ultralytics 8.0.229 add model.embed() method (#7098)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/engine/model.py

@@ -94,7 +94,7 @@ class Model(nn.Module):
             self._load(model, task)
 
     def __call__(self, source=None, stream=False, **kwargs):
-        """Calls the 'predict' function with given arguments to perform object detection."""
+        """Calls the predict() method with given arguments to perform object detection."""
         return self.predict(source, stream, **kwargs)
 
     @staticmethod
@@ -201,6 +201,24 @@ class Model(nn.Module):
         self._check_is_pytorch_model()
         self.model.fuse()
 
+    def embed(self, source=None, stream=False, **kwargs):
+        """
+        Calls the predict() method and returns image embeddings.
+
+        Args:
+            source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
+                Accepts all source types accepted by the YOLO model.
+            stream (bool): Whether to stream the predictions or not. Defaults to False.
+            **kwargs : Additional keyword arguments passed to the predictor.
+                Check the 'configuration' section in the documentation for all available options.
+
+        Returns:
+            (List[torch.Tensor]): A list of image embeddings.
+        """
+        if not kwargs.get('embed'):
+            kwargs['embed'] = [len(self.model.model) - 2]  # embed second-to-last layer if no indices passed
+        return self.predict(source, stream, **kwargs)
+
     def predict(self, source=None, stream=False, predictor=None, **kwargs):
         """
         Perform prediction using the YOLO model.

ultralytics/engine/predictor.py

@@ -134,7 +134,7 @@ class BasePredictor:
         """Runs inference on a given image using the specified model and arguments."""
         visualize = increment_path(self.save_dir / Path(self.batch[0][0]).stem,
                                    mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False
-        return self.model(im, augment=self.args.augment, visualize=visualize)
+        return self.model(im, augment=self.args.augment, visualize=visualize, embed=self.args.embed, *args, **kwargs)
 
     def pre_transform(self, im):
         """
@@ -263,6 +263,9 @@ class BasePredictor:
                 # Inference
                 with profilers[1]:
                     preds = self.inference(im, *args, **kwargs)
+                    if self.args.embed:
+                        yield from [preds] if isinstance(preds, torch.Tensor) else preds  # yield embedding tensors
+                        continue
 
                 # Postprocess
                 with profilers[2]: