standalone val (#56)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/utils/modeling/__init__.py

@@ -113,8 +113,8 @@ def get_model(model='s.pt', pretrained=True):
         model = model.split(".")[0]
 
     if Path(f"{model}.pt").is_file():  # local file
-        return torch.load(f"{model}.pt", map_location='cpu')
+        return attempt_load_weights(f"{model}.pt", device='cpu')
     elif model in torchvision.models.__dict__:  # TorchVision models i.e. resnet50, efficientnet_b0
         return torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None)
     else:  # Ultralytics assets
-        return torch.load(attempt_download(f"{model}.pt"), map_location='cpu')
+        return attempt_load_weights(f"{model}.pt", device='cpu')

ultralytics/yolo/utils/modeling/autobackend.py

@@ -304,7 +304,7 @@ class AutoBackend(nn.Module):
     def _model_type(p='path/to/model.pt'):
         # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
         # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
-        from export import export_formats
+        from ultralytics.yolo.engine.exporter import export_formats
         sf = list(export_formats().Suffix)  # export suffixes
         if not is_url(p, check=False):
             check_suffix(p, sf)  # checks

ultralytics/yolo/utils/modeling/tasks.py

@@ -172,7 +172,7 @@ class DetectionModel(BaseModel):
         csd = weights['model'].float().state_dict()  # checkpoint state_dict as FP32
         csd = intersect_state_dicts(csd, self.state_dict())  # intersect
         self.load_state_dict(csd, strict=False)  # load
-        LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from {weights}')
+        LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights')
 
 
 class SegmentationModel(DetectionModel):