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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, DiscriminatorFullModel
from torch.optim.lr_scheduler import MultiStepLR
from sync_batchnorm import DataParallelWithCallback
from frames_dataset import DatasetRepeater
def train(config, generator, discriminator, kp_detector, he_estimator, 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))
optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=train_params["lr_discriminator"], betas=(0.5, 0.999))
optimizer_kp_detector = torch.optim.Adam(kp_detector.parameters(), lr=train_params["lr_kp_detector"], betas=(0.5, 0.999))
optimizer_he_estimator = torch.optim.Adam(he_estimator.parameters(), lr=train_params["lr_he_estimator"], betas=(0.5, 0.999))
if checkpoint is not None:
start_epoch = Logger.load_cpk(
checkpoint,
generator,
discriminator,
kp_detector,
he_estimator,
optimizer_generator,
optimizer_discriminator,
optimizer_kp_detector,
optimizer_he_estimator,
)
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_kp_detector = MultiStepLR(
optimizer_kp_detector, train_params["epoch_milestones"], gamma=0.1, last_epoch=-1 + start_epoch * (train_params["lr_kp_detector"] != 0)
)
scheduler_he_estimator = MultiStepLR(
optimizer_he_estimator, train_params["epoch_milestones"], gamma=0.1, last_epoch=-1 + start_epoch * (train_params["lr_kp_detector"] != 0)
)
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=16, drop_last=True)
generator_full = GeneratorFullModel(
kp_detector,
he_estimator,
generator,
discriminator,
train_params,
estimate_jacobian=config["model_params"]["common_params"]["estimate_jacobian"],
)
discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params)
if torch.cuda.is_available():
generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids)
discriminator_full = DataParallelWithCallback(discriminator_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()
optimizer_kp_detector.step()
optimizer_kp_detector.zero_grad()
optimizer_he_estimator.step()
optimizer_he_estimator.zero_grad()
if train_params["loss_weights"]["generator_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)
scheduler_generator.step()
scheduler_discriminator.step()
scheduler_kp_detector.step()
scheduler_he_estimator.step()
logger.log_epoch(
epoch,
{
"generator": generator,
"discriminator": discriminator,
"kp_detector": kp_detector,
"he_estimator": he_estimator,
"optimizer_generator": optimizer_generator,
"optimizer_discriminator": optimizer_discriminator,
"optimizer_kp_detector": optimizer_kp_detector,
"optimizer_he_estimator": optimizer_he_estimator,
},
inp=x,
out=generated,
)
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