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7896def | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 | from abc import ABC, abstractmethod
from typing import Dict, Any, Optional, List, Literal
import torch
import numpy as np
from copy import deepcopy
from ..utils.normalizer import LinearNormalizer, NormMode
from fastwam.utils.pytorch_utils import dict_apply
class BaseProcessor(ABC):
def __init__(
self,
# keys
shape_meta: Dict[str, Any],
num_obs_steps: int,
num_output_cameras: int,
action_output_dim: int,
proprio_output_dim: int,
action_state_transforms: Optional[List[Any]],
# action & state normalization
use_stepwise_action_norm: bool,
norm_default_mode: NormMode,
norm_exception_mode: Dict[str, Dict[str, NormMode]],
action_state_merger,
# image transform
train_transforms: Dict[str, List[Any]] | None,
val_transforms: Dict[str, List[Any]] | None,
# instruction transform
drop_high_level_prob: float,
use_zh_instruction: bool,
tokenizer: Any
):
self.shape_meta = shape_meta
self.num_obs_steps = num_obs_steps
self.num_output_cameras = num_output_cameras
self.action_output_dim = action_output_dim
self.proprio_output_dim = proprio_output_dim
self.drop_high_level_prob = drop_high_level_prob
self.use_zh_instruction = use_zh_instruction
# image
self.train_transforms = train_transforms
self.val_transforms = val_transforms
self._is_train = None
self.action_state_transforms = action_state_transforms
self.action_state_merger = action_state_merger
self.action_state_merger.set_shape_meta(self.shape_meta)
self.use_stepwise_action_norm = use_stepwise_action_norm
self.norm_default_mode = norm_default_mode
self.norm_exception_mode = norm_exception_mode
self._normalizer = None
self.tokenizer = tokenizer
@property
def is_train(self):
if self._is_train is None:
raise ValueError("is_train has not been set. Please call train() and eval() first.")
return self._is_train
@property
def normalizer(self) -> LinearNormalizer:
if self._normalizer is None:
raise ValueError("normalizer has not been set. Please call set_normalizer_from_stats() first.")
return self._normalizer
def train(self):
self._is_train = True
return self
def eval(self):
self._is_train = False
return self
def set_normalizer_from_stats(self, dataset_stats: Dict[str, Any] = None):
self._normalizer = LinearNormalizer(
use_stepwise_action_norm=self.use_stepwise_action_norm,
shape_meta=self.shape_meta,
default_mode=self.norm_default_mode,
exception_mode=self.norm_exception_mode,
stats=dataset_stats,
)
def augment_instruction(self, data: Dict[str, str] | List[str]) -> List[str]:
"""
Args:
data: Dict[str, str] | List[str], lerobot sample in raw mcap
Returns:
List[str], processed instructions
"""
# if single instruction, convert to list
if "coarse_task" in data:
high_level_instruction = data["coarse_task"]
else:
high_level_instruction = ""
if "task" not in data:
return f"[high] {high_level_instruction}"
low_level_instruction = data["task"]
# Galaxea lerobot use @ to split Chinese and English instruction
if "@" in low_level_instruction:
zh, eng = low_level_instruction.split("@")
low_level_instruction = zh if self.use_zh_instruction else eng
if np.random.rand() < self.drop_high_level_prob:
instruction = f"[Low]: {low_level_instruction}"
else:
instruction = f"[High]: {high_level_instruction}, [Low]: {low_level_instruction}"
return instruction
def action_state_transform(self, batch):
if "action" in batch:
for meta in self.shape_meta["action"]:
k, meta_shape = meta["key"], meta["raw_shape"]
actual_shape = batch["action"][k].shape[-1]
assert actual_shape == meta_shape, \
f"Action key {k} actual raw shape {actual_shape} mismatch with meta raw shape {meta_shape}."
for meta in self.shape_meta["state"]:
k, meta_shape = meta["key"], meta["raw_shape"]
actual_shape = batch["state"][k].shape[-1]
assert actual_shape == meta_shape, \
f"State key {k} actual raw shape {actual_shape} mismatch with meta raw shape{meta_shape}."
if self.action_state_transforms is not None:
for trans in self.action_state_transforms:
batch = trans.forward(batch)
if "action" in batch:
for meta in self.shape_meta["action"]:
k, meta_shape = meta["key"], meta["shape"]
actual_shape = batch["action"][k].shape[-1]
assert actual_shape == meta_shape, \
f"Action key {k} actual transformed shape {actual_shape} mismatch with meta shape {meta_shape}."
for meta in self.shape_meta["state"]:
k, meta_shape = meta["key"], meta["shape"]
actual_shape = batch["state"][k].shape[-1]
assert actual_shape == meta_shape, \
f"State key {k} actual transformed shape {actual_shape} mismatch with meta raw shape {meta_shape}."
return batch
def preprocess(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""
Preprocess the data for the policy model.
Args:
Data: Dict[str, Any], lerobot sample in raw mcap obtained from dataset __getitem__:
- "action": Optional, Dict[str, torch.Tensor] -> [action_horizon, action_dim]
- "state": Dict[str, torch.Tensor] -> [num_obs_steps, state_dim]
- "images": Dict[str, torch.Tensor] -> [num_obs_steps, C, H, W]
- "action_is_pad": Optional, torch.Tensor -> [action_horizon,]
- "state_is_pad": torch.Tensor -> [num_obs_steps,]
- "image_is_pad": torch.Tensor -> [num_obs_steps,]
- "idx": int, sample index
Returns:
Sample: Dict[str, Any], which can collated:
- "input_ids": torch.Tensor -> [max_image_text_tokens,]
- "attention_mask": torch.Tensor -> [max_image_text_tokens,]
- "pixel_values": torch.Tensor -> [num_input_cameras, C, H, W]
- "image_is_pad": torch.Tensor -> [num_obs_steps,]
- "proprio": torch.Tensor -> [num_obs_steps, proprio_dim]
- "state_is_pad": torch.Tensor -> [num_obs_steps,]
- "action": Optional, torch.Tensor -> [action_horizon, action_dim]
- "action_is_pad": Optional, torch.Tensor -> [action_horizon,]
- "gt_action: Optional, deepcopy of input action for open loop eval, which is left untouched
- "idx": int, sample index
"""
sample = {}
# 1. instruction
sample["instruction"] = self.augment_instruction(data)
sample["image_is_pad"] = data["image_is_pad"]
# 2. image
processed_images = []
for meta in self.shape_meta["images"]:
key, shape = meta["key"], meta["shape"]
image = data["images"][key] # [num_obs_steps, C, H, W]
assert image.ndim == 4, f"Expected 4 dimensions (num_obs_steps, C, H, W), got shape {image.shape}"
# Apply transforms efficiently on the merged batch
transforms = self.train_transforms if self.is_train else self.val_transforms
for trans in transforms[key]:
image = trans(image)
meta_shape = [self.num_obs_steps] + shape
assert image.shape == meta_shape, \
f"Expected shape {meta_shape}, got {image.shape} after transforms for key {key}"
processed_images.append(image)
pixel_values = torch.cat(processed_images, dim=0) # [num_input_cameras, C, H, W]
if self.num_output_cameras > pixel_values.shape[0]:
out = torch.zeros((self.num_output_cameras,) + pixel_values.shape[1:], device=pixel_values.device, dtype=pixel_values.dtype)
out[0: pixel_values.shape[0]] = pixel_values
sample["pixel_values"] = out
else:
sample["pixel_values"] = pixel_values
# Copy action before transform for open-loop evaluation,
# disabled for training dataset as it may cause collating key problem.
if not self.is_train and "action" in data:
sample["gt_action"] = deepcopy(data["action"])
# 3. action & state
data = self.action_state_transform(data)
data = self.normalizer.forward(data)
data = self.action_state_merger.forward(data)
if "action" in data:
sample["action"] = data["action"] # [action_horizon, action_dim]
sample["action_is_pad"] = data["action_is_pad"] # [action_horizon,]
sample["action_dim_is_pad"] = data["action_dim_is_pad"] # [action_dim,]
assert sample["action"].shape[-1] == self.action_output_dim
# TODO: rename all "state" into "proprio"
sample["proprio"] = data["state"] # [num_obs_steps, proprio_dim]
sample["proprio_is_pad"] = data["state_is_pad"] # [num_obs_steps,]
sample["proprio_dim_is_pad"] = data["state_dim_is_pad"] # [proprio_dim,]
assert sample["proprio"].shape[-1] == self.proprio_output_dim
sample["idx"] = data["idx"]
sample = self.tokenizer(sample)
return sample
def postprocess(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""
Postprocess the data for the policy model.
Args:
data: Dict[str, Any], lerobot sample in raw mcap
Returns:
data: Dict[str, Any], processed data including unnormalized action
"""
assert "action" in data, "Action is required in postprocess"
data["state"] = data.pop("proprio")
data = self.action_state_merger.backward(data)
data = self.normalizer.backward(data)
if self.action_state_transforms is not None:
for trans in reversed(self.action_state_transforms):
data = trans.backward(data)
start_obs_step = self.num_obs_steps - 1
data["action"] = dict_apply(data["action"], lambda x: x[:, start_obs_step:, :])
return data
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