Some changes to the tensorboard code and hypernetwork support

1 files changed, 17 insertions, 1 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 74300122..5e919775 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -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)