_ori_modulation_config" - # script = "Generator_ori_config" - class_name = "Generator" - arcface_ckpt= "arcface_ckpt/arcface_checkpoint.tar" - model_config={ - "id_dim": 512, - "g_kernel_size": 3, - "in_channel":64, - "res_num": 3, - # "up_mode": "nearest", - "up_mode": "bilinear", - "aggregator": "eca_invo", - "res_mode": "conv", - "norm": "bn" - } - - - os.environ['CUDA_VISIBLE_DEVICES'] = str(0) - print("GPU used : ", os.environ['CUDA_VISIBLE_DEVICES']) - - gscript_name = "components." + script - - - package = __import__(gscript_name, fromlist=True) - gen_class= getattr(package, class_name) - gen = gen_class(**model_config) - model = gen.cuda().eval().requires_grad_(False) - arcface1 = torch.load(arcface_ckpt, map_location=torch.device("cpu")) - arcface = arcface1['model'].module - arcface = arcface.cuda() - arcface.eval().requires_grad_(False) - - attr_img = torch.rand((1,3,512,512)).cuda() - id_img = torch.rand((1,3,112,112)).cuda() - id_latent = torch.rand((1,512)).cuda() - - macs, params = profile(model, inputs=(attr_img, id_latent)) - macs, params = clever_format([macs, params], "%.3f") - print(macs) - print(params) \ No newline at end of file diff --git a/id_cos.py b/id_cos.py deleted file mode 100644 index 7a363de..0000000 --- a/id_cos.py +++ /dev/null @@ -1,76 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: id_cos.py -# Created Date: Friday March 25th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 29th March 2022 11:58:30 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# -import cv2 -from PIL import Image - -import torch -import torch.nn.functional as F -from torchvision import transforms -from insightface_func.face_detect_crop_single import Face_detect_crop - -from arcface_torch.backbones.iresnet import iresnet100 - -if __name__ == "__main__": - imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - arcface_ckpt = "./arcface_ckpt/arcface_checkpoint.tar" - arcface1 = torch.load(arcface_ckpt, map_location=torch.device("cpu")) - arcface = arcface1['model'].module - arcface.eval() - - root1 = "G:/VGGFace2-HQ/VGGface2_ffhq_align_256_9_28_512_bygfpgan/n000002/" - root2 = "G:/VGGFace2-HQ/VGGface2_None_norm_512_true_bygfpgan/n000002/" - - # arcface_ckpt = "./arcface_torch/checkpoints/backbone.pth" # backbone.pth glint360k_cosface_r100_fp16_backbone.pth - # arcface = iresnet100(pretrained=False, fp16=False) - # arcface.load_state_dict(torch.load(arcface_ckpt, map_location='cpu')) - # arcface.eval() - - # id1 = "G:/swap_data/ID/hinton.jpg" - # id2 = "G:/hififace-master/hififace-master/assets/inference_samples/hififace/img-172.jpg" - id1 = root2 + "0003_01.jpg" - id2 = root2 + "0036_01.jpg" - - mode = "none" - cos_loss = torch.nn.CosineSimilarity() - # detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - # detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - id_img = cv2.imread(id1) - # id_img_align_crop, _ = detect.get(id_img,256) - # cv2.imwrite("id1_crop.png",id_img_align_crop[0]) - # id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img,cv2.COLOR_BGR2RGB)) - id_img = transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0) - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - # id_img = (id_img-0.5)*2.0 - latend_id = arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - - id_img2 = cv2.imread(id2) - # id_img_align_crop2, _ = detect.get(id_img2,256) - # cv2.imwrite("id2_crop.png",id_img_align_crop2[0]) - # id_img_align_crop_pil2 = Image.fromarray(cv2.cvtColor(id_img_align_crop2[0],cv2.COLOR_BGR2RGB)) - id_img_align_crop_pil2 = Image.fromarray(cv2.cvtColor(id_img2,cv2.COLOR_BGR2RGB)) - id_img2 = transformer_Arcface(id_img_align_crop_pil2) - id_img2 = id_img2.unsqueeze(0) - id_img2 = F.interpolate(id_img2,size=(112,112), mode='bicubic') - # id_img2 = (id_img2-0.5)*2.0 - latend_id2 = arcface(id_img2) - latend_id2 = F.normalize(latend_id2, p=2, dim=1) - - cos_dis = 1 - cos_loss(latend_id, latend_id2) - print("cosine similarity:", cos_dis.item()) \ No newline at end of file diff --git a/losses/KA.py b/losses/KA.py deleted file mode 100644 index d6d907e..0000000 --- a/losses/KA.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: KA.py -# Created Date: Wednesday February 23rd 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 23rd February 2022 12:12:05 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -def KA(X, Y): - X_ = X.view(X.size(0), -1) - Y_ = Y.view(Y.size(0), -1) - assert X_.shape[0] == Y_.shape[ - 0], f'X_ and Y_ must have the same shape on dim 0, but got {X_.shape[0]} for X_ and {Y_.shape[0]} for Y_.' - X_vec = X_ @ X_.T - Y_vec = Y_ @ Y_.T - ret = (X_vec * Y_vec).sum() / ((X_vec**2).sum() * (Y_vec**2).sum())**0.5 - return ret \ No newline at end of file diff --git a/losses/PatchNCE.py b/losses/PatchNCE.py deleted file mode 100644 index ed8db2c..0000000 --- a/losses/PatchNCE.py +++ /dev/null @@ -1,229 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: PatchNCE.py -# Created Date: Friday January 21st 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 24th January 2022 9:56:24 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch -import torch.nn as nn -from torch.nn import init -import torch.nn.functional as F -from packaging import version - -import numpy as np - - - -def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[], debug=False, initialize_weights=True): - """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights - Parameters: - net (network) -- the network to be initialized - init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal - gain (float) -- scaling factor for normal, xavier and orthogonal. - gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 - - Return an initialized network. - """ - if len(gpu_ids) > 0: - assert(torch.cuda.is_available()) - net.to(gpu_ids[0]) - # if not amp: - # net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs for non-AMP training - if initialize_weights: - init_weights(net, init_type, init_gain=init_gain, debug=debug) - return net - -def init_weights(net, init_type='normal', init_gain=0.02, debug=False): - """Initialize network weights. - - Parameters: - net (network) -- network to be initialized - init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal - init_gain (float) -- scaling factor for normal, xavier and orthogonal. - - We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might - work better for some applications. Feel free to try yourself. - """ - def init_func(m): # define the initialization function - classname = m.__class__.__name__ - if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): - if debug: - print(classname) - if init_type == 'normal': - init.normal_(m.weight.data, 0.0, init_gain) - elif init_type == 'xavier': - init.xavier_normal_(m.weight.data, gain=init_gain) - elif init_type == 'kaiming': - init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') - elif init_type == 'orthogonal': - init.orthogonal_(m.weight.data, gain=init_gain) - else: - raise NotImplementedError('initialization method [%s] is not implemented' % init_type) - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies. - init.normal_(m.weight.data, 1.0, init_gain) - init.constant_(m.bias.data, 0.0) - - net.apply(init_func) # apply the initialization function - -class Normalize(nn.Module): - - def __init__(self, power=2): - super(Normalize, self).__init__() - self.power = power - - def forward(self, x): - norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power) - out = x.div(norm + 1e-7) - return out - -class PatchSampleF(nn.Module): - def __init__(self, use_mlp=False, init_type='normal', init_gain=0.02, nc=256, gpu_ids=[]): - # potential issues: currently, we use the same patch_ids for multiple images in the batch - super(PatchSampleF, self).__init__() - self.l2norm = Normalize(2) - self.use_mlp = use_mlp - self.nc = nc # hard-coded - self.mlp_init = False - self.init_type = init_type - self.init_gain = init_gain - self.gpu_ids = gpu_ids - - def create_mlp(self, feats): - for mlp_id, feat in enumerate(feats): - input_nc = feat.shape[1] - mlp = nn.Sequential(*[nn.Linear(input_nc, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)]) - if len(self.gpu_ids) > 0: - mlp.cuda() - setattr(self, 'mlp_%d' % mlp_id, mlp) - init_net(self, self.init_type, self.init_gain, self.gpu_ids) - self.mlp_init = True - - def forward(self, feats, num_patches=64, patch_ids=None): - return_ids = [] - return_feats = [] - if self.use_mlp and not self.mlp_init: - self.create_mlp(feats) - for feat_id, feat in enumerate(feats): - B, H, W = feat.shape[0], feat.shape[2], feat.shape[3] - feat_reshape = feat.permute(0, 2, 3, 1).flatten(1, 2) - if num_patches > 0: - if patch_ids is not None: - patch_id = patch_ids[feat_id] - else: - # torch.randperm produces cudaErrorIllegalAddress for newer versions of PyTorch. https://github.com/taesungp/contrastive-unpaired-translation/issues/83 - #patch_id = torch.randperm(feat_reshape.shape[1], device=feats[0].device) - patch_id = np.random.permutation(feat_reshape.shape[1]) - patch_id = patch_id[:int(min(num_patches, patch_id.shape[0]))] # .to(patch_ids.device) - x_sample = feat_reshape[:, patch_id, :].flatten(0, 1) # reshape(-1, x.shape[1]) - else: - x_sample = feat_reshape - patch_id = [] - if self.use_mlp: - mlp = getattr(self, 'mlp_%d' % feat_id) - x_sample = mlp(x_sample) - return_ids.append(patch_id) - x_sample = self.l2norm(x_sample) - - if num_patches == 0: - x_sample = x_sample.permute(0, 2, 1).reshape([B, x_sample.shape[-1], H, W]) - return_feats.append(x_sample) - return return_feats, return_ids - - - -# def calculate_NCE_loss(src, tgt): -# n_layers = len(self.nce_layers) -# feat_q = self.netG(tgt, self.nce_layers, encode_only=True) - -# if self.opt.flip_equivariance and self.flipped_for_equivariance: -# feat_q = [torch.flip(fq, [3]) for fq in feat_q] - -# feat_k = self.netG(src, self.nce_layers, encode_only=True) -# feat_k_pool, sample_ids = self.netF(feat_k, self.opt.num_patches, None) -# feat_q_pool, _ = self.netF(feat_q, self.opt.num_patches, sample_ids) - -# total_nce_loss = 0.0 -# for f_q, f_k, crit, nce_layer in zip(feat_q_pool, feat_k_pool, self.criterionNCE, self.nce_layers): -# loss = crit(f_q, f_k) * self.opt.lambda_NCE -# total_nce_loss += loss.mean() - - # return total_nce_loss / n_layers - -class PatchNCELoss(nn.Module): - def __init__(self,batch_size, nce_T = 0.07): - super().__init__() - self.nce_T = nce_T - self.batch_size = batch_size - self.cross_entropy_loss = torch.nn.CrossEntropyLoss(reduction='none') - self.mask_dtype = torch.uint8 if version.parse(torch.__version__) < version.parse('1.2.0') else torch.bool - - def forward(self, feat_q, feat_k): - num_patches = feat_q.shape[0] - dim = feat_q.shape[1] - feat_k = feat_k.detach() - - # pos logit - l_pos = torch.bmm( - feat_q.view(num_patches, 1, -1), feat_k.view(num_patches, -1, 1)) - l_pos = l_pos.view(num_patches, 1) - - # neg logit - - # Should the negatives from the other samples of a minibatch be utilized? - # In CUT and FastCUT, we found that it's best to only include negatives - # from the same image. Therefore, we set - # --nce_includes_all_negatives_from_minibatch as False - # However, for single-image translation, the minibatch consists of - # crops from the "same" high-resolution image. - # Therefore, we will include the negatives from the entire minibatch. - # if self.opt.nce_includes_all_negatives_from_minibatch: - # # reshape features as if they are all negatives of minibatch of size 1. - # batch_dim_for_bmm = 1 - # else: - batch_dim_for_bmm = self.batch_size - - # reshape features to batch size - feat_q = feat_q.view(batch_dim_for_bmm, -1, dim) - feat_k = feat_k.view(batch_dim_for_bmm, -1, dim) - npatches = feat_q.size(1) - l_neg_curbatch = torch.bmm(feat_q, feat_k.transpose(2, 1)) - - # diagonal entries are similarity between same features, and hence meaningless. - # just fill the diagonal with very small number, which is exp(-10) and almost zero - diagonal = torch.eye(npatches, device=feat_q.device, dtype=self.mask_dtype)[None, :, :] - l_neg_curbatch.masked_fill_(diagonal, -10.0) - l_neg = l_neg_curbatch.view(-1, npatches) - - out = torch.cat((l_pos, l_neg), dim=1) / self.nce_T - - loss = self.cross_entropy_loss(out, torch.zeros(out.size(0), dtype=torch.long, - device=feat_q.device)) - - return loss - -if __name__ == "__main__": - batch = 16 - nc = 256 - num_patches = 64 - netF = PatchSampleF(use_mlp=True, init_type='normal', init_gain=0.02, gpu_ids=["cuda:0"], nc=nc) - - feat_q = [torch.ones((batch,nc,32,32)).cuda()] - - # if self.opt.flip_equivariance and self.flipped_for_equivariance: - # feat_q = [torch.flip(fq, [3]) for fq in feat_q] - crit = PatchNCELoss(batch) - feat_k = [torch.ones((batch,nc,32,32)).cuda()] - feat_k_pool, sample_ids = netF(feat_k, num_patches, None) - print(feat_k_pool[0].shape) - feat_q_pool, _ = netF(feat_q, num_patches, sample_ids) - print(feat_q_pool[0].shape) - loss = crit(feat_q_pool[0], feat_k_pool[0]) - print(loss) \ No newline at end of file diff --git a/losses/PerceptualLoss.py b/losses/PerceptualLoss.py deleted file mode 100644 index ba62399..0000000 --- a/losses/PerceptualLoss.py +++ /dev/null @@ -1,248 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: PerceptualLoss.py -# Created Date: Wednesday January 13th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 6th March 2021 4:42:26 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -import torch -from torch import nn as nn -from torch.nn import functional as F -from torchvision.models import vgg as vgg - -from collections import OrderedDict - - -NAMES = { - 'vgg11': [ - 'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', - 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', - 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', - 'conv5_2', 'relu5_2', 'pool5' - ], - 'vgg13': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', - 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', - 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', - 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5' - ], - 'vgg16': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', - 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', - 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', - 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', - 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', - 'pool5' - ], - 'vgg19': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', - 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', - 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', - 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', - 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', - 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', - 'pool5' - ] -} - - -def insert_bn(names): - """Insert bn layer after each conv. - - Args: - names (list): The list of layer names. - - Returns: - list: The list of layer names with bn layers. - """ - names_bn = [] - for name in names: - names_bn.append(name) - if 'conv' in name: - position = name.replace('conv', '') - names_bn.append('bn' + position) - return names_bn - - -class VGGFeatureExtractor(nn.Module): - """VGG network for feature extraction. - - In this implementation, we allow users to choose whether use normalization - in the input feature and the type of vgg network. Note that the pretrained - path must fit the vgg type. - - Args: - layer_name_list (list[str]): Forward function returns the corresponding - features according to the layer_name_list. - Example: {'relu1_1', 'relu2_1', 'relu3_1'}. - vgg_type (str): Set the type of vgg network. Default: 'vgg19'. - use_input_norm (bool): If True, normalize the input image. Importantly, - the input feature must in the range [0, 1]. Default: True. - requires_grad (bool): If true, the parameters of VGG network will be - optimized. Default: False. - remove_pooling (bool): If true, the max pooling operations in VGG net - will be removed. Default: False. - pooling_stride (int): The stride of max pooling operation. Default: 2. - """ - - def __init__(self, - layer_name_list, - vgg_type='vgg19', - use_input_norm=True, - requires_grad=False, - remove_pooling=False, - pooling_stride=2): - super(VGGFeatureExtractor, self).__init__() - - self.layer_name_list = layer_name_list - self.use_input_norm = use_input_norm - - self.names = NAMES[vgg_type.replace('_bn', '')] - if 'bn' in vgg_type: - self.names = insert_bn(self.names) - - # only borrow layers that will be used to avoid unused params - max_idx = 0 - for v in layer_name_list: - idx = self.names.index(v) - if idx > max_idx: - max_idx = idx - features = getattr(vgg, - vgg_type)(pretrained=True).features[:max_idx + 1] - - modified_net = OrderedDict() - for k, v in zip(self.names, features): - if 'pool' in k: - # if remove_pooling is true, pooling operation will be removed - if remove_pooling: - continue - else: - # in some cases, we may want to change the default stride - modified_net[k] = nn.MaxPool2d( - kernel_size=2, stride=pooling_stride) - else: - modified_net[k] = v - - self.vgg_net = nn.Sequential(modified_net) - - if not requires_grad: - self.vgg_net.eval() - for param in self.parameters(): - param.requires_grad = False - else: - self.vgg_net.train() - for param in self.parameters(): - param.requires_grad = True - - if self.use_input_norm: - # the mean is for image with range [0, 1] - self.register_buffer( - 'mean', - torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) - # the std is for image with range [0, 1] - self.register_buffer( - 'std', - torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) - - def forward(self, x): - """Forward function. - - Args: - x (Tensor): Input tensor with shape (n, c, h, w). - - Returns: - Tensor: Forward results. - """ - - if self.use_input_norm: - x = (x - self.mean) / self.std - - output = {} - for key, layer in self.vgg_net._modules.items(): - x = layer(x) - if key in self.layer_name_list: - output[key] = x.clone() - - return output - -class PerceptualLoss(nn.Module): - """Perceptual loss with commonly used style loss. - - Args: - layer_weights (dict): The weight for each layer of vgg feature. - Here is an example: {'conv5_4': 1.}, which means the conv5_4 - feature layer (before relu5_4) will be extracted with weight - 1.0 in calculting losses. - vgg_type (str): The type of vgg network used as feature extractor. - Default: 'vgg19'. - use_input_norm (bool): If True, normalize the input image in vgg. - Default: True. - perceptual_weight (float): If `perceptual_weight > 0`, the perceptual - loss will be calculated and the loss will multiplied by the - weight. Default: 1.0. - style_weight (float): If `style_weight > 0`, the style loss will be - calculated and the loss will multiplied by the weight. - Default: 0. - norm_img (bool): If True, the image will be normed to [0, 1]. Note that - this is different from the `use_input_norm` which norm the input in - in forward function of vgg according to the statistics of dataset. - Importantly, the input image must be in range [-1, 1]. - Default: False. - criterion (str): Criterion used for perceptual loss. Default: 'l1'. - """ - - def __init__(self, - layer_weights, - vgg_type='vgg19', - use_input_norm=True, - perceptual_weight=1.0, - criterion='l1'): - super(PerceptualLoss, self).__init__() - - self.perceptual_weight = perceptual_weight - self.layer_weights = layer_weights - self.vgg = VGGFeatureExtractor( - layer_name_list=list(layer_weights.keys()), - vgg_type=vgg_type, - use_input_norm=use_input_norm) - - self.criterion_type = criterion - if self.criterion_type == 'l1': - self.criterion = torch.nn.L1Loss() - elif self.criterion_type == 'l2': - self.criterion = torch.nn.L2loss() - else: - raise NotImplementedError( - f'{criterion} criterion has not been supported.') - - def forward(self, x, gt): - """Forward function. - - Args: - x (Tensor): Input tensor with shape (n, c, h, w). - gt (Tensor): Ground-truth tensor with shape (n, c, h, w). - - Returns: - Tensor: Forward results. - """ - - # extract vgg features - x_features = self.vgg(x) - gt_features = self.vgg(gt.detach()) - - # calculate perceptual loss - if self.perceptual_weight > 0: - percep_loss = 0 - for k in x_features.keys(): - percep_loss += self.criterion( - x_features[k], gt_features[k]) * self.layer_weights[k] - percep_loss *= self.perceptual_weight - else: - percep_loss = None - - return percep_loss \ No newline at end of file diff --git a/losses/SliceWassersteinDistance.py b/losses/SliceWassersteinDistance.py deleted file mode 100644 index eb2def9..0000000 --- a/losses/SliceWassersteinDistance.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: SliceWassersteinDistance.py -# Created Date: Tuesday October 12th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 12th October 2021 3:11:23 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -import torch - -from torch import nn -import torch.nn.functional as F - - -class SWD(nn.Module): - """ Slicing layer: computes projections and returns sorted vector """ - def __init__(self, channel, direction_num=16): - super().__init__() - # Number of directions - self.direc_num = direction_num - self.channel = channel - self.seed = nn.Parameter(torch.normal(mean=0.0, std=torch.ones(self.direc_num, self.channel)),requires_grad=False) - - def update(self): - """ Update random directions """ - # Generate random directions - self.seed.normal_() - # norm = self.directions.norm(dim=-1,keepdim=True) - self.directions = F.normalize(self.seed) - - # Normalize directions - # self.directions = self.directions/norm - # print("self.directions shape:", self.directions.shape) - # print("self.directions:", self.directions) - - def forward(self, input): - """ Implementation of figure 2 """ - input = input.flatten(-2) - sliced = self.directions @ input - sliced, _ = sliced.sort() - - return sliced - -if __name__ == "__main__": - wocao = torch.ones((4,3,5,5)) - slice = SWD(wocao.shape[1]) - slice.update() - wocao_slice = slice(wocao) - print(wocao_slice.shape) - print(wocao_slice) \ No newline at end of file diff --git a/losses/cos.py b/losses/cos.py deleted file mode 100644 index b89a62f..0000000 --- a/losses/cos.py +++ /dev/null @@ -1,17 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: cos.py -# Created Date: Monday February 7th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 7th February 2022 6:26:23 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch - -def cosin_metric(x1, x2): - #return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2)) - return torch.sum(x1 * x2, dim=1) / (torch.norm(x1, dim=1) * torch.norm(x2, dim=1)) \ No newline at end of file diff --git a/metrics/equivariance.py b/metrics/equivariance.py deleted file mode 100644 index d5559ac..0000000 --- a/metrics/equivariance.py +++ /dev/null @@ -1,267 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Equivariance metrics (EQ-T, EQ-T_frac, and EQ-R) from the paper -"Alias-Free Generative Adversarial Networks".""" - -import copy -import numpy as np -import torch -import torch.fft -from torch_utils.ops import upfirdn2d -from . import metric_utils - -#---------------------------------------------------------------------------- -# Utilities. - -def sinc(x): - y = (x * np.pi).abs() - z = torch.sin(y) / y.clamp(1e-30, float('inf')) - return torch.where(y < 1e-30, torch.ones_like(x), z) - -def lanczos_window(x, a): - x = x.abs() / a - return torch.where(x < 1, sinc(x), torch.zeros_like(x)) - -def rotation_matrix(angle): - angle = torch.as_tensor(angle).to(torch.float32) - mat = torch.eye(3, device=angle.device) - mat[0, 0] = angle.cos() - mat[0, 1] = angle.sin() - mat[1, 0] = -angle.sin() - mat[1, 1] = angle.cos() - return mat - -#---------------------------------------------------------------------------- -# Apply integer translation to a batch of 2D images. Corresponds to the -# operator T_x in Appendix E.1. - -def apply_integer_translation(x, tx, ty): - _N, _C, H, W = x.shape - tx = torch.as_tensor(tx * W).to(dtype=torch.float32, device=x.device) - ty = torch.as_tensor(ty * H).to(dtype=torch.float32, device=x.device) - ix = tx.round().to(torch.int64) - iy = ty.round().to(torch.int64) - - z = torch.zeros_like(x) - m = torch.zeros_like(x) - if abs(ix) < W and abs(iy) < H: - y = x[:, :, max(-iy,0) : H+min(-iy,0), max(-ix,0) : W+min(-ix,0)] - z[:, :, max(iy,0) : H+min(iy,0), max(ix,0) : W+min(ix,0)] = y - m[:, :, max(iy,0) : H+min(iy,0), max(ix,0) : W+min(ix,0)] = 1 - return z, m - -#---------------------------------------------------------------------------- -# Apply integer translation to a batch of 2D images. Corresponds to the -# operator T_x in Appendix E.2. - -def apply_fractional_translation(x, tx, ty, a=3): - _N, _C, H, W = x.shape - tx = torch.as_tensor(tx * W).to(dtype=torch.float32, device=x.device) - ty = torch.as_tensor(ty * H).to(dtype=torch.float32, device=x.device) - ix = tx.floor().to(torch.int64) - iy = ty.floor().to(torch.int64) - fx = tx - ix - fy = ty - iy - b = a - 1 - - z = torch.zeros_like(x) - zx0 = max(ix - b, 0) - zy0 = max(iy - b, 0) - zx1 = min(ix + a, 0) + W - zy1 = min(iy + a, 0) + H - if zx0 < zx1 and zy0 < zy1: - taps = torch.arange(a * 2, device=x.device) - b - filter_x = (sinc(taps - fx) * sinc((taps - fx) / a)).unsqueeze(0) - filter_y = (sinc(taps - fy) * sinc((taps - fy) / a)).unsqueeze(1) - y = x - y = upfirdn2d.filter2d(y, filter_x / filter_x.sum(), padding=[b,a,0,0]) - y = upfirdn2d.filter2d(y, filter_y / filter_y.sum(), padding=[0,0,b,a]) - y = y[:, :, max(b-iy,0) : H+b+a+min(-iy-a,0), max(b-ix,0) : W+b+a+min(-ix-a,0)] - z[:, :, zy0:zy1, zx0:zx1] = y - - m = torch.zeros_like(x) - mx0 = max(ix + a, 0) - my0 = max(iy + a, 0) - mx1 = min(ix - b, 0) + W - my1 = min(iy - b, 0) + H - if mx0 < mx1 and my0 < my1: - m[:, :, my0:my1, mx0:mx1] = 1 - return z, m - -#---------------------------------------------------------------------------- -# Construct an oriented low-pass filter that applies the appropriate -# bandlimit with respect to the input and output of the given affine 2D -# image transformation. - -def construct_affine_bandlimit_filter(mat, a=3, amax=16, aflt=64, up=4, cutoff_in=1, cutoff_out=1): - assert a <= amax < aflt - mat = torch.as_tensor(mat).to(torch.float32) - - # Construct 2D filter taps in input & output coordinate spaces. - taps = ((torch.arange(aflt * up * 2 - 1, device=mat.device) + 1) / up - aflt).roll(1 - aflt * up) - yi, xi = torch.meshgrid(taps, taps) - xo, yo = (torch.stack([xi, yi], dim=2) @ mat[:2, :2].t()).unbind(2) - - # Convolution of two oriented 2D sinc filters. - fi = sinc(xi * cutoff_in) * sinc(yi * cutoff_in) - fo = sinc(xo * cutoff_out) * sinc(yo * cutoff_out) - f = torch.fft.ifftn(torch.fft.fftn(fi) * torch.fft.fftn(fo)).real - - # Convolution of two oriented 2D Lanczos windows. - wi = lanczos_window(xi, a) * lanczos_window(yi, a) - wo = lanczos_window(xo, a) * lanczos_window(yo, a) - w = torch.fft.ifftn(torch.fft.fftn(wi) * torch.fft.fftn(wo)).real - - # Construct windowed FIR filter. - f = f * w - - # Finalize. - c = (aflt - amax) * up - f = f.roll([aflt * up - 1] * 2, dims=[0,1])[c:-c, c:-c] - f = torch.nn.functional.pad(f, [0, 1, 0, 1]).reshape(amax * 2, up, amax * 2, up) - f = f / f.sum([0,2], keepdim=True) / (up ** 2) - f = f.reshape(amax * 2 * up, amax * 2 * up)[:-1, :-1] - return f - -#---------------------------------------------------------------------------- -# Apply the given affine transformation to a batch of 2D images. - -def apply_affine_transformation(x, mat, up=4, **filter_kwargs): - _N, _C, H, W = x.shape - mat = torch.as_tensor(mat).to(dtype=torch.float32, device=x.device) - - # Construct filter. - f = construct_affine_bandlimit_filter(mat, up=up, **filter_kwargs) - assert f.ndim == 2 and f.shape[0] == f.shape[1] and f.shape[0] % 2 == 1 - p = f.shape[0] // 2 - - # Construct sampling grid. - theta = mat.inverse() - theta[:2, 2] *= 2 - theta[0, 2] += 1 / up / W - theta[1, 2] += 1 / up / H - theta[0, :] *= W / (W + p / up * 2) - theta[1, :] *= H / (H + p / up * 2) - theta = theta[:2, :3].unsqueeze(0).repeat([x.shape[0], 1, 1]) - g = torch.nn.functional.affine_grid(theta, x.shape, align_corners=False) - - # Resample image. - y = upfirdn2d.upsample2d(x=x, f=f, up=up, padding=p) - z = torch.nn.functional.grid_sample(y, g, mode='bilinear', padding_mode='zeros', align_corners=False) - - # Form mask. - m = torch.zeros_like(y) - c = p * 2 + 1 - m[:, :, c:-c, c:-c] = 1 - m = torch.nn.functional.grid_sample(m, g, mode='nearest', padding_mode='zeros', align_corners=False) - return z, m - -#---------------------------------------------------------------------------- -# Apply fractional rotation to a batch of 2D images. Corresponds to the -# operator R_\alpha in Appendix E.3. - -def apply_fractional_rotation(x, angle, a=3, **filter_kwargs): - angle = torch.as_tensor(angle).to(dtype=torch.float32, device=x.device) - mat = rotation_matrix(angle) - return apply_affine_transformation(x, mat, a=a, amax=a*2, **filter_kwargs) - -#---------------------------------------------------------------------------- -# Modify the frequency content of a batch of 2D images as if they had undergo -# fractional rotation -- but without actually rotating them. Corresponds to -# the operator R^*_\alpha in Appendix E.3. - -def apply_fractional_pseudo_rotation(x, angle, a=3, **filter_kwargs): - angle = torch.as_tensor(angle).to(dtype=torch.float32, device=x.device) - mat = rotation_matrix(-angle) - f = construct_affine_bandlimit_filter(mat, a=a, amax=a*2, up=1, **filter_kwargs) - y = upfirdn2d.filter2d(x=x, f=f) - m = torch.zeros_like(y) - c = f.shape[0] // 2 - m[:, :, c:-c, c:-c] = 1 - return y, m - -#---------------------------------------------------------------------------- -# Compute the selected equivariance metrics for the given generator. - -def compute_equivariance_metrics(opts, num_samples, batch_size, translate_max=0.125, rotate_max=1, compute_eqt_int=False, compute_eqt_frac=False, compute_eqr=False): - assert compute_eqt_int or compute_eqt_frac or compute_eqr - - # Setup generator and labels. - G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device) - I = torch.eye(3, device=opts.device) - M = getattr(getattr(getattr(G, 'synthesis', None), 'input', None), 'transform', None) - if M is None: - raise ValueError('Cannot compute equivariance metrics; the given generator does not support user-specified image transformations') - c_iter = metric_utils.iterate_random_labels(opts=opts, batch_size=batch_size) - - # Sampling loop. - sums = None - progress = opts.progress.sub(tag='eq sampling', num_items=num_samples) - for batch_start in range(0, num_samples, batch_size * opts.num_gpus): - progress.update(batch_start) - s = [] - - # Randomize noise buffers, if any. - for name, buf in G.named_buffers(): - if name.endswith('.noise_const'): - buf.copy_(torch.randn_like(buf)) - - # Run mapping network. - z = torch.randn([batch_size, G.z_dim], device=opts.device) - c = next(c_iter) - ws = G.mapping(z=z, c=c) - - # Generate reference image. - M[:] = I - orig = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs) - - # Integer translation (EQ-T). - if compute_eqt_int: - t = (torch.rand(2, device=opts.device) * 2 - 1) * translate_max - t = (t * G.img_resolution).round() / G.img_resolution - M[:] = I - M[:2, 2] = -t - img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs) - ref, mask = apply_integer_translation(orig, t[0], t[1]) - s += [(ref - img).square() * mask, mask] - - # Fractional translation (EQ-T_frac). - if compute_eqt_frac: - t = (torch.rand(2, device=opts.device) * 2 - 1) * translate_max - M[:] = I - M[:2, 2] = -t - img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs) - ref, mask = apply_fractional_translation(orig, t[0], t[1]) - s += [(ref - img).square() * mask, mask] - - # Rotation (EQ-R). - if compute_eqr: - angle = (torch.rand([], device=opts.device) * 2 - 1) * (rotate_max * np.pi) - M[:] = rotation_matrix(-angle) - img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs) - ref, ref_mask = apply_fractional_rotation(orig, angle) - pseudo, pseudo_mask = apply_fractional_pseudo_rotation(img, angle) - mask = ref_mask * pseudo_mask - s += [(ref - pseudo).square() * mask, mask] - - # Accumulate results. - s = torch.stack([x.to(torch.float64).sum() for x in s]) - sums = sums + s if sums is not None else s - progress.update(num_samples) - - # Compute PSNRs. - if opts.num_gpus > 1: - torch.distributed.all_reduce(sums) - sums = sums.cpu() - mses = sums[0::2] / sums[1::2] - psnrs = np.log10(2) * 20 - mses.log10() * 10 - psnrs = tuple(psnrs.numpy()) - return psnrs[0] if len(psnrs) == 1 else psnrs - -#---------------------------------------------------------------------------- diff --git a/metrics/frechet_inception_distance.py b/metrics/frechet_inception_distance.py deleted file mode 100644 index f99c828..0000000 --- a/metrics/frechet_inception_distance.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Frechet Inception Distance (FID) from the paper -"GANs trained by a two time-scale update rule converge to a local Nash -equilibrium". Matches the original implementation by Heusel et al. at -https://github.com/bioinf-jku/TTUR/blob/master/fid.py""" - -import numpy as np -import scipy.linalg -from . import metric_utils - -#---------------------------------------------------------------------------- - -def compute_fid(opts, max_real, num_gen, swav=False, sfid=False): - # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz - detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' - detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer. - - mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - rel_lo=0, rel_hi=0, capture_mean_cov=True, max_items=max_real, swav=swav, sfid=sfid).get_mean_cov() - - mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen, swav=swav, sfid=sfid).get_mean_cov() - - if opts.rank != 0: - return float('nan') - - m = np.square(mu_gen - mu_real).sum() - s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member - fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) - return float(fid) - -#---------------------------------------------------------------------------- diff --git a/metrics/inception_score.py b/metrics/inception_score.py deleted file mode 100644 index e0a3a44..0000000 --- a/metrics/inception_score.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Inception Score (IS) from the paper "Improved techniques for training -GANs". Matches the original implementation by Salimans et al. at -https://github.com/openai/improved-gan/blob/master/inception_score/model.py""" - -import numpy as np -from . import metric_utils - -#---------------------------------------------------------------------------- - -def compute_is(opts, num_gen, num_splits): - # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz - detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' - detector_kwargs = dict(no_output_bias=True) # Match the original implementation by not applying bias in the softmax layer. - - gen_probs = metric_utils.compute_feature_stats_for_generator( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - capture_all=True, max_items=num_gen).get_all() - - if opts.rank != 0: - return float('nan'), float('nan') - - scores = [] - for i in range(num_splits): - part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits] - kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True))) - kl = np.mean(np.sum(kl, axis=1)) - scores.append(np.exp(kl)) - return float(np.mean(scores)), float(np.std(scores)) - -#---------------------------------------------------------------------------- diff --git a/metrics/kernel_inception_distance.py b/metrics/kernel_inception_distance.py deleted file mode 100644 index d69325c..0000000 --- a/metrics/kernel_inception_distance.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Kernel Inception Distance (KID) from the paper "Demystifying MMD -GANs". Matches the original implementation by Binkowski et al. at -https://github.com/mbinkowski/MMD-GAN/blob/master/gan/compute_scores.py""" - -import numpy as np -from . import metric_utils - -#---------------------------------------------------------------------------- - -def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size): - # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz - detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' - detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer. - - real_features = metric_utils.compute_feature_stats_for_dataset( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all() - - gen_features = metric_utils.compute_feature_stats_for_generator( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all() - - if opts.rank != 0: - return float('nan') - - n = real_features.shape[1] - m = min(min(real_features.shape[0], gen_features.shape[0]), max_subset_size) - t = 0 - for _subset_idx in range(num_subsets): - x = gen_features[np.random.choice(gen_features.shape[0], m, replace=False)] - y = real_features[np.random.choice(real_features.shape[0], m, replace=False)] - a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3 - b = (x @ y.T / n + 1) ** 3 - t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m - kid = t / num_subsets / m - return float(kid) - -#---------------------------------------------------------------------------- diff --git a/metrics/metric_main.py b/metrics/metric_main.py deleted file mode 100644 index 27adc6e..0000000 --- a/metrics/metric_main.py +++ /dev/null @@ -1,151 +0,0 @@ -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Main API for computing and reporting quality metrics.""" - -import os -import time -import json -import torch -import dnnlib - -from . import metric_utils -from . import frechet_inception_distance -from . import kernel_inception_distance -from . import precision_recall -from . import perceptual_path_length -from . import inception_score -from . import equivariance - -#---------------------------------------------------------------------------- - -_metric_dict = dict() # name => fn - -def register_metric(fn): - assert callable(fn) - _metric_dict[fn.__name__] = fn - return fn - -def is_valid_metric(metric): - return metric in _metric_dict - -def list_valid_metrics(): - return list(_metric_dict.keys()) - -#---------------------------------------------------------------------------- - -def calc_metric(metric, **kwargs): # See metric_utils.MetricOptions for the full list of arguments. - assert is_valid_metric(metric) - opts = metric_utils.MetricOptions(**kwargs) - - # Calculate. - start_time = time.time() - results = _metric_dict[metric](opts) - total_time = time.time() - start_time - - # Broadcast results. - for key, value in list(results.items()): - if opts.num_gpus > 1: - value = torch.as_tensor(value, dtype=torch.float64, device=opts.device) - torch.distributed.broadcast(tensor=value, src=0) - value = float(value.cpu()) - results[key] = value - - # Decorate with metadata. - return dnnlib.EasyDict( - results = dnnlib.EasyDict(results), - metric = metric, - total_time = total_time, - total_time_str = dnnlib.util.format_time(total_time), - num_gpus = opts.num_gpus, - ) - -#---------------------------------------------------------------------------- - -def report_metric(result_dict, run_dir=None, snapshot_pkl=None): - metric = result_dict['metric'] - assert is_valid_metric(metric) - if run_dir is not None and snapshot_pkl is not None: - snapshot_pkl = os.path.relpath(snapshot_pkl, run_dir) - - jsonl_line = json.dumps(dict(result_dict, snapshot_pkl=snapshot_pkl, timestamp=time.time())) - print(jsonl_line) - if run_dir is not None and os.path.isdir(run_dir): - with open(os.path.join(run_dir, f'metric-{metric}.jsonl'), 'at') as f: - f.write(jsonl_line + '\n') - -#---------------------------------------------------------------------------- -# Recommended metrics. - -@register_metric -def fid50k_full(opts): - opts.dataset_kwargs.update(max_size=None, xflip=False) - fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=50000) - return dict(fid50k_full=fid) - -@register_metric -def fid10k_full(opts): - opts.dataset_kwargs.update(max_size=None, xflip=False) - fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=10000) - return dict(fid10k_full=fid) - -@register_metric -def kid50k_full(opts): - opts.dataset_kwargs.update(max_size=None, xflip=False) - kid = kernel_inception_distance.compute_kid(opts, max_real=1000000, num_gen=50000, num_subsets=100, max_subset_size=1000) - return dict(kid50k_full=kid) - -@register_metric -def pr50k3_full(opts): - opts.dataset_kwargs.update(max_size=None, xflip=False) - precision, recall = precision_recall.compute_pr(opts, max_real=200000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000) - return dict(pr50k3_full_precision=precision, pr50k3_full_recall=recall) - -@register_metric -def ppl2_wend(opts): - ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='w', sampling='end', crop=False, batch_size=2) - return dict(ppl2_wend=ppl) - -@register_metric -def eqt50k_int(opts): - opts.G_kwargs.update(force_fp32=True) - psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqt_int=True) - return dict(eqt50k_int=psnr) - -@register_metric -def eqt50k_frac(opts): - opts.G_kwargs.update(force_fp32=True) - psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqt_frac=True) - return dict(eqt50k_frac=psnr) - -@register_metric -def eqr50k(opts): - opts.G_kwargs.update(force_fp32=True) - psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqr=True) - return dict(eqr50k=psnr) - -# Legacy metrics. - -@register_metric -def fid50k(opts): - opts.dataset_kwargs.update(max_size=None) - fid = frechet_inception_distance.compute_fid(opts, max_real=50000, num_gen=50000) - return dict(fid50k=fid) - -@register_metric -def kid50k(opts): - opts.dataset_kwargs.update(max_size=None) - kid = kernel_inception_distance.compute_kid(opts, max_real=50000, num_gen=50000, num_subsets=100, max_subset_size=1000) - return dict(kid50k=kid) - -@register_metric -def pr50k3(opts): - opts.dataset_kwargs.update(max_size=None) - precision, recall = precision_recall.compute_pr(opts, max_real=50000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000) - return dict(pr50k3_precision=precision, pr50k3_recall=recall) - -@register_metric -def is50k(opts): - opts.dataset_kwargs.update(max_size=None, xflip=False) - mean, std = inception_score.compute_is(opts, num_gen=50000, num_splits=10) - return dict(is50k_mean=mean, is50k_std=std) diff --git a/metrics/metric_utils.py b/metrics/metric_utils.py deleted file mode 100644 index d7e3960..0000000 --- a/metrics/metric_utils.py +++ /dev/null @@ -1,298 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Miscellaneous utilities used internally by the quality metrics.""" - -import os -import time -import hashlib -import pickle -import copy -import uuid -import numpy as np -import torch -import dnnlib -from tqdm import tqdm - -#---------------------------------------------------------------------------- - -class MetricOptions: - def __init__(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1, rank=0, device=None, progress=None, cache=True, run_dir=None, cur_nimg=None, snapshot_pkl=None): - assert 0 <= rank < num_gpus - self.G = G - self.G_kwargs = dnnlib.EasyDict(G_kwargs) - self.dataset_kwargs = dnnlib.EasyDict(dataset_kwargs) - self.num_gpus = num_gpus - self.rank = rank - self.device = device if device is not None else torch.device('cuda', rank) - self.progress = progress.sub() if progress is not None and rank == 0 else ProgressMonitor() - self.cache = cache - self.run_dir = run_dir - self.cur_nimg = cur_nimg - self.snapshot_pkl = snapshot_pkl - -#---------------------------------------------------------------------------- - -_feature_detector_cache = dict() - -def get_feature_detector_name(url): - return os.path.splitext(url.split('/')[-1])[0] - -def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False): - assert 0 <= rank < num_gpus - key = (url, device) - if key not in _feature_detector_cache: - is_leader = (rank == 0) - if not is_leader and num_gpus > 1: - torch.distributed.barrier() # leader goes first - with dnnlib.util.open_url(url, verbose=(verbose and is_leader)) as f: - _feature_detector_cache[key] = pickle.load(f).to(device) - if is_leader and num_gpus > 1: - torch.distributed.barrier() # others follow - return _feature_detector_cache[key] - -#---------------------------------------------------------------------------- - -def iterate_random_labels(opts, batch_size): - if opts.G.c_dim == 0: - c = torch.zeros([batch_size, opts.G.c_dim], device=opts.device) - while True: - yield c - else: - dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) - while True: - c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_size)] - c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device) - yield c - -#---------------------------------------------------------------------------- - -class FeatureStats: - def __init__(self, capture_all=False, capture_mean_cov=False, max_items=None): - self.capture_all = capture_all - self.capture_mean_cov = capture_mean_cov - self.max_items = max_items - self.num_items = 0 - self.num_features = None - self.all_features = None - self.raw_mean = None - self.raw_cov = None - - def set_num_features(self, num_features): - if self.num_features is not None: - assert num_features == self.num_features - else: - self.num_features = num_features - self.all_features = [] - self.raw_mean = np.zeros([num_features], dtype=np.float64) - self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64) - - def is_full(self): - return (self.max_items is not None) and (self.num_items >= self.max_items) - - def append(self, x): - x = np.asarray(x, dtype=np.float32) - assert x.ndim == 2 - if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items): - if self.num_items >= self.max_items: - return - x = x[:self.max_items - self.num_items] - - self.set_num_features(x.shape[1]) - self.num_items += x.shape[0] - if self.capture_all: - self.all_features.append(x) - if self.capture_mean_cov: - x64 = x.astype(np.float64) - self.raw_mean += x64.sum(axis=0) - self.raw_cov += x64.T @ x64 - - def append_torch(self, x, num_gpus=1, rank=0): - assert isinstance(x, torch.Tensor) and x.ndim == 2 - assert 0 <= rank < num_gpus - if num_gpus > 1: - ys = [] - for src in range(num_gpus): - y = x.clone() - torch.distributed.broadcast(y, src=src) - ys.append(y) - x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples - self.append(x.cpu().numpy()) - - def get_all(self): - assert self.capture_all - return np.concatenate(self.all_features, axis=0) - - def get_all_torch(self): - return torch.from_numpy(self.get_all()) - - def get_mean_cov(self): - assert self.capture_mean_cov - mean = self.raw_mean / self.num_items - cov = self.raw_cov / self.num_items - cov = cov - np.outer(mean, mean) - return mean, cov - - def save(self, pkl_file): - with open(pkl_file, 'wb') as f: - pickle.dump(self.__dict__, f) - - @staticmethod - def load(pkl_file): - with open(pkl_file, 'rb') as f: - s = dnnlib.EasyDict(pickle.load(f)) - obj = FeatureStats(capture_all=s.capture_all, max_items=s.max_items) - obj.__dict__.update(s) - return obj - -#---------------------------------------------------------------------------- - -class ProgressMonitor: - def __init__(self, tag=None, num_items=None, flush_interval=1000, verbose=False, progress_fn=None, pfn_lo=0, pfn_hi=1000, pfn_total=1000): - self.tag = tag - self.num_items = num_items - self.verbose = verbose - self.flush_interval = flush_interval - self.progress_fn = progress_fn - self.pfn_lo = pfn_lo - self.pfn_hi = pfn_hi - self.pfn_total = pfn_total - self.start_time = time.time() - self.batch_time = self.start_time - self.batch_items = 0 - if self.progress_fn is not None: - self.progress_fn(self.pfn_lo, self.pfn_total) - - def update(self, cur_items): - assert (self.num_items is None) or (cur_items <= self.num_items) - if (cur_items < self.batch_items + self.flush_interval) and (self.num_items is None or cur_items < self.num_items): - return - cur_time = time.time() - total_time = cur_time - self.start_time - time_per_item = (cur_time - self.batch_time) / max(cur_items - self.batch_items, 1) - if (self.verbose) and (self.tag is not None): - print(f'{self.tag:<19s} items {cur_items:<7d} time {dnnlib.util.format_time(total_time):<12s} ms/item {time_per_item*1e3:.2f}') - self.batch_time = cur_time - self.batch_items = cur_items - - if (self.progress_fn is not None) and (self.num_items is not None): - self.progress_fn(self.pfn_lo + (self.pfn_hi - self.pfn_lo) * (cur_items / self.num_items), self.pfn_total) - - def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0, rel_hi=1): - return ProgressMonitor( - tag = tag, - num_items = num_items, - flush_interval = flush_interval, - verbose = self.verbose, - progress_fn = self.progress_fn, - pfn_lo = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_lo, - pfn_hi = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_hi, - pfn_total = self.pfn_total, - ) - -#---------------------------------------------------------------------------- - -def compute_feature_stats_for_dataset(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, data_loader_kwargs=None, max_items=None, swav=False, sfid=False, **stats_kwargs): - dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) - if data_loader_kwargs is None: - data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2) - - # Try to lookup from cache. - cache_file = None - if opts.cache: - det_name = get_feature_detector_name(detector_url) - - # Choose cache file name. - args = dict(dataset_kwargs=opts.dataset_kwargs, detector_url=detector_url, detector_kwargs=detector_kwargs, stats_kwargs=stats_kwargs) - md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8')) - cache_tag = f'{dataset.name}-{det_name}-{md5.hexdigest()}' - cache_file = os.path.join('.', 'dnnlib', 'gan-metrics', cache_tag + '.pkl') - # cache_file = dnnlib.make_cache_dir_path('gan-metrics', cache_tag + '.pkl') - - # Check if the file exists (all processes must agree). - flag = os.path.isfile(cache_file) if opts.rank == 0 else False - if opts.num_gpus > 1: - flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device) - torch.distributed.broadcast(tensor=flag, src=0) - flag = (float(flag.cpu()) != 0) - - # Load. - if flag: - return FeatureStats.load(cache_file) - - print('Calculating the stats for this dataset the first time\n') - print(f'Saving them to {cache_file}') - - # Initialize. - num_items = len(dataset) - if max_items is not None: - num_items = min(num_items, max_items) - stats = FeatureStats(max_items=num_items, **stats_kwargs) - progress = opts.progress.sub(tag='dataset features', num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi) - - # get detector - detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) - - # Main loop. - item_subset = [(i * opts.num_gpus + opts.rank) % num_items for i in range((num_items - 1) // opts.num_gpus + 1)] - for images, _labels in tqdm(torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset, batch_size=batch_size, **data_loader_kwargs)): - if images.shape[1] == 1: - images = images.repeat([1, 3, 1, 1]) - - with torch.no_grad(): - features = detector(images.to(opts.device), **detector_kwargs) - - stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) - progress.update(stats.num_items) - - # Save to cache. - if cache_file is not None and opts.rank == 0: - os.makedirs(os.path.dirname(cache_file), exist_ok=True) - temp_file = cache_file + '.' + uuid.uuid4().hex - stats.save(temp_file) - os.replace(temp_file, cache_file) # atomic - return stats - -#---------------------------------------------------------------------------- - -def compute_feature_stats_for_generator(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, swav=False, sfid=False, **stats_kwargs): - if batch_gen is None: - batch_gen = min(batch_size, 4) - assert batch_size % batch_gen == 0 - - # Setup generator and labels. - G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device) - c_iter = iterate_random_labels(opts=opts, batch_size=batch_gen) - - # Initialize. - stats = FeatureStats(**stats_kwargs) - assert stats.max_items is not None - progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi) - - # get detector - detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) - - # Main loop. - while not stats.is_full(): - images = [] - for _i in range(batch_size // batch_gen): - z = torch.randn([batch_gen, G.z_dim], device=opts.device) - # img = G(z=z, c=next(c_iter), truncation_psi=0.1, **opts.G_kwargs) - img = G(z=z, c=next(c_iter), **opts.G_kwargs) - img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8) - images.append(img) - images = torch.cat(images) - if images.shape[1] == 1: - images = images.repeat([1, 3, 1, 1]) - - with torch.no_grad(): - features = detector(images.to(opts.device), **detector_kwargs) - - stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) - progress.update(stats.num_items) - return stats diff --git a/metrics/perceptual_path_length.py b/metrics/perceptual_path_length.py deleted file mode 100644 index c68519f..0000000 --- a/metrics/perceptual_path_length.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Perceptual Path Length (PPL) from the paper "A Style-Based Generator -Architecture for Generative Adversarial Networks". Matches the original -implementation by Karras et al. at -https://github.com/NVlabs/stylegan/blob/master/metrics/perceptual_path_length.py""" - -import copy -import numpy as np -import torch -from . import metric_utils - -#---------------------------------------------------------------------------- - -# Spherical interpolation of a batch of vectors. -def slerp(a, b, t): - a = a / a.norm(dim=-1, keepdim=True) - b = b / b.norm(dim=-1, keepdim=True) - d = (a * b).sum(dim=-1, keepdim=True) - p = t * torch.acos(d) - c = b - d * a - c = c / c.norm(dim=-1, keepdim=True) - d = a * torch.cos(p) + c * torch.sin(p) - d = d / d.norm(dim=-1, keepdim=True) - return d - -#---------------------------------------------------------------------------- - -class PPLSampler(torch.nn.Module): - def __init__(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16): - assert space in ['z', 'w'] - assert sampling in ['full', 'end'] - super().__init__() - self.G = copy.deepcopy(G) - self.G_kwargs = G_kwargs - self.epsilon = epsilon - self.space = space - self.sampling = sampling - self.crop = crop - self.vgg16 = copy.deepcopy(vgg16) - - def forward(self, c): - # Generate random latents and interpolation t-values. - t = torch.rand([c.shape[0]], device=c.device) * (1 if self.sampling == 'full' else 0) - z0, z1 = torch.randn([c.shape[0] * 2, self.G.z_dim], device=c.device).chunk(2) - - # Interpolate in W or Z. - if self.space == 'w': - w0, w1 = self.G.mapping(z=torch.cat([z0,z1]), c=torch.cat([c,c])).chunk(2) - wt0 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2)) - wt1 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2) + self.epsilon) - else: # space == 'z' - zt0 = slerp(z0, z1, t.unsqueeze(1)) - zt1 = slerp(z0, z1, t.unsqueeze(1) + self.epsilon) - wt0, wt1 = self.G.mapping(z=torch.cat([zt0,zt1]), c=torch.cat([c,c])).chunk(2) - - # Randomize noise buffers. - for name, buf in self.G.named_buffers(): - if name.endswith('.noise_const'): - buf.copy_(torch.randn_like(buf)) - - # Generate images. - img = self.G.synthesis(ws=torch.cat([wt0,wt1]), noise_mode='const', force_fp32=True, **self.G_kwargs) - - # Center crop. - if self.crop: - assert img.shape[2] == img.shape[3] - c = img.shape[2] // 8 - img = img[:, :, c*3 : c*7, c*2 : c*6] - - # Downsample to 256x256. - factor = self.G.img_resolution // 256 - if factor > 1: - img = img.reshape([-1, img.shape[1], img.shape[2] // factor, factor, img.shape[3] // factor, factor]).mean([3, 5]) - - # Scale dynamic range from [-1,1] to [0,255]. - img = (img + 1) * (255 / 2) - if self.G.img_channels == 1: - img = img.repeat([1, 3, 1, 1]) - - # Evaluate differential LPIPS. - lpips_t0, lpips_t1 = self.vgg16(img, resize_images=False, return_lpips=True).chunk(2) - dist = (lpips_t0 - lpips_t1).square().sum(1) / self.epsilon ** 2 - return dist - -#---------------------------------------------------------------------------- - -def compute_ppl(opts, num_samples, epsilon, space, sampling, crop, batch_size): - vgg16_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl' - vgg16 = metric_utils.get_feature_detector(vgg16_url, num_gpus=opts.num_gpus, rank=opts.rank, verbose=opts.progress.verbose) - - # Setup sampler and labels. - sampler = PPLSampler(G=opts.G, G_kwargs=opts.G_kwargs, epsilon=epsilon, space=space, sampling=sampling, crop=crop, vgg16=vgg16) - sampler.eval().requires_grad_(False).to(opts.device) - c_iter = metric_utils.iterate_random_labels(opts=opts, batch_size=batch_size) - - # Sampling loop. - dist = [] - progress = opts.progress.sub(tag='ppl sampling', num_items=num_samples) - for batch_start in range(0, num_samples, batch_size * opts.num_gpus): - progress.update(batch_start) - x = sampler(next(c_iter)) - for src in range(opts.num_gpus): - y = x.clone() - if opts.num_gpus > 1: - torch.distributed.broadcast(y, src=src) - dist.append(y) - progress.update(num_samples) - - # Compute PPL. - if opts.rank != 0: - return float('nan') - dist = torch.cat(dist)[:num_samples].cpu().numpy() - lo = np.percentile(dist, 1, interpolation='lower') - hi = np.percentile(dist, 99, interpolation='higher') - ppl = np.extract(np.logical_and(dist >= lo, dist <= hi), dist).mean() - return float(ppl) - -#---------------------------------------------------------------------------- diff --git a/metrics/precision_recall.py b/metrics/precision_recall.py deleted file mode 100644 index 120ef80..0000000 --- a/metrics/precision_recall.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Precision/Recall (PR) from the paper "Improved Precision and Recall -Metric for Assessing Generative Models". Matches the original implementation -by Kynkaanniemi et al. at -https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py""" - -import torch -from . import metric_utils - -#---------------------------------------------------------------------------- - -def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size): - assert 0 <= rank < num_gpus - num_cols = col_features.shape[0] - num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus - col_batches = torch.nn.functional.pad(col_features, [0, 0, 0, -num_cols % num_batches]).chunk(num_batches) - dist_batches = [] - for col_batch in col_batches[rank :: num_gpus]: - dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0] - for src in range(num_gpus): - dist_broadcast = dist_batch.clone() - if num_gpus > 1: - torch.distributed.broadcast(dist_broadcast, src=src) - dist_batches.append(dist_broadcast.cpu() if rank == 0 else None) - return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None - -#---------------------------------------------------------------------------- - -def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size): - detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl' - detector_kwargs = dict(return_features=True) - - real_features = metric_utils.compute_feature_stats_for_dataset( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all_torch().to(torch.float16).to(opts.device) - - gen_features = metric_utils.compute_feature_stats_for_generator( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all_torch().to(torch.float16).to(opts.device) - - results = dict() - for name, manifold, probes in [('precision', real_features, gen_features), ('recall', gen_features, real_features)]: - kth = [] - for manifold_batch in manifold.split(row_batch_size): - dist = compute_distances(row_features=manifold_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size) - kth.append(dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) if opts.rank == 0 else None) - kth = torch.cat(kth) if opts.rank == 0 else None - pred = [] - for probes_batch in probes.split(row_batch_size): - dist = compute_distances(row_features=probes_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size) - pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None) - results[name] = float(torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else 'nan') - return results['precision'], results['recall'] - -#---------------------------------------------------------------------------- diff --git a/models/__init__.py b/models/__init__.py deleted file mode 100644 index 289de91..0000000 --- a/models/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .arcface_models import ArcMarginModel -from .arcface_models import ResNet -from .arcface_models import IRBlock -from .arcface_models import SEBlock \ No newline at end of file diff --git a/models/arcface_models.py b/models/arcface_models.py deleted file mode 100644 index c678011..0000000 --- a/models/arcface_models.py +++ /dev/null @@ -1,162 +0,0 @@ -import math -import torch -import torch.nn.functional as F -from torch import nn -from torch.nn import Parameter -from .config import device, num_classes - - - -class SEBlock(nn.Module): - def __init__(self, channel, reduction=16): - super(SEBlock, self).__init__() - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Sequential( - nn.Linear(channel, channel // reduction), - nn.PReLU(), - nn.Linear(channel // reduction, channel), - nn.Sigmoid() - ) - - def forward(self, x): - b, c, _, _ = x.size() - y = self.avg_pool(x).view(b, c) - y = self.fc(y).view(b, c, 1, 1) - return x * y - - -class IRBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): - super(IRBlock, self).__init__() - self.bn0 = nn.BatchNorm2d(inplanes) - self.conv1 = conv3x3(inplanes, inplanes) - self.bn1 = nn.BatchNorm2d(inplanes) - self.prelu = nn.PReLU() - self.conv2 = conv3x3(inplanes, planes, stride) - self.bn2 = nn.BatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - self.use_se = use_se - if self.use_se: - self.se = SEBlock(planes) - - def forward(self, x): - residual = x - out = self.bn0(x) - out = self.conv1(out) - out = self.bn1(out) - out = self.prelu(out) - - out = self.conv2(out) - out = self.bn2(out) - if self.use_se: - out = self.se(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.prelu(out) - - return out - - -class ResNet(nn.Module): - - def __init__(self, block, layers, use_se=True): - self.inplanes = 64 - self.use_se = use_se - super(ResNet, self).__init__() - self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.prelu = nn.PReLU() - self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) - self.layer1 = self._make_layer(block, 64, layers[0]) - self.layer2 = self._make_layer(block, 128, layers[1], stride=2) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2) - self.bn2 = nn.BatchNorm2d(512) - self.dropout = nn.Dropout() - self.fc = nn.Linear(512 * 7 * 7, 512) - self.bn3 = nn.BatchNorm1d(512) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.xavier_normal_(m.weight) - elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.Linear): - nn.init.xavier_normal_(m.weight) - nn.init.constant_(m.bias, 0) - - def _make_layer(self, block, planes, blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes * block.expansion, - kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(planes * block.expansion), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se)) - self.inplanes = planes - for i in range(1, blocks): - layers.append(block(self.inplanes, planes, use_se=self.use_se)) - - return nn.Sequential(*layers) - - def forward(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.prelu(x) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - - x = self.bn2(x) - x = self.dropout(x) - x = x.view(x.size(0), -1) - x = self.fc(x) - x = self.bn3(x) - - return x - - -class ArcMarginModel(nn.Module): - def __init__(self, args): - super(ArcMarginModel, self).__init__() - - self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size)) - nn.init.xavier_uniform_(self.weight) - - self.easy_margin = args.easy_margin - self.m = args.margin_m - self.s = args.margin_s - - self.cos_m = math.cos(self.m) - self.sin_m = math.sin(self.m) - self.th = math.cos(math.pi - self.m) - self.mm = math.sin(math.pi - self.m) * self.m - - def forward(self, input, label): - x = F.normalize(input) - W = F.normalize(self.weight) - cosine = F.linear(x, W) - sine = torch.sqrt(1.0 - torch.pow(cosine, 2)) - phi = cosine * self.cos_m - sine * self.sin_m # cos(theta + m) - if self.easy_margin: - phi = torch.where(cosine > 0, phi, cosine) - else: - phi = torch.where(cosine > self.th, phi, cosine - self.mm) - one_hot = torch.zeros(cosine.size(), device=device) - one_hot.scatter_(1, label.view(-1, 1).long(), 1) - output = (one_hot * phi) + ((1.0 - one_hot) * cosine) - output *= self.s - return output \ No newline at end of file diff --git a/models/config.py b/models/config.py deleted file mode 100644 index eb83edb..0000000 --- a/models/config.py +++ /dev/null @@ -1,28 +0,0 @@ -import os - -import torch - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # sets device for model and PyTorch tensors - -# Model parameters -image_w = 112 -image_h = 112 -channel = 3 -emb_size = 512 - -# Training parameters -num_workers = 1 # for data-loading; right now, only 1 works with h5py -grad_clip = 5. # clip gradients at an absolute value of -print_freq = 100 # print training/validation stats every __ batches -checkpoint = None # path to checkpoint, None if none - -# Data parameters -num_classes = 93431 -num_samples = 5179510 -DATA_DIR = 'data' -# faces_ms1m_folder = 'data/faces_ms1m_112x112' -faces_ms1m_folder = 'data/ms1m-retinaface-t1' -path_imgidx = os.path.join(faces_ms1m_folder, 'train.idx') -path_imgrec = os.path.join(faces_ms1m_folder, 'train.rec') -IMG_DIR = 'data/images' -pickle_file = 'data/faces_ms1m_112x112.pickle' diff --git a/similarity.py b/similarity.py deleted file mode 100644 index 4aeca0a..0000000 --- a/similarity.py +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: similarity.py -# Created Date: Thursday January 27th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 27th January 2022 3:48:25 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - diff --git a/speed_test.py b/speed_test.py deleted file mode 100644 index bea143a..0000000 --- a/speed_test.py +++ /dev/null @@ -1,74 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: speed_test.py -# Created Date: Thursday February 10th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 3rd April 2022 6:39:26 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# -import os -import time - -import torch -from torch.backends import cudnn - - - -if __name__ == '__main__': - # cudnn.benchmark = True - # cudnn.enabled = True - # script = "Generator_modulation_up" - # script = "Generator_modulation_up" - # script = "Generator_Invobn_config3" - script = "Generator_maskhead_config" - # script = "Generator_ori_config" - class_name = "Generator" - arcface_ckpt= "arcface_ckpt/arcface_checkpoint.tar" - model_config={ - "id_dim": 512, - "g_kernel_size": 3, - "in_channel":64, - "res_num": 0, - # "up_mode": "nearest", - "up_mode": "bilinear", - "aggregator": "eca_invo", - "res_mode": "eca_invo", - "norm": "bn" - } - os.environ['CUDA_VISIBLE_DEVICES'] = str(0) - print("GPU used : ", os.environ['CUDA_VISIBLE_DEVICES']) - - gscript_name = "components." + script - - - package = __import__(gscript_name, fromlist=True) - gen_class= getattr(package, class_name) - gen = gen_class(**model_config) - model = gen.cuda().eval().requires_grad_(False) - arcface1 = torch.load(arcface_ckpt, map_location=torch.device("cpu")) - arcface = arcface1['model'].module - arcface = arcface.cuda() - arcface.eval().requires_grad_(False) - - - id_img = torch.rand((4,3,112,112)).cuda() - id_latent = torch.rand((4,512)).cuda() - # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0] - - attr = torch.rand((4,3,512,512)).cuda() - - import datetime - start_time = time.time() - for i in range(100): - with torch.no_grad(): - - id_latent = arcface(id_img) - - results = model(attr, id_latent) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - information="Elapsed [{}]".format(elapsed) - print(information) \ No newline at end of file diff --git a/start_train.sh b/start_train.sh deleted file mode 100644 index 852f856..0000000 --- a/start_train.sh +++ /dev/null @@ -1 +0,0 @@ -nohup python train_multigpu.py > 2maskloss2_1.log 2>&1 & \ No newline at end of file diff --git a/test.py b/test.py deleted file mode 100644 index 777ec55..0000000 --- a/test.py +++ /dev/null @@ -1,287 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: test.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 23rd April 2022 10:03:56 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - -import os -import argparse -from torch.backends import cudnn -from utilities.json_config import readConfig -from utilities.reporter import Reporter -from utilities.sshupload import fileUploaderClass -import warnings - -warnings.filterwarnings('ignore') - -def str2bool(v): - return v.lower() in ('true') - -#################################################################################### -# To configure the seting of training\finetune\test -# -#################################################################################### -def getParameters(): - - parser = argparse.ArgumentParser() - # general settings - parser.add_argument('-v', '--version', type=str, default='maskhead_recfm_2', # maskhead_recfm_2 maskloss_2 resskip_recfm_1 maskhead_recfm_1 maskhead_recfm_2 resskip_2 resskip_3 resskip_4 resskip_9 cycle_res1 cycle_res2 cycle_res3 cycle_lstu1 depthwise depthwise_config0 Invobn_resinvo1 - help="version name for train, test, finetune") - - parser.add_argument('-c', '--cuda', type=int, default=0) # >0 if it is set as -1, program will use CPU - parser.add_argument('-s', '--checkpoint_step', type=int, default=480000, - help="checkpoint epoch for test phase or finetune phase") - parser.add_argument('--start_checkpoint_step', type=int, default=10000, - help="checkpoint epoch for test phase or finetune phase") - - # test - parser.add_argument('-t', '--test_script_name', type=str, default='tester_video') # video image_w_mask image_list_w_mask image_list image_nofusion - parser.add_argument('-b', '--batch_size', type=int, default=1) - parser.add_argument('-n', '--node_ip', type=str, default='localhost') # localhost 119.29.91.52 101.33.242.26 2001:da8:8000:6880:f284:d61c:3c76:f9cb - parser.add_argument('--crop_mode', type=str, default="vggface", choices=['ffhq','vggface'], help='crop mode for face detector') - - - parser.add_argument('-i', '--id_imgs', type=str, default='G:/simswap/inputdata/2/2/10.jpg') # G:/simswap/inputdata/2/2/10.jpg G:\\swap_data\\ID\\dlrb2.jpeg 'G:\\swap_data\\FF++\\996_img_00288.jpg' G:\\swap_data\\ID\\hinton.jpg - # parser.add_argument('-i', '--id_imgs', type=str, default='G:\\VGGFace2-HQ\\VGGface2_ffhq_align_256_9_28_512_bygfpgan\\n000002\\0027_01.jpg') - parser.add_argument('-a', '--attr_files', type=str, default='G:/simswap/inputdata/3/100297.mp4', # G:/swap_data/video/1 G:\\swap_data\\ID\\bengio.jpg G:\\swap_data\\FF++\\056_img_00228.jpg - help="file path for attribute images or video") # G:/swap_data/video/2/G2218_Trim.mp4 - parser.add_argument('--img_list_txt', type=str, default='./test_imgs_list.txt', # G:\\swap_data\\ID\\bengio.jpg G:\\swap_data\\FF++\\056_img_00228.jpg - help="file path for image list txt") - parser.add_argument('--record_metric', type=str2bool, default='False', - help="Whether to record the cosine similarity") - parser.add_argument('--save_mask', type=str2bool, default='False', - help="Whether to save the mask") - - parser.add_argument('--preprocess', type=str2bool, default='False', help='Whether to employ preprocess') - - parser.add_argument('--use_specified_data', action='store_true') - parser.add_argument('--specified_data_paths', type=str, nargs='+', default=[""], help='paths to specified files') - parser.add_argument('--use_specified_data_paths', type=str2bool, default='False', help='use the specified save dir') - parser.add_argument('--specified_save_path', type=str, default="G:/swap_data/video/results3", help='save results to specified dir') - - # # logs (does not to be changed in most time) - # parser.add_argument('--dataloader_workers', type=int, default=6) - # parser.add_argument('--use_tensorboard', type=str2bool, default='True', - # choices=['True', 'False'], help='enable the tensorboard') - # parser.add_argument('--log_step', type=int, default=100) - # parser.add_argument('--sample_step', type=int, default=100) - - # # template (onece editing finished, it should be deleted) - # parser.add_argument('--str_parameter', type=str, default="default", help='str parameter') - # parser.add_argument('--str_parameter_choices', type=str, - # default="default", choices=['choice1', 'choice2','choice3'], help='str parameter with choices list') - # parser.add_argument('--int_parameter', type=int, default=0, help='int parameter') - # parser.add_argument('--float_parameter', type=float, default=0.0, help='float parameter') - # parser.add_argument('--bool_parameter', type=str2bool, default='True', choices=['True', 'False'], help='bool parameter') - # parser.add_argument('--list_str_parameter', type=str, nargs='+', default=["element1","element2"], help='str list parameter') - # parser.add_argument('--list_int_parameter', type=int, nargs='+', default=[0,1], help='int list parameter') - return parser.parse_args() - -ignoreKey = [ - "dataloader_workers", - "log_root_path", - "project_root", - "project_summary", - "project_checkpoints", - "project_samples", - "project_scripts", - "reporter_path", - "use_specified_data", - "specified_data_paths", - "dataset_path","cuda", - "test_script_name", - "test_dataloader", - "test_dataset_path", - "save_test_result", - "test_batch_size", - "node_name", - "checkpoint_epoch", - "test_dataset_path", - "test_dataset_name", - "use_my_test_date"] - -#################################################################################### -# This function will create the related directories before the -# training\fintune\test starts -# Your_log_root (version name) -# |---summary/... -# |---samples/... (save evaluated images) -# |---checkpoints/... -# |---scripts/... -# -#################################################################################### -def createDirs(sys_state): - # the base dir - if not os.path.exists(sys_state["log_root_path"]): - os.makedirs(sys_state["log_root_path"]) - - # create dirs - sys_state["project_root"] = os.path.join(sys_state["log_root_path"], - sys_state["version"]) - - project_root = sys_state["project_root"] - if not os.path.exists(project_root): - os.makedirs(project_root) - - sys_state["project_summary"] = os.path.join(project_root, "summary") - if not os.path.exists(sys_state["project_summary"]): - os.makedirs(sys_state["project_summary"]) - - sys_state["project_checkpoints"] = os.path.join(project_root, "checkpoints") - if not os.path.exists(sys_state["project_checkpoints"]): - os.makedirs(sys_state["project_checkpoints"]) - - sys_state["project_samples"] = os.path.join(project_root, "samples") - if not os.path.exists(sys_state["project_samples"]): - os.makedirs(sys_state["project_samples"]) - - sys_state["project_scripts"] = os.path.join(project_root, "scripts") - if not os.path.exists(sys_state["project_scripts"]): - os.makedirs(sys_state["project_scripts"]) - - sys_state["reporter_path"] = os.path.join(project_root,sys_state["version"]+"_report") - -def main(): - - config = getParameters() - # speed up the program - cudnn.benchmark = True - - sys_state = {} - - # set the GPU number - if config.cuda >= 0: - os.environ["CUDA_VISIBLE_DEVICES"] = str(config.cuda) - - # read system environment paths - env_config = readConfig('env/env.json') - env_config = env_config["path"] - sys_state["env_config"] = env_config - - # obtain all configurations in argparse - config_dic = vars(config) - for config_key in config_dic.keys(): - sys_state[config_key] = config_dic[config_key] - - #=======================Test Phase=========================# - - # TODO modify below lines to obtain the configuration - sys_state["log_root_path"] = env_config["train_log_root"] - - sys_state["test_samples_path"] = os.path.join(env_config["test_log_root"], - sys_state["version"] , "samples") - # if not config.use_my_test_date: - # print("Use public benchmark...") - # data_key = config.test_dataset_name.lower() - # sys_state["test_dataset_path"] = env_config["test_dataset_paths"][data_key] - # if config.test_dataset_name.lower() == "set5" or config.test_dataset_name.lower() =="set14": - # sys_state["test_dataloader"] = "setx" - # else: - # sys_state["test_dataloader"] = config.test_dataset_name.lower() - - # sys_state["test_dataset_name"] = config.test_dataset_name - - if not os.path.exists(sys_state["test_samples_path"]): - os.makedirs(sys_state["test_samples_path"]) - - # Create dirs - createDirs(sys_state) - config_json = os.path.join(sys_state["project_root"], env_config["config_json_name"]) - - #fetch checkpoints, model_config.json and scripts from remote machine - if sys_state["node_ip"]!="localhost": - machine_config = env_config["machine_config"] - machine_config = readConfig(machine_config) - nodeinf = None - for item in machine_config: - if item["ip"] == sys_state["node_ip"]: - nodeinf = item - break - if not nodeinf: - raise Exception(print("Configuration of node %s is unavaliable"%sys_state["node_ip"])) - sys_state["remote_machine"] = nodeinf - print("ready to fetch related files from server: %s ......"%nodeinf["ip"]) - uploader = fileUploaderClass(nodeinf["ip"],nodeinf["user"],nodeinf["passwd"]) - - remotebase = os.path.join(nodeinf['path'],"train_logs",sys_state["version"]).replace('\\','/') - - # Get the config.json - print("ready to get the config.json...") - remoteFile = os.path.join(remotebase, env_config["config_json_name"]).replace('\\','/') - localFile = config_json - - ssh_state = uploader.sshScpGet(remoteFile, localFile) - if not ssh_state: - raise Exception(print("Get file %s failed! config.json does not exist!"%remoteFile)) - print("success get the config.json from server %s"%nodeinf['ip']) - - # Get scripts - remoteDir = os.path.join(remotebase, "scripts").replace('\\','/') - localDir = os.path.join(sys_state["project_scripts"]) - ssh_state = uploader.sshScpGetDir(remoteDir, localDir) - if not ssh_state: - raise Exception(print("Get file %s failed! Program exists!"%remoteFile)) - print("Get the scripts successful!") - # Read model_config.json - json_obj = readConfig(config_json) - for item in json_obj.items(): - if item[0] in ignoreKey: - pass - else: - sys_state[item[0]] = item[1] - - # Get checkpoints - if sys_state["node_ip"]!="localhost": - - ckpt_name = "step%d_%s.pth"%(sys_state["checkpoint_step"], - sys_state["checkpoint_names"]["generator_name"]) - localFile = os.path.join(sys_state["project_checkpoints"],ckpt_name) - if not os.path.exists(localFile): - - remoteFile = os.path.join(remotebase, "checkpoints", ckpt_name).replace('\\','/') - ssh_state = uploader.sshScpGet(remoteFile, localFile, True) - if not ssh_state: - raise Exception(print("Get file %s failed! Checkpoint file does not exist!"%remoteFile)) - print("Get the checkpoint %s successfully!"%(ckpt_name)) - else: - print("%s exists!"%(ckpt_name)) - - - # TODO get the checkpoint file path - sys_state["ckp_name"] = {} - # for data_key in sys_state["checkpoint_names"].keys(): - # sys_state["ckp_name"][data_key] = os.path.join(sys_state["project_checkpoints"], - # "%d_%s.pth"%(sys_state["checkpoint_epoch"], - # sys_state["checkpoint_names"][data_key])) - - # Get the test configurations - sys_state["com_base"] = "train_logs.%s.scripts."%sys_state["version"] - - # make a reporter - report_path = os.path.join(env_config["test_log_root"], sys_state["version"], - sys_state["version"]+"_report") - reporter = Reporter(report_path) - reporter.writeConfig(sys_state) - - # Display the test information - # TODO modify below lines to display your configuration information - moduleName = "test_scripts." + sys_state["test_script_name"] - print("Start to run test script: {}".format(moduleName)) - print("Test version: %s"%sys_state["version"]) - print("Test Script Name: %s"%sys_state["test_script_name"]) - - package = __import__(moduleName, fromlist=True) - testerClass = getattr(package, 'Tester') - tester = testerClass(sys_state,reporter) - tester.test() - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/test_arcface.py b/test_arcface.py deleted file mode 100644 index 8238b16..0000000 --- a/test_arcface.py +++ /dev/null @@ -1,17 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: test_arcface.py -# Created Date: Thursday March 17th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 17th March 2022 12:34:57 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# -import torch - -if __name__ == "__main__": - arcface1 = torch.load("./arcface_ckpt/arcface_checkpoint.tar", map_location=torch.device("cpu")) - print(arcface1) - arcface = arcface1['model'].module \ No newline at end of file diff --git a/test_imgs_list.txt b/test_imgs_list.txt deleted file mode 100644 index cc7c842..0000000 --- a/test_imgs_list.txt +++ /dev/null @@ -1,47 +0,0 @@ -G:/swap_data/ID/hsw.jpg;G:/swap_data/ID/tom-cruise-wallpaper-hd-wallpaper-43864908.jpg;fusion -G:/swap_data/ID/06.jpg;G:/swap_data/ID/hm.jpg;fusion -G:/swap_data/ID/1.png;G:/swap_data/ID/2.jpg;fusion -G:/swap_data/ID/hinton.jpg;G:/swap_data/ID/bengio.jpg;fusion -G:/swap_data/ID/hsw.jpg;G:/swap_data/ID/ts1.jpg;fusion -G:/swap_data/ID/hsw.jpg;G:/swap_data/ID/lyf2.jpeg;fusion -G:/swap_data/FF++/996_img_00288.jpg;G:/swap_data/FF++/056_img_00228.jpg;no -G:/swap_data/ID/gxt3.jpeg;G:/swap_data/ID/lyf5.jpeg;fusion -G:/swap_data/ID/hsw.jpg;G:/swap_data/ID/hb.jpeg;fusion -G:/swap_data/ID/06.jpg;G:/swap_data/ID/2130429-1216_tom_cruise_genes.jpg;fusion -G:/swap_data/FF++/019_img_00139.jpg;G:/swap_data/FF++/018_img_00088.jpg;no -G:/swap_data/FF++/052_img_00033.jpg;G:/swap_data/FF++/108_img_00150.jpg;no -G:/swap_data/FF++/011_img_00448.jpg;G:/swap_data/FF++/805_img_00252.jpg;no -G:/swap_data/FF++/638_img_00000.jpg;G:/swap_data/FF++/640_img_00248.jpg;no -G:/swap_data/FF++/819_img_00651.jpg;G:/swap_data/FF++/786_img_00156.jpg;no -G:/swap_data/FF++/416_img_00032.jpg;G:/swap_data/FF++/342_img_00062.jpg;no -G:/swap_data/ID/hsw.jpg;G:/swap_data/ID/lyf4.jpeg;fusion -G:/swap_data/ID/lxq.jpeg;G:/swap_data/ID/zyq2.jpeg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/ID/zyq.jpeg;fusion -G:/swap_data/ID/jl.jpg;G:/swap_data/ID/fbb2.jpg;fusion -G:/swap_data/ID/bengio.jpg;G:/swap_data/ID/messi.jpg;fusion -G:/swap_data/ID/elon-musk-hero-image.jpeg;G:/swap_data/ID/pexels-ichad-windhiagiri-3989151.jpg;fusion -G:/swap_data/ID/hsw.jpg;G:/swap_data/ID/ts.jpeg;fusion -G:/swap_data/ID/hsw.jpg;G:/swap_data/ID/zzq.jpeg;fusion -G:/swap_data/ID/06.jpg;G:/swap_data/ID/audrey-hepburn-63115_960_720.jpg;fusion -G:/swap_data/ID/ScarlettJohansson1.jpg;G:/swap_data/ID/chris-evans-captain-america.jpg;fusion -G:/swap_data/ID/sjch.jpeg;G:/swap_data/ID/zym9.jpeg;fusion -G:/swap_data/ID/ScarlettJohansson1.jpg;G:/swap_data/ID/captainamerica.jpg;fusion -G:/swap_data/ID/lyf2.jpeg;G:/swap_data/ID/Lane-Ten-Things-about-Wonder-Woman.jpg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/ID/lyf2.jpeg;fusion -G:/swap_data/ID/hsw.jpg;G:/swap_data/ID/hb.jpeg;fusion -G:/swap_data/ID/RobertDowneyJr2.jpg;G:/swap_data/ID/GettyImages.png;fusion -G:/swap_data/ID/RobertDowneyJr2.jpg;G:/swap_data/ID/leonardo.jpg;fusion -G:/swap_data/ID/06.jpg;G:/swap_data/ID/RobertDowneyJr2.jpg;fusion -G:/swap_data/ID/lyf2.jpeg;G:/swap_data/video/5/FMQpZa5aIAAGsvb.jpg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/video/5/FMQpZa5aIAAGsvb.jpg;fusion -G:/swap_data/ID/lyf2.jpeg;G:/swap_data/video/5/FIpgTdIaIAAsA6f.jpg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/video/5/FIpgTdIaIAAsA6f.jpg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/video/5/FKilL2takAAESu4.jpg;fusion -G:/swap_data/ID/lyf2.jpeg;G:/swap_data/video/5/FKilL2takAAESu4.jpg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/video/5/FKilL2xaIAAup9D.jpg;fusion -G:/swap_data/ID/lyf2.jpeg;G:/swap_data/video/5/FKilL2xaIAAup9D.jpg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/video/5/FKilL2xaQAA9zzV.jpg;fusion -G:/swap_data/ID/lyf2.jpeg;G:/swap_data/video/5/FKilL2xaQAA9zzV.jpg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/video/5/FLy-vUWaMAMsQVf.jpg;fusion -G:/swap_data/ID/lyf2.jpeg;G:/swap_data/video/5/FLy-vUWaMAMsQVf.jpg;fusion -G:/swap_data/ID/dlrb2.jpeg;G:/swap_data/video/5/waaa00103jp-1.jpg;fusion \ No newline at end of file diff --git a/test_json.py b/test_json.py deleted file mode 100644 index 42214d8..0000000 --- a/test_json.py +++ /dev/null @@ -1,283 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: test.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 6th April 2022 4:09:28 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - -import os -import argparse -from torch.backends import cudnn -from utilities.json_config import readConfig -from utilities.reporter import Reporter -from utilities.sshupload import fileUploaderClass -import warnings - -warnings.filterwarnings('ignore') - -def str2bool(v): - return v.lower() in ('true') - -#################################################################################### -# To configure the seting of training\finetune\test -# -#################################################################################### -def getParameters(): - - parser = argparse.ArgumentParser() - # general settings - parser.add_argument('-v', '--version', type=str, default='maskhead_recfm_2', # maskhead_recfm_2 maskloss_2 resskip_recfm_1 maskhead_recfm_1 maskhead_recfm_2 resskip_2 resskip_3 resskip_4 resskip_9 cycle_res1 cycle_res2 cycle_res3 cycle_lstu1 depthwise depthwise_config0 Invobn_resinvo1 - help="version name for train, test, finetune") - - parser.add_argument('-c', '--cuda', type=int, default=0) # >0 if it is set as -1, program will use CPU - parser.add_argument('-s', '--checkpoint_step', type=int, default=480000, - help="checkpoint epoch for test phase or finetune phase") - parser.add_argument('--start_checkpoint_step', type=int, default=10000, - help="checkpoint epoch for test phase or finetune phase") - - # test - parser.add_argument('-t', '--test_script_name', type=str, default='video') # video image_w_mask image_list_w_mask image_list image_nofusion - parser.add_argument('-b', '--batch_size', type=int, default=1) - parser.add_argument('-n', '--node_ip', type=str, default='2001:da8:8000:6880:f284:d61c:3c76:f9cb') # localhost 119.29.91.52 101.33.242.26 2001:da8:8000:6880:f284:d61c:3c76:f9cb - parser.add_argument('--crop_mode', type=str, default="vggface", choices=['ffhq','vggface'], help='crop mode for face detector') - - - parser.add_argument('-i', '--id_imgs', type=str, default='G:\\swap_data\\ID\\dlrb2.jpeg') # G:\\swap_data\\ID\\dlrb2.jpeg 'G:\\swap_data\\FF++\\996_img_00288.jpg' G:\\swap_data\\ID\\hinton.jpg - # parser.add_argument('-i', '--id_imgs', type=str, default='G:\\VGGFace2-HQ\\VGGface2_ffhq_align_256_9_28_512_bygfpgan\\n000002\\0027_01.jpg') - parser.add_argument('-a', '--attr_files', type=str, default='G:/swap_data/video/2/G2218_Trim.mp4', # G:/swap_data/video/1 G:\\swap_data\\ID\\bengio.jpg G:\\swap_data\\FF++\\056_img_00228.jpg - help="file path for attribute images or video") # G:/swap_data/video/2/G2218_Trim.mp4 - parser.add_argument('--img_list_txt', type=str, default='./test_imgs_list.txt', # G:\\swap_data\\ID\\bengio.jpg G:\\swap_data\\FF++\\056_img_00228.jpg - help="file path for image list txt") - parser.add_argument('--record_metric', type=str2bool, default='False', choices=['True', 'False'], - help="Whether to record the cosine similarity") - - parser.add_argument('--use_specified_data', action='store_true') - parser.add_argument('--specified_data_paths', type=str, nargs='+', default=[""], help='paths to specified files') - parser.add_argument('--use_specified_data_paths', type=str2bool, default='True', choices=['True', 'False'], help='use the specified save dir') - parser.add_argument('--specified_save_path', type=str, default="G:/swap_data/video/results3", help='save results to specified dir') - - # # logs (does not to be changed in most time) - # parser.add_argument('--dataloader_workers', type=int, default=6) - # parser.add_argument('--use_tensorboard', type=str2bool, default='True', - # choices=['True', 'False'], help='enable the tensorboard') - # parser.add_argument('--log_step', type=int, default=100) - # parser.add_argument('--sample_step', type=int, default=100) - - # # template (onece editing finished, it should be deleted) - # parser.add_argument('--str_parameter', type=str, default="default", help='str parameter') - # parser.add_argument('--str_parameter_choices', type=str, - # default="default", choices=['choice1', 'choice2','choice3'], help='str parameter with choices list') - # parser.add_argument('--int_parameter', type=int, default=0, help='int parameter') - # parser.add_argument('--float_parameter', type=float, default=0.0, help='float parameter') - # parser.add_argument('--bool_parameter', type=str2bool, default='True', choices=['True', 'False'], help='bool parameter') - # parser.add_argument('--list_str_parameter', type=str, nargs='+', default=["element1","element2"], help='str list parameter') - # parser.add_argument('--list_int_parameter', type=int, nargs='+', default=[0,1], help='int list parameter') - return parser.parse_args() - -ignoreKey = [ - "dataloader_workers", - "log_root_path", - "project_root", - "project_summary", - "project_checkpoints", - "project_samples", - "project_scripts", - "reporter_path", - "use_specified_data", - "specified_data_paths", - "dataset_path","cuda", - "test_script_name", - "test_dataloader", - "test_dataset_path", - "save_test_result", - "test_batch_size", - "node_name", - "checkpoint_epoch", - "test_dataset_path", - "test_dataset_name", - "use_my_test_date"] - -#################################################################################### -# This function will create the related directories before the -# training\fintune\test starts -# Your_log_root (version name) -# |---summary/... -# |---samples/... (save evaluated images) -# |---checkpoints/... -# |---scripts/... -# -#################################################################################### -def createDirs(sys_state): - # the base dir - if not os.path.exists(sys_state["log_root_path"]): - os.makedirs(sys_state["log_root_path"]) - - # create dirs - sys_state["project_root"] = os.path.join(sys_state["log_root_path"], - sys_state["version"]) - - project_root = sys_state["project_root"] - if not os.path.exists(project_root): - os.makedirs(project_root) - - sys_state["project_summary"] = os.path.join(project_root, "summary") - if not os.path.exists(sys_state["project_summary"]): - os.makedirs(sys_state["project_summary"]) - - sys_state["project_checkpoints"] = os.path.join(project_root, "checkpoints") - if not os.path.exists(sys_state["project_checkpoints"]): - os.makedirs(sys_state["project_checkpoints"]) - - sys_state["project_samples"] = os.path.join(project_root, "samples") - if not os.path.exists(sys_state["project_samples"]): - os.makedirs(sys_state["project_samples"]) - - sys_state["project_scripts"] = os.path.join(project_root, "scripts") - if not os.path.exists(sys_state["project_scripts"]): - os.makedirs(sys_state["project_scripts"]) - - sys_state["reporter_path"] = os.path.join(project_root,sys_state["version"]+"_report") - -def main(): - - config = getParameters() - # speed up the program - cudnn.benchmark = True - - sys_state = {} - - # set the GPU number - if config.cuda >= 0: - os.environ["CUDA_VISIBLE_DEVICES"] = str(config.cuda) - - # read system environment paths - env_config = readConfig('env/env.json') - env_config = env_config["path"] - sys_state["env_config"] = env_config - - # obtain all configurations in argparse - config_dic = vars(config) - for config_key in config_dic.keys(): - sys_state[config_key] = config_dic[config_key] - - #=======================Test Phase=========================# - - # TODO modify below lines to obtain the configuration - sys_state["log_root_path"] = env_config["train_log_root"] - - sys_state["test_samples_path"] = os.path.join(env_config["test_log_root"], - sys_state["version"] , "samples") - # if not config.use_my_test_date: - # print("Use public benchmark...") - # data_key = config.test_dataset_name.lower() - # sys_state["test_dataset_path"] = env_config["test_dataset_paths"][data_key] - # if config.test_dataset_name.lower() == "set5" or config.test_dataset_name.lower() =="set14": - # sys_state["test_dataloader"] = "setx" - # else: - # sys_state["test_dataloader"] = config.test_dataset_name.lower() - - # sys_state["test_dataset_name"] = config.test_dataset_name - - if not os.path.exists(sys_state["test_samples_path"]): - os.makedirs(sys_state["test_samples_path"]) - - # Create dirs - createDirs(sys_state) - config_json = os.path.join(sys_state["project_root"], env_config["config_json_name"]) - - #fetch checkpoints, model_config.json and scripts from remote machine - if sys_state["node_ip"]!="localhost": - machine_config = env_config["machine_config"] - machine_config = readConfig(machine_config) - nodeinf = None - for item in machine_config: - if item["ip"] == sys_state["node_ip"]: - nodeinf = item - break - if not nodeinf: - raise Exception(print("Configuration of node %s is unavaliable"%sys_state["node_ip"])) - sys_state["remote_machine"] = nodeinf - print("ready to fetch related files from server: %s ......"%nodeinf["ip"]) - uploader = fileUploaderClass(nodeinf["ip"],nodeinf["user"],nodeinf["passwd"]) - - remotebase = os.path.join(nodeinf['path'],"train_logs",sys_state["version"]).replace('\\','/') - - # Get the config.json - print("ready to get the config.json...") - remoteFile = os.path.join(remotebase, env_config["config_json_name"]).replace('\\','/') - localFile = config_json - - ssh_state = uploader.sshScpGet(remoteFile, localFile) - if not ssh_state: - raise Exception(print("Get file %s failed! config.json does not exist!"%remoteFile)) - print("success get the config.json from server %s"%nodeinf['ip']) - - # Get scripts - remoteDir = os.path.join(remotebase, "scripts").replace('\\','/') - localDir = os.path.join(sys_state["project_scripts"]) - ssh_state = uploader.sshScpGetDir(remoteDir, localDir) - if not ssh_state: - raise Exception(print("Get file %s failed! Program exists!"%remoteFile)) - print("Get the scripts successful!") - # Read model_config.json - json_obj = readConfig(config_json) - for item in json_obj.items(): - if item[0] in ignoreKey: - pass - else: - sys_state[item[0]] = item[1] - - # Get checkpoints - if sys_state["node_ip"]!="localhost": - - ckpt_name = "step%d_%s.pth"%(sys_state["checkpoint_step"], - sys_state["checkpoint_names"]["generator_name"]) - localFile = os.path.join(sys_state["project_checkpoints"],ckpt_name) - if not os.path.exists(localFile): - - remoteFile = os.path.join(remotebase, "checkpoints", ckpt_name).replace('\\','/') - ssh_state = uploader.sshScpGet(remoteFile, localFile, True) - if not ssh_state: - raise Exception(print("Get file %s failed! Checkpoint file does not exist!"%remoteFile)) - print("Get the checkpoint %s successfully!"%(ckpt_name)) - else: - print("%s exists!"%(ckpt_name)) - - - # TODO get the checkpoint file path - sys_state["ckp_name"] = {} - # for data_key in sys_state["checkpoint_names"].keys(): - # sys_state["ckp_name"][data_key] = os.path.join(sys_state["project_checkpoints"], - # "%d_%s.pth"%(sys_state["checkpoint_epoch"], - # sys_state["checkpoint_names"][data_key])) - - # Get the test configurations - sys_state["com_base"] = "train_logs.%s.scripts."%sys_state["version"] - - # make a reporter - report_path = os.path.join(env_config["test_log_root"], sys_state["version"], - sys_state["version"]+"_report") - reporter = Reporter(report_path) - reporter.writeConfig(sys_state) - - # Display the test information - # TODO modify below lines to display your configuration information - moduleName = "test_scripts.tester_" + sys_state["test_script_name"] - print("Start to run test script: {}".format(moduleName)) - print("Test version: %s"%sys_state["version"]) - print("Test Script Name: %s"%sys_state["test_script_name"]) - - package = __import__(moduleName, fromlist=True) - testerClass = getattr(package, 'Tester') - tester = testerClass(sys_state,reporter) - tester.test() - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/test_scripts/tester_ID_Pose.py b/test_scripts/tester_ID_Pose.py deleted file mode 100644 index 795f2df..0000000 --- a/test_scripts/tester_ID_Pose.py +++ /dev/null @@ -1,210 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_ID_Pose.py -# Created Date: Friday March 4th 2022 -# Author: Liu Naiyuan -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 5th March 2022 1:00:29 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import os -import cv2 -import time -import glob -from tqdm import tqdm - -import torch -import torch.nn.functional as F -from torchvision import transforms -from torch.utils import data - -import numpy as np - -import PIL -from PIL import Image - - -class TotalDataset(data.Dataset): - """Dataset class for the vggface dataset with precalulated face landmarks.""" - - def __init__(self,image_dir,content_transform): - self.image_dir= image_dir - self.content_transform= content_transform - self.dataset = [] - self.preprocess() - self.num_images = len(self.dataset) - - def preprocess(self): - """Preprocess the Face++ original frames.""" - filenames = sorted(glob.glob(os.path.join(self.image_dir, '*'), recursive=False)) - # self.total_num = len(lines) - for filename in filenames: - self.dataset.append(filename) - - print('Finished preprocessing the Face++ original frames dataset...') - - - def __getitem__(self, index): - """Return two src domain images and two dst domain images.""" - src_filename = self.dataset[index] - - split_tmp = src_filename.split('/') - - save_filename = split_tmp[-1] - - src_image1 = self.content_transform(Image.open(src_filename)) - - return src_image1, save_filename - - - def __len__(self): - """Return the number of images.""" - return len(self.dataset) - -def getLoader(c_image_dir, batch_size=16): - """Build and return a data loader.""" - num_workers = 8 - - c_transforms = [] - - c_transforms.append(transforms.ToTensor()) - c_transforms.append(transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])) - # c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) - - c_transforms = transforms.Compose(c_transforms) - - content_dataset = TotalDataset(c_image_dir, c_transforms) - content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, - drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True) - return content_data_loader, len(content_dataset) - - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - version = self.config["version"] - batch_size = self.config["batch_size"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - save_dir = os.path.join(save_dir,"v_%s_step_%d"%(version,self.config["checkpoint_step"])) - if not os.path.exists(save_dir): - os.makedirs(save_dir) - - source_loader, dataet_len = getLoader( - self.config["env_config"]["dataset_paths"]["id_pose_source_root"], batch_size=batch_size) - target_loader, dataet_len = getLoader( - self.config["env_config"]["dataset_paths"]["id_pose_source_root"], batch_size=batch_size) - - source_iter = iter(source_loader) - target_iter = iter(target_loader) - - # models - self.__init_framework__() - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - with torch.no_grad(): - for profile_batch, filename_batch in tqdm(source_iter): - profile_batch = profile_batch.cuda() - profile_id_downsample = F.interpolate(profile_batch, (112,112), mode='bicubic') - profile_latent_id = self.arcface(profile_id_downsample) - profile_latent_id = F.normalize(profile_latent_id, p=2, dim=1) - if init_batch ==True: - wholeid_batch = profile_latent_id.cpu() - init_batch = False - else: - wholeid_batch = torch.cat([wholeid_batch,profile_latent_id.cpu()],dim=0) - - target_source_pair_dict = np.load( - self.config["env_config"]["dataset_paths"]["pairs_dict"] ,allow_pickle=True).item() - - for target_batch, filename_batch in tqdm(target_iter): - target_index_list = [] - init_id_batch = True - - for filename_tmp in filename_batch: - source_index = int(filename_tmp.split('_')[0]) - target_index = target_source_pair_dict[source_index] - target_index_list.append(target_index) - if init_id_batch: - batch_id = wholeid_batch[target_index][None].cuda() - init_id_batch = False - else: - batch_id = torch.cat([batch_id, wholeid_batch[target_index][None].cuda()],dim = 0) - - img_fakes = self.network(target_batch.cuda(), batch_id) - - for img_fake, target_index_tmp,filename_tmp in zip(img_fakes, target_index_list,filename_batch): - filename_tmp_split = filename_tmp.split('_') - final_filename = filename_tmp_split[0] + '_' +str(target_index_tmp) + '_' + filename_tmp_split[-1] - save_path = os.path.join(save_dir,final_filename) - img_fake = img_fake * self.imagenet_std + self.imagenet_mean - img_fake = img_fake.numpy().transpose(1,2,0) - img_fake = np.clip(img_fake,0.0,1.0) * 255 - PIL.Image.fromarray(img_fake).save(save_path,quality=100) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) \ No newline at end of file diff --git a/test_scripts/tester_arcface_Rec.py b/test_scripts/tester_arcface_Rec.py deleted file mode 100644 index 99fd765..0000000 --- a/test_scripts/tester_arcface_Rec.py +++ /dev/null @@ -1,158 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 29th January 2022 12:41:01 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms -from torchvision.utils import save_image - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).view(3,1,1) - if self.config["cuda"] >=0: - self.imagenet_std = self.imagenet_std .cuda() - self.imagenet_mean = self.imagenet_mean.cuda() - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path)) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - attr_files = self.config["attr_files"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - if os.path.isdir(attr_files): - print("Input a dir....") - imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) - for item in imgs: - imgs_list.append(item) - print(imgs_list) - else: - print("Input an image....") - imgs_list.append(attr_files) - img_num = len(imgs_list) - - - # models - self.__init_framework__() - - mode = None - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - index = 0 - with torch.no_grad(): - for img in imgs_list[1:]: - print(img) - attr_img_ori= cv2.imread(img) - # try: - # attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - # except: - # print("No face detected!") - # continue - # attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB)) - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_ori,cv2.COLOR_BGR2RGB)) - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc= F.interpolate(attr_img,size=(112,112), mode='bicubic') - attr_id = self.arcface(attr_img_arc) - results = self.network(attr_id) - - results = results * self.imagenet_std + self.imagenet_mean - results = results.clamp_(0, 1) - attr = attr_img_arc * self.imagenet_std + self.imagenet_mean - results = torch.concat((attr, results), dim=2) - if index == 0: - final_img = results - else: - final_img = torch.concat((final_img, results), dim=0) - index += 1 - save_filename = os.path.join(save_dir, "ckp_%s_v_%s.png"%(ckp_step, version)) - mark = 0 - while(True): - if os.path.exists(save_filename): - save_filename = os.path.join(save_dir, "ckp_%s_v_%s_%d.png"%(ckp_step, version,mark)) - mark += 1 - else: - break - save_image(final_img, save_filename, nrow=img_num//8) - - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) \ No newline at end of file diff --git a/test_scripts/tester_common copy.py b/test_scripts/tester_common copy.py deleted file mode 100644 index 30ec590..0000000 --- a/test_scripts/tester_common copy.py +++ /dev/null @@ -1,124 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 4th July 2021 11:32:14 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time - -import torch -from utilities.utilities import tensor2img - -# from utilities.Reporter import Reporter -from tqdm import tqdm - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - #============build evaluation dataloader==============# - print("Prepare the test dataloader...") - dlModulename = config["test_dataloader"] - package = __import__("data_tools.test_dataloader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'TestDataset') - dataloader = dataloaderClass(config["test_data_path"], - config["batch_size"], - ["png","jpg"]) - self.test_loader= dataloader - - self.test_iter = len(dataloader)//config["batch_size"] - if len(dataloader)%config["batch_size"]>0: - self.test_iter+=1 - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - script_name = "components."+self.config["module_script_name"] - class_name = self.config["class_name"] - package = __import__(script_name, fromlist=True) - network_class = getattr(package, class_name) - n_class = len(self.config["selectedStyleDir"]) - - # TODO replace below lines to define the model framework - self.network = network_class(self.config["GConvDim"], - self.config["GKS"], - self.config["resNum"], - n_class - #**self.config["module_params"] - ) - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - # loader1 = torch.load(self.config["ckp_name"]["generator_name"]) - # print(loader1.key()) - # pathwocao = "H:\\Multi Scale Kernel Prediction Networks\\Mobile_Oriented_KPN\\train_logs\\repsr_pixel_0\\checkpoints\\epoch%d_RepSR_Plain.pth"%self.config["checkpoint_epoch"] - self.network.load_state_dict(torch.load(self.config["ckp_name"]["generator_name"])["g_model"]) - # self.network.load_state_dict(torch.load(pathwocao)) - print('loaded trained backbone model epoch {}...!'.format(self.config["checkpoint_epoch"])) - - def test(self): - - # save_result = self.config["saveTestResult"] - save_dir = self.config["test_samples_path"] - ckp_epoch = self.config["checkpoint_epoch"] - version = self.config["version"] - batch_size = self.config["batch_size"] - style_names = self.config["selectedStyleDir"] - n_class = len(style_names) - - # models - self.__init_framework__() - - condition_labels = torch.ones((n_class, batch_size, 1)).long() - for i in range(n_class): - condition_labels[i,:,:] = condition_labels[i,:,:]*i - if self.config["cuda"] >=0: - condition_labels = condition_labels.cuda() - total = len(self.test_loader) - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - with torch.no_grad(): - for _ in tqdm(range(total//batch_size)): - contents, img_names = self.test_loader() - for i in range(n_class): - if self.config["cuda"] >=0: - contents = contents.cuda() - res, _ = self.network(contents, condition_labels[i, 0, :]) - res = tensor2img(res.cpu()) - for t in range(batch_size): - temp_img = res[t,:,:,:] - temp_img = cv2.cvtColor(temp_img, cv2.COLOR_RGB2BGR) - cv2.imwrite(os.path.join(save_dir,'{}_version_{}_step{}_style_{}.png'.format( - img_names[t], version, ckp_epoch, style_names[i])),temp_img) - - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) \ No newline at end of file diff --git a/test_scripts/tester_common.py b/test_scripts/tester_common.py deleted file mode 100644 index 30ec590..0000000 --- a/test_scripts/tester_common.py +++ /dev/null @@ -1,124 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 4th July 2021 11:32:14 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time - -import torch -from utilities.utilities import tensor2img - -# from utilities.Reporter import Reporter -from tqdm import tqdm - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - #============build evaluation dataloader==============# - print("Prepare the test dataloader...") - dlModulename = config["test_dataloader"] - package = __import__("data_tools.test_dataloader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'TestDataset') - dataloader = dataloaderClass(config["test_data_path"], - config["batch_size"], - ["png","jpg"]) - self.test_loader= dataloader - - self.test_iter = len(dataloader)//config["batch_size"] - if len(dataloader)%config["batch_size"]>0: - self.test_iter+=1 - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - script_name = "components."+self.config["module_script_name"] - class_name = self.config["class_name"] - package = __import__(script_name, fromlist=True) - network_class = getattr(package, class_name) - n_class = len(self.config["selectedStyleDir"]) - - # TODO replace below lines to define the model framework - self.network = network_class(self.config["GConvDim"], - self.config["GKS"], - self.config["resNum"], - n_class - #**self.config["module_params"] - ) - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - # loader1 = torch.load(self.config["ckp_name"]["generator_name"]) - # print(loader1.key()) - # pathwocao = "H:\\Multi Scale Kernel Prediction Networks\\Mobile_Oriented_KPN\\train_logs\\repsr_pixel_0\\checkpoints\\epoch%d_RepSR_Plain.pth"%self.config["checkpoint_epoch"] - self.network.load_state_dict(torch.load(self.config["ckp_name"]["generator_name"])["g_model"]) - # self.network.load_state_dict(torch.load(pathwocao)) - print('loaded trained backbone model epoch {}...!'.format(self.config["checkpoint_epoch"])) - - def test(self): - - # save_result = self.config["saveTestResult"] - save_dir = self.config["test_samples_path"] - ckp_epoch = self.config["checkpoint_epoch"] - version = self.config["version"] - batch_size = self.config["batch_size"] - style_names = self.config["selectedStyleDir"] - n_class = len(style_names) - - # models - self.__init_framework__() - - condition_labels = torch.ones((n_class, batch_size, 1)).long() - for i in range(n_class): - condition_labels[i,:,:] = condition_labels[i,:,:]*i - if self.config["cuda"] >=0: - condition_labels = condition_labels.cuda() - total = len(self.test_loader) - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - with torch.no_grad(): - for _ in tqdm(range(total//batch_size)): - contents, img_names = self.test_loader() - for i in range(n_class): - if self.config["cuda"] >=0: - contents = contents.cuda() - res, _ = self.network(contents, condition_labels[i, 0, :]) - res = tensor2img(res.cpu()) - for t in range(batch_size): - temp_img = res[t,:,:,:] - temp_img = cv2.cvtColor(temp_img, cv2.COLOR_RGB2BGR) - cv2.imwrite(os.path.join(save_dir,'{}_version_{}_step{}_style_{}.png'.format( - img_names[t], version, ckp_epoch, style_names[i])),temp_img) - - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) \ No newline at end of file diff --git a/test_scripts/tester_image.py b/test_scripts/tester_image.py deleted file mode 100644 index bbdad11..0000000 --- a/test_scripts/tester_image.py +++ /dev/null @@ -1,236 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 23rd February 2022 12:30:12 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - crop_mode = self.config["crop_mode"] - attr_files = self.config["attr_files"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - - if os.path.isdir(attr_files): - print("Input a dir....") - imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) - for item in imgs: - imgs_list.append(item) - print(imgs_list) - else: - print("Input an image....") - imgs_list.append(attr_files) - id_basename = os.path.basename(id_imgs) - id_basename = os.path.splitext(os.path.basename(id_imgs))[0] - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - id_img = cv2.imread(id_imgs) - id_img_align_crop, _ = self.detect.get(id_img,512) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - print(img) - attr_img_ori= cv2.imread(img) - try: - attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - except: - continue - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB)) - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - - mat = mat[0] - results = self.network(attr_img, latend_id) - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) - img_white = np.full((512,512), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0]) - - target_image = cv2.warpAffine(results, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - img1 = np.array(attr_img_ori, dtype=np.float) - img1 = img_mask * target_image + (1-img_mask) * img1 - final_img = img1.astype(np.uint8) - attr_basename = os.path.splitext(os.path.basename(img))[0] - final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) - final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - - cv2.imwrite(save_filename, final_img) - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_image_allstep.py b/test_scripts/tester_image_allstep.py deleted file mode 100644 index 6645d4c..0000000 --- a/test_scripts/tester_image_allstep.py +++ /dev/null @@ -1,215 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 3rd March 2022 9:03:57 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - crop_mode = self.config["crop_mode"] - attr_files = self.config["attr_files"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - - if os.path.isdir(attr_files): - print("Input a dir....") - imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) - for item in imgs: - imgs_list.append(item) - print(imgs_list) - else: - print("Input an image....") - imgs_list.append(attr_files) - id_basename = os.path.basename(id_imgs) - id_basename = os.path.splitext(os.path.basename(id_imgs))[0] - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - id_img = cv2.imread(id_imgs) - id_img_align_crop, _ = self.detect.get(id_img,512) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - - total_dict = {} - - from utilities.sshupload import fileUploaderClass - nodeinf = self.config["remote_machine"] - - uploader = fileUploaderClass(nodeinf["ip"],nodeinf["user"],nodeinf["passwd"]) - - remotebase = os.path.join(nodeinf['path'],"train_logs",self.config["version"]).replace('\\','/') - - - for istep in range(self.config["start_checkpoint_step"],self.config["checkpoint_step"]+1,10000): - ckpt_name = "step%d_%s.pth"%(istep, - self.config["checkpoint_names"]["generator_name"]) - localFile = os.path.join(self.config["project_checkpoints"],ckpt_name) - - if self.config["node_ip"]!="localhost": - if not os.path.exists(localFile): - remoteFile = os.path.join(remotebase, "checkpoints", ckpt_name).replace('\\','/') - ssh_state = uploader.sshScpGet(remoteFile, localFile, True) - if not ssh_state: - raise Exception(print("Get file %s failed! Checkpoint file does not exist!"%remoteFile)) - print("Get the checkpoint %s successfully!"%(ckpt_name)) - else: - print("%s exists!"%(ckpt_name)) - self.network.load_state_dict(torch.load(localFile, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(istep)) - cos_dict = {} - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - print(img) - attr_img_ori= cv2.imread(img) - try: - attr_img_align_crop, _ = self.detect.get(attr_img_ori,512) - except: - continue - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB)) - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - - results = self.network(attr_img, latend_id) - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - cos_dict[img] = results_cos_dis.item() - average_cos += results_cos_dis - - average_cos /= len(imgs_list) - total_dict[str(istep)] = { - "step":istep, - "Average_cosin": average_cos.item(), - "images": cos_dict - } - - print("Step: [{}], average cosin similarity between ID and results [{}]".format(istep, average_cos.item())) - self.reporter.writeInfo("Step: [{}], average cosin similarity between ID and results [{}]".format(istep, average_cos.item())) - self.reporter.writeJson(total_dict) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) \ No newline at end of file diff --git a/test_scripts/tester_image_list.py b/test_scripts/tester_image_list.py deleted file mode 100644 index 8d3038b..0000000 --- a/test_scripts/tester_image_list.py +++ /dev/null @@ -1,259 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 25th March 2022 2:07:24 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - crop_mode = self.config["crop_mode"] - list_txt = self.config["img_list_txt"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - imgs_list = [] - with open(list_txt,'r') as logf: - for line in logf: - cells = line.split(";") - imgs_list.append([cells[0],cells[1],cells[2].replace("\n","")]) - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - id_img_n, attr_img_n, fusion= img - print("id image:%s---attr image:%s"%(id_img_n, attr_img_n)) - id_img = cv2.imread(id_img_n) - print(fusion) - if fusion.lower() == "fusion": - try: - id_img_align_crop, _ = self.detect.get(id_img,512) - except: - print("Do not detect a face!") - continue - # id_basename = os.path.splitext(os.path.basename(id_img_n))[0] - # cv2.imwrite(os.path.join(save_dir, "id_%s.png"%(id_basename)),id_img_align_crop[0]) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - else: - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img,cv2.COLOR_BGR2RGB)) - - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - attr_img_ori= cv2.imread(attr_img_n) - - if fusion.lower() == "fusion": - try: - attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - except: - print("Do not detect a face!") - continue - - # attr_basename = os.path.splitext(os.path.basename(attr_img_n))[0] - # cv2.imwrite(os.path.join(save_dir, "attr_%s.png"%(attr_basename)),attr_img_align_crop[0]) - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB)) - - else: - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_ori,cv2.COLOR_BGR2RGB)) - - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - - - results = self.network(attr_img, latend_id) - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) - if fusion.lower() == "fusion": - mat = mat[0] - img_white = np.full((512,512), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0]) - - target_image = cv2.warpAffine(results, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - img1 = np.array(attr_img_ori, dtype=np.float) - img1 = img_mask * target_image + (1-img_mask) * img1 - else: - results = results*255 - img1 = cv2.cvtColor(results,cv2.COLOR_RGB2BGR) - - final_img = img1.astype(np.uint8) - id_basename = os.path.basename(id_img_n) - id_basename = os.path.splitext(os.path.basename(id_img_n))[0] - attr_basename = os.path.splitext(os.path.basename(attr_img_n))[0] - final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) - final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) - print(save_dir) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - - cv2.imwrite(save_filename, final_img) - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_image_list_w_2mask.py b/test_scripts/tester_image_list_w_2mask.py deleted file mode 100644 index bb6c658..0000000 --- a/test_scripts/tester_image_list_w_2mask.py +++ /dev/null @@ -1,279 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 12th April 2022 9:04:01 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - crop_mode = self.config["crop_mode"] - list_txt = self.config["img_list_txt"] - record_metric= self.config["record_metric"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - imgs_list = [] - with open(list_txt,'r') as logf: - for line in logf: - cells = line.split(";") - imgs_list.append([cells[0],cells[1],cells[2].replace("\n","")]) - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - id_img_n, attr_img_n, fusion= img - print("id image:%s---attr image:%s"%(id_img_n, attr_img_n)) - id_img = cv2.imread(id_img_n) - print(fusion) - if fusion.lower() == "fusion": - try: - id_img_align_crop, _ = self.detect.get(id_img,512) - except: - print("Image %s Do not detect a face!"%id_img_n) - continue - # id_basename = os.path.splitext(os.path.basename(id_img_n))[0] - # cv2.imwrite(os.path.join(save_dir, "id_%s.png"%(id_basename)),id_img_align_crop[0]) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - else: - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img,cv2.COLOR_BGR2RGB)) - - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - attr_img_ori= cv2.imread(attr_img_n) - - if fusion.lower() == "fusion": - try: - attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - except: - print("Image %s Do not detect a face!"%attr_img_n) - continue - - # attr_basename = os.path.splitext(os.path.basename(attr_img_n))[0] - # cv2.imwrite(os.path.join(save_dir, "attr_%s.png"%(attr_basename)),attr_img_align_crop[0]) - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB)) - - else: - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_ori,cv2.COLOR_BGR2RGB)) - - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - - - results,mask_lr,mask_hr= self.network(attr_img, latend_id) - - mask_lr = mask_lr.cpu().permute(0,2,3,1)[0,...] - mask_lr = mask_lr.numpy() - # mask_lr = (mask_lr - np.min(mask_lr))/np.max(mask_lr) - mask_lr = np.clip(mask_lr,0.0,1.0) * 255 - mask_hr = mask_hr.cpu().permute(0,2,3,1)[0,...] - mask_hr = mask_hr.numpy() - # mask_hr = (mask_hr - np.min(mask_hr))/np.max(mask_hr) - mask_hr = np.clip(mask_hr,0.0,1.0) * 255 - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) - if fusion.lower() == "fusion": - mat = mat[0] - img_white = np.full((512,512), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0]) - - target_image = cv2.warpAffine(results, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - img1 = np.array(attr_img_ori, dtype=np.float) - img1 = img_mask * target_image + (1-img_mask) * img1 - else: - results = results*255 - img1 = cv2.cvtColor(results,cv2.COLOR_RGB2BGR) - - final_img = img1.astype(np.uint8) - id_basename = os.path.basename(id_img_n) - id_basename = os.path.splitext(os.path.basename(id_img_n))[0] - attr_basename = os.path.splitext(os.path.basename(attr_img_n))[0] - if record_metric: - final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) - final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) - print(save_dir) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - - cv2.imwrite(save_filename, final_img) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask_lr.png"%(id_basename, - attr_basename,ckp_step,version)) - cv2.imwrite(save_filename,mask_lr) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask_hr.png"%(id_basename, - attr_basename,ckp_step,version)) - cv2.imwrite(save_filename,mask_hr) - - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_image_list_w_mask.py b/test_scripts/tester_image_list_w_mask.py deleted file mode 100644 index de762d8..0000000 --- a/test_scripts/tester_image_list_w_mask.py +++ /dev/null @@ -1,328 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 23rd April 2022 10:04:51 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - - if self.config["preprocess"]: - print("Employ GFPGAN to upsampling detected face images!") - from face_enhancer.gfpgan import GFPGANer - version = '1.2' - if version == '1': - arch = 'original' - channel_multiplier = 1 - model_name = 'GFPGANv1' - elif version == '1.2': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANCleanv1-NoCE-C2' - elif version == '1.3': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANv1.3' - - # determine model paths - model_path = os.path.join('./face_enhancer/experiments/pretrained_models', model_name + '.pth') - if not os.path.isfile(model_path): - model_path = os.path.join('./face_enhancer/realesrgan/weights', model_name + '.pth') - if not os.path.isfile(model_path): - raise ValueError(f'Model {model_name} does not exist.') - - self.restorer = GFPGANer( - model_path=model_path, - upscale=1, - arch=arch, - channel_multiplier=channel_multiplier, - bg_upsampler=None) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - crop_mode = self.config["crop_mode"] - list_txt = self.config["img_list_txt"] - record_metric= self.config["record_metric"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - imgs_list = [] - with open(list_txt,'r') as logf: - for line in logf: - cells = line.split(";") - imgs_list.append([cells[0],cells[1],cells[2].replace("\n","")]) - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - id_img_n, attr_img_n, fusion= img - print("id image:%s---attr image:%s"%(id_img_n, attr_img_n)) - id_img = cv2.imread(id_img_n) - print(fusion) - if fusion.lower() == "fusion": - try: - id_img_align_crop, _ = self.detect.get(id_img,512) - except: - print("Image %s Do not detect a face!"%id_img_n) - continue - - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - else: - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img,cv2.COLOR_BGR2RGB)) - - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - attr_img_ori= cv2.imread(attr_img_n) - - if fusion.lower() == "fusion": - try: - attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - except: - print("Image %s Do not detect a face!"%attr_img_n) - continue - - # attr_basename = os.path.splitext(os.path.basename(attr_img_n))[0] - # cv2.imwrite(os.path.join(save_dir, "attr_%s.png"%(attr_basename)),attr_img_align_crop[0]) - restored_face = attr_img_align_crop[0] - if self.config["preprocess"]: - _, _, restored_face = self.restorer.enhance( - restored_face, has_aligned=False, only_center_face=True, paste_back=True) - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(restored_face,cv2.COLOR_BGR2RGB)) - - else: - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_ori,cv2.COLOR_BGR2RGB)) - - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - - - # results,mask= self.network(attr_img, latend_id) - pred = self.network(attr_img, latend_id) - results = pred[0] - - - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) - if fusion.lower() == "fusion": - mat = mat[0] - img_white = np.full((512,512), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0]) - - target_image = cv2.warpAffine(results, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - img1 = np.array(attr_img_ori, dtype=np.float) - img1 = img_mask * target_image + (1-img_mask) * img1 - else: - results = results*255 - img1 = cv2.cvtColor(results,cv2.COLOR_RGB2BGR) - - final_img = img1.astype(np.uint8) - id_basename = os.path.basename(id_img_n) - id_basename = os.path.splitext(os.path.basename(id_img_n))[0] - attr_basename = os.path.splitext(os.path.basename(attr_img_n))[0] - if record_metric: - final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) - final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) - print(save_dir) - if self.config["preprocess"]: - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_gfpgan.png"%(id_basename, - attr_basename,ckp_step,version)) - else: - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - - cv2.imwrite(save_filename, final_img) - - if self.config["save_mask"]: - num = 0 - - for mask in pred[1:]: - - mask = mask.cpu().permute(0,2,3,1)[0,...] - mask = mask.numpy() - mask = (mask - np.min(mask))/np.max(mask) - mask = np.clip(mask,0.0,1.0) * 255 - - if self.config["preprocess"]: - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask%d_gfpgan.png"%(id_basename, - attr_basename,ckp_step,version,num)) - else: - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask%d.png"%(id_basename, - attr_basename,ckp_step,version,num)) - - - cv2.imwrite(save_filename,mask) - num += 1 - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_image_nofusion.py b/test_scripts/tester_image_nofusion.py deleted file mode 100644 index 18ae36c..0000000 --- a/test_scripts/tester_image_nofusion.py +++ /dev/null @@ -1,184 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 24th March 2022 12:40:35 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - crop_mode = self.config["crop_mode"] - attr_files = self.config["attr_files"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - - if os.path.isdir(attr_files): - print("Input a dir....") - imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) - for item in imgs: - imgs_list.append(item) - print(imgs_list) - else: - print("Input an image....") - imgs_list.append(attr_files) - id_basename = os.path.basename(id_imgs) - id_basename = os.path.splitext(os.path.basename(id_imgs))[0] - - # models - self.__init_framework__() - - id_img = cv2.imread(id_imgs) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img,cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - cos_loss = torch.nn.CosineSimilarity() - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - print(img) - attr_img_ori= cv2.imread(img) - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_ori,cv2.COLOR_BGR2RGB)) - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - results = self.network(attr_img, latend_id) - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) * 255 - results = cv2.cvtColor(results, cv2.COLOR_RGB2BGR) - - final_img = results.astype(np.uint8) - attr_basename = os.path.splitext(os.path.basename(img))[0] - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - - cv2.imwrite(save_filename, final_img) - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_image_w_2mask.py b/test_scripts/tester_image_w_2mask.py deleted file mode 100644 index 0a1a65f..0000000 --- a/test_scripts/tester_image_w_2mask.py +++ /dev/null @@ -1,255 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 12th April 2022 10:09:21 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - crop_mode = self.config["crop_mode"] - attr_files = self.config["attr_files"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - - if os.path.isdir(attr_files): - print("Input a dir....") - imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) - for item in imgs: - imgs_list.append(item) - print(imgs_list) - else: - print("Input an image....") - imgs_list.append(attr_files) - id_basename = os.path.basename(id_imgs) - id_basename = os.path.splitext(os.path.basename(id_imgs))[0] - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - id_img = cv2.imread(id_imgs) - id_img_align_crop, _ = self.detect.get(id_img,512) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - print(img) - attr_img_ori= cv2.imread(img) - try: - attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - except: - continue - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB)) - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - - mat = mat[0] - results,mask_lr,mask_hr= self.network(attr_img, latend_id) - - mask_lr = mask_lr.cpu().permute(0,2,3,1)[0,...] - mask_lr = mask_lr.numpy() - # mask_lr = (mask_lr - np.min(mask_lr))/np.max(mask_lr) - mask_lr = np.clip(mask_lr,0.0,1.0) * 255 - mask_hr = mask_hr.cpu().permute(0,2,3,1)[0,...] - mask_hr = mask_hr.numpy() - # mask_hr = (mask_hr - np.min(mask_hr))/np.max(mask_hr) - mask_hr = np.clip(mask_hr,0.0,1.0) * 255 - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) - img_white = np.full((512,512), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0]) - - target_image = cv2.warpAffine(results, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - img1 = np.array(attr_img_ori, dtype=np.float) - img1 = img_mask * target_image + (1-img_mask) * img1 - final_img = img1.astype(np.uint8) - attr_basename = os.path.splitext(os.path.basename(img))[0] - final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) - final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - - cv2.imwrite(save_filename, final_img) - - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask_lr.png"%(id_basename, - attr_basename,ckp_step,version)) - cv2.imwrite(save_filename,mask_lr) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask_hr.png"%(id_basename, - attr_basename,ckp_step,version)) - cv2.imwrite(save_filename,mask_hr) - - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_image_w_2mask_gfpgan.py b/test_scripts/tester_image_w_2mask_gfpgan.py deleted file mode 100644 index 17c2cf7..0000000 --- a/test_scripts/tester_image_w_2mask_gfpgan.py +++ /dev/null @@ -1,286 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 16th April 2022 5:20:54 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop -from face_enhancer.gfpgan import GFPGANer - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - version = '1.2' - if version == '1': - arch = 'original' - channel_multiplier = 1 - model_name = 'GFPGANv1' - elif version == '1.2': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANCleanv1-NoCE-C2' - elif version == '1.3': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANv1.3' - - # determine model paths - model_path = os.path.join('./face_enhancer/experiments/pretrained_models', model_name + '.pth') - if not os.path.isfile(model_path): - model_path = os.path.join('./face_enhancer/realesrgan/weights', model_name + '.pth') - if not os.path.isfile(model_path): - raise ValueError(f'Model {model_name} does not exist.') - - self.restorer = GFPGANer( - model_path=model_path, - upscale=1, - arch=arch, - channel_multiplier=channel_multiplier, - bg_upsampler=None) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - crop_mode = self.config["crop_mode"] - attr_files = self.config["attr_files"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - - if os.path.isdir(attr_files): - print("Input a dir....") - imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) - for item in imgs: - imgs_list.append(item) - print(imgs_list) - else: - print("Input an image....") - imgs_list.append(attr_files) - id_basename = os.path.basename(id_imgs) - id_basename = os.path.splitext(os.path.basename(id_imgs))[0] - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - id_img = cv2.imread(id_imgs) - id_img_align_crop, _ = self.detect.get(id_img,512) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - print(img) - attr_img_ori= cv2.imread(img) - try: - attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - except: - continue - _, _, restored_face = self.restorer.enhance( - attr_img_align_crop[0], has_aligned=False, only_center_face=True, paste_back=True) - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(restored_face,cv2.COLOR_BGR2RGB)) - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - - mat = mat[0] - results,mask_lr,mask_hr= self.network(attr_img, latend_id) - - mask_lr = mask_lr.cpu().permute(0,2,3,1)[0,...] - mask_lr = mask_lr.numpy() - # mask_lr = (mask_lr - np.min(mask_lr))/np.max(mask_lr) - mask_lr = np.clip(mask_lr,0.0,1.0) * 255 - mask_hr = mask_hr.cpu().permute(0,2,3,1)[0,...] - mask_hr = mask_hr.numpy() - # mask_hr = (mask_hr - np.min(mask_hr))/np.max(mask_hr) - mask_hr = np.clip(mask_hr,0.0,1.0) * 255 - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) - img_white = np.full((512,512), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0]) - - target_image = cv2.warpAffine(results, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - img1 = np.array(attr_img_ori, dtype=np.float) - img1 = img_mask * target_image + (1-img_mask) * img1 - final_img = img1.astype(np.uint8) - attr_basename = os.path.splitext(os.path.basename(img))[0] - final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) - final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - - cv2.imwrite(save_filename, final_img) - - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask_lr.png"%(id_basename, - attr_basename,ckp_step,version)) - cv2.imwrite(save_filename,mask_lr) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask_hr.png"%(id_basename, - attr_basename,ckp_step,version)) - cv2.imwrite(save_filename,mask_hr) - - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_image_w_mask.py b/test_scripts/tester_image_w_mask.py deleted file mode 100644 index 9588789..0000000 --- a/test_scripts/tester_image_w_mask.py +++ /dev/null @@ -1,301 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 23rd April 2022 10:05:22 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - if self.config["preprocess"]: - print("Employ GFPGAN to upsampling detected face images!") - from face_enhancer.gfpgan import GFPGANer - version = '1.2' - if version == '1': - arch = 'original' - channel_multiplier = 1 - model_name = 'GFPGANv1' - elif version == '1.2': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANCleanv1-NoCE-C2' - elif version == '1.3': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANv1.3' - - # determine model paths - model_path = os.path.join('./face_enhancer/experiments/pretrained_models', model_name + '.pth') - if not os.path.isfile(model_path): - model_path = os.path.join('./face_enhancer/realesrgan/weights', model_name + '.pth') - if not os.path.isfile(model_path): - raise ValueError(f'Model {model_name} does not exist.') - - self.restorer = GFPGANer( - model_path=model_path, - upscale=1, - arch=arch, - channel_multiplier=channel_multiplier, - bg_upsampler=None) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - crop_mode = self.config["crop_mode"] - attr_files = self.config["attr_files"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - - if os.path.isdir(attr_files): - print("Input a dir....") - imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) - for item in imgs: - imgs_list.append(item) - print(imgs_list) - else: - print("Input an image....") - imgs_list.append(attr_files) - id_basename = os.path.basename(id_imgs) - id_basename = os.path.splitext(os.path.basename(id_imgs))[0] - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - id_img = cv2.imread(id_imgs) - id_img_align_crop, _ = self.detect.get(id_img,512) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - print(img) - attr_img_ori= cv2.imread(img) - try: - attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - except: - continue - restored_face = attr_img_align_crop[0] - if self.config["preprocess"]: - _, _, restored_face = self.restorer.enhance( - restored_face, has_aligned=False, only_center_face=True, paste_back=True) - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(restored_face,cv2.COLOR_BGR2RGB)) - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - - mat = mat[0] - pred = self.network(attr_img, latend_id) - results = pred[0] - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) - img_white = np.full((512,512), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0]) - - target_image = cv2.warpAffine(results, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - img1 = np.array(attr_img_ori, dtype=np.float) - img1 = img_mask * target_image + (1-img_mask) * img1 - final_img = img1.astype(np.uint8) - attr_basename = os.path.splitext(os.path.basename(img))[0] - if self.config["record_metric"]: - final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) - final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) - if self.config["preprocess"]: - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_gfpgan.png"%(id_basename, - attr_basename,ckp_step,version)) - else: - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - cv2.imwrite(save_filename, final_img) - - if self.config["save_mask"]: - num = 0 - - for mask in pred[1:]: - - mask = mask.cpu().permute(0,2,3,1)[0,...] - mask = mask.numpy() - mask = (mask - np.min(mask))/np.max(mask) - mask = np.clip(mask,0.0,1.0) * 255 - - if self.config["preprocess"]: - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask%d_gfpgan.png"%(id_basename, - attr_basename,ckp_step,version,num)) - else: - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask%d.png"%(id_basename, - attr_basename,ckp_step,version,num)) - - - cv2.imwrite(save_filename,mask) - num += 1 - - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_image_w_mask_gfpgan.py b/test_scripts/tester_image_w_mask_gfpgan.py deleted file mode 100644 index 40ce8b3..0000000 --- a/test_scripts/tester_image_w_mask_gfpgan.py +++ /dev/null @@ -1,280 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 14th April 2022 1:48:18 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import glob - -import torch -import torch.nn.functional as F -from torchvision import transforms - -import numpy as np -from PIL import Image - -from insightface_func.face_detect_crop_single import Face_detect_crop -from face_enhancer.gfpgan import GFPGANer - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # for name in self.network.state_dict(): - # print(name) - self.features = {} - mapping_layers = [ - "first_layer", - "down4", - "BottleNeck.2" - ] - - - - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - - version = '1.2' - if version == '1': - arch = 'original' - channel_multiplier = 1 - model_name = 'GFPGANv1' - elif version == '1.2': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANCleanv1-NoCE-C2' - elif version == '1.3': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANv1.3' - - # determine model paths - model_path = os.path.join('./face_enhancer/experiments/pretrained_models', model_name + '.pth') - if not os.path.isfile(model_path): - model_path = os.path.join('./face_enhancer/realesrgan/weights', model_name + '.pth') - if not os.path.isfile(model_path): - raise ValueError(f'Model {model_name} does not exist.') - - self.restorer = GFPGANer( - model_path=model_path, - upscale=1, - arch=arch, - channel_multiplier=channel_multiplier, - bg_upsampler=None) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - - - - def test(self): - - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - crop_mode = self.config["crop_mode"] - attr_files = self.config["attr_files"] - specified_save_path = self.config["specified_save_path"] - self.arcface_ckpt= self.config["arcface_ckpt"] - imgs_list = [] - - self.reporter.writeInfo("Version %s"%version) - - if os.path.isdir(specified_save_path): - print("Input a legal specified save path!") - save_dir = specified_save_path - - if os.path.isdir(attr_files): - print("Input a dir....") - imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) - for item in imgs: - imgs_list.append(item) - print(imgs_list) - else: - print("Input an image....") - imgs_list.append(attr_files) - id_basename = os.path.basename(id_imgs) - id_basename = os.path.splitext(os.path.basename(id_imgs))[0] - - # models - self.__init_framework__() - - mode = crop_mode.lower() - if mode == "vggface": - mode = "none" - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - id_img = cv2.imread(id_imgs) - id_img_align_crop, _ = self.detect.get(id_img,512) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - cos_loss = torch.nn.CosineSimilarity() - font = cv2.FONT_HERSHEY_SIMPLEX - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - cos_dict = {} - average_cos = 0 - with torch.no_grad(): - for img in imgs_list: - print(img) - attr_img_ori= cv2.imread(img) - try: - attr_img_align_crop, mat = self.detect.get(attr_img_ori,512) - except: - continue - _, _, restored_face = self.restorer.enhance( - attr_img_align_crop[0], has_aligned=False, only_center_face=True, paste_back=True) - # cv2.imwrite("id_wocao.png",restored_face) - attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(restored_face,cv2.COLOR_BGR2RGB)) - attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() - - attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') - # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) - attr_id = self.arcface(attr_img_arc) - attr_id = F.normalize(attr_id, p=2, dim=1) - cos_dis = 1 - cos_loss(latend_id, attr_id) - - mat = mat[0] - results,mask= self.network(attr_img, latend_id) - - mask = mask.cpu().permute(0,2,3,1)[0,...] - mask = mask.numpy() - mask = (mask - np.min(mask))/np.max(mask) - mask = np.clip(mask,0.0,1.0) * 255 - - results_arc = F.interpolate(results,size=(112,112), mode='bicubic') - results_arc = self.arcface(results_arc) - results_arc = F.normalize(results_arc, p=2, dim=1) - results_cos_dis = 1 - cos_loss(latend_id, results_arc) - average_cos += results_cos_dis - - results = results * self.imagenet_std + self.imagenet_mean - results = results.cpu().permute(0,2,3,1)[0,...] - results = results.numpy() - results = np.clip(results,0.0,1.0) - img_white = np.full((512,512), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (attr_img_ori.shape[1], attr_img_ori.shape[0]) - - target_image = cv2.warpAffine(results, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - img1 = np.array(attr_img_ori, dtype=np.float) - img1 = img_mask * target_image + (1-img_mask) * img1 - final_img = img1.astype(np.uint8) - attr_basename = os.path.splitext(os.path.basename(img))[0] - final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) - final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, - attr_basename,ckp_step,version)) - - cv2.imwrite(save_filename, final_img) - - save_filename = os.path.join(save_dir, - "id_%s--attr_%s_ckp_%s_v_%s_mask.png"%(id_basename, - attr_basename,ckp_step,version)) - cv2.imwrite(save_filename,mask) - - average_cos /= len(imgs_list) - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) - print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) - self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) \ No newline at end of file diff --git a/test_scripts/tester_video.py b/test_scripts/tester_video.py deleted file mode 100644 index 08f188e..0000000 --- a/test_scripts/tester_video.py +++ /dev/null @@ -1,286 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 22nd April 2022 11:20:19 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import shutil - -import torch -import torch.nn.functional as F -from torchvision import transforms - -from moviepy.editor import AudioFileClip, VideoFileClip -from moviepy.video.io.ImageSequenceClip import ImageSequenceClip - -import numpy as np -from tqdm import tqdm -from PIL import Image -import glob - -from utilities.ImagenetNorm import ImagenetNorm -from parsing_model.model import BiSeNet -from insightface_func.face_detect_crop_single import Face_detect_crop -from utilities.reverse2original import reverse2wholeimage -from face_enhancer.gfpgan import GFPGANer -from utilities.utilities import load_file_from_url - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - def cv2totensor(self, cv2_img): - """ - cv2_img: an image read by cv2, H*W*C - return: an 1*C*H*W tensor - """ - cv2_img = cv2.cvtColor(cv2_img,cv2.COLOR_BGR2RGB) - cv2_img = torch.from_numpy(cv2_img) - cv2_img = cv2_img.permute(2,0,1).cuda() - temp = cv2_img / 255.0 - temp -= self.imagenet_mean - temp /= self.imagenet_std - return temp.unsqueeze(0) - - def video_swap( - self, - video_path, - gfpgan, - id_vetor, - save_path, - temp_results_dir='./temp_results', - crop_size=512, - use_mask =False - ): - - video_forcheck = VideoFileClip(video_path) - if video_forcheck.audio is None: - no_audio = True - else: - no_audio = False - - del video_forcheck - - if not no_audio: - video_audio_clip = AudioFileClip(video_path) - - video = cv2.VideoCapture(video_path) - ret = True - frame_index = 0 - - frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) - - # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) - - # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) - - fps = video.get(cv2.CAP_PROP_FPS) - if os.path.exists(temp_results_dir): - shutil.rmtree(temp_results_dir) - spNorm =ImagenetNorm() - if use_mask: - n_classes = 19 - net = BiSeNet(n_classes=n_classes) - net.cuda() - save_pth = os.path.join('./parsing_model', '79999_iter.pth') - net.load_state_dict(torch.load(save_pth)) - net.eval() - else: - net =None - - # while ret: - for frame_index in tqdm(range(frame_count)): - ret, frame = video.read() - if ret: - detect_results = self.detect.get(frame,crop_size) - - if detect_results is not None: - # print(frame_index) - if not os.path.exists(temp_results_dir): - os.mkdir(temp_results_dir) - frame_align_crop_list = detect_results[0] - frame_mat_list = detect_results[1] - swap_result_list = [] - frame_align_crop_tenor_list = [] - for frame_align_crop in frame_align_crop_list: - if gfpgan: - _, _, frame_align_crop = gfpgan.enhance( - frame_align_crop, has_aligned=False, only_center_face=True, paste_back=True) - frame_align_crop_tenor = self.cv2totensor(frame_align_crop) - swap_result = self.network(frame_align_crop_tenor, id_vetor)[0][0] - swap_result = swap_result* self.imagenet_std + self.imagenet_mean - swap_result = torch.clip(swap_result,0.0,1.0) - cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame) - swap_result_list.append(swap_result) - frame_align_crop_tenor_list.append(frame_align_crop_tenor) - reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame,\ - os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),pasring_model =net,use_mask=use_mask, norm = spNorm) - - else: - if not os.path.exists(temp_results_dir): - os.mkdir(temp_results_dir) - frame = frame.astype(np.uint8) - cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame) - else: - break - - video.release() - - # image_filename_list = [] - path = os.path.join(temp_results_dir,'*.jpg') - image_filenames = sorted(glob.glob(path)) - - clips = ImageSequenceClip(image_filenames,fps = fps) - - if not no_audio: - clips = clips.set_audio(video_audio_clip) - basename = os.path.basename(video_path) - basename = os.path.splitext(basename)[0] - save_filename = os.path.join(save_path, basename+".mp4") - index = 0 - while(True): - if os.path.exists(save_filename): - save_filename = os.path.join(save_path, basename+"_%d.mp4"%index) - index += 1 - else: - break - clips.write_videofile(save_filename,audio_codec='aac') - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - # loader1 = torch.load(self.config["ckp_name"]["generator_name"]) - # print(loader1.key()) - # pathwocao = "H:\\Multi Scale Kernel Prediction Networks\\Mobile_Oriented_KPN\\train_logs\\repsr_pixel_0\\checkpoints\\epoch%d_RepSR_Plain.pth"%self.config["checkpoint_epoch"] - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path)) - # self.network.load_state_dict(torch.load(pathwocao)) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - def test(self): - - # save_result = self.config["saveTestResult"] - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - attr_files = self.config["attr_files"] - self.arcface_ckpt= self.config["arcface_ckpt"] - - # models - self.__init_framework__() - - if self.config["preprocess"]: - print("Employ GFPGAN to upsampling detected face images!") - version = '1.2' - if version == '1': - arch = 'original' - channel_multiplier = 1 - model_name = 'GFPGANv1' - elif version == '1.2': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANCleanv1-NoCE-C2' - elif version == '1.3': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANv1.3' - - # determine model paths - model_path = os.path.join('./face_enhancer/experiments/pretrained_models', model_name + '.pth') - url_path = "https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth" - if not os.path.isfile(model_path): - # raise ValueError(f'Model {model_name} does not exist.') - print(f'Model {model_name} does not exist. Prepare to download it......') - model_path = load_file_from_url( - url=url_path, model_dir=model_path, progress=True, file_name=None) - restorer = GFPGANer( - model_path=model_path, - upscale=1, - arch=arch, - channel_multiplier=channel_multiplier, - bg_upsampler=None) - else: - restorer = None - - - - mode = None - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - id_img = cv2.imread(id_imgs) - id_img_align_crop, _ = self.detect.get(id_img,512) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - with torch.no_grad(): - self.video_swap(attr_files, restorer, latend_id, save_dir, temp_results_dir="./.temples",\ - use_mask=False,crop_size=512) - - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) \ No newline at end of file diff --git a/test_scripts/tester_video_gfpgan.py b/test_scripts/tester_video_gfpgan.py deleted file mode 100644 index c941d70..0000000 --- a/test_scripts/tester_video_gfpgan.py +++ /dev/null @@ -1,285 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: tester_commonn.py -# Created Date: Saturday July 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 14th April 2022 11:40:45 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - - - -import os -import cv2 -import time -import shutil - -import torch -import torch.nn.functional as F -from torchvision import transforms - -from moviepy.editor import AudioFileClip, VideoFileClip -from moviepy.video.io.ImageSequenceClip import ImageSequenceClip - -import numpy as np -from tqdm import tqdm -from PIL import Image -import glob - -from utilities.ImagenetNorm import ImagenetNorm -from parsing_model.model import BiSeNet -from insightface_func.face_detect_crop_single import Face_detect_crop -from utilities.reverse2original import reverse2wholeimage -from face_enhancer.gfpgan import GFPGANer -from utilities.utilities import load_file_from_url - -class Tester(object): - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - self.transformer_Arcface = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]) - self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) - self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) - - def cv2totensor(self, cv2_img): - """ - cv2_img: an image read by cv2, H*W*C - return: an 1*C*H*W tensor - """ - cv2_img = cv2.cvtColor(cv2_img,cv2.COLOR_BGR2RGB) - cv2_img = torch.from_numpy(cv2_img) - cv2_img = cv2_img.permute(2,0,1).cuda() - temp = cv2_img / 255.0 - temp -= self.imagenet_mean - temp /= self.imagenet_std - return temp.unsqueeze(0) - - def video_swap( - self, - video_path, - gfpgan, - id_vetor, - save_path, - temp_results_dir='./temp_results', - crop_size=512, - use_mask =False - ): - - video_forcheck = VideoFileClip(video_path) - if video_forcheck.audio is None: - no_audio = True - else: - no_audio = False - - del video_forcheck - - if not no_audio: - video_audio_clip = AudioFileClip(video_path) - - video = cv2.VideoCapture(video_path) - ret = True - frame_index = 0 - - frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) - - # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) - - # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) - - fps = video.get(cv2.CAP_PROP_FPS) - if os.path.exists(temp_results_dir): - shutil.rmtree(temp_results_dir) - spNorm =ImagenetNorm() - if use_mask: - n_classes = 19 - net = BiSeNet(n_classes=n_classes) - net.cuda() - save_pth = os.path.join('./parsing_model', '79999_iter.pth') - net.load_state_dict(torch.load(save_pth)) - net.eval() - else: - net =None - - # while ret: - for frame_index in tqdm(range(frame_count)): - ret, frame = video.read() - if ret: - detect_results = self.detect.get(frame,crop_size) - - if detect_results is not None: - # print(frame_index) - if not os.path.exists(temp_results_dir): - os.mkdir(temp_results_dir) - frame_align_crop_list = detect_results[0] - frame_mat_list = detect_results[1] - swap_result_list = [] - frame_align_crop_tenor_list = [] - for frame_align_crop in frame_align_crop_list: - _, _, restored_face = gfpgan.enhance( - frame_align_crop, has_aligned=False, only_center_face=True, paste_back=True) - frame_align_crop_tenor = self.cv2totensor(restored_face) - swap_result = self.network(frame_align_crop_tenor, id_vetor)[0][0] - swap_result = swap_result* self.imagenet_std + self.imagenet_mean - swap_result = torch.clip(swap_result,0.0,1.0) - cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame) - swap_result_list.append(swap_result) - frame_align_crop_tenor_list.append(frame_align_crop_tenor) - reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame,\ - os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),pasring_model =net,use_mask=use_mask, norm = spNorm) - - else: - if not os.path.exists(temp_results_dir): - os.mkdir(temp_results_dir) - frame = frame.astype(np.uint8) - cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame) - else: - break - - video.release() - - # image_filename_list = [] - path = os.path.join(temp_results_dir,'*.jpg') - image_filenames = sorted(glob.glob(path)) - - clips = ImageSequenceClip(image_filenames,fps = fps) - - if not no_audio: - clips = clips.set_audio(video_audio_clip) - basename = os.path.basename(video_path) - basename = os.path.splitext(basename)[0] - save_filename = os.path.join(save_path, basename+".mp4") - index = 0 - while(True): - if os.path.exists(save_filename): - save_filename = os.path.join(save_path, basename+"_%d.mp4"%index) - index += 1 - else: - break - clips.write_videofile(save_filename,audio_codec='aac') - - - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - model_config = self.config["model_configs"] - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.network = gen_class(**model_config["g_model"]["module_params"]) - - # TODO replace below lines to define the model framework - self.network = gen_class(**model_config["g_model"]["module_params"]) - self.network = self.network.eval() - # print and recorde model structure - self.reporter.writeInfo("Model structure:") - self.reporter.writeModel(self.network.__str__()) - - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - self.arcface.eval() - self.arcface.requires_grad_(False) - - # train in GPU - if self.config["cuda"] >=0: - self.network = self.network.cuda() - self.arcface = self.arcface.cuda() - # loader1 = torch.load(self.config["ckp_name"]["generator_name"]) - # print(loader1.key()) - # pathwocao = "H:\\Multi Scale Kernel Prediction Networks\\Mobile_Oriented_KPN\\train_logs\\repsr_pixel_0\\checkpoints\\epoch%d_RepSR_Plain.pth"%self.config["checkpoint_epoch"] - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.network.load_state_dict(torch.load(model_path)) - # self.network.load_state_dict(torch.load(pathwocao)) - print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) - - def test(self): - - # save_result = self.config["saveTestResult"] - save_dir = self.config["test_samples_path"] - ckp_step = self.config["checkpoint_step"] - version = self.config["version"] - id_imgs = self.config["id_imgs"] - attr_files = self.config["attr_files"] - self.arcface_ckpt= self.config["arcface_ckpt"] - - # models - self.__init_framework__() - version = '1.2' - if version == '1': - arch = 'original' - channel_multiplier = 1 - model_name = 'GFPGANv1' - elif version == '1.2': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANCleanv1-NoCE-C2' - elif version == '1.3': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANv1.3' - - # determine model paths - model_path = os.path.join('./face_enhancer/experiments/pretrained_models', model_name + '.pth') - url_path = "https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth" - - if not os.path.isfile(model_path): - # raise ValueError(f'Model {model_name} does not exist.') - print(f'Model {model_name} does not exist. Prepare to download it......') - model_path = load_file_from_url( - url=url_path, model_dir=model_path, progress=True, file_name=None) - - restorer = GFPGANer( - model_path=model_path, - upscale=1, - arch=arch, - channel_multiplier=channel_multiplier, - bg_upsampler=None) - - - - mode = None - self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models') - self.detect.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode = mode) - - id_img = cv2.imread(id_imgs) - id_img_align_crop, _ = self.detect.get(id_img,512) - # _, _, restored_face = restorer.enhance( - # id_img_align_crop[0], has_aligned=False, only_center_face=True, paste_back=True) - # cv2.imwrite("id_wocao.png",restored_face) - id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB)) - id_img = self.transformer_Arcface(id_img_align_crop_pil) - id_img = id_img.unsqueeze(0).cuda() - - #create latent id - id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') - latend_id = self.arcface(id_img) - latend_id = F.normalize(latend_id, p=2, dim=1) - # Start time - import datetime - print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - print('Start =================================== test...') - start_time = time.time() - self.network.eval() - with torch.no_grad(): - self.video_swap(attr_files, restorer, latend_id, save_dir, temp_results_dir="./.temples",\ - use_mask=False,crop_size=512) - - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - print("Elapsed [{}]".format(elapsed)) \ No newline at end of file diff --git a/torch_utils/__init__.py b/torch_utils/__init__.py deleted file mode 100644 index 939e7c6..0000000 --- a/torch_utils/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -# empty diff --git a/torch_utils/custom_ops.py b/torch_utils/custom_ops.py deleted file mode 100644 index dd7cc04..0000000 --- a/torch_utils/custom_ops.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -import glob -import hashlib -import importlib -import os -import re -import shutil -import uuid - -import torch -import torch.utils.cpp_extension -from torch.utils.file_baton import FileBaton - -#---------------------------------------------------------------------------- -# Global options. - -verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full' - -#---------------------------------------------------------------------------- -# Internal helper funcs. - -def _find_compiler_bindir(): - patterns = [ - 'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64', - 'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64', - 'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64', - 'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin', - ] - for pattern in patterns: - matches = sorted(glob.glob(pattern)) - if len(matches): - return matches[-1] - return None - -#---------------------------------------------------------------------------- - -def _get_mangled_gpu_name(): - name = torch.cuda.get_device_name().lower() - out = [] - for c in name: - if re.match('[a-z0-9_-]+', c): - out.append(c) - else: - out.append('-') - return ''.join(out) - -#---------------------------------------------------------------------------- -# Main entry point for compiling and loading C++/CUDA plugins. - -_cached_plugins = dict() - -def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwargs): - assert verbosity in ['none', 'brief', 'full'] - if headers is None: - headers = [] - if source_dir is not None: - sources = [os.path.join(source_dir, fname) for fname in sources] - headers = [os.path.join(source_dir, fname) for fname in headers] - - # Already cached? - if module_name in _cached_plugins: - return _cached_plugins[module_name] - - # Print status. - if verbosity == 'full': - print(f'Setting up PyTorch plugin "{module_name}"...') - elif verbosity == 'brief': - print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) - verbose_build = (verbosity == 'full') - - # Compile and load. - try: # pylint: disable=too-many-nested-blocks - # Make sure we can find the necessary compiler binaries. - if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0: - compiler_bindir = _find_compiler_bindir() - if compiler_bindir is None: - raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') - os.environ['PATH'] += ';' + compiler_bindir - - # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either - # break the build or unnecessarily restrict what's available to nvcc. - # Unset it to let nvcc decide based on what's available on the - # machine. - os.environ['TORCH_CUDA_ARCH_LIST'] = '' - - # Incremental build md5sum trickery. Copies all the input source files - # into a cached build directory under a combined md5 digest of the input - # source files. Copying is done only if the combined digest has changed. - # This keeps input file timestamps and filenames the same as in previous - # extension builds, allowing for fast incremental rebuilds. - # - # This optimization is done only in case all the source files reside in - # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR - # environment variable is set (we take this as a signal that the user - # actually cares about this.) - # - # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work - # around the *.cu dependency bug in ninja config. - # - all_source_files = sorted(sources + headers) - all_source_dirs = set(os.path.dirname(fname) for fname in all_source_files) - if len(all_source_dirs) == 1: # and ('TORCH_EXTENSIONS_DIR' in os.environ): - - # Compute combined hash digest for all source files. - hash_md5 = hashlib.md5() - for src in all_source_files: - with open(src, 'rb') as f: - hash_md5.update(f.read()) - - # Select cached build directory name. - source_digest = hash_md5.hexdigest() - build_top_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access - cached_build_dir = os.path.join(build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}') - - if not os.path.isdir(cached_build_dir): - tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}' - os.makedirs(tmpdir) - for src in all_source_files: - shutil.copyfile(src, os.path.join(tmpdir, os.path.basename(src))) - try: - os.replace(tmpdir, cached_build_dir) # atomic - except OSError: - # source directory already exists, delete tmpdir and its contents. - shutil.rmtree(tmpdir) - if not os.path.isdir(cached_build_dir): raise - - # Compile. - cached_sources = [os.path.join(cached_build_dir, os.path.basename(fname)) for fname in sources] - torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir, - verbose=verbose_build, sources=cached_sources, **build_kwargs) - else: - torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) - - # Load. - module = importlib.import_module(module_name) - - except: - if verbosity == 'brief': - print('Failed!') - raise - - # Print status and add to cache dict. - if verbosity == 'full': - print(f'Done setting up PyTorch plugin "{module_name}".') - elif verbosity == 'brief': - print('Done.') - _cached_plugins[module_name] = module - return module - -#---------------------------------------------------------------------------- diff --git a/torch_utils/misc.py b/torch_utils/misc.py deleted file mode 100644 index 3173c48..0000000 --- a/torch_utils/misc.py +++ /dev/null @@ -1,272 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -import re -import contextlib -import numpy as np -import torch -import warnings -import dnnlib - -#---------------------------------------------------------------------------- -# Cached construction of constant tensors. Avoids CPU=>GPU copy when the -# same constant is used multiple times. - -_constant_cache = dict() - -def constant(value, shape=None, dtype=None, device=None, memory_format=None): - value = np.asarray(value) - if shape is not None: - shape = tuple(shape) - if dtype is None: - dtype = torch.get_default_dtype() - if device is None: - device = torch.device('cpu') - if memory_format is None: - memory_format = torch.contiguous_format - - key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format) - tensor = _constant_cache.get(key, None) - if tensor is None: - tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) - if shape is not None: - tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape)) - tensor = tensor.contiguous(memory_format=memory_format) - _constant_cache[key] = tensor - return tensor - -#---------------------------------------------------------------------------- -# Replace NaN/Inf with specified numerical values. - -try: - nan_to_num = torch.nan_to_num # 1.8.0a0 -except AttributeError: - def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin - assert isinstance(input, torch.Tensor) - if posinf is None: - posinf = torch.finfo(input.dtype).max - if neginf is None: - neginf = torch.finfo(input.dtype).min - assert nan == 0 - return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) - -#---------------------------------------------------------------------------- -# Symbolic assert. - -try: - symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access -except AttributeError: - symbolic_assert = torch.Assert # 1.7.0 - -#---------------------------------------------------------------------------- -# Context manager to temporarily suppress known warnings in torch.jit.trace(). -# Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672 - -@contextlib.contextmanager -def suppress_tracer_warnings(): - flt = ('ignore', None, torch.jit.TracerWarning, None, 0) - warnings.filters.insert(0, flt) - yield - warnings.filters.remove(flt) - -#---------------------------------------------------------------------------- -# Assert that the shape of a tensor matches the given list of integers. -# None indicates that the size of a dimension is allowed to vary. -# Performs symbolic assertion when used in torch.jit.trace(). - -def assert_shape(tensor, ref_shape): - if tensor.ndim != len(ref_shape): - raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') - for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): - if ref_size is None: - pass - elif isinstance(ref_size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}') - elif isinstance(size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') - elif size != ref_size: - raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') - -#---------------------------------------------------------------------------- -# Function decorator that calls torch.autograd.profiler.record_function(). - -def profiled_function(fn): - def decorator(*args, **kwargs): - with torch.autograd.profiler.record_function(fn.__name__): - return fn(*args, **kwargs) - decorator.__name__ = fn.__name__ - return decorator - -#---------------------------------------------------------------------------- -# Sampler for torch.utils.data.DataLoader that loops over the dataset -# indefinitely, shuffling items as it goes. - -class InfiniteSampler(torch.utils.data.Sampler): - def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): - assert len(dataset) > 0 - assert num_replicas > 0 - assert 0 <= rank < num_replicas - assert 0 <= window_size <= 1 - super().__init__(dataset) - self.dataset = dataset - self.rank = rank - self.num_replicas = num_replicas - self.shuffle = shuffle - self.seed = seed - self.window_size = window_size - - def __iter__(self): - order = np.arange(len(self.dataset)) - rnd = None - window = 0 - if self.shuffle: - rnd = np.random.RandomState(self.seed) - rnd.shuffle(order) - window = int(np.rint(order.size * self.window_size)) - - idx = 0 - while True: - i = idx % order.size - if idx % self.num_replicas == self.rank: - yield order[i] - if window >= 2: - j = (i - rnd.randint(window)) % order.size - order[i], order[j] = order[j], order[i] - idx += 1 - -#---------------------------------------------------------------------------- -# Utilities for operating with torch.nn.Module parameters and buffers. - -def params_and_buffers(module): - assert isinstance(module, torch.nn.Module) - return list(module.parameters()) + list(module.buffers()) - -def named_params_and_buffers(module): - assert isinstance(module, torch.nn.Module) - return list(module.named_parameters()) + list(module.named_buffers()) - -def copy_params_and_buffers(src_module, dst_module, require_all=False): - assert isinstance(src_module, torch.nn.Module) - assert isinstance(dst_module, torch.nn.Module) - src_tensors = dict(named_params_and_buffers(src_module)) - for name, tensor in named_params_and_buffers(dst_module): - assert (name in src_tensors) or (not require_all) - if name in src_tensors: - tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad) - -#---------------------------------------------------------------------------- -# Context manager for easily enabling/disabling DistributedDataParallel -# synchronization. - -@contextlib.contextmanager -def ddp_sync(module, sync): - assert isinstance(module, torch.nn.Module) - if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel): - yield - else: - with module.no_sync(): - yield - -#---------------------------------------------------------------------------- -# Check DistributedDataParallel consistency across processes. - -def check_ddp_consistency(module, ignore_regex=None): - assert isinstance(module, torch.nn.Module) - for name, tensor in named_params_and_buffers(module): - fullname = type(module).__name__ + '.' + name - if ignore_regex is not None and re.fullmatch(ignore_regex, fullname): - continue - tensor = tensor.detach() - if tensor.is_floating_point(): - tensor = nan_to_num(tensor) - other = tensor.clone() - torch.distributed.broadcast(tensor=other, src=0) - assert (tensor == other).all(), fullname - -#---------------------------------------------------------------------------- -# Print summary table of module hierarchy. - -def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): - assert isinstance(module, torch.nn.Module) - assert not isinstance(module, torch.jit.ScriptModule) - assert isinstance(inputs, (tuple, list)) - - # Register hooks. - entries = [] - nesting = [0] - def pre_hook(_mod, _inputs): - nesting[0] += 1 - def post_hook(mod, _inputs, outputs): - nesting[0] -= 1 - if nesting[0] <= max_nesting: - outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs] - outputs = [t for t in outputs if isinstance(t, torch.Tensor)] - entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs)) - hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()] - hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()] - - # Run module. - outputs = module(*inputs) - for hook in hooks: - hook.remove() - - # Identify unique outputs, parameters, and buffers. - tensors_seen = set() - for e in entries: - e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen] - e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen] - e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen] - tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs} - - # Filter out redundant entries. - if skip_redundant: - entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)] - - # Construct table. - rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']] - rows += [['---'] * len(rows[0])] - param_total = 0 - buffer_total = 0 - submodule_names = {mod: name for name, mod in module.named_modules()} - for e in entries: - name = '' if e.mod is module else submodule_names[e.mod] - param_size = sum(t.numel() for t in e.unique_params) - buffer_size = sum(t.numel() for t in e.unique_buffers) - output_shapes = [str(list(t.shape)) for t in e.outputs] - output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs] - rows += [[ - name + (':0' if len(e.outputs) >= 2 else ''), - str(param_size) if param_size else '-', - str(buffer_size) if buffer_size else '-', - (output_shapes + ['-'])[0], - (output_dtypes + ['-'])[0], - ]] - for idx in range(1, len(e.outputs)): - rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]] - param_total += param_size - buffer_total += buffer_size - rows += [['---'] * len(rows[0])] - rows += [['Total', str(param_total), str(buffer_total), '-', '-']] - - # Print table. - widths = [max(len(cell) for cell in column) for column in zip(*rows)] - print() - for row in rows: - print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths))) - print() - return outputs - -#---------------------------------------------------------------------------- - -# Added by Katja -import os - -def get_ckpt_path(run_dir): - return os.path.join(run_dir, f'network-snapshot.pkl') diff --git a/torch_utils/ops/__init__.py b/torch_utils/ops/__init__.py deleted file mode 100644 index 939e7c6..0000000 --- a/torch_utils/ops/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -# empty diff --git a/torch_utils/ops/bias_act.cpp b/torch_utils/ops/bias_act.cpp deleted file mode 100644 index 3adaeee..0000000 --- a/torch_utils/ops/bias_act.cpp +++ /dev/null @@ -1,99 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include -#include -#include -#include "bias_act.h" - -//------------------------------------------------------------------------ - -static bool has_same_layout(torch::Tensor x, torch::Tensor y) -{ - if (x.dim() != y.dim()) - return false; - for (int64_t i = 0; i < x.dim(); i++) - { - if (x.size(i) != y.size(i)) - return false; - if (x.size(i) >= 2 && x.stride(i) != y.stride(i)) - return false; - } - return true; -} - -//------------------------------------------------------------------------ - -static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp) -{ - // Validate arguments. - TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); - TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x"); - TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x"); - TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x"); - TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x"); - TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); - TORCH_CHECK(b.dim() == 1, "b must have rank 1"); - TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds"); - TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements"); - TORCH_CHECK(grad >= 0, "grad must be non-negative"); - - // Validate layout. - TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense"); - TORCH_CHECK(b.is_contiguous(), "b must be contiguous"); - TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x"); - TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x"); - TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x"); - - // Create output tensor. - const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); - torch::Tensor y = torch::empty_like(x); - TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x"); - - // Initialize CUDA kernel parameters. - bias_act_kernel_params p; - p.x = x.data_ptr(); - p.b = (b.numel()) ? b.data_ptr() : NULL; - p.xref = (xref.numel()) ? xref.data_ptr() : NULL; - p.yref = (yref.numel()) ? yref.data_ptr() : NULL; - p.dy = (dy.numel()) ? dy.data_ptr() : NULL; - p.y = y.data_ptr(); - p.grad = grad; - p.act = act; - p.alpha = alpha; - p.gain = gain; - p.clamp = clamp; - p.sizeX = (int)x.numel(); - p.sizeB = (int)b.numel(); - p.stepB = (b.numel()) ? (int)x.stride(dim) : 1; - - // Choose CUDA kernel. - void* kernel; - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] - { - kernel = choose_bias_act_kernel(p); - }); - TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func"); - - // Launch CUDA kernel. - p.loopX = 4; - int blockSize = 4 * 32; - int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1; - void* args[] = {&p}; - AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); - return y; -} - -//------------------------------------------------------------------------ - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) -{ - m.def("bias_act", &bias_act); -} - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.cu b/torch_utils/ops/bias_act.cu deleted file mode 100644 index ed1d16f..0000000 --- a/torch_utils/ops/bias_act.cu +++ /dev/null @@ -1,173 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include -#include "bias_act.h" - -//------------------------------------------------------------------------ -// Helpers. - -template struct InternalType; -template <> struct InternalType { typedef double scalar_t; }; -template <> struct InternalType { typedef float scalar_t; }; -template <> struct InternalType { typedef float scalar_t; }; - -//------------------------------------------------------------------------ -// CUDA kernel. - -template -__global__ void bias_act_kernel(bias_act_kernel_params p) -{ - typedef typename InternalType::scalar_t scalar_t; - int G = p.grad; - scalar_t alpha = (scalar_t)p.alpha; - scalar_t gain = (scalar_t)p.gain; - scalar_t clamp = (scalar_t)p.clamp; - scalar_t one = (scalar_t)1; - scalar_t two = (scalar_t)2; - scalar_t expRange = (scalar_t)80; - scalar_t halfExpRange = (scalar_t)40; - scalar_t seluScale = (scalar_t)1.0507009873554804934193349852946; - scalar_t seluAlpha = (scalar_t)1.6732632423543772848170429916717; - - // Loop over elements. - int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x; - for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x) - { - // Load. - scalar_t x = (scalar_t)((const T*)p.x)[xi]; - scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0; - scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0; - scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0; - scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one; - scalar_t yy = (gain != 0) ? yref / gain : 0; - scalar_t y = 0; - - // Apply bias. - ((G == 0) ? x : xref) += b; - - // linear - if (A == 1) - { - if (G == 0) y = x; - if (G == 1) y = x; - } - - // relu - if (A == 2) - { - if (G == 0) y = (x > 0) ? x : 0; - if (G == 1) y = (yy > 0) ? x : 0; - } - - // lrelu - if (A == 3) - { - if (G == 0) y = (x > 0) ? x : x * alpha; - if (G == 1) y = (yy > 0) ? x : x * alpha; - } - - // tanh - if (A == 4) - { - if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); } - if (G == 1) y = x * (one - yy * yy); - if (G == 2) y = x * (one - yy * yy) * (-two * yy); - } - - // sigmoid - if (A == 5) - { - if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one); - if (G == 1) y = x * yy * (one - yy); - if (G == 2) y = x * yy * (one - yy) * (one - two * yy); - } - - // elu - if (A == 6) - { - if (G == 0) y = (x >= 0) ? x : exp(x) - one; - if (G == 1) y = (yy >= 0) ? x : x * (yy + one); - if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one); - } - - // selu - if (A == 7) - { - if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one); - if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha); - if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha); - } - - // softplus - if (A == 8) - { - if (G == 0) y = (x > expRange) ? x : log(exp(x) + one); - if (G == 1) y = x * (one - exp(-yy)); - if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); } - } - - // swish - if (A == 9) - { - if (G == 0) - y = (x < -expRange) ? 0 : x / (exp(-x) + one); - else - { - scalar_t c = exp(xref); - scalar_t d = c + one; - if (G == 1) - y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d); - else - y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d); - yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain; - } - } - - // Apply gain. - y *= gain * dy; - - // Clamp. - if (clamp >= 0) - { - if (G == 0) - y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp; - else - y = (yref > -clamp & yref < clamp) ? y : 0; - } - - // Store. - ((T*)p.y)[xi] = (T)y; - } -} - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template void* choose_bias_act_kernel(const bias_act_kernel_params& p) -{ - if (p.act == 1) return (void*)bias_act_kernel; - if (p.act == 2) return (void*)bias_act_kernel; - if (p.act == 3) return (void*)bias_act_kernel; - if (p.act == 4) return (void*)bias_act_kernel; - if (p.act == 5) return (void*)bias_act_kernel; - if (p.act == 6) return (void*)bias_act_kernel; - if (p.act == 7) return (void*)bias_act_kernel; - if (p.act == 8) return (void*)bias_act_kernel; - if (p.act == 9) return (void*)bias_act_kernel; - return NULL; -} - -//------------------------------------------------------------------------ -// Template specializations. - -template void* choose_bias_act_kernel (const bias_act_kernel_params& p); -template void* choose_bias_act_kernel (const bias_act_kernel_params& p); -template void* choose_bias_act_kernel (const bias_act_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.h b/torch_utils/ops/bias_act.h deleted file mode 100644 index 60b81c6..0000000 --- a/torch_utils/ops/bias_act.h +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct bias_act_kernel_params -{ - const void* x; // [sizeX] - const void* b; // [sizeB] or NULL - const void* xref; // [sizeX] or NULL - const void* yref; // [sizeX] or NULL - const void* dy; // [sizeX] or NULL - void* y; // [sizeX] - - int grad; - int act; - float alpha; - float gain; - float clamp; - - int sizeX; - int sizeB; - int stepB; - int loopX; -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template void* choose_bias_act_kernel(const bias_act_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.py b/torch_utils/ops/bias_act.py deleted file mode 100644 index 5c485c0..0000000 --- a/torch_utils/ops/bias_act.py +++ /dev/null @@ -1,209 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Custom PyTorch ops for efficient bias and activation.""" - -import os -import numpy as np -import torch -import dnnlib - -from .. import custom_ops -from .. import misc - -#---------------------------------------------------------------------------- - -activation_funcs = { - 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), - 'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False), - 'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False), - 'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True), - 'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True), - 'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True), - 'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True), - 'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True), - 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True), -} - -#---------------------------------------------------------------------------- - -_plugin = None -_null_tensor = torch.empty([0]) - -def _init(): - global _plugin - if _plugin is None: - _plugin = custom_ops.get_plugin( - module_name='bias_act_plugin', - sources=['bias_act.cpp', 'bias_act.cu'], - headers=['bias_act.h'], - source_dir=os.path.dirname(__file__), - extra_cuda_cflags=['--use_fast_math'], - ) - return True - -#---------------------------------------------------------------------------- - -def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): - r"""Fused bias and activation function. - - Adds bias `b` to activation tensor `x`, evaluates activation function `act`, - and scales the result by `gain`. Each of the steps is optional. In most cases, - the fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports first and second order gradients, - but not third order gradients. - - Args: - x: Input activation tensor. Can be of any shape. - b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type - as `x`. The shape must be known, and it must match the dimension of `x` - corresponding to `dim`. - dim: The dimension in `x` corresponding to the elements of `b`. - The value of `dim` is ignored if `b` is not specified. - act: Name of the activation function to evaluate, or `"linear"` to disable. - Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. - See `activation_funcs` for a full list. `None` is not allowed. - alpha: Shape parameter for the activation function, or `None` to use the default. - gain: Scaling factor for the output tensor, or `None` to use default. - See `activation_funcs` for the default scaling of each activation function. - If unsure, consider specifying 1. - clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable - the clamping (default). - impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). - - Returns: - Tensor of the same shape and datatype as `x`. - """ - assert isinstance(x, torch.Tensor) - assert impl in ['ref', 'cuda'] - if impl == 'cuda' and x.device.type == 'cuda' and _init(): - return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b) - return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp) - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None): - """Slow reference implementation of `bias_act()` using standard TensorFlow ops. - """ - assert isinstance(x, torch.Tensor) - assert clamp is None or clamp >= 0 - spec = activation_funcs[act] - alpha = float(alpha if alpha is not None else spec.def_alpha) - gain = float(gain if gain is not None else spec.def_gain) - clamp = float(clamp if clamp is not None else -1) - - # Add bias. - if b is not None: - assert isinstance(b, torch.Tensor) and b.ndim == 1 - assert 0 <= dim < x.ndim - assert b.shape[0] == x.shape[dim] - x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]) - - # Evaluate activation function. - alpha = float(alpha) - x = spec.func(x, alpha=alpha) - - # Scale by gain. - gain = float(gain) - if gain != 1: - x = x * gain - - # Clamp. - if clamp >= 0: - x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type - return x - -#---------------------------------------------------------------------------- - -_bias_act_cuda_cache = dict() - -def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): - """Fast CUDA implementation of `bias_act()` using custom ops. - """ - # Parse arguments. - assert clamp is None or clamp >= 0 - spec = activation_funcs[act] - alpha = float(alpha if alpha is not None else spec.def_alpha) - gain = float(gain if gain is not None else spec.def_gain) - clamp = float(clamp if clamp is not None else -1) - - # Lookup from cache. - key = (dim, act, alpha, gain, clamp) - if key in _bias_act_cuda_cache: - return _bias_act_cuda_cache[key] - - # Forward op. - class BiasActCuda(torch.autograd.Function): - @staticmethod - def forward(ctx, x, b): # pylint: disable=arguments-differ - ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(1) == 1 else torch.contiguous_format - x = x.contiguous(memory_format=ctx.memory_format) - b = b.contiguous() if b is not None else _null_tensor - y = x - if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor: - y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) - ctx.save_for_backward( - x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, - b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, - y if 'y' in spec.ref else _null_tensor) - return y - - @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ - dy = dy.contiguous(memory_format=ctx.memory_format) - x, b, y = ctx.saved_tensors - dx = None - db = None - - if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: - dx = dy - if act != 'linear' or gain != 1 or clamp >= 0: - dx = BiasActCudaGrad.apply(dy, x, b, y) - - if ctx.needs_input_grad[1]: - db = dx.sum([i for i in range(dx.ndim) if i != dim]) - - return dx, db - - # Backward op. - class BiasActCudaGrad(torch.autograd.Function): - @staticmethod - def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ - ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(1) == 1 else torch.contiguous_format - dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp) - ctx.save_for_backward( - dy if spec.has_2nd_grad else _null_tensor, - x, b, y) - return dx - - @staticmethod - def backward(ctx, d_dx): # pylint: disable=arguments-differ - d_dx = d_dx.contiguous(memory_format=ctx.memory_format) - dy, x, b, y = ctx.saved_tensors - d_dy = None - d_x = None - d_b = None - d_y = None - - if ctx.needs_input_grad[0]: - d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) - - if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]): - d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) - - if spec.has_2nd_grad and ctx.needs_input_grad[2]: - d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) - - return d_dy, d_x, d_b, d_y - - # Add to cache. - _bias_act_cuda_cache[key] = BiasActCuda - return BiasActCuda - -#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/conv2d_gradfix.py b/torch_utils/ops/conv2d_gradfix.py deleted file mode 100644 index 388778f..0000000 --- a/torch_utils/ops/conv2d_gradfix.py +++ /dev/null @@ -1,198 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Custom replacement for `torch.nn.functional.conv2d` that supports -arbitrarily high order gradients with zero performance penalty.""" - -import contextlib -import torch - -# pylint: disable=redefined-builtin -# pylint: disable=arguments-differ -# pylint: disable=protected-access - -#---------------------------------------------------------------------------- - -enabled = False # Enable the custom op by setting this to true. -weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights. - -@contextlib.contextmanager -def no_weight_gradients(disable=True): - global weight_gradients_disabled - old = weight_gradients_disabled - if disable: - weight_gradients_disabled = True - yield - weight_gradients_disabled = old - -#---------------------------------------------------------------------------- - -def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): - if _should_use_custom_op(input): - return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias) - return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) - -def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1): - if _should_use_custom_op(input): - return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias) - return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation) - -#---------------------------------------------------------------------------- - -def _should_use_custom_op(input): - assert isinstance(input, torch.Tensor) - if (not enabled) or (not torch.backends.cudnn.enabled): - return False - if input.device.type != 'cuda': - return False - return True - -def _tuple_of_ints(xs, ndim): - xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim - assert len(xs) == ndim - assert all(isinstance(x, int) for x in xs) - return xs - -#---------------------------------------------------------------------------- - -_conv2d_gradfix_cache = dict() -_null_tensor = torch.empty([0]) - -def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups): - # Parse arguments. - ndim = 2 - weight_shape = tuple(weight_shape) - stride = _tuple_of_ints(stride, ndim) - padding = _tuple_of_ints(padding, ndim) - output_padding = _tuple_of_ints(output_padding, ndim) - dilation = _tuple_of_ints(dilation, ndim) - - # Lookup from cache. - key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) - if key in _conv2d_gradfix_cache: - return _conv2d_gradfix_cache[key] - - # Validate arguments. - assert groups >= 1 - assert len(weight_shape) == ndim + 2 - assert all(stride[i] >= 1 for i in range(ndim)) - assert all(padding[i] >= 0 for i in range(ndim)) - assert all(dilation[i] >= 0 for i in range(ndim)) - if not transpose: - assert all(output_padding[i] == 0 for i in range(ndim)) - else: # transpose - assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim)) - - # Helpers. - common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups) - def calc_output_padding(input_shape, output_shape): - if transpose: - return [0, 0] - return [ - input_shape[i + 2] - - (output_shape[i + 2] - 1) * stride[i] - - (1 - 2 * padding[i]) - - dilation[i] * (weight_shape[i + 2] - 1) - for i in range(ndim) - ] - - # Forward & backward. - class Conv2d(torch.autograd.Function): - @staticmethod - def forward(ctx, input, weight, bias): - assert weight.shape == weight_shape - ctx.save_for_backward( - input if weight.requires_grad else _null_tensor, - weight if input.requires_grad else _null_tensor, - ) - ctx.input_shape = input.shape - - # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere). - if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0): - a = weight.reshape(groups, weight_shape[0] // groups, weight_shape[1]) - b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1) - c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, 2, 0, 3).flatten(2) - c = c.reshape(-1, input.shape[0], *input.shape[2:]).transpose(0, 1) - c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3) - return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format)) - - # General case => cuDNN. - if transpose: - return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs) - return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) - - @staticmethod - def backward(ctx, grad_output): - input, weight = ctx.saved_tensors - input_shape = ctx.input_shape - grad_input = None - grad_weight = None - grad_bias = None - - if ctx.needs_input_grad[0]: - p = calc_output_padding(input_shape=input_shape, output_shape=grad_output.shape) - op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) - grad_input = op.apply(grad_output, weight, None) - assert grad_input.shape == input_shape - - if ctx.needs_input_grad[1] and not weight_gradients_disabled: - grad_weight = Conv2dGradWeight.apply(grad_output, input) - assert grad_weight.shape == weight_shape - - if ctx.needs_input_grad[2]: - grad_bias = grad_output.sum([0, 2, 3]) - - return grad_input, grad_weight, grad_bias - - # Gradient with respect to the weights. - class Conv2dGradWeight(torch.autograd.Function): - @staticmethod - def forward(ctx, grad_output, input): - ctx.save_for_backward( - grad_output if input.requires_grad else _null_tensor, - input if grad_output.requires_grad else _null_tensor, - ) - ctx.grad_output_shape = grad_output.shape - ctx.input_shape = input.shape - - # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere). - if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0): - a = grad_output.reshape(grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) - b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) - c = (b @ a.transpose(1, 2) if transpose else a @ b.transpose(1, 2)).reshape(weight_shape) - return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format)) - - # General case => cuDNN. - name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight' - flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] - return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags) - - @staticmethod - def backward(ctx, grad2_grad_weight): - grad_output, input = ctx.saved_tensors - grad_output_shape = ctx.grad_output_shape - input_shape = ctx.input_shape - grad2_grad_output = None - grad2_input = None - - if ctx.needs_input_grad[0]: - grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) - assert grad2_grad_output.shape == grad_output_shape - - if ctx.needs_input_grad[1]: - p = calc_output_padding(input_shape=input_shape, output_shape=grad_output_shape) - op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) - grad2_input = op.apply(grad_output, grad2_grad_weight, None) - assert grad2_input.shape == input_shape - - return grad2_grad_output, grad2_input - - _conv2d_gradfix_cache[key] = Conv2d - return Conv2d - -#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/conv2d_resample.py b/torch_utils/ops/conv2d_resample.py deleted file mode 100644 index 5eb5877..0000000 --- a/torch_utils/ops/conv2d_resample.py +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""2D convolution with optional up/downsampling.""" - -import torch - -from .. import misc -from . import conv2d_gradfix -from . import upfirdn2d -from .upfirdn2d import _parse_padding -from .upfirdn2d import _get_filter_size - -#---------------------------------------------------------------------------- - -def _get_weight_shape(w): - with misc.suppress_tracer_warnings(): # this value will be treated as a constant - shape = [int(sz) for sz in w.shape] - misc.assert_shape(w, shape) - return shape - -#---------------------------------------------------------------------------- - -def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): - """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. - """ - _out_channels, _in_channels_per_group, kh, kw = _get_weight_shape(w) - - # Flip weight if requested. - # Note: conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). - if not flip_weight and (kw > 1 or kh > 1): - w = w.flip([2, 3]) - - # Execute using conv2d_gradfix. - op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d - return op(x, w, stride=stride, padding=padding, groups=groups) - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False): - r"""2D convolution with optional up/downsampling. - - Padding is performed only once at the beginning, not between the operations. - - Args: - x: Input tensor of shape - `[batch_size, in_channels, in_height, in_width]`. - w: Weight tensor of shape - `[out_channels, in_channels//groups, kernel_height, kernel_width]`. - f: Low-pass filter for up/downsampling. Must be prepared beforehand by - calling upfirdn2d.setup_filter(). None = identity (default). - up: Integer upsampling factor (default: 1). - down: Integer downsampling factor (default: 1). - padding: Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - groups: Split input channels into N groups (default: 1). - flip_weight: False = convolution, True = correlation (default: True). - flip_filter: False = convolution, True = correlation (default: False). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - # Validate arguments. - assert isinstance(x, torch.Tensor) and (x.ndim == 4) - assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) - assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) - assert isinstance(up, int) and (up >= 1) - assert isinstance(down, int) and (down >= 1) - assert isinstance(groups, int) and (groups >= 1) - out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) - fw, fh = _get_filter_size(f) - px0, px1, py0, py1 = _parse_padding(padding) - - # Adjust padding to account for up/downsampling. - if up > 1: - px0 += (fw + up - 1) // 2 - px1 += (fw - up) // 2 - py0 += (fh + up - 1) // 2 - py1 += (fh - up) // 2 - if down > 1: - px0 += (fw - down + 1) // 2 - px1 += (fw - down) // 2 - py0 += (fh - down + 1) // 2 - py1 += (fh - down) // 2 - - # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. - if kw == 1 and kh == 1 and (down > 1 and up == 1): - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - return x - - # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. - if kw == 1 and kh == 1 and (up > 1 and down == 1): - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) - return x - - # Fast path: downsampling only => use strided convolution. - if down > 1 and up == 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) - x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) - return x - - # Fast path: upsampling with optional downsampling => use transpose strided convolution. - if up > 1: - if groups == 1: - w = w.transpose(0, 1) - else: - w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) - w = w.transpose(1, 2) - w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) - px0 -= kw - 1 - px1 -= kw - up - py0 -= kh - 1 - py1 -= kh - up - pxt = max(min(-px0, -px1), 0) - pyt = max(min(-py0, -py1), 0) - x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) - if down > 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) - return x - - # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. - if up == 1 and down == 1: - if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: - return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight) - - # Fallback: Generic reference implementation. - x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - if down > 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) - return x - -#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/filtered_lrelu.cpp b/torch_utils/ops/filtered_lrelu.cpp deleted file mode 100644 index ff4149b..0000000 --- a/torch_utils/ops/filtered_lrelu.cpp +++ /dev/null @@ -1,300 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include -#include -#include -#include "filtered_lrelu.h" - -//------------------------------------------------------------------------ - -static std::tuple filtered_lrelu( - torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si, - int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns) -{ - // Set CUDA device. - TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); - const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); - - // Validate arguments. - TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device"); - TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32"); - TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype"); - TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32"); - TORCH_CHECK(x.dim() == 4, "x must be rank 4"); - TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large"); - TORCH_CHECK(x.numel() > 0, "x is empty"); - TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2"); - TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large"); - TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large"); - TORCH_CHECK(fu.numel() > 0, "fu is empty"); - TORCH_CHECK(fd.numel() > 0, "fd is empty"); - TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x"); - TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1"); - - // Figure out how much shared memory is available on the device. - int maxSharedBytes = 0; - AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index())); - int sharedKB = maxSharedBytes >> 10; - - // Populate enough launch parameters to check if a CUDA kernel exists. - filtered_lrelu_kernel_params p; - p.up = up; - p.down = down; - p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter. - p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0); - filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel(p, sharedKB); - if (!test_spec.exec) - { - // No kernel found - return empty tensors and indicate missing kernel with return code of -1. - return std::make_tuple(torch::Tensor(), torch::Tensor(), -1); - } - - // Input/output element size. - int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4; - - // Input sizes. - int64_t xw = (int)x.size(3); - int64_t xh = (int)x.size(2); - int64_t fut_w = (int)fu.size(-1) - 1; - int64_t fut_h = (int)fu.size(0) - 1; - int64_t fdt_w = (int)fd.size(-1) - 1; - int64_t fdt_h = (int)fd.size(0) - 1; - - // Logical size of upsampled buffer. - int64_t cw = xw * up + (px0 + px1) - fut_w; - int64_t ch = xh * up + (py0 + py1) - fut_h; - TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter"); - TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large"); - - // Compute output size and allocate. - int64_t yw = (cw - fdt_w + (down - 1)) / down; - int64_t yh = (ch - fdt_h + (down - 1)) / down; - TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1"); - TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large"); - torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format()); - - // Allocate sign tensor. - torch::Tensor so; - torch::Tensor s = si; - bool readSigns = !!s.numel(); - int64_t sw_active = 0; // Active width of sign tensor. - if (writeSigns) - { - sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements. - int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height. - int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16. - TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large"); - s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous); - } - else if (readSigns) - sw_active = s.size(3) << 2; - - // Validate sign tensor if in use. - if (readSigns || writeSigns) - { - TORCH_CHECK(s.is_contiguous(), "signs must be contiguous"); - TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8"); - TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x"); - TORCH_CHECK(s.dim() == 4, "signs must be rank 4"); - TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x"); - TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large"); - } - - // Populate rest of CUDA kernel parameters. - p.x = x.data_ptr(); - p.y = y.data_ptr(); - p.b = b.data_ptr(); - p.s = (readSigns || writeSigns) ? s.data_ptr() : 0; - p.fu = fu.data_ptr(); - p.fd = fd.data_ptr(); - p.pad0 = make_int2(px0, py0); - p.gain = gain; - p.slope = slope; - p.clamp = clamp; - p.flip = (flip_filters) ? 1 : 0; - p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); - p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); - p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous. - p.sOfs = make_int2(sx, sy); - p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes. - - // x, y, b strides are in bytes. - p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0)); - p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0)); - p.bStride = sz * b.stride(0); - - // fu, fd strides are in elements. - p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0); - p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0); - - // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those. - bool index64b = false; - if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true; - if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true; - if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true; - if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true; - if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true; - if (s.numel() > INT_MAX) index64b = true; - - // Choose CUDA kernel. - filtered_lrelu_kernel_spec spec = { 0 }; - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&] - { - if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation. - { - // Choose kernel based on index type, datatype and sign read/write modes. - if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); - else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); - else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); - else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); - else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); - else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); - } - }); - TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists. - - // Launch CUDA kernel. - void* args[] = {&p}; - int bx = spec.numWarps * 32; - int gx = (p.yShape.x - 1) / spec.tileOut.x + 1; - int gy = (p.yShape.y - 1) / spec.tileOut.y + 1; - int gz = p.yShape.z * p.yShape.w; - - // Repeat multiple horizontal tiles in a CTA? - if (spec.xrep) - { - p.tilesXrep = spec.xrep; - p.tilesXdim = gx; - - gx = (gx + p.tilesXrep - 1) / p.tilesXrep; - std::swap(gx, gy); - } - else - { - p.tilesXrep = 0; - p.tilesXdim = 0; - } - - // Launch filter setup kernel. - AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream())); - - // Copy kernels to constant memory. - if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream()))); - else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream()))); - else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream()))); - - // Set cache and shared memory configurations for main kernel. - AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared)); - if (spec.dynamicSharedKB) // Need dynamically allocated shared memory? - AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10)); - AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte)); - - // Launch main kernel. - const int maxSubGz = 65535; // CUDA maximum for block z dimension. - for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big. - { - p.blockZofs = zofs; - int subGz = std::min(maxSubGz, gz - zofs); - AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream())); - } - - // Done. - return std::make_tuple(y, so, 0); -} - -//------------------------------------------------------------------------ - -static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns) -{ - // Set CUDA device. - TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); - const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); - - // Validate arguments. - TORCH_CHECK(x.dim() == 4, "x must be rank 4"); - TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large"); - TORCH_CHECK(x.numel() > 0, "x is empty"); - TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64"); - - // Output signs if we don't have sign input. - torch::Tensor so; - torch::Tensor s = si; - bool readSigns = !!s.numel(); - if (writeSigns) - { - int64_t sw = x.size(3); - sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing. - s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous); - } - - // Validate sign tensor if in use. - if (readSigns || writeSigns) - { - TORCH_CHECK(s.is_contiguous(), "signs must be contiguous"); - TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8"); - TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x"); - TORCH_CHECK(s.dim() == 4, "signs must be rank 4"); - TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x"); - TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large"); - } - - // Initialize CUDA kernel parameters. - filtered_lrelu_act_kernel_params p; - p.x = x.data_ptr(); - p.s = (readSigns || writeSigns) ? s.data_ptr() : 0; - p.gain = gain; - p.slope = slope; - p.clamp = clamp; - p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); - p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0)); - p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous. - p.sOfs = make_int2(sx, sy); - - // Choose CUDA kernel. - void* func = 0; - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&] - { - if (writeSigns) - func = choose_filtered_lrelu_act_kernel(); - else if (readSigns) - func = choose_filtered_lrelu_act_kernel(); - else - func = choose_filtered_lrelu_act_kernel(); - }); - TORCH_CHECK(func, "internal error - CUDA kernel not found"); - - // Launch CUDA kernel. - void* args[] = {&p}; - int bx = 128; // 4 warps per block. - - // Logical size of launch = writeSigns ? p.s : p.x - uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x; - uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y; - uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use. - gx = (gx - 1) / bx + 1; - - // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest. - const uint32_t gmax = 65535; - gy = std::min(gy, gmax); - gz = std::min(gz, gmax); - - // Launch. - AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream())); - return so; -} - -//------------------------------------------------------------------------ - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) -{ - m.def("filtered_lrelu", &filtered_lrelu); // The whole thing. - m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place. -} - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.cu b/torch_utils/ops/filtered_lrelu.cu deleted file mode 100644 index 8e6f47f..0000000 --- a/torch_utils/ops/filtered_lrelu.cu +++ /dev/null @@ -1,1284 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include -#include "filtered_lrelu.h" -#include - -//------------------------------------------------------------------------ -// Helpers. - -enum // Filter modes. -{ - MODE_SUSD = 0, // Separable upsampling, separable downsampling. - MODE_FUSD = 1, // Full upsampling, separable downsampling. - MODE_SUFD = 2, // Separable upsampling, full downsampling. - MODE_FUFD = 3, // Full upsampling, full downsampling. -}; - -template struct InternalType; -template <> struct InternalType -{ - typedef double scalar_t; typedef double2 vec2_t; typedef double4 vec4_t; - __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_double2(0, 0); } - __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_double4(0, 0, 0, 0); } - __device__ __forceinline__ static double clamp(double x, double c) { return fmin(fmax(x, -c), c); } -}; -template <> struct InternalType -{ - typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t; - __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); } - __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); } - __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); } -}; -template <> struct InternalType -{ - typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t; - __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); } - __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); } - __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); } -}; - -#define MIN(A, B) ((A) < (B) ? (A) : (B)) -#define MAX(A, B) ((A) > (B) ? (A) : (B)) -#define CEIL_DIV(A, B) (((B)==1) ? (A) : \ - ((B)==2) ? ((int)((A)+1) >> 1) : \ - ((B)==4) ? ((int)((A)+3) >> 2) : \ - (((A) + ((A) > 0 ? (B) - 1 : 0)) / (B))) - -// This works only up to blocks of size 256 x 256 and for all N that are powers of two. -template __device__ __forceinline__ void fast_div_mod(int& x, int& y, unsigned int i) -{ - if ((N & (N-1)) && N <= 256) - y = (i * ((1<<24)/N + 1)) >> 24; // Assumes N <= 256, i < N*256. - else - y = i/N; - - x = i - y*N; -} - -// Type cast stride before reading it. -template __device__ __forceinline__ T get_stride(const int64_t& x) -{ - return *reinterpret_cast(&x); -} - -//------------------------------------------------------------------------ -// Filters, setup kernel, copying function. - -#define MAX_FILTER_SIZE 32 - -// Combined up/down filter buffers so that transfer can be done with one copy. -__device__ float g_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in global memory, written by setup kernel. -__device__ __constant__ float c_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in constant memory, read by main kernel. - -// Accessors to combined buffers to index up/down filters individually. -#define c_fu (c_fbuf) -#define c_fd (c_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE) -#define g_fu (g_fbuf) -#define g_fd (g_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE) - -// Set up filters into global memory buffer. -static __global__ void setup_filters_kernel(filtered_lrelu_kernel_params p) -{ - for (int idx = threadIdx.x; idx < MAX_FILTER_SIZE * MAX_FILTER_SIZE; idx += blockDim.x) - { - int x, y; - fast_div_mod(x, y, idx); - - int fu_x = p.flip ? x : (p.fuShape.x - 1 - x); - int fu_y = p.flip ? y : (p.fuShape.y - 1 - y); - if (p.fuShape.y > 0) - g_fu[idx] = (x >= p.fuShape.x || y >= p.fuShape.y) ? 0.0f : p.fu[fu_x * p.fuStride.x + fu_y * p.fuStride.y]; - else - g_fu[idx] = (x >= p.fuShape.x || y > 0) ? 0.0f : p.fu[fu_x * p.fuStride.x]; - - int fd_x = p.flip ? x : (p.fdShape.x - 1 - x); - int fd_y = p.flip ? y : (p.fdShape.y - 1 - y); - if (p.fdShape.y > 0) - g_fd[idx] = (x >= p.fdShape.x || y >= p.fdShape.y) ? 0.0f : p.fd[fd_x * p.fdStride.x + fd_y * p.fdStride.y]; - else - g_fd[idx] = (x >= p.fdShape.x || y > 0) ? 0.0f : p.fd[fd_x * p.fdStride.x]; - } -} - -// Host function to copy filters written by setup kernel into constant buffer for main kernel. -template static cudaError_t copy_filters(cudaStream_t stream) -{ - void* src = 0; - cudaError_t err = cudaGetSymbolAddress(&src, g_fbuf); - if (err) return err; - return cudaMemcpyToSymbolAsync(c_fbuf, src, 2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream); -} - -//------------------------------------------------------------------------ -// Coordinate spaces: -// - Relative to input tensor: inX, inY, tileInX, tileInY -// - Relative to input tile: relInX, relInY, tileInW, tileInH -// - Relative to upsampled tile: relUpX, relUpY, tileUpW, tileUpH -// - Relative to output tile: relOutX, relOutY, tileOutW, tileOutH -// - Relative to output tensor: outX, outY, tileOutX, tileOutY -// -// Relationships between coordinate spaces: -// - inX = tileInX + relInX -// - inY = tileInY + relInY -// - relUpX = relInX * up + phaseInX -// - relUpY = relInY * up + phaseInY -// - relUpX = relOutX * down -// - relUpY = relOutY * down -// - outX = tileOutX + relOutX -// - outY = tileOutY + relOutY - -extern __shared__ char s_buf_raw[]; // When sharedKB <= 48, allocate shared memory statically inside the kernel, otherwise use the externally allocated shared memory buffer. - -template -static __global__ void filtered_lrelu_kernel(filtered_lrelu_kernel_params p) -{ - // Check that we don't try to support non-existing filter modes. - static_assert(up == 1 || up == 2 || up == 4, "only up=1, up=2, up=4 scales supported"); - static_assert(down == 1 || down == 2 || down == 4, "only down=1, down=2, down=4 scales supported"); - static_assert(fuSize >= up, "upsampling filter size must be at least upsampling factor"); - static_assert(fdSize >= down, "downsampling filter size must be at least downsampling factor"); - static_assert(fuSize % up == 0, "upsampling filter size must be divisible with upsampling factor"); - static_assert(fdSize % down == 0, "downsampling filter size must be divisible with downsampling factor"); - static_assert(fuSize <= MAX_FILTER_SIZE && fdSize <= MAX_FILTER_SIZE, "filter size greater than MAX_FILTER_SIZE"); - static_assert(up != 1 || (fuSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "up=1 supported only for 1x1 full filters"); - static_assert(down != 1 || (fdSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "down=1 supported only for 1x1 full filters"); - static_assert(!(up == 4 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "full filters not supported for up=4"); - static_assert(!(down == 4 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "full filters not supported for down=4"); - - // Static definitions. - typedef typename InternalType::scalar_t scalar_t; - typedef typename InternalType::vec2_t vec2_t; - typedef typename InternalType::vec4_t vec4_t; - const int tileUpW = (tileOutW * down + (fdSize - 1) - (down - 1) + 3) & ~3; // Upsampled tile width, rounded up to multiple of 4. - const int tileUpH = tileOutH * down + (fdSize - 1) - (down - 1); // Upsampled tile height. - const int tileInW = CEIL_DIV(tileUpW + (fuSize - 1), up); // Input tile width. - const int tileInH = CEIL_DIV(tileUpH + (fuSize - 1), up); // Input tile height. - const int tileUpH_up = CEIL_DIV(tileUpH, up) * up; // Upsampled tile height rounded up to a multiple of up. - const int tileInH_up = CEIL_DIV(tileUpH_up + (fuSize - 1), up); // For allocations only, to avoid shared memory read overruns with up=2 and up=4. - - // Merge 1x1 downsampling into last upsampling step for upf1 and ups2. - const bool downInline = (down == 1) && ((up == 1 && filterMode == MODE_FUFD) || (up == 2 && filterMode == MODE_SUFD)); - - // Sizes of logical buffers. - const int szIn = tileInH_up * tileInW; - const int szUpX = tileInH_up * tileUpW; - const int szUpXY = downInline ? 0 : (tileUpH * tileUpW); - const int szDownX = tileUpH * tileOutW; - - // Sizes for shared memory arrays. - const int s_buf0_size_base = - (filterMode == MODE_SUSD) ? MAX(szIn, szUpXY) : - (filterMode == MODE_FUSD) ? MAX(szIn, szDownX) : - (filterMode == MODE_SUFD) ? MAX(szIn, szUpXY) : - (filterMode == MODE_FUFD) ? szIn : - -1; - const int s_buf1_size_base = - (filterMode == MODE_SUSD) ? MAX(szUpX, szDownX) : - (filterMode == MODE_FUSD) ? szUpXY : - (filterMode == MODE_SUFD) ? szUpX : - (filterMode == MODE_FUFD) ? szUpXY : - -1; - - // Ensure U128 alignment. - const int s_buf0_size = (s_buf0_size_base + 3) & ~3; - const int s_buf1_size = (s_buf1_size_base + 3) & ~3; - - // Check at compile time that we don't use too much shared memory. - static_assert((s_buf0_size + s_buf1_size) * sizeof(scalar_t) <= (sharedKB << 10), "shared memory overflow"); - - // Declare shared memory arrays. - scalar_t* s_buf0; - scalar_t* s_buf1; - if (sharedKB <= 48) - { - // Allocate shared memory arrays here. - __shared__ scalar_t s_buf0_st[(sharedKB > 48) ? (1<<24) : (s_buf0_size + s_buf1_size)]; // Prevent launching if this isn't optimized away when unused. - s_buf0 = s_buf0_st; - s_buf1 = s_buf0 + s_buf0_size; - } - else - { - // Use the dynamically allocated shared memory array. - s_buf0 = (scalar_t*)s_buf_raw; - s_buf1 = s_buf0 + s_buf0_size; - } - - // Pointers to the buffers. - scalar_t* s_tileIn; // Input tile: [relInX * tileInH + relInY] - scalar_t* s_tileUpX; // After horizontal upsampling: [relInY * tileUpW + relUpX] - scalar_t* s_tileUpXY; // After upsampling: [relUpY * tileUpW + relUpX] - scalar_t* s_tileDownX; // After horizontal downsampling: [relUpY * tileOutW + relOutX] - if (filterMode == MODE_SUSD) - { - s_tileIn = s_buf0; - s_tileUpX = s_buf1; - s_tileUpXY = s_buf0; - s_tileDownX = s_buf1; - } - else if (filterMode == MODE_FUSD) - { - s_tileIn = s_buf0; - s_tileUpXY = s_buf1; - s_tileDownX = s_buf0; - } - else if (filterMode == MODE_SUFD) - { - s_tileIn = s_buf0; - s_tileUpX = s_buf1; - s_tileUpXY = s_buf0; - } - else if (filterMode == MODE_FUFD) - { - s_tileIn = s_buf0; - s_tileUpXY = s_buf1; - } - - // Allow large grids in z direction via per-launch offset. - int channelIdx = blockIdx.z + p.blockZofs; - int batchIdx = channelIdx / p.yShape.z; - channelIdx -= batchIdx * p.yShape.z; - - // Offset to output feature map. In bytes. - index_t mapOfsOut = channelIdx * get_stride(p.yStride.z) + batchIdx * get_stride(p.yStride.w); - - // Sign shift amount. - uint32_t signXo = ((threadIdx.x + p.sOfs.x) << 1) & 6; - - // Inner tile loop. - #pragma unroll 1 - for (int tileIdx = 0; !enableXrep || (tileIdx < MIN(p.tilesXrep, p.tilesXdim - p.tilesXrep * blockIdx.y)); tileIdx++) - { - // Locate output tile. - int tileX = enableXrep ? blockIdx.y * p.tilesXrep + tileIdx : blockIdx.x; - int tileOutX = tileX * tileOutW; - int tileOutY = (enableXrep ? blockIdx.x : blockIdx.y) * tileOutH; - - // Locate input tile. - int tmpX = tileOutX * down - p.pad0.x; - int tmpY = tileOutY * down - p.pad0.y; - int tileInX = CEIL_DIV(tmpX, up); - int tileInY = CEIL_DIV(tmpY, up); - const int phaseInX = tileInX * up - tmpX; - const int phaseInY = tileInY * up - tmpY; - - // Extra sync if input and output buffers are the same and we are not on first tile. - if (enableXrep && tileIdx > 0 && (filterMode == MODE_FUSD || (filterMode == MODE_SUFD && !downInline) || (filterMode == MODE_FUFD && downInline))) - __syncthreads(); - - // Load input tile & apply bias. Unrolled. - scalar_t b = (scalar_t)*(const T*)((const char*)p.b + (channelIdx * get_stride(p.bStride))); - index_t mapOfsIn = channelIdx * get_stride(p.xStride.z) + batchIdx * get_stride(p.xStride.w); - int idx = threadIdx.x; - const int loopCountIN = CEIL_DIV(tileInW * tileInH, threadsPerBlock); - #pragma unroll - for (int loop = 0; loop < loopCountIN; loop++) - { - int relInX, relInY; - fast_div_mod(relInX, relInY, idx); - int inX = tileInX + relInX; - int inY = tileInY + relInY; - scalar_t v = 0; - - if ((uint32_t)inX < p.xShape.x && (uint32_t)inY < p.xShape.y) - v = (scalar_t)*((const T*)((const char*)p.x + (inX * get_stride(p.xStride.x) + inY * get_stride(p.xStride.y) + mapOfsIn))) + b; - - bool skip = (loop == loopCountIN-1) && (idx >= tileInW * tileInH); - if (!skip) - s_tileIn[idx] = v; - - idx += threadsPerBlock; - } - - if (filterMode == MODE_SUSD || filterMode == MODE_SUFD) // Separable upsampling filter. - { - // Horizontal upsampling. - __syncthreads(); - if (up == 4) - { - for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up) - { - int relUpX0, relInY; - fast_div_mod(relUpX0, relInY, idx); - int relInX0 = relUpX0 / up; - int src0 = relInX0 + tileInW * relInY; - int dst = relInY * tileUpW + relUpX0; - vec4_t v = InternalType::zero_vec4(); - scalar_t a = s_tileIn[src0]; - if (phaseInX == 0) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileIn[src0 + step + 1]; - v.y += a * (scalar_t)c_fu[step * up + 3]; - v.z += a * (scalar_t)c_fu[step * up + 2]; - v.w += a * (scalar_t)c_fu[step * up + 1]; - } - } - else if (phaseInX == 1) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 1]; - v.y += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileIn[src0 + step + 1]; - v.z += a * (scalar_t)c_fu[step * up + 3]; - v.w += a * (scalar_t)c_fu[step * up + 2]; - } - } - else if (phaseInX == 2) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 2]; - v.y += a * (scalar_t)c_fu[step * up + 1]; - v.z += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileIn[src0 + step + 1]; - v.w += a * (scalar_t)c_fu[step * up + 3]; - } - } - else // (phaseInX == 3) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 3]; - v.y += a * (scalar_t)c_fu[step * up + 2]; - v.z += a * (scalar_t)c_fu[step * up + 1]; - v.w += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileIn[src0 + step + 1]; - } - } - s_tileUpX[dst+0] = v.x; - s_tileUpX[dst+1] = v.y; - s_tileUpX[dst+2] = v.z; - s_tileUpX[dst+3] = v.w; - } - } - else if (up == 2) - { - bool p0 = (phaseInX == 0); - for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up) - { - int relUpX0, relInY; - fast_div_mod(relUpX0, relInY, idx); - int relInX0 = relUpX0 / up; - int src0 = relInX0 + tileInW * relInY; - int dst = relInY * tileUpW + relUpX0; - vec2_t v = InternalType::zero_vec2(); - scalar_t a = s_tileIn[src0]; - if (p0) // (phaseInX == 0) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileIn[src0 + step + 1]; - v.y += a * (scalar_t)c_fu[step * up + 1]; - } - } - else // (phaseInX == 1) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 1]; - v.y += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileIn[src0 + step + 1]; - } - } - s_tileUpX[dst+0] = v.x; - s_tileUpX[dst+1] = v.y; - } - } - - // Vertical upsampling & nonlinearity. - - __syncthreads(); - int groupMask = 15 << ((threadIdx.x & 31) & ~3); - int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs. - int sShapeMaxY = MIN(p.sShape.y, tileOutY * down + tileUpH); // Avoid out-of-tile sign writes. - if (up == 4) - { - minY -= 3; // Adjust according to block height. - for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x) - { - int relUpX, relInY0; - fast_div_mod(relUpX, relInY0, idx); - int relUpY0 = relInY0 * up; - int src0 = relInY0 * tileUpW + relUpX; - int dst = relUpY0 * tileUpW + relUpX; - vec4_t v = InternalType::zero_vec4(); - - scalar_t a = s_tileUpX[src0]; - if (phaseInY == 0) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileUpX[src0 + (step + 1) * tileUpW]; - v.y += a * (scalar_t)c_fu[step * up + 3]; - v.z += a * (scalar_t)c_fu[step * up + 2]; - v.w += a * (scalar_t)c_fu[step * up + 1]; - } - } - else if (phaseInY == 1) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 1]; - v.y += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileUpX[src0 + (step + 1) * tileUpW]; - v.z += a * (scalar_t)c_fu[step * up + 3]; - v.w += a * (scalar_t)c_fu[step * up + 2]; - } - } - else if (phaseInY == 2) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 2]; - v.y += a * (scalar_t)c_fu[step * up + 1]; - v.z += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileUpX[src0 + (step + 1) * tileUpW]; - v.w += a * (scalar_t)c_fu[step * up + 3]; - } - } - else // (phaseInY == 3) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 3]; - v.y += a * (scalar_t)c_fu[step * up + 2]; - v.z += a * (scalar_t)c_fu[step * up + 1]; - v.w += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileUpX[src0 + (step + 1) * tileUpW]; - } - } - - int x = tileOutX * down + relUpX; - int y = tileOutY * down + relUpY0; - int signX = x + p.sOfs.x; - int signY = y + p.sOfs.y; - int signZ = blockIdx.z + p.blockZofs; - int signXb = signX >> 2; - index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); - index_t si1 = si0 + p.sShape.x; - index_t si2 = si0 + p.sShape.x * 2; - index_t si3 = si0 + p.sShape.x * 3; - - v.x *= (scalar_t)((float)up * (float)up * p.gain); - v.y *= (scalar_t)((float)up * (float)up * p.gain); - v.z *= (scalar_t)((float)up * (float)up * p.gain); - v.w *= (scalar_t)((float)up * (float)up * p.gain); - - if (signWrite) - { - if (!enableWriteSkip) - { - // Determine and write signs. - int sx = __float_as_uint(v.x) >> 31 << 0; - int sy = __float_as_uint(v.y) >> 31 << 8; - int sz = __float_as_uint(v.z) >> 31 << 16; - int sw = __float_as_uint(v.w) >> 31 << 24; - if (sx) v.x *= p.slope; - if (sy) v.y *= p.slope; - if (sz) v.z *= p.slope; - if (sw) v.w *= p.slope; - if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } - if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } - if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType::clamp(v.z, p.clamp); } - if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType::clamp(v.w, p.clamp); } - - if ((uint32_t)signXb < p.swLimit && signY >= minY) - { - // Combine signs. - uint32_t s = sx + sy + sw + sz; - s <<= (signX & 3) << 1; - s |= __shfl_xor_sync(groupMask, s, 1); - s |= __shfl_xor_sync(groupMask, s, 2); - - // Write signs. - if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } - if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } - if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); } - if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); } - } - } - else - { - // Determine and write signs. - if ((uint32_t)signXb < p.swLimit && signY >= minY) - { - int sx = __float_as_uint(v.x) >> 31 << 0; - int sy = __float_as_uint(v.y) >> 31 << 8; - int sz = __float_as_uint(v.z) >> 31 << 16; - int sw = __float_as_uint(v.w) >> 31 << 24; - if (sx) v.x *= p.slope; - if (sy) v.y *= p.slope; - if (sz) v.z *= p.slope; - if (sw) v.w *= p.slope; - if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } - if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } - if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType::clamp(v.z, p.clamp); } - if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType::clamp(v.w, p.clamp); } - - // Combine signs. - uint32_t s = sx + sy + sw + sz; - s <<= (signX & 3) << 1; - s |= __shfl_xor_sync(groupMask, s, 1); - s |= __shfl_xor_sync(groupMask, s, 2); - - // Write signs. - if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } - if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } - if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); } - if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); } - } - else - { - // Just compute the values. - if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); - if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); - if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); - if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); - } - } - } - else if (signRead) // Read signs and apply. - { - if ((uint32_t)signXb < p.swLimit) - { - int ss = (signX & 3) << 1; - if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> ss; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; } - if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> ss; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; } - if ((uint32_t)(signY + 2) < p.sShape.y) { int s = p.s[si2] >> ss; if (s & 1) v.z *= p.slope; if (s & 2) v.z = 0.f; } - if ((uint32_t)(signY + 3) < p.sShape.y) { int s = p.s[si3] >> ss; if (s & 1) v.w *= p.slope; if (s & 2) v.w = 0.f; } - } - } - else // Forward pass with no sign write. - { - if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); - if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); - if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); - if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); - } - - s_tileUpXY[dst + 0 * tileUpW] = v.x; - if (relUpY0 + 1 < tileUpH) s_tileUpXY[dst + 1 * tileUpW] = v.y; - if (relUpY0 + 2 < tileUpH) s_tileUpXY[dst + 2 * tileUpW] = v.z; - if (relUpY0 + 3 < tileUpH) s_tileUpXY[dst + 3 * tileUpW] = v.w; - } - } - else if (up == 2) - { - minY -= 1; // Adjust according to block height. - for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x) - { - int relUpX, relInY0; - fast_div_mod(relUpX, relInY0, idx); - int relUpY0 = relInY0 * up; - int src0 = relInY0 * tileUpW + relUpX; - int dst = relUpY0 * tileUpW + relUpX; - vec2_t v = InternalType::zero_vec2(); - - scalar_t a = s_tileUpX[src0]; - if (phaseInY == 0) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileUpX[src0 + (step + 1) * tileUpW]; - v.y += a * (scalar_t)c_fu[step * up + 1]; - } - } - else // (phaseInY == 1) - { - #pragma unroll - for (int step = 0; step < fuSize / up; step++) - { - v.x += a * (scalar_t)c_fu[step * up + 1]; - v.y += a * (scalar_t)c_fu[step * up + 0]; - a = s_tileUpX[src0 + (step + 1) * tileUpW]; - } - } - - int x = tileOutX * down + relUpX; - int y = tileOutY * down + relUpY0; - int signX = x + p.sOfs.x; - int signY = y + p.sOfs.y; - int signZ = blockIdx.z + p.blockZofs; - int signXb = signX >> 2; - index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); - index_t si1 = si0 + p.sShape.x; - - v.x *= (scalar_t)((float)up * (float)up * p.gain); - v.y *= (scalar_t)((float)up * (float)up * p.gain); - - if (signWrite) - { - if (!enableWriteSkip) - { - // Determine and write signs. - int sx = __float_as_uint(v.x) >> 31 << 0; - int sy = __float_as_uint(v.y) >> 31 << 8; - if (sx) v.x *= p.slope; - if (sy) v.y *= p.slope; - if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } - if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } - - if ((uint32_t)signXb < p.swLimit && signY >= minY) - { - // Combine signs. - int s = sx + sy; - s <<= signXo; - s |= __shfl_xor_sync(groupMask, s, 1); - s |= __shfl_xor_sync(groupMask, s, 2); - - // Write signs. - if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } - if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } - } - } - else - { - // Determine and write signs. - if ((uint32_t)signXb < p.swLimit && signY >= minY) - { - int sx = __float_as_uint(v.x) >> 31 << 0; - int sy = __float_as_uint(v.y) >> 31 << 8; - if (sx) v.x *= p.slope; - if (sy) v.y *= p.slope; - if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } - if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } - - // Combine signs. - int s = sx + sy; - s <<= signXo; - s |= __shfl_xor_sync(groupMask, s, 1); - s |= __shfl_xor_sync(groupMask, s, 2); - - // Write signs. - if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } - if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } - } - else - { - // Just compute the values. - if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); - if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); - } - } - } - else if (signRead) // Read signs and apply. - { - if ((uint32_t)signXb < p.swLimit) - { - if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> signXo; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; } - if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> signXo; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; } - } - } - else // Forward pass with no sign write. - { - if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); - if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); - } - - if (!downInline) - { - // Write into temporary buffer. - s_tileUpXY[dst] = v.x; - if (relUpY0 < tileUpH - 1) - s_tileUpXY[dst + tileUpW] = v.y; - } - else - { - // Write directly into output buffer. - if ((uint32_t)x < p.yShape.x) - { - int ymax = MIN(p.yShape.y, tileUpH + tileOutY * down); - index_t ofs = x * get_stride(p.yStride.x) + y * get_stride(p.yStride.y) + mapOfsOut; - if ((uint32_t)y + 0 < p.yShape.y) *((T*)((char*)p.y + ofs)) = (T)(v.x * (scalar_t)c_fd[0]); - if ((uint32_t)y + 1 < ymax) *((T*)((char*)p.y + ofs + get_stride(p.yStride.y))) = (T)(v.y * (scalar_t)c_fd[0]); - } - } - } - } - } - else if (filterMode == MODE_FUSD || filterMode == MODE_FUFD) - { - // Full upsampling filter. - - if (up == 2) - { - // 2 x 2-wide. - __syncthreads(); - int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH + p.sOfs.y : 0; // Skip already written signs. - for (int idx = threadIdx.x * 4; idx < tileUpW * tileUpH; idx += blockDim.x * 4) - { - int relUpX0, relUpY0; - fast_div_mod(relUpX0, relUpY0, idx); - int relInX0 = CEIL_DIV(relUpX0 - phaseInX, up); - int relInY0 = CEIL_DIV(relUpY0 - phaseInY, up); - int src0 = relInX0 + tileInW * relInY0; - int tap0y = (relInY0 * up + phaseInY - relUpY0); - - #define X_LOOP(TAPY, PX) \ - for (int sx = 0; sx < fuSize / up; sx++) \ - { \ - v.x += a * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \ - v.z += b * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 0) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \ - v.y += a * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \ - v.w += b * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 1) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \ - } - - vec4_t v = InternalType::zero_vec4(); - if (tap0y == 0 && phaseInX == 0) - #pragma unroll - for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; - #pragma unroll - X_LOOP(0, 0) } - if (tap0y == 0 && phaseInX == 1) - #pragma unroll - for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; - #pragma unroll - X_LOOP(0, 1) } - if (tap0y == 1 && phaseInX == 0) - #pragma unroll - for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; - #pragma unroll - X_LOOP(1, 0) } - if (tap0y == 1 && phaseInX == 1) - #pragma unroll - for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; - #pragma unroll - X_LOOP(1, 1) } - - #undef X_LOOP - - int x = tileOutX * down + relUpX0; - int y = tileOutY * down + relUpY0; - int signX = x + p.sOfs.x; - int signY = y + p.sOfs.y; - int signZ = blockIdx.z + p.blockZofs; - int signXb = signX >> 2; - index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); - - v.x *= (scalar_t)((float)up * (float)up * p.gain); - v.y *= (scalar_t)((float)up * (float)up * p.gain); - v.z *= (scalar_t)((float)up * (float)up * p.gain); - v.w *= (scalar_t)((float)up * (float)up * p.gain); - - if (signWrite) - { - if (!enableWriteSkip) - { - // Determine and write signs. - int sx = __float_as_uint(v.x) >> 31; - int sy = __float_as_uint(v.y) >> 31; - int sz = __float_as_uint(v.z) >> 31; - int sw = __float_as_uint(v.w) >> 31; - if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType::clamp(v.x, p.clamp); } - if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType::clamp(v.y, p.clamp); } - if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType::clamp(v.z, p.clamp); } - if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType::clamp(v.w, p.clamp); } - - if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) - { - p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6); - } - } - else - { - // Determine and write signs. - if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) - { - int sx = __float_as_uint(v.x) >> 31; - int sy = __float_as_uint(v.y) >> 31; - int sz = __float_as_uint(v.z) >> 31; - int sw = __float_as_uint(v.w) >> 31; - if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType::clamp(v.x, p.clamp); } - if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType::clamp(v.y, p.clamp); } - if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType::clamp(v.z, p.clamp); } - if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType::clamp(v.w, p.clamp); } - - p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6); - } - else - { - // Just compute the values. - if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); - if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); - if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); - if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); - } - } - } - else if (signRead) // Read sign and apply. - { - if ((uint32_t)signY < p.sShape.y) - { - int s = 0; - if ((uint32_t)signXb < p.swLimit) s = p.s[si]; - if ((uint32_t)signXb + 1 < p.swLimit) s |= p.s[si + 1] << 8; - s >>= (signX & 3) << 1; - if (s & 0x01) v.x *= p.slope; if (s & 0x02) v.x = 0.f; - if (s & 0x04) v.y *= p.slope; if (s & 0x08) v.y = 0.f; - if (s & 0x10) v.z *= p.slope; if (s & 0x20) v.z = 0.f; - if (s & 0x40) v.w *= p.slope; if (s & 0x80) v.w = 0.f; - } - } - else // Forward pass with no sign write. - { - if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); - if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); - if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); - if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); - } - - s_tileUpXY[idx + 0] = v.x; - s_tileUpXY[idx + 1] = v.y; - s_tileUpXY[idx + 2] = v.z; - s_tileUpXY[idx + 3] = v.w; - } - } - else if (up == 1) - { - __syncthreads(); - uint32_t groupMask = 15 << ((threadIdx.x & 31) & ~3); - int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs. - for (int idx = threadIdx.x; idx < tileUpW * tileUpH; idx += blockDim.x) - { - int relUpX0, relUpY0; - fast_div_mod(relUpX0, relUpY0, idx); - scalar_t v = s_tileIn[idx] * (scalar_t)c_fu[0]; // 1x1 filter. - - int x = tileOutX * down + relUpX0; - int y = tileOutY * down + relUpY0; - int signX = x + p.sOfs.x; - int signY = y + p.sOfs.y; - int signZ = blockIdx.z + p.blockZofs; - int signXb = signX >> 2; - index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); - v *= (scalar_t)((float)up * (float)up * p.gain); - - if (signWrite) - { - if (!enableWriteSkip) - { - // Determine and write sign. - uint32_t s = 0; - uint32_t signXbit = (1u << signXo); - if (v < 0.f) - { - s = signXbit; - v *= p.slope; - } - if (fabsf(v) > p.clamp) - { - s = signXbit * 2; - v = InternalType::clamp(v, p.clamp); - } - if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) - { - s += __shfl_xor_sync(groupMask, s, 1); // Coalesce. - s += __shfl_xor_sync(groupMask, s, 2); // Coalesce. - p.s[si] = s; // Write. - } - } - else - { - // Determine and write sign. - if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) - { - uint32_t s = 0; - uint32_t signXbit = (1u << signXo); - if (v < 0.f) - { - s = signXbit; - v *= p.slope; - } - if (fabsf(v) > p.clamp) - { - s = signXbit * 2; - v = InternalType::clamp(v, p.clamp); - } - s += __shfl_xor_sync(groupMask, s, 1); // Coalesce. - s += __shfl_xor_sync(groupMask, s, 2); // Coalesce. - p.s[si] = s; // Write. - } - else - { - // Just compute the value. - if (v < 0.f) v *= p.slope; - v = InternalType::clamp(v, p.clamp); - } - } - } - else if (signRead) - { - // Read sign and apply if within sign tensor bounds. - if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y) - { - int s = p.s[si]; - s >>= signXo; - if (s & 1) v *= p.slope; - if (s & 2) v = 0.f; - } - } - else // Forward pass with no sign write. - { - if (v < 0.f) v *= p.slope; - v = InternalType::clamp(v, p.clamp); - } - - if (!downInline) // Write into temporary buffer. - s_tileUpXY[idx] = v; - else if ((uint32_t)x < p.yShape.x && (uint32_t)y < p.yShape.y) // Write directly into output buffer - *((T*)((char*)p.y + (x * get_stride(p.yStride.x) + y * get_stride(p.yStride.y) + mapOfsOut))) = (T)(v * (scalar_t)c_fd[0]); - } - } - } - - // Downsampling. - if (filterMode == MODE_SUSD || filterMode == MODE_FUSD) - { - // Horizontal downsampling. - __syncthreads(); - if (down == 4 && tileOutW % 4 == 0) - { - // Calculate 4 pixels at a time. - for (int idx = threadIdx.x * 4; idx < tileOutW * tileUpH; idx += blockDim.x * 4) - { - int relOutX0, relUpY; - fast_div_mod(relOutX0, relUpY, idx); - int relUpX0 = relOutX0 * down; - int src0 = relUpY * tileUpW + relUpX0; - vec4_t v = InternalType::zero_vec4(); - #pragma unroll - for (int step = 0; step < fdSize; step++) - { - v.x += s_tileUpXY[src0 + 0 + step] * (scalar_t)c_fd[step]; - v.y += s_tileUpXY[src0 + 4 + step] * (scalar_t)c_fd[step]; - v.z += s_tileUpXY[src0 + 8 + step] * (scalar_t)c_fd[step]; - v.w += s_tileUpXY[src0 + 12 + step] * (scalar_t)c_fd[step]; - } - s_tileDownX[idx+0] = v.x; - s_tileDownX[idx+1] = v.y; - s_tileDownX[idx+2] = v.z; - s_tileDownX[idx+3] = v.w; - } - } - else if ((down == 2 || down == 4) && (tileOutW % 2 == 0)) - { - // Calculate 2 pixels at a time. - for (int idx = threadIdx.x * 2; idx < tileOutW * tileUpH; idx += blockDim.x * 2) - { - int relOutX0, relUpY; - fast_div_mod(relOutX0, relUpY, idx); - int relUpX0 = relOutX0 * down; - int src0 = relUpY * tileUpW + relUpX0; - vec2_t v = InternalType::zero_vec2(); - #pragma unroll - for (int step = 0; step < fdSize; step++) - { - v.x += s_tileUpXY[src0 + 0 + step] * (scalar_t)c_fd[step]; - v.y += s_tileUpXY[src0 + down + step] * (scalar_t)c_fd[step]; - } - s_tileDownX[idx+0] = v.x; - s_tileDownX[idx+1] = v.y; - } - } - else - { - // Calculate 1 pixel at a time. - for (int idx = threadIdx.x; idx < tileOutW * tileUpH; idx += blockDim.x) - { - int relOutX0, relUpY; - fast_div_mod(relOutX0, relUpY, idx); - int relUpX0 = relOutX0 * down; - int src = relUpY * tileUpW + relUpX0; - scalar_t v = 0.f; - #pragma unroll - for (int step = 0; step < fdSize; step++) - v += s_tileUpXY[src + step] * (scalar_t)c_fd[step]; - s_tileDownX[idx] = v; - } - } - - // Vertical downsampling & store output tile. - __syncthreads(); - for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x) - { - int relOutX, relOutY0; - fast_div_mod(relOutX, relOutY0, idx); - int relUpY0 = relOutY0 * down; - int src0 = relUpY0 * tileOutW + relOutX; - scalar_t v = 0; - #pragma unroll - for (int step = 0; step < fdSize; step++) - v += s_tileDownX[src0 + step * tileOutW] * (scalar_t)c_fd[step]; - - int outX = tileOutX + relOutX; - int outY = tileOutY + relOutY0; - - if (outX < p.yShape.x & outY < p.yShape.y) - *((T*)((char*)p.y + (outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut))) = (T)v; - } - } - else if (filterMode == MODE_SUFD || filterMode == MODE_FUFD) - { - // Full downsampling filter. - if (down == 2) - { - // 2-wide. - __syncthreads(); - for (int idx = threadIdx.x * 2; idx < tileOutW * tileOutH; idx += blockDim.x * 2) - { - int relOutX0, relOutY0; - fast_div_mod(relOutX0, relOutY0, idx); - int relUpX0 = relOutX0 * down; - int relUpY0 = relOutY0 * down; - int src0 = relUpY0 * tileUpW + relUpX0; - vec2_t v = InternalType::zero_vec2(); - #pragma unroll - for (int sy = 0; sy < fdSize; sy++) - #pragma unroll - for (int sx = 0; sx < fdSize; sx++) - { - v.x += s_tileUpXY[src0 + 0 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE]; - v.y += s_tileUpXY[src0 + 2 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE]; - } - - int outX = tileOutX + relOutX0; - int outY = tileOutY + relOutY0; - if ((uint32_t)outY < p.yShape.y) - { - index_t ofs = outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut; - if (outX + 0 < p.yShape.x) *((T*)((char*)p.y + ofs)) = (T)v.x; - if (outX + 1 < p.yShape.x) *((T*)((char*)p.y + ofs + get_stride(p.yStride.x))) = (T)v.y; - } - } - } - else if (down == 1 && !downInline) - { - // Thread per pixel. - __syncthreads(); - for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x) - { - int relOutX0, relOutY0; - fast_div_mod(relOutX0, relOutY0, idx); - scalar_t v = s_tileUpXY[idx] * (scalar_t)c_fd[0]; // 1x1 filter. - - int outX = tileOutX + relOutX0; - int outY = tileOutY + relOutY0; - if ((uint32_t)outX < p.yShape.x && (uint32_t)outY < p.yShape.y) - *((T*)((char*)p.y + (outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut))) = (T)v; - } - } - } - - if (!enableXrep) - break; - } -} - -//------------------------------------------------------------------------ -// Compute activation function and signs for upsampled data tensor, modifying data tensor in-place. Used for accelerating the generic variant. -// Sign tensor is known to be contiguous, and p.x and p.s have the same z, w dimensions. 64-bit indexing is always used. - -template -static __global__ void filtered_lrelu_act_kernel(filtered_lrelu_act_kernel_params p) -{ - typedef typename InternalType::scalar_t scalar_t; - - // Indexing. - int32_t x = threadIdx.x + blockIdx.x * blockDim.x; - int32_t ymax = signWrite ? p.sShape.y : p.xShape.y; - int32_t qmax = p.xShape.z * p.xShape.w; // Combined minibatch*channel maximum index. - - // Loop to accommodate oversized tensors. - for (int32_t q = blockIdx.z; q < qmax; q += gridDim.z) - for (int32_t y = blockIdx.y; y < ymax; y += gridDim.y) - { - // Extract z and w (channel, minibatch index). - int32_t w = q / p.xShape.z; - int32_t z = q - w * p.xShape.z; - - // Choose behavior based on sign read/write mode. - if (signWrite) - { - // Process value if in p.x. - uint32_t s = 0; - if (x < p.xShape.x && y < p.xShape.y) - { - int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; - T* pv = ((T*)p.x) + ix; - scalar_t v = (scalar_t)(*pv); - - // Gain, LReLU, clamp. - v *= p.gain; - if (v < 0.f) - { - v *= p.slope; - s = 1; // Sign. - } - if (fabsf(v) > p.clamp) - { - v = InternalType::clamp(v, p.clamp); - s = 2; // Clamp. - } - - *pv = (T)v; // Write value. - } - - // Coalesce into threads 0 and 16 of warp. - uint32_t m = (threadIdx.x & 16) ? 0xffff0000u : 0x0000ffffu; - s <<= ((threadIdx.x & 15) << 1); // Shift into place. - s |= __shfl_xor_sync(m, s, 1); // Distribute. - s |= __shfl_xor_sync(m, s, 2); - s |= __shfl_xor_sync(m, s, 4); - s |= __shfl_xor_sync(m, s, 8); - - // Write signs if leader and in p.s. - if (!(threadIdx.x & 15) && x < p.sShape.x) // y is always in. - { - uint64_t is = x + p.sShape.x * (y + (int64_t)p.sShape.y * q); // Contiguous. - ((uint32_t*)p.s)[is >> 4] = s; - } - } - else if (signRead) - { - // Process value if in p.x. - if (x < p.xShape.x) // y is always in. - { - int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; - T* pv = ((T*)p.x) + ix; - scalar_t v = (scalar_t)(*pv); - v *= p.gain; - - // Apply sign buffer offset. - uint32_t sx = x + p.sOfs.x; - uint32_t sy = y + p.sOfs.y; - - // Read and apply signs if we land inside valid region of sign buffer. - if (sx < p.sShape.x && sy < p.sShape.y) - { - uint64_t is = (sx >> 2) + (p.sShape.x >> 2) * (sy + (uint64_t)p.sShape.y * q); // Contiguous. - unsigned char s = p.s[is]; - s >>= (sx & 3) << 1; // Shift into place. - if (s & 1) // Sign? - v *= p.slope; - if (s & 2) // Clamp? - v = 0.f; - } - - *pv = (T)v; // Write value. - } - } - else - { - // Forward pass with no sign write. Process value if in p.x. - if (x < p.xShape.x) // y is always in. - { - int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; - T* pv = ((T*)p.x) + ix; - scalar_t v = (scalar_t)(*pv); - v *= p.gain; - if (v < 0.f) - v *= p.slope; - if (fabsf(v) > p.clamp) - v = InternalType::clamp(v, p.clamp); - *pv = (T)v; // Write value. - } - } - } -} - -template void* choose_filtered_lrelu_act_kernel(void) -{ - return (void*)filtered_lrelu_act_kernel; -} - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB) -{ - filtered_lrelu_kernel_spec s = { 0 }; - - // Return the first matching kernel. -#define CASE(SH, U, FU, D, FD, MODE, TW, TH, W, XR, WS) \ - if (sharedKB >= SH) \ - if ((p.fuShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_SUFD)) || (p.fuShape.y > 0 && (MODE == MODE_FUSD || MODE == MODE_FUFD))) \ - if ((p.fdShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_FUSD)) || (p.fdShape.y > 0 && (MODE == MODE_SUFD || MODE == MODE_FUFD))) \ - if (p.up == U && p.fuShape.x <= FU && p.fuShape.y <= FU && p.down == D && p.fdShape.x <= FD && p.fdShape.y <= FD) \ - { \ - static_assert((D*TW % 4) == 0, "down * tileWidth must be divisible by 4"); \ - static_assert(FU % U == 0, "upscaling filter size must be multiple of upscaling factor"); \ - static_assert(FD % D == 0, "downscaling filter size must be multiple of downscaling factor"); \ - s.setup = (void*)setup_filters_kernel; \ - s.exec = (void*)filtered_lrelu_kernel; \ - s.tileOut = make_int2(TW, TH); \ - s.numWarps = W; \ - s.xrep = XR; \ - s.dynamicSharedKB = (SH == 48) ? 0 : SH; \ - return s; \ - } - - // Launch parameters for various kernel specializations. - // Small filters must be listed before large filters, otherwise the kernel for larger filter will always match first. - // Kernels that use more shared memory must be listed before those that use less, for the same reason. - - CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/1,1, /*mode*/MODE_FUFD, /*tw,th,warps,xrep,wskip*/64, 178, 32, 0, 0) // 1t-upf1-downf1 - CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/152, 95, 16, 0, 0) // 4t-ups2-downf1 - CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,8, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56, 22, 16, 0, 0) // 4t-upf1-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56, 29, 16, 11, 0) // 4t-ups2-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/60, 28, 16, 0, 0) // 4t-upf2-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56, 28, 16, 0, 0) // 4t-ups2-downf2 - CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56, 31, 16, 11, 0) // 4t-ups4-downs2 - CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56, 36, 16, 0, 0) // 4t-ups4-downf2 - CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/4,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 22, 16, 12, 0) // 4t-ups2-downs4 - CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/4,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/29, 15, 16, 0, 0) // 4t-upf2-downs4 - CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/96, 150, 28, 0, 0) // 6t-ups2-downf1 - CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32, 35, 24, 0, 0) // 6t-upf1-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 46, 16, 10, 0) // 6t-ups2-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/58, 28, 24, 8, 0) // 6t-upf2-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/52, 28, 16, 0, 0) // 6t-ups2-downf2 - CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 51, 16, 5, 0) // 6t-ups4-downs2 - CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 56, 16, 6, 0) // 6t-ups4-downf2 - CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 18, 16, 12, 0) // 6t-ups2-downs4 - CASE(/*sharedKB*/96, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27, 31, 32, 6, 0) // 6t-upf2-downs4 96kB - CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27, 13, 24, 0, 0) // 6t-upf2-downs4 - CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/148, 89, 24, 0, 0) // 8t-ups2-downf1 - CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32, 31, 16, 5, 0) // 8t-upf1-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 41, 16, 9, 0) // 8t-ups2-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56, 26, 24, 0, 0) // 8t-upf2-downs2 - CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 40, 16, 0, 0) // 8t-ups2-downf2 - CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 46, 24, 5, 0) // 8t-ups4-downs2 - CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 50, 16, 0, 0) // 8t-ups4-downf2 - CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/24, 24, 32, 12, 1) // 8t-ups2-downs4 96kB - CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 13, 16, 10, 1) // 8t-ups2-downs4 - CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25, 28, 28, 4, 0) // 8t-upf2-downs4 96kB - CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25, 10, 24, 0, 0) // 8t-upf2-downs4 - - #undef CASE - return s; // No kernel found. -} - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.h b/torch_utils/ops/filtered_lrelu.h deleted file mode 100644 index 2c403e3..0000000 --- a/torch_utils/ops/filtered_lrelu.h +++ /dev/null @@ -1,90 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct filtered_lrelu_kernel_params -{ - // These parameters decide which kernel to use. - int up; // upsampling ratio (1, 2, 4) - int down; // downsampling ratio (1, 2, 4) - int2 fuShape; // [size, 1] | [size, size] - int2 fdShape; // [size, 1] | [size, size] - - int _dummy; // Alignment. - - // Rest of the parameters. - const void* x; // Input tensor. - void* y; // Output tensor. - const void* b; // Bias tensor. - unsigned char* s; // Sign tensor in/out. NULL if unused. - const float* fu; // Upsampling filter. - const float* fd; // Downsampling filter. - - int2 pad0; // Left/top padding. - float gain; // Additional gain factor. - float slope; // Leaky ReLU slope on negative side. - float clamp; // Clamp after nonlinearity. - int flip; // Filter kernel flip for gradient computation. - - int tilesXdim; // Original number of horizontal output tiles. - int tilesXrep; // Number of horizontal tiles per CTA. - int blockZofs; // Block z offset to support large minibatch, channel dimensions. - - int4 xShape; // [width, height, channel, batch] - int4 yShape; // [width, height, channel, batch] - int2 sShape; // [width, height] - width is in bytes. Contiguous. Zeros if unused. - int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. - int swLimit; // Active width of sign tensor in bytes. - - longlong4 xStride; // Strides of all tensors except signs, same component order as shapes. - longlong4 yStride; // - int64_t bStride; // - longlong3 fuStride; // - longlong3 fdStride; // -}; - -struct filtered_lrelu_act_kernel_params -{ - void* x; // Input/output, modified in-place. - unsigned char* s; // Sign tensor in/out. NULL if unused. - - float gain; // Additional gain factor. - float slope; // Leaky ReLU slope on negative side. - float clamp; // Clamp after nonlinearity. - - int4 xShape; // [width, height, channel, batch] - longlong4 xStride; // Input/output tensor strides, same order as in shape. - int2 sShape; // [width, height] - width is in elements. Contiguous. Zeros if unused. - int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. -}; - -//------------------------------------------------------------------------ -// CUDA kernel specialization. - -struct filtered_lrelu_kernel_spec -{ - void* setup; // Function for filter kernel setup. - void* exec; // Function for main operation. - int2 tileOut; // Width/height of launch tile. - int numWarps; // Number of warps per thread block, determines launch block size. - int xrep; // For processing multiple horizontal tiles per thread block. - int dynamicSharedKB; // How much dynamic shared memory the exec kernel wants. -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template void* choose_filtered_lrelu_act_kernel(void); -template cudaError_t copy_filters(cudaStream_t stream); - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.py b/torch_utils/ops/filtered_lrelu.py deleted file mode 100644 index b606f7f..0000000 --- a/torch_utils/ops/filtered_lrelu.py +++ /dev/null @@ -1,275 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -import os -import numpy as np -import torch -import warnings - -from .. import custom_ops -from .. import misc -from . import upfirdn2d -from . import bias_act - -#---------------------------------------------------------------------------- - -_plugin = None - -def _init(): - global _plugin - if _plugin is None: - _plugin = custom_ops.get_plugin( - module_name='filtered_lrelu_plugin', - sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'], - headers=['filtered_lrelu.h', 'filtered_lrelu.cu'], - source_dir=os.path.dirname(__file__), - extra_cuda_cflags=['--use_fast_math'], - ) - return True - -def _get_filter_size(f): - if f is None: - return 1, 1 - assert isinstance(f, torch.Tensor) - assert 1 <= f.ndim <= 2 - return f.shape[-1], f.shape[0] # width, height - -def _parse_padding(padding): - if isinstance(padding, int): - padding = [padding, padding] - assert isinstance(padding, (list, tuple)) - assert all(isinstance(x, (int, np.integer)) for x in padding) - padding = [int(x) for x in padding] - if len(padding) == 2: - px, py = padding - padding = [px, px, py, py] - px0, px1, py0, py1 = padding - return px0, px1, py0, py1 - -#---------------------------------------------------------------------------- - -def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'): - r"""Filtered leaky ReLU for a batch of 2D images. - - Performs the following sequence of operations for each channel: - - 1. Add channel-specific bias if provided (`b`). - - 2. Upsample the image by inserting N-1 zeros after each pixel (`up`). - - 3. Pad the image with the specified number of zeros on each side (`padding`). - Negative padding corresponds to cropping the image. - - 4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it - so that the footprint of all output pixels lies within the input image. - - 5. Multiply each value by the provided gain factor (`gain`). - - 6. Apply leaky ReLU activation function to each value. - - 7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided. - - 8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking - it so that the footprint of all output pixels lies within the input image. - - 9. Downsample the image by keeping every Nth pixel (`down`). - - The fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports gradients of arbitrary order. - - Args: - x: Float32/float16/float64 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - fu: Float32 upsampling FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - fd: Float32 downsampling FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type - as `x`. The length of vector must must match the channel dimension of `x`. - up: Integer upsampling factor (default: 1). - down: Integer downsampling factor. (default: 1). - padding: Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - gain: Overall scaling factor for signal magnitude (default: sqrt(2)). - slope: Slope on the negative side of leaky ReLU (default: 0.2). - clamp: Maximum magnitude for leaky ReLU output (default: None). - flip_filter: False = convolution, True = correlation (default: False). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - assert isinstance(x, torch.Tensor) - assert impl in ['ref', 'cuda'] - if impl == 'cuda' and x.device.type == 'cuda' and _init(): - return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0) - return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter) - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): - """Slow and memory-inefficient reference implementation of `filtered_lrelu()` using - existing `upfirdn2n()` and `bias_act()` ops. - """ - assert isinstance(x, torch.Tensor) and x.ndim == 4 - fu_w, fu_h = _get_filter_size(fu) - fd_w, fd_h = _get_filter_size(fd) - if b is not None: - assert isinstance(b, torch.Tensor) and b.dtype == x.dtype - misc.assert_shape(b, [x.shape[1]]) - assert isinstance(up, int) and up >= 1 - assert isinstance(down, int) and down >= 1 - px0, px1, py0, py1 = _parse_padding(padding) - assert gain == float(gain) and gain > 0 - assert slope == float(slope) and slope >= 0 - assert clamp is None or (clamp == float(clamp) and clamp >= 0) - - # Calculate output size. - batch_size, channels, in_h, in_w = x.shape - in_dtype = x.dtype - out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down - out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down - - # Compute using existing ops. - x = bias_act.bias_act(x=x, b=b) # Apply bias. - x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. - x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp. - x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample. - - # Check output shape & dtype. - misc.assert_shape(x, [batch_size, channels, out_h, out_w]) - assert x.dtype == in_dtype - return x - -#---------------------------------------------------------------------------- - -_filtered_lrelu_cuda_cache = dict() - -def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): - """Fast CUDA implementation of `filtered_lrelu()` using custom ops. - """ - assert isinstance(up, int) and up >= 1 - assert isinstance(down, int) and down >= 1 - px0, px1, py0, py1 = _parse_padding(padding) - assert gain == float(gain) and gain > 0 - gain = float(gain) - assert slope == float(slope) and slope >= 0 - slope = float(slope) - assert clamp is None or (clamp == float(clamp) and clamp >= 0) - clamp = float(clamp if clamp is not None else 'inf') - - # Lookup from cache. - key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter) - if key in _filtered_lrelu_cuda_cache: - return _filtered_lrelu_cuda_cache[key] - - # Forward op. - class FilteredLReluCuda(torch.autograd.Function): - @staticmethod - def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ - assert isinstance(x, torch.Tensor) and x.ndim == 4 - - # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable). - if fu is None: - fu = torch.ones([1, 1], dtype=torch.float32, device=x.device) - if fd is None: - fd = torch.ones([1, 1], dtype=torch.float32, device=x.device) - assert 1 <= fu.ndim <= 2 - assert 1 <= fd.ndim <= 2 - - # Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1. - if up == 1 and fu.ndim == 1 and fu.shape[0] == 1: - fu = fu.square()[None] - if down == 1 and fd.ndim == 1 and fd.shape[0] == 1: - fd = fd.square()[None] - - # Missing sign input tensor. - if si is None: - si = torch.empty([0]) - - # Missing bias tensor. - if b is None: - b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device) - - # Construct internal sign tensor only if gradients are needed. - write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad) - - # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout. - x = x.contiguous() - strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1] - if any(a < b for a, b in zip(strides[:-1], strides[1:])): - warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning) - - # Call C++/Cuda plugin if datatype is supported. - if x.dtype in [torch.float16, torch.float32]: - if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device): - warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning) - y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs) - else: - return_code = -1 - - # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because - # only the bit-packed sign tensor is retained for gradient computation. - if return_code < 0: - warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning) - - y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias. - y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. - so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place. - y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample. - - # Prepare for gradient computation. - ctx.save_for_backward(fu, fd, (si if si.numel() else so)) - ctx.x_shape = x.shape - ctx.y_shape = y.shape - ctx.s_ofs = sx, sy - return y - - @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ - fu, fd, si = ctx.saved_tensors - _, _, xh, xw = ctx.x_shape - _, _, yh, yw = ctx.y_shape - sx, sy = ctx.s_ofs - dx = None # 0 - dfu = None; assert not ctx.needs_input_grad[1] - dfd = None; assert not ctx.needs_input_grad[2] - db = None # 3 - dsi = None; assert not ctx.needs_input_grad[4] - dsx = None; assert not ctx.needs_input_grad[5] - dsy = None; assert not ctx.needs_input_grad[6] - - if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]: - pp = [ - (fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0, - xw * up - yw * down + px0 - (up - 1), - (fu.shape[0] - 1) + (fd.shape[0] - 1) - py0, - xh * up - yh * down + py0 - (up - 1), - ] - gg = gain * (up ** 2) / (down ** 2) - ff = (not flip_filter) - sx = sx - (fu.shape[-1] - 1) + px0 - sy = sy - (fu.shape[0] - 1) + py0 - dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy) - - if ctx.needs_input_grad[3]: - db = dx.sum([0, 2, 3]) - - return dx, dfu, dfd, db, dsi, dsx, dsy - - # Add to cache. - _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda - return FilteredLReluCuda - -#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/filtered_lrelu_ns.cu b/torch_utils/ops/filtered_lrelu_ns.cu deleted file mode 100644 index ef5d948..0000000 --- a/torch_utils/ops/filtered_lrelu_ns.cu +++ /dev/null @@ -1,27 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include "filtered_lrelu.cu" - -// Template/kernel specializations for no signs mode (no gradients required). - -// Full op, 32-bit indexing. -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); - -// Full op, 64-bit indexing. -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); - -// Activation/signs only for generic variant. 64-bit indexing. -template void* choose_filtered_lrelu_act_kernel(void); -template void* choose_filtered_lrelu_act_kernel(void); -template void* choose_filtered_lrelu_act_kernel(void); - -// Copy filters to constant memory. -template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/filtered_lrelu_rd.cu b/torch_utils/ops/filtered_lrelu_rd.cu deleted file mode 100644 index 9683478..0000000 --- a/torch_utils/ops/filtered_lrelu_rd.cu +++ /dev/null @@ -1,27 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include "filtered_lrelu.cu" - -// Template/kernel specializations for sign read mode. - -// Full op, 32-bit indexing. -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); - -// Full op, 64-bit indexing. -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); - -// Activation/signs only for generic variant. 64-bit indexing. -template void* choose_filtered_lrelu_act_kernel(void); -template void* choose_filtered_lrelu_act_kernel(void); -template void* choose_filtered_lrelu_act_kernel(void); - -// Copy filters to constant memory. -template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/filtered_lrelu_wr.cu b/torch_utils/ops/filtered_lrelu_wr.cu deleted file mode 100644 index a4c6a24..0000000 --- a/torch_utils/ops/filtered_lrelu_wr.cu +++ /dev/null @@ -1,27 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include "filtered_lrelu.cu" - -// Template/kernel specializations for sign write mode. - -// Full op, 32-bit indexing. -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); - -// Full op, 64-bit indexing. -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); - -// Activation/signs only for generic variant. 64-bit indexing. -template void* choose_filtered_lrelu_act_kernel(void); -template void* choose_filtered_lrelu_act_kernel(void); -template void* choose_filtered_lrelu_act_kernel(void); - -// Copy filters to constant memory. -template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/fma.py b/torch_utils/ops/fma.py deleted file mode 100644 index 51a45df..0000000 --- a/torch_utils/ops/fma.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`.""" - -import torch - -#---------------------------------------------------------------------------- - -def fma(a, b, c): # => a * b + c - return _FusedMultiplyAdd.apply(a, b, c) - -#---------------------------------------------------------------------------- - -class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c - @staticmethod - def forward(ctx, a, b, c): # pylint: disable=arguments-differ - out = torch.addcmul(c, a, b) - ctx.save_for_backward(a, b) - ctx.c_shape = c.shape - return out - - @staticmethod - def backward(ctx, dout): # pylint: disable=arguments-differ - a, b = ctx.saved_tensors - c_shape = ctx.c_shape - da = None - db = None - dc = None - - if ctx.needs_input_grad[0]: - da = _unbroadcast(dout * b, a.shape) - - if ctx.needs_input_grad[1]: - db = _unbroadcast(dout * a, b.shape) - - if ctx.needs_input_grad[2]: - dc = _unbroadcast(dout, c_shape) - - return da, db, dc - -#---------------------------------------------------------------------------- - -def _unbroadcast(x, shape): - extra_dims = x.ndim - len(shape) - assert extra_dims >= 0 - dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)] - if len(dim): - x = x.sum(dim=dim, keepdim=True) - if extra_dims: - x = x.reshape(-1, *x.shape[extra_dims+1:]) - assert x.shape == shape - return x - -#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/grid_sample_gradfix.py b/torch_utils/ops/grid_sample_gradfix.py deleted file mode 100644 index 979ee83..0000000 --- a/torch_utils/ops/grid_sample_gradfix.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Custom replacement for `torch.nn.functional.grid_sample` that -supports arbitrarily high order gradients between the input and output. -Only works on 2D images and assumes -`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`.""" - -import torch - -# pylint: disable=redefined-builtin -# pylint: disable=arguments-differ -# pylint: disable=protected-access - -#---------------------------------------------------------------------------- - -enabled = False # Enable the custom op by setting this to true. - -#---------------------------------------------------------------------------- - -def grid_sample(input, grid): - if _should_use_custom_op(): - return _GridSample2dForward.apply(input, grid) - return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) - -#---------------------------------------------------------------------------- - -def _should_use_custom_op(): - return enabled - -#---------------------------------------------------------------------------- - -class _GridSample2dForward(torch.autograd.Function): - @staticmethod - def forward(ctx, input, grid): - assert input.ndim == 4 - assert grid.ndim == 4 - output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) - ctx.save_for_backward(input, grid) - return output - - @staticmethod - def backward(ctx, grad_output): - input, grid = ctx.saved_tensors - grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid) - return grad_input, grad_grid - -#---------------------------------------------------------------------------- - -class _GridSample2dBackward(torch.autograd.Function): - @staticmethod - def forward(ctx, grad_output, input, grid): - op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward') - grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False) - ctx.save_for_backward(grid) - return grad_input, grad_grid - - @staticmethod - def backward(ctx, grad2_grad_input, grad2_grad_grid): - _ = grad2_grad_grid # unused - grid, = ctx.saved_tensors - grad2_grad_output = None - grad2_input = None - grad2_grid = None - - if ctx.needs_input_grad[0]: - grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid) - - assert not ctx.needs_input_grad[2] - return grad2_grad_output, grad2_input, grad2_grid - -#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/upfirdn2d.cpp b/torch_utils/ops/upfirdn2d.cpp deleted file mode 100644 index 44fa337..0000000 --- a/torch_utils/ops/upfirdn2d.cpp +++ /dev/null @@ -1,107 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include -#include -#include -#include "upfirdn2d.h" - -//------------------------------------------------------------------------ - -static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain) -{ - // Validate arguments. - TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); - TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x"); - TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32"); - TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); - TORCH_CHECK(f.numel() <= INT_MAX, "f is too large"); - TORCH_CHECK(x.numel() > 0, "x has zero size"); - TORCH_CHECK(f.numel() > 0, "f has zero size"); - TORCH_CHECK(x.dim() == 4, "x must be rank 4"); - TORCH_CHECK(f.dim() == 2, "f must be rank 2"); - TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large"); - TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1"); - TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1"); - TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1"); - - // Create output tensor. - const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); - int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx; - int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy; - TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1"); - torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format()); - TORCH_CHECK(y.numel() <= INT_MAX, "output is too large"); - TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large"); - - // Initialize CUDA kernel parameters. - upfirdn2d_kernel_params p; - p.x = x.data_ptr(); - p.f = f.data_ptr(); - p.y = y.data_ptr(); - p.up = make_int2(upx, upy); - p.down = make_int2(downx, downy); - p.pad0 = make_int2(padx0, pady0); - p.flip = (flip) ? 1 : 0; - p.gain = gain; - p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); - p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0)); - p.filterSize = make_int2((int)f.size(1), (int)f.size(0)); - p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0)); - p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); - p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0)); - p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z; - p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1; - - // Choose CUDA kernel. - upfirdn2d_kernel_spec spec; - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] - { - spec = choose_upfirdn2d_kernel(p); - }); - - // Set looping options. - p.loopMajor = (p.sizeMajor - 1) / 16384 + 1; - p.loopMinor = spec.loopMinor; - p.loopX = spec.loopX; - p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1; - p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1; - - // Compute grid size. - dim3 blockSize, gridSize; - if (spec.tileOutW < 0) // large - { - blockSize = dim3(4, 32, 1); - gridSize = dim3( - ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor, - (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1, - p.launchMajor); - } - else // small - { - blockSize = dim3(256, 1, 1); - gridSize = dim3( - ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor, - (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1, - p.launchMajor); - } - - // Launch CUDA kernel. - void* args[] = {&p}; - AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); - return y; -} - -//------------------------------------------------------------------------ - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) -{ - m.def("upfirdn2d", &upfirdn2d); -} - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.cu b/torch_utils/ops/upfirdn2d.cu deleted file mode 100644 index 3a33e31..0000000 --- a/torch_utils/ops/upfirdn2d.cu +++ /dev/null @@ -1,384 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include -#include "upfirdn2d.h" - -//------------------------------------------------------------------------ -// Helpers. - -template struct InternalType; -template <> struct InternalType { typedef double scalar_t; }; -template <> struct InternalType { typedef float scalar_t; }; -template <> struct InternalType { typedef float scalar_t; }; - -static __device__ __forceinline__ int floor_div(int a, int b) -{ - int t = 1 - a / b; - return (a + t * b) / b - t; -} - -//------------------------------------------------------------------------ -// Generic CUDA implementation for large filters. - -template static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p) -{ - typedef typename InternalType::scalar_t scalar_t; - - // Calculate thread index. - int minorBase = blockIdx.x * blockDim.x + threadIdx.x; - int outY = minorBase / p.launchMinor; - minorBase -= outY * p.launchMinor; - int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y; - int majorBase = blockIdx.z * p.loopMajor; - if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor) - return; - - // Setup Y receptive field. - int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y; - int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y); - int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY; - int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y; - if (p.flip) - filterY = p.filterSize.y - 1 - filterY; - - // Loop over major, minor, and X. - for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) - for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor) - { - int nc = major * p.sizeMinor + minor; - int n = nc / p.inSize.z; - int c = nc - n * p.inSize.z; - for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y) - { - // Setup X receptive field. - int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x; - int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x); - int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX; - int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x; - if (p.flip) - filterX = p.filterSize.x - 1 - filterX; - - // Initialize pointers. - const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; - const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y]; - int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x; - int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y; - - // Inner loop. - scalar_t v = 0; - for (int y = 0; y < h; y++) - { - for (int x = 0; x < w; x++) - { - v += (scalar_t)(*xp) * (scalar_t)(*fp); - xp += p.inStride.x; - fp += filterStepX; - } - xp += p.inStride.y - w * p.inStride.x; - fp += filterStepY - w * filterStepX; - } - - // Store result. - v *= p.gain; - ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; - } - } -} - -//------------------------------------------------------------------------ -// Specialized CUDA implementation for small filters. - -template -static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p) -{ - typedef typename InternalType::scalar_t scalar_t; - const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1; - const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1; - __shared__ volatile scalar_t sf[filterH][filterW]; - __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor]; - - // Calculate tile index. - int minorBase = blockIdx.x; - int tileOutY = minorBase / p.launchMinor; - minorBase -= tileOutY * p.launchMinor; - minorBase *= loopMinor; - tileOutY *= tileOutH; - int tileOutXBase = blockIdx.y * p.loopX * tileOutW; - int majorBase = blockIdx.z * p.loopMajor; - if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor) - return; - - // Load filter (flipped). - for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x) - { - int fy = tapIdx / filterW; - int fx = tapIdx - fy * filterW; - scalar_t v = 0; - if (fx < p.filterSize.x & fy < p.filterSize.y) - { - int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx; - int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy; - v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y]; - } - sf[fy][fx] = v; - } - - // Loop over major and X. - for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) - { - int baseNC = major * p.sizeMinor + minorBase; - int n = baseNC / p.inSize.z; - int baseC = baseNC - n * p.inSize.z; - for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW) - { - // Load input pixels. - int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x; - int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y; - int tileInX = floor_div(tileMidX, upx); - int tileInY = floor_div(tileMidY, upy); - __syncthreads(); - for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x) - { - int relC = inIdx; - int relInX = relC / loopMinor; - int relInY = relInX / tileInW; - relC -= relInX * loopMinor; - relInX -= relInY * tileInW; - int c = baseC + relC; - int inX = tileInX + relInX; - int inY = tileInY + relInY; - scalar_t v = 0; - if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z) - v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; - sx[relInY][relInX][relC] = v; - } - - // Loop over output pixels. - __syncthreads(); - for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x) - { - int relC = outIdx; - int relOutX = relC / loopMinor; - int relOutY = relOutX / tileOutW; - relC -= relOutX * loopMinor; - relOutX -= relOutY * tileOutW; - int c = baseC + relC; - int outX = tileOutX + relOutX; - int outY = tileOutY + relOutY; - - // Setup receptive field. - int midX = tileMidX + relOutX * downx; - int midY = tileMidY + relOutY * downy; - int inX = floor_div(midX, upx); - int inY = floor_div(midY, upy); - int relInX = inX - tileInX; - int relInY = inY - tileInY; - int filterX = (inX + 1) * upx - midX - 1; // flipped - int filterY = (inY + 1) * upy - midY - 1; // flipped - - // Inner loop. - if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z) - { - scalar_t v = 0; - #pragma unroll - for (int y = 0; y < filterH / upy; y++) - #pragma unroll - for (int x = 0; x < filterW / upx; x++) - v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx]; - v *= p.gain; - ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; - } - } - } - } -} - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p) -{ - int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y; - upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large, -1,-1,1, 4}; // contiguous - if (s == 1) spec = {(void*)upfirdn2d_kernel_large, -1,-1,4, 1}; // channels_last - - // No up/downsampling. - if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; - if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; - if (s != 1 && fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - if (s != 1 && fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - if (s != 1 && fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - if (s != 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; - if (s != 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; - if (s != 1 && fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; - if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - // channels_last - if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s == 1 && fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - if (s == 1 && fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - if (s == 1 && fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - if (s == 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; - if (s == 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; - if (s == 1 && fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; - if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; - if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; - if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; - } - - // 2x upsampling. - if (p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; - if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; - if (s != 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - if (s != 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; - // channels_last - if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s == 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - if (s == 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; - } - if (p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; - if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; - if (s != 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; - // channels_last - if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; - if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; - if (s == 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; - } - if (p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - // channels_last - if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; - if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; - if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; - } - - // 2x downsampling. - if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) - { - // contiguous - if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; - if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; - if (s != 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; - if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; - if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; - if (s != 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; - // channels_last - if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 16,16,1, 1}; - if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 16,16,1, 1}; - if (s == 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; - if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; - if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; - if (s == 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; - } - if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; - if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; - if (s != 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; - // channels_last - if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; - if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; - if (s == 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; - } - if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) - { - // contiguous - if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; - if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; - if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; - // channels_last - if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; - if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; - if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; - } - - // 4x upsampling. - if (p.up.x == 4 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; - if (s != 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; - // channels_last - if (s == 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s == 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - } - if (p.up.x == 4 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; - if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; - // channels_last - if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; - if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; - } - if (p.up.x == 1 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; - // channels_last - if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; - if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; - } - - // 4x downsampling (inefficient). - if (p.up.x == 1 && p.up.y == 1 && p.down.x == 4 && p.down.y == 1) - { - // contiguous - if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; - if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; - // channels_last - if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,1,8, 1}; - if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,1,8, 1}; - } - if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 4) - { - // contiguous - if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; - if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; - // channels_last - if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 1,32,8, 1}; - if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 1,32,8, 1}; - } - return spec; -} - -//------------------------------------------------------------------------ -// Template specializations. - -template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); -template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); -template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.h b/torch_utils/ops/upfirdn2d.h deleted file mode 100644 index 2793daf..0000000 --- a/torch_utils/ops/upfirdn2d.h +++ /dev/null @@ -1,59 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct upfirdn2d_kernel_params -{ - const void* x; - const float* f; - void* y; - - int2 up; - int2 down; - int2 pad0; - int flip; - float gain; - - int4 inSize; // [width, height, channel, batch] - int4 inStride; - int2 filterSize; // [width, height] - int2 filterStride; - int4 outSize; // [width, height, channel, batch] - int4 outStride; - int sizeMinor; - int sizeMajor; - - int loopMinor; - int loopMajor; - int loopX; - int launchMinor; - int launchMajor; -}; - -//------------------------------------------------------------------------ -// CUDA kernel specialization. - -struct upfirdn2d_kernel_spec -{ - void* kernel; - int tileOutW; - int tileOutH; - int loopMinor; - int loopX; -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.py b/torch_utils/ops/upfirdn2d.py deleted file mode 100644 index b544be1..0000000 --- a/torch_utils/ops/upfirdn2d.py +++ /dev/null @@ -1,389 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Custom PyTorch ops for efficient resampling of 2D images.""" - -import os -import numpy as np -import torch - -from .. import custom_ops -from .. import misc -from . import conv2d_gradfix - -#---------------------------------------------------------------------------- - -_plugin = None - -def _init(): - global _plugin - if _plugin is None: - _plugin = custom_ops.get_plugin( - module_name='upfirdn2d_plugin', - sources=['upfirdn2d.cpp', 'upfirdn2d.cu'], - headers=['upfirdn2d.h'], - source_dir=os.path.dirname(__file__), - extra_cuda_cflags=['--use_fast_math'], - ) - return True - -def _parse_scaling(scaling): - if isinstance(scaling, int): - scaling = [scaling, scaling] - assert isinstance(scaling, (list, tuple)) - assert all(isinstance(x, int) for x in scaling) - sx, sy = scaling - assert sx >= 1 and sy >= 1 - return sx, sy - -def _parse_padding(padding): - if isinstance(padding, int): - padding = [padding, padding] - assert isinstance(padding, (list, tuple)) - assert all(isinstance(x, int) for x in padding) - if len(padding) == 2: - padx, pady = padding - padding = [padx, padx, pady, pady] - padx0, padx1, pady0, pady1 = padding - return padx0, padx1, pady0, pady1 - -def _get_filter_size(f): - if f is None: - return 1, 1 - assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] - fw = f.shape[-1] - fh = f.shape[0] - with misc.suppress_tracer_warnings(): - fw = int(fw) - fh = int(fh) - misc.assert_shape(f, [fh, fw][:f.ndim]) - assert fw >= 1 and fh >= 1 - return fw, fh - -#---------------------------------------------------------------------------- - -def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None): - r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. - - Args: - f: Torch tensor, numpy array, or python list of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), - `[]` (impulse), or - `None` (identity). - device: Result device (default: cpu). - normalize: Normalize the filter so that it retains the magnitude - for constant input signal (DC)? (default: True). - flip_filter: Flip the filter? (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - separable: Return a separable filter? (default: select automatically). - - Returns: - Float32 tensor of the shape - `[filter_height, filter_width]` (non-separable) or - `[filter_taps]` (separable). - """ - # Validate. - if f is None: - f = 1 - f = torch.as_tensor(f, dtype=torch.float32) - assert f.ndim in [0, 1, 2] - assert f.numel() > 0 - if f.ndim == 0: - f = f[np.newaxis] - - # Separable? - if separable is None: - separable = (f.ndim == 1 and f.numel() >= 8) - if f.ndim == 1 and not separable: - f = f.ger(f) - assert f.ndim == (1 if separable else 2) - - # Apply normalize, flip, gain, and device. - if normalize: - f /= f.sum() - if flip_filter: - f = f.flip(list(range(f.ndim))) - f = f * (gain ** (f.ndim / 2)) - f = f.to(device=device) - return f - -#---------------------------------------------------------------------------- - -def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'): - r"""Pad, upsample, filter, and downsample a batch of 2D images. - - Performs the following sequence of operations for each channel: - - 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). - - 2. Pad the image with the specified number of zeros on each side (`padding`). - Negative padding corresponds to cropping the image. - - 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it - so that the footprint of all output pixels lies within the input image. - - 4. Downsample the image by keeping every Nth pixel (`down`). - - This sequence of operations bears close resemblance to scipy.signal.upfirdn(). - The fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports gradients of arbitrary order. - - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - up: Integer upsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - down: Integer downsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - padding: Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - assert isinstance(x, torch.Tensor) - assert impl in ['ref', 'cuda'] - if impl == 'cuda' and x.device.type == 'cuda' and _init(): - return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f) - return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain) - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): - """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops. - """ - # Validate arguments. - assert isinstance(x, torch.Tensor) and x.ndim == 4 - if f is None: - f = torch.ones([1, 1], dtype=torch.float32, device=x.device) - assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] - assert f.dtype == torch.float32 and not f.requires_grad - batch_size, num_channels, in_height, in_width = x.shape - upx, upy = _parse_scaling(up) - downx, downy = _parse_scaling(down) - padx0, padx1, pady0, pady1 = _parse_padding(padding) - - # Check that upsampled buffer is not smaller than the filter. - upW = in_width * upx + padx0 + padx1 - upH = in_height * upy + pady0 + pady1 - assert upW >= f.shape[-1] and upH >= f.shape[0] - - # Upsample by inserting zeros. - x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) - x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1]) - x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) - - # Pad or crop. - x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) - x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)] - - # Setup filter. - f = f * (gain ** (f.ndim / 2)) - f = f.to(x.dtype) - if not flip_filter: - f = f.flip(list(range(f.ndim))) - - # Convolve with the filter. - f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim) - if f.ndim == 4: - x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels) - else: - x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) - x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) - - # Downsample by throwing away pixels. - x = x[:, :, ::downy, ::downx] - return x - -#---------------------------------------------------------------------------- - -_upfirdn2d_cuda_cache = dict() - -def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): - """Fast CUDA implementation of `upfirdn2d()` using custom ops. - """ - # Parse arguments. - upx, upy = _parse_scaling(up) - downx, downy = _parse_scaling(down) - padx0, padx1, pady0, pady1 = _parse_padding(padding) - - # Lookup from cache. - key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) - if key in _upfirdn2d_cuda_cache: - return _upfirdn2d_cuda_cache[key] - - # Forward op. - class Upfirdn2dCuda(torch.autograd.Function): - @staticmethod - def forward(ctx, x, f): # pylint: disable=arguments-differ - assert isinstance(x, torch.Tensor) and x.ndim == 4 - if f is None: - f = torch.ones([1, 1], dtype=torch.float32, device=x.device) - if f.ndim == 1 and f.shape[0] == 1: - f = f.square().unsqueeze(0) # Convert separable-1 into full-1x1. - assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] - y = x - if f.ndim == 2: - y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) - else: - y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, 1.0) - y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, gain) - ctx.save_for_backward(f) - ctx.x_shape = x.shape - return y - - @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ - f, = ctx.saved_tensors - _, _, ih, iw = ctx.x_shape - _, _, oh, ow = dy.shape - fw, fh = _get_filter_size(f) - p = [ - fw - padx0 - 1, - iw * upx - ow * downx + padx0 - upx + 1, - fh - pady0 - 1, - ih * upy - oh * downy + pady0 - upy + 1, - ] - dx = None - df = None - - if ctx.needs_input_grad[0]: - dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f) - - assert not ctx.needs_input_grad[1] - return dx, df - - # Add to cache. - _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda - return Upfirdn2dCuda - -#---------------------------------------------------------------------------- - -def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): - r"""Filter a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape matches the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - padding: Padding with respect to the output. Can be a single number or a - list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - padx0, padx1, pady0, pady1 = _parse_padding(padding) - fw, fh = _get_filter_size(f) - p = [ - padx0 + fw // 2, - padx1 + (fw - 1) // 2, - pady0 + fh // 2, - pady1 + (fh - 1) // 2, - ] - return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) - -#---------------------------------------------------------------------------- - -def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): - r"""Upsample a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape is a multiple of the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - up: Integer upsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - padding: Padding with respect to the output. Can be a single number or a - list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - upx, upy = _parse_scaling(up) - padx0, padx1, pady0, pady1 = _parse_padding(padding) - fw, fh = _get_filter_size(f) - p = [ - padx0 + (fw + upx - 1) // 2, - padx1 + (fw - upx) // 2, - pady0 + (fh + upy - 1) // 2, - pady1 + (fh - upy) // 2, - ] - return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl) - -#---------------------------------------------------------------------------- - -def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'): - r"""Downsample a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape is a fraction of the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - down: Integer downsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - padding: Padding with respect to the input. Can be a single number or a - list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - downx, downy = _parse_scaling(down) - padx0, padx1, pady0, pady1 = _parse_padding(padding) - fw, fh = _get_filter_size(f) - p = [ - padx0 + (fw - downx + 1) // 2, - padx1 + (fw - downx) // 2, - pady0 + (fh - downy + 1) // 2, - pady1 + (fh - downy) // 2, - ] - return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) - -#---------------------------------------------------------------------------- diff --git a/torch_utils/persistence.py b/torch_utils/persistence.py deleted file mode 100644 index f90ce85..0000000 --- a/torch_utils/persistence.py +++ /dev/null @@ -1,251 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Facilities for pickling Python code alongside other data. - -The pickled code is automatically imported into a separate Python module -during unpickling. This way, any previously exported pickles will remain -usable even if the original code is no longer available, or if the current -version of the code is not consistent with what was originally pickled.""" - -import sys -import pickle -import io -import inspect -import copy -import uuid -import types -import dnnlib - -#---------------------------------------------------------------------------- - -_version = 6 # internal version number -_decorators = set() # {decorator_class, ...} -_import_hooks = [] # [hook_function, ...] -_module_to_src_dict = dict() # {module: src, ...} -_src_to_module_dict = dict() # {src: module, ...} - -#---------------------------------------------------------------------------- - -def persistent_class(orig_class): - r"""Class decorator that extends a given class to save its source code - when pickled. - - Example: - - from torch_utils import persistence - - @persistence.persistent_class - class MyNetwork(torch.nn.Module): - def __init__(self, num_inputs, num_outputs): - super().__init__() - self.fc = MyLayer(num_inputs, num_outputs) - ... - - @persistence.persistent_class - class MyLayer(torch.nn.Module): - ... - - When pickled, any instance of `MyNetwork` and `MyLayer` will save its - source code alongside other internal state (e.g., parameters, buffers, - and submodules). This way, any previously exported pickle will remain - usable even if the class definitions have been modified or are no - longer available. - - The decorator saves the source code of the entire Python module - containing the decorated class. It does *not* save the source code of - any imported modules. Thus, the imported modules must be available - during unpickling, also including `torch_utils.persistence` itself. - - It is ok to call functions defined in the same module from the - decorated class. However, if the decorated class depends on other - classes defined in the same module, they must be decorated as well. - This is illustrated in the above example in the case of `MyLayer`. - - It is also possible to employ the decorator just-in-time before - calling the constructor. For example: - - cls = MyLayer - if want_to_make_it_persistent: - cls = persistence.persistent_class(cls) - layer = cls(num_inputs, num_outputs) - - As an additional feature, the decorator also keeps track of the - arguments that were used to construct each instance of the decorated - class. The arguments can be queried via `obj.init_args` and - `obj.init_kwargs`, and they are automatically pickled alongside other - object state. A typical use case is to first unpickle a previous - instance of a persistent class, and then upgrade it to use the latest - version of the source code: - - with open('old_pickle.pkl', 'rb') as f: - old_net = pickle.load(f) - new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs) - misc.copy_params_and_buffers(old_net, new_net, require_all=True) - """ - assert isinstance(orig_class, type) - if is_persistent(orig_class): - return orig_class - - assert orig_class.__module__ in sys.modules - orig_module = sys.modules[orig_class.__module__] - orig_module_src = _module_to_src(orig_module) - - class Decorator(orig_class): - _orig_module_src = orig_module_src - _orig_class_name = orig_class.__name__ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self._init_args = copy.deepcopy(args) - self._init_kwargs = copy.deepcopy(kwargs) - assert orig_class.__name__ in orig_module.__dict__ - _check_pickleable(self.__reduce__()) - - @property - def init_args(self): - return copy.deepcopy(self._init_args) - - @property - def init_kwargs(self): - return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs)) - - def __reduce__(self): - fields = list(super().__reduce__()) - fields += [None] * max(3 - len(fields), 0) - if fields[0] is not _reconstruct_persistent_obj: - meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2]) - fields[0] = _reconstruct_persistent_obj # reconstruct func - fields[1] = (meta,) # reconstruct args - fields[2] = None # state dict - return tuple(fields) - - Decorator.__name__ = orig_class.__name__ - _decorators.add(Decorator) - return Decorator - -#---------------------------------------------------------------------------- - -def is_persistent(obj): - r"""Test whether the given object or class is persistent, i.e., - whether it will save its source code when pickled. - """ - try: - if obj in _decorators: - return True - except TypeError: - pass - return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck - -#---------------------------------------------------------------------------- - -def import_hook(hook): - r"""Register an import hook that is called whenever a persistent object - is being unpickled. A typical use case is to patch the pickled source - code to avoid errors and inconsistencies when the API of some imported - module has changed. - - The hook should have the following signature: - - hook(meta) -> modified meta - - `meta` is an instance of `dnnlib.EasyDict` with the following fields: - - type: Type of the persistent object, e.g. `'class'`. - version: Internal version number of `torch_utils.persistence`. - module_src Original source code of the Python module. - class_name: Class name in the original Python module. - state: Internal state of the object. - - Example: - - @persistence.import_hook - def wreck_my_network(meta): - if meta.class_name == 'MyNetwork': - print('MyNetwork is being imported. I will wreck it!') - meta.module_src = meta.module_src.replace("True", "False") - return meta - """ - assert callable(hook) - _import_hooks.append(hook) - -#---------------------------------------------------------------------------- - -def _reconstruct_persistent_obj(meta): - r"""Hook that is called internally by the `pickle` module to unpickle - a persistent object. - """ - meta = dnnlib.EasyDict(meta) - meta.state = dnnlib.EasyDict(meta.state) - for hook in _import_hooks: - meta = hook(meta) - assert meta is not None - - assert meta.version == _version - module = _src_to_module(meta.module_src) - - assert meta.type == 'class' - orig_class = module.__dict__[meta.class_name] - decorator_class = persistent_class(orig_class) - obj = decorator_class.__new__(decorator_class) - - setstate = getattr(obj, '__setstate__', None) - if callable(setstate): - setstate(meta.state) # pylint: disable=not-callable - else: - obj.__dict__.update(meta.state) - return obj - -#---------------------------------------------------------------------------- - -def _module_to_src(module): - r"""Query the source code of a given Python module. - """ - src = _module_to_src_dict.get(module, None) - if src is None: - src = inspect.getsource(module) - _module_to_src_dict[module] = src - _src_to_module_dict[src] = module - return src - -def _src_to_module(src): - r"""Get or create a Python module for the given source code. - """ - module = _src_to_module_dict.get(src, None) - if module is None: - module_name = "_imported_module_" + uuid.uuid4().hex - module = types.ModuleType(module_name) - sys.modules[module_name] = module - _module_to_src_dict[module] = src - _src_to_module_dict[src] = module - exec(src, module.__dict__) # pylint: disable=exec-used - return module - -#---------------------------------------------------------------------------- - -def _check_pickleable(obj): - r"""Check that the given object is pickleable, raising an exception if - it is not. This function is expected to be considerably more efficient - than actually pickling the object. - """ - def recurse(obj): - if isinstance(obj, (list, tuple, set)): - return [recurse(x) for x in obj] - if isinstance(obj, dict): - return [[recurse(x), recurse(y)] for x, y in obj.items()] - if isinstance(obj, (str, int, float, bool, bytes, bytearray)): - return None # Python primitive types are pickleable. - if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor', 'torch.nn.parameter.Parameter']: - return None # NumPy arrays and PyTorch tensors are pickleable. - if is_persistent(obj): - return None # Persistent objects are pickleable, by virtue of the constructor check. - return obj - with io.BytesIO() as f: - pickle.dump(recurse(obj), f) - -#---------------------------------------------------------------------------- diff --git a/torch_utils/training_stats.py b/torch_utils/training_stats.py deleted file mode 100644 index 5de4134..0000000 --- a/torch_utils/training_stats.py +++ /dev/null @@ -1,268 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Facilities for reporting and collecting training statistics across -multiple processes and devices. The interface is designed to minimize -synchronization overhead as well as the amount of boilerplate in user -code.""" - -import re -import numpy as np -import torch -import dnnlib - -from . import misc - -#---------------------------------------------------------------------------- - -_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] -_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction. -_counter_dtype = torch.float64 # Data type to use for the internal counters. -_rank = 0 # Rank of the current process. -_sync_device = None # Device to use for multiprocess communication. None = single-process. -_sync_called = False # Has _sync() been called yet? -_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor -_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor - -#---------------------------------------------------------------------------- - -def init_multiprocessing(rank, sync_device): - r"""Initializes `torch_utils.training_stats` for collecting statistics - across multiple processes. - - This function must be called after - `torch.distributed.init_process_group()` and before `Collector.update()`. - The call is not necessary if multi-process collection is not needed. - - Args: - rank: Rank of the current process. - sync_device: PyTorch device to use for inter-process - communication, or None to disable multi-process - collection. Typically `torch.device('cuda', rank)`. - """ - global _rank, _sync_device - assert not _sync_called - _rank = rank - _sync_device = sync_device - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def report(name, value): - r"""Broadcasts the given set of scalars to all interested instances of - `Collector`, across device and process boundaries. - - This function is expected to be extremely cheap and can be safely - called from anywhere in the training loop, loss function, or inside a - `torch.nn.Module`. - - Warning: The current implementation expects the set of unique names to - be consistent across processes. Please make sure that `report()` is - called at least once for each unique name by each process, and in the - same order. If a given process has no scalars to broadcast, it can do - `report(name, [])` (empty list). - - Args: - name: Arbitrary string specifying the name of the statistic. - Averages are accumulated separately for each unique name. - value: Arbitrary set of scalars. Can be a list, tuple, - NumPy array, PyTorch tensor, or Python scalar. - - Returns: - The same `value` that was passed in. - """ - if name not in _counters: - _counters[name] = dict() - - elems = torch.as_tensor(value) - if elems.numel() == 0: - return value - - elems = elems.detach().flatten().to(_reduce_dtype) - moments = torch.stack([ - torch.ones_like(elems).sum(), - elems.sum(), - elems.square().sum(), - ]) - assert moments.ndim == 1 and moments.shape[0] == _num_moments - moments = moments.to(_counter_dtype) - - device = moments.device - if device not in _counters[name]: - _counters[name][device] = torch.zeros_like(moments) - _counters[name][device].add_(moments) - return value - -#---------------------------------------------------------------------------- - -def report0(name, value): - r"""Broadcasts the given set of scalars by the first process (`rank = 0`), - but ignores any scalars provided by the other processes. - See `report()` for further details. - """ - report(name, value if _rank == 0 else []) - return value - -#---------------------------------------------------------------------------- - -class Collector: - r"""Collects the scalars broadcasted by `report()` and `report0()` and - computes their long-term averages (mean and standard deviation) over - user-defined periods of time. - - The averages are first collected into internal counters that are not - directly visible to the user. They are then copied to the user-visible - state as a result of calling `update()` and can then be queried using - `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the - internal counters for the next round, so that the user-visible state - effectively reflects averages collected between the last two calls to - `update()`. - - Args: - regex: Regular expression defining which statistics to - collect. The default is to collect everything. - keep_previous: Whether to retain the previous averages if no - scalars were collected on a given round - (default: True). - """ - def __init__(self, regex='.*', keep_previous=True): - self._regex = re.compile(regex) - self._keep_previous = keep_previous - self._cumulative = dict() - self._moments = dict() - self.update() - self._moments.clear() - - def names(self): - r"""Returns the names of all statistics broadcasted so far that - match the regular expression specified at construction time. - """ - return [name for name in _counters if self._regex.fullmatch(name)] - - def update(self): - r"""Copies current values of the internal counters to the - user-visible state and resets them for the next round. - - If `keep_previous=True` was specified at construction time, the - operation is skipped for statistics that have received no scalars - since the last update, retaining their previous averages. - - This method performs a number of GPU-to-CPU transfers and one - `torch.distributed.all_reduce()`. It is intended to be called - periodically in the main training loop, typically once every - N training steps. - """ - if not self._keep_previous: - self._moments.clear() - for name, cumulative in _sync(self.names()): - if name not in self._cumulative: - self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) - delta = cumulative - self._cumulative[name] - self._cumulative[name].copy_(cumulative) - if float(delta[0]) != 0: - self._moments[name] = delta - - def _get_delta(self, name): - r"""Returns the raw moments that were accumulated for the given - statistic between the last two calls to `update()`, or zero if - no scalars were collected. - """ - assert self._regex.fullmatch(name) - if name not in self._moments: - self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) - return self._moments[name] - - def num(self, name): - r"""Returns the number of scalars that were accumulated for the given - statistic between the last two calls to `update()`, or zero if - no scalars were collected. - """ - delta = self._get_delta(name) - return int(delta[0]) - - def mean(self, name): - r"""Returns the mean of the scalars that were accumulated for the - given statistic between the last two calls to `update()`, or NaN if - no scalars were collected. - """ - delta = self._get_delta(name) - if int(delta[0]) == 0: - return float('nan') - return float(delta[1] / delta[0]) - - def std(self, name): - r"""Returns the standard deviation of the scalars that were - accumulated for the given statistic between the last two calls to - `update()`, or NaN if no scalars were collected. - """ - delta = self._get_delta(name) - if int(delta[0]) == 0 or not np.isfinite(float(delta[1])): - return float('nan') - if int(delta[0]) == 1: - return float(0) - mean = float(delta[1] / delta[0]) - raw_var = float(delta[2] / delta[0]) - return np.sqrt(max(raw_var - np.square(mean), 0)) - - def as_dict(self): - r"""Returns the averages accumulated between the last two calls to - `update()` as an `dnnlib.EasyDict`. The contents are as follows: - - dnnlib.EasyDict( - NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT), - ... - ) - """ - stats = dnnlib.EasyDict() - for name in self.names(): - stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name)) - return stats - - def __getitem__(self, name): - r"""Convenience getter. - `collector[name]` is a synonym for `collector.mean(name)`. - """ - return self.mean(name) - -#---------------------------------------------------------------------------- - -def _sync(names): - r"""Synchronize the global cumulative counters across devices and - processes. Called internally by `Collector.update()`. - """ - if len(names) == 0: - return [] - global _sync_called - _sync_called = True - - # Collect deltas within current rank. - deltas = [] - device = _sync_device if _sync_device is not None else torch.device('cpu') - for name in names: - delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) - for counter in _counters[name].values(): - delta.add_(counter.to(device)) - counter.copy_(torch.zeros_like(counter)) - deltas.append(delta) - deltas = torch.stack(deltas) - - # Sum deltas across ranks. - if _sync_device is not None: - torch.distributed.all_reduce(deltas) - - # Update cumulative values. - deltas = deltas.cpu() - for idx, name in enumerate(names): - if name not in _cumulative: - _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) - _cumulative[name].add_(deltas[idx]) - - # Return name-value pairs. - return [(name, _cumulative[name]) for name in names] - -#---------------------------------------------------------------------------- diff --git a/torch_utils/utils_spectrum.py b/torch_utils/utils_spectrum.py deleted file mode 100644 index 2931ea1..0000000 --- a/torch_utils/utils_spectrum.py +++ /dev/null @@ -1,155 +0,0 @@ -import torch -from torch.fft import fftn - - -def roll_quadrants(data, backwards=False): - """ - Shift low frequencies to the center of fourier transform, i.e. [-N/2, ..., +N/2] -> [0, ..., N-1] - Args: - data: fourier transform, (NxHxW) - backwards: bool, if True shift high frequencies back to center - - Returns: - Shifted fourier transform. - """ - dim = data.ndim - 1 - - if dim != 2: - raise AttributeError(f'Data must be 2d but it is {dim}d.') - if any(s % 2 == 0 for s in data.shape[1:]): - raise RuntimeWarning('Roll quadrants for 2d input should only be used with uneven spatial sizes.') - - # for each dimension swap left and right half - dims = tuple(range(1, dim+1)) # add one for batch dimension - shifts = torch.tensor(data.shape[1:]) // 2 #.div(2, rounding_mode='floor') # N/2 if N even, (N-1)/2 if N odd - if backwards: - shifts *= -1 - return data.roll(shifts.tolist(), dims=dims) - - -def batch_fft(data, normalize=False): - """ - Compute fourier transform of batch. - Args: - data: input tensor, (NxHxW) - - Returns: - Batch fourier transform of input data. - """ - - dim = data.ndim - 1 # subtract one for batch dimension - if dim != 2: - raise AttributeError(f'Data must be 2d but it is {dim}d.') - - dims = tuple(range(1, dim + 1)) # add one for batch dimension - if normalize: - norm = 'ortho' - else: - norm = 'backward' - - if not torch.is_complex(data): - data = torch.complex(data, torch.zeros_like(data)) - freq = fftn(data, dim=dims, norm=norm) - - return freq - - -def azimuthal_average(image, center=None): - # modified to tensor inputs from https://www.astrobetter.com/blog/2010/03/03/fourier-transforms-of-images-in-python/ - """ - Calculate the azimuthally averaged radial profile. - Requires low frequencies to be at the center of the image. - Args: - image: Batch of 2D images, NxHxW - center: The [x,y] pixel coordinates used as the center. The default is - None, which then uses the center of the image (including - fracitonal pixels). - - Returns: - Azimuthal average over the image around the center - """ - # Check input shapes - assert center is None or (len(center) == 2), f'Center has to be None or len(center)=2 ' \ - f'(but it is len(center)={len(center)}.' - # Calculate the indices from the image - H, W = image.shape[-2:] - h, w = torch.meshgrid(torch.arange(0, H), torch.arange(0, W)) - - if center is None: - center = torch.tensor([(w.max() - w.min()) / 2.0, (h.max() - h.min()) / 2.0]) - - # Compute radius for each pixel wrt center - r = torch.stack([w-center[0], h-center[1]]).norm(2, 0) - - # Get sorted radii - r_sorted, ind = r.flatten().sort() - i_sorted = image.flatten(-2, -1)[..., ind] - - # Get the integer part of the radii (bin size = 1) - r_int = r_sorted.long() # attribute to the smaller integer - - # Find all pixels that fall within each radial bin. - deltar = r_int[1:] - r_int[:-1] # Assumes all radii represented, computes bin change between subsequent radii - rind = torch.where(deltar)[0] # location of changed radius - - # compute number of elements in each bin - nind = rind + 1 # number of elements = idx + 1 - nind = torch.cat([torch.tensor([0]), nind, torch.tensor([H*W])]) # add borders - nr = nind[1:] - nind[:-1] # number of radius bin, i.e. counter for bins belonging to each radius - - # Cumulative sum to figure out sums for each radius bin - if H % 2 == 0: - raise NotImplementedError('Not sure if implementation correct, please check') - rind = torch.cat([torch.tensor([0]), rind, torch.tensor([H * W - 1])]) # add borders - else: - rind = torch.cat([rind, torch.tensor([H * W - 1])]) # add borders - csim = i_sorted.cumsum(-1, dtype=torch.float64) # integrate over all values with smaller radius - tbin = csim[..., rind[1:]] - csim[..., rind[:-1]] - # add mean - tbin = torch.cat([csim[:, 0:1], tbin], 1) - - radial_prof = tbin / nr.to(tbin.device) # normalize by counted bins - - return radial_prof - - -def get_spectrum(data, normalize=False): - dim = data.ndim - 1 # subtract one for batch dimension - if dim != 2: - raise AttributeError(f'Data must be 2d but it is {dim}d.') - - freq = batch_fft(data, normalize=normalize) - power_spec = freq.real ** 2 + freq.imag ** 2 - N = data.shape[1] - if N % 2 == 0: # duplicate value for N/2 so it is put at the end of the spectrum - # and is not averaged with the mean value - N_2 = N//2 - power_spec = torch.cat([power_spec[:, :N_2+1], power_spec[:, N_2:N_2+1], power_spec[:, N_2+1:]], dim=1) - power_spec = torch.cat([power_spec[:, :, :N_2+1], power_spec[:, :, N_2:N_2+1], power_spec[:, :, N_2+1:]], dim=2) - - power_spec = roll_quadrants(power_spec) - power_spec = azimuthal_average(power_spec) - return power_spec - - -def plot_std(mean, std, x=None, ax=None, **kwargs): - import matplotlib.pyplot as plt - if ax is None: - fig, ax = plt.subplots(1) - - # plot error margins in same color as line - err_kwargs = { - 'alpha': 0.3 - } - - if 'c' in kwargs.keys(): - err_kwargs['color'] = kwargs['c'] - elif 'color' in kwargs.keys(): - err_kwargs['color'] = kwargs['color'] - - if x is None: - x = torch.linspace(0, 1, len(mean)) # use normalized x axis - ax.plot(x, mean, **kwargs) - ax.fill_between(x, mean-std, mean+std, **err_kwargs) - - return ax diff --git a/train.py b/train.py deleted file mode 100644 index 5951bc5..0000000 --- a/train.py +++ /dev/null @@ -1,246 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: train.py -# Created Date: Tuesday April 28th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 29th January 2022 3:25:24 am -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - - -import os -import shutil -import argparse -from torch.backends import cudnn -from utilities.json_config import readConfig, writeConfig -from utilities.reporter import Reporter -from utilities.yaml_config import getConfigYaml - - -def str2bool(v): - return v.lower() in ('true') - -#################################################################################### -# To configure the seting of training\finetune\test -# -#################################################################################### -def getParameters(): - - parser = argparse.ArgumentParser() - # general settings - parser.add_argument('-v', '--version', type=str, default='arcface_rec', - help="version name for train, test, finetune") - parser.add_argument('-t', '--tag', type=str, default='arcface_rec', - help="tag for current experiment") - - parser.add_argument('-p', '--phase', type=str, default="train", - choices=['train', 'finetune','debug'], - help="The phase of current project") - - parser.add_argument('-c', '--cuda', type=int, default=0) # <0 if it is set as -1, program will use CPU - parser.add_argument('-e', '--ckpt', type=int, default=74, - help="checkpoint epoch for test phase or finetune phase") - - # training - parser.add_argument('--experiment_description', type=str, - default="用arcface作编ç å™¨ï¼Œè¿›è¡Œå›¾åƒé‡æž„") - - parser.add_argument('--train_yaml', type=str, default="train_arcface_rec.yaml") - - # system logger - parser.add_argument('--logger', type=str, - default="wandb", choices=['tensorboard', 'wandb','none'], help='system logger') - - # # logs (does not to be changed in most time) - # parser.add_argument('--dataloader_workers', type=int, default=6) - # parser.add_argument('--use_tensorboard', type=str2bool, default='True', - # choices=['True', 'False'], help='enable the tensorboard') - # parser.add_argument('--log_step', type=int, default=100) - # parser.add_argument('--sample_step', type=int, default=100) - - # # template (onece editing finished, it should be deleted) - # parser.add_argument('--str_parameter', type=str, default="default", help='str parameter') - # parser.add_argument('--str_parameter_choices', type=str, - # default="default", choices=['choice1', 'choice2','choice3'], help='str parameter with choices list') - # parser.add_argument('--int_parameter', type=int, default=0, help='int parameter') - # parser.add_argument('--float_parameter', type=float, default=0.0, help='float parameter') - # parser.add_argument('--bool_parameter', type=str2bool, default='True', choices=['True', 'False'], help='bool parameter') - # parser.add_argument('--list_str_parameter', type=str, nargs='+', default=["element1","element2"], help='str list parameter') - # parser.add_argument('--list_int_parameter', type=int, nargs='+', default=[0,1], help='int list parameter') - return parser.parse_args() - -ignoreKey = [ - "dataloader_workers", - "log_root_path", - "project_root", - "project_summary", - "project_checkpoints", - "project_samples", - "project_scripts", - "reporter_path", - "use_specified_data", - "specified_data_paths", - "dataset_path","cuda", - "test_script_name", - "test_dataloader", - "test_dataset_path", - "save_test_result", - "test_batch_size", - "node_name", - "checkpoint_epoch", - "test_dataset_path", - "test_dataset_name", - "use_my_test_date"] - -#################################################################################### -# This function will create the related directories before the -# training\fintune\test starts -# Your_log_root (version name) -# |---summary/... -# |---samples/... (save evaluated images) -# |---checkpoints/... -# |---scripts/... -# -#################################################################################### -def createDirs(sys_state): - # the base dir - if not os.path.exists(sys_state["log_root_path"]): - os.makedirs(sys_state["log_root_path"]) - - # create dirs - sys_state["project_root"] = os.path.join(sys_state["log_root_path"], - sys_state["version"]) - - project_root = sys_state["project_root"] - if not os.path.exists(project_root): - os.makedirs(project_root) - - sys_state["project_summary"] = os.path.join(project_root, "summary") - if not os.path.exists(sys_state["project_summary"]): - os.makedirs(sys_state["project_summary"]) - - sys_state["project_checkpoints"] = os.path.join(project_root, "checkpoints") - if not os.path.exists(sys_state["project_checkpoints"]): - os.makedirs(sys_state["project_checkpoints"]) - - sys_state["project_samples"] = os.path.join(project_root, "samples") - if not os.path.exists(sys_state["project_samples"]): - os.makedirs(sys_state["project_samples"]) - - sys_state["project_scripts"] = os.path.join(project_root, "scripts") - if not os.path.exists(sys_state["project_scripts"]): - os.makedirs(sys_state["project_scripts"]) - - sys_state["reporter_path"] = os.path.join(project_root,sys_state["version"]+"_report") - -def main(): - - config = getParameters() - # speed up the program - cudnn.benchmark = True - - from utilities.logo_class import logo_class - logo_class.print_group_logo() - - sys_state = {} - - # set the GPU number - if config.cuda >= 0: - os.environ["CUDA_VISIBLE_DEVICES"] = str(config.cuda) - - # read system environment paths - env_config = readConfig('env/env.json') - env_config = env_config["path"] - - # obtain all configurations in argparse - config_dic = vars(config) - for config_key in config_dic.keys(): - sys_state[config_key] = config_dic[config_key] - - #=======================Train Phase=========================# - if config.phase == "train": - # read training configurations from yaml file - ymal_config = getConfigYaml(os.path.join(env_config["train_config_path"], config.train_yaml)) - for item in ymal_config.items(): - sys_state[item[0]] = item[1] - - # create related dirs - sys_state["log_root_path"] = env_config["train_log_root"] - createDirs(sys_state) - - # create reporter file - reporter = Reporter(sys_state["reporter_path"]) - - # save the config json - config_json = os.path.join(sys_state["project_root"], env_config["config_json_name"]) - writeConfig(config_json, sys_state) - - # save the dependent scripts - # TODO and copy the scripts to the project dir - - # save the trainer script into [train_logs_root]\[version name]\scripts\ - file1 = os.path.join(env_config["train_scripts_path"], - "trainer_%s.py"%sys_state["train_script_name"]) - tgtfile1 = os.path.join(sys_state["project_scripts"], - "trainer_%s.py"%sys_state["train_script_name"]) - shutil.copyfile(file1,tgtfile1) - - # save the yaml file - file1 = os.path.join(env_config["train_config_path"], config.train_yaml) - tgtfile1 = os.path.join(sys_state["project_scripts"], config.train_yaml) - shutil.copyfile(file1,tgtfile1) - - # TODO replace below lines, here to save the critical scripts - - #=====================Finetune Phase=====================# - elif config.phase == "finetune": - sys_state["log_root_path"] = env_config["train_log_root"] - sys_state["project_root"] = os.path.join(sys_state["log_root_path"], sys_state["version"]) - - config_json = os.path.join(sys_state["project_root"], env_config["config_json_name"]) - train_config = readConfig(config_json) - for item in train_config.items(): - if item[0] in ignoreKey: - pass - else: - sys_state[item[0]] = item[1] - - createDirs(sys_state) - reporter = Reporter(sys_state["reporter_path"]) - sys_state["com_base"] = "train_logs.%s.scripts."%sys_state["version"] - - - # get the dataset path - sys_state["dataset_paths"] = {} - for data_key in env_config["dataset_paths"].keys(): - sys_state["dataset_paths"][data_key] = env_config["dataset_paths"][data_key] - - # display the training information - moduleName = "train_scripts.trainer_" + sys_state["train_script_name"] - if config.phase == "finetune": - moduleName = sys_state["com_base"] + "trainer_" + sys_state["train_script_name"] - - # print some important information - # TODO - print("Start to run training script: {}".format(moduleName)) - print("Traning version: %s"%sys_state["version"]) - print("Dataloader Name: %s"%sys_state["dataloader"]) - # print("Image Size: %d"%sys_state["imsize"]) - print("Batch size: %d"%(sys_state["batch_size"])) - - - - # Load the training script and start to train - reporter.writeConfig(sys_state) - - package = __import__(moduleName, fromlist=True) - trainerClass= getattr(package, 'Trainer') - trainer = trainerClass(sys_state, reporter) - trainer.train() - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/train_distillation_mgpu.py b/train_distillation_mgpu.py deleted file mode 100644 index 3ad79f1..0000000 --- a/train_distillation_mgpu.py +++ /dev/null @@ -1,329 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: train.py -# Created Date: Tuesday April 28th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 23rd February 2022 2:30:03 am -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - - -from curses.panel import version -import os -import shutil -import argparse -from torch.backends import cudnn -from utilities.json_config import readConfig, writeConfig -from utilities.reporter import Reporter -from utilities.yaml_config import getConfigYaml - - - -def str2bool(v): - return v.lower() in ('true') - -#################################################################################### -# To configure the seting of training\finetune\test -# -#################################################################################### -def getParameters(): - - parser = argparse.ArgumentParser() - # general settings - parser.add_argument('-v', '--version', type=str, default='distillation', - help="version name for train, test, finetune") - parser.add_argument('-t', '--tag', type=str, default='distillation', - help="tag for current experiment") - - parser.add_argument('-p', '--phase', type=str, default="train", - choices=['train', 'finetune','debug'], - help="The phase of current project") - - parser.add_argument('-c', '--gpus', type=int, nargs='+', default=[0,1]) # <0 if it is set as -1, program will use CPU - parser.add_argument('-e', '--ckpt', type=int, default=74, - help="checkpoint epoch for test phase or finetune phase") - - # training - parser.add_argument('--experiment_description', type=str, - default="测试蒸é¦ä»£ç ") - - parser.add_argument('--train_yaml', type=str, default="train_distillation.yaml") - - # system logger - parser.add_argument('--logger', type=str, - default="none", choices=['tensorboard', 'wandb','none'], help='system logger') - - # # logs (does not to be changed in most time) - # parser.add_argument('--dataloader_workers', type=int, default=6) - # parser.add_argument('--use_tensorboard', type=str2bool, default='True', - # choices=['True', 'False'], help='enable the tensorboard') - # parser.add_argument('--log_step', type=int, default=100) - # parser.add_argument('--sample_step', type=int, default=100) - - # # template (onece editing finished, it should be deleted) - # parser.add_argument('--str_parameter', type=str, default="default", help='str parameter') - # parser.add_argument('--str_parameter_choices', type=str, - # default="default", choices=['choice1', 'choice2','choice3'], help='str parameter with choices list') - # parser.add_argument('--int_parameter', type=int, default=0, help='int parameter') - # parser.add_argument('--float_parameter', type=float, default=0.0, help='float parameter') - # parser.add_argument('--bool_parameter', type=str2bool, default='True', choices=['True', 'False'], help='bool parameter') - # parser.add_argument('--list_str_parameter', type=str, nargs='+', default=["element1","element2"], help='str list parameter') - # parser.add_argument('--list_int_parameter', type=int, nargs='+', default=[0,1], help='int list parameter') - return parser.parse_args() - -ignoreKey = [ - "dataloader_workers", - "log_root_path", - "project_root", - "project_summary", - "project_checkpoints", - "project_samples", - "project_scripts", - "reporter_path", - "use_specified_data", - "specified_data_paths", - "dataset_path","cuda", - "test_script_name", - "test_dataloader", - "test_dataset_path", - "save_test_result", - "test_batch_size", - "node_name", - "checkpoint_epoch", - "test_dataset_path", - "test_dataset_name", - "use_my_test_date"] - -#################################################################################### -# This function will create the related directories before the -# training\fintune\test starts -# Your_log_root (version name) -# |---summary/... -# |---samples/... (save evaluated images) -# |---checkpoints/... -# |---scripts/... -# -#################################################################################### -def createDirs(sys_state): - # the base dir - if not os.path.exists(sys_state["log_root_path"]): - os.makedirs(sys_state["log_root_path"]) - - # create dirs - sys_state["project_root"] = os.path.join(sys_state["log_root_path"], - sys_state["version"]) - - project_root = sys_state["project_root"] - if not os.path.exists(project_root): - os.makedirs(project_root) - - sys_state["project_summary"] = os.path.join(project_root, "summary") - if not os.path.exists(sys_state["project_summary"]): - os.makedirs(sys_state["project_summary"]) - - sys_state["project_checkpoints"] = os.path.join(project_root, "checkpoints") - if not os.path.exists(sys_state["project_checkpoints"]): - os.makedirs(sys_state["project_checkpoints"]) - - sys_state["project_samples"] = os.path.join(project_root, "samples") - if not os.path.exists(sys_state["project_samples"]): - os.makedirs(sys_state["project_samples"]) - - sys_state["project_scripts"] = os.path.join(project_root, "scripts") - if not os.path.exists(sys_state["project_scripts"]): - os.makedirs(sys_state["project_scripts"]) - - sys_state["reporter_path"] = os.path.join(project_root,sys_state["version"]+"_report") - -def fetch_teacher_files(sys_state, env_config): - - version = sys_state["teacher_model"]["version"] - if not os.path.exists(sys_state["log_root_path"]): - os.makedirs(sys_state["log_root_path"]) - # create dirs - sys_state["teacher_model"]["project_root"] = os.path.join(sys_state["log_root_path"], version) - - project_root = sys_state["teacher_model"]["project_root"] - if not os.path.exists(project_root): - os.makedirs(project_root) - - sys_state["teacher_model"]["project_checkpoints"] = os.path.join(project_root, "checkpoints") - if not os.path.exists(sys_state["teacher_model"]["project_checkpoints"]): - os.makedirs(sys_state["teacher_model"]["project_checkpoints"]) - - sys_state["teacher_model"]["project_scripts"] = os.path.join(project_root, "scripts") - if not os.path.exists(sys_state["teacher_model"]["project_scripts"]): - os.makedirs(sys_state["teacher_model"]["project_scripts"]) - if sys_state["teacher_model"]["node_ip"] != "localhost": - from utilities.sshupload import fileUploaderClass - machine_config = env_config["machine_config"] - machine_config = readConfig(machine_config) - nodeinf = None - for item in machine_config: - if item["ip"] == sys_state["teacher_model"]["node_ip"]: - nodeinf = item - break - if not nodeinf: - raise Exception(print("Configuration of node %s is unavaliable"%sys_state["node_ip"])) - print("ready to fetch related files from server: %s ......"%nodeinf["ip"]) - uploader = fileUploaderClass(nodeinf["ip"],nodeinf["user"],nodeinf["passwd"]) - - remotebase = os.path.join(nodeinf['path'],"train_logs",version).replace('\\','/') - - # Get the config.json - print("ready to get the teacher's config.json...") - remoteFile = os.path.join(remotebase, env_config["config_json_name"]).replace('\\','/') - localFile = os.path.join(project_root, env_config["config_json_name"]) - - ssh_state = uploader.sshScpGet(remoteFile, localFile) - if not ssh_state: - raise Exception(print("Get file %s failed! config.json does not exist!"%remoteFile)) - print("success get the teacher's config.json from server %s"%nodeinf['ip']) - - # Get scripts - remoteDir = os.path.join(remotebase, "scripts").replace('\\','/') - localDir = os.path.join(sys_state["teacher_model"]["project_scripts"]) - ssh_state = uploader.sshScpGetDir(remoteDir, localDir) - if not ssh_state: - raise Exception(print("Get file %s failed! Program exists!"%remoteFile)) - print("Get the teacher's scripts successful!") - # Read model_config.json - config_json = os.path.join(project_root, env_config["config_json_name"]) - json_obj = readConfig(config_json) - for item in json_obj.items(): - if item[0] in ignoreKey: - pass - else: - sys_state["teacher_model"][item[0]] = item[1] - - # Get checkpoints - if sys_state["teacher_model"]["node_ip"] != "localhost": - ckpt_name = "step%d_%s.pth"%(sys_state["teacher_model"]["checkpoint_step"], - sys_state["teacher_model"]["checkpoint_names"]["generator_name"]) - localFile = os.path.join(sys_state["teacher_model"]["project_checkpoints"],ckpt_name) - if not os.path.exists(localFile): - remoteFile = os.path.join(remotebase, "checkpoints", ckpt_name).replace('\\','/') - ssh_state = uploader.sshScpGet(remoteFile, localFile, True) - if not ssh_state: - raise Exception(print("Get file %s failed! Checkpoint file does not exist!"%remoteFile)) - print("Get the teacher's checkpoint %s successfully!"%(ckpt_name)) - else: - print("%s exists!"%(ckpt_name)) - -def main(): - - config = getParameters() - # speed up the program - cudnn.benchmark = True - cudnn.enabled = True - - from utilities.logo_class import logo_class - logo_class.print_group_logo() - - sys_state = {} - - # set the GPU number - gpus = [str(i) for i in config.gpus] - os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpus) - - # read system environment paths - env_config = readConfig('env/env.json') - env_config = env_config["path"] - - # obtain all configurations in argparse - config_dic = vars(config) - for config_key in config_dic.keys(): - sys_state[config_key] = config_dic[config_key] - - #=======================Train Phase=========================# - if config.phase == "train": - # read training configurations from yaml file - ymal_config = getConfigYaml(os.path.join(env_config["train_config_path"], config.train_yaml)) - for item in ymal_config.items(): - sys_state[item[0]] = item[1] - - # create related dirs - sys_state["log_root_path"] = env_config["train_log_root"] - createDirs(sys_state) - - # create reporter file - reporter = Reporter(sys_state["reporter_path"]) - - # save the config json - config_json = os.path.join(sys_state["project_root"], env_config["config_json_name"]) - writeConfig(config_json, sys_state) - - # save the dependent scripts - # TODO and copy the scripts to the project dir - - # save the trainer script into [train_logs_root]\[version name]\scripts\ - file1 = os.path.join(env_config["train_scripts_path"], - "trainer_%s.py"%sys_state["train_script_name"]) - tgtfile1 = os.path.join(sys_state["project_scripts"], - "trainer_%s.py"%sys_state["train_script_name"]) - shutil.copyfile(file1,tgtfile1) - - # save the yaml file - file1 = os.path.join(env_config["train_config_path"], config.train_yaml) - tgtfile1 = os.path.join(sys_state["project_scripts"], config.train_yaml) - shutil.copyfile(file1,tgtfile1) - - # TODO replace below lines, here to save the critical scripts - - #=====================Finetune Phase=====================# - elif config.phase == "finetune": - sys_state["log_root_path"] = env_config["train_log_root"] - sys_state["project_root"] = os.path.join(sys_state["log_root_path"], sys_state["version"]) - - config_json = os.path.join(sys_state["project_root"], env_config["config_json_name"]) - train_config = readConfig(config_json) - for item in train_config.items(): - if item[0] in ignoreKey: - pass - else: - sys_state[item[0]] = item[1] - - createDirs(sys_state) - reporter = Reporter(sys_state["reporter_path"]) - sys_state["com_base"] = "train_logs.%s.scripts."%sys_state["version"] - - fetch_teacher_files(sys_state,env_config) - # get the dataset path - sys_state["dataset_paths"] = {} - for data_key in env_config["dataset_paths"].keys(): - sys_state["dataset_paths"][data_key] = env_config["dataset_paths"][data_key] - - # display the training information - moduleName = "train_scripts.trainer_" + sys_state["train_script_name"] - if config.phase == "finetune": - moduleName = sys_state["com_base"] + "trainer_" + sys_state["train_script_name"] - - # print some important information - # TODO - # print("Start to run training script: {}".format(moduleName)) - # print("Traning version: %s"%sys_state["version"]) - # print("Dataloader Name: %s"%sys_state["dataloader"]) - # # print("Image Size: %d"%sys_state["imsize"]) - # print("Batch size: %d"%(sys_state["batch_size"])) - # print("GPUs:", gpus) - print("\n========================================================================\n") - print(sys_state) - for data_key in sys_state.keys(): - print("[%s]---[%s]"%(data_key,sys_state[data_key])) - print("\n========================================================================\n") - - - # Load the training script and start to train - reporter.writeConfig(sys_state) - - package = __import__(moduleName, fromlist=True) - trainerClass= getattr(package, 'Trainer') - trainer = trainerClass(sys_state, reporter) - trainer.train() - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/train_multigpu.py b/train_multigpu.py deleted file mode 100644 index 5d98b15..0000000 --- a/train_multigpu.py +++ /dev/null @@ -1,252 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: train.py -# Created Date: Tuesday April 28th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 19th April 2022 6:58:59 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - - -import os -import shutil -import argparse -from torch.backends import cudnn -from utilities.json_config import readConfig, writeConfig -from utilities.reporter import Reporter -from utilities.yaml_config import getConfigYaml - - -def str2bool(v): - return v.lower() in ('true') - -#################################################################################### -# To configure the seting of training\finetune\test -# -#################################################################################### -def getParameters(): - - parser = argparse.ArgumentParser() - # general settings - parser.add_argument('-v', '--version', type=str, default='2maskloss256_1', - help="version name for train, test, finetune") - parser.add_argument('-t', '--tag', type=str, default='256', - help="tag for current experiment") - - parser.add_argument('-p', '--phase', type=str, default="train", - choices=['train', 'finetune','debug'], - help="The phase of current project") - - parser.add_argument('-c', '--gpus', type=int, nargs='+', default=[0,1,2,3]) # <0 if it is set as -1, program will use CPU - parser.add_argument('-e', '--ckpt', type=int, default=10000, - help="checkpoint epoch for test phase or finetune phase") - - # training - parser.add_argument('--experiment_description', type=str, - default="使用了一个128*128çš„mask以åŠ256*256çš„mask,mask loss调整到100使得mask能比较完整,å¦å¤–我始终认为mask head应该从encoder的输出引出,如果从decoder引出显然逻辑上\\\ - 就有很大的问题,因为这个时候mask与decoder共用åŒä¸€ä¸ªfeature,那么如果被ID改å˜åŽçš„feature脸型å‘生了较大的改å˜,岂ä¸æ˜¯mask也会跟ç€å˜,mask应该å映的是encoder输入的target imageçš„\\\ - mask,那么是å¦åº”该使用encoderçš„ä¿¡æ¯å‘¢.æ­¤å‰ä»Ženc中引head去生æˆmask会生æˆç©ºæ´žè¾ƒå¤§çš„mask,增大weight应该是å¯ä»¥æ”¹å–„这个问题å§,将生æˆå™¨æœ€åŽçš„两层改回adain注入") - - parser.add_argument('--train_yaml', type=str, default="train_2maskhead_256.yaml") - - # system logger - parser.add_argument('--logger', type=str, - default="wandb", choices=['tensorboard', 'wandb','none'], help='system logger') - - # # logs (does not to be changed in most time) - # parser.add_argument('--dataloader_workers', type=int, default=6) - # parser.add_argument('--use_tensorboard', type=str2bool, default='True', - # choices=['True', 'False'], help='enable the tensorboard') - # parser.add_argument('--log_step', type=int, default=100) - # parser.add_argument('--sample_step', type=int, default=100) - - # # template (onece editing finished, it should be deleted) - # parser.add_argument('--str_parameter', type=str, default="default", help='str parameter') - # parser.add_argument('--str_parameter_choices', type=str, - # default="default", choices=['choice1', 'choice2','choice3'], help='str parameter with choices list') - # parser.add_argument('--int_parameter', type=int, default=0, help='int parameter') - # parser.add_argument('--float_parameter', type=float, default=0.0, help='float parameter') - # parser.add_argument('--bool_parameter', type=str2bool, default='True', choices=['True', 'False'], help='bool parameter') - # parser.add_argument('--list_str_parameter', type=str, nargs='+', default=["element1","element2"], help='str list parameter') - # parser.add_argument('--list_int_parameter', type=int, nargs='+', default=[0,1], help='int list parameter') - return parser.parse_args() - -ignoreKey = [ - "dataloader_workers", - "log_root_path", - "project_root", - "project_summary", - "project_checkpoints", - "project_samples", - "project_scripts", - "reporter_path", - "use_specified_data", - "specified_data_paths", - "dataset_path","cuda", - "test_script_name", - "test_dataloader", - "test_dataset_path", - "save_test_result", - "test_batch_size", - "node_name", - "checkpoint_epoch", - "test_dataset_path", - "test_dataset_name", - "use_my_test_date"] - -#################################################################################### -# This function will create the related directories before the -# training\fintune\test starts -# Your_log_root (version name) -# |---summary/... -# |---samples/... (save evaluated images) -# |---checkpoints/... -# |---scripts/... -# -#################################################################################### -def createDirs(sys_state): - # the base dir - if not os.path.exists(sys_state["log_root_path"]): - os.makedirs(sys_state["log_root_path"]) - - # create dirs - sys_state["project_root"] = os.path.join(sys_state["log_root_path"], - sys_state["version"]) - - project_root = sys_state["project_root"] - if not os.path.exists(project_root): - os.makedirs(project_root) - - sys_state["project_summary"] = os.path.join(project_root, "summary") - if not os.path.exists(sys_state["project_summary"]): - os.makedirs(sys_state["project_summary"]) - - sys_state["project_checkpoints"] = os.path.join(project_root, "checkpoints") - if not os.path.exists(sys_state["project_checkpoints"]): - os.makedirs(sys_state["project_checkpoints"]) - - sys_state["project_samples"] = os.path.join(project_root, "samples") - if not os.path.exists(sys_state["project_samples"]): - os.makedirs(sys_state["project_samples"]) - - sys_state["project_scripts"] = os.path.join(project_root, "scripts") - if not os.path.exists(sys_state["project_scripts"]): - os.makedirs(sys_state["project_scripts"]) - - sys_state["reporter_path"] = os.path.join(project_root,sys_state["version"]+"_report") - -def main(): - - config = getParameters() - # speed up the program - cudnn.benchmark = True - cudnn.enabled = True - - from utilities.logo_class import logo_class - logo_class.print_group_logo() - - sys_state = {} - - # set the GPU number - gpus = [str(i) for i in config.gpus] - os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpus) - - # read system environment paths - env_config = readConfig('env/env.json') - env_config = env_config["path"] - - # obtain all configurations in argparse - config_dic = vars(config) - for config_key in config_dic.keys(): - sys_state[config_key] = config_dic[config_key] - - #=======================Train Phase=========================# - if config.phase == "train": - # read training configurations from yaml file - ymal_config = getConfigYaml(os.path.join(env_config["train_config_path"], config.train_yaml)) - for item in ymal_config.items(): - sys_state[item[0]] = item[1] - - # create related dirs - sys_state["log_root_path"] = env_config["train_log_root"] - createDirs(sys_state) - - # create reporter file - reporter = Reporter(sys_state["reporter_path"]) - - # save the config json - config_json = os.path.join(sys_state["project_root"], env_config["config_json_name"]) - writeConfig(config_json, sys_state) - - # save the dependent scripts - # TODO and copy the scripts to the project dir - - # save the trainer script into [train_logs_root]\[version name]\scripts\ - file1 = os.path.join(env_config["train_scripts_path"], - "trainer_%s.py"%sys_state["train_script_name"]) - tgtfile1 = os.path.join(sys_state["project_scripts"], - "trainer_%s.py"%sys_state["train_script_name"]) - shutil.copyfile(file1,tgtfile1) - - # save the yaml file - file1 = os.path.join(env_config["train_config_path"], config.train_yaml) - tgtfile1 = os.path.join(sys_state["project_scripts"], config.train_yaml) - shutil.copyfile(file1,tgtfile1) - - # TODO replace below lines, here to save the critical scripts - - #=====================Finetune Phase=====================# - elif config.phase == "finetune": - sys_state["log_root_path"] = env_config["train_log_root"] - sys_state["project_root"] = os.path.join(sys_state["log_root_path"], sys_state["version"]) - - config_json = os.path.join(sys_state["project_root"], env_config["config_json_name"]) - train_config = readConfig(config_json) - for item in train_config.items(): - if item[0] in ignoreKey: - pass - else: - sys_state[item[0]] = item[1] - - createDirs(sys_state) - reporter = Reporter(sys_state["reporter_path"]) - sys_state["com_base"] = "train_logs.%s.scripts."%sys_state["version"] - - - # get the dataset path - sys_state["dataset_paths"] = {} - for data_key in env_config["dataset_paths"].keys(): - sys_state["dataset_paths"][data_key] = env_config["dataset_paths"][data_key] - - # display the training information - moduleName = "train_scripts.trainer_" + sys_state["train_script_name"] - if config.phase == "finetune": - moduleName = sys_state["com_base"] + "trainer_" + sys_state["train_script_name"] - - # print some important information - # TODO - # print("Start to run training script: {}".format(moduleName)) - # print("Traning version: %s"%sys_state["version"]) - # print("Dataloader Name: %s"%sys_state["dataloader"]) - # # print("Image Size: %d"%sys_state["imsize"]) - # print("Batch size: %d"%(sys_state["batch_size"])) - # print("GPUs:", gpus) - print("\n========================================================================\n") - print(sys_state) - print("\n========================================================================\n") - - - # Load the training script and start to train - reporter.writeConfig(sys_state) - - package = __import__(moduleName, fromlist=True) - trainerClass= getattr(package, 'Trainer') - trainer = trainerClass(sys_state, reporter) - trainer.train() - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/train_scripts/trainer_2layer_FM.py b/train_scripts/trainer_2layer_FM.py deleted file mode 100644 index 6d687ce..0000000 --- a/train_scripts/trainer_2layer_FM.py +++ /dev/null @@ -1,343 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 22nd January 2022 12:42:28 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random - -import numpy as np - -import torch -import torch.nn.functional as F -from utilities.plot import plot_batch - -from train_scripts.trainer_base import TrainerBase - -class Trainer(TrainerBase): - - def __init__(self, config, reporter): - super(Trainer, self).__init__(config, reporter) - - self.img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - self.img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - # TODO modify this function to build your models - def init_framework(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - - model_config = self.config["model_configs"] - - if self.config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - dscript_name = "components." + model_config["d_model"]["script"] - - elif self.config["phase"] == "finetune": - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - dscript_name = self.config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - self.reporter.writeInfo("Generator structure:") - self.reporter.writeModel(self.gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - self.dis = dis_class(**model_config["d_model"]["module_params"]) - self.dis.feature_network.requires_grad_(False) - - # print and recorde model structure - self.reporter.writeInfo("Discriminator structure:") - self.reporter.writeModel(self.dis.__str__()) - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - - # train in GPU - if self.config["cuda"] >=0: - self.gen = self.gen.cuda() - self.dis = self.dis.cuda() - self.arcface= self.arcface.cuda() - - self.arcface.eval() - self.arcface.requires_grad_(False) - - # if in finetune phase, load the pretrained checkpoint - if self.config["phase"] == "finetune": - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.gen.load_state_dict(torch.load(model_path)) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["discriminator_name"])) - self.dis.load_state_dict(torch.load(model_path)) - - print('loaded trained backbone model step {}...!'.format(self.config["project_checkpoints"])) - - # TODO modify this function to configurate the optimizer of your pipeline - def __setup_optimizers__(self): - g_train_opt = self.config['g_optim_config'] - d_train_opt = self.config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in self.gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in self.dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = self.config['optim_type'] - - if optim_type == 'Adam': - self.g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - self.d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if self.config["phase"] == "finetune": - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["generator_name"])) - self.g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["discriminator_name"])) - self.d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(self.config["project_checkpoints"])) - - - # TODO modify this function to evaluate your model - # Evaluate the checkpoint - def __evaluation__( - self, - step = 0, - **kwargs - ): - src_image1 = kwargs["src1"] - src_image2 = kwargs["src2"] - batch_size = self.batch_size - self.gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* self.img_std + self.img_mean).numpy() - for r in range(batch_size): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = self.arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_size): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_size, 1, 1, 1) - img_fake = self.gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * self.img_std - img_fake = img_fake + self.img_mean - img_fake = img_fake.numpy() - for j in range(batch_size): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(self.sample_dir, 'step_'+str(step+1)+'.jpg')) - - - - - def train(self): - - ckpt_dir = self.config["project_checkpoints"] - log_frep = self.config["log_step"] - model_freq = self.config["model_save_step"] - sample_freq = self.config["sample_step"] - total_step = self.config["total_step"] - random_seed = self.config["dataset_params"]["random_seed"] - - self.batch_size = self.config["batch_size"] - self.sample_dir = self.config["project_samples"] - self.arcface_ckpt= self.config["arcface_ckpt"] - - - # prep_weights= self.config["layersWeight"] - id_w = self.config["id_weight"] - rec_w = self.config["reconstruct_weight"] - feat_w = self.config["feature_match_weight"] - - - - super().train() - - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - random.seed(random_seed) - randindex = [i for i in range(self.batch_size)] - random.shuffle(randindex) - import datetime - for step in range(self.start, total_step): - self.gen.train() - self.dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = self.train_loader.next() - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = self.arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - if interval: - - img_fake = self.gen(src_image1, latent_id) - gen_logits,_ = self.dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = self.dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - self.d_optimizer.zero_grad() - loss_D.backward() - self.d_optimizer.step() - else: - - # model.netD.requires_grad_(True) - img_fake = self.gen(src_image1, latent_id) - # G loss - gen_logits,feat = self.dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = self.arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = self.dis.get_feature(src_image1) - feat_match_loss = l1_loss(feat["3"],real_feat["3"]) + l1_loss(feat["2"],real_feat["2"]) - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - self.g_optimizer.zero_grad() - loss_G.backward() - self.g_optimizer.step() - - # Print out log info - if (step + 1) % log_frep == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(self.config["version"], elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - self.reporter.writeInfo(epochinformation) - - if self.config["logger"] == "tensorboard": - self.logger.add_scalar('G/G_loss', loss_G.item(), step) - self.logger.add_scalar('G/Rec_loss', loss_G_Rec.item(), step) - self.logger.add_scalar('G/Fm_loss', feat_match_loss.item(), step) - self.logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - self.logger.add_scalar('D/D_loss', loss_D.item(), step) - self.logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - self.logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif self.config["logger"] == "wandb": - self.logger.log({"G_loss": loss_G.item()}, step = step) - self.logger.log({"Rec_loss": loss_G_Rec.item()}, step = step) - self.logger.log({"Fm_loss": feat_match_loss.item()}, step = step) - self.logger.log({"G_ID": loss_G_ID.item()}, step = step) - self.logger.log({"D_loss": loss_D.item()}, step = step) - self.logger.log({"D_fake": loss_Dgen.item()}, step = step) - self.logger.log({"D_real": loss_Dreal.item()}, step = step) - if (step + 1) % sample_freq == 0: - self.__evaluation__( - step = step, - **{ - "src1": src_image1, - "src2": src_image2 - }) - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if (step+1) % model_freq==0: - - torch.save(self.gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - torch.save(self.dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["discriminator_name"]))) - - torch.save(self.g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - - torch.save(self.d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - - self.__evaluation__( - step = step, - **{ - "src1": src_image1, - "src2": src_image2 - }) \ No newline at end of file diff --git a/train_scripts/trainer_FM.py b/train_scripts/trainer_FM.py deleted file mode 100644 index f838550..0000000 --- a/train_scripts/trainer_FM.py +++ /dev/null @@ -1,349 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 24th January 2022 6:56:17 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random - -import numpy as np - -import torch -import torch.nn.functional as F -from utilities.plot import plot_batch - -from train_scripts.trainer_base import TrainerBase - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - self.img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - self.img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - # TODO modify this function to build your models - def init_framework(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - - model_config = self.config["model_configs"] - - if self.config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - dscript_name = "components." + model_config["d_model"]["script"] - - elif self.config["phase"] == "finetune": - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - dscript_name = self.config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - self.reporter.writeInfo("Generator structure:") - self.reporter.writeModel(self.gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - self.dis = dis_class(**model_config["d_model"]["module_params"]) - self.dis.feature_network.requires_grad_(False) - - # print and recorde model structure - self.reporter.writeInfo("Discriminator structure:") - self.reporter.writeModel(self.dis.__str__()) - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - - # train in GPU - if self.config["cuda"] >=0: - self.gen = self.gen.cuda() - self.dis = self.dis.cuda() - self.arcface= self.arcface.cuda() - - self.arcface.eval() - self.arcface.requires_grad_(False) - - # if in finetune phase, load the pretrained checkpoint - if self.config["phase"] == "finetune": - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.gen.load_state_dict(torch.load(model_path)) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["discriminator_name"])) - self.dis.load_state_dict(torch.load(model_path)) - - print('loaded trained backbone model step {}...!'.format(self.config["project_checkpoints"])) - - # TODO modify this function to configurate the optimizer of your pipeline - def __setup_optimizers__(self): - g_train_opt = self.config['g_optim_config'] - d_train_opt = self.config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in self.gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in self.dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = self.config['optim_type'] - - if optim_type == 'Adam': - self.g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - self.d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if self.config["phase"] == "finetune": - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["generator_name"])) - self.g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["discriminator_name"])) - self.d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(self.config["project_checkpoints"])) - - - # TODO modify this function to evaluate your model - # Evaluate the checkpoint - def __evaluation__(self, - step = 0, - **kwargs - ): - src_image1 = kwargs["src1"] - src_image2 = kwargs["src2"] - batch_size = self.batch_size - self.gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* self.img_std + self.img_mean).numpy() - for r in range(batch_size): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = self.arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_size): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_size, 1, 1, 1) - img_fake = self.gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * self.img_std - img_fake = img_fake + self.img_mean - img_fake = img_fake.numpy() - for j in range(batch_size): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(self.sample_dir, 'step_'+str(step+1)+'.jpg')) - - - - - def train(self): - - ckpt_dir = self.config["project_checkpoints"] - log_freq = self.config["log_step"] - model_freq = self.config["model_save_step"] - sample_freq = self.config["sample_step"] - total_step = self.config["total_step"] - random_seed = self.config["dataset_params"]["random_seed"] - - self.batch_size = self.config["batch_size"] - self.sample_dir = self.config["project_samples"] - self.arcface_ckpt= self.config["arcface_ckpt"] - - - # prep_weights= self.config["layersWeight"] - id_w = self.config["id_weight"] - rec_w = self.config["reconstruct_weight"] - feat_w = self.config["feature_match_weight"] - - - - super().train() - - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - random.seed(random_seed) - randindex = [i for i in range(self.batch_size)] - random.shuffle(randindex) - import datetime - for step in range(self.start, total_step): - self.gen.train() - self.dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = self.train_loader.next() - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = self.arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - if interval: - - img_fake = self.gen(src_image1, latent_id) - gen_logits,_ = self.dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = self.dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - self.d_optimizer.zero_grad() - loss_D.backward() - self.d_optimizer.step() - else: - - # model.netD.requires_grad_(True) - img_fake = self.gen(src_image1, latent_id) - # G loss - gen_logits,feat = self.dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = self.arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = self.dis.get_feature(src_image1) - feat_match_loss = l1_loss(feat["3"],real_feat["3"]) - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - self.g_optimizer.zero_grad() - loss_G.backward() - self.g_optimizer.step() - - # Print out log info - if (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(self.config["version"], elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - self.reporter.writeInfo(epochinformation) - - if self.config["logger"] == "tensorboard": - self.logger.add_scalar('G/G_loss', loss_G.item(), step) - self.logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - self.logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) - self.logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - self.logger.add_scalar('D/D_loss', loss_D.item(), step) - self.logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - self.logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif self.config["logger"] == "wandb": - self.logger.log({"G_loss": loss_G.item()}, step = step) - self.logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - self.logger.log({"G_feat_match": feat_match_loss.item()}, step = step) - self.logger.log({"G_ID": loss_G_ID.item()}, step = step) - self.logger.log({"D_loss": loss_D.item()}, step = step) - self.logger.log({"D_fake": loss_Dgen.item()}, step = step) - self.logger.log({"D_real": loss_Dreal.item()}, step = step) - - if (step + 1) % sample_freq == 0: - self.__evaluation__( - step = step, - **{ - "src1": src_image1, - "src2": src_image2 - }) - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if (step+1) % model_freq==0: - - torch.save(self.gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - torch.save(self.dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["discriminator_name"]))) - - torch.save(self.g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - - torch.save(self.d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - - self.__evaluation__( - step = step, - **{ - "src1": src_image1, - "src2": src_image2 - }) \ No newline at end of file diff --git a/train_scripts/trainer_GramFM.py b/train_scripts/trainer_GramFM.py deleted file mode 100644 index 863a00a..0000000 --- a/train_scripts/trainer_GramFM.py +++ /dev/null @@ -1,350 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 25th January 2022 3:25:56 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import numpy as np - -import torch -import torch.nn.functional as F - -from utilities.plot import plot_batch -from utilities.utilities import Gram - -from train_scripts.trainer_base import TrainerBase - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - self.img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - self.img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - # TODO modify this function to build your models - def init_framework(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - - model_config = self.config["model_configs"] - - if self.config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - dscript_name = "components." + model_config["d_model"]["script"] - - elif self.config["phase"] == "finetune": - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - dscript_name = self.config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - self.reporter.writeInfo("Generator structure:") - self.reporter.writeModel(self.gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - self.dis = dis_class(**model_config["d_model"]["module_params"]) - self.dis.feature_network.requires_grad_(False) - - # print and recorde model structure - self.reporter.writeInfo("Discriminator structure:") - self.reporter.writeModel(self.dis.__str__()) - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - - # train in GPU - if self.config["cuda"] >=0: - self.gen = self.gen.cuda() - self.dis = self.dis.cuda() - self.arcface= self.arcface.cuda() - - self.arcface.eval() - self.arcface.requires_grad_(False) - - # if in finetune phase, load the pretrained checkpoint - if self.config["phase"] == "finetune": - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.gen.load_state_dict(torch.load(model_path)) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["discriminator_name"])) - self.dis.load_state_dict(torch.load(model_path)) - - print('loaded trained backbone model step {}...!'.format(self.config["project_checkpoints"])) - - # TODO modify this function to configurate the optimizer of your pipeline - def __setup_optimizers__(self): - g_train_opt = self.config['g_optim_config'] - d_train_opt = self.config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in self.gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in self.dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = self.config['optim_type'] - - if optim_type == 'Adam': - self.g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - self.d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if self.config["phase"] == "finetune": - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["generator_name"])) - self.g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["discriminator_name"])) - self.d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(self.config["project_checkpoints"])) - - - # TODO modify this function to evaluate your model - # Evaluate the checkpoint - def __evaluation__(self, - step = 0, - **kwargs - ): - src_image1 = kwargs["src1"] - src_image2 = kwargs["src2"] - batch_size = self.batch_size - self.gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* self.img_std + self.img_mean).numpy() - for r in range(batch_size): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = self.arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_size): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_size, 1, 1, 1) - img_fake = self.gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * self.img_std - img_fake = img_fake + self.img_mean - img_fake = img_fake.numpy() - for j in range(batch_size): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(self.sample_dir, 'step_'+str(step+1)+'.jpg')) - - - - - def train(self): - - ckpt_dir = self.config["project_checkpoints"] - log_freq = self.config["log_step"] - model_freq = self.config["model_save_step"] - sample_freq = self.config["sample_step"] - total_step = self.config["total_step"] - random_seed = self.config["dataset_params"]["random_seed"] - - self.batch_size = self.config["batch_size"] - self.sample_dir = self.config["project_samples"] - self.arcface_ckpt= self.config["arcface_ckpt"] - - - # prep_weights= self.config["layersWeight"] - id_w = self.config["id_weight"] - rec_w = self.config["reconstruct_weight"] - feat_w = self.config["feature_match_weight"] - - - - super().train() - - #===============build losses===================# - # TODO replace below lines to build your losses - MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - random.seed(random_seed) - randindex = [i for i in range(self.batch_size)] - random.shuffle(randindex) - import datetime - for step in range(self.start, total_step): - self.gen.train() - self.dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = self.train_loader.next() - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = self.arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - if interval: - - img_fake = self.gen(src_image1, latent_id) - gen_logits,_ = self.dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = self.dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - self.d_optimizer.zero_grad() - loss_D.backward() - self.d_optimizer.step() - else: - - # model.netD.requires_grad_(True) - img_fake = self.gen(src_image1, latent_id) - # G loss - gen_logits,feat = self.dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = self.arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = self.dis.get_feature(src_image1) - feat_match_loss = l1_loss(Gram(feat["3"]), Gram(real_feat["3"])) + \ - l1_loss(Gram(feat["2"]), Gram(real_feat["2"])) - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - self.g_optimizer.zero_grad() - loss_G.backward() - self.g_optimizer.step() - - # Print out log info - if (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(self.config["version"], elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - self.reporter.writeInfo(epochinformation) - - if self.config["logger"] == "tensorboard": - self.logger.add_scalar('G/G_loss', loss_G.item(), step) - self.logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - self.logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) - self.logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - self.logger.add_scalar('D/D_loss', loss_D.item(), step) - self.logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - self.logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif self.config["logger"] == "wandb": - self.logger.log({"G_loss": loss_G.item()}, step = step) - self.logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - self.logger.log({"G_feat_match": feat_match_loss.item()}, step = step) - self.logger.log({"G_ID": loss_G_ID.item()}, step = step) - self.logger.log({"D_loss": loss_D.item()}, step = step) - self.logger.log({"D_fake": loss_Dgen.item()}, step = step) - self.logger.log({"D_real": loss_Dreal.item()}, step = step) - - if (step + 1) % sample_freq == 0: - self.__evaluation__( - step = step, - **{ - "src1": src_image1, - "src2": src_image2 - }) - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if (step+1) % model_freq==0: - - torch.save(self.gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - torch.save(self.dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["discriminator_name"]))) - - torch.save(self.g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - - torch.save(self.d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - - self.__evaluation__( - step = step, - **{ - "src1": src_image1, - "src2": src_image2 - }) \ No newline at end of file diff --git a/train_scripts/trainer_arcface_rec.py b/train_scripts/trainer_arcface_rec.py deleted file mode 100644 index ccb4fb6..0000000 --- a/train_scripts/trainer_arcface_rec.py +++ /dev/null @@ -1,237 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 29th January 2022 3:54:06 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -from cv2 import sqrt - -import numpy as np - -import torch -import torch.nn.functional as F -from torchvision.utils import save_image - -from train_scripts.trainer_base import TrainerBase - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - self.img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - self.img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - # TODO modify this function to build your models - def init_framework(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - - model_config = self.config["model_configs"] - - if self.config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(self.config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif self.config["phase"] == "finetune": - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - self.reporter.writeInfo("Generator structure:") - self.reporter.writeModel(self.gen.__str__()) - - - # print and recorde model structure - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - - # train in GPU - if self.config["cuda"] >=0: - self.gen = self.gen.cuda() - self.arcface= self.arcface.cuda() - - self.arcface.eval() - self.arcface.requires_grad_(False) - - # if in finetune phase, load the pretrained checkpoint - if self.config["phase"] == "finetune": - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.gen.load_state_dict(torch.load(model_path)) - - - print('loaded trained backbone model step {}...!'.format(self.config["project_checkpoints"])) - - # TODO modify this function to configurate the optimizer of your pipeline - def __setup_optimizers__(self): - g_train_opt = self.config['g_optim_config'] - - g_optim_params = [] - for k, v in self.gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = self.config['optim_type'] - - if optim_type == 'Adam': - self.g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if self.config["phase"] == "finetune": - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["generator_name"])) - self.g_optimizer.load_state_dict(torch.load(opt_path)) - - - print('loaded trained optimizer step {}...!'.format(self.config["project_checkpoints"])) - - - # TODO modify this function to evaluate your model - # Evaluate the checkpoint - def __evaluation__(self, - step = 0, - **kwargs - ): - src_image1 = kwargs["src1"] - self.gen.eval() - with torch.no_grad(): - id_vector_src1 = self.arcface(src_image1) - img_fake = self.gen(id_vector_src1).cpu() - img_fake = img_fake * self.img_std - img_fake = img_fake + self.img_mean - img_fake = img_fake.clamp_(0, 1) - print("Save test data") - save_image(img_fake, - os.path.join(self.sample_dir, 'step_'+str(step+1)+'.jpg'), - nrow=8) - - - - - def train(self): - - ckpt_dir = self.config["project_checkpoints"] - log_freq = self.config["log_step"] - model_freq = self.config["model_save_step"] - sample_freq = self.config["sample_step"] - total_step = self.config["total_step"] - random_seed = self.config["dataset_params"]["random_seed"] - - self.batch_size = self.config["batch_size"] - self.sample_dir = self.config["project_samples"] - self.arcface_ckpt= self.config["arcface_ckpt"] - - - super().train() - - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - - - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - random.seed(random_seed) - randindex = [i for i in range(self.batch_size)] - random.shuffle(randindex) - import datetime - for step in range(self.start, total_step): - self.gen.train() - src_image1 = self.train_loader.next() - - latent_id = self.arcface(src_image1) - img_fake = self.gen(latent_id.detach()) - loss = l1_loss(img_fake, src_image1) - - self.g_optimizer.zero_grad() - loss.backward() - self.g_optimizer.step() - - # Print out log info - if (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], Reconstruction: {:.4f}". \ - format(self.config["version"], elapsed, step, total_step, loss.item()) - print(epochinformation) - self.reporter.writeInfo(epochinformation) - - if self.config["logger"] == "tensorboard": - self.logger.add_scalar('Rec_loss', loss.item(), step) - elif self.config["logger"] == "wandb": - self.logger.log({"Rec_loss": loss.item()}, step = step) - - if (step + 1) % sample_freq == 0: - self.__evaluation__( - step = step, - **{ - "src1": src_image1 - }) - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if (step+1) % model_freq==0: - - torch.save(self.gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - - torch.save(self.g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - - self.__evaluation__( - step = step, - **{ - "src1": src_image1 - }) \ No newline at end of file diff --git a/train_scripts/trainer_base.py b/train_scripts/trainer_base.py deleted file mode 100644 index 8d6cb49..0000000 --- a/train_scripts/trainer_base.py +++ /dev/null @@ -1,114 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_base.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 17th January 2022 1:08:25 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -class TrainerBase(object): - - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - # Data loader - #============build train dataloader==============# - # TODO to modify the key: "your_train_dataset" to get your train dataset path - self.train_dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - self.dataloader_class = dataloaderClass - dataloader = self.dataloader_class(self.train_dataset, - config["batch_size"], - **config["dataset_params"]) - - self.train_loader= dataloader - - #========build evaluation dataloader=============# - # TODO to modify the key: "your_eval_dataset" to get your evaluation dataset path - # eval_dataset = config["dataset_paths"][config["eval_dataset_name"]] - - # #================================================# - # print("Prepare the evaluation dataloader...") - # dlModulename = config["eval_dataloader"] - # package = __import__("data_tools.eval_dataloader_%s"%dlModulename, fromlist=True) - # dataloaderClass = getattr(package, 'EvalDataset') - # dataloader = dataloaderClass(eval_dataset, - # config["eval_batch_size"]) - # self.eval_loader= dataloader - - # self.eval_iter = len(dataloader)//config["eval_batch_size"] - # if len(dataloader)%config["eval_batch_size"]>0: - # self.eval_iter+=1 - - #==============build tensorboard=================# - if self.config["logger"] == "tensorboard": - from utilities.utilities import build_tensorboard - tensorboard_writer = build_tensorboard(self.config["project_summary"]) - self.logger = tensorboard_writer - elif self.config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[self.config["tag"]], name=self.config["version"]) - - wandb.config = { - "total_step": self.config["total_step"], - "batch_size": self.config["batch_size"] - } - self.logger = wandb - - # TODO modify this function to build your models - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - pass - - # TODO modify this function to configurate the optimizer of your pipeline - def __setup_optimizers__(self): - pass - - - # TODO modify this function to evaluate your model - # Evaluate the checkpoint - def __evaluation__(self, - step = 0, - **kwargs - ): - pass - - - def train(self): - #===============build framework================# - self.init_framework() - - #===============build optimizer================# - # Optimizer - # TODO replace below lines to build your optimizer - print("build the optimizer...") - self.__setup_optimizers__() - - # set the start point for training loop - if self.config["phase"] == "finetune": - self.start = self.config["checkpoint_step"] - else: - self.start = 0 - - # Start time - import datetime - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() \ No newline at end of file diff --git a/train_scripts/trainer_cycleloss.py b/train_scripts/trainer_cycleloss.py deleted file mode 100644 index d1191bd..0000000 --- a/train_scripts/trainer_cycleloss.py +++ /dev/null @@ -1,350 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 19th January 2022 4:21:03 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random - -import numpy as np - -import torch -import torch.nn.functional as F -from utilities.plot import plot_batch - -from train_scripts.trainer_base import TrainerBase - -class Trainer(TrainerBase): - - def __init__(self, config, reporter): - super(Trainer, self).__init__(config, reporter) - - self.img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - self.img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - # TODO modify this function to build your models - def init_framework(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - - model_config = self.config["model_configs"] - - if self.config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - dscript_name = "components." + model_config["d_model"]["script"] - - elif self.config["phase"] == "finetune": - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - dscript_name = self.config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - self.reporter.writeInfo("Generator structure:") - self.reporter.writeModel(self.gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - self.dis = dis_class(**model_config["d_model"]["module_params"]) - self.dis.feature_network.requires_grad_(False) - - # print and recorde model structure - self.reporter.writeInfo("Discriminator structure:") - self.reporter.writeModel(self.dis.__str__()) - arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface1['model'].module - - # train in GPU - if self.config["cuda"] >=0: - self.gen = self.gen.cuda() - self.dis = self.dis.cuda() - self.arcface= self.arcface.cuda() - - self.arcface.eval() - self.arcface.requires_grad_(False) - - # if in finetune phase, load the pretrained checkpoint - if self.config["phase"] == "finetune": - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.gen.load_state_dict(torch.load(model_path)) - - model_path = os.path.join(self.config["project_checkpoints"], - "step%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["discriminator_name"])) - self.dis.load_state_dict(torch.load(model_path)) - - print('loaded trained backbone model step {}...!'.format(self.config["project_checkpoints"])) - - # TODO modify this function to configurate the optimizer of your pipeline - def __setup_optimizers__(self): - g_train_opt = self.config['g_optim_config'] - d_train_opt = self.config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in self.gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in self.dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = self.config['optim_type'] - - if optim_type == 'Adam': - self.g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - self.d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if self.config["phase"] == "finetune": - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["generator_name"])) - self.g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(self.config["project_checkpoints"], - "step%d_optim_%s.pth"%(self.config["checkpoint_step"], - self.config["optimizer_names"]["discriminator_name"])) - self.d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(self.config["project_checkpoints"])) - - - # TODO modify this function to evaluate your model - # Evaluate the checkpoint - def __evaluation__(self, - step = 0, - **kwargs - ): - src_image1 = kwargs["src1"] - src_image2 = kwargs["src2"] - batch_size = self.batch_size - self.gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* self.img_std + self.img_mean).numpy() - for r in range(batch_size): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = self.arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_size): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_size, 1, 1, 1) - img_fake = self.gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * self.img_std - img_fake = img_fake + self.img_mean - img_fake = img_fake.numpy() - for j in range(batch_size): - imgs.append(img_fake[j,...]) - print("Save sample image at step = % ..............."%step) - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(self.sample_dir, 'step_'+str(step+1)+'.jpg')) - - - - - def train(self): - - ckpt_dir = self.config["project_checkpoints"] - log_frep = self.config["log_step"] - model_freq = self.config["model_save_step"] - sample_freq = self.config["sample_step"] - total_step = self.config["total_step"] - random_seed = self.config["dataset_params"]["random_seed"] - - self.batch_size = self.config["batch_size"] - self.sample_dir = self.config["project_samples"] - self.arcface_ckpt= self.config["arcface_ckpt"] - - - # prep_weights= self.config["layersWeight"] - id_w = self.config["id_weight"] - rec_w = self.config["reconstruct_weight"] - feat_w = self.config["feature_match_weight"] - cyc_w = self.config["cycle_weight"] - - - - super().train() - - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - random.seed(random_seed) - randindex = [i for i in range(self.batch_size)] - random.shuffle(randindex) - import datetime - for step in range(self.start, total_step): - self.gen.train() - self.dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = self.train_loader.next() - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = self.arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - if interval: - - img_fake = self.gen(src_image1, latent_id) - gen_logits,_ = self.dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = self.dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - self.d_optimizer.zero_grad() - loss_D.backward() - self.d_optimizer.step() - else: - - # model.netD.requires_grad_(True) - img_fake = self.gen(src_image1, latent_id) - # G loss - gen_logits,feat = self.dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = self.arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = self.dis.get_feature(src_image1) - feat_match_loss = l1_loss(feat["3"],real_feat["3"]) + l1_loss(feat["2"],real_feat["2"]) - - src1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic') - src1_id = self.arcface(src1_down) - cyc_fake = self.gen(img_fake, src1_id) - loss_cyc = l1_loss(cyc_fake, src_image1) - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w + cyc_w * loss_cyc - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - self.g_optimizer.zero_grad() - loss_G.backward() - self.g_optimizer.step() - - # Print out log info - if (step + 1) % log_frep == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(self.config["version"], elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - self.reporter.writeInfo(epochinformation) - - if self.config["logger"] == "tensorboard": - self.logger.add_scalar('G/G_loss', loss_G.item(), step) - self.logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - self.logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) - self.logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - self.logger.add_scalar('G/Cycle', loss_cyc.item(), step) - self.logger.add_scalar('D/D_loss', loss_D.item(), step) - self.logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - self.logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif self.config["logger"] == "wandb": - self.logger.log({"G_loss": loss_G.item()}, step = step) - self.logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - self.logger.log({"G_feat_match": feat_match_loss.item()}, step = step) - self.logger.log({"G_ID": loss_G_ID.item()}, step = step) - self.logger.log({"Cycle": loss_cyc.item()}, step = step) - self.logger.log({"D_loss": loss_D.item()}, step = step) - self.logger.log({"D_fake": loss_Dgen.item()}, step = step) - self.logger.log({"D_real": loss_Dreal.item()}, step = step) - if (step + 1) % sample_freq == 0: - self.__evaluation__( - step = step, - **{ - "src1": src_image1, - "src2": src_image2 - }) - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if (step+1) % model_freq==0: - - torch.save(self.gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - torch.save(self.dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - self.config["checkpoint_names"]["discriminator_name"]))) - - torch.save(self.g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["generator_name"]))) - - torch.save(self.d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - self.config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - - self.__evaluation__( - step = step, - **{ - "src1": src_image1, - "src2": src_image2 - }) \ No newline at end of file diff --git a/train_scripts/trainer_distillation_mgpu.py b/train_scripts/trainer_distillation_mgpu.py deleted file mode 100644 index 751bcbe..0000000 --- a/train_scripts/trainer_distillation_mgpu.py +++ /dev/null @@ -1,575 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 23rd February 2022 3:39:20 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from losses.KA import KA -from utilities.plot import plot_batch -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -def add_mapping_hook(network, features,mapping_layers): - mapping_hooks = [] - - def get_activation(mem, name): - def get_output_hook(module, input, output): - mem[name] = output - - return get_output_hook - - def add_hook(net, mem, mapping_layers): - for n, m in net.named_modules(): - if n in mapping_layers: - mapping_hooks.append( - m.register_forward_hook(get_activation(mem, n))) - - add_hook(network, features, mapping_layers) - - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - com_base = "train_logs."+config["teacher_model"]["version"]+".scripts" - tscript_name = com_base +"."+ config["teacher_model"]["model_configs"]["g_model"]["script"] - class_name = config["teacher_model"]["model_configs"]["g_model"]["class_name"] - package = __import__(tscript_name, fromlist=True) - gen_class = getattr(package, class_name) - tgen = gen_class(**config["teacher_model"]["model_configs"]["g_model"]["module_params"]) - tgen = tgen.eval() - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - reporter.writeInfo("Teacher structure:") - reporter.writeModel(tgen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - model_path = os.path.join(config["teacher_model"]["project_checkpoints"], - "step%d_%s.pth"%(config["teacher_model"]["model_step"], - config["teacher_model"]["checkpoint_names"]["generator_name"])) - tgen.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained teacher backbone model step {}...!'.format(config["teacher_model"]["model_step"])) - tgen = tgen.to(device) - tgen.requires_grad_(False) - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - t_features = {} - s_features = {} - add_mapping_hook(tgen,t_features,config["feature_list"]) - add_mapping_hook(gen,s_features,config["feature_list"]) - - return tgen, gen, dis, arcface, t_features, s_features - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - feat_w = config["feature_match_weight"] - distill_w = config["distillation_weight"] - feat_num = len(config["feature_list"]) - - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - tgen, gen, dis, arcface, t_feat, s_feat = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface, tgen]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - dis.feature_network.requires_grad_(False) - - for step in range(start, total_step): - gen.train() - dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = dataloader.next() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("dataloader:",elapsed) - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - - - img_fake = gen(src_image1, latent_id) - gen_logits,_ = dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Discriminator training:",elapsed) - else: - - # model.netD.requires_grad_(True) - img_t = tgen(src_image1, latent_id) - img_fake = gen(src_image1, latent_id) - - Sacts = [ - s_feat[key] for key in sorted(s_feat.keys()) - ] - Tacts = [ - t_feat[key] for key in sorted(t_feat.keys()) - ] - loss_distill = 0 - for Sact, Tact in zip(Sacts, Tacts): - loss_distill += -KA(Sact, Tact) - # G loss - loss_distill /= feat_num - gen_logits,feat = dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = dis.get_feature(src_image1) - feat_match_loss = l1_loss(feat["3"],real_feat["3"]) - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w + loss_distill * distill_w - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Generator training:",elapsed) - - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("ready to report losses") - # ID_Total= loss_G_ID - # torch.distributed.all_reduce(ID_Total) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - Distillaton_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_distill.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) - logger.add_scalar('G/G_distillation', loss_distill.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"G_feat_match": feat_match_loss.item()}, step = step) - logger.log({"G_distillation": loss_distill.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_fake": loss_Dgen.item()}, step = step) - logger.log({"D_real": loss_Dreal.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake = gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_distillation_mgpu_withrec_importweight.py b/train_scripts/trainer_distillation_mgpu_withrec_importweight.py deleted file mode 100644 index d425e9a..0000000 --- a/train_scripts/trainer_distillation_mgpu_withrec_importweight.py +++ /dev/null @@ -1,590 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 4th March 2022 7:02:04 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from losses.KA import KA -from utilities.plot import plot_batch -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -def add_mapping_hook(network, features,mapping_layers): - mapping_hooks = [] - - def get_activation(mem, name): - def get_output_hook(module, input, output): - mem[name] = output - - return get_output_hook - - def add_hook(net, mem, mapping_layers): - for n, m in net.named_modules(): - if n in mapping_layers: - mapping_hooks.append( - m.register_forward_hook(get_activation(mem, n))) - - add_hook(network, features, mapping_layers) - - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - com_base = "train_logs."+config["teacher_model"]["version"]+".scripts" - tscript_name = com_base +"."+ config["teacher_model"]["model_configs"]["g_model"]["script"] - class_name = config["teacher_model"]["model_configs"]["g_model"]["class_name"] - package = __import__(tscript_name, fromlist=True) - gen_class = getattr(package, class_name) - tgen = gen_class(**config["teacher_model"]["model_configs"]["g_model"]["module_params"]) - tgen = tgen.eval() - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - reporter.writeInfo("Teacher structure:") - reporter.writeModel(tgen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - model_path = os.path.join(config["teacher_model"]["project_checkpoints"], - "step%d_%s.pth"%(config["teacher_model"]["model_step"], - config["teacher_model"]["checkpoint_names"]["generator_name"])) - tgen.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) - print('loaded trained teacher backbone model step {}...!'.format(config["teacher_model"]["model_step"])) - tgen = tgen.to(device) - tgen.requires_grad_(False) - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - t_features = {} - s_features = {} - add_mapping_hook(tgen,t_features,config["feature_list"]) - add_mapping_hook(gen,s_features,config["feature_list"]) - - return tgen, gen, dis, arcface, t_features, s_features - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - feat_w = config["feature_match_weight"] - distill_w = config["distillation_weight"] - distill_rec_w = config["teacher_reconstruction"] - distill_feat_w = config["teacher_featurematching"] - - feat_num = len(config["feature_list"]) - - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - tgen, gen, dis, arcface, t_feat, s_feat = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface, tgen]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - l1_loss_import = torch.nn.L1Loss(reduce=False) - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - dis.feature_network.requires_grad_(False) - - for step in range(start, total_step): - gen.train() - dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = dataloader.next() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("dataloader:",elapsed) - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - - - img_fake = gen(src_image1, latent_id) - gen_logits,_ = dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Discriminator training:",elapsed) - else: - - # model.netD.requires_grad_(True) - t_fake = tgen(src_image1, latent_id) - t_id = arcface(t_fake.detach()) - t_feat = dis.get_feature(t_fake.detach()) - realism = cos_loss(t_id, latent_id) - - - - img_fake = gen(src_image1, latent_id) - - Sacts = [ - s_feat[key] for key in sorted(s_feat.keys()) - ] - Tacts = [ - t_feat[key] for key in sorted(t_feat.keys()) - ] - loss_distill = 0 - for Sact, Tact in zip(Sacts, Tacts): - loss_distill += -KA(Sact, Tact) - # G loss - loss_distill /= feat_num - gen_logits,feat = dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = dis.get_feature(src_image1) - feat_match_loss = l1_loss(feat["3"],real_feat["3"]) - - feat_match_ts = (realism * l1_loss_import(feat["3"],t_feat)).mean() - t_rec_loss = (realism * l1_loss_import(t_fake.detach(), img_fake)).mean() - - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w + loss_distill * distill_w +\ - distill_feat_w * feat_match_ts + distill_rec_w * t_rec_loss - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Generator training:",elapsed) - - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("ready to report losses") - # ID_Total= loss_G_ID - # torch.distributed.all_reduce(ID_Total) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - Distillaton_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_distill.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) - logger.add_scalar('G/G_distillation', loss_distill.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"G_feat_match": feat_match_loss.item()}, step = step) - logger.log({"G_distillation": loss_distill.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_fake": loss_Dgen.item()}, step = step) - logger.log({"D_real": loss_Dreal.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake = gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_mgpu_2maskloss.py b/train_scripts/trainer_mgpu_2maskloss.py deleted file mode 100644 index b1c80c6..0000000 --- a/train_scripts/trainer_mgpu_2maskloss.py +++ /dev/null @@ -1,588 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 12th April 2022 1:51:44 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from utilities.plot import plot_batch -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - # arcface = iresnet100(pretrained=False, fp16=False) - # arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - # arcface.eval() - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - -def d_logistic_loss(real_pred, fake_pred): - real_loss = F.softplus(-real_pred) - fake_loss = F.softplus(fake_pred) - - return real_loss.mean() + fake_loss.mean() - - -def d_r1_loss(d_out, x_in): - # zero-centered gradient penalty for real images - batch_size = x_in.size(0) - grad_dout = torch.autograd.grad( - outputs=d_out.sum(), inputs=x_in, - create_graph=True, retain_graph=True, only_inputs=True - )[0] - grad_dout2 = grad_dout.pow(2) - assert(grad_dout2.size() == x_in.size()) - reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) - return reg - - -def g_nonsaturating_loss(fake_pred): - loss = F.softplus(-fake_pred).mean() - - return loss - -def requires_grad(model, flag=True): - for p in model.parameters(): - p.requires_grad = flag - - -# def r1_reg(d_out, x_in): -# # zero-centered gradient penalty for real images -# batch_size = x_in.size(0) -# grad_dout = torch.autograd.grad( -# outputs=d_out.sum(), inputs=x_in, -# create_graph=True, retain_graph=True, only_inputs=True -# )[0] -# grad_dout2 = grad_dout.pow(2) -# assert(grad_dout2.size() == x_in.size()) -# reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) -# return reg - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - d_reg_freq = config["d_reg_freq"] - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - rec_fm_w = config["rec_feature_match_weight"] - mask_w = config["mask_weight"] - cycle_w = config["cycle_weight"] - reg_w = config["reg_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - for step in range(start, total_step): - gen.train() - dis.train() - - for interval in range(2): - - src_image1, src_image2, mask_label = dataloader.next() - - if step%2 == 0: - img_id = src_image2 - else: - random.shuffle(randindex) - img_id = src_image2[randindex] - mask_label = mask_label[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - requires_grad(dis, True) - requires_grad(gen, False) - - d_regularize = step % d_reg_freq == 0 - if d_regularize: - src_image1.requires_grad_() - - real_logits,_ = dis(src_image1) - with torch.no_grad(): - img_fake,_,_ = gen(src_image1, latent_id.detach()) - fake_logits,_ = dis(img_fake.detach()) - - loss_D = d_logistic_loss(real_logits, fake_logits) - - if d_regularize: - loss_reg = d_r1_loss(real_logits, src_image1) - loss_D += loss_reg * reg_w * d_reg_freq - - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - - - #================================Generator interval======================================# - else: - requires_grad(dis, False) - requires_grad(gen, True) - # model.netD.requires_grad_(True) - img_fake,lr_mask,hr_mask= gen(src_image1, latent_id.detach()) - # G loss - gen_logits,fake_feat= dis(img_fake) - # real_feat = dis.get_feature(src_image1) - loss_Gmain = g_nonsaturating_loss(gen_logits) - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id.detach())).mean() - mask_label_lr = F.interpolate(mask_label, size=(128,128), mode='bicubic') - loss_mask = l1_loss(lr_mask, mask_label_lr) + l1_loss(hr_mask, mask_label) - loss_G = loss_Gmain + loss_G_ID * id_w + loss_mask * mask_w - if step%2 == 0: - #G_Rec - real_feat = dis.get_feature(src_image1) - rec_fm = l1_loss(fake_feat, real_feat) - loss_G_Rec = l1_loss(img_fake, src_image1) - # lpips_loss = loss_fn_vgg(img_fake, src_image1).mean() - loss_G += (loss_G_Rec * rec_w + rec_fm_w * rec_fm) #+ rec_fm * rec_fm_w - else: - source1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic') - latent_source1 = arcface(source1_down) - latent_source1 = F.normalize(latent_source1, p=2, dim=1) - cycle_src,_,_ = gen(img_fake, latent_source1) - cycle_loss = l1_loss(src_image1,cycle_src) - # cycle_feat = dis.get_feature(cycle_src) - # cycle_fm = l1_loss(real_feat["3"],cycle_feat["3"]) + l1_loss(real_feat["2"],cycle_feat["2"]) - loss_G += cycle_loss * cycle_w #+ cycle_fm * cycle_fm_w - - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_fm: {:.4f}, \ - # rec_fm: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - # format(version, elapsed, step, total_step, \ - # loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_fm.item(), \ - # rec_fm.item(), cycle_loss.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_loss: {:.4f}, rec_fm: {:.4f}, loss_mask: {:.4f}, D_loss: {:.4f}, D_R1: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_loss.item(), rec_fm.item(), loss_mask.item(), loss_D.item(), loss_reg.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/cycle_loss', cycle_loss.item(), step) - # logger.add_scalar('G/cycle_fm', cycle_fm.item(), step) - # logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/loss_mask', loss_mask.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_reg', loss_reg.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"cycle_loss": cycle_loss.item()}, step = step) - # logger.log({"cycle_fm": cycle_fm.item()}, step = step) - # logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"loss_mask": loss_mask.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_reg": loss_reg.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake,_,_ = gen(image_infer, id_vector_src1) - - img_fake = img_fake.cpu() * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - # pred_mask = pred_mask.cpu().numpy() * 255 - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_mgpu_2maskloss_256.py b/train_scripts/trainer_mgpu_2maskloss_256.py deleted file mode 100644 index 08be608..0000000 --- a/train_scripts/trainer_mgpu_2maskloss_256.py +++ /dev/null @@ -1,592 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 19th April 2022 6:57:10 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from utilities.plot import plot_batch -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - # arcface = iresnet100(pretrained=False, fp16=False) - # arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - # arcface.eval() - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - -def d_logistic_loss(real_pred, fake_pred): - real_loss = F.softplus(-real_pred) - fake_loss = F.softplus(fake_pred) - - return real_loss.mean() + fake_loss.mean() - - -def d_r1_loss(d_out, x_in): - # zero-centered gradient penalty for real images - batch_size = x_in.size(0) - grad_dout = torch.autograd.grad( - outputs=d_out.sum(), inputs=x_in, - create_graph=True, retain_graph=True, only_inputs=True - )[0] - grad_dout2 = grad_dout.pow(2) - assert(grad_dout2.size() == x_in.size()) - reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) - return reg - - -def g_nonsaturating_loss(fake_pred): - loss = F.softplus(-fake_pred).mean() - - return loss - -def requires_grad(model, flag=True): - for p in model.parameters(): - p.requires_grad = flag - - -# def r1_reg(d_out, x_in): -# # zero-centered gradient penalty for real images -# batch_size = x_in.size(0) -# grad_dout = torch.autograd.grad( -# outputs=d_out.sum(), inputs=x_in, -# create_graph=True, retain_graph=True, only_inputs=True -# )[0] -# grad_dout2 = grad_dout.pow(2) -# assert(grad_dout2.size() == x_in.size()) -# reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) -# return reg - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - d_reg_freq = config["d_reg_freq"] - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - rec_fm_w = config["rec_feature_match_weight"] - mask_w = config["mask_weight"] - cycle_w = config["cycle_weight"] - reg_w = config["reg_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - for step in range(start, total_step): - gen.train() - dis.train() - - for interval in range(2): - - src_image1, src_image2, mask_label = dataloader.next() - - src_image1 = F.interpolate(src_image1,size=(256,256), mode='bicubic') - src_image2 = F.interpolate(src_image2,size=(256,256), mode='bicubic') - - if step%2 == 0: - img_id = src_image2 - else: - random.shuffle(randindex) - img_id = src_image2[randindex] - mask_label = mask_label[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - requires_grad(dis, True) - requires_grad(gen, False) - - d_regularize = step % d_reg_freq == 0 - if d_regularize: - src_image1.requires_grad_() - - real_logits,_ = dis(src_image1) - with torch.no_grad(): - img_fake,_,_ = gen(src_image1, latent_id.detach()) - fake_logits,_ = dis(img_fake.detach()) - - loss_D = d_logistic_loss(real_logits, fake_logits) - - if d_regularize: - loss_reg = d_r1_loss(real_logits, src_image1) - loss_D += loss_reg * reg_w * d_reg_freq - - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - - - #================================Generator interval======================================# - else: - requires_grad(dis, False) - requires_grad(gen, True) - # model.netD.requires_grad_(True) - img_fake,lr_mask,hr_mask= gen(src_image1, latent_id.detach()) - # G loss - gen_logits,fake_feat= dis(img_fake) - # real_feat = dis.get_feature(src_image1) - loss_Gmain = g_nonsaturating_loss(gen_logits) - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id.detach())).mean() - mask_label_lr = F.interpolate(mask_label, size=(64,64), mode='bicubic') - mask_label = F.interpolate(mask_label, size=(256,256), mode='bicubic') - loss_mask = l1_loss(lr_mask, mask_label_lr) + l1_loss(hr_mask, mask_label) - loss_G = loss_Gmain + loss_G_ID * id_w + loss_mask * mask_w - if step%2 == 0: - #G_Rec - real_feat = dis.get_feature(src_image1) - rec_fm = l1_loss(fake_feat, real_feat) - loss_G_Rec = l1_loss(img_fake, src_image1) - # lpips_loss = loss_fn_vgg(img_fake, src_image1).mean() - loss_G += (loss_G_Rec * rec_w + rec_fm_w * rec_fm) #+ rec_fm * rec_fm_w - else: - source1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic') - latent_source1 = arcface(source1_down) - latent_source1 = F.normalize(latent_source1, p=2, dim=1) - cycle_src,_,_ = gen(img_fake, latent_source1) - cycle_loss = l1_loss(src_image1,cycle_src) - # cycle_feat = dis.get_feature(cycle_src) - # cycle_fm = l1_loss(real_feat["3"],cycle_feat["3"]) + l1_loss(real_feat["2"],cycle_feat["2"]) - loss_G += cycle_loss * cycle_w #+ cycle_fm * cycle_fm_w - - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_fm: {:.4f}, \ - # rec_fm: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - # format(version, elapsed, step, total_step, \ - # loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_fm.item(), \ - # rec_fm.item(), cycle_loss.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_loss: {:.4f}, rec_fm: {:.4f}, loss_mask: {:.4f}, D_loss: {:.4f}, D_R1: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_loss.item(), rec_fm.item(), loss_mask.item(), loss_D.item(), loss_reg.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/cycle_loss', cycle_loss.item(), step) - # logger.add_scalar('G/cycle_fm', cycle_fm.item(), step) - # logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/loss_mask', loss_mask.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_reg', loss_reg.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"cycle_loss": cycle_loss.item()}, step = step) - # logger.log({"cycle_fm": cycle_fm.item()}, step = step) - # logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"loss_mask": loss_mask.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_reg": loss_reg.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake,_,_ = gen(image_infer, id_vector_src1) - - img_fake = img_fake.cpu() * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - # pred_mask = pred_mask.cpu().numpy() * 255 - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_mgpu_fm.py b/train_scripts/trainer_mgpu_fm.py deleted file mode 100644 index 8920688..0000000 --- a/train_scripts/trainer_mgpu_fm.py +++ /dev/null @@ -1,582 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 2nd April 2022 1:48:32 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from utilities.plot import plot_batch -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - # arcface = iresnet100(pretrained=False, fp16=False) - # arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - # arcface.eval() - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - -def d_logistic_loss(real_pred, fake_pred): - real_loss = F.softplus(-real_pred) - fake_loss = F.softplus(fake_pred) - - return real_loss.mean() + fake_loss.mean() - - -def d_r1_loss(d_out, x_in): - # zero-centered gradient penalty for real images - batch_size = x_in.size(0) - grad_dout = torch.autograd.grad( - outputs=d_out.sum(), inputs=x_in, - create_graph=True, retain_graph=True, only_inputs=True - )[0] - grad_dout2 = grad_dout.pow(2) - assert(grad_dout2.size() == x_in.size()) - reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) - return reg - - -def g_nonsaturating_loss(fake_pred): - loss = F.softplus(-fake_pred).mean() - - return loss - -def requires_grad(model, flag=True): - for p in model.parameters(): - p.requires_grad = flag - - -# def r1_reg(d_out, x_in): -# # zero-centered gradient penalty for real images -# batch_size = x_in.size(0) -# grad_dout = torch.autograd.grad( -# outputs=d_out.sum(), inputs=x_in, -# create_graph=True, retain_graph=True, only_inputs=True -# )[0] -# grad_dout2 = grad_dout.pow(2) -# assert(grad_dout2.size() == x_in.size()) -# reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) -# return reg - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - d_reg_freq = config["d_reg_freq"] - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - rec_fm_w = config["rec_feature_match_weight"] - cycle_fm_w = config["cycle_feature_match_weight"] - cycle_w = config["cycle_weight"] - reg_w = config["reg_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - for step in range(start, total_step): - gen.train() - dis.train() - - for interval in range(2): - - src_image1, src_image2 = dataloader.next() - - if step%2 == 0: - img_id = src_image2 - else: - random.shuffle(randindex) - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - requires_grad(dis, True) - requires_grad(gen, False) - - d_regularize = step % d_reg_freq == 0 - if d_regularize: - src_image1.requires_grad_() - - real_logits,_ = dis(src_image1) - with torch.no_grad(): - img_fake = gen(src_image1, latent_id.detach()) - fake_logits,_ = dis(img_fake.detach()) - - loss_D = d_logistic_loss(real_logits, fake_logits) - - if d_regularize: - loss_reg = d_r1_loss(real_logits, src_image1) - loss_D += loss_reg * reg_w * d_reg_freq - - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - - - #================================Generator interval======================================# - else: - requires_grad(dis, False) - requires_grad(gen, True) - # model.netD.requires_grad_(True) - img_fake = gen(src_image1, latent_id.detach()) - # G loss - gen_logits,fake_feat= dis(img_fake) - # real_feat = dis.get_feature(src_image1) - loss_Gmain = g_nonsaturating_loss(gen_logits) - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id.detach())).mean() - loss_G = loss_Gmain + loss_G_ID * id_w - if step%2 == 0: - #G_Rec - real_feat = dis.get_feature(src_image1) - rec_fm = l1_loss(fake_feat, real_feat) - loss_G_Rec = l1_loss(img_fake, src_image1) - # lpips_loss = loss_fn_vgg(img_fake, src_image1).mean() - loss_G += (loss_G_Rec * rec_w + rec_fm_w * rec_fm) #+ rec_fm * rec_fm_w - else: - source1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic') - latent_source1 = arcface(source1_down) - latent_source1 = F.normalize(latent_source1, p=2, dim=1) - cycle_src = gen(img_fake, latent_source1) - cycle_loss = l1_loss(src_image1,cycle_src) - # cycle_feat = dis.get_feature(cycle_src) - # cycle_fm = l1_loss(real_feat["3"],cycle_feat["3"]) + l1_loss(real_feat["2"],cycle_feat["2"]) - loss_G += cycle_loss * cycle_w #+ cycle_fm * cycle_fm_w - - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_fm: {:.4f}, \ - # rec_fm: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - # format(version, elapsed, step, total_step, \ - # loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_fm.item(), \ - # rec_fm.item(), cycle_loss.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_loss: {:.4f}, rec_fm: {:.4f}, D_loss: {:.4f}, D_R1: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_loss.item(), rec_fm.item(), loss_D.item(), loss_reg.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/cycle_loss', cycle_loss.item(), step) - # logger.add_scalar('G/cycle_fm', cycle_fm.item(), step) - # logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/lpips_loss', rec_fm.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_reg', loss_reg.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"cycle_loss": cycle_loss.item()}, step = step) - # logger.log({"cycle_fm": cycle_fm.item()}, step = step) - # logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"lpips_loss": rec_fm.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_reg": loss_reg.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake = gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_mgpu_fm_w_mask.py b/train_scripts/trainer_mgpu_fm_w_mask.py deleted file mode 100644 index e4425c5..0000000 --- a/train_scripts/trainer_mgpu_fm_w_mask.py +++ /dev/null @@ -1,581 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Wednesday, 13th April 2022 5:37:02 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from utilities.plot import plot_batch -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - # arcface = iresnet100(pretrained=False, fp16=False) - # arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - # arcface.eval() - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - -def d_logistic_loss(real_pred, fake_pred): - real_loss = F.softplus(-real_pred) - fake_loss = F.softplus(fake_pred) - - return real_loss.mean() + fake_loss.mean() - - -def d_r1_loss(d_out, x_in): - # zero-centered gradient penalty for real images - batch_size = x_in.size(0) - grad_dout = torch.autograd.grad( - outputs=d_out.sum(), inputs=x_in, - create_graph=True, retain_graph=True, only_inputs=True - )[0] - grad_dout2 = grad_dout.pow(2) - assert(grad_dout2.size() == x_in.size()) - reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) - return reg - - -def g_nonsaturating_loss(fake_pred): - loss = F.softplus(-fake_pred).mean() - - return loss - -def requires_grad(model, flag=True): - for p in model.parameters(): - p.requires_grad = flag - - -# def r1_reg(d_out, x_in): -# # zero-centered gradient penalty for real images -# batch_size = x_in.size(0) -# grad_dout = torch.autograd.grad( -# outputs=d_out.sum(), inputs=x_in, -# create_graph=True, retain_graph=True, only_inputs=True -# )[0] -# grad_dout2 = grad_dout.pow(2) -# assert(grad_dout2.size() == x_in.size()) -# reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) -# return reg - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - d_reg_freq = config["d_reg_freq"] - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - rec_fm_w = config["rec_feature_match_weight"] - cycle_w = config["cycle_weight"] - reg_w = config["reg_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - for step in range(start, total_step): - gen.train() - dis.train() - - for interval in range(2): - - src_image1, src_image2 = dataloader.next() - - if step%2 == 0: - img_id = src_image2 - else: - random.shuffle(randindex) - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - requires_grad(dis, True) - requires_grad(gen, False) - - d_regularize = step % d_reg_freq == 0 - if d_regularize: - src_image1.requires_grad_() - - real_logits,_ = dis(src_image1) - with torch.no_grad(): - img_fake,_ = gen(src_image1, latent_id.detach()) - fake_logits,_ = dis(img_fake.detach()) - - loss_D = d_logistic_loss(real_logits, fake_logits) - - if d_regularize: - loss_reg = d_r1_loss(real_logits, src_image1) - loss_D += loss_reg * reg_w * d_reg_freq - - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - - - #================================Generator interval======================================# - else: - requires_grad(dis, False) - requires_grad(gen, True) - # model.netD.requires_grad_(True) - img_fake,_ = gen(src_image1, latent_id.detach()) - # G loss - gen_logits,fake_feat= dis(img_fake) - # real_feat = dis.get_feature(src_image1) - loss_Gmain = g_nonsaturating_loss(gen_logits) - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id.detach())).mean() - loss_G = loss_Gmain + loss_G_ID * id_w - if step%2 == 0: - #G_Rec - real_feat = dis.get_feature(src_image1) - rec_fm = l1_loss(fake_feat, real_feat) - loss_G_Rec = l1_loss(img_fake, src_image1) - # lpips_loss = loss_fn_vgg(img_fake, src_image1).mean() - loss_G += (loss_G_Rec * rec_w + rec_fm_w * rec_fm) #+ rec_fm * rec_fm_w - else: - source1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic') - latent_source1 = arcface(source1_down) - latent_source1 = F.normalize(latent_source1, p=2, dim=1) - cycle_src,_ = gen(img_fake, latent_source1) - cycle_loss = l1_loss(src_image1,cycle_src) - # cycle_feat = dis.get_feature(cycle_src) - # cycle_fm = l1_loss(real_feat["3"],cycle_feat["3"]) + l1_loss(real_feat["2"],cycle_feat["2"]) - loss_G += cycle_loss * cycle_w #+ cycle_fm * cycle_fm_w - - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_fm: {:.4f}, \ - # rec_fm: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - # format(version, elapsed, step, total_step, \ - # loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_fm.item(), \ - # rec_fm.item(), cycle_loss.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_loss: {:.4f}, rec_fm: {:.4f}, D_loss: {:.4f}, D_R1: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_loss.item(), rec_fm.item(), loss_D.item(), loss_reg.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/cycle_loss', cycle_loss.item(), step) - # logger.add_scalar('G/cycle_fm', cycle_fm.item(), step) - # logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/lpips_loss', rec_fm.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_reg', loss_reg.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"cycle_loss": cycle_loss.item()}, step = step) - # logger.log({"cycle_fm": cycle_fm.item()}, step = step) - # logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"lpips_loss": rec_fm.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_reg": loss_reg.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake,_= gen(image_infer, id_vector_src1) - - img_fake = img_fake.cpu() * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_mgpu_maskloss.py b/train_scripts/trainer_mgpu_maskloss.py deleted file mode 100644 index ac9f005..0000000 --- a/train_scripts/trainer_mgpu_maskloss.py +++ /dev/null @@ -1,588 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 15th April 2022 2:21:12 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from utilities.plot import plot_batch -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - # arcface = iresnet100(pretrained=False, fp16=False) - # arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - # arcface.eval() - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path), map_location=torch.device("cpu")) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path), map_location=torch.device("cpu")) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - -def d_logistic_loss(real_pred, fake_pred): - real_loss = F.softplus(-real_pred) - fake_loss = F.softplus(fake_pred) - - return real_loss.mean() + fake_loss.mean() - - -def d_r1_loss(d_out, x_in): - # zero-centered gradient penalty for real images - batch_size = x_in.size(0) - grad_dout = torch.autograd.grad( - outputs=d_out.sum(), inputs=x_in, - create_graph=True, retain_graph=True, only_inputs=True - )[0] - grad_dout2 = grad_dout.pow(2) - assert(grad_dout2.size() == x_in.size()) - reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) - return reg - - -def g_nonsaturating_loss(fake_pred): - loss = F.softplus(-fake_pred).mean() - - return loss - -def requires_grad(model, flag=True): - for p in model.parameters(): - p.requires_grad = flag - - -# def r1_reg(d_out, x_in): -# # zero-centered gradient penalty for real images -# batch_size = x_in.size(0) -# grad_dout = torch.autograd.grad( -# outputs=d_out.sum(), inputs=x_in, -# create_graph=True, retain_graph=True, only_inputs=True -# )[0] -# grad_dout2 = grad_dout.pow(2) -# assert(grad_dout2.size() == x_in.size()) -# reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) -# return reg - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - d_reg_freq = config["d_reg_freq"] - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - rec_fm_w = config["rec_feature_match_weight"] - mask_w = config["mask_weight"] - cycle_w = config["cycle_weight"] - reg_w = config["reg_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - for step in range(start, total_step): - gen.train() - dis.train() - - for interval in range(2): - - src_image1, src_image2, mask_label = dataloader.next() - - if step%2 == 0: - img_id = src_image2 - else: - random.shuffle(randindex) - img_id = src_image2[randindex] - mask_label = mask_label[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - requires_grad(dis, True) - requires_grad(gen, False) - - d_regularize = step % d_reg_freq == 0 - if d_regularize: - src_image1.requires_grad_() - - real_logits,_ = dis(src_image1) - with torch.no_grad(): - img_fake,_ = gen(src_image1, latent_id.detach()) - fake_logits,_ = dis(img_fake.detach()) - - loss_D = d_logistic_loss(real_logits, fake_logits) - - if d_regularize: - loss_reg = d_r1_loss(real_logits, src_image1) - loss_D += loss_reg * reg_w * d_reg_freq - - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - - - #================================Generator interval======================================# - else: - requires_grad(dis, False) - requires_grad(gen, True) - # model.netD.requires_grad_(True) - img_fake,pred_mask= gen(src_image1, latent_id.detach()) - # G loss - gen_logits,fake_feat= dis(img_fake) - # real_feat = dis.get_feature(src_image1) - loss_Gmain = g_nonsaturating_loss(gen_logits) - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id.detach())).mean() - mask_label = F.interpolate(mask_label, size=(128,128), mode='bilinear') - loss_mask = l1_loss(pred_mask, mask_label) - loss_G = loss_Gmain + loss_G_ID * id_w + loss_mask * mask_w - if step%2 == 0: - #G_Rec - real_feat = dis.get_feature(src_image1) - rec_fm = l1_loss(fake_feat, real_feat) - loss_G_Rec = l1_loss(img_fake, src_image1) - # lpips_loss = loss_fn_vgg(img_fake, src_image1).mean() - loss_G += (loss_G_Rec * rec_w + rec_fm_w * rec_fm) #+ rec_fm * rec_fm_w - else: - source1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic') - latent_source1 = arcface(source1_down) - latent_source1 = F.normalize(latent_source1, p=2, dim=1) - cycle_src,_ = gen(img_fake, latent_source1) - cycle_loss = l1_loss(src_image1,cycle_src) - # cycle_feat = dis.get_feature(cycle_src) - # cycle_fm = l1_loss(real_feat["3"],cycle_feat["3"]) + l1_loss(real_feat["2"],cycle_feat["2"]) - loss_G += cycle_loss * cycle_w #+ cycle_fm * cycle_fm_w - - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_fm: {:.4f}, \ - # rec_fm: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - # format(version, elapsed, step, total_step, \ - # loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_fm.item(), \ - # rec_fm.item(), cycle_loss.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_loss: {:.4f}, rec_fm: {:.4f}, loss_mask: {:.4f}, D_loss: {:.4f}, D_R1: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_loss.item(), rec_fm.item(), loss_mask.item(), loss_D.item(), loss_reg.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/cycle_loss', cycle_loss.item(), step) - # logger.add_scalar('G/cycle_fm', cycle_fm.item(), step) - # logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/loss_mask', loss_mask.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_reg', loss_reg.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"cycle_loss": cycle_loss.item()}, step = step) - # logger.log({"cycle_fm": cycle_fm.item()}, step = step) - # logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"loss_mask": loss_mask.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_reg": loss_reg.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake,pred_mask = gen(image_infer, id_vector_src1) - - img_fake = img_fake.cpu() * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - pred_mask = pred_mask.cpu().numpy() * 255 - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_multi_gpu.py b/train_scripts/trainer_multi_gpu.py deleted file mode 100644 index 73b5607..0000000 --- a/train_scripts/trainer_multi_gpu.py +++ /dev/null @@ -1,522 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Saturday, 26th March 2022 4:58:52 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from arcface_torch.backbones.iresnet import iresnet100 - -from utilities.plot import plot_batch -from losses.cos import cosin_metric -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - arcface = iresnet100(pretrained=False, fp16=False) - arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - arcface.eval() - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - feat_w = config["feature_match_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - dis.feature_network.requires_grad_(False) - - for step in range(start, total_step): - gen.train() - dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = dataloader.next() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("dataloader:",elapsed) - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - - img_fake = gen(src_image1, latent_id) - gen_logits,_ = dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Discriminator training:",elapsed) - else: - - # model.netD.requires_grad_(True) - img_fake = gen(src_image1, latent_id) - # G loss - gen_logits,feat = dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = dis.get_feature(src_image1) - feat_match_loss = l1_loss(feat["3"],real_feat["3"]) - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Generator training:",elapsed) - - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"G_feat_match": feat_match_loss.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_fake": loss_Dgen.item()}, step = step) - logger.log({"D_real": loss_Dreal.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake = gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_multi_gpu1.py b/train_scripts/trainer_multi_gpu1.py deleted file mode 100644 index 26169c1..0000000 --- a/train_scripts/trainer_multi_gpu1.py +++ /dev/null @@ -1,521 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 15th February 2022 1:25:28 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from utilities.plot import plot_batch -from losses.cos import cosin_metric -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - feat_w = config["feature_match_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - dis.feature_network.requires_grad_(False) - # dataloader = iter(dataloader) - for step in range(start, total_step): - gen.train() - dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = dataloader.next() - # src_image1, src_image2 = next(dataloader) - # src_image1, src_image2 = src_image1.to(device), src_image2.to(device) - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("dataloader:",elapsed) - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - - img_fake = gen(src_image1, latent_id) - gen_logits,_ = dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Discriminator training:",elapsed) - else: - - # model.netD.requires_grad_(True) - img_fake = gen(src_image1, latent_id) - # G loss - gen_logits,feat = dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = dis.get_feature(src_image1) - feat_match_loss = l1_loss(feat["3"],real_feat["3"]) - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Generator training:",elapsed) - - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("ready to report losses") - # ID_Total= loss_G_ID - # torch.distributed.all_reduce(ID_Total) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"G_feat_match": feat_match_loss.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_fake": loss_Dgen.item()}, step = step) - logger.log({"D_real": loss_Dreal.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake = gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_multi_gpu_CUT.py b/train_scripts/trainer_multi_gpu_CUT.py deleted file mode 100644 index 2763ac4..0000000 --- a/train_scripts/trainer_multi_gpu_CUT.py +++ /dev/null @@ -1,525 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 17th March 2022 1:01:52 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from arcface_torch.backbones.iresnet import iresnet100 - -from utilities.plot import plot_batch -from losses.cos import cosin_metric -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - arcface = iresnet100(pretrained=False, fp16=False) - arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - arcface.eval() - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - feat_w = config["feature_match_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - dis.feature_network.requires_grad_(False) - - for step in range(start, total_step): - gen.train() - dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = dataloader.next() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("dataloader:",elapsed) - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - - img_fake = gen(src_image1, latent_id) - gen_logits,_ = dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Discriminator training:",elapsed) - else: - - # model.netD.requires_grad_(True) - img_fake = gen(src_image1, latent_id) - # G loss - gen_logits,feat = dis(img_fake, None) - - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - real_feat = dis.get_feature(src_image1) - feat_match_loss = l1_loss(feat["3"],real_feat["3"]) - loss_G = loss_Gmain + loss_G_ID * id_w + \ - feat_match_loss * feat_w - if step%2 == 0: - #G_Rec - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Generator training:",elapsed) - - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("ready to report losses") - # ID_Total= loss_G_ID - # torch.distributed.all_reduce(ID_Total) - - epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ - G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ - D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ - loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"G_feat_match": feat_match_loss.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_fake": loss_Dgen.item()}, step = step) - logger.log({"D_real": loss_Dreal.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake = gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_multi_gpu_cycle.py b/train_scripts/trainer_multi_gpu_cycle.py deleted file mode 100644 index 9abccb7..0000000 --- a/train_scripts/trainer_multi_gpu_cycle.py +++ /dev/null @@ -1,543 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 27th March 2022 12:58:54 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from arcface_torch.backbones.iresnet import iresnet100 - -from utilities.plot import plot_batch -from losses.cos import cosin_metric -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - # arcface = iresnet100(pretrained=False, fp16=False) - # arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - # arcface.eval() - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - rec_fm_w = config["rec_feature_match_weight"] - cycle_fm_w = config["cycle_feature_match_weight"] - cycle_w = config["cycle_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - dis.feature_network.requires_grad_(False) - - for step in range(start, total_step): - gen.train() - dis.train() - for interval in range(2): - random.shuffle(randindex) - src_image1, src_image2 = dataloader.next() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("dataloader:",elapsed) - - if step%2 == 0: - img_id = src_image2 - else: - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - img_fake = gen(src_image1, latent_id) - gen_logits,_ = dis(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits,_ = dis(src_image2,None) - loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() - - loss_D = loss_Dgen + loss_Dreal - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Discriminator training:",elapsed) - else: - - # model.netD.requires_grad_(True) - img_fake = gen(src_image1, latent_id) - # G loss - # gen_logits,feat = dis(img_fake, None) - gen_logits,_ = dis(img_fake, None) - # real_feat = dis.get_feature(src_image1) - loss_Gmain = (-gen_logits).mean() - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() - loss_G = loss_Gmain + loss_G_ID * id_w - if step%2 == 0: - #G_Rec - # rec_fm = l1_loss(feat["3"],real_feat["3"]) + l1_loss(feat["2"],real_feat["2"]) - loss_G_Rec = l1_loss(img_fake, src_image1) - loss_G += loss_G_Rec * rec_w #+ rec_fm * rec_fm_w - else: - source1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic') - latent_source1 = arcface(source1_down) - latent_source1 = F.normalize(latent_source1, p=2, dim=1) - cycle_src = gen(img_fake, latent_source1) - cycle_loss = l1_loss(src_image1,cycle_src) - # cycle_feat = dis.get_feature(cycle_src) - # cycle_fm = l1_loss(real_feat["3"],cycle_feat["3"]) + l1_loss(real_feat["2"],cycle_feat["2"]) - loss_G += cycle_loss * cycle_w #+ cycle_fm * cycle_fm_w - - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - # if rank ==0: - - # elapsed = time.time() - start_time - # elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("Generator training:",elapsed) - - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # print("ready to report losses") - # ID_Total= loss_G_ID - # torch.distributed.all_reduce(ID_Total) - - # epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_fm: {:.4f}, \ - # rec_fm: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - # format(version, elapsed, step, total_step, \ - # loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_fm.item(), \ - # rec_fm.item(), cycle_loss.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_loss.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/cycle_loss', cycle_loss.item(), step) - # logger.add_scalar('G/cycle_fm', cycle_fm.item(), step) - # logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_fake', loss_Dgen.item(), step) - logger.add_scalar('D/D_real', loss_Dreal.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"cycle_loss": cycle_loss.item()}, step = step) - # logger.log({"cycle_fm": cycle_fm.item()}, step = step) - # logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_fake": loss_Dgen.item()}, step = step) - logger.log({"D_real": loss_Dreal.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake = gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_multi_gpu_cycle_nonstatue_dis.py b/train_scripts/trainer_multi_gpu_cycle_nonstatue_dis.py deleted file mode 100644 index 8c80e15..0000000 --- a/train_scripts/trainer_multi_gpu_cycle_nonstatue_dis.py +++ /dev/null @@ -1,586 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 29th March 2022 9:16:41 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random -import shutil -import tempfile - -import lpips - -import numpy as np - -import torch -import torch.nn.functional as F - -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -from arcface_torch.backbones.iresnet import iresnet100 - -from utilities.plot import plot_batch -from losses.cos import cosin_metric -from train_scripts.trainer_multigpu_base import TrainerBase - - -class Trainer(TrainerBase): - - def __init__(self, - config, - reporter): - super(Trainer, self).__init__(config, reporter) - - import inspect - print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) - - def train(self): - # Launch processes. - num_gpus = len(self.config["gpus"]) - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) - -# TODO modify this function to build your models -def init_framework(config, reporter, device, rank): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - model_config = config["model_configs"] - - if config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - file1 = os.path.join("components", model_config["g_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - dscript_name = "components." + model_config["d_model"]["script"] - file1 = os.path.join("components", model_config["d_model"]["script"]+".py") - tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") - shutil.copyfile(file1,tgtfile1) - - elif config["phase"] == "finetune": - gscript_name = config["com_base"] + model_config["g_model"]["script"] - dscript_name = config["com_base"] + model_config["d_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - reporter.writeInfo("Generator structure:") - reporter.writeModel(gen.__str__()) - - class_name = model_config["d_model"]["class_name"] - package = __import__(dscript_name, fromlist=True) - dis_class = getattr(package, class_name) - dis = dis_class(**model_config["d_model"]["module_params"]) - - - # print and recorde model structure - reporter.writeInfo("Discriminator structure:") - reporter.writeModel(dis.__str__()) - - # arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - # arcface = arcface1['model'].module - - # arcface = iresnet100(pretrained=False, fp16=False) - # arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu')) - # arcface.eval() - arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) - arcface = arcface1['model'].module - - # train in GPU - - # if in finetune phase, load the pretrained checkpoint - if config["phase"] == "finetune": - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["generator_name"])) - gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - model_path = os.path.join(config["project_checkpoints"], - "step%d_%s.pth"%(config["ckpt"], - config["checkpoint_names"]["discriminator_name"])) - dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu")) - - print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) - - gen = gen.to(device) - dis = dis.to(device) - arcface= arcface.to(device) - arcface.requires_grad_(False) - arcface.eval() - - - - return gen, dis, arcface - -# TODO modify this function to configurate the optimizer of your pipeline -def setup_optimizers(config, reporter, gen, dis, rank): - - torch.cuda.set_device(rank) - torch.cuda.empty_cache() - g_train_opt = config['g_optim_config'] - d_train_opt = config['d_optim_config'] - - g_optim_params = [] - d_optim_params = [] - for k, v in gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - for k, v in dis.named_parameters(): - if v.requires_grad: - d_optim_params.append(v) - else: - reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = config['optim_type'] - - if optim_type == 'Adam': - g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - if config["phase"] == "finetune": - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["generator_name"])) - g_optimizer.load_state_dict(torch.load(opt_path)) - - opt_path = os.path.join(config["project_checkpoints"], - "step%d_optim_%s.pth"%(config["ckpt"], - config["optimizer_names"]["discriminator_name"])) - d_optimizer.load_state_dict(torch.load(opt_path)) - - print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) - return g_optimizer, d_optimizer - -def d_logistic_loss(real_pred, fake_pred): - real_loss = F.softplus(-real_pred) - fake_loss = F.softplus(fake_pred) - - return real_loss.mean() + fake_loss.mean() - - -def d_r1_loss(d_out, x_in): - # zero-centered gradient penalty for real images - batch_size = x_in.size(0) - grad_dout = torch.autograd.grad( - outputs=d_out.sum(), inputs=x_in, - create_graph=True, retain_graph=True, only_inputs=True - )[0] - grad_dout2 = grad_dout.pow(2) - assert(grad_dout2.size() == x_in.size()) - reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) - return reg - - -def g_nonsaturating_loss(fake_pred): - loss = F.softplus(-fake_pred).mean() - - return loss - -def requires_grad(model, flag=True): - for p in model.parameters(): - p.requires_grad = flag - - -# def r1_reg(d_out, x_in): -# # zero-centered gradient penalty for real images -# batch_size = x_in.size(0) -# grad_dout = torch.autograd.grad( -# outputs=d_out.sum(), inputs=x_in, -# create_graph=True, retain_graph=True, only_inputs=True -# )[0] -# grad_dout2 = grad_dout.pow(2) -# assert(grad_dout2.size() == x_in.size()) -# reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0) -# return reg - -def train_loop( - rank, - config, - reporter, - temp_dir - ): - - version = config["version"] - - ckpt_dir = config["project_checkpoints"] - sample_dir = config["project_samples"] - - log_freq = config["log_step"] - model_freq = config["model_save_step"] - sample_freq = config["sample_step"] - total_step = config["total_step"] - random_seed = config["dataset_params"]["random_seed"] - d_reg_freq = config["d_reg_freq"] - - id_w = config["id_weight"] - rec_w = config["reconstruct_weight"] - rec_fm_w = config["rec_feature_match_weight"] - cycle_fm_w = config["cycle_feature_match_weight"] - cycle_w = config["cycle_weight"] - reg_w = config["reg_weight"] - lpips_w = config["lpips_weight"] - num_gpus = len(config["gpus"]) - batch_gpu = config["batch_size"] // num_gpus - - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - - - - if rank == 0: - img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) - img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) - - - # Initialize. - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. - - # Create dataloader. - if rank == 0: - print('Loading training set...') - - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - rank, - num_gpus, - batch_gpu, - **config["dataset_params"]) - - # Construct networks. - if rank == 0: - print('Constructing networks...') - gen, dis, arcface = init_framework(config, reporter, device, rank) - - # Check for existing checkpoint - - # Print network summary tables. - # if rank == 0: - # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) - # id = torch.empty([batch_gpu, 3, 112, 112], device=device) - # latent = misc.print_module_summary(arcface, [id]) - # img = misc.print_module_summary(gen, [attr, latent]) - # misc.print_module_summary(dis, [img, None]) - # del attr - # del id - # del latent - # del img - # torch.cuda.empty_cache() - - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [gen, dis, arcface]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - #===============build losses===================# - # TODO replace below lines to build your losses - # MSE_loss = torch.nn.MSELoss() - l1_loss = torch.nn.L1Loss() - cos_loss = torch.nn.CosineSimilarity() - loss_fn_vgg = lpips.LPIPS(net='vgg') - - g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - #==============build tensorboard=================# - if config["logger"] == "tensorboard": - import torch.utils.tensorboard as tensorboard - tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) - logger = tensorboard_writer - - elif config["logger"] == "wandb": - import wandb - wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - tags=[config["tag"]], name=version) - - wandb.config = { - "total_step": config["total_step"], - "batch_size": config["batch_size"] - } - logger = wandb - - - random.seed(random_seed) - randindex = [i for i in range(batch_gpu)] - - # set the start point for training loop - if config["phase"] == "finetune": - start = config["ckpt"] - else: - start = 0 - if rank == 0: - import datetime - start_time = time.time() - - # Caculate the epoch number - print("Total step = %d"%total_step) - - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - - for step in range(start, total_step): - gen.train() - dis.train() - - for interval in range(2): - - src_image1, src_image2 = dataloader.next() - - if step%2 == 0: - img_id = src_image2 - else: - random.shuffle(randindex) - img_id = src_image2[randindex] - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - if interval == 0: - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - latent_id = arcface(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - requires_grad(dis, True) - requires_grad(gen, False) - - d_regularize = step % d_reg_freq == 0 - if d_regularize: - src_image1.requires_grad_() - - real_logits = dis(src_image1) - with torch.no_grad(): - img_fake = gen(src_image1, latent_id.detach()) - fake_logits = dis(img_fake.detach()) - - loss_D = d_logistic_loss(real_logits, fake_logits) - - if d_regularize: - loss_reg = d_r1_loss(real_logits, src_image1) - loss_D += loss_reg * reg_w * d_reg_freq - - d_optimizer.zero_grad(set_to_none=True) - loss_D.backward() - - with torch.autograd.profiler.record_function('discriminator_opt'): - # params = [param for param in dis.parameters() if param.grad is not None] - # if len(params) > 0: - # flat = torch.cat([param.grad.flatten() for param in params]) - # if num_gpus > 1: - # torch.distributed.all_reduce(flat) - # flat /= num_gpus - # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - # grads = flat.split([param.numel() for param in params]) - # for param, grad in zip(params, grads): - # param.grad = grad.reshape(param.shape) - params = [param for param in dis.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - d_optimizer.step() - - - #================================Generator interval======================================# - else: - requires_grad(dis, False) - requires_grad(gen, True) - # model.netD.requires_grad_(True) - img_fake = gen(src_image1, latent_id.detach()) - # G loss - gen_logits = dis(img_fake) - # real_feat = dis.get_feature(src_image1) - loss_Gmain = g_nonsaturating_loss(gen_logits) - img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') - latent_fake = arcface(img_fake_down) - latent_fake = F.normalize(latent_fake, p=2, dim=1) - loss_G_ID = (1 - cos_loss(latent_fake, latent_id.detach())).mean() - loss_G = loss_Gmain + loss_G_ID * id_w - if step%2 == 0: - #G_Rec - # rec_fm = l1_loss(feat["3"],real_feat["3"]) + l1_loss(feat["2"],real_feat["2"]) - loss_G_Rec = l1_loss(img_fake, src_image1) - lpips_loss = loss_fn_vgg(img_fake, src_image1).mean() - loss_G += loss_G_Rec * rec_w + lpips_w * lpips_loss #+ rec_fm * rec_fm_w - else: - source1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic') - latent_source1 = arcface(source1_down) - latent_source1 = F.normalize(latent_source1, p=2, dim=1) - cycle_src = gen(img_fake, latent_source1) - cycle_loss = l1_loss(src_image1,cycle_src) - # cycle_feat = dis.get_feature(cycle_src) - # cycle_fm = l1_loss(real_feat["3"],cycle_feat["3"]) + l1_loss(real_feat["2"],cycle_feat["2"]) - loss_G += cycle_loss * cycle_w #+ cycle_fm * cycle_fm_w - - - g_optimizer.zero_grad(set_to_none=True) - loss_G.backward() - with torch.autograd.profiler.record_function('generator_opt'): - params = [param for param in gen.parameters() if param.grad is not None] - flat = torch.cat([param.grad.flatten() for param in params]) - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - g_optimizer.step() - - # Print out log info - if rank == 0 and (step + 1) % log_freq == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - # epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_fm: {:.4f}, \ - # rec_fm: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ - # format(version, elapsed, step, total_step, \ - # loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_fm.item(), \ - # rec_fm.item(), cycle_loss.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) - epochinformation="[{}], Elapsed [{}], Step [{}/{}], G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, cycle_loss: {:.4f}, D_loss: {:.4f}, D_R1: {:.4f}". \ - format(version, elapsed, step, total_step, \ - loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), cycle_loss.item(), loss_D.item(), loss_reg.item()) - print(epochinformation) - reporter.writeInfo(epochinformation) - - if config["logger"] == "tensorboard": - logger.add_scalar('G/G_loss', loss_G.item(), step) - logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) - logger.add_scalar('G/cycle_loss', cycle_loss.item(), step) - # logger.add_scalar('G/cycle_fm', cycle_fm.item(), step) - # logger.add_scalar('G/rec_fm', rec_fm.item(), step) - logger.add_scalar('G/G_ID', loss_G_ID.item(), step) - logger.add_scalar('D/D_loss', loss_D.item(), step) - logger.add_scalar('D/D_reg', loss_reg.item(), step) - elif config["logger"] == "wandb": - logger.log({"G_Loss": loss_G.item()}, step = step) - logger.log({"G_Rec": loss_G_Rec.item()}, step = step) - logger.log({"cycle_loss": cycle_loss.item()}, step = step) - # logger.log({"cycle_fm": cycle_fm.item()}, step = step) - # logger.log({"rec_fm": rec_fm.item()}, step = step) - logger.log({"G_ID": loss_G_ID.item()}, step = step) - logger.log({"D_loss": loss_D.item()}, step = step) - logger.log({"D_reg": loss_reg.item()}, step = step) - torch.cuda.empty_cache() - - if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): - gen.eval() - with torch.no_grad(): - imgs = [] - zero_img = (torch.zeros_like(src_image1[0,...])) - imgs.append(zero_img.cpu().numpy()) - save_img = ((src_image1.cpu())* img_std + img_mean).numpy() - for r in range(batch_gpu): - imgs.append(save_img[r,...]) - arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') - id_vector_src1 = arcface(arcface_112) - id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) - - for i in range(batch_gpu): - - imgs.append(save_img[i,...]) - image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) - img_fake = gen(image_infer, id_vector_src1).cpu() - - img_fake = img_fake * img_std - img_fake = img_fake + img_mean - img_fake = img_fake.numpy() - for j in range(batch_gpu): - imgs.append(img_fake[j,...]) - print("Save test data") - imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) - plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) - torch.cuda.empty_cache() - - - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if rank == 0 and (step+1) % model_freq==0: - - torch.save(gen.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - torch.save(dis.state_dict(), - os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - - torch.save(g_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["generator_name"]))) - - torch.save(d_optimizer.state_dict(), - os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, - config["checkpoint_names"]["discriminator_name"]))) - print("Save step %d model checkpoint!"%(step+1)) - torch.cuda.empty_cache() - print("Rank %d process done!"%rank) - torch.distributed.barrier() \ No newline at end of file diff --git a/train_scripts/trainer_multigpu_base.py b/train_scripts/trainer_multigpu_base.py deleted file mode 100644 index 6b21878..0000000 --- a/train_scripts/trainer_multigpu_base.py +++ /dev/null @@ -1,122 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_base.py -# Created Date: Sunday January 16th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 6th February 2022 3:06:45 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -class TrainerBase(object): - - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - - - #========build evaluation dataloader=============# - # TODO to modify the key: "your_eval_dataset" to get your evaluation dataset path - # eval_dataset = config["dataset_paths"][config["eval_dataset_name"]] - - # #================================================# - # print("Prepare the evaluation dataloader...") - # dlModulename = config["eval_dataloader"] - # package = __import__("data_tools.eval_dataloader_%s"%dlModulename, fromlist=True) - # dataloaderClass = getattr(package, 'EvalDataset') - # dataloader = dataloaderClass(eval_dataset, - # config["eval_batch_size"]) - # self.eval_loader= dataloader - - # self.eval_iter = len(dataloader)//config["eval_batch_size"] - # if len(dataloader)%config["eval_batch_size"]>0: - # self.eval_iter+=1 - - # #==============build tensorboard=================# - # if self.config["logger"] == "tensorboard": - # from utilities.utilities import build_tensorboard - # tensorboard_writer = build_tensorboard(self.config["project_summary"]) - # self.logger = tensorboard_writer - # elif self.config["logger"] == "wandb": - # import wandb - # wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", - # tags=[self.config["tag"]], name=self.config["version"]) - - # wandb.config = { - # "total_step": self.config["total_step"], - # "batch_size": self.config["batch_size"] - # } - # self.logger = wandb - - # TODO modify this function to build your models - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - pass - - # TODO modify this function to configurate the optimizer of your pipeline - def __setup_optimizers__(self): - pass - - - # TODO modify this function to evaluate your model - # Evaluate the checkpoint - def __evaluation__(self, - step = 0, - **kwargs - ): - pass - - - def __create_dataloader__(self, - config, - cur_gpu - ): - # Data loader - #============build train dataloader==============# - # TODO to modify the key: "your_train_dataset" to get your train dataset path - dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - dataloader_class= dataloaderClass - dataloader = dataloader_class(dataset, - cur_gpu, - config["batch_size"], - **config["dataset_params"]) - - return dataloader - - - def train(self): - #===============build framework================# - self.init_framework() - - #===============build optimizer================# - # Optimizer - # TODO replace below lines to build your optimizer - print("build the optimizer...") - self.__setup_optimizers__() - - # set the start point for training loop - if self.config["phase"] == "finetune": - self.start = self.config["checkpoint_step"] - else: - self.start = 0 - - # Start time - import datetime - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() \ No newline at end of file diff --git a/train_scripts/trainer_naiv512.py b/train_scripts/trainer_naiv512.py deleted file mode 100644 index 5a0823c..0000000 --- a/train_scripts/trainer_naiv512.py +++ /dev/null @@ -1,322 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: trainer_naiv512.py -# Created Date: Sunday January 9th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 11th January 2022 3:06:14 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import os -import time -import random - -import torch -import torch.nn.functional as F -from torchvision.utils import save_image - -from utilities.utilities import denorm - - -class Trainer(object): - - def __init__(self, config, reporter): - - self.config = config - # logger - self.reporter = reporter - - # Data loader - #============build train dataloader==============# - # TODO to modify the key: "your_train_dataset" to get your train dataset path - self.train_dataset = config["dataset_paths"][config["dataset_name"]] - #================================================# - print("Prepare the train dataloader...") - dlModulename = config["dataloader"] - package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) - dataloaderClass = getattr(package, 'GetLoader') - self.dataloader_class = dataloaderClass - dataloader = self.dataloader_class(self.train_dataset, - config["batch_size"], - config["imcrop_size"], - **config["dataset_params"]) - - self.train_loader= dataloader - - #========build evaluation dataloader=============# - # TODO to modify the key: "your_eval_dataset" to get your evaluation dataset path - # eval_dataset = config["dataset_paths"][config["eval_dataset_name"]] - - # #================================================# - # print("Prepare the evaluation dataloader...") - # dlModulename = config["eval_dataloader"] - # package = __import__("data_tools.eval_dataloader_%s"%dlModulename, fromlist=True) - # dataloaderClass = getattr(package, 'EvalDataset') - # dataloader = dataloaderClass(eval_dataset, - # config["eval_batch_size"]) - # self.eval_loader= dataloader - - # self.eval_iter = len(dataloader)//config["eval_batch_size"] - # if len(dataloader)%config["eval_batch_size"]>0: - # self.eval_iter+=1 - - #==============build tensorboard=================# - if self.config["use_tensorboard"]: - from utilities.utilities import build_tensorboard - self.tensorboard_writer = build_tensorboard(self.config["project_summary"]) - - # TODO modify this function to build your models - def __init_framework__(self): - ''' - This function is designed to define the framework, - and print the framework information into the log file - ''' - #===============build models================# - print("build models...") - # TODO [import models here] - - model_config = self.config["model_configs"] - - if self.config["phase"] == "train": - gscript_name = "components." + model_config["g_model"]["script"] - - elif self.config["phase"] == "finetune": - gscript_name = self.config["com_base"] + model_config["g_model"]["script"] - - class_name = model_config["g_model"]["class_name"] - package = __import__(gscript_name, fromlist=True) - gen_class = getattr(package, class_name) - self.gen = gen_class(**model_config["g_model"]["module_params"]) - - # print and recorde model structure - self.reporter.writeInfo("Generator structure:") - self.reporter.writeModel(self.gen.__str__()) - - - - - # id extractor network - arcface_ckpt = self.config["arcface_ckpt"] - arcface_ckpt = torch.load(arcface_ckpt, map_location=torch.device("cpu")) - self.arcface = arcface_ckpt['model'].module - - - - - # train in GPU - if self.config["cuda"] >=0: - self.gen = self.gen.cuda() - self.arcface = self.arcface.cuda() - - self.arcface.eval() - self.arcface.requires_grad_(False) - - # if in finetune phase, load the pretrained checkpoint - if self.config["phase"] == "finetune": - model_path = os.path.join(self.config["project_checkpoints"], - "epoch%d_%s.pth"%(self.config["checkpoint_step"], - self.config["checkpoint_names"]["generator_name"])) - self.gen.load_state_dict(torch.load(model_path)) - - print('loaded trained backbone model epoch {}...!'.format(self.config["project_checkpoints"])) - - - # TODO modify this function to evaluate your model - def __evaluation__(self, epoch, step = 0): - # Evaluate the checkpoint - self.network.eval() - total_psnr = 0 - total_num = 0 - with torch.no_grad(): - for _ in range(self.eval_iter): - hr, lr = self.eval_loader() - - if self.config["cuda"] >=0: - hr = hr.cuda() - lr = lr.cuda() - hr = (hr + 1.0)/2.0 * 255.0 - hr = torch.clamp(hr,0.0,255.0) - lr = (lr + 1.0)/2.0 * 255.0 - lr = torch.clamp(lr,0.0,255.0) - res = self.network(lr) - # res = (res + 1.0)/2.0 * 255.0 - # hr = (hr + 1.0)/2.0 * 255.0 - res = torch.clamp(res,0.0,255.0) - diff = (res-hr) ** 2 - diff = diff.mean(dim=-1).mean(dim=-1).mean(dim=-1).sqrt() - psnrs = 20. * (255. / diff).log10() - total_psnr+= psnrs.sum() - total_num+=res.shape[0] - final_psnr = total_psnr/total_num - print("[{}], Epoch [{}], psnr: {:.4f}".format(self.config["version"], - epoch, final_psnr)) - self.reporter.writeTrainLog(epoch,step,"psnr: {:.4f}".format(final_psnr)) - self.tensorboard_writer.add_scalar('metric/loss', final_psnr, epoch) - - # TODO modify this function to configurate the optimizer of your pipeline - def __setup_optimizers__(self): - g_train_opt = self.config['g_optim_config'] - g_optim_params = [] - for k, v in self.gen.named_parameters(): - if v.requires_grad: - g_optim_params.append(v) - else: - self.reporter.writeInfo(f'Params {k} will not be optimized.') - print(f'Params {k} will not be optimized.') - - optim_type = self.config['optim_type'] - - if optim_type == 'Adam': - self.g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) - else: - raise NotImplementedError( - f'optimizer {optim_type} is not supperted yet.') - # self.optimizers.append(self.optimizer_g) - - - def train(self): - - ckpt_dir = self.config["project_checkpoints"] - log_frep = self.config["log_step"] - model_freq = self.config["model_save_epoch"] - total_epoch = self.config["total_epoch"] - batch_size = self.config["batch_size"] - - # prep_weights= self.config["layersWeight"] - content_w = self.config["content_weight"] - style_w = self.config["style_weight"] - - sample_dir = self.config["project_samples"] - - - #===============build framework================# - self.__init_framework__() - - #===============build optimizer================# - # Optimizer - # TODO replace below lines to build your optimizer - print("build the optimizer...") - self.__setup_optimizers__() - - #===============build losses===================# - # TODO replace below lines to build your losses - MSE_loss = torch.nn.MSELoss() - - - # set the start point for training loop - if self.config["phase"] == "finetune": - start = self.config["checkpoint_epoch"] - 1 - else: - start = 0 - - # print("prepare the fixed labels...") - # fix_label = [i for i in range(n_class)] - # fix_label = torch.tensor(fix_label).long().cuda() - # fix_label = fix_label.view(n_class,1) - # fix_label = torch.zeros(n_class, n_class).cuda().scatter_(1, fix_label, 1) - - # Start time - import datetime - print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - from utilities.logo_class import logo_class - logo_class.print_start_training() - start_time = time.time() - - # Caculate the epoch number - step_epoch = len(self.train_loader) - step_epoch = step_epoch // batch_size - print("Total step = %d in each epoch"%step_epoch) - - randindex = [i for i in range(batch_size)] - - - # step_epoch = 2 - for epoch in range(start, total_epoch): - for step in range(step_epoch): - self.gen.train() - image1, image2 = self.train_loader.next() - random.shuffle(randindex) - - img_att = image1 - - if step%2 == 0: - img_id = image2 # swap with same id, different pose - else: - img_id = image2[randindex] # swap with different face - - img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') - - latent_id = self.arcface(img_id_112) - - latent_id = F.normalize(latent_id, p=2, dim=1) - - losses, img_fake= self.gen(image1, latent_id) - - # update Generator weights - losses = [ torch.mean(x) if not isinstance(x, int) else x for x in losses ] - loss_dict = dict(zip(model.loss_names, losses)) - - loss_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat', 0) + loss_dict['G_ID'] * opt.lambda_id - if step%2 == 0: - loss_G += loss_dict['G_Rec'] - - optimizer_G.zero_grad() - loss_G.backward(retain_graph=True) - optimizer_G.step() - - loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5 + loss_dict['D_GP'] - optimizer_D.zero_grad() - loss_D.backward() - optimizer_D.step() - - - # backward & optimize - g_loss = content_loss* content_w + style_loss* style_w - self.g_optimizer.zero_grad() - g_loss.backward() - self.g_optimizer.step() - - - # Print out log info - if (step + 1) % log_frep == 0: - elapsed = time.time() - start_time - elapsed = str(datetime.timedelta(seconds=elapsed)) - - # cumulative steps - cum_step = (step_epoch * epoch + step + 1) - - epochinformation="[{}], Elapsed [{}], Epoch [{}/{}], Step [{}/{}], content_loss: {:.4f}, style_loss: {:.4f}, g_loss: {:.4f}".format(self.config["version"], elapsed, epoch + 1, total_epoch, step + 1, step_epoch, content_loss.item(), style_loss.item(), g_loss.item()) - print(epochinformation) - self.reporter.writeInfo(epochinformation) - - if self.config["use_tensorboard"]: - self.tensorboard_writer.add_scalar('data/g_loss', g_loss.item(), cum_step) - self.tensorboard_writer.add_scalar('data/content_loss', content_loss.item(), cum_step) - self.tensorboard_writer.add_scalar('data/style_loss', style_loss, cum_step) - - #===============adjust learning rate============# - # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: - # print("Learning rate decay") - # for p in self.optimizer.param_groups: - # p['lr'] *= self.config["lr_decay"] - # print("Current learning rate is %f"%p['lr']) - - #===============save checkpoints================# - if (epoch+1) % model_freq==0: - print("Save epoch %d model checkpoint!"%(epoch+1)) - torch.save(self.gen.state_dict(), - os.path.join(ckpt_dir, 'epoch{}_{}.pth'.format(epoch + 1, - self.config["checkpoint_names"]["generator_name"]))) - - torch.cuda.empty_cache() - print('Sample images {}_fake.jpg'.format(epoch + 1)) - self.gen.eval() - with torch.no_grad(): - sample = fake_image - saved_image1 = denorm(sample.cpu().data) - save_image(saved_image1, - os.path.join(sample_dir, '{}_fake.jpg'.format(epoch + 1)),nrow=4) diff --git a/train_yamls/train_1maskhead.yaml b/train_yamls/train_1maskhead.yaml deleted file mode 100644 index 2c6d1cb..0000000 --- a/train_yamls/train_1maskhead.yaml +++ /dev/null @@ -1,75 +0,0 @@ -# Related scripts -train_script_name: mgpu_maskloss - -# models' scripts -model_configs: - g_model: - script: Generator_starganv2 - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 16 - -# Dataset -dataloader: VGGFace2HQ_multigpu_w_mask -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 8 -id_weight: 30.0 -reconstruct_weight: 10.0 -rec_feature_match_weight: 3.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 -mask_weight: 100.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_2layer_FM.yaml b/train_yamls/train_2layer_FM.yaml deleted file mode 100644 index d073535..0000000 --- a/train_yamls/train_2layer_FM.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Related scripts -train_script_name: 2layer_FM - -# models' scripts -model_configs: - g_model: - script: Generator - class_name: Generator - module_params: - g_conv_dim: 512 - g_kernel_size: 3 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 12 - -# Dataset -dataloader: VGGFace2HQ -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 8 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 1.0 -feature_match_weight: 1.0 - -# Log -log_step: 300 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_2maskhead.yaml b/train_yamls/train_2maskhead.yaml deleted file mode 100644 index e0658d1..0000000 --- a/train_yamls/train_2maskhead.yaml +++ /dev/null @@ -1,75 +0,0 @@ -# Related scripts -train_script_name: mgpu_2maskloss - -# models' scripts -model_configs: - g_model: - script: Generator_2mask - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 8 - -# Dataset -dataloader: VGGFace2HQ_multigpu_w_mask -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 30.0 -reconstruct_weight: 10.0 -rec_feature_match_weight: 3.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 -mask_weight: 100.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_2maskhead2.yaml b/train_yamls/train_2maskhead2.yaml deleted file mode 100644 index 2cd61c4..0000000 --- a/train_yamls/train_2maskhead2.yaml +++ /dev/null @@ -1,75 +0,0 @@ -# Related scripts -train_script_name: mgpu_2maskloss - -# models' scripts -model_configs: - g_model: - script: Generator_2mask2 - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 16 - -# Dataset -dataloader: VGGFace2HQ_multigpu_w_mask -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 35.0 -reconstruct_weight: 10.0 -rec_feature_match_weight: 3.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 -mask_weight: 100.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_2maskhead_256.yaml b/train_yamls/train_2maskhead_256.yaml deleted file mode 100644 index 9a02489..0000000 --- a/train_yamls/train_2maskhead_256.yaml +++ /dev/null @@ -1,75 +0,0 @@ -# Related scripts -train_script_name: mgpu_2maskloss_256 - -# models' scripts -model_configs: - g_model: - script: Generator_256 - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 256 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 32 - -# Dataset -dataloader: VGGFace2HQ_multigpu_w_mask -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 35.0 -reconstruct_weight: 10.0 -rec_feature_match_weight: 3.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 -mask_weight: 100.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_2maskhead_DWConv.yaml b/train_yamls/train_2maskhead_DWConv.yaml deleted file mode 100644 index 47136b7..0000000 --- a/train_yamls/train_2maskhead_DWConv.yaml +++ /dev/null @@ -1,75 +0,0 @@ -# Related scripts -train_script_name: mgpu_2maskloss - -# models' scripts -model_configs: - g_model: - script: Generator_2mask_DWConv - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 16 - -# Dataset -dataloader: VGGFace2HQ_multigpu_w_mask -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 35.0 -reconstruct_weight: 10.0 -rec_feature_match_weight: 3.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 -mask_weight: 100.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_512FM.yaml b/train_yamls/train_512FM.yaml deleted file mode 100644 index d7fe3b4..0000000 --- a/train_yamls/train_512FM.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Related scripts -train_script_name: FM - -# models' scripts -model_configs: - g_model: - script: Generator - class_name: Generator - module_params: - g_conv_dim: 512 - g_kernel_size: 3 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 12 - -# Dataset -dataloader: VGGFace2HQ -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 8 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_512FM_Modulation.yaml b/train_yamls/train_512FM_Modulation.yaml deleted file mode 100644 index 7d119ad..0000000 --- a/train_yamls/train_512FM_Modulation.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Related scripts -train_script_name: FM - -# models' scripts -model_configs: - g_model: - script: Generator_reduce - class_name: Generator - module_params: - g_conv_dim: 512 - g_kernel_size: 3 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 12 - -# Dataset -dataloader: VGGFace2HQ -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 8 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_Depthwise.yaml b/train_yamls/train_Depthwise.yaml deleted file mode 100644 index dab4d2f..0000000 --- a/train_yamls/train_Depthwise.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Related scripts -train_script_name: multi_gpu - -# models' scripts -model_configs: - g_model: - script: Generator_modulation_depthwise - class_name: Generator - module_params: - g_conv_dim: 512 - g_kernel_size: 3 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 16 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_GramFM.yaml b/train_yamls/train_GramFM.yaml deleted file mode 100644 index 2962cc6..0000000 --- a/train_yamls/train_GramFM.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Related scripts -train_script_name: GramFM - -# models' scripts -model_configs: - g_model: - script: Generator - class_name: Generator - module_params: - g_conv_dim: 512 - g_kernel_size: 3 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 12 - -# Dataset -dataloader: VGGFace2HQ -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 8 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 100.0 - -# Log -log_step: 300 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_Invobn_config.yaml b/train_yamls/train_Invobn_config.yaml deleted file mode 100644 index e9e6066..0000000 --- a/train_yamls/train_Invobn_config.yaml +++ /dev/null @@ -1,67 +0,0 @@ -# Related scripts -train_script_name: multi_gpu - -# models' scripts -model_configs: - g_model: - script: Generator_Invobn_config3 - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 16 - res_num: 9 - up_mode: bilinear - aggregator: "invo" - res_mode: "invo" - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 64 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_Invoup.yaml b/train_yamls/train_Invoup.yaml deleted file mode 100644 index 2005f02..0000000 --- a/train_yamls/train_Invoup.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Related scripts -train_script_name: multi_gpu - -# models' scripts -model_configs: - g_model: - script: Generator_upsample - class_name: Generator - module_params: - g_conv_dim: 512 - g_kernel_size: 3 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 28 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 4 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_arcface_rec.yaml b/train_yamls/train_arcface_rec.yaml deleted file mode 100644 index d2fa9a3..0000000 --- a/train_yamls/train_arcface_rec.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# Related scripts -train_script_name: arcface_rec - -# models' scripts -model_configs: - g_model: - script: arcface_decoder - class_name: Decoder - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 64 - -# Dataset -dataloader: VGGFace2HQ_Rec -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 8 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0008 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -# Log -log_step: 200 -model_save_step: 2000 -total_step: 100000 -sample_step: 500 -checkpoint_names: - generator_name: Decoder \ No newline at end of file diff --git a/train_yamls/train_cycleloss.yaml b/train_yamls/train_cycleloss.yaml deleted file mode 100644 index b58ffeb..0000000 --- a/train_yamls/train_cycleloss.yaml +++ /dev/null @@ -1,70 +0,0 @@ -# Related scripts -train_script_name: multi_gpu_cycle - -# models' scripts -model_configs: - g_model: - script: Generator_LSTU_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 9 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 20 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -rec_feature_match_weight: 10.0 -cycle_feature_match_weight: 10.0 -cycle_weight: 10.0 - -# Log -log_step: 400 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_cycleloss_fm_nonstatu.yaml b/train_yamls/train_cycleloss_fm_nonstatu.yaml deleted file mode 100644 index cc6d6f3..0000000 --- a/train_yamls/train_cycleloss_fm_nonstatu.yaml +++ /dev/null @@ -1,76 +0,0 @@ -# Related scripts -train_script_name: mgpu_fm - -# models' scripts -model_configs: - g_model: - script: Generator_featout_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - lstu_script: LSTU_Config - lstu_class: LSTU - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 16 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 30.0 -reconstruct_weight: 1.0 -rec_feature_match_weight: 1.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 6.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_cycleloss_res.yaml b/train_yamls/train_cycleloss_res.yaml deleted file mode 100644 index bc144ce..0000000 --- a/train_yamls/train_cycleloss_res.yaml +++ /dev/null @@ -1,70 +0,0 @@ -# Related scripts -train_script_name: multi_gpu_cycle - -# models' scripts -model_configs: - g_model: - script: Generator_Res_config2 - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 8 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 25.0 -reconstruct_weight: 10.0 -rec_feature_match_weight: 10.0 -cycle_feature_match_weight: 10.0 -cycle_weight: 10.0 - -# Log -log_step: 400 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_cycleloss_resskip.yaml b/train_yamls/train_cycleloss_resskip.yaml deleted file mode 100644 index 4fc1f31..0000000 --- a/train_yamls/train_cycleloss_resskip.yaml +++ /dev/null @@ -1,70 +0,0 @@ -# Related scripts -train_script_name: multi_gpu_cycle - -# models' scripts -model_configs: - g_model: - script: Generator_ResSkip_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 8 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 5.0 -rec_feature_match_weight: 1.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 5.0 - -# Log -log_step: 400 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_cycleloss_resskip_nonstatu.yaml b/train_yamls/train_cycleloss_resskip_nonstatu.yaml deleted file mode 100644 index 2f404dc..0000000 --- a/train_yamls/train_cycleloss_resskip_nonstatu.yaml +++ /dev/null @@ -1,76 +0,0 @@ -# Related scripts -train_script_name: multi_gpu_cycle_nonstatue_dis - -# models' scripts -model_configs: - g_model: - script: Generator_ResSkip_config1 - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - lstu_script: LSTU_Config - lstu_class: LSTU - - d_model: - script: Nonstau_Discriminator - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 8 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 20.0 -reconstruct_weight: 1.0 -rec_feature_match_weight: 1.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 5.0 - -# Log -log_step: 400 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_depthwise_modulation.yaml b/train_yamls/train_depthwise_modulation.yaml deleted file mode 100644 index b68b87b..0000000 --- a/train_yamls/train_depthwise_modulation.yaml +++ /dev/null @@ -1,66 +0,0 @@ -# Related scripts -train_script_name: multi_gpu - -# models' scripts -model_configs: - g_model: - script: Generator_modulation_depthwise_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 16 - res_num: 9 - up_mode: bilinear - res_mode: depthwise - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 64 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_distillation.yaml b/train_yamls/train_distillation.yaml deleted file mode 100644 index 22618c5..0000000 --- a/train_yamls/train_distillation.yaml +++ /dev/null @@ -1,73 +0,0 @@ -# Related scripts -train_script_name: distillation_mgpu - -# models' scripts -model_configs: - g_model: - script: Generator_modulation_depthwise_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 16 - res_num: 9 - up_mode: bilinear - res_mode: depthwise - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -teacher_model: - node_ip: localhost - version: depthwise - model_step: 430000 - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 64 -feature_list: ["down4","BN1"] - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 -distillation_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_maskhead_fm_nonstatu.yaml b/train_yamls/train_maskhead_fm_nonstatu.yaml deleted file mode 100644 index 015661a..0000000 --- a/train_yamls/train_maskhead_fm_nonstatu.yaml +++ /dev/null @@ -1,74 +0,0 @@ -# Related scripts -train_script_name: mgpu_fm_w_mask - -# models' scripts -model_configs: - g_model: - script: Generator_maskhead_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 8 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 30.0 -reconstruct_weight: 1.0 -rec_feature_match_weight: 1.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_maskhead_fm_vggstyle.yaml b/train_yamls/train_maskhead_fm_vggstyle.yaml deleted file mode 100644 index 179567c..0000000 --- a/train_yamls/train_maskhead_fm_vggstyle.yaml +++ /dev/null @@ -1,74 +0,0 @@ -# Related scripts -train_script_name: mgpu_fm_w_mask - -# models' scripts -model_configs: - g_model: - script: Generator_VGGStyle_maskhead_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 16 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 30.0 -reconstruct_weight: 1.0 -rec_feature_match_weight: 1.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_maskhead_hififace.yaml b/train_yamls/train_maskhead_hififace.yaml deleted file mode 100644 index 16560b8..0000000 --- a/train_yamls/train_maskhead_hififace.yaml +++ /dev/null @@ -1,75 +0,0 @@ -# Related scripts -train_script_name: mgpu_maskloss - -# models' scripts -model_configs: - g_model: - script: Generator_maskhead_config1 - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 8 - -# Dataset -dataloader: VGGFace2HQ_multigpu_w_mask -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 25.0 -reconstruct_weight: 3.0 -rec_feature_match_weight: 1.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 -mask_weight: 30.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_maskhead_hififace1.yaml b/train_yamls/train_maskhead_hififace1.yaml deleted file mode 100644 index 1c0ab9a..0000000 --- a/train_yamls/train_maskhead_hififace1.yaml +++ /dev/null @@ -1,75 +0,0 @@ -# Related scripts -train_script_name: mgpu_maskloss - -# models' scripts -model_configs: - g_model: - script: Generator_maskhead_config2 - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 16 - -# Dataset -dataloader: VGGFace2HQ_multigpu_w_mask -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 25.0 -reconstruct_weight: 3.0 -rec_feature_match_weight: 1.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 -mask_weight: 30.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_maskloss.yaml b/train_yamls/train_maskloss.yaml deleted file mode 100644 index 022197b..0000000 --- a/train_yamls/train_maskloss.yaml +++ /dev/null @@ -1,75 +0,0 @@ -# Related scripts -train_script_name: mgpu_maskloss - -# models' scripts -model_configs: - g_model: - script: Generator_maskhead_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 64 - res_num: 3 - up_mode: bilinear - aggregator: "conv" - res_mode: "conv" - norm: "bn" - - d_model: - script: Nonstau_Discriminator_FM - class_name: Discriminator - module_params: - img_size: 512 - max_conv_dim: 512 - norm: "bn" - -# arcface_ckpt: arcface_torch/checkpoints/glint360k_cosface_r100_fp16_backbone.pth -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 16 - -# Dataset -dataloader: VGGFace2HQ_multigpu_w_mask -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 6 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - - -d_reg_freq: 16 -id_weight: 30.0 -reconstruct_weight: 1.0 -rec_feature_match_weight: 1.0 -cycle_feature_match_weight: 1.0 -cycle_weight: 1.0 -reg_weight: 8.0 -mask_weight: 10.0 - -# Log -log_step: 500 -model_save_step: 10000 -total_step: 1000000 -sample_step: 1000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_multigpu.yaml b/train_yamls/train_multigpu.yaml deleted file mode 100644 index b164fd7..0000000 --- a/train_yamls/train_multigpu.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Related scripts -train_script_name: multi_gpu - -# models' scripts -model_configs: - g_model: - script: Generator_ori - class_name: Generator - module_params: - g_conv_dim: 512 - g_kernel_size: 3 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 28 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 8 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_ori_modulation_config.yaml b/train_yamls/train_ori_modulation_config.yaml deleted file mode 100644 index 388ae9e..0000000 --- a/train_yamls/train_ori_modulation_config.yaml +++ /dev/null @@ -1,64 +0,0 @@ -# Related scripts -train_script_name: multi_gpu - -# models' scripts -model_configs: - g_model: - script: Generator_ori_modulation_config - class_name: Generator - module_params: - id_dim: 512 - g_kernel_size: 3 - in_channel: 8 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 32 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 4 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0006 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/train_yamls/train_oriae_modulation.yaml b/train_yamls/train_oriae_modulation.yaml deleted file mode 100644 index 2f24fc3..0000000 --- a/train_yamls/train_oriae_modulation.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# Related scripts -train_script_name: multi_gpu - -# models' scripts -model_configs: - g_model: - script: Generator_oriae_modulation - class_name: Generator - module_params: - g_conv_dim: 512 - g_kernel_size: 3 - res_num: 9 - - d_model: - script: projected_discriminator - class_name: ProjectedDiscriminator - module_params: - diffaug: False - interp224: False - backbone_kwargs: {} - -arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar - -# Training information -batch_size: 64 - -# Dataset -dataloader: VGGFace2HQ_multigpu -dataset_name: vggface2_hq -dataset_params: - random_seed: 1234 - dataloader_workers: 4 - -eval_dataloader: DIV2K_hdf5 -eval_dataset_name: DF2K_H5_Eval -eval_batch_size: 2 - -# Dataset - -# Optimizer -optim_type: Adam -g_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -d_optim_config: - lr: 0.0004 - betas: [ 0, 0.99] - eps: !!float 1e-8 - -id_weight: 20.0 -reconstruct_weight: 10.0 -feature_match_weight: 10.0 - -# Log -log_step: 300 -model_save_step: 10000 -sample_step: 1000 -total_step: 1000000 -checkpoint_names: - generator_name: Generator - discriminator_name: Discriminator \ No newline at end of file diff --git a/translation_list2json.py b/translation_list2json.py deleted file mode 100644 index f3e864b..0000000 --- a/translation_list2json.py +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: translation_list2json.py -# Created Date: Thursday March 24th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 24th March 2022 3:20:06 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import json - -if __name__ == "__main__": - savePath = "./vggface2hq_failed.txt" - log_txt = "./vggface2hq_failed.json" - images = {} - - with open(savePath,'r') as logf: - for line in logf: - cells = line.split("/") - if images.__contains__(cells[0]): - images[cells[0]] += [cells[1]] - else: - images[cells[0]] = [cells[1]] - with open(log_txt, 'w') as cf: - configjson = json.dumps(images, indent=4) - cf.writelines(configjson) \ No newline at end of file diff --git a/utilities/ImagenetNorm.py b/utilities/ImagenetNorm.py deleted file mode 100644 index 89c57ca..0000000 --- a/utilities/ImagenetNorm.py +++ /dev/null @@ -1,38 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: ImagenetNorm.py -# Created Date: Friday January 21st 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 21st January 2022 10:41:50 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch.nn as nn -import numpy as np -import torch -class ImagenetNorm(nn.Module): - def __init__(self, epsilon=1e-8): - """ - @notice: avoid in-place ops. - https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 - """ - super(ImagenetNorm, self).__init__() - self.mean = np.array([0.485, 0.456, 0.406]) - self.mean = torch.from_numpy(self.mean).float().cuda() - self.mean = self.mean.view([1, 3, 1, 1]) - - self.std = np.array([0.229, 0.224, 0.225]) - self.std = torch.from_numpy(self.std).float().cuda() - self.std = self.std.view([1, 3, 1, 1]) - - def forward(self, x): - mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]]) - std = self.std.expand([1, 3, x.shape[2], x.shape[3]]) - - x = (x - mean) / std - - return x \ No newline at end of file diff --git a/utilities/checkpoint_manager.py b/utilities/checkpoint_manager.py deleted file mode 100644 index bbcace0..0000000 --- a/utilities/checkpoint_manager.py +++ /dev/null @@ -1,100 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: checkpoint_manager.py -# Created Date: Sunday July 12th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 27th July 2020 11:01:16 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - -import os -import torch - -class CheckpointManager(object): - def __init__(self): - pass - self.maxCkpNum = -1 - self.ckpList = [] - self.modelsDict = {} # key model name, value model - self.currentEpoch = 0 - - - def registerModels(self): - pass - - def __updateCkpList__(self): - pass - - def saveModel(self): - pass - - def loadModel(self): - pass - - def saveLR(self): - pass - - def loadLR(self): - pass - - - - -def loadPretrainedModel(chechpointStep,modelSavePath,gModel,dModel,cuda,**kwargs): - gModel.load_state_dict(torch.load(os.path.join( - modelSavePath, 'Epoch{}_LocalG.pth'.format(chechpointStep)),map_location=cuda)) - dModel.load_state_dict(torch.load(os.path.join( - modelSavePath, 'Epoch{}_GlobalD.pth'.format(chechpointStep)),map_location=cuda)) - print('loaded trained models (epoch: {}) successful!'.format(chechpointStep)) - if not kwargs: - return - for k,v in kwargs.items(): - v.load_state_dict(torch.load(os.path.join( - modelSavePath, 'Epoch{}_{}.pth'.format(chechpointStep,k)),map_location=cuda)) - print("Loaded param %s"%k) - -def loadPretrainedModelByDict(chechpointStep,modelSavePath,cuda,**kwargs): - if not kwargs: - return - for k,v in kwargs.items(): - v.load_state_dict(torch.load(os.path.join( - modelSavePath, 'Epoch{}_{}.pth'.format(chechpointStep,k)),map_location=cuda)) - print("Loaded param %s"%k) - -def loadLR(chechpointStep,modelSavePath,dlr,glr): - glr.load_state_dict(torch.load(os.path.join( - modelSavePath, 'Epoch{}_LocalGlr.pth'.format(chechpointStep)))) - dlr.load_state_dict(torch.load(os.path.join( - modelSavePath, 'Epoch{}_GlobalDlr.pth'.format(chechpointStep)))) - print("Generator learning rate:%f"%glr.get_lr()[0]) - print("Discriminator learning rate:%f"%dlr.get_lr()[0]) - -def saveLR(step,modelSavePath,dlr,glr): - torch.save(glr.state_dict(),os.path.join(modelSavePath, 'Epoch{}_LocalGlr.pth'.format(step + 1))) - torch.save(dlr.state_dict(),os.path.join(modelSavePath, 'Epoch{}_GlobalDlr.pth'.format(step + 1))) - print("Epoch:{} models learning rate saved!".format(step+1)) - - -def saveModel(step,modelSavePath,gModel,dModel,**kwargs): - torch.save(gModel.state_dict(), - os.path.join(modelSavePath, 'Epoch{}_LocalG.pth'.format(step + 1))) - torch.save(dModel.state_dict(), - os.path.join(modelSavePath, 'Epoch{}_GlobalD.pth'.format(step + 1))) - print("Epoch:{} models saved!".format(step+1)) - if not kwargs: - return - for k,v in kwargs.items(): - torch.save(v.state_dict(), - os.path.join(modelSavePath, 'Epoch{}_{}.pth'.format(step + 1,k))) - print("Epoch:{} models param {} saved!".format(step+1,k)) - -def saveModelByDict(step,modelSavePath,**kwargs): - if not kwargs: - return - for k,v in kwargs.items(): - torch.save(v.state_dict(), - os.path.join(modelSavePath, 'Epoch{}_{}.pth'.format(step + 1,k))) - print("Epoch:{} models param {} saved!".format(step+1,k)) \ No newline at end of file diff --git a/utilities/figure.py b/utilities/figure.py deleted file mode 100644 index 3d8336b..0000000 --- a/utilities/figure.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: figure.py -# Created Date: Tuesday October 13th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 13th October 2020 2:54:30 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - -import matplotlib.pyplot as plt - -def plot_loss_curve(losses, save_path): - for key in losses.keys(): - plt.plot(range(len(losses[key])), losses[key], label=key) - plt.xlabel('iteration') - plt.title(f'loss curve') - plt.legend() - plt.savefig(save_path) - plt.clf() \ No newline at end of file diff --git a/utilities/json_config.py b/utilities/json_config.py deleted file mode 100644 index c68fbff..0000000 --- a/utilities/json_config.py +++ /dev/null @@ -1,15 +0,0 @@ -import json - - -def readConfig(path): - with open(path,'r') as cf: - nodelocaltionstr = cf.read() - nodelocaltioninf = json.loads(nodelocaltionstr) - if isinstance(nodelocaltioninf,str): - nodelocaltioninf = json.loads(nodelocaltioninf) - return nodelocaltioninf - -def writeConfig(path, info): - with open(path, 'w') as cf: - configjson = json.dumps(info, indent=4) - cf.writelines(configjson) \ No newline at end of file diff --git a/utilities/learningrate_scheduler.py b/utilities/learningrate_scheduler.py deleted file mode 100644 index 6877495..0000000 --- a/utilities/learningrate_scheduler.py +++ /dev/null @@ -1,135 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: learningrate_scheduler.py -# Created Date: Tuesday January 5th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 5th January 2021 2:04:00 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -# Refer to basicSR https://github.com/xinntao/BasicSR - - -import math -from collections import Counter -from torch.optim.lr_scheduler import _LRScheduler - - -class MultiStepRestartLR(_LRScheduler): - """ MultiStep with restarts learning rate scheme. - - Args: - optimizer (torch.nn.optimizer): Torch optimizer. - milestones (list): Iterations that will decrease learning rate. - gamma (float): Decrease ratio. Default: 0.1. - restarts (list): Restart iterations. Default: [0]. - restart_weights (list): Restart weights at each restart iteration. - Default: [1]. - last_epoch (int): Used in _LRScheduler. Default: -1. - """ - - def __init__(self, - optimizer, - milestones, - gamma=0.1, - restarts=(0,), - restart_weights=(1,), - last_epoch=-1): - self.milestones = Counter(milestones) - self.gamma = gamma - self.restarts = restarts - self.restart_weights = restart_weights - print(type(self.restarts),self.restarts) - print(type(self.restart_weights),self.restart_weights) - assert len(self.restarts) == len( - self.restart_weights), 'restarts and their weights do not match.' - super(MultiStepRestartLR, self).__init__(optimizer, last_epoch) - - def get_lr(self): - if self.last_epoch in self.restarts: - weight = self.restart_weights[self.restarts.index(self.last_epoch)] - return [ - group['initial_lr'] * weight - for group in self.optimizer.param_groups - ] - if self.last_epoch not in self.milestones: - return [group['lr'] for group in self.optimizer.param_groups] - return [ - group['lr'] * self.gamma**self.milestones[self.last_epoch] - for group in self.optimizer.param_groups - ] - - -def get_position_from_periods(iteration, cumulative_period): - """Get the position from a period list. - - It will return the index of the right-closest number in the period list. - For example, the cumulative_period = [100, 200, 300, 400], - if iteration == 50, return 0; - if iteration == 210, return 2; - if iteration == 300, return 2. - - Args: - iteration (int): Current iteration. - cumulative_period (list[int]): Cumulative period list. - - Returns: - int: The position of the right-closest number in the period list. - """ - for i, period in enumerate(cumulative_period): - if iteration <= period: - return i - - -class CosineAnnealingRestartLR(_LRScheduler): - """ Cosine annealing with restarts learning rate scheme. - - An example of config: - periods = [10, 10, 10, 10] - restart_weights = [1, 0.5, 0.5, 0.5] - eta_min=1e-7 - - It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the - scheduler will restart with the weights in restart_weights. - - Args: - optimizer (torch.nn.optimizer): Torch optimizer. - periods (list): Period for each cosine anneling cycle. - restart_weights (list): Restart weights at each restart iteration. - Default: [1]. - eta_min (float): The mimimum lr. Default: 0. - last_epoch (int): Used in _LRScheduler. Default: -1. - """ - - def __init__(self, - optimizer, - periods, - restart_weights=(1), - eta_min=0, - last_epoch=-1): - self.periods = periods - self.restart_weights = restart_weights - self.eta_min = eta_min - assert (len(self.periods) == len(self.restart_weights) - ), 'periods and restart_weights should have the same length.' - self.cumulative_period = [ - sum(self.periods[0:i + 1]) for i in range(0, len(self.periods)) - ] - super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch) - - def get_lr(self): - idx = get_position_from_periods(self.last_epoch, - self.cumulative_period) - current_weight = self.restart_weights[idx] - nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1] - current_period = self.periods[idx] - - return [ - self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) * - (1 + math.cos(math.pi * ( - (self.last_epoch - nearest_restart) / current_period))) - for base_lr in self.base_lrs - ] \ No newline at end of file diff --git a/utilities/logo_class.py b/utilities/logo_class.py deleted file mode 100644 index 044dce3..0000000 --- a/utilities/logo_class.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: logo_class.py -# Created Date: Tuesday June 29th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 11th October 2021 12:39:55 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -class logo_class: - - @staticmethod - def print_group_logo(): - logo_str = """ - -███╗ ██╗██████╗ ███████╗██╗ ██████╗ ███████╗ ██╗████████╗██╗ ██╗ -████╗ ██║██╔â•â•â–ˆâ–ˆâ•—██╔â•â•â•â•â•â–ˆâ–ˆâ•‘██╔â•â•â•â•â• ██╔â•â•â•â•â• ██║╚â•â•â–ˆâ–ˆâ•”â•â•â•â–ˆâ–ˆâ•‘ ██║ -██╔██╗ ██║██████╔â•â–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ•—██║██║ ███╗ ███████╗ ██║ ██║ ██║ ██║ -██║╚██╗██║██╔â•â•â–ˆâ–ˆâ•—â•šâ•â•â•â•â–ˆâ–ˆâ•‘██║██║ ██║ â•šâ•â•â•â•â–ˆâ–ˆâ•‘██ ██║ ██║ ██║ ██║ -██║ ╚████║██║ ██║███████║██║╚██████╔╠███████║╚█████╔╠██║ ╚██████╔╠-â•šâ•â• â•šâ•â•â•â•â•šâ•â• â•šâ•â•â•šâ•â•â•â•â•â•â•â•šâ•â• â•šâ•â•â•â•â•â• â•šâ•â•â•â•â•â•â• â•šâ•â•â•â•â• â•šâ•â• â•šâ•â•â•â•â•â• -Neural Rendering Special Interesting Group of SJTU - - """ - print(logo_str) - - @staticmethod - def print_start_training(): - logo_str = """ - _____ __ __ ______ _ _ - / ___/ / /_ ____ _ _____ / /_ /_ __/_____ ____ _ (_)____ (_)____ ____ _ - \__ \ / __// __ `// ___// __/ / / / ___// __ `// // __ \ / // __ \ / __ `/ - ___/ // /_ / /_/ // / / /_ / / / / / /_/ // // / / // // / / // /_/ / -/____/ \__/ \__,_//_/ \__/ /_/ /_/ \__,_//_//_/ /_//_//_/ /_/ \__, / - /____/ - """ - print(logo_str) - -if __name__=="__main__": - # logo_class.print_group_logo() - logo_class.print_start_training() \ No newline at end of file diff --git a/utilities/plot.py b/utilities/plot.py deleted file mode 100644 index 0da1c75..0000000 --- a/utilities/plot.py +++ /dev/null @@ -1,37 +0,0 @@ -import numpy as np -import math -import PIL - -def postprocess(x): - """[0,1] to uint8.""" - - x = np.clip(255 * x, 0, 255) - x = np.cast[np.uint8](x) - return x - -def tile(X, rows, cols): - """Tile images for display.""" - tiling = np.zeros((rows * X.shape[1], cols * X.shape[2], X.shape[3]), dtype = X.dtype) - for i in range(rows): - for j in range(cols): - idx = i * cols + j - if idx < X.shape[0]: - img = X[idx,...] - tiling[ - i*X.shape[1]:(i+1)*X.shape[1], - j*X.shape[2]:(j+1)*X.shape[2], - :] = img - return tiling - - -def plot_batch(X, out_path): - """Save batch of images tiled.""" - n_channels = X.shape[3] - if n_channels > 3: - X = X[:,:,:,np.random.choice(n_channels, size = 3)] - X = postprocess(X) - rc = math.sqrt(X.shape[0]) - rows = cols = math.ceil(rc) - canvas = tile(X, rows, cols) - canvas = np.squeeze(canvas) - PIL.Image.fromarray(canvas).save(out_path) \ No newline at end of file diff --git a/utilities/reporter.py b/utilities/reporter.py deleted file mode 100644 index a4998af..0000000 --- a/utilities/reporter.py +++ /dev/null @@ -1,62 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Reporter.py -# Created Date: Tuesday September 24th 2019 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 3rd March 2022 8:42:13 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2019 Shanghai Jiao Tong University -############################################################# - -import datetime -import os -import json - -class Reporter: - def __init__(self,reportPath): - self.path = reportPath - self.withTimeStamp = False - self.index = 1 - self.timeStrFormat = '%Y-%m-%d %H:%M:%S' - timeStr = datetime.datetime.strftime(datetime.datetime.now(),'%Y%m%d%H%M%S') - self.path = self.path + "-%s.log"%timeStr - if not os.path.exists(self.path): - f = open(self.path,'w') - f.close() - - def writeInfo(self,strLine): - with open(self.path,'a+') as logf: - timeStr = datetime.datetime.strftime(datetime.datetime.now(),self.timeStrFormat) - logf.writelines("[%d]-[%s]-[info] %s\n"%(self.index,timeStr,strLine)) - self.index += 1 - - def writeConfig(self,config): - with open(self.path,'a+') as logf: - for item in config.items(): - text = "[%d]-[parameters] %s--%s\n"%(self.index,item[0],str(item[1])) - logf.writelines(text) - self.index +=1 - - def writeModel(self,modelText): - with open(self.path,'a+') as logf: - logf.writelines("[%d]-[model] %s\n"%(self.index,modelText)) - self.index += 1 - - def writeRawInfo(self, strLine): - with open(self.path,'a+') as logf: - timeStr = datetime.datetime.strftime(datetime.datetime.now(),self.timeStrFormat) - logf.writelines("[%d]-[info] %s\n"%(self.index,timeStr,strLine)) - self.index += 1 - - def writeTrainLog(self, epoch, step, logText): - with open(self.path,'a+') as logf: - timeStr = datetime.datetime.strftime(datetime.datetime.now(),self.timeStrFormat) - logf.writelines("[%d]-[%s]-[logInfo]-epoch[%d]-step[%d] %s\n"%(self.index,timeStr,epoch,step,logText)) - self.index += 1 - - def writeJson(self, info): - with open(self.path, 'a+') as cf: - configjson = json.dumps(info, indent=4) - cf.writelines(configjson) diff --git a/utilities/reverse2original.py b/utilities/reverse2original.py deleted file mode 100644 index acd9c80..0000000 --- a/utilities/reverse2original.py +++ /dev/null @@ -1,173 +0,0 @@ -import cv2 -import numpy as np -# import time -import torch -from torch.nn import functional as F -import torch.nn as nn - - -def encode_segmentation_rgb(segmentation, no_neck=True): - parse = segmentation - - face_part_ids = [1, 2, 3, 4, 5, 6, 10, 12, 13] if no_neck else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14] - mouth_id = 11 - # hair_id = 17 - face_map = np.zeros([parse.shape[0], parse.shape[1]]) - mouth_map = np.zeros([parse.shape[0], parse.shape[1]]) - # hair_map = np.zeros([parse.shape[0], parse.shape[1]]) - - for valid_id in face_part_ids: - valid_index = np.where(parse==valid_id) - face_map[valid_index] = 255 - valid_index = np.where(parse==mouth_id) - mouth_map[valid_index] = 255 - # valid_index = np.where(parse==hair_id) - # hair_map[valid_index] = 255 - #return np.stack([face_map, mouth_map,hair_map], axis=2) - return np.stack([face_map, mouth_map], axis=2) - - -class SoftErosion(nn.Module): - def __init__(self, kernel_size=15, threshold=0.6, iterations=1): - super(SoftErosion, self).__init__() - r = kernel_size // 2 - self.padding = r - self.iterations = iterations - self.threshold = threshold - - # Create kernel - y_indices, x_indices = torch.meshgrid(torch.arange(0., kernel_size), torch.arange(0., kernel_size)) - dist = torch.sqrt((x_indices - r) ** 2 + (y_indices - r) ** 2) - kernel = dist.max() - dist - kernel /= kernel.sum() - kernel = kernel.view(1, 1, *kernel.shape) - self.register_buffer('weight', kernel) - - def forward(self, x): - x = x.float() - for i in range(self.iterations - 1): - x = torch.min(x, F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding)) - x = F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding) - - mask = x >= self.threshold - x[mask] = 1.0 - x[~mask] /= x[~mask].max() - - return x, mask - - -def postprocess(swapped_face, target, target_mask,smooth_mask): - # target_mask = cv2.resize(target_mask, (self.size, self.size)) - - mask_tensor = torch.from_numpy(target_mask.copy().transpose((2, 0, 1))).float().mul_(1/255.0).cuda() - face_mask_tensor = mask_tensor[0] + mask_tensor[1] - - soft_face_mask_tensor, _ = smooth_mask(face_mask_tensor.unsqueeze_(0).unsqueeze_(0)) - soft_face_mask_tensor.squeeze_() - - soft_face_mask = soft_face_mask_tensor.cpu().numpy() - soft_face_mask = soft_face_mask[:, :, np.newaxis] - - result = swapped_face * soft_face_mask + target * (1 - soft_face_mask) - result = result[:,:,::-1]# .astype(np.uint8) - return result - -def reverse2wholeimage(b_align_crop_tenor_list,swaped_imgs, mats, crop_size, oriimg, save_path = '', \ - pasring_model =None, norm = None, use_mask = False): - - target_image_list = [] - img_mask_list = [] - if use_mask: - smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=7).cuda() - else: - pass - - # print(len(swaped_imgs)) - # print(mats) - # print(len(b_align_crop_tenor_list)) - for swaped_img, mat ,source_img in zip(swaped_imgs, mats,b_align_crop_tenor_list): - swaped_img = swaped_img.cpu().numpy().transpose((1, 2, 0)) - img_white = np.full((crop_size,crop_size), 255, dtype=float) - - # inverse the Affine transformation matrix - mat_rev = np.zeros([2,3]) - div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0] - mat_rev[0][0] = mat[1][1]/div1 - mat_rev[0][1] = -mat[0][1]/div1 - mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1 - div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1] - mat_rev[1][0] = mat[1][0]/div2 - mat_rev[1][1] = -mat[0][0]/div2 - mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2 - - orisize = (oriimg.shape[1], oriimg.shape[0]) - if use_mask: - source_img_norm = norm(source_img) - source_img_512 = F.interpolate(source_img_norm,size=(512,512)) - out = pasring_model(source_img_512)[0] - parsing = out.squeeze(0).detach().cpu().numpy().argmax(0) - vis_parsing_anno = parsing.copy().astype(np.uint8) - tgt_mask = encode_segmentation_rgb(vis_parsing_anno) - if tgt_mask.sum() >= 5000: - # face_mask_tensor = tgt_mask[...,0] + tgt_mask[...,1] - target_mask = cv2.resize(tgt_mask, (crop_size, crop_size)) - # print(source_img) - target_image_parsing = postprocess(swaped_img, source_img[0].cpu().detach().numpy().transpose((1, 2, 0)), target_mask,smooth_mask) - - - target_image = cv2.warpAffine(target_image_parsing, mat_rev, orisize) - # target_image_parsing = cv2.warpAffine(swaped_img, mat_rev, orisize) - else: - target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)[..., ::-1] - else: - target_image = cv2.warpAffine(swaped_img, mat_rev, orisize) - # source_image = cv2.warpAffine(source_img, mat_rev, orisize) - - img_white = cv2.warpAffine(img_white, mat_rev, orisize) - - - img_white[img_white>20] =255 - - img_mask = img_white - - # if use_mask: - # kernel = np.ones((40,40),np.uint8) - # img_mask = cv2.erode(img_mask,kernel,iterations = 1) - # else: - kernel = np.ones((40,40),np.uint8) - img_mask = cv2.erode(img_mask,kernel,iterations = 1) - kernel_size = (20, 20) - blur_size = tuple(2*i+1 for i in kernel_size) - img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) - - # kernel = np.ones((10,10),np.uint8) - # img_mask = cv2.erode(img_mask,kernel,iterations = 1) - - - - img_mask /= 255 - - img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) - - # pasing mask - - # target_image_parsing = postprocess(target_image, source_image, tgt_mask) - - if use_mask: - target_image = np.array(target_image, dtype=np.float) * 255 - else: - target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255 - - - img_mask_list.append(img_mask) - target_image_list.append(target_image) - - - # target_image /= 255 - # target_image = 0 - img = np.array(oriimg, dtype=np.float) - for img_mask, target_image in zip(img_mask_list, target_image_list): - img = img_mask * target_image + (1-img_mask) * img - - final_img = img.astype(np.uint8) - cv2.imwrite(save_path, final_img) diff --git a/utilities/save_heatmap.py b/utilities/save_heatmap.py deleted file mode 100644 index f47d352..0000000 --- a/utilities/save_heatmap.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: save_heatmap.py -# Created Date: Friday January 15th 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 15th January 2021 10:23:13 am -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# - -import os -import shutil -import seaborn as sns -import matplotlib.pyplot as plt -import cv2 -import numpy as np - -def SaveHeatmap(heatmaps, path, row=-1, dpi=72): - """ - The input tensor must be B X 1 X H X W - """ - batch_size = heatmaps.shape[0] - temp_path = ".temp/" - if not os.path.exists(temp_path): - os.makedirs(temp_path) - final_img = None - if row < 1: - col = batch_size - row = 1 - else: - col = batch_size // row - if row * col = col: - col_i = 0 - row_i += 1 - cv2.imwrite(path,final_img) - -if __name__ == "__main__": - random_map = np.random.randn(16,1,10,10) - SaveHeatmap(random_map,"./wocao.png",1) diff --git a/utilities/sshupload.py b/utilities/sshupload.py deleted file mode 100644 index 18f3c91..0000000 --- a/utilities/sshupload.py +++ /dev/null @@ -1,156 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: sshupload.py -# Created Date: Tuesday September 24th 2019 -# Author: Lcx -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 14th April 2022 12:33:07 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2019 Shanghai Jiao Tong University -############################################################# - -try: - import paramiko -except: - from pip._internal import main - main(['install', 'paramiko']) - import paramiko -import os -from pathlib import Path -# ssh传输类： - -class fileUploaderClass(object): - def __init__(self,serverIp,userName,passWd,port=22): - self.__ip__ = serverIp - self.__userName__ = userName - self.__passWd__ = passWd - self.__port__ = port - self.__ssh__ = paramiko.SSHClient() - self.__ssh__.set_missing_host_key_policy(paramiko.AutoAddPolicy()) - - def sshScpPut(self,localFile,remoteFile): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - remoteDir = remoteFile.split("/") - if remoteFile[0]=='/': - sftp.chdir('/') - - for item in remoteDir[0:-1]: - if item == "": - continue - try: - sftp.chdir(item) - except: - sftp.mkdir(item) - sftp.chdir(item) - sftp.put(localFile,remoteDir[-1]) - sftp.close() - self.__ssh__.close() - print("ssh localfile:%s remotefile:%s success"%(localFile,remoteFile)) - - def sshScpGetNames(self,remoteDir): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - wocao = sftp.listdir(remoteDir) - return wocao - - def sshScpGetDir(self, remoteDir, localDir, showProgress=False): - self.__ssh__.connect(self.__ip__, self.__port__, self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - try: - sftp.stat(remoteDir) - print("Remote dir exists!") - except: - print("Remote dir does not exist!") - return False - files = sftp.listdir(remoteDir) - for i_f in files: - i_remote_file = Path(remoteDir,i_f).as_posix() - local_file = Path(localDir,i_f) - if showProgress: - sftp.get(i_remote_file, local_file,callback=self.__putCallBack__) - else: - sftp.get(i_remote_file, local_file) - sftp.close() - self.__ssh__.close() - return True - - def sshScpGet(self, remoteFile, localFile, showProgress=False): - self.__ssh__.connect(self.__ip__, self.__port__, self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - try: - sftp.stat(remoteFile) - print("Remote file exists!") - except: - print("Remote file does not exist!") - return False - sftp = self.__ssh__.open_sftp() - if showProgress: - sftp.get(remoteFile, localFile,callback=self.__putCallBack__) - else: - sftp.get(remoteFile, localFile) - sftp.close() - self.__ssh__.close() - return True - - def __putCallBack__(self,transferred,total): - print("current transferred %3.1f percent"%(transferred/total*100),end='\r') - - def sshScpGetmd5(self, file_path): - self.__ssh__.connect(self.__ip__, self.__port__, self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - try: - file = sftp.open(file_path, 'rb') - res = (True,hashlib.new('md5', file.read()).hexdigest()) - sftp.close() - self.__ssh__.close() - return res - except: - sftp.close() - self.__ssh__.close() - return (False,None) - - def sshScpRename(self, oldpath, newpath): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - sftp.rename(oldpath,newpath) - sftp.close() - self.__ssh__.close() - print("ssh oldpath:%s newpath:%s success"%(oldpath,newpath)) - - def sshScpDelete(self,path): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - sftp.remove(path) - sftp.close() - self.__ssh__.close() - print("ssh delete:%s success"%(path)) - - def sshScpDeleteDir(self,path): - self.__ssh__.connect(self.__ip__, self.__port__ , self.__userName__, self.__passWd__) - sftp = paramiko.SFTPClient.from_transport(self.__ssh__.get_transport()) - sftp = self.__ssh__.open_sftp() - self.__rm__(sftp,path) - sftp.close() - self.__ssh__.close() - - def __rm__(self,sftp,path): - try: - files = sftp.listdir(path=path) - print(files) - for f in files: - filepath = os.path.join(path, f).replace('\\','/') - self.__rm__(sftp,filepath) - sftp.rmdir(path) - print("ssh delete:%s success"%(path)) - except: - print(path) - sftp.remove(path) - print("ssh delete:%s success"%(path)) \ No newline at end of file diff --git a/utilities/transfer_checkpoint.py b/utilities/transfer_checkpoint.py deleted file mode 100644 index a6c3dd9..0000000 --- a/utilities/transfer_checkpoint.py +++ /dev/null @@ -1,145 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: transfer_checkpoint.py -# Created Date: Wednesday February 3rd 2021 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Monday, 17th January 2022 1:25:56 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2021 Shanghai Jiao Tong University -############################################################# -import torch -from torch import nn as nn -from torch.nn import functional as F -from torch.nn import init as init -import os - - -class RepSRPlain_pixel(nn.Module): - """Networks consisting of Residual in Residual Dense Block, which is used - in ESRGAN. - ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. - Currently, it supports x4 upsampling scale factor. - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_feat (int): Channel number of intermediate features. - Default: 64 - num_block (int): Block number in the trunk network. Defaults: 23 - num_grow_ch (int): Channels for each growth. Default: 32. - """ - - def __init__(self, - num_in_ch, - num_out_ch, - num_feat=32, - num_layer = 3, - upsampling=4): - super(RepSRPlain_pixel, self).__init__() - - self.scale = upsampling - self.ssqu = upsampling ** 2 - - self.rep1 = nn.Conv2d(num_in_ch, num_feat,3,1,1) - self.rep2 = nn.Conv2d(num_feat, num_feat*2,3,1,1) - self.rep3 = nn.Conv2d(num_feat*2, num_feat*2,3,1,1) - self.rep4 = nn.Conv2d(num_feat*2, num_feat*2,3,1,1) - self.rep5 = nn.Conv2d(num_feat*2, num_feat*2,3,1,1) - self.rep6 = nn.Conv2d(num_feat*2, num_feat,3,1,1) - - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - - self.activator = nn.LeakyReLU(negative_slope=0.2, inplace=True) - # self.activator = nn.ReLU(inplace=True) - - # default_init_weights([self.conv_up1,self.conv_up2,self.conv_hr,self.conv_last], 0.1) - - def forward(self, x): - - f_d = self.activator(self.rep1(x)) - f_d = self.activator(self.rep2(f_d)) - f_d = self.activator(self.rep3(f_d)) - f_d = self.activator(self.rep4(f_d)) - f_d = self.activator(self.rep5(f_d)) - f_d = self.activator(self.rep6(f_d)) - - feat = self.activator( - self.conv_up1(F.interpolate(f_d, scale_factor=2, mode='nearest'))) - feat = self.activator( - self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest'))) - out = self.conv_last(self.activator(self.conv_hr(feat))) - return out - -def create_identity_conv(dim,kernel_size=3): - zeros = torch.zeros((dim,dim,kernel_size,kernel_size)).cuda() - for i_dim in range(dim): - zeros[i_dim,i_dim,kernel_size//2,kernel_size//2] = 1.0 - return zeros - -def fill_conv_kernel(in_tensor,kernel_size=3): - shape = in_tensor.shape - zeros = torch.zeros(shape[0],shape[1],kernel_size,kernel_size).cuda() - for i_dim in range(shape[0]): - zeros[i_dim,:,kernel_size//2,kernel_size//2] = in_tensor[i_dim,:,0,0] - return zeros - -if __name__ == "__main__": - os.environ["CUDA_VISIBLE_DEVICES"] = str(0) - base_path = "H:\\Multi Scale Kernel Prediction Networks\\Mobile_Oriented_KPN\\train_logs\\" - version = "repsr_pixel_0" - epoch = 73 - path_ckp= os.path.join(base_path,version,"checkpoints\\epoch%d_RepSR.pth"%epoch) - path_plain_ckp= os.path.join(base_path,version,"checkpoints\\epoch%d_RepSR_Plain.pth"%epoch) - network = RepSRPlain_pixel(3, - 3, - 64, - 3, - 4 - ) - network = network.cuda() - - - - wocao = network.state_dict() - # for data_key in wocao.keys(): - # print(data_key) - # print(wocao[data_key].shape) - wocao_cpk = torch.load(path_ckp) - - # for data_key in wocao_cpk.keys(): - # print(data_key) - # print(wocao_cpk[data_key].shape) - name_list = ["rep1","rep2","rep3","rep4","rep5","rep6"] - other_list = ["conv_up1","conv_up2","conv_hr","conv_last"] - for i_name in name_list: - temp= wocao_cpk[i_name+".conv3.weight"] + fill_conv_kernel(wocao_cpk[i_name+".conv1x1.weight"]) - wocao[i_name+".weight"] = temp - temp= wocao_cpk[i_name+".conv3.bias"] + wocao_cpk[i_name+".conv1x1.bias"] - wocao[i_name+".bias"] = temp - - if wocao_cpk[i_name+".conv3.weight"].shape[0] == wocao_cpk[i_name+".conv3.weight"].shape[1]: - print("include identity") - temp = wocao[i_name+".weight"] + create_identity_conv(wocao_cpk[i_name+".conv3.weight"].shape[0]) - wocao[i_name+".weight"] = temp - - for i_name in other_list: - wocao[i_name+".weight"] = wocao_cpk[i_name+".weight"] - wocao[i_name+".bias"] = wocao_cpk[i_name+".bias"] - - torch.save(wocao,path_plain_ckp) - - # wocao = torch.load(path_plain_ckp) - # for data_key in wocao.keys(): - # result1 = wocao[data_key].cpu().numpy() - # # np.savetxt(i_name+"_conv3_weight.txt",result1) - # str_temp = ("%s"%data_key).replace(".","_") - # io.savemat(str_temp+".mat",{str_temp:result1}) - - # for data_key in wocao.keys(): - # print(data_key) - # print(wocao[data_key].shape) \ No newline at end of file diff --git a/utilities/utilities.py b/utilities/utilities.py deleted file mode 100644 index 3f942cf..0000000 --- a/utilities/utilities.py +++ /dev/null @@ -1,369 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: utilities.py -# Created Date: Monday April 6th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 14th April 2022 11:34:54 am -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - -import os -import cv2 -import torch -from PIL import Image -import numpy as np -from torchvision import transforms -from torch.hub import download_url_to_file, get_dir -from urllib.parse import urlparse - -def load_file_from_url(url, model_dir=None, progress=True, file_name=None): - """Load file form http url, will download models if necessary. - - Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py - - Args: - url (str): URL to be downloaded. - model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. - Default: None. - progress (bool): Whether to show the download progress. Default: True. - file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. - - Returns: - str: The path to the downloaded file. - """ - if model_dir is None: # use the pytorch hub_dir - hub_dir = get_dir() - model_dir = os.path.join(hub_dir, 'checkpoints') - - os.makedirs(model_dir, exist_ok=True) - - parts = urlparse(url) - filename = os.path.basename(parts.path) - if file_name is not None: - filename = file_name - cached_file = os.path.abspath(os.path.join(model_dir, filename)) - if not os.path.exists(cached_file): - print(f'Downloading: "{url}" to {cached_file}\n') - download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) - return cached_file - -# Gram Matrix -def Gram(tensor: torch.Tensor): - B, C, H, W = tensor.shape - x = tensor.view(B, C, H*W) - x_t = x.transpose(1, 2) - return torch.bmm(x, x_t) / (C*H*W) - -def build_tensorboard(summary_path): - from tensorboardX import SummaryWriter - # from logger import Logger - # self.logger = Logger(self.log_path) - writer = SummaryWriter(log_dir=summary_path) - return writer - - - -def denorm(x): - out = (x + 1) / 2 - return out.clamp_(0, 1) - -def tensor2img(img_tensor): - """ - Input image tensor shape must be [B C H W] - the return image numpy array shape is [B H W C] - """ - res = img_tensor.numpy() - res = (res + 1) / 2 - res = np.clip(res, 0.0, 1.0) - res = res * 255 - res = res.transpose((0,2,3,1)) - return res - -def img2tensor255(path, max_size=None): - - image = Image.open(path) - # Rescale the image - if (max_size==None): - itot_t = transforms.Compose([ - #transforms.ToPILImage(), - transforms.ToTensor(), - transforms.Lambda(lambda x: x.mul(255)) - ]) - else: - H, W, C = image.shape - image_size = tuple([int((float(max_size) / max([H,W]))*x) for x in [H, W]]) - itot_t = transforms.Compose([ - transforms.ToPILImage(), - transforms.Resize(image_size), - transforms.ToTensor(), - transforms.Lambda(lambda x: x.mul(255)) - ]) - - # Convert image to tensor - tensor = itot_t(image) - - # Add the batch_size dimension - tensor = tensor.unsqueeze(dim=0) - return tensor - -def img2tensor255crop(path, crop_size=256): - - image = Image.open(path) - # Rescale the image - itot_t = transforms.Compose([ - transforms.CenterCrop(crop_size), - transforms.ToTensor(), - transforms.Lambda(lambda x: x.mul(255)) - ]) - - # Convert image to tensor - tensor = itot_t(image) - - # Add the batch_size dimension - tensor = tensor.unsqueeze(dim=0) - return tensor - -# def img2tensor255(path, crop_size=None): -# """ -# Input image tensor shape must be [B C H W] -# the return image numpy array shape is [B H W C] -# """ -# img = cv2.imread(path) -# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float) -# img = torch.from_numpy(img).transpose((2,0,1)).unsqueeze(0) -# return img - -def img2tensor1(img_tensor): - """ - Input image tensor shape must be [B C H W] - the return image numpy array shape is [B H W C] - """ - res = img_tensor.numpy() - res = (res + 1) / 2 - res = np.clip(res, 0.0, 1.0) - res = res * 255 - res = res.transpose((0,2,3,1)) - return res - -def _convert_input_type_range(img): - """Convert the type and range of the input image. - - It converts the input image to np.float32 type and range of [0, 1]. - It is mainly used for pre-processing the input image in colorspace - convertion functions such as rgb2ycbcr and ycbcr2rgb. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - (ndarray): The converted image with type of np.float32 and range of - [0, 1]. - """ - img_type = img.dtype - img = img.astype(np.float32) - if img_type == np.float32: - pass - elif img_type == np.uint8: - img /= 255. - else: - raise TypeError('The img type should be np.float32 or np.uint8, ' - f'but got {img_type}') - return img - -def _convert_output_type_range(img, dst_type): - """Convert the type and range of the image according to dst_type. - - It converts the image to desired type and range. If `dst_type` is np.uint8, - images will be converted to np.uint8 type with range [0, 255]. If - `dst_type` is np.float32, it converts the image to np.float32 type with - range [0, 1]. - It is mainly used for post-processing images in colorspace convertion - functions such as rgb2ycbcr and ycbcr2rgb. - - Args: - img (ndarray): The image to be converted with np.float32 type and - range [0, 255]. - dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it - converts the image to np.uint8 type with range [0, 255]. If - dst_type is np.float32, it converts the image to np.float32 type - with range [0, 1]. - - Returns: - (ndarray): The converted image with desired type and range. - """ - if dst_type not in (np.uint8, np.float32): - raise TypeError('The dst_type should be np.float32 or np.uint8, ' - f'but got {dst_type}') - if dst_type == np.uint8: - img = img.round() - else: - img /= 255. - return img.astype(dst_type) - - -def bgr2ycbcr(img, y_only=False): - """Convert a BGR image to YCbCr image. - - The bgr version of rgb2ycbcr. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - ndarray: The converted YCbCr image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) - if y_only: - # out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0 - out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0 #RGB - else: - out_img = np.matmul( - img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], - [65.481, -37.797, 112.0]]) + [16, 128, 128] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - -def to_y_channel(img): - """Change to Y channel of YCbCr. - - Args: - img (ndarray): Images with range [0, 255]. - - Returns: - (ndarray): Images with range [0, 255] (float type) without round. - """ - img = img.astype(np.float32) / 255. - if img.ndim == 3 and img.shape[2] == 3: - img = bgr2ycbcr(img, y_only=True) - img = img[..., None] - return img * 255. - -def calculate_psnr(img1, - img2, - # crop_border=0, - test_y_channel=True): - """Calculate PSNR (Peak Signal-to-Noise Ratio). - - Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio - - Args: - img1 (ndarray): Images with range [0, 255]. - img2 (ndarray): Images with range [0, 255]. - crop_border (int): Cropped pixels in each edge of an image. These - pixels are not involved in the PSNR calculation. - input_order (str): Whether the input order is 'HWC' or 'CHW'. - Default: 'HWC'. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: psnr result. - """ - - # if crop_border != 0: - # img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...] - # img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] - - img1 = img1.astype(np.float64) - img2 = img2.astype(np.float64) - - if test_y_channel: - img1 = to_y_channel(img1) - img2 = to_y_channel(img2) - - mse = np.mean((img1 - img2)**2) - if mse == 0: - return float('inf') - return 20. * np.log10(255. / np.sqrt(mse)) - - -def _ssim(img1, img2): - """Calculate SSIM (structural similarity) for one channel images. - - It is called by func:`calculate_ssim`. - - Args: - img1 (ndarray): Images with range [0, 255] with order 'HWC'. - img2 (ndarray): Images with range [0, 255] with order 'HWC'. - - Returns: - float: ssim result. - """ - - C1 = (0.01 * 255)**2 - C2 = (0.03 * 255)**2 - - img1 = img1.astype(np.float64) - img2 = img2.astype(np.float64) - kernel = cv2.getGaussianKernel(11, 1.5) - window = np.outer(kernel, kernel.transpose()) - - mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] - mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] - mu1_sq = mu1**2 - mu2_sq = mu2**2 - mu1_mu2 = mu1 * mu2 - sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq - sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq - sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 - - ssim_map = ((2 * mu1_mu2 + C1) * - (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * - (sigma1_sq + sigma2_sq + C2)) - return ssim_map.mean() - - -def calculate_ssim(img1, - img2, - test_y_channel=True): - """Calculate SSIM (structural similarity). - - Ref: - Image quality assessment: From error visibility to structural similarity - - The results are the same as that of the official released MATLAB code in - https://ece.uwaterloo.ca/~z70wang/research/ssim/. - - For three-channel images, SSIM is calculated for each channel and then - averaged. - - Args: - img1 (ndarray): Images with range [0, 255]. - img2 (ndarray): Images with range [0, 255]. - crop_border (int): Cropped pixels in each edge of an image. These - pixels are not involved in the SSIM calculation. - input_order (str): Whether the input order is 'HWC' or 'CHW'. - Default: 'HWC'. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: ssim result. - """ - - img1 = img1.astype(np.float64) - img2 = img2.astype(np.float64) - - if test_y_channel: - img1 = to_y_channel(img1) - img2 = to_y_channel(img2) - - ssims = [] - for i in range(img1.shape[2]): - ssims.append(_ssim(img1[..., i], img2[..., i])) - return np.array(ssims).mean() \ No newline at end of file diff --git a/utilities/yaml_config.py b/utilities/yaml_config.py deleted file mode 100644 index 1a920ed..0000000 --- a/utilities/yaml_config.py +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: Config_from_yaml.py -# Created Date: Monday February 17th 2020 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 28th February 2020 4:30:01 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - - -import yaml - -def getConfigYaml(yaml_file): - with open(yaml_file, 'r') as config_file: - try: - config_dict = yaml.load(config_file, Loader=yaml.FullLoader) - return config_dict - except ValueError: - print('INVALID YAML file format.. Please provide a good yaml file') - exit(-1) - -if __name__ == "__main__": - a= getConfigYaml("./train_256.yaml") - sys_state = {} - for item in a.items(): - sys_state[item[0]] = item[1] \ No newline at end of file diff --git a/vggface2hq_failed.json b/vggface2hq_failed.json deleted file mode 100644 index 3b7ed18..0000000 --- a/vggface2hq_failed.json +++ /dev/null @@ -1,67621 +0,0 @@ -{ - "n000002": [ - "0054_01.jpg" - ], - "n000004": [ - "0026_01.jpg", - "0084_01.jpg" - ], - "n000005": [ - "0138_01.jpg" - ], - "n000006": [ - "0014_01.jpg", - "0036_02.jpg", - "0091_01.jpg", - "0300_01.jpg", - "0519_01.jpg", - "0351_01.jpg" - ], - "n000007": [ - "0106_02.jpg", - "0115_01.jpg", - "0119_01.jpg", - "0181_01.jpg", - "0174_01.jpg", - "0148_02.jpg", - "0140_02.jpg" - ], - "n000008": [ - "0072_01.jpg" - ], - "n000009": [ - "0150_02.jpg", - "0096_01.jpg", - "0068_01.jpg" - ], - "n000014": [ - "0163_01.jpg" - ], - "n000015": [ - "0402_01.jpg", - "0392_02.jpg" - ], - "n000016": [ - "0047_03.jpg", 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