trying gpu use and tagging implentation with batch: fin (includes mod…

… for ryo_grid env).

tagging.py

@@ -126,8 +126,7 @@ def load_model(self) -> None:
             return
 
         self.tagger_model_path = hf_hub_download(repo_id=TAGGER_VIT_MODEL_REPO, filename=MODEL_FILE_NAME)
-        # self.tagger_model = rt.InferenceSession(self.tagger_model_path, providers=['CPUExecutionProvider'])
-        self.tagger_model = rt.InferenceSession(self.tagger_model_path, providers=['CUDAExecutionProvider'])
+        self.tagger_model = rt.InferenceSession(self.tagger_model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
         _, height, _, _ = self.tagger_model.get_inputs()[0].shape
 
         self.model_target_size = height
@@ -163,10 +162,15 @@ def predict(
                 inputs = np.vstack([inputs, self.prepare_image(img)])
         print(inputs.shape)
 
-        preds: List[np.ndarray] = self.tagger_model.run([label_name], {input_name: np.array(inputs)})
+        start = time.perf_counter()
+        preds: np.ndarray = self.tagger_model.run([label_name], {input_name: inputs})
+        end = time.perf_counter()
+        # print time per file
+        print('{:.4f} seconds per file'.format((end - start) / len(images)), flush=True)
+
         ret_strings: List[str] = []
-        for idx in range(len(preds)):
-            labels: List[Tuple[str, float]] = list(zip(self.tag_names, preds[idx].astype(float)))
+        for idx in range(0, len(images)):
+            labels: List[Tuple[str, float]] = list(zip(self.tag_names, preds[0][idx].astype(float)))
 
             general_names: List[Tuple[str, float]] = [labels[i] for i in self.general_indexes]
 
@@ -222,9 +226,13 @@ def process_directory(self, directory: str) -> None:
 
         self.load_model()
 
+        # reverse the list to process from last (for ryo_grid env)
+        file_list.reverse()
+
         imgs: List[Image.Image] = []
         fpathes: List[str] = []
         start: float = time.perf_counter()
+        last_cnt: int = 0
         cnt: int = 0
         for file_path in file_list:
             try:
@@ -239,7 +247,9 @@ def process_directory(self, directory: str) -> None:
                     imgs = []
                     fpathes = []
 
-                if cnt % 100 == 0:
+                cnt += 1
+
+                if last_cnt - cnt >= 100:
                     now: float = time.perf_counter()
                     print(f'{cnt} files processed')
                     diff: float = now - start
@@ -248,8 +258,8 @@ def process_directory(self, directory: str) -> None:
                         time_per_file: float = diff / cnt
                         print('{:.4f} seconds per file'.format(time_per_file))
                     print("", flush=True)
+                    last_cnt = cnt
 
-                cnt += 1
             except Exception as e:
                 error_class: type = type(e)
                 error_description: str = str(e)