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

Fix log off by 1 #3847

3 files changed, 20 insertions, 18 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 87cf3cf3..2e84583b 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -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()

diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index 2062726a..3a736065 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -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:

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ff002d3e..17dfb223 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -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)