OpenVINO metadata fix (#7952)

* Rename OpenVINO meta.yaml to model name

* Rename OpenVINO meta.yaml to model name

* Rename OpenVINO meta.yaml to model name

* fix

export.py

@@ -180,7 +180,7 @@ def export_openvino(model, file, half, prefix=colorstr('OpenVINO:')):
 
         cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
         subprocess.check_output(cmd.split())  # export
-        with open(Path(f) / 'meta.yaml', 'w') as g:
+        with open(Path(f) / file.with_suffix('.yaml'), 'w') as g:
             yaml.dump({'stride': int(max(model.stride)), 'names': model.names}, g)  # add metadata.yaml
 
         LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')

models/common.py

@@ -323,9 +323,12 @@ class DetectMultiBackend(nn.Module):
         super().__init__()
         w = str(weights[0] if isinstance(weights, list) else weights)
         pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self.model_type(w)  # get backend
-        stride, names = 32, [f'class{i}' for i in range(1000)]  # assign defaults
         w = attempt_download(w)  # download if not local
         fp16 &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16
+        stride, names = 32, [f'class{i}' for i in range(1000)]  # assign defaults
+        if data:  # assign class names (optional)
+            with open(data, errors='ignore') as f:
+                names = yaml.safe_load(f)['names']
 
         if pt:  # PyTorch
             model = attempt_load(weights if isinstance(weights, list) else w, map_location=device)
@@ -365,7 +368,9 @@ class DetectMultiBackend(nn.Module):
             network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
             executable_network = ie.compile_model(model=network, device_name="CPU")
             output_layer = next(iter(executable_network.outputs))
-            self._load_metadata(w.parent / 'meta.yaml')  # load metadata
+            meta = w.with_suffix('.yaml')
+            if meta.exists():
+                stride, names = self._load_metadata(meta)  # load metadata
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
             import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
@@ -431,11 +436,7 @@ class DetectMultiBackend(nn.Module):
                 output_details = interpreter.get_output_details()  # outputs
             elif tfjs:
                 raise Exception('ERROR: YOLOv5 TF.js inference is not supported')
-
         self.__dict__.update(locals())  # assign all variables to self
-        if not hasattr(self, 'names') and data:  # assign class names (optional)
-            with open(data, errors='ignore') as f:
-                names = yaml.safe_load(f)['names']
 
     def forward(self, im, augment=False, visualize=False, val=False):
         # YOLOv5 MultiBackend inference
@@ -495,13 +496,6 @@ class DetectMultiBackend(nn.Module):
             y = torch.tensor(y, device=self.device)
         return (y, []) if val else y
 
-    def _load_metadata(self, f='path/to/meta.yaml'):
-        # Load metadata from meta.yaml if it exists
-        if Path(f).is_file():
-            with open(f, errors='ignore') as f:
-                for k, v in yaml.safe_load(f).items():
-                    setattr(self, k, v)  # assign stride, names
-
     def warmup(self, imgsz=(1, 3, 640, 640)):
         # Warmup model by running inference once
         warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb
@@ -522,6 +516,13 @@ class DetectMultiBackend(nn.Module):
         tflite &= not edgetpu  # *.tflite
         return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs
 
+    @staticmethod
+    def _load_metadata(f='path/to/meta.yaml'):
+        # Load metadata from meta.yaml if it exists
+        with open(f, errors='ignore') as f:
+            d = yaml.safe_load(f)
+        return d['stride'], d['names']  # assign stride, names
+
 
 class AutoShape(nn.Module):
     # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS