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remove duplicate code for log loss, add step, make it read from options rather than gradio input

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AUTOMATIC
2022-10-14 22:43:55 +03:00
parent 326fe7d44b
commit 03d62538ae
4 changed files with 38 additions and 32 deletions
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44
modules/textual_inversion/textual_inversion.py
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@@ -173,6 +173,32 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
return fn
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:
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:
csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
if write_csv_header:
csv_writer.writeheader()
epoch = step // epoch_len
epoch_step = step - epoch * epoch_len
csv_writer.writerow({
"step": step + 1,
"epoch": epoch + 1,
"epoch_step": epoch_step + 1,
**values,
})
def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
assert embedding_name, 'embedding not selected'
@@ -257,20 +283,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
embedding.save(last_saved_file)
if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0:
write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True
with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:
csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss", "learn_rate"])
if write_csv_header:
csv_writer.writeheader()
csv_writer.writerow({"epoch": epoch_num + 1,
"epoch_step": epoch_step - 1,
"loss": f"{losses.mean():.7f}",
"learn_rate": scheduler.learn_rate})
write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
"loss": f"{losses.mean():.7f}",
"learn_rate": scheduler.learn_rate
})
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')