ultralytics 8.3.59 Add ability to load any torchvision model as module (#18564)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/nn/modules/block.py

@@ -49,6 +49,7 @@ __all__ = (
     "Attention",
     "PSA",
     "SCDown",
+    "TorchVision",
 )
 
 
@@ -1107,3 +1108,51 @@ class SCDown(nn.Module):
     def forward(self, x):
         """Applies convolution and downsampling to the input tensor in the SCDown module."""
         return self.cv2(self.cv1(x))
+
+
+class TorchVision(nn.Module):
+    """
+    TorchVision module to allow loading any torchvision model.
+
+    This class provides a way to load a model from the torchvision library, optionally load pre-trained weights, and customize the model by truncating or unwrapping layers.
+
+    Attributes:
+        m (nn.Module): The loaded torchvision model, possibly truncated and unwrapped.
+
+    Args:
+        c1 (int): Input channels.
+        c2 (): Output channels.
+        model (str): Name of the torchvision model to load.
+        weights (str, optional): Pre-trained weights to load. Default is "DEFAULT".
+        unwrap (bool, optional): If True, unwraps the model to a sequential containing all but the last `truncate` layers. Default is True.
+        truncate (int, optional): Number of layers to truncate from the end if `unwrap` is True. Default is 2.
+        split (bool, optional): Returns output from intermediate child modules as list. Default is False.
+    """
+
+    def __init__(self, c1, c2, model, weights="DEFAULT", unwrap=True, truncate=2, split=False):
+        """Load the model and weights from torchvision."""
+        import torchvision
+
+        super().__init__()
+        if hasattr(torchvision.models, "get_model"):
+            self.m = torchvision.models.get_model(model, weights=weights)
+        else:
+            self.m = torchvision.models.__dict__[model](pretrained=bool(weights))
+        if unwrap:
+            layers = list(self.m.children())[:-truncate]
+            if isinstance(layers[0], nn.Sequential):  # Second-level for some models like EfficientNet, Swin
+                layers = [*list(layers[0].children()), *layers[1:]]
+            self.m = nn.Sequential(*layers)
+            self.split = split
+        else:
+            self.split = False
+            self.m.head = self.m.heads = nn.Identity()
+
+    def forward(self, x):
+        """Forward pass through the model."""
+        if self.split:
+            y = [x]
+            y.extend(m(y[-1]) for m in self.m)
+        else:
+            y = self.m(x)
+        return y