fix images are used twice, update debug dataset

library/train_util.py

@@ -481,8 +481,7 @@ class BaseDataset(torch.utils.data.Dataset):
         else:
             if subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0:
                 tokens = [t.strip() for t in caption.strip().split(",")]
-                print(subset.token_warmup_min, subset.token_warmup_step)
+                if subset.token_warmup_step < 1: # 初回に上書きする
-                if subset.token_warmup_step < 1:
                     subset.token_warmup_step = math.floor(subset.token_warmup_step * self.max_train_steps)
                 if subset.token_warmup_step and self.current_step < subset.token_warmup_step:
                     tokens_len = (
@@ -1342,17 +1341,22 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
 
 def debug_dataset(train_dataset, show_input_ids=False):
     print(f"Total dataset length (steps) / データセットの長さ（ステップ数）: {len(train_dataset)}")
-    print("`E` for increment (pseudo) epoch no. , Escape for exit. / Eキーで疑似的にエポック番号を+1、Escキーで中断、終了します")
+    print("`S` for next step, `E` for next epoch no. , Escape for exit. / Sキーで次のステップ、Eキーで次のエポック、Escキーで中断、終了します")
 
     epoch = 1
-    steps = 1
+    while True:
-    train_dataset.set_current_epoch(epoch)
+        print(f"epoch: {epoch}")
-    train_dataset.set_current_step(steps)
 
-    k = 0
+        steps = (epoch - 1) * len(train_dataset) + 1
         indices = list(range(len(train_dataset)))
         random.shuffle(indices)
+
+        k = 0
         for i, idx in enumerate(indices):
+            train_dataset.set_current_epoch(epoch)
+            train_dataset.set_current_step(steps)
+            print(f"steps: {steps} ({i + 1}/{len(train_dataset)})")
+
             example = train_dataset[idx]
             if example["latents"] is not None:
                 print(f"sample has latents from npz file: {example['latents'].size()}")
@@ -1372,19 +1376,19 @@ def debug_dataset(train_dataset, show_input_ids=False):
                         cv2.imshow("img", im)
                     k = cv2.waitKey()
                     cv2.destroyAllWindows()
+                    if k == 27 or k == ord("s") or k == ord("e"):
+                        break
+            steps += 1
+
+            if k == ord("e"):
+                break
+            if k == 27 or (example["images"] is None and i >= 8):
+                k = 27
+                break
         if k == 27:
             break
-                if k == ord("e"):
+
         epoch += 1
-                    steps = len(train_dataset) * (epoch - 1)
-                    train_dataset.set_current_epoch(epoch)
-                    print(f"epoch: {epoch}")
-            
-        steps += 1
-        train_dataset.set_current_step(steps)
-
-        if k == 27 or (example["images"] is None and i >= 8):
-            break
 
 
 def glob_images(directory, base="*"):
@@ -1394,8 +1398,8 @@ def glob_images(directory, base="*"):
             img_paths.extend(glob.glob(os.path.join(glob.escape(directory), base + ext)))
         else:
             img_paths.extend(glob.glob(glob.escape(os.path.join(directory, base + ext))))
-    # img_paths = list(set(img_paths))                    # 重複を排除
+    img_paths = list(set(img_paths))  # 重複を排除
-    # img_paths.sort()
+    img_paths.sort()
     return img_paths
 
 
@@ -1407,8 +1411,8 @@ def glob_images_pathlib(dir_path, recursive):
     else:
         for ext in IMAGE_EXTENSIONS:
             image_paths += list(dir_path.glob("*" + ext))
-    # image_paths = list(set(image_paths))        # 重複を排除
+    image_paths = list(set(image_paths))  # 重複を排除
-    # image_paths.sort()
+    image_paths.sort()
     return image_paths