pytorch tagging script worked at M1 MBA.

colab_env/tagging.py

@@ -47,7 +47,7 @@
 
 EXTENSIONS: List[str] = ['.png', '.jpg', '.jpeg', ".PNG", ".JPG", ".JPEG"]
 
-BATCH_SIZE: int = 100
+BATCH_SIZE: int = 10
 
 torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 # for apple silicon
@@ -138,9 +138,9 @@ def __init__(self) -> None:
         self.last_loaded_repo: Optional[str] = None
         self.tagger_model: Optional[nn.Module] = None
         self.tag_names: Optional[List[str]] = None
-        self.rating_indexes: Optional[List[int]] = None
-        self.general_indexes: Optional[List[int]] = None
-        self.character_indexes: Optional[List[int]] = None
+        self.rating_index: Optional[List[int]] = None
+        self.general_index: Optional[List[int]] = None
+        self.character_index: Optional[List[int]] = None
         self.transform: Optional[Callable] = None
 
     def list_files_recursive(self, tarfile_path: str) -> List[str]:
@@ -152,7 +152,7 @@ def list_files_recursive(self, tarfile_path: str) -> List[str]:
                     file_list.append(file_path)
         return file_list
 
-    def prepare_image(self, image: Image.Image) -> np.ndarray:
+    def prepare_image(self, image: Image.Image) -> Image.Image:
         # target_size: int = self.model_target_size
 
         if image.mode in ('RGBA', 'LA'):
@@ -176,10 +176,12 @@ def prepare_image(self, image: Image.Image) -> np.ndarray:
         #         Image.BICUBIC,
         #     )
 
-        image_array: np.ndarray = np.asarray(padded_image, dtype=np.float32)
-        image_array = image_array[:, :, ::-1]
+        # image_array: np.ndarray = np.asarray(padded_image, dtype=np.float32)
+        # image_array = image_array[:, :, ::-1]
+        #
+        # return np.expand_dims(image_array, axis=0)
 
-        return np.expand_dims(image_array, axis=0)
+        return padded_image
 
     def load_labels_hf(
             self,
@@ -197,8 +199,8 @@ def load_labels_hf(
 
         df: pd.DataFrame = pd.read_csv(csv_path, usecols=["name", "category"])
         self.rating_index = list(np.where(df["category"] == 9)[0])
-        self.general_index = list(np.where(df["category"] == 0)[0]),
-        self.character_index = list(np.where(df["category"] == 4)[0]),
+        self.general_index = list(np.where(df["category"] == 0)[0])
+        self.character_index = list(np.where(df["category"] == 4)[0])
         self.tag_names = df["name"].tolist()
 
     def load_model(self) -> None:
@@ -278,19 +280,21 @@ def predict(
         #     char_threshold=opts.char_threshold,
         # )
 
+        # print(preds)
+        # exit(1)
         ret_strings: List[str] = []
         for idx in range(0, len(images)):
-            labels: List[Tuple[str, float]] = list(zip(self.tag_names, preds[0][idx].astype(float)))
+            labels: List[Tuple[str, float]] = list(zip(self.tag_names, preds[idx].astype(float)))
 
-            general_names: List[Tuple[str, float]] = [labels[i] for i in self.general_indexes]
+            general_names: List[Tuple[str, float]] = [labels[i] for i in self.general_index]
 
             if general_mcut_enabled:
                 general_probs: np.ndarray = np.array([x[1] for x in general_names])
                 general_thresh = mcut_threshold(general_probs)
 
             general_res: Dict[str, float] = {x[0]: x[1] for x in general_names if x[1] > general_thresh}
 
-            character_names: List[Tuple[str, float]] = [labels[i] for i in self.character_indexes]
+            character_names: List[Tuple[str, float]] = [labels[i] for i in self.character_index]
 
             if character_mcut_enabled:
                 character_probs: np.ndarray = np.array([x[1] for x in character_names])