from tqdm import trange import torch import torch.nn as nn from torch.utils.data import DataLoader from logger import Logger from modules.model import DiscriminatorFullModel, TrainPart1Model, TrainPart2Model import itertools from torch.optim.lr_scheduler import MultiStepLR from sync_batchnorm import DataParallelWithCallback from frames_dataset import DatasetRepeater,TestsetRepeater import time from tensorboardX import SummaryWriter def train_part1(config, generator, discriminator, kp_detector, kp_detector_a,audio_feature, checkpoint, audio_checkpoint, log_dir, dataset, test_dataset, device_ids, name): train_params = config['train_params'] optimizer_audio_feature = torch.optim.Adam(itertools.chain(audio_feature.parameters(),kp_detector_a.parameters()), lr=train_params['lr_audio_feature'], betas=(0.5, 0.999)) optimizer_generator = None optimizer_discriminator = None optimizer_kp_detector = None if checkpoint is not None: start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, audio_feature, optimizer_generator, optimizer_discriminator, None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector, None if train_params['lr_audio_feature'] == 0 else optimizer_audio_feature) # audio_feature load wav2lip wav2lip_ckpt_path = "/data/liujin/Wav2Lip-master/checkpoints/wav2lip.pth" checkpoint = torch.load(wav2lip_ckpt_path) s = checkpoint["state_dict"] new_s = {} for k, v in s.items(): new_s[k.replace('module.', '')] = v audio_feature.load_state_dict(new_s, strict=False) if audio_checkpoint is not None: pretrain = torch.load(audio_checkpoint) kp_detector_a.load_state_dict(pretrain['kp_detector_a']) audio_feature.load_state_dict(pretrain['audio_feature']) optimizer_audio_feature.load_state_dict(pretrain['optimizer_audio_feature']) start_epoch = pretrain['epoch'] else: start_epoch = 0 scheduler_audio_feature = MultiStepLR(optimizer_audio_feature, train_params['epoch_milestones'], gamma=0.1, last_epoch=-1 + start_epoch * (train_params['lr_audio_feature'] != 0)) if 'num_repeats' in train_params or train_params['num_repeats'] != 1: dataset = DatasetRepeater(dataset, train_params['num_repeats']) test_dataset = TestsetRepeater(test_dataset, train_params['num_repeats']) dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6 test_dataloader = DataLoader(test_dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6 num_steps_per_epoch = len(dataloader) num_steps_test_epoch = len(test_dataloader) generator_full = TrainPart1Model(kp_detector, kp_detector_a, audio_feature, generator, discriminator, train_params,device_ids) discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params) if len(device_ids)>1: generator_full=torch.nn.DataParallel(generator_full) discriminator_full=torch.nn.DataParallel(discriminator_full) if torch.cuda.is_available(): if len(device_ids) == 1: generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids) discriminator_full = DataParallelWithCallback(discriminator_full, device_ids=device_ids) elif len(device_ids)>1: generator_full = generator_full.to(device_ids[0]) discriminator_full = discriminator_full.to(device_ids[0]) step = 0 t0 = time.time() writer=SummaryWriter(comment=name) train_itr=0 test_itr=0 with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], checkpoint_freq=train_params['checkpoint_freq']) as logger: for epoch in trange(start_epoch, train_params['num_epochs']): for x in dataloader: losses_generator, generated = generator_full(x) loss_values = [val.mean() for val in losses_generator.values()] loss = sum(loss_values) writer.add_scalar('Train',loss,train_itr) writer.add_scalar('Train_value',loss_values[0],train_itr) writer.add_scalar('Train_heatmap',loss_values[1],train_itr) writer.add_scalar('Train_jacobian',loss_values[2],train_itr) train_itr+=1 loss.backward() optimizer_audio_feature.step() optimizer_audio_feature.zero_grad() d = time.time() # if train_params['loss_weights']['generator_gan'] != 0: # optimizer_discriminator.zero_grad() # else: # losses_discriminator = {} # losses_generator.update(losses_discriminator) losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} logger.log_iter(losses=losses) e = time.time() step += 1 if(step % 2500 == 0): print('Save ckpt and training visualization!') logger.log_epoch(epoch,step, {'audio_feature': audio_feature, 'kp_detector_a':kp_detector_a, 'optimizer_audio_feature': optimizer_audio_feature}, inp=x, out=generated) scheduler_audio_feature.step() for x in test_dataloader: with torch.no_grad(): losses_generator, generated = generator_full(x) loss_values = [val.mean() for val in losses_generator.values()] loss = sum(loss_values) writer.add_scalar('Test',loss,test_itr) writer.add_scalar('Test_value',loss_values[0],test_itr) writer.add_scalar('Test_heatmap',loss_values[1],test_itr) writer.add_scalar('Test_jacobian',loss_values[2],test_itr) test_itr+=1 def train_part1_fine_tune(config, generator, discriminator, kp_detector, kp_detector_a,audio_feature, checkpoint, audio_checkpoint, log_dir, dataset, test_dataset, device_ids, name): train_params = config['train_params'] optimizer_generator = torch.optim.Adam(generator.parameters(), lr=train_params['lr_generator'], betas=(0.5, 0.999)) optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=train_params['lr_discriminator'], betas=(0.5, 0.999)) optimizer_audio_feature = torch.optim.Adam(itertools.chain(audio_feature.parameters(),kp_detector_a.parameters()), lr=train_params['lr_audio_feature'], betas=(0.5, 0.999)) # optimizer_kp_detector_a = torch.optim.Adam(kp_detector_a.parameters(), lr=train_params['lr_audio_feature'], betas=(0.5, 0.999)) optimizer_kp_detector = None if checkpoint is not None: start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, audio_feature, optimizer_generator, optimizer_discriminator, None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector, None if train_params['lr_audio_feature'] == 0 else optimizer_audio_feature) if audio_checkpoint is not None: pretrain = torch.load(audio_checkpoint) kp_detector_a.load_state_dict(pretrain['kp_detector_a']) audio_feature.load_state_dict(pretrain['audio_feature']) # optimizer_kp_detector_a.load_state_dict(pretrain['optimizer_kp_detector_a']) optimizer_audio_feature.load_state_dict(pretrain['optimizer_audio_feature']) start_epoch = pretrain['epoch'] else: start_epoch = 0 scheduler_generator = MultiStepLR(optimizer_generator, train_params['epoch_milestones'], gamma=0.1, last_epoch=start_epoch - 1) scheduler_discriminator = MultiStepLR(optimizer_discriminator, train_params['epoch_milestones'], gamma=0.1, last_epoch=start_epoch - 1) scheduler_audio_feature = MultiStepLR(optimizer_audio_feature, train_params['epoch_milestones'], gamma=0.1, last_epoch=-1 + start_epoch * (train_params['lr_audio_feature'] != 0)) if 'num_repeats' in train_params or train_params['num_repeats'] != 1: dataset = DatasetRepeater(dataset, train_params['num_repeats']) test_dataset = TestsetRepeater(test_dataset, train_params['num_repeats']) dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6 test_dataloader = DataLoader(test_dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6 num_steps_per_epoch = len(dataloader) num_steps_test_epoch = len(test_dataloader) # generator_full = TrainFullModel(kp_detector, kp_detector_a, audio_feature, generator, discriminator, train_params,device_ids) generator_full = TrainPart1Model(kp_detector, kp_detector_a, audio_feature, generator, discriminator, train_params, device_ids) discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params) print('End dataload ', file=open('log/MEAD_LRW_test_a.txt', 'a')) if len(device_ids)>1: generator_full=torch.nn.DataParallel(generator_full) discriminator_full=torch.nn.DataParallel(discriminator_full) if torch.cuda.is_available(): if len(device_ids) == 1: generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids) discriminator_full = DataParallelWithCallback(discriminator_full, device_ids=device_ids) elif len(device_ids)>1: generator_full = generator_full.to(device_ids[0]) discriminator_full = discriminator_full.to(device_ids[0]) step = 0 t0 = time.time() writer=SummaryWriter(comment=name) train_itr=0 test_itr=0 with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], checkpoint_freq=train_params['checkpoint_freq']) as logger: for epoch in trange(start_epoch, train_params['num_epochs']): for x in dataloader: losses_generator, generated = generator_full(x) loss_values = [val.mean() for val in losses_generator.values()] loss = sum(loss_values) writer.add_scalar('Train',loss,train_itr) writer.add_scalar('Train_value',loss_values[0],train_itr) writer.add_scalar('Train_heatmap',loss_values[1],train_itr) writer.add_scalar('Train_jacobian',loss_values[2],train_itr) writer.add_scalar('Train_perceptual',loss_values[3],train_itr) train_itr+=1 loss.backward() optimizer_audio_feature.step() optimizer_audio_feature.zero_grad() optimizer_generator.step() optimizer_generator.zero_grad() # optimizer_kp_detector_a.step() # optimizer_kp_detector_a.zero_grad() if train_params['loss_weights']['discriminator_gan'] != 0: optimizer_discriminator.zero_grad() # losses_discriminator = discriminator_full(x, generated) # loss_values = [val.mean() for val in losses_discriminator.values()] # loss = sum(loss_values) # loss.backward() # optimizer_discriminator.step() # optimizer_discriminator.zero_grad() else: losses_discriminator = {} losses_generator.update(losses_discriminator) losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} logger.log_iter(losses=losses) step += 1 if(step % 5000 == 0): logger.log_epoch(epoch,step, {'audio_feature': audio_feature, 'kp_detector_a':kp_detector_a, 'generator': generator, 'optimizer_generator':optimizer_generator, 'optimizer_audio_feature': optimizer_audio_feature}, inp=x, out=generated) scheduler_generator.step() scheduler_discriminator.step() scheduler_audio_feature.step() for x in test_dataloader: with torch.no_grad(): losses_generator, generated = generator_full(x) loss_values = [val.mean() for val in losses_generator.values()] loss = sum(loss_values) writer.add_scalar('Test',loss,test_itr) writer.add_scalar('Test_value',loss_values[0],test_itr) writer.add_scalar('Test_heatmap',loss_values[1],test_itr) writer.add_scalar('Test_jacobian',loss_values[2],test_itr) writer.add_scalar('Test_perceptual',loss_values[3],test_itr) test_itr+=1 def train_part2(config, generator, discriminator, kp_detector, emo_detector, kp_detector_a,audio_feature, checkpoint, audio_checkpoint, emo_checkpoint, log_dir, dataset, test_dataset, device_ids, exp_name): train_params = config['train_params'] optimizer_emo_detector = torch.optim.Adam(emo_detector.parameters(), lr=train_params['lr_audio_feature'], betas=(0.5, 0.999)) if checkpoint is not None: start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, audio_feature, optimizer_generator, optimizer_discriminator, None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector, None if train_params['lr_audio_feature'] == 0 else optimizer_audio_feature) if emo_checkpoint is not None: pretrain = torch.load(emo_checkpoint) tgt_state = emo_detector.state_dict() strip = 'module.' if 'emo_detector' in pretrain: emo_detector.load_state_dict(pretrain['emo_detector']) optimizer_emo_detector.load_state_dict(pretrain['optimizer_emo_detector']) print('emo_detector in pretrain + load', file=open('log/'+exp_name+'.txt', 'a')) for name, param in pretrain.items(): if isinstance(param, nn.Parameter): param = param.data if strip is not None and name.startswith(strip): name = name[len(strip):] if name not in tgt_state: continue tgt_state[name].copy_(param) print(name) if audio_checkpoint is not None: pretrain = torch.load(audio_checkpoint) kp_detector_a.load_state_dict(pretrain['kp_detector_a']) audio_feature.load_state_dict(pretrain['audio_feature']) optimizer_audio_feature.load_state_dict(pretrain['optimizer_audio_feature']) if 'emo_detector' in pretrain: emo_detector.load_state_dict(pretrain['emo_detector']) optimizer_emo_detector.load_state_dict(pretrain['optimizer_emo_detector']) start_epoch = pretrain['epoch'] else: start_epoch = 0 scheduler_emo_detector = MultiStepLR(optimizer_emo_detector, train_params['epoch_milestones'], gamma=0.1, last_epoch=-1 + start_epoch * (train_params['lr_audio_feature'] != 0)) if 'num_repeats' in train_params or train_params['num_repeats'] != 1: dataset = DatasetRepeater(dataset, train_params['num_repeats']) test_dataset = TestsetRepeater(test_dataset, train_params['num_repeats']) dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6 test_dataloader = DataLoader(test_dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6 num_steps_per_epoch = len(dataloader) num_steps_test_epoch = len(test_dataloader) generator_full = TrainPart2Model(kp_detector, emo_detector,kp_detector_a, audio_feature,generator, discriminator, train_params,device_ids) discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params) if len(device_ids)>1: generator_full=torch.nn.DataParallel(generator_full) discriminator_full=torch.nn.DataParallel(discriminator_full) if torch.cuda.is_available(): if len(device_ids) == 1: generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids) discriminator_full = DataParallelWithCallback(discriminator_full, device_ids=device_ids) elif len(device_ids)>1: generator_full = generator_full.to(device_ids[0]) discriminator_full = discriminator_full.to(device_ids[0]) step = 0 t0 = time.time() writer=SummaryWriter(comment=exp_name) train_itr=0 test_itr=0 with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], checkpoint_freq=train_params['checkpoint_freq']) as logger: for epoch in trange(start_epoch, train_params['num_epochs']): for x in dataloader: losses_generator, generated = generator_full(x) loss_values = [val.mean() for val in losses_generator.values()] loss = sum(loss_values) writer.add_scalar('Train',loss,train_itr) writer.add_scalar('Train_value',loss_values[0],train_itr) # writer.add_scalar('Train_heatmap',loss_values[1],train_itr) writer.add_scalar('Train_jacobian',loss_values[1],train_itr) writer.add_scalar('Train_classify',loss_values[2],train_itr) train_itr+=1 loss.backward() optimizer_emo_detector.step() optimizer_emo_detector.zero_grad() losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} logger.log_iter(losses=losses) step += 1 if(step % 1000 == 0): logger.log_epoch(epoch,step, {'audio_feature': audio_feature, 'kp_detector_a':kp_detector_a, 'emo_detector':emo_detector, 'optimizer_emo_detector': optimizer_emo_detector, # 'optimizer_kp_detector_a':optimizer_kp_detector_a, 'optimizer_audio_feature': optimizer_audio_feature}, inp=x, out=generated) scheduler_emo_detector.step() for x in test_dataloader: with torch.no_grad(): losses_generator, generated = generator_full(x) loss_values = [val.mean() for val in losses_generator.values()] loss = sum(loss_values) writer.add_scalar('Test',loss,test_itr) writer.add_scalar('Test_value',loss_values[0],test_itr) # writer.add_scalar('Test_heatmap',loss_values[1],test_itr) writer.add_scalar('Test_jacobian',loss_values[1],test_itr) writer.add_scalar('Test_classify',loss_values[2],test_itr) test_itr+=1