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import argparse
import os
from pathlib import Path
import torch
from torch import autocast
from torch.cuda.amp import GradScaler
from tqdm import tqdm
from config import parser
from dataloaders.CXR14 import build_dataloaders
from models.diffusion_model import DiffusionModel
from trainers.utils import (TensorboardLogger, compare_configs, sample_plot_image,
seed_everything)
def train(config, model, optimizer, train_loader, val_loader, logger, scaler, step):
best_val_loss = float('inf')
train_losses = []
pbar = tqdm(total=config.val_freq, desc='Training')
while True:
for x in train_loader:
pbar.update(1)
step += 1
x = x.to(config.device)
# Forward pass
optimizer.zero_grad()
with autocast(device_type=config.device, enabled=config.mixed_precision):
loss = model.train_step(x)
scaler.scale(loss).backward()
optimizer.step()
train_losses.append(loss.item())
pbar.set_description(f'Training loss: {loss.item():.4f}')
if step % config.log_freq == 0 or config.debug:
avg_train_loss = sum(train_losses) / len(train_losses)
print(f'Step {step} - Train loss: {avg_train_loss:.4f}')
logger.log({'train/loss': avg_train_loss}, step=step)
if step % config.val_freq == 0 or config.debug:
val_results = validate(config, model, val_loader)
logger.log(val_results, step=step)
if val_results['val/loss'] < best_val_loss and not config.debug:
print(f'Step {step} - New best validation loss: '
f'{val_results["val/loss"]:.4f}, saving model '
f'in {config.log_dir}')
best_val_loss = val_results['val/loss']
save(
model,
optimizer,
config,
config.log_dir + '/best_model.pt',
step
)
if step >= config.max_steps or config.debug:
return model
@torch.no_grad()
def validate(config, model, val_loader):
model.eval()
losses = []
for i, x in tqdm(enumerate(val_loader), desc='Validating'):
x = x.to(config.device)
with autocast(device_type=config.device, enabled=config.mixed_precision):
loss = model.train_step(x)
losses.append(loss.item())
if i + 1 == config.max_val_steps or config.debug:
break
avg_loss = sum(losses) / len(losses)
print(f'Validation loss: {avg_loss:.4f}')
with autocast(device_type=config.device, enabled=config.mixed_precision):
sampled_imgs = sample_plot_image(
model,
config.timesteps,
config.img_size,
config.n_sampled_imgs if not config.debug else 1,
normalized=config.normalize,
)
model.train()
return {
'val/loss': avg_loss,
'val/sampled images': sampled_imgs
}
def save(model, optimizer, config, path, step):
torch.save({
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'config': config,
'step': step
}, path)
def load(new_config, path):
checkpoint = torch.load(path, map_location=torch.device(new_config.device))
old_config = checkpoint['config']
compare_configs(old_config, new_config)
model = DiffusionModel(old_config).to(new_config.device)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer = torch.optim.Adam(model.parameters(), lr=new_config.lr)
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
step = checkpoint['step']
return model, optimizer, step
def main(config):
# adjust logdir to include experiment name
config.log_dir = Path(config.log_dir).parent / "CXR14" / Path(config.log_dir).name
os.makedirs(config.log_dir, exist_ok=True)
# save config namespace into logdir
with open(config.log_dir / 'config.txt', 'w') as f:
for k, v in vars(config).items():
if type(v) not in [str, int, float, bool]:
f.write(f'{k}: {str(v)}\n')
else:
f.write(f'{k}: {v}\n')
# Random seed
seed_everything(config.seed)
# Init model and optimizer
if config.resume_path is not None:
print('Loading model from', config.resume_path)
diffusion_model, optimizer, step = load(config, config.resume_path)
else:
diffusion_model = DiffusionModel(config)
optimizer = torch.optim.Adam(diffusion_model.parameters(), lr=config.lr) # , betas=config.adam_betas)
step = 0
diffusion_model.to(config.device)
diffusion_model.train()
scaler = GradScaler()
# Load data
dataloaders = build_dataloaders(
config.data_dir,
config.img_size,
config.batch_size,
config.num_workers,
)
train_dl = dataloaders['train']
val_dl = dataloaders['val']
# Logger
logger = TensorboardLogger(config.log_dir, enabled=not config.debug)
train(config, diffusion_model, optimizer, train_dl, val_dl, logger, scaler, step)
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