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Change val loss calculate method

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Hina Chen
2024-12-27 17:28:05 +08:00
parent 05bb9183fa
commit 62164e5792
1 changed files with 12 additions and 8 deletions
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20
train_network.py
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@@ -1383,16 +1383,20 @@ class NetworkTrainer:
else: else:
target = noise target = noise
huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler) # huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c) # loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
if weighting is not None: # if weighting is not None:
loss = loss * weighting # loss = loss * weighting
if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None): # if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
loss = apply_masked_loss(loss, batch) # loss = apply_masked_loss(loss, batch)
loss = loss.mean([1, 2, 3]) # loss = loss.mean([1, 2, 3])
# min snr gamma, scale v pred loss like noise pred, v pred like loss, debiased estimation etc. # min snr gamma, scale v pred loss like noise pred, v pred like loss, debiased estimation etc.
loss = self.post_process_loss(loss, args, timesteps, noise_scheduler) # loss = self.post_process_loss(loss, args, timesteps, noise_scheduler)
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
loss = loss.mean([1, 2, 3])
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし