draft

lerobot/common/policies/act/configuration_act.py

@@ -103,12 +103,21 @@ class ActionChunkingTransformerConfig:
     def __post_init__(self):
         """Input validation (not exhaustive)."""
         if not self.vision_backbone.startswith("resnet"):
-            raise ValueError("`vision_backbone` must be one of the ResNet variants.")
+            raise ValueError(
+                f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}."
+            )
         if self.use_temporal_aggregation:
             raise NotImplementedError("Temporal aggregation is not yet implemented.")
         if self.n_action_steps > self.chunk_size:
             raise ValueError(
-                "The chunk size is the upper bound for the number of action steps per model invocation."
+                f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
+                f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
+            )
+        if self.n_obs_steps != 1:
+            raise ValueError(
+                f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
             )
         if self.camera_names != ["top"]:
-            raise ValueError("For now, `camera_names` can only be ['top']")
+            raise ValueError(f"For now, `camera_names` can only be ['top']. Got {self.camera_names}.")
+        if len(set(self.camera_names)) != len(self.camera_names):
+            raise ValueError(f"`camera_names` should not have any repeated entries. Got {self.camera_names}.")

lerobot/common/policies/act/modeling_act.py

@@ -20,7 +20,9 @@ from torch import Tensor, nn
 from torchvision.models._utils import IntermediateLayerGetter
 from torchvision.ops.misc import FrozenBatchNorm2d
 
-from lerobot.common.policies.act.configuration_act import ActionChunkingTransformerConfig
+from lerobot.common.policies.act.configuration_act import (
+    ActionChunkingTransformerConfig,
+)
 
 
 class ActionChunkingTransformerPolicy(nn.Module):
@@ -61,9 +63,6 @@ class ActionChunkingTransformerPolicy(nn.Module):
     """
 
     name = "act"
-    _multiple_obs_steps_not_handled_msg = (
-        "ActionChunkingTransformerPolicy does not handle multiple observation steps."
-    )
 
     def __init__(self, cfg: ActionChunkingTransformerConfig | None = None):
         """
@@ -74,8 +73,6 @@ class ActionChunkingTransformerPolicy(nn.Module):
         super().__init__()
         if cfg is None:
             cfg = ActionChunkingTransformerConfig()
-        if cfg.n_obs_steps != 1:
-            raise ValueError(self._multiple_obs_steps_not_handled_msg)
         self.cfg = cfg
 
         # BERT style VAE encoder with input [cls, *joint_space_configuration, *action_sequence].
@@ -102,7 +99,11 @@ class ActionChunkingTransformerPolicy(nn.Module):
             mean=cfg.image_normalization_mean, std=cfg.image_normalization_std
         )
         backbone_model = getattr(torchvision.models, cfg.vision_backbone)(
-            replace_stride_with_dilation=[False, False, cfg.replace_final_stride_with_dilation],
+            replace_stride_with_dilation=[
+                False,
+                False,
+                cfg.replace_final_stride_with_dilation,
+            ],
             pretrained=cfg.use_pretrained_backbone,
             norm_layer=FrozenBatchNorm2d,
         )
@@ -176,82 +177,16 @@ class ActionChunkingTransformerPolicy(nn.Module):
         environment. It works by managing the actions in a queue and only calling `select_actions` when the
         queue is empty.
         """
+        self.eval()
         if len(self._action_queue) == 0:
-            # `select_actions` returns a (batch_size, n_action_steps, *) tensor, but the queue effectively has
-            # shape (n_action_steps, batch_size, *), hence the transpose.
-            self._action_queue.extend(self._select_actions(batch).transpose(0, 1))
+            # `_forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue effectively
+            # has shape (n_action_steps, batch_size, *), hence the transpose.
+            self._action_queue.extend(self._forward(batch)[0][: self.cfg.n_action_steps].transpose(0, 1))
         return self._action_queue.popleft()
 
-    @torch.no_grad
-    def _select_actions(self, batch: dict[str, Tensor]) -> Tensor:
-        """Use the action chunking transformer to generate a sequence of actions."""
-        self.eval()
-        batch = self._reshape_batch(batch, add_obs_steps_dim=True)
-        actions, _ = self._forward(
-            batch["observation.state"], self.image_normalizer(batch["observation.images.top"])
-        )
-        return actions[: self.cfg.n_action_steps]
-
-    def _reshape_batch(self, batch: dict[str, Tensor], add_obs_steps_dim: bool = False) -> dict[str, Tensor]:
-        """Reshapes the batch items to account for various requirements of this policy.
-
-        This function expects `batch` to have (at least):
-        {
-            "observation.state": (B, 1, J) OR (B, J) tensor of robot states (joint configuration).
-            "observation.images.top": (B, 1, C, H, W) OR (B, C, H, W) tensor of images.
-        }
-
-        TODO(alexander-soare): Right now this method does and undoes reshaping operations. This is just to
-        separate out the core logic from the temporary logic. See comments below.
-        """
-        # Create a shallow copy.
-        batch = dict(batch)
-
-        # Add a dimension for observation steps.
-        if add_obs_steps_dim:
-            # Add a dimension for the observations steps. Since n_obs_steps > 1 is not supported right now,
-            # this just amounts to an unsqueeze.
-            for k in batch:
-                if k.startswith("observation."):
-                    batch[k] = batch[k].unsqueeze(1)
-
-        # Temporary logic to remove the observation step dimension as the policy does not yet handle it.
-        # TODO(alexander-soare): generalize this to multiple observations steps.
-        # Check that there is only 1 observation step (policy does not yet handle more).
-        if not all(batch[k].shape[1] == 1 for k in batch if k.startswith("observation.")):
-            raise ValueError(self._multiple_obs_steps_not_handled_msg)
-        # Remove observation steps dimension.
-        for k in batch:
-            if k.startswith("observation."):
-                batch[k] = batch[k].squeeze(1)
-
-        # Temporary logic to add the multiple image dimension back in.
-        # TODO(alexander-soare): generalize this to multiple images. Once resolved, this logic will stack all
-        # images.
-        assert (
-            sum(k.startswith("observation.images.") and not k.endswith("is_pad") for k in batch) == 1
-        ), f"{self.__class__.__name__} only handles one image for now."
-        # Since we only handle one image, just unsqueeze instead of stacking.
-        batch["observation.images.top"] = batch["observation.images.top"].unsqueeze(1)
-
-        return batch
-
     def compute_loss(self, batch, **_) -> float:
-        batch = self._reshape_batch(batch)
-
-        self.train()
-
-        num_slices = self.cfg.batch_size
-        batch_size = self.cfg.chunk_size * num_slices
-
-        assert batch_size % self.cfg.chunk_size == 0
-        assert batch_size % num_slices == 0
-
-        actions_hat, (mu_hat, log_sigma_x2_hat) = self._forward(
-            batch["observation.state"],
-            self.image_normalizer(batch["observation.images.top"]),
-            batch["action"],
-        )
+        """Runs the batch through the model and computes the loss for training or validation."""
+        actions_hat, (mu_hat, log_sigma_x2_hat) = self._forward(batch)
 
         l1_loss = (
             F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
@@ -274,6 +209,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
     def update(self, batch, **_) -> dict:
         """Run the model in train mode, compute the loss, and do an optimization step."""
         start_time = time.time()
+        self.train()
         loss = self.compute_loss(batch)
         loss.backward()
 
@@ -295,35 +231,64 @@ class ActionChunkingTransformerPolicy(nn.Module):
 
         return info
 
-    def _forward(
-        self, robot_state: Tensor, image: Tensor, actions: Tensor | None = None
-    ) -> tuple[Tensor, tuple[Tensor | None, Tensor | None]]:
+    def _stack_images(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        """Stacks all the images in a batch and puts them in a new key: "observation.images".
+
+        This function expects `batch` to have (at least):
+        {
+            "observation.state": (B, state_dim) batch of robot states.
+            "observation.images.{name}": (B, C, H, W) tensor of images.
+        }
         """
-        Args:
-            robot_state: (B, J) batch of robot joint configurations.
-            image: (B, N, C, H, W) batch of N camera frames.
-            actions: (B, S, A) batch of actions from the target dataset which must be provided if the
-                VAE is enabled and the model is in training mode.
+        # Check that there is only one image.
+        # TODO(alexander-soare): generalize this to multiple images.
+        provided_cameras = {k.rsplit(".", 1)[-1] for k in batch if k.startswith("observation.images.")}
+        if len(missing := set(self.cfg.camera_names).difference(provided_cameras)) > 0:
+            raise ValueError(
+                f"The following camera images are missing from the provided batch: {missing}. Check the "
+                "configuration parameter: `camera_names`."
+            )
+        # Stack images in the order dictated by the camera names.
+        batch["observation.images"] = torch.stack(
+            [batch[f"observation.images.{name}"] for name in self.cfg.camera_names],
+            dim=-4,
+        )
+
+    def _forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tensor] | tuple[None, None]]:
+        """A forward pass through the Action Chunking Transformer (with optional VAE encoder).
+
+        `batch` should have the following structure:
+
+        {
+            "observation.state": (B, state_dim) batch of robot states.
+            "observation.images": (B, n_cameras, C, H, W) batch of images.
+            "action" (optional, only if training with VAE): (B, chunk_size, action dim) batch of actions.
+        }
+
         Returns:
-            (B, S, A) batch of action sequences
+            (B, chunk_size, action_dim) batch of action sequences
             Tuple containing the latent PDF's parameters (mean, log(σ²)) both as (B, L) tensors where L is the
             latent dimension.
         """
         if self.cfg.use_vae and self.training:
             assert (
-                actions is not None
+                "action" in batch
             ), "actions must be provided when using the variational objective in training mode."
 
-        batch_size = robot_state.shape[0]
+        self._stack_images(batch)
+
+        batch_size = batch["observation.state"].shape[0]
 
         # Prepare the latent for input to the transformer encoder.
-        if self.cfg.use_vae and actions is not None:
+        if self.cfg.use_vae and "action" in batch:
             # Prepare the input to the VAE encoder: [cls, *joint_space_configuration, *action_sequence].
             cls_embed = einops.repeat(
                 self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size
             )  # (B, 1, D)
-            robot_state_embed = self.vae_encoder_robot_state_input_proj(robot_state).unsqueeze(1)  # (B, 1, D)
-            action_embed = self.vae_encoder_action_input_proj(actions)  # (B, S, D)
+            robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"]).unsqueeze(
+                1
+            )  # (B, 1, D)
+            action_embed = self.vae_encoder_action_input_proj(batch["action"])  # (B, S, D)
             vae_encoder_input = torch.cat([cls_embed, robot_state_embed, action_embed], axis=1)  # (B, S+2, D)
 
             # Prepare fixed positional embedding.
@@ -345,15 +310,16 @@ class ActionChunkingTransformerPolicy(nn.Module):
             # When not using the VAE encoder, we set the latent to be all zeros.
             mu = log_sigma_x2 = None
             latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
-                robot_state.device
+                batch["observation.state"].device
             )
 
         # Prepare all other transformer encoder inputs.
         # Camera observation features and positional embeddings.
         all_cam_features = []
         all_cam_pos_embeds = []
-        for cam_id, _ in enumerate(self.cfg.camera_names):
-            cam_features = self.backbone(image[:, cam_id])["feature_map"]
+        images = self.image_normalizer(batch["observation.images"])
+        for cam_index in range(len(self.cfg.camera_names)):
+            cam_features = self.backbone(images[:, cam_index])["feature_map"]
             cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype)
             cam_features = self.encoder_img_feat_input_proj(cam_features)  # (B, C, h, w)
             all_cam_features.append(cam_features)
@@ -363,7 +329,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
         cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=3)
 
         # Get positional embeddings for robot state and latent.
-        robot_state_embed = self.encoder_robot_state_input_proj(robot_state)
+        robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])
         latent_embed = self.encoder_latent_input_proj(latent_sample)
 
         # Stack encoder input and positional embeddings moving to (S, B, C).
@@ -479,7 +445,10 @@ class _TransformerDecoder(nn.Module):
     ) -> Tensor:
         for layer in self.layers:
             x = layer(
-                x, encoder_out, decoder_pos_embed=decoder_pos_embed, encoder_pos_embed=encoder_pos_embed
+                x,
+                encoder_out,
+                decoder_pos_embed=decoder_pos_embed,
+                encoder_pos_embed=encoder_pos_embed,
             )
         if self.norm is not None:
             x = self.norm(x)

lerobot/configs/policy/act.yaml

@@ -67,6 +67,4 @@ policy:
   utd: 1
 
   delta_timestamps:
-    observation.images.top: "[i / ${fps} for i in range(1 - ${n_obs_steps}, 1)]"
-    observation.state: "[i / ${fps} for i in range(1 - ${n_obs_steps}, 1)]"
     action: "[i / ${fps} for i in range(${policy.chunk_size})]"