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e94400c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import numpy as np
import torch
from pydantic import Field
from ..schema import DatasetMetadata, StateActionMetadata
from .base import InvertibleModalityTransform
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.",
)
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 apply(self, data: dict) -> dict:
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.append(6) # Allow for rotation_6d
# if key in ["state.right_arm", "state.right_hand"]:
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.append(3) # Allow for axis angle
assert (
self.action_dims[key] == data[key].shape[-1]
), f"Action dim mismatch for {key=}, {self.action_dims[key]=}, {data[key].shape[-1]=}"
# 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.action_dims[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.state_dims[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 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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