fix caching and dataset stats is optional

lerobot/common/policies/sac/configuration_sac.py

@@ -120,7 +120,7 @@ class SACConfig(PreTrainedConfig):
         }
     )
 
-    dataset_stats: dict[str, dict[str, list[float]]] = field(
+    dataset_stats: dict[str, dict[str, list[float]]] | None = field(
         default_factory=lambda: {
             "observation.image": {
                 "mean": [0.485, 0.456, 0.406],

lerobot/common/policies/sac/modeling_sac.py

@@ -65,16 +65,21 @@ class SACPolicy(
         else:
             self.normalize_inputs = nn.Identity()
 
-        output_normalization_params = _convert_normalization_params_to_tensor(config.dataset_stats)
 
-        # HACK: This is hacky and should be removed
-        dataset_stats = dataset_stats or output_normalization_params
-        self.normalize_targets = Normalize(
-            config.output_features, config.normalization_mapping, dataset_stats
-        )
-        self.unnormalize_outputs = Unnormalize(
-            config.output_features, config.normalization_mapping, dataset_stats
-        )
+        if config.dataset_stats is not None:
+            output_normalization_params = _convert_normalization_params_to_tensor(config.dataset_stats)
+
+            # HACK: This is hacky and should be removed
+            dataset_stats = dataset_stats or output_normalization_params
+            self.normalize_targets = Normalize(
+                config.output_features, config.normalization_mapping, dataset_stats
+            )
+            self.unnormalize_outputs = Unnormalize(
+                config.output_features, config.normalization_mapping, dataset_stats
+            )
+        else:
+            self.normalize_targets = nn.Identity()
+            self.unnormalize_outputs = nn.Identity()
 
         # NOTE: For images the encoder should be shared between the actor and critic
         if config.shared_encoder:
@@ -192,7 +197,7 @@ class SACPolicy(
         # We cached the encoder output to avoid recomputing it
         observations_features = None
         if self.shared_encoder:
-            observations_features = self.actor.encoder.get_image_features(batch)
+            observations_features = self.actor.encoder.get_image_features(batch, normalize=True)
 
         actions, _, _ = self.actor(batch, observations_features)
         actions = self.unnormalize_outputs({"action": actions})["action"]
@@ -568,8 +573,7 @@ class SACObservationEncoder(nn.Module):
         feat = []
         obs_dict = self.input_normalization(obs_dict)
         if len(self.all_image_keys) > 0 and vision_encoder_cache is None:
-            vision_encoder_cache = self.get_image_features(obs_dict)
-            feat.append(vision_encoder_cache)
+            vision_encoder_cache = self.get_image_features(obs_dict, normalize=False)
 
         if vision_encoder_cache is not None:
             feat.append(vision_encoder_cache)
@@ -584,8 +588,10 @@ class SACObservationEncoder(nn.Module):
 
         return features
 
-    def get_image_features(self, batch: dict[str, Tensor]) -> torch.Tensor:
+    def get_image_features(self, batch: dict[str, Tensor], normalize: bool = True) -> torch.Tensor:
         # [N*B, C, H, W]
+        if normalize:
+            batch = self.input_normalization(batch)
         if len(self.all_image_keys) > 0:
             # Batch all images along the batch dimension, then encode them.
             images_batched = torch.cat([batch[key] for key in self.all_image_keys], dim=0)

lerobot/scripts/server/learner_server.py

@@ -1026,8 +1026,8 @@ def get_observation_features(
         return None, None
 
     with torch.no_grad():
-        observation_features = policy.actor.encoder.get_image_features(observations)
-        next_observation_features = policy.actor.encoder.get_image_features(next_observations)
+        observation_features = policy.actor.encoder.get_image_features(observations, normalize=True)
+        next_observation_features = policy.actor.encoder.get_image_features(next_observations, normalize=True)
 
     return observation_features, next_observation_features