Handle crop_shape=None in Diffusion Policy (#219)

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -155,7 +155,7 @@ def __post_init__(self):
                 f"{self.__class__.__name__} only handles one image for now. Got image keys {image_keys}."
             )
         image_key = next(iter(image_keys))
-        if (
+        if self.crop_shape is not None and (
             self.crop_shape[0] > self.input_shapes[image_key][1]
             or self.crop_shape[1] > self.input_shapes[image_key][2]
         ):

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -427,11 +427,15 @@ def __init__(self, config: DiffusionConfig):
         # Set up pooling and final layers.
         # Use a dry run to get the feature map shape.
         # The dummy input should take the number of image channels from `config.input_shapes` and it should
-        # use the height and width from `config.crop_shape`.
+        # use the height and width from `config.crop_shape` if it is provided, otherwise it should use the
+        # height and width from `config.input_shapes`.
         image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
         assert len(image_keys) == 1
         image_key = image_keys[0]
-        dummy_input = torch.zeros(size=(1, config.input_shapes[image_key][0], *config.crop_shape))
+        dummy_input_h_w = (
+            config.crop_shape if config.crop_shape is not None else config.input_shapes[image_key][1:]
+        )
+        dummy_input = torch.zeros(size=(1, config.input_shapes[image_key][0], *dummy_input_h_w))
         with torch.inference_mode():
             dummy_feature_map = self.backbone(dummy_input)
         feature_map_shape = tuple(dummy_feature_map.shape[1:])