chenxuanhong
575 lines
24 KiB
Python
575 lines
24 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: trainer_naiv512.py
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# Created Date: Sunday January 9th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Wednesday, 23rd February 2022 3:39:20 pm
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 Shanghai Jiao Tong University
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#############################################################
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import os
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import time
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import random
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import shutil
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import tempfile
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch_utils import misc
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from torch_utils import training_stats
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from torch_utils.ops import conv2d_gradfix
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from torch_utils.ops import grid_sample_gradfix
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from losses.KA import KA
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from utilities.plot import plot_batch
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from train_scripts.trainer_multigpu_base import TrainerBase
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class Trainer(TrainerBase):
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def __init__(self,
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config,
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reporter):
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super(Trainer, self).__init__(config, reporter)
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import inspect
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print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe()))
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def train(self):
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# Launch processes.
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num_gpus = len(self.config["gpus"])
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print('Launching processes...')
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torch.multiprocessing.set_start_method('spawn')
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with tempfile.TemporaryDirectory() as temp_dir:
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torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus)
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def add_mapping_hook(network, features,mapping_layers):
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mapping_hooks = []
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def get_activation(mem, name):
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def get_output_hook(module, input, output):
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mem[name] = output
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return get_output_hook
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def add_hook(net, mem, mapping_layers):
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for n, m in net.named_modules():
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if n in mapping_layers:
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mapping_hooks.append(
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m.register_forward_hook(get_activation(mem, n)))
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add_hook(network, features, mapping_layers)
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# TODO modify this function to build your models
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def init_framework(config, reporter, device, rank):
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'''
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This function is designed to define the framework,
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and print the framework information into the log file
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'''
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#===============build models================#
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print("build models...")
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# TODO [import models here]
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torch.cuda.set_device(rank)
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torch.cuda.empty_cache()
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model_config = config["model_configs"]
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if config["phase"] == "train":
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gscript_name = "components." + model_config["g_model"]["script"]
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file1 = os.path.join("components", model_config["g_model"]["script"]+".py")
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tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py")
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shutil.copyfile(file1,tgtfile1)
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dscript_name = "components." + model_config["d_model"]["script"]
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file1 = os.path.join("components", model_config["d_model"]["script"]+".py")
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tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py")
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shutil.copyfile(file1,tgtfile1)
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elif config["phase"] == "finetune":
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gscript_name = config["com_base"] + model_config["g_model"]["script"]
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dscript_name = config["com_base"] + model_config["d_model"]["script"]
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com_base = "train_logs."+config["teacher_model"]["version"]+".scripts"
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tscript_name = com_base +"."+ config["teacher_model"]["model_configs"]["g_model"]["script"]
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class_name = config["teacher_model"]["model_configs"]["g_model"]["class_name"]
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package = __import__(tscript_name, fromlist=True)
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gen_class = getattr(package, class_name)
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tgen = gen_class(**config["teacher_model"]["model_configs"]["g_model"]["module_params"])
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tgen = tgen.eval()
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class_name = model_config["g_model"]["class_name"]
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package = __import__(gscript_name, fromlist=True)
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gen_class = getattr(package, class_name)
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gen = gen_class(**model_config["g_model"]["module_params"])
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# print and recorde model structure
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reporter.writeInfo("Generator structure:")
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reporter.writeModel(gen.__str__())
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reporter.writeInfo("Teacher structure:")
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reporter.writeModel(tgen.__str__())
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class_name = model_config["d_model"]["class_name"]
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package = __import__(dscript_name, fromlist=True)
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dis_class = getattr(package, class_name)
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dis = dis_class(**model_config["d_model"]["module_params"])
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# print and recorde model structure
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reporter.writeInfo("Discriminator structure:")
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reporter.writeModel(dis.__str__())
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arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu"))
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arcface = arcface1['model'].module
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# train in GPU
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# if in finetune phase, load the pretrained checkpoint
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if config["phase"] == "finetune":
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model_path = os.path.join(config["project_checkpoints"],
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"step%d_%s.pth"%(config["ckpt"],
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config["checkpoint_names"]["generator_name"]))
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gen.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
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model_path = os.path.join(config["project_checkpoints"],
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"step%d_%s.pth"%(config["ckpt"],
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config["checkpoint_names"]["discriminator_name"]))
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dis.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
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print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"]))
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model_path = os.path.join(config["teacher_model"]["project_checkpoints"],
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"step%d_%s.pth"%(config["teacher_model"]["model_step"],
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config["teacher_model"]["checkpoint_names"]["generator_name"]))
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tgen.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
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print('loaded trained teacher backbone model step {}...!'.format(config["teacher_model"]["model_step"]))
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tgen = tgen.to(device)
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tgen.requires_grad_(False)
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gen = gen.to(device)
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dis = dis.to(device)
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arcface= arcface.to(device)
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arcface.requires_grad_(False)
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arcface.eval()
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t_features = {}
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s_features = {}
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add_mapping_hook(tgen,t_features,config["feature_list"])
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add_mapping_hook(gen,s_features,config["feature_list"])
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return tgen, gen, dis, arcface, t_features, s_features
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# TODO modify this function to configurate the optimizer of your pipeline
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def setup_optimizers(config, reporter, gen, dis, rank):
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torch.cuda.set_device(rank)
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torch.cuda.empty_cache()
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g_train_opt = config['g_optim_config']
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d_train_opt = config['d_optim_config']
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g_optim_params = []
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d_optim_params = []
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for k, v in gen.named_parameters():
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if v.requires_grad:
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g_optim_params.append(v)
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else:
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reporter.writeInfo(f'Params {k} will not be optimized.')
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print(f'Params {k} will not be optimized.')
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for k, v in dis.named_parameters():
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if v.requires_grad:
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d_optim_params.append(v)
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else:
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reporter.writeInfo(f'Params {k} will not be optimized.')
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print(f'Params {k} will not be optimized.')
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optim_type = config['optim_type']
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if optim_type == 'Adam':
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g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt)
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d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt)
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else:
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raise NotImplementedError(
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f'optimizer {optim_type} is not supperted yet.')
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# self.optimizers.append(self.optimizer_g)
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if config["phase"] == "finetune":
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opt_path = os.path.join(config["project_checkpoints"],
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"step%d_optim_%s.pth"%(config["ckpt"],
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config["optimizer_names"]["generator_name"]))
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g_optimizer.load_state_dict(torch.load(opt_path))
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opt_path = os.path.join(config["project_checkpoints"],
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"step%d_optim_%s.pth"%(config["ckpt"],
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config["optimizer_names"]["discriminator_name"]))
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d_optimizer.load_state_dict(torch.load(opt_path))
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print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"]))
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return g_optimizer, d_optimizer
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def train_loop(
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rank,
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config,
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reporter,
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temp_dir
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):
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version = config["version"]
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ckpt_dir = config["project_checkpoints"]
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sample_dir = config["project_samples"]
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log_freq = config["log_step"]
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model_freq = config["model_save_step"]
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sample_freq = config["sample_step"]
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total_step = config["total_step"]
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random_seed = config["dataset_params"]["random_seed"]
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id_w = config["id_weight"]
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rec_w = config["reconstruct_weight"]
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feat_w = config["feature_match_weight"]
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distill_w = config["distillation_weight"]
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feat_num = len(config["feature_list"])
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num_gpus = len(config["gpus"])
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batch_gpu = config["batch_size"] // num_gpus
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init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
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if os.name == 'nt':
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init_method = 'file:///' + init_file.replace('\\', '/')
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torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus)
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else:
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init_method = f'file://{init_file}'
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torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus)
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# Init torch_utils.
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sync_device = torch.device('cuda', rank)
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training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
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if rank == 0:
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img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1)
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img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1)
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# Initialize.
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device = torch.device('cuda', rank)
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np.random.seed(random_seed * num_gpus + rank)
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torch.manual_seed(random_seed * num_gpus + rank)
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torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy.
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torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy.
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conv2d_gradfix.enabled = True # Improves training speed.
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grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe.
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# Create dataloader.
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if rank == 0:
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print('Loading training set...')
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dataset = config["dataset_paths"][config["dataset_name"]]
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#================================================#
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print("Prepare the train dataloader...")
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dlModulename = config["dataloader"]
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package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True)
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dataloaderClass = getattr(package, 'GetLoader')
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dataloader_class= dataloaderClass
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dataloader = dataloader_class(dataset,
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rank,
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num_gpus,
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batch_gpu,
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**config["dataset_params"])
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# Construct networks.
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if rank == 0:
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print('Constructing networks...')
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tgen, gen, dis, arcface, t_feat, s_feat = init_framework(config, reporter, device, rank)
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# Check for existing checkpoint
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# Print network summary tables.
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# if rank == 0:
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# attr = torch.empty([batch_gpu, 3, 512, 512], device=device)
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# id = torch.empty([batch_gpu, 3, 112, 112], device=device)
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# latent = misc.print_module_summary(arcface, [id])
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# img = misc.print_module_summary(gen, [attr, latent])
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# misc.print_module_summary(dis, [img, None])
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# del attr
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# del id
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# del latent
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# del img
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# torch.cuda.empty_cache()
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# Distribute across GPUs.
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if rank == 0:
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print(f'Distributing across {num_gpus} GPUs...')
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for module in [gen, dis, arcface, tgen]:
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if module is not None and num_gpus > 1:
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for param in misc.params_and_buffers(module):
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torch.distributed.broadcast(param, src=0)
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# Setup training phases.
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if rank == 0:
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print('Setting up training phases...')
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#===============build losses===================#
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# TODO replace below lines to build your losses
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# MSE_loss = torch.nn.MSELoss()
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l1_loss = torch.nn.L1Loss()
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cos_loss = torch.nn.CosineSimilarity()
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g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank)
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# Initialize logs.
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if rank == 0:
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print('Initializing logs...')
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#==============build tensorboard=================#
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if config["logger"] == "tensorboard":
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import torch.utils.tensorboard as tensorboard
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tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"])
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logger = tensorboard_writer
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elif config["logger"] == "wandb":
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import wandb
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wandb.init(project="Simswap_HQ", entity="xhchen", notes="512",
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tags=[config["tag"]], name=version)
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wandb.config = {
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"total_step": config["total_step"],
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"batch_size": config["batch_size"]
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}
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logger = wandb
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random.seed(random_seed)
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randindex = [i for i in range(batch_gpu)]
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# set the start point for training loop
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if config["phase"] == "finetune":
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start = config["ckpt"]
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else:
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start = 0
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if rank == 0:
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import datetime
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start_time = time.time()
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# Caculate the epoch number
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print("Total step = %d"%total_step)
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print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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from utilities.logo_class import logo_class
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logo_class.print_start_training()
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dis.feature_network.requires_grad_(False)
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for step in range(start, total_step):
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gen.train()
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dis.train()
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for interval in range(2):
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random.shuffle(randindex)
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src_image1, src_image2 = dataloader.next()
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# if rank ==0:
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# elapsed = time.time() - start_time
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# elapsed = str(datetime.timedelta(seconds=elapsed))
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# print("dataloader:",elapsed)
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if step%2 == 0:
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img_id = src_image2
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else:
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img_id = src_image2[randindex]
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img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic')
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latent_id = arcface(img_id_112)
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latent_id = F.normalize(latent_id, p=2, dim=1)
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if interval == 0:
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img_fake = gen(src_image1, latent_id)
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gen_logits,_ = dis(img_fake.detach(), None)
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loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean()
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real_logits,_ = dis(src_image2,None)
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loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean()
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loss_D = loss_Dgen + loss_Dreal
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d_optimizer.zero_grad(set_to_none=True)
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loss_D.backward()
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with torch.autograd.profiler.record_function('discriminator_opt'):
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# params = [param for param in dis.parameters() if param.grad is not None]
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# if len(params) > 0:
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# flat = torch.cat([param.grad.flatten() for param in params])
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# if num_gpus > 1:
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# torch.distributed.all_reduce(flat)
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# flat /= num_gpus
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# misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
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# grads = flat.split([param.numel() for param in params])
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# for param, grad in zip(params, grads):
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# param.grad = grad.reshape(param.shape)
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params = [param for param in dis.parameters() if param.grad is not None]
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flat = torch.cat([param.grad.flatten() for param in params])
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torch.distributed.all_reduce(flat)
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flat /= num_gpus
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misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
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grads = flat.split([param.numel() for param in params])
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for param, grad in zip(params, grads):
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param.grad = grad.reshape(param.shape)
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d_optimizer.step()
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# if rank ==0:
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# elapsed = time.time() - start_time
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# elapsed = str(datetime.timedelta(seconds=elapsed))
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# print("Discriminator training:",elapsed)
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else:
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# model.netD.requires_grad_(True)
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img_t = tgen(src_image1, latent_id)
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img_fake = gen(src_image1, latent_id)
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Sacts = [
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s_feat[key] for key in sorted(s_feat.keys())
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]
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Tacts = [
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t_feat[key] for key in sorted(t_feat.keys())
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]
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loss_distill = 0
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for Sact, Tact in zip(Sacts, Tacts):
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loss_distill += -KA(Sact, Tact)
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# G loss
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loss_distill /= feat_num
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gen_logits,feat = dis(img_fake, None)
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loss_Gmain = (-gen_logits).mean()
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img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic')
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latent_fake = arcface(img_fake_down)
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latent_fake = F.normalize(latent_fake, p=2, dim=1)
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loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean()
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real_feat = dis.get_feature(src_image1)
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feat_match_loss = l1_loss(feat["3"],real_feat["3"])
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loss_G = loss_Gmain + loss_G_ID * id_w + \
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feat_match_loss * feat_w + loss_distill * distill_w
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if step%2 == 0:
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#G_Rec
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loss_G_Rec = l1_loss(img_fake, src_image1)
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loss_G += loss_G_Rec * rec_w
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g_optimizer.zero_grad(set_to_none=True)
|
|
loss_G.backward()
|
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with torch.autograd.profiler.record_function('generator_opt'):
|
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params = [param for param in gen.parameters() if param.grad is not None]
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flat = torch.cat([param.grad.flatten() for param in params])
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|
torch.distributed.all_reduce(flat)
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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])
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|
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() |