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Merge pull request #3858 from R-N/log-csv 

Fix log off by 1 #3847
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AUTOMATIC1111 2022-10-29 07:55:20 +03:00 committed by GitHub
parent 3019452927 9ceef81f77
commit 810e6a407d
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 20 additions and 18 deletions
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12
modules/hypernetworks/hypernetwork.py
View File

@ -429,7 +429,9 @@ 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:
@ -439,9 +441,9 @@ 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)
@ -450,8 +452,8 @@ 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()

2
modules/textual_inversion/learn_schedule.py
View File

@ -52,7 +52,7 @@ class LearnRateScheduler:
self.finished = False
def apply(self, optimizer, step_number):
if step_number <= self.end_step:
if step_number < self.end_step:
return
try:

24
modules/textual_inversion/textual_inversion.py
View File

@ -184,9 +184,8 @@ def write_loss(log_directory, filename, step, epoch_len, values):
if shared.opts.training_write_csv_every == 0:
return
if step % shared.opts.training_write_csv_every != 0:
if (step + 1) % shared.opts.training_write_csv_every != 0:
return
write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
@ -196,11 +195,11 @@ def write_loss(log_directory, filename, step, epoch_len, values):
csv_writer.writeheader()
epoch = step // epoch_len
epoch_step = step - epoch * epoch_len
epoch_step = step % epoch_len
csv_writer.writerow({
"step": step + 1,
"epoch": epoch + 1,
"epoch": epoch,
"epoch_step": epoch_step + 1,
**values,
})
@ -282,15 +281,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
loss.backward()
optimizer.step()
steps_done = embedding.step + 1
epoch_num = embedding.step // len(ds)
epoch_step = embedding.step - (epoch_num * len(ds)) + 1
epoch_step = embedding.step % len(ds)
pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
if embedding_dir is not None and steps_done % save_embedding_every == 0:
# Before saving, change name to match current checkpoint.
embedding.name = f'{embedding_name}-{embedding.step}'
embedding.name = f'{embedding_name}-{steps_done}'
last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
embedding.save(last_saved_file)
embedding_yet_to_be_embedded = True
@ -300,8 +300,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
"learn_rate": scheduler.learn_rate
})
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
forced_filename = f'{embedding_name}-{embedding.step}'
if images_dir is not None and steps_done % create_image_every == 0:
forced_filename = f'{embedding_name}-{steps_done}'
last_saved_image = os.path.join(images_dir, forced_filename)
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
@ -334,7 +334,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
info = PngImagePlugin.PngInfo()
data = torch.load(last_saved_file)
@ -350,7 +350,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
checkpoint = sd_models.select_checkpoint()
footer_left = checkpoint.model_name
footer_mid = '[{}]'.format(checkpoint.hash)
footer_right = '{}v {}s'.format(vectorSize, embedding.step)
footer_right = '{}v {}s'.format(vectorSize, steps_done)
captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
captioned_image = insert_image_data_embed(captioned_image, data)