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chenxuanhong
2022-03-03 21:19:52 +08:00
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commit 39964bf613
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test_scripts/tester_image_allstep.py
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#!/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))