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Some changes to the tensorboard code and hypernetwork support

This commit is contained in:
Melan 2022-10-20 22:37:16 +02:00
parent a6d593a6b5
commit 8f59129847
2 changed files with 44 additions and 19 deletions
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18
modules/hypernetworks/hypernetwork.py
View File

@ -4,6 +4,7 @@ import html
import os
import sys
import traceback
import tensorboard
import tqdm
import csv
@ -18,7 +19,6 @@ import modules.textual_inversion.dataset
from modules.textual_inversion import textual_inversion
from modules.textual_inversion.learn_schedule import LearnRateScheduler
class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
@ -291,6 +291,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
if shared.opts.training_enable_tensorboard:
tensorboard_writer = textual_inversion.tensorboard_setup(log_directory)
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
for i, entries in pbar:
hypernetwork.step = i + ititial_step
@ -315,6 +318,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
optimizer.zero_grad()
loss.backward()
optimizer.step()
mean_loss = losses.mean()
if torch.isnan(mean_loss):
raise RuntimeError("Loss diverged.")
@ -323,6 +327,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
hypernetwork.save(last_saved_file)
if shared.opts.training_enable_tensorboard:
epoch_num = hypernetwork.step // len(ds)
epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss,
global_step=hypernetwork.step, step=epoch_step,
learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
"loss": f"{mean_loss:.7f}",
@ -360,6 +372,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images)>0 else None
if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
textual_inversion.tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}",
image, hypernetwork.step)
if unload:
shared.sd_model.cond_stage_model.to(devices.cpu)
shared.sd_model.first_stage_model.to(devices.cpu)

45
modules/textual_inversion/textual_inversion.py
View File

@ -201,19 +201,30 @@ def write_loss(log_directory, filename, step, epoch_len, values):
**values,
})
def tensorboard_setup(log_directory):
os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
return SummaryWriter(
log_dir=os.path.join(log_directory, "tensorboard"),
flush_secs=shared.opts.training_tensorboard_flush_every)
def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epoch_num):
tensorboard_add_scaler(tensorboard_writer, "Loss/train", loss, global_step)
tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", loss, step)
tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", learn_rate, global_step)
tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)
def tensorboard_add_scaler(tensorboard_writer, tag, value, step):
if shared.opts.training_enable_tensorboard:
tensorboard_writer.add_scalar(tag=tag,
scalar_value=value, global_step=step)
tensorboard_writer.add_scalar(tag=tag,
scalar_value=value, global_step=step)
def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):
if shared.opts.training_enable_tensorboard:
# Convert a pil image to a torch tensor
img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], len(pil_image.getbands()))
img_tensor = img_tensor.permute((2, 0, 1))
# Convert a pil image to a torch tensor
img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0],
len(pil_image.getbands()))
img_tensor = img_tensor.permute((2, 0, 1))
tensorboard_writer.add_image(tag, img_tensor, global_step=step)
tensorboard_writer.add_image(tag, img_tensor, global_step=step)
def train_embedding(embedding_name, learn_rate, batch_size, 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'
@ -268,10 +279,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
if shared.opts.training_enable_tensorboard:
os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
tensorboard_writer = SummaryWriter(
log_dir=os.path.join(log_directory, "tensorboard"),
flush_secs=shared.opts.training_tensorboard_flush_every)
tensorboard_writer = tensorboard_setup(log_directory)
pbar = tqdm.tqdm(enumerate(ds), total=steps-initial_step)
for i, entries in pbar:
@ -308,10 +316,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
embedding_yet_to_be_embedded = True
if shared.opts.training_enable_tensorboard:
tensorboard_add_scaler(tensorboard_writer, "Loss/train", losses.mean(), embedding.step)
tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", losses.mean(), epoch_step)
tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", scheduler.learn_rate, embedding.step)
tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", scheduler.learn_rate, epoch_step)
tensorboard_add(tensorboard_writer, loss=losses.mean(), global_step=embedding.step,
step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
"loss": f"{losses.mean():.7f}",
@ -377,7 +383,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
embedding_yet_to_be_embedded = False
image.save(last_saved_image)
tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)
if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}",
image, embedding.step)
last_saved_image += f", prompt: {preview_text}"