ready for review

lerobot/common/policies/diffusion/model/multi_image_obs_encoder.py

@@ -1,5 +1,5 @@
 import copy
-from typing import Dict, Tuple, Union
+from typing import Dict, Optional, Tuple, Union
 
 import timm
 import torch
@@ -46,7 +46,7 @@ class MultiImageObsEncoder(ModuleAttrMixin):
         share_rgb_model: bool = False,
         # renormalize rgb input with imagenet normalization
         # assuming input in [0,1]
-        imagenet_norm: bool = False,
+        norm_mean_std: Optional[tuple[float, float]] = None,
     ):
         """
         Assumes rgb input: B,C,H,W
@@ -120,13 +120,9 @@ class MultiImageObsEncoder(ModuleAttrMixin):
                         this_normalizer = torchvision.transforms.CenterCrop(size=(h, w))
                 # configure normalizer
                 this_normalizer = nn.Identity()
-                if imagenet_norm:
+                if norm_mean_std is not None:
-                    # TODO(rcadene): move normalizer to dataset and env
                     this_normalizer = torchvision.transforms.Normalize(
-                        # Note: This matches the normalization in the original impl. for PushT Image. This may not be
+                        mean=norm_mean_std[0], std=norm_mean_std[1]
-                        # the case for other tasks.
-                        mean=[127.5, 127.5, 127.5],
-                        std=[127.5, 127.5, 127.5],
                     )
 
                 this_transform = nn.Sequential(this_resizer, this_randomizer, this_normalizer)

lerobot/configs/policy/diffusion.yaml

@@ -81,7 +81,7 @@ obs_encoder:
   # random_crop: True
   use_group_norm: True
   share_rgb_model: False
-  imagenet_norm: True
+  norm_mean_std: [0.5, 0.5]  # for PushT the original impl normalizes to [-1, 1] (maybe not the case for robomimic envs)
 
 rgb_model:
   model_name: resnet18