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Update train_db.py

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gesen2egee
2024-08-04 15:15:42 +08:00
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parent b9bdd10129
commit 3d68754def
1 changed files with 16 additions and 18 deletions
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train_db.py
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@@ -503,28 +503,26 @@ def train(args):
avr_loss: float = loss_recorder.moving_average
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if len(val_dataloader) > 0:
if (args.validation_every_n_step is not None and global_step % args.validation_every_n_step == 0) or (args.validation_every_n_step is None and step == len(train_dataloader) - 1) or global_step >= args.max_train_steps:
accelerator.print("Validating バリデーション処理...")
total_loss = 0.0
with torch.no_grad():
validation_steps = min(args.max_validation_steps, len(val_dataloader)) if args.max_validation_steps is not None else len(val_dataloader)
for val_step in tqdm(range(validation_steps), desc='Validation Steps'):
batch = next(cyclic_val_dataloader)
loss = self.process_val_batch(batch, tokenizers, text_encoders, unet, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args)
total_loss += loss.detach().item()
current_loss = total_loss / validation_steps
val_loss_recorder.add(epoch=0, step=global_step, loss=current_loss)
accelerator.print("Validating バリデーション処理...")
total_loss = 0.0
with torch.no_grad():
validation_steps = min(args.max_validation_steps, len(val_dataloader)) if args.max_validation_steps is not None else len(val_dataloader)
for val_step in tqdm(range(validation_steps), desc='Validation Steps'):
batch = next(cyclic_val_dataloader)
loss = self.process_val_batch(batch, tokenizers, text_encoders, unet, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args)
total_loss += loss.detach().item()
current_loss = total_loss / validation_steps
val_loss_recorder.add(epoch=0, step=global_step, loss=current_loss)
if args.logging_dir is not None:
logs = {"loss/current_val_loss": current_loss}
accelerator.log(logs, step=global_step)
avr_loss: float = val_loss_recorder.moving_average
logs = {"loss/average_val_loss": avr_loss}
accelerator.log(logs, step=global_step)
if args.logging_dir is not None:
logs = {"loss/current_val_loss": current_loss}
accelerator.log(logs, step=global_step)
avr_loss: float = val_loss_recorder.moving_average
logs = {"loss/average_val_loss": avr_loss}
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break