fix(groot): compatibility fixes for gr00t in v0.5 (#3182)

* fix(groot): apply groot 0.5 fixes

* fix(groot): correct indentation and add tile count in Eagle25VL processor

* Fixed lint7/style

src/lerobot/policies/groot/action_head/flow_matching_action_head.py

@@ -204,7 +204,9 @@ class FlowmatchingActionHead(nn.Module):
             self.position_embedding = nn.Embedding(config.max_seq_len, self.input_embedding_dim)
             nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)
 
-        self.beta_dist = Beta(config.noise_beta_alpha, config.noise_beta_beta)
+        self._noise_beta_alpha = config.noise_beta_alpha
+        self._noise_beta_beta = config.noise_beta_beta
+        self._beta_dist = None
         self.num_timestep_buckets = config.num_timestep_buckets
         self.config = config
         self.set_trainable_parameters(config.tune_projector, config.tune_diffusion_model)
@@ -249,7 +251,9 @@ class FlowmatchingActionHead(nn.Module):
                 self.model.eval()
 
     def sample_time(self, batch_size, device, dtype):
-        sample = self.beta_dist.sample([batch_size]).to(device, dtype=dtype)
+        if self._beta_dist is None:
+            self._beta_dist = Beta(self._noise_beta_alpha, self._noise_beta_beta, validate_args=False)
+        sample = self._beta_dist.sample([batch_size]).to(device, dtype=dtype)
         return (self.config.noise_s - sample) / self.config.noise_s
 
     def prepare_input(self, batch: dict) -> BatchFeature:

src/lerobot/policies/groot/eagle2_hg_model/processing_eagle2_5_vl.py

@@ -222,6 +222,13 @@ class Eagle25VLProcessor(ProcessorMixin):
                         videos=None,
                         **output_kwargs["images_kwargs"],
                     )
+                    if isinstance(image_inputs["pixel_values"], list):
+                        _pv = image_inputs["pixel_values"]
+                        if _pv and isinstance(_pv[0], list):
+                            _pv = [t for sub in _pv for t in sub]
+                        image_inputs["pixel_values"] = torch.stack(
+                            [t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in _pv]
+                        )
                     num_all_tiles = image_inputs["pixel_values"].shape[0]
                     special_placeholder = f"<image {idx_in_list + 1}>{self.image_start_token}{self.image_token * num_all_tiles * self.tokens_per_tile}{self.image_end_token}"
                     unified_frame_list.append(image_inputs)
@@ -233,6 +240,13 @@ class Eagle25VLProcessor(ProcessorMixin):
                         videos=[video_list[idx_in_list]],
                         **output_kwargs["videos_kwargs"],
                     )
+                    if isinstance(video_inputs["pixel_values"], list):
+                        _pv = video_inputs["pixel_values"]
+                        if _pv and isinstance(_pv[0], list):
+                            _pv = [t for sub in _pv for t in sub]
+                        video_inputs["pixel_values"] = torch.stack(
+                            [t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in _pv]
+                        )
                     num_all_tiles = video_inputs["pixel_values"].shape[0]
                     image_sizes = video_inputs["image_sizes"]
                     if timestamps_list is not None and -1 not in timestamps_list:
@@ -288,8 +302,18 @@ class Eagle25VLProcessor(ProcessorMixin):
 
         text = replace_in_text(text)
         if len(unified_frame_list) > 0:
-            pixel_values = torch.cat([frame["pixel_values"] for frame in unified_frame_list])
+
-            image_sizes = torch.cat([frame["image_sizes"] for frame in unified_frame_list])
+            def _to_tensor(v):
+                if isinstance(v, torch.Tensor):
+                    return v
+                if isinstance(v, list):
+                    if v and isinstance(v[0], list):
+                        v = [t for sub in v for t in sub]
+                    return torch.stack([t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in v])
+                return torch.as_tensor(v)
+
+            pixel_values = torch.cat([_to_tensor(frame["pixel_values"]) for frame in unified_frame_list])
+            image_sizes = torch.cat([_to_tensor(frame["image_sizes"]) for frame in unified_frame_list])
         else:
             pixel_values = None
             image_sizes = None

src/lerobot/policies/groot/groot_n1.py

@@ -221,6 +221,7 @@ class GR00TN15(PreTrainedModel):
         self.action_horizon = config.action_horizon
         self.action_dim = config.action_dim
         self.compute_dtype = config.compute_dtype
+        self.post_init()
 
     def validate_inputs(self, inputs):
         # NOTE -- this should be handled internally by the model