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import numpy as np
from pydantic import Field, PrivateAttr
import torch
from groot.vla.data.schema import DatasetMetadata, StateActionMetadata
from groot.vla.data.transform.base import InvertibleModalityTransform, ModalityTransform
class ConcatTransform(InvertibleModalityTransform):
"""
Concatenate the keys according to specified order.
"""
# -- We inherit from ModalityTransform, so we keep apply_to as well --
apply_to: list[str] = Field(
default_factory=list, description="Not used in this transform, kept for compatibility."
)
video_concat_order: list[str] = Field(
...,
description="Concatenation order for each video modality. "
"Format: ['video.ego_view_pad_res224_freq20', ...]",
)
state_concat_order: Optional[list[str]] = Field(
default=None,
description="Concatenation order for each state modality. "
"Format: ['state.position', 'state.velocity', ...].",
)
action_concat_order: Optional[list[str]] = Field(
default=None,
description="Concatenation order for each action modality. "
"Format: ['action.position', 'action.velocity', ...].",
)
action_dims: dict[str, int] = Field(
default_factory=dict,
description="The dimensions of the action keys.",
)
state_dims: dict[str, int] = Field(
default_factory=dict,
description="The dimensions of the state keys.",
)
action_dims_post_transform: dict[str, int] = Field(
default_factory=dict,
description="The new dimensions of the action keys after transform is applied.",
)
state_dims_post_transform: dict[str, int] = Field(
default_factory=dict,
description="The new dimensions of the state keys after transform is applied.",
)
# Store the transform pipeline to examine for dimension changes
_transform_pipeline: List[ModalityTransform] = PrivateAttr(default_factory=list)
def model_dump(self, *args, **kwargs):
if kwargs.get("mode", "python") == "json":
include = {
"apply_to",
"video_concat_order",
"state_concat_order",
"action_concat_order",
}
else:
include = kwargs.pop("include", None)
return super().model_dump(*args, include=include, **kwargs)
def set_transform_pipeline(self, transforms: List[ModalityTransform]):
"""Set the transform pipeline so this transform can examine it for dimension changes."""
self._transform_pipeline = transforms
def _get_target_rotations_from_pipeline(self) -> Dict[str, str]:
"""Extract target_rotations from StateActionTransform instances in the pipeline."""
target_rotations = {}
for transform in self._transform_pipeline:
if hasattr(transform, "target_rotations"):
transform_target_rotations = getattr(transform, "target_rotations", {})
if transform_target_rotations:
target_rotations.update(transform_target_rotations)
return target_rotations
def apply(self, data: Dict[str, Any]) -> Dict[str, Any]:
grouped_keys = {}
for key in data.keys():
try:
modality, _ = key.split(".")
except: # noqa: E722
### Handle language annotation special case
if "annotation" in key:
modality = "language"
else:
modality = "others"
if modality not in grouped_keys:
grouped_keys[modality] = []
grouped_keys[modality].append(key)
if "video" in grouped_keys:
# Check if keys in video_concat_order, state_concat_order, action_concat_order are
# ineed contained in the data. If not, then the keys are misspecified
video_keys = grouped_keys["video"]
assert self.video_concat_order is not None, f"{self.video_concat_order=}, {video_keys=}"
assert all(
item in video_keys for item in self.video_concat_order
), f"keys in video_concat_order are misspecified, \n{video_keys=}, \n{self.video_concat_order=}"
# Process each video view
unsqueezed_videos = []
for video_key in self.video_concat_order:
video_data = data.pop(video_key)
unsqueezed_video = np.expand_dims(
video_data, axis=-4
) # [..., H, W, C] -> [..., 1, H, W, C]
unsqueezed_videos.append(unsqueezed_video)
# Concatenate along the new axis
unsqueezed_video = np.concatenate(unsqueezed_videos, axis=-4) # [..., V, H, W, C]
# Video
data["video"] = unsqueezed_video
# "state"
if "state" in grouped_keys:
state_keys = grouped_keys["state"]
assert self.state_concat_order is not None, f"{self.state_concat_order=}"
assert all(
item in state_keys for item in self.state_concat_order
), f"keys in state_concat_order are misspecified, \n{state_keys=}, \n{self.state_concat_order=}"
# Check the state dims
for key in self.state_concat_order:
target_shapes = [self.state_dims[key]]
if self.is_rotation_key(key):
target_shapes.extend(
[3, 4, 6]
) # 3 -> axis_angle, 4 -> quaternion, 6 -> rotation_6d
target_shapes.append(self.state_dims[key] * 2) # Allow for sin-cos transform
assert (
data[key].shape[-1] in target_shapes
), f"State dim mismatch for {key=}, {data[key].shape[-1]=}, {target_shapes=}"
# Concatenate the state keys
# We'll have StateActionToTensor before this transform, so here we use torch.cat
data["state"] = torch.cat(
[data.pop(key) for key in self.state_concat_order], dim=-1
) # [T, D_state]
if "action" in grouped_keys:
action_keys = grouped_keys["action"]
assert self.action_concat_order is not None, f"{self.action_concat_order=}"
# Check if all keys in concat_order are present
assert set(self.action_concat_order) == set(
action_keys
), f"{set(self.action_concat_order)=}, {set(action_keys)=}"
# Record the action dims
for key in self.action_concat_order:
target_shapes = [self.action_dims[key]]
if self.is_rotation_key(key):
target_shapes.extend(
[3, 4, 6]
) # 3 -> axis_angle, 4 -> quaternion, 6 -> rotation_6d
assert (
data[key].shape[-1] in target_shapes
), f"Action dim mismatch for {key=}, {data[key].shape[-1]=}, {target_shapes=}"
# Concatenate the action keys
# We'll have StateActionToTensor before this transform, so here we use torch.cat
data["action"] = torch.cat(
[data.pop(key) for key in self.action_concat_order], dim=-1
) # [T, D_action]
return data
def unapply(self, data: dict) -> dict:
start_dim = 0
assert "action" in data, f"{data.keys()=}"
# For those dataset without actions (LAPA), we'll never run unapply
assert self.action_concat_order is not None, f"{self.action_concat_order=}"
action_tensor = data.pop("action")
for key in self.action_concat_order:
if key not in self.action_dims:
raise ValueError(f"Action dim {key} not found in action_dims.")
end_dim = start_dim + self.get_state_action_dims_post_transform(key)
data[key] = action_tensor[..., start_dim:end_dim]
start_dim = end_dim
if "state" in data:
assert self.state_concat_order is not None, f"{self.state_concat_order=}"
start_dim = 0
state_tensor = data.pop("state")
for key in self.state_concat_order:
end_dim = start_dim + self.get_state_action_dims_post_transform(key)
data[key] = state_tensor[..., start_dim:end_dim]
start_dim = end_dim
return data
def __call__(self, data: dict) -> dict:
return self.apply(data)
def get_modality_metadata(self, key: str) -> StateActionMetadata:
modality, subkey = key.split(".")
assert self.dataset_metadata is not None, "Metadata not set"
modality_config = getattr(self.dataset_metadata.modalities, modality)
assert subkey in modality_config, f"{subkey=} not found in {modality_config=}"
assert isinstance(
modality_config[subkey], StateActionMetadata
), f"Expected {StateActionMetadata} for {subkey=}, got {type(modality_config[subkey])=}"
return modality_config[subkey]
def get_state_action_dims(self, key: str) -> int:
"""Get the dimension of a state or action key from the dataset metadata."""
modality_config = self.get_modality_metadata(key)
shape = modality_config.shape
assert len(shape) == 1, f"{shape=}"
return shape[0]
def get_state_action_dims_post_transform(self, key: str) -> int:
"""
This function is used to get the dims of the state/action keys after transform is applied.
It is different from the `get_state_action_dims` function, because this function accounts for
the case where we apply transforms and the # of dims is change eg. after applying axis_angle transform on
quaternion, the dims change from 4D to 3D.
"""
modality_config = self.get_modality_metadata(key)
shape = modality_config.shape
assert len(shape) == 1, f"{shape=}"
if self.is_rotation_key(key):
target_rotations = self._get_target_rotations_from_pipeline()
if key in target_rotations:
target_rotation = target_rotations[key]
if target_rotation == "axis_angle":
return 3
elif target_rotation == "quaternion":
return 4
elif target_rotation == "rotation_6d":
return 6
elif target_rotation == "euler_angles":
return 3
else:
raise ValueError(f"Unknown target rotation type: {target_rotation}")
else:
# No target rotation specified, return original dimension
return shape[0]
else:
return shape[0]
def is_rotation_key(self, key: str) -> bool:
modality_config = self.get_modality_metadata(key)
return modality_config.rotation_type is not None
def set_metadata(self, dataset_metadata: DatasetMetadata):
"""Set the metadata and compute the dimensions of the state and action keys."""
super().set_metadata(dataset_metadata)
# Pre-compute the dimensions of the state and action keys
if self.action_concat_order is not None:
for key in self.action_concat_order:
self.action_dims[key] = self.get_state_action_dims(key)
if self.state_concat_order is not None:
for key in self.state_concat_order:
self.state_dims[key] = self.get_state_action_dims(key)
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