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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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20
modules/hypernetworks/hypernetwork.py
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@@ -15,6 +15,7 @@ import torch
from torch import einsum
from einops import rearrange, repeat
import modules.textual_inversion.dataset
from modules.textual_inversion import textual_inversion
from modules.textual_inversion.learn_schedule import LearnRateScheduler
@@ -210,7 +211,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"):
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
if unload:
shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -263,19 +264,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
hypernetwork.save(last_saved_file)
if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0:
write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True
with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:
csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss", "learn_rate"])
if write_csv_header:
csv_writer.writeheader()
csv_writer.writerow({"step": hypernetwork.step,
"loss": f"{losses.mean():.7f}",
"learn_rate": scheduler.learn_rate})
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
"loss": f"{losses.mean():.7f}",
"learn_rate": scheduler.learn_rate
})
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')