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688e1f3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 | from collections.abc import Sequence
import logging
import torch
from openpi_value.shared import image_tools
import openpi_value.transforms as _transforms
import kornia.augmentation as K
logger = logging.getLogger("openpi")
# Constants moved from model.py
IMAGE_RESOLUTION = (224, 224)
def preprocess_observation_pytorch(
observation,
*,
train: bool = False,
# image_keys: Sequence[str] = IMAGE_KEYS,
image_keys: Sequence[str] = None,
image_resolution: tuple[int, int] = IMAGE_RESOLUTION,
return_full_obs: bool = False,
apply_shape_visual_aug: bool = False,
apply_blur_visual_aug: bool = False,
p_mask_base: float = 0.0,
state_noise_snr: float | None = None,
):
"""Torch.compile-compatible version of preprocess_observation_pytorch with simplified type annotations.
This function avoids complex type annotations that can cause torch.compile issues.
"""
assert image_keys is None, "Deprecated: cannot use image_key anymore"
# assert not (apply_blur_visual_aug and apply_shape_visual_aug), "Cannot apply both custom and official visual augmentations"
batch_shape = observation.state.shape[:-1]
image_keys = list(observation.images.keys())
part_order = {'base': 0, 'left_wrist': 1, 'right_wrist': 2}
def simple_sort_key(k):
part, timestep_str, _ = k.rsplit('_', 2)
timestep = int(timestep_str)
return (timestep, part_order[part])
image_keys = sorted(image_keys, key=simple_sort_key)
out_images = {}
for key in image_keys:
image = observation.images[key]
# Handle both [B, C, H, W] and [B, H, W, C] formats
is_channels_first = image.shape[1] == 3 # Check if channels are in dimension 1
if is_channels_first:
# Convert [B, C, H, W] to [B, H, W, C] for processing
image = image.permute(0, 2, 3, 1)
if image.shape[1:3] != image_resolution:
logger.info(f"Resizing image {key} from {image.shape[1:3]} to {image_resolution}")
image = image_tools.resize_with_pad_torch(image, *image_resolution)
if train:
# Convert from [-1, 1] to [0, 1] for PyTorch augmentations
image = image / 2.0 + 0.5
# Apply PyTorch-based augmentations
if "wrist" not in key and apply_shape_visual_aug:
# Geometric augmentations for non-wrist cameras
height, width = image.shape[1:3]
# Random crop and resize
crop_height = int(height * 0.95)
crop_width = int(width * 0.95)
# Random crop
max_h = height - crop_height
max_w = width - crop_width
if max_h > 0 and max_w > 0:
# Use tensor operations instead of .item() for torch.compile compatibility
start_h = torch.randint(0, max_h + 1, (1,), device=image.device)
start_w = torch.randint(0, max_w + 1, (1,), device=image.device)
image = image[:, start_h : start_h + crop_height, start_w : start_w + crop_width, :]
# Resize back to original size
image = torch.nn.functional.interpolate(
image.permute(0, 3, 1, 2), # [b, h, w, c] -> [b, c, h, w]
size=(height, width),
mode="bilinear",
align_corners=False,
).permute(0, 2, 3, 1) # [b, c, h, w] -> [b, h, w, c]
# Random rotation (small angles)
# Use tensor operations instead of .item() for torch.compile compatibility
angle = torch.rand(1, device=image.device) * 10 - 5 # Random angle between -5 and 5 degrees
if torch.abs(angle) > 0.1: # Only rotate if angle is significant
# Convert to radians
angle_rad = angle * torch.pi / 180.0
# Create rotation matrix
cos_a = torch.cos(angle_rad)
sin_a = torch.sin(angle_rad)
# Apply rotation using grid_sample
grid_x = torch.linspace(-1, 1, width, device=image.device)
grid_y = torch.linspace(-1, 1, height, device=image.device)
# Create meshgrid
grid_y, grid_x = torch.meshgrid(grid_y, grid_x, indexing="ij")
# Expand to batch dimension
grid_x = grid_x.unsqueeze(0).expand(image.shape[0], -1, -1)
grid_y = grid_y.unsqueeze(0).expand(image.shape[0], -1, -1)
# Apply rotation transformation
grid_x_rot = grid_x * cos_a - grid_y * sin_a
grid_y_rot = grid_x * sin_a + grid_y * cos_a
# Stack and reshape for grid_sample
grid = torch.stack([grid_x_rot, grid_y_rot], dim=-1)
image = torch.nn.functional.grid_sample(
image.permute(0, 3, 1, 2), # [b, h, w, c] -> [b, c, h, w]
grid,
mode="bilinear",
padding_mode="zeros",
align_corners=False,
).permute(0, 2, 3, 1) # [b, c, h, w] -> [b, h, w, c]
# * add motionblur and gaussian blur
if apply_blur_visual_aug:
image_nchw = image.permute(0, 3, 1, 2).contiguous()
aug = K.AugmentationSequential(
K.RandomMedianBlur(kernel_size=(3, 5), p=0.1), # * prob too high
K.RandomMotionBlur(kernel_size=(3, 5), angle=35., direction=0.5, p=0.1), # * smaller aug. Since the sensor is already blurry.
keepdim=True,
)
# Apply
image_nchw = aug(image_nchw)
# Permute back to [B, H, W, C]
image = image_nchw.permute(0, 2, 3, 1).contiguous()
# Clamp values to [0, 1]
image = torch.clamp(image, 0, 1)
# Back to [-1, 1]
image = image * 2.0 - 1.0
# Convert back to [B, C, H, W] format if it was originally channels-first
if is_channels_first:
image = image.permute(0, 3, 1, 2) # [B, H, W, C] -> [B, C, H, W]
out_images[key] = image
out_masks = {}
for key in out_images:
if key not in observation.image_masks:
# do not mask by default
out_masks[key] = torch.ones(batch_shape, dtype=torch.bool, device=observation.state.device)
else:
out_masks[key] = observation.image_masks[key]
if 'base' in key and train and p_mask_base > 0.0:
# Randomly mask base images
random_tensor = torch.rand(batch_shape, device=out_masks[key].device)
base_mask = random_tensor > p_mask_base
out_masks[key] = out_masks[key] & base_mask # Combine with existing mask
# * State augmentation
# * Only for conveyor? using norm04
state_std = [
0.2079681158065796,
0.7834290266036987,
0.5441722273826599,
0.14168238639831543,
0.1750941127538681,
0.15182428061962128,
0.024107031524181366,
0.19041913747787476,
0.6899408102035522,
0.4627247452735901,
0.10430814325809479,
0.1795605719089508,
0.11770003288984299,
0.03210258111357689,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0
],
states = observation.state
if state_noise_snr is not None:
# 1. Calculate the noise standard deviation (sigma)
# math.sqrt or **0.5 works fine for scalar operations here
state_std = torch.tensor(state_std).to(states).reshape(1, -1) # [1, state_dim]
epsilon = 1e-6
noise_scale = state_std / torch.sqrt(torch.tensor(10) ** (state_noise_snr / 10) + epsilon)
noise_scale = noise_scale.expand(states.shape) # Now noise_scale has shape [4, 32]
# 2. Add Gaussian noise
# torch.randn_like(states) creates N(0,1) noise on the correct device (GPU/CPU)
# We then multiply by noise_scale to adjust the spread
states += torch.randn_like(states) * noise_scale
# Create a simple object with the required attributes instead of using the complex Observation class
class SimpleProcessedObservation:
def __init__(self, **kwargs):
for key, value in kwargs.items():
setattr(self, key, value)
if return_full_obs:
return SimpleProcessedObservation(
images=out_images,
image_masks=out_masks,
state=states,
tokenized_prompt=observation.tokenized_prompt,
tokenized_prompt_mask=observation.tokenized_prompt_mask,
token_ar_mask=observation.token_ar_mask,
token_loss_mask=observation.token_loss_mask,
action_advantage=observation.action_advantage,
action_advantage_original=observation.action_advantage_original,
frame_index=observation.frame_index,
frame_index_progress=observation.frame_index_progress,
is_failure_data=observation.is_failure_data,
is_infer_data=observation.is_infer_data,
episode_length=observation.episode_length,
image_original=observation.image_original,
episode_index=observation.episode_index,
inferred_action=observation.inferred_action,
noise=observation.noise,
)
else:
# * Simplified for sampling value
return SimpleProcessedObservation(
images=out_images,
image_masks=out_masks,
state=states,
tokenized_prompt=observation.tokenized_prompt,
tokenized_prompt_mask=observation.tokenized_prompt_mask,
)
|