def main():
parser = ArgumentParser()
parser.add_argument("--augmentation", action='store_true')
parser.add_argument("--train-dataset-percentage", type=float, default=100)
parser.add_argument("--val-dataset-percentage", type=int, default=100)
parser.add_argument("--label-smoothing", type=float, default=0.9)
parser.add_argument("--validation-frequency", type=int, default=1)
args = parser.parse_args()
ENABLE_AUGMENTATION = args.augmentation
TRAIN_DATASET_PERCENTAGE = args.train_dataset_percentage
VAL_DATASET_PERCENTAGE = args.val_dataset_percentage
LABEL_SMOOTHING_FACTOR = args.label_smoothing
VALIDATION_FREQUENCY = args.validation_frequency
if ENABLE_AUGMENTATION:
augment_batch = AugmentPipe()
augment_batch.to(device)
else:
augment_batch = lambda x: x
augment_batch.p = 0
NUM_ADV_EPOCHS = round(NUM_ADV_BASELINE_EPOCHS /
(TRAIN_DATASET_PERCENTAGE / 100))
NUM_PRETRAIN_EPOCHS = round(NUM_BASELINE_PRETRAIN_EPOCHS /
(TRAIN_DATASET_PERCENTAGE / 100))
VALIDATION_FREQUENCY = round(VALIDATION_FREQUENCY /
(TRAIN_DATASET_PERCENTAGE / 100))
training_start = datetime.datetime.now().isoformat()
train_set = TrainDatasetFromFolder(train_dataset_dir,
patch_size=PATCH_SIZE,
upscale_factor=UPSCALE_FACTOR)
len_train_set = len(train_set)
train_set = Subset(
train_set,
list(
np.random.choice(
np.arange(len_train_set),
int(len_train_set * TRAIN_DATASET_PERCENTAGE / 100), False)))
val_set = ValDatasetFromFolder(val_dataset_dir,
upscale_factor=UPSCALE_FACTOR)
len_val_set = len(val_set)
val_set = Subset(
val_set,
list(
np.random.choice(np.arange(len_val_set),
int(len_val_set * VAL_DATASET_PERCENTAGE / 100),
False)))
train_loader = DataLoader(dataset=train_set,
num_workers=8,
batch_size=BATCH_SIZE,
shuffle=True,
pin_memory=True,
prefetch_factor=8)
val_loader = DataLoader(dataset=val_set,
num_workers=2,
batch_size=VAL_BATCH_SIZE,
shuffle=False,
pin_memory=True,
prefetch_factor=2)
epoch_validation_hr_dataset = HrValDatasetFromFolder(
val_dataset_dir) # Useful to compute FID metric
results_folder = Path(
f"results_{training_start}_CS:{PATCH_SIZE}_US:{UPSCALE_FACTOR}x_TRAIN:{TRAIN_DATASET_PERCENTAGE}%_AUGMENTATION:{ENABLE_AUGMENTATION}"
)
results_folder.mkdir(exist_ok=True)
writer = SummaryWriter(str(results_folder / "tensorboard_log"))
g_net = Generator(n_residual_blocks=NUM_RESIDUAL_BLOCKS,
upsample_factor=UPSCALE_FACTOR)
d_net = Discriminator(patch_size=PATCH_SIZE)
lpips_metric = lpips.LPIPS(net='alex')
g_net.to(device=device)
d_net.to(device=device)
lpips_metric.to(device=device)
g_optimizer = optim.Adam(g_net.parameters(), lr=1e-4)
d_optimizer = optim.Adam(d_net.parameters(), lr=1e-4)
bce_loss = BCELoss()
mse_loss = MSELoss()
bce_loss.to(device=device)
mse_loss.to(device=device)
results = {
'd_total_loss': [],
'g_total_loss': [],
'g_adv_loss': [],
'g_content_loss': [],
'd_real_mean': [],
'd_fake_mean': [],
'psnr': [],
'ssim': [],
'lpips': [],
'fid': [],
'rt': [],
'augment_probability': []
}
augment_probability = 0
num_images = len(train_set) * (NUM_PRETRAIN_EPOCHS + NUM_ADV_EPOCHS)
prediction_list = []
rt = 0
for epoch in range(1, NUM_PRETRAIN_EPOCHS + NUM_ADV_EPOCHS + 1):
train_bar = tqdm(train_loader, ncols=200)
running_results = {
'batch_sizes': 0,
'd_epoch_total_loss': 0,
'g_epoch_total_loss': 0,
'g_epoch_adv_loss': 0,
'g_epoch_content_loss': 0,
'd_epoch_real_mean': 0,
'd_epoch_fake_mean': 0,
'rt': 0,
'augment_probability': 0
}
image_percentage = epoch / (NUM_PRETRAIN_EPOCHS + NUM_ADV_EPOCHS) * 100
g_net.train()
d_net.train()
for data, target in train_bar:
augment_batch.p = torch.tensor([augment_probability],
device=device)
batch_size = data.size(0)
running_results["batch_sizes"] += batch_size
target = target.to(device)
data = data.to(device)
real_labels = torch.ones(batch_size, device=device)
fake_labels = torch.zeros(batch_size, device=device)
if epoch > NUM_PRETRAIN_EPOCHS:
# Discriminator training
d_optimizer.zero_grad(set_to_none=True)
d_real_output = d_net(augment_batch(target))
d_real_output_loss = bce_loss(
d_real_output, real_labels * LABEL_SMOOTHING_FACTOR)
fake_img = g_net(data)
d_fake_output = d_net(augment_batch(fake_img))
d_fake_output_loss = bce_loss(d_fake_output, fake_labels)
d_total_loss = d_real_output_loss + d_fake_output_loss
d_total_loss.backward()
d_optimizer.step()
d_real_mean = d_real_output.mean()
d_fake_mean = d_fake_output.mean()
# Generator training
g_optimizer.zero_grad(set_to_none=True)
fake_img = g_net(data)
if epoch > NUM_PRETRAIN_EPOCHS:
adversarial_loss = bce_loss(d_net(augment_batch(fake_img)),
real_labels) * ADV_LOSS_BALANCER
content_loss = mse_loss(fake_img, target)
g_total_loss = content_loss + adversarial_loss
else:
adversarial_loss = mse_loss(torch.zeros(
1, device=device), torch.zeros(
1,
device=device)) # Logging purposes, it is always zero
content_loss = mse_loss(fake_img, target)
g_total_loss = content_loss
g_total_loss.backward()
g_optimizer.step()
if epoch > NUM_PRETRAIN_EPOCHS and ENABLE_AUGMENTATION:
prediction_list.append(
(torch.sign(d_real_output - 0.5)).tolist())
if len(prediction_list) == RT_BATCH_SMOOTHING_FACTOR:
rt_list = [
prediction for sublist in prediction_list
for prediction in sublist
]
rt = mean(rt_list)
if mean(rt_list) > AUGMENT_PROB_TARGET:
augment_probability = min(
0.85,
augment_probability + AUGMENT_PROBABABILITY_STEP)
else:
augment_probability = max(
0.,
augment_probability - AUGMENT_PROBABABILITY_STEP)
prediction_list.clear()
running_results['g_epoch_total_loss'] += g_total_loss.to(
'cpu', non_blocking=True).detach() * batch_size
running_results['g_epoch_adv_loss'] += adversarial_loss.to(
'cpu', non_blocking=True).detach() * batch_size
running_results['g_epoch_content_loss'] += content_loss.to(
'cpu', non_blocking=True).detach() * batch_size
if epoch > NUM_PRETRAIN_EPOCHS:
running_results['d_epoch_total_loss'] += d_total_loss.to(
'cpu', non_blocking=True).detach() * batch_size
running_results['d_epoch_real_mean'] += d_real_mean.to(
'cpu', non_blocking=True).detach() * batch_size
running_results['d_epoch_fake_mean'] += d_fake_mean.to(
'cpu', non_blocking=True).detach() * batch_size
running_results['rt'] += rt * batch_size
running_results[
'augment_probability'] += augment_probability * batch_size
train_bar.set_description(
desc=f'[{epoch}/{NUM_ADV_EPOCHS + NUM_PRETRAIN_EPOCHS}] '
f'Loss_D: {running_results["d_epoch_total_loss"] / running_results["batch_sizes"]:.4f} '
f'Loss_G: {running_results["g_epoch_total_loss"] / running_results["batch_sizes"]:.4f} '
f'Loss_G_adv: {running_results["g_epoch_adv_loss"] / running_results["batch_sizes"]:.4f} '
f'Loss_G_content: {running_results["g_epoch_content_loss"] / running_results["batch_sizes"]:.4f} '
f'D(x): {running_results["d_epoch_real_mean"] / running_results["batch_sizes"]:.4f} '
f'D(G(z)): {running_results["d_epoch_fake_mean"] / running_results["batch_sizes"]:.4f} '
f'rt: {running_results["rt"] / running_results["batch_sizes"]:.4f} '
f'augment_probability: {running_results["augment_probability"] / running_results["batch_sizes"]:.4f}'
)
if epoch == 1 or epoch == (
NUM_PRETRAIN_EPOCHS + NUM_ADV_EPOCHS
) or epoch % VALIDATION_FREQUENCY == 0 or VALIDATION_FREQUENCY == 1:
torch.cuda.empty_cache()
gc.collect()
g_net.eval()
# ...
images_path = results_folder / Path(f'training_images_results')
images_path.mkdir(exist_ok=True)
with torch.no_grad():
val_bar = tqdm(val_loader, ncols=160)
val_results = {
'epoch_mse': 0,
'epoch_ssim': 0,
'epoch_psnr': 0,
'epoch_avg_psnr': 0,
'epoch_avg_ssim': 0,
'epoch_lpips': 0,
'epoch_avg_lpips': 0,
'epoch_fid': 0,
'batch_sizes': 0
}
val_images = torch.empty((0, 0))
epoch_validation_sr_dataset = None
for lr, val_hr_restore, hr in val_bar:
batch_size = lr.size(0)
val_results['batch_sizes'] += batch_size
hr = hr.to(device=device)
lr = lr.to(device=device)
sr = g_net(lr)
sr = torch.clamp(sr, 0., 1.)
if not epoch_validation_sr_dataset:
epoch_validation_sr_dataset = SingleTensorDataset(
(sr.cpu() * 255).to(torch.uint8))
else:
epoch_validation_sr_dataset = ConcatDataset(
(epoch_validation_sr_dataset,
SingleTensorDataset(
(sr.cpu() * 255).to(torch.uint8))))
batch_mse = ((sr - hr)**2).data.mean() # Pixel-wise MSE
val_results['epoch_mse'] += batch_mse * batch_size
batch_ssim = pytorch_ssim.ssim(sr, hr).item()
val_results['epoch_ssim'] += batch_ssim * batch_size
val_results['epoch_avg_ssim'] = val_results[
'epoch_ssim'] / val_results['batch_sizes']
val_results['epoch_psnr'] += 20 * log10(
hr.max() / (batch_mse / batch_size)) * batch_size
val_results['epoch_avg_psnr'] = val_results[
'epoch_psnr'] / val_results['batch_sizes']
val_results['epoch_lpips'] += torch.mean(
lpips_metric(hr * 2 - 1, sr * 2 - 1)).to(
'cpu', non_blocking=True).detach() * batch_size
val_results['epoch_avg_lpips'] = val_results[
'epoch_lpips'] / val_results['batch_sizes']
val_bar.set_description(
desc=
f"[converting LR images to SR images] PSNR: {val_results['epoch_avg_psnr']:4f} dB "
f"SSIM: {val_results['epoch_avg_ssim']:4f} "
f"LPIPS: {val_results['epoch_avg_lpips']:.4f} ")
if val_images.size(0) * val_images.size(
1) < NUM_LOGGED_VALIDATION_IMAGES * 3:
if val_images.size(0) == 0:
val_images = torch.hstack(
(display_transform(CENTER_CROP_SIZE)
(val_hr_restore).unsqueeze(0).transpose(0, 1),
display_transform(CENTER_CROP_SIZE)(
hr.data.cpu()).unsqueeze(0).transpose(
0, 1),
display_transform(CENTER_CROP_SIZE)(
sr.data.cpu()).unsqueeze(0).transpose(
0, 1)))
else:
val_images = torch.cat((
val_images,
torch.hstack(
(display_transform(CENTER_CROP_SIZE)(
val_hr_restore).unsqueeze(0).transpose(
0, 1),
display_transform(CENTER_CROP_SIZE)(
hr.data.cpu()).unsqueeze(0).transpose(
0, 1),
display_transform(CENTER_CROP_SIZE)(
sr.data.cpu()).unsqueeze(0).transpose(
0, 1)))))
val_results['epoch_fid'] = calculate_metrics(
epoch_validation_sr_dataset,
epoch_validation_hr_dataset,
cuda=True,
fid=True,
verbose=True
)['frechet_inception_distance'] # Set batch_size=1 if you get memory error (inside calculate metric function)
val_images = val_images.view(
(NUM_LOGGED_VALIDATION_IMAGES // 4, -1, 3,
CENTER_CROP_SIZE, CENTER_CROP_SIZE))
val_save_bar = tqdm(val_images,
desc='[saving validation results]',
ncols=160)
for index, image_batch in enumerate(val_save_bar, start=1):
image_grid = utils.make_grid(image_batch,
nrow=3,
padding=5)
writer.add_image(
f'progress{image_percentage:.1f}_index_{index}.png',
image_grid)
# save loss / scores / psnr /ssim
results['d_total_loss'].append(running_results['d_epoch_total_loss'] /
running_results['batch_sizes'])
results['g_total_loss'].append(running_results['g_epoch_total_loss'] /
running_results['batch_sizes'])
results['g_adv_loss'].append(running_results['g_epoch_adv_loss'] /
running_results['batch_sizes'])
results['g_content_loss'].append(
running_results['g_epoch_content_loss'] /
running_results['batch_sizes'])
results['d_real_mean'].append(running_results['d_epoch_real_mean'] /
running_results['batch_sizes'])
results['d_fake_mean'].append(running_results['d_epoch_fake_mean'] /
running_results['batch_sizes'])
results['rt'].append(running_results['rt'] /
running_results['batch_sizes'])
results['augment_probability'].append(
running_results['augment_probability'] /
running_results['batch_sizes'])
if epoch == 1 or epoch == (
NUM_PRETRAIN_EPOCHS + NUM_ADV_EPOCHS
) or epoch % VALIDATION_FREQUENCY == 0 or VALIDATION_FREQUENCY == 1:
results['psnr'].append(val_results['epoch_avg_psnr'])
results['ssim'].append(val_results['epoch_avg_ssim'])
results['lpips'].append(val_results['epoch_avg_lpips'])
results['fid'].append(val_results['epoch_fid'])
for metric, metric_values in results.items():
if epoch == 1 or epoch == (
NUM_PRETRAIN_EPOCHS + NUM_ADV_EPOCHS) or epoch % VALIDATION_FREQUENCY == 0 or VALIDATION_FREQUENCY == 1 or \
metric not in ["psnr", "ssim", "lpips", "fid"]:
writer.add_scalar(metric, metric_values[-1],
int(image_percentage * num_images * 0.01))
if epoch == 1 or epoch == (
NUM_PRETRAIN_EPOCHS + NUM_ADV_EPOCHS
) or epoch % VALIDATION_FREQUENCY == 0 or VALIDATION_FREQUENCY == 1:
# save model parameters
models_path = results_folder / "saved_models"
models_path.mkdir(exist_ok=True)
torch.save(
{
'progress': image_percentage,
'g_net': g_net.state_dict(),
'd_net': g_net.state_dict(),
# 'g_optimizer': g_optimizer.state_dict(), Uncomment this if you want resume training
# 'd_optimizer': d_optimizer.state_dict(),
},
str(models_path / f'progress_{image_percentage:.1f}.tar'))
total_g += _par.numel()
print("parameters generator", total_g)
print(d)
total_d = 0
for _n, _par in d.state_dict().items():
total_d += _par.numel()
print("parameters discriminator", total_d)
'''
# ================================================= Training ================================================== #
# loss function
tb = SummaryWriter(comment="DC_SAGAN_batch" + str(batch_size) + "_wd" +
w_decay_str + "_lr" + lrate_str)
loss = BCELoss()
loss = loss.to(device)
# main train loop
if epochs >= num_epochs:
raise SyntaxError("we have already trained for this amount of epochs")
for e in range(epochs, num_epochs):
for id, data in dataloader:
# first, train the discriminator
d.zero_grad()
g.zero_grad()
data = data.to(device)
batch_size = data.size()[0]
# labels: all 1s
labels_t = torch.ones(batch_size).unsqueeze_(0)
labels_t = labels_t.to(device)
# get the prediction of the D model
# D(x)