ultralytics 8.0.37 add TFLite metadata in AutoBackend (#953)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Aarni Koskela <akx@iki.fi>

ultralytics/yolo/engine/exporter.py

@@ -73,7 +73,7 @@ from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, __version__, callbacks,
 from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
 from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.ops import Profile
-from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode, get_latest_opset
+from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
 
 MACOS = platform.system() == 'Darwin'  # macOS environment
 
@@ -508,7 +508,7 @@ class Exporter:
         onnx = self.file.with_suffix('.onnx')
 
         # Export to TF SavedModel
-        subprocess.run(f'onnx2tf -i {onnx} --output_signaturedefs -o {f}', shell=True)
+        subprocess.run(f'onnx2tf -i {onnx} -o {f} --non_verbose', shell=True)
 
         # Add TFLite metadata
         for tflite_file in Path(f).rglob('*.tflite'):

ultralytics/yolo/engine/model.py

@@ -108,8 +108,8 @@ class YOLO:
         Raises TypeError is model is not a PyTorch model
         """
         if not isinstance(self.model, nn.Module):
-            raise TypeError(f"model='{self.model}' must be a PyTorch model, but is a different type. PyTorch models "
-                            f"can be used to train, val, predict and export, i.e. "
+            raise TypeError(f"model='{self.model}' must be a *.pt PyTorch model, but is a different type. "
+                            f"PyTorch models can be used to train, val, predict and export, i.e. "
                             f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
                             f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
 
@@ -240,7 +240,7 @@ class YOLO:
         if RANK in {0, -1}:
             self.model, _ = attempt_load_one_weight(str(self.trainer.best))
             self.overrides = self.model.args
-        self.metrics_data = self.trainer.validator.metrics
+            self.metrics_data = self.trainer.validator.metrics
 
     def to(self, device):
         """