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from tqdm import trange |
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import torch |
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from torch.utils.data import DataLoader |
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from logger import Logger |
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from modules.model import GeneratorFullModel |
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from torch.optim.lr_scheduler import MultiStepLR |
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from torch.nn.utils import clip_grad_norm_ |
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from frames_dataset import DatasetRepeater |
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import math |
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def train(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset): |
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train_params = config['train_params'] |
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optimizer = torch.optim.Adam( |
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[{'params': list(inpainting_network.parameters()) + |
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list(dense_motion_network.parameters()) + |
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list(kp_detector.parameters()), 'initial_lr': train_params['lr_generator']}],lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4) |
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optimizer_bg_predictor = None |
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if bg_predictor: |
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optimizer_bg_predictor = torch.optim.Adam( |
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[{'params':bg_predictor.parameters(),'initial_lr': train_params['lr_generator']}], |
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lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4) |
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if checkpoint is not None: |
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start_epoch = Logger.load_cpk( |
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checkpoint, inpainting_network = inpainting_network, dense_motion_network = dense_motion_network, |
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kp_detector = kp_detector, bg_predictor = bg_predictor, |
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optimizer = optimizer, optimizer_bg_predictor = optimizer_bg_predictor) |
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print('load success:', start_epoch) |
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start_epoch += 1 |
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else: |
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start_epoch = 0 |
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scheduler_optimizer = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1, |
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last_epoch=start_epoch - 1) |
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if bg_predictor: |
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scheduler_bg_predictor = MultiStepLR(optimizer_bg_predictor, train_params['epoch_milestones'], |
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gamma=0.1, last_epoch=start_epoch - 1) |
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if 'num_repeats' in train_params or train_params['num_repeats'] != 1: |
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dataset = DatasetRepeater(dataset, train_params['num_repeats']) |
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dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, |
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num_workers=train_params['dataloader_workers'], drop_last=True) |
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generator_full = GeneratorFullModel(kp_detector, bg_predictor, dense_motion_network, inpainting_network, train_params) |
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if torch.cuda.is_available(): |
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generator_full = torch.nn.DataParallel(generator_full).cuda() |
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bg_start = train_params['bg_start'] |
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with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], |
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checkpoint_freq=train_params['checkpoint_freq']) as logger: |
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for epoch in trange(start_epoch, train_params['num_epochs']): |
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for x in dataloader: |
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if(torch.cuda.is_available()): |
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x['driving'] = x['driving'].cuda() |
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x['source'] = x['source'].cuda() |
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losses_generator, generated = generator_full(x, epoch) |
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loss_values = [val.mean() for val in losses_generator.values()] |
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loss = sum(loss_values) |
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loss.backward() |
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clip_grad_norm_(kp_detector.parameters(), max_norm=10, norm_type = math.inf) |
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clip_grad_norm_(dense_motion_network.parameters(), max_norm=10, norm_type = math.inf) |
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if bg_predictor and epoch>=bg_start: |
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clip_grad_norm_(bg_predictor.parameters(), max_norm=10, norm_type = math.inf) |
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optimizer.step() |
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optimizer.zero_grad() |
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if bg_predictor and epoch>=bg_start: |
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optimizer_bg_predictor.step() |
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optimizer_bg_predictor.zero_grad() |
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losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} |
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logger.log_iter(losses=losses) |
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scheduler_optimizer.step() |
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if bg_predictor: |
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scheduler_bg_predictor.step() |
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model_save = { |
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'inpainting_network': inpainting_network, |
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'dense_motion_network': dense_motion_network, |
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'kp_detector': kp_detector, |
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'optimizer': optimizer, |
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} |
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if bg_predictor and epoch>=bg_start: |
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model_save['bg_predictor'] = bg_predictor |
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model_save['optimizer_bg_predictor'] = optimizer_bg_predictor |
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logger.log_epoch(epoch, model_save, inp=x, out=generated) |
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