🔀 [Merge] branch 'DEPLOY' into TEST

yolo/tools/data_loader.py

@@ -255,7 +255,7 @@ class StreamDataLoader:
     def load_video_file(self, video_path):
         import cv2
 
-        cap = cv2.VideoCapture(video_path)
+        cap = cv2.VideoCapture(str(video_path))
         while self.running:
             ret, frame = cap.read()
             if not ret:

yolo/tools/solver.py

@@ -224,7 +224,7 @@ class ModelValidator:
 
         with contextlib.redirect_stdout(io.StringIO()):
             # TODO: load with config file
-            json_path, _ = locate_label_paths(Path(dataset_cfg.path), dataset_cfg.get("val", "val"))
+            json_path, _ = locate_label_paths(Path(dataset_cfg.path), dataset_cfg.get("validation", "val"))
             if json_path:
                 self.coco_gt = COCO(json_path)
 

yolo/utils/bounding_box_utils.py

@@ -1,5 +1,5 @@
 import math
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
@@ -368,7 +368,7 @@ class Anc2Box:
         return preds_cls, None, preds_box, preds_cnf.sigmoid()
 
 
-def create_converter(model_version: str = "v9-c", *args, **kwargs):
+def create_converter(model_version: str = "v9-c", *args, **kwargs) -> Union[Anc2Box, Vec2Box]:
     if "v7" in model_version:  # check model if v7
         converter = Anc2Box(*args, **kwargs)
     else:

yolo/utils/deploy_utils.py

@@ -45,6 +45,8 @@ class FastModelLoader:
                 if idx % 3 == 2:
                     model_outputs.append(layer_output)
                     layer_output = []
+            if len(model_outputs) == 6:
+                model_outputs = model_outputs[:3]
             return {"Main": model_outputs}
 
         InferenceSession.__call__ = onnx_forward