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

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

docs/en/guides/workouts-monitoring.md

@@ -125,5 +125,6 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 | `visualize`     | `bool`         | `False`                | visualize model features                                                   |
 | `augment`       | `bool`         | `False`                | apply image augmentation to prediction sources                             |
 | `agnostic_nms`  | `bool`         | `False`                | class-agnostic NMS                                                         |
+| `classes`       | `list[int]`    | `None`                 | filter results by class, i.e. classes=0, or classes=[0,2,3]                |
 | `retina_masks`  | `bool`         | `False`                | use high-resolution segmentation masks                                     |
-| `classes`       | `None or list` | `None`                 | filter results by class, i.e. classes=0, or classes=[0,2,3]                |
+| `embed`         | `list[int]`    | `None`                 | return feature vectors/embeddings from given layers                        |

docs/en/modes/predict.md

@@ -355,8 +355,9 @@ Inference arguments:
 | `visualize`     | `bool`         | `False`                | visualize model features                                                   |
 | `augment`       | `bool`         | `False`                | apply image augmentation to prediction sources                             |
 | `agnostic_nms`  | `bool`         | `False`                | class-agnostic NMS                                                         |
+| `classes`       | `list[int]`    | `None`                 | filter results by class, i.e. classes=0, or classes=[0,2,3]                |
 | `retina_masks`  | `bool`         | `False`                | use high-resolution segmentation masks                                     |
-| `classes`       | `None or list` | `None`                 | filter results by class, i.e. classes=0, or classes=[0,2,3]                |
+| `embed`         | `list[int]`    | `None`                 | return feature vectors/embeddings from given layers                        |
 
 Visualization arguments:
 

docs/en/reference/nn/autobackend.md

@@ -18,3 +18,7 @@ keywords: Ultralytics, AutoBackend, check_class_names, YOLO, YOLO models, optimi
 ## ::: ultralytics.nn.autobackend.check_class_names
 
 <br><br>
+
+## ::: ultralytics.nn.autobackend.default_class_names
+
+<br><br>

docs/en/usage/cfg.md

@@ -156,8 +156,9 @@ Inference arguments:
 | `visualize`     | `bool`         | `False`                | visualize model features                                                   |
 | `augment`       | `bool`         | `False`                | apply image augmentation to prediction sources                             |
 | `agnostic_nms`  | `bool`         | `False`                | class-agnostic NMS                                                         |
+| `classes`       | `list[int]`    | `None`                 | filter results by class, i.e. classes=0, or classes=[0,2,3]                |
 | `retina_masks`  | `bool`         | `False`                | use high-resolution segmentation masks                                     |
-| `classes`       | `None or list` | `None`                 | filter results by class, i.e. classes=0, or classes=[0,2,3]                |
+| `embed`         | `list[int]`    | `None`                 | return feature vectors/embeddings from given layers                        |
 
 Visualization arguments:
 

tests/test_python.py

@@ -511,3 +511,13 @@ def test_model_tune():
     """Tune YOLO model for performance."""
     YOLO('yolov8n-pose.pt').tune(data='coco8-pose.yaml', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
     YOLO('yolov8n-cls.pt').tune(data='imagenet10', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
+
+
+def test_model_embeddings():
+    """Test YOLO model embeddings."""
+    model_detect = YOLO(MODEL)
+    model_segment = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
+
+    for batch in [SOURCE], [SOURCE, SOURCE]:  # test batch size 1 and 2
+        assert len(model_detect.embed(source=batch, imgsz=32)) == len(batch)
+        assert len(model_segment.embed(source=batch, imgsz=32)) == len(batch)

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.228'
+__version__ = '8.0.229'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/cfg/default.yaml

@@ -61,6 +61,7 @@ augment: False  # (bool) apply image augmentation to prediction sources
 agnostic_nms: False  # (bool) class-agnostic NMS
 classes:  # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
 retina_masks: False  # (bool) use high-resolution segmentation masks
+embed:  # (list[int], optional) return feature vectors/embeddings from given layers
 
 # Visualize settings ---------------------------------------------------------------------------------------------------
 show: False  # (bool) show predicted images and videos if environment allows

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]:

ultralytics/nn/autobackend.py

@@ -333,7 +333,7 @@ class AutoBackend(nn.Module):
 
         self.__dict__.update(locals())  # assign all variables to self
 
-    def forward(self, im, augment=False, visualize=False):
+    def forward(self, im, augment=False, visualize=False, embed=None):
         """
         Runs inference on the YOLOv8 MultiBackend model.
 
@@ -341,6 +341,7 @@ class AutoBackend(nn.Module):
             im (torch.Tensor): The image tensor to perform inference on.
             augment (bool): whether to perform data augmentation during inference, defaults to False
             visualize (bool): whether to visualize the output predictions, defaults to False
+            embed (list, optional): A list of feature vectors/embeddings to return.
 
         Returns:
             (tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True)
@@ -352,7 +353,7 @@ class AutoBackend(nn.Module):
             im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)
 
         if self.pt or self.nn_module:  # PyTorch
-            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
+            y = self.model(im, augment=augment, visualize=visualize, embed=embed)
         elif self.jit:  # TorchScript
             y = self.model(im)
         elif self.dnn:  # ONNX OpenCV DNN

ultralytics/nn/tasks.py

@@ -41,7 +41,7 @@ class BaseModel(nn.Module):
             return self.loss(x, *args, **kwargs)
         return self.predict(x, *args, **kwargs)
 
-    def predict(self, x, profile=False, visualize=False, augment=False):
+    def predict(self, x, profile=False, visualize=False, augment=False, embed=None):
         """
         Perform a forward pass through the network.
 
@@ -50,15 +50,16 @@ class BaseModel(nn.Module):
             profile (bool):  Print the computation time of each layer if True, defaults to False.
             visualize (bool): Save the feature maps of the model if True, defaults to False.
             augment (bool): Augment image during prediction, defaults to False.
+            embed (list, optional): A list of feature vectors/embeddings to return.
 
         Returns:
             (torch.Tensor): The last output of the model.
         """
         if augment:
             return self._predict_augment(x)
-        return self._predict_once(x, profile, visualize)
+        return self._predict_once(x, profile, visualize, embed)
 
-    def _predict_once(self, x, profile=False, visualize=False):
+    def _predict_once(self, x, profile=False, visualize=False, embed=None):
         """
         Perform a forward pass through the network.
 
@@ -66,11 +67,12 @@ class BaseModel(nn.Module):
             x (torch.Tensor): The input tensor to the model.
             profile (bool):  Print the computation time of each layer if True, defaults to False.
             visualize (bool): Save the feature maps of the model if True, defaults to False.
+            embed (list, optional): A list of feature vectors/embeddings to return.
 
         Returns:
             (torch.Tensor): The last output of the model.
         """
-        y, dt = [], []  # outputs
+        y, dt, embeddings = [], [], []  # outputs
         for m in self.model:
             if m.f != -1:  # if not from previous layer
                 x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
@@ -80,6 +82,10 @@ class BaseModel(nn.Module):
             y.append(x if m.i in self.save else None)  # save output
             if visualize:
                 feature_visualization(x, m.type, m.i, save_dir=visualize)
+            if embed and m.i in embed:
+                embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
+                if m.i == max(embed):
+                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
         return x
 
     def _predict_augment(self, x):
@@ -454,7 +460,7 @@ class RTDETRDetectionModel(DetectionModel):
         return sum(loss.values()), torch.as_tensor([loss[k].detach() for k in ['loss_giou', 'loss_class', 'loss_bbox']],
                                                    device=img.device)
 
-    def predict(self, x, profile=False, visualize=False, batch=None, augment=False):
+    def predict(self, x, profile=False, visualize=False, batch=None, augment=False, embed=None):
         """
         Perform a forward pass through the model.
 
@@ -464,11 +470,12 @@ class RTDETRDetectionModel(DetectionModel):
             visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
             batch (dict, optional): Ground truth data for evaluation. Defaults to None.
             augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
+            embed (list, optional): A list of feature vectors/embeddings to return.
 
         Returns:
             (torch.Tensor): Model's output tensor.
         """
-        y, dt = [], []  # outputs
+        y, dt, embeddings = [], [], []  # outputs
         for m in self.model[:-1]:  # except the head part
             if m.f != -1:  # if not from previous layer
                 x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
@@ -478,6 +485,10 @@ class RTDETRDetectionModel(DetectionModel):
             y.append(x if m.i in self.save else None)  # save output
             if visualize:
                 feature_visualization(x, m.type, m.i, save_dir=visualize)
+            if embed and m.i in embed:
+                embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
+                if m.i == max(embed):
+                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
         head = self.model[-1]
         x = head([y[j] for j in head.f], batch)  # head inference
         return x