From 9ceef81f77ecce89f0c8f412c4d849210d852e82 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Fri, 28 Oct 2022 20:48:08 +0700
Subject: Fix log off by 1

---
 modules/textual_inversion/textual_inversion.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

(limited to 'modules/textual_inversion/textual_inversion.py')

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)
-- 
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