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train_avd.py
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train_avd.py
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from tqdm import trange
import torch
from torch.utils.data import DataLoader
from logger import Logger
from torch.optim.lr_scheduler import MultiStepLR
from frames_dataset import DatasetRepeater
def random_scale(kp_params, scale):
theta = torch.rand(kp_params['fg_kp'].shape[0], 2) * (2 * scale) + (1 - scale)
theta = torch.diag_embed(theta).unsqueeze(1).type(kp_params['fg_kp'].type())
new_kp_params = {'fg_kp': torch.matmul(theta, kp_params['fg_kp'].unsqueeze(-1)).squeeze(-1)}
return new_kp_params
def train_avd(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network,
avd_network, checkpoint, log_dir, dataset):
train_params = config['train_avd_params']
optimizer = torch.optim.Adam(avd_network.parameters(), lr=train_params['lr'], betas=(0.5, 0.999))
if checkpoint is not None:
Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector,
bg_predictor=bg_predictor, avd_network=avd_network,
dense_motion_network= dense_motion_network,optimizer_avd=optimizer)
start_epoch = 0
else:
raise AttributeError("Checkpoint should be specified for mode='train_avd'.")
scheduler = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1)
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=train_params['dataloader_workers'], drop_last=True)
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']):
avd_network.train()
for x in dataloader:
with torch.no_grad():
kp_source = kp_detector(x['source'].cuda())
kp_driving_gt = kp_detector(x['driving'].cuda())
kp_driving_random = random_scale(kp_driving_gt, scale=train_params['random_scale'])
rec = avd_network(kp_source, kp_driving_random)
reconstruction_kp = train_params['lambda_shift'] * \
torch.abs(kp_driving_gt['fg_kp'] - rec['fg_kp']).mean()
loss_dict = {'rec_kp': reconstruction_kp}
loss = reconstruction_kp
loss.backward()
optimizer.step()
optimizer.zero_grad()
losses = {key: value.mean().detach().data.cpu().numpy() for key, value in loss_dict.items()}
logger.log_iter(losses=losses)
# Visualization
avd_network.eval()
with torch.no_grad():
source = x['source'][:6].cuda()
driving = torch.cat([x['driving'][[0, 1]].cuda(), source[[2, 3, 2, 1]]], dim=0)
kp_source = kp_detector(source)
kp_driving = kp_detector(driving)
out = avd_network(kp_source, kp_driving)
kp_driving = out
dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving,
kp_source=kp_source)
generated = inpainting_network(source, dense_motion)
generated.update({'kp_source': kp_source, 'kp_driving': kp_driving})
scheduler.step(epoch)
model_save = {
'inpainting_network': inpainting_network,
'dense_motion_network': dense_motion_network,
'kp_detector': kp_detector,
'avd_network': avd_network,
'optimizer_avd': optimizer
}
if bg_predictor :
model_save['bg_predictor'] = bg_predictor
logger.log_epoch(epoch, model_save,
inp={'source': source, 'driving': driving},
out=generated)