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Merge branch 'master' into gradient-clipping

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This commit is contained in:
Muhammad Rizqi Nur
2022-10-29 15:04:21 +07:00
parent 16451ca573 35c45df28b
commit 05e2e40537
27 changed files with 2910 additions and 918 deletions
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13
modules/hypernetworks/hypernetwork.py
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@@ -25,6 +25,7 @@ from statistics import stdev, mean
class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
activation_dict = {
"linear": torch.nn.Identity,
"relu": torch.nn.ReLU,
"leakyrelu": torch.nn.LeakyReLU,
"elu": torch.nn.ELU,
@@ -443,7 +444,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
optimizer.step()
if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
steps_done = hypernetwork.step + 1
if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
raise RuntimeError("Loss diverged.")
if len(previous_mean_losses) > 1:
@@ -453,9 +456,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
pbar.set_description(dataset_loss_info)
if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
# Before saving, change name to match current checkpoint.
hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
hypernetwork.save(last_saved_file)
@@ -464,8 +467,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
"learn_rate": scheduler.learn_rate
})
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
if images_dir is not None and steps_done % create_image_every == 0:
forced_filename = f'{hypernetwork_name}-{steps_done}'
last_saved_image = os.path.join(images_dir, forced_filename)
optimizer.zero_grad()