early training loss as expected

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

@@ -1,15 +1,37 @@
 import copy
 from typing import Dict, Tuple, Union
 
+import timm
 import torch
 import torch.nn as nn
 import torchvision
+from robomimic.models.base_nets import SpatialSoftmax
 
 from lerobot.common.policies.diffusion.model.crop_randomizer import CropRandomizer
 from lerobot.common.policies.diffusion.model.module_attr_mixin import ModuleAttrMixin
 from lerobot.common.policies.diffusion.pytorch_utils import replace_submodules
 
 
+class RgbEncoder(nn.Module):
+    """Following `VisualCore` from Robomimic 0.2.0."""
+
+    def __init__(self, input_shape, model_name="resnet18", pretrained=False, num_keypoints=32):
+        """
+        resnet_name: a timm model name.
+        pretrained: whether to use timm pretrained weights.
+        num_keypoints: Number of keypoints for SpatialSoftmax (default value of 32 matches PushT Image).
+        """
+        super().__init__()
+        self.backbone = timm.create_model(model_name, pretrained, num_classes=0, global_pool="")
+        # Figure out the feature map shape.
+        with torch.inference_mode():
+            feat_map_shape = tuple(self.backbone(torch.zeros(size=(1, *input_shape))).shape[1:])
+        self.pool = SpatialSoftmax(feat_map_shape, num_kp=num_keypoints)
+
+    def forward(self, x):
+        return torch.flatten(self.pool(self.backbone(x)), start_dim=1)
+
+
 class MultiImageObsEncoder(ModuleAttrMixin):
     def __init__(
         self,
@@ -101,7 +123,8 @@ class MultiImageObsEncoder(ModuleAttrMixin):
                 if imagenet_norm:
                     # TODO(rcadene): move normalizer to dataset and env
                     this_normalizer = torchvision.transforms.Normalize(
-                        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                        mean=[127.5, 127.5, 127.5],
+                        std=[127.5, 127.5, 127.5],
                     )
 
                 this_transform = nn.Sequential(this_resizer, this_randomizer, this_normalizer)