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train.py
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train.py
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from tqdm import trange
import torch
from torch.utils.data import DataLoader
from logger import Logger
from modules.model import GeneratorFullModel
from torch.optim.lr_scheduler import MultiStepLR
from sync_batchnorm import DataParallelWithCallback
from frames_dataset import DatasetRepeater
def train(config, generator, kp_detector, checkpoint, log_dir, dataset, device_ids):
train_params = config['train_params']
optimizer_generator = torch.optim.Adam(generator.parameters(), lr=train_params['lr_generator'], betas=(0.5, 0.999))
if checkpoint is not None:
start_epoch = Logger.load_cpk(checkpoint, generator, kp_detector, optimizer_generator)
else:
start_epoch = 0
scheduler_generator = MultiStepLR(optimizer_generator, train_params['epoch_milestones'], gamma=0.1,
last_epoch=start_epoch - 1)
if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
dataset = DatasetRepeater(dataset, train_params['num_repeats'])
dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=4, drop_last=True)
generator_full = GeneratorFullModel(kp_detector, generator, train_params)
if torch.cuda.is_available():
generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids)
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)
loss.backward()
optimizer_generator.step()
optimizer_generator.zero_grad()
losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()}
logger.log_iter(losses=losses)
scheduler_generator.step()
logger.log_epoch(epoch, {'generator': generator,
'kp_detector': kp_detector,
'optimizer_generator': optimizer_generator}, inp=x, out=generated)