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| def preprocess_observation_pytorch(
observation,
*,
train: bool = False,
image_keys: Sequence[str] = IMAGE_KEYS,
image_resolution: tuple[int, int] = IMAGE_RESOLUTION,
):
"""Torch.compile-compatible version of preprocess_observation_pytorch with simplified type annotations.
This function avoids complex type annotations that can cause torch.compile issues.
"""
if not set(image_keys).issubset(observation.images):
raise ValueError(f"images dict missing keys: expected {image_keys}, got {list(observation.images)}")
batch_shape = observation.state.shape[:-1]
out_images = {}
for key in image_keys:
image = observation.images[key]
# TODO: This is a hack to handle both [B, C, H, W] and [B, H, W, C] formats
# 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:
# 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]
# Color augmentations for all cameras
# Random brightness
# Use tensor operations instead of .item() for torch.compile compatibility
brightness_factor = 0.7 + torch.rand(1, device=image.device) * 0.6 # Random factor between 0.7 and 1.3
image = image * brightness_factor
# Random contrast
# Use tensor operations instead of .item() for torch.compile compatibility
contrast_factor = 0.6 + torch.rand(1, device=image.device) * 0.8 # Random factor between 0.6 and 1.4
mean = image.mean(dim=[1, 2, 3], keepdim=True)
image = (image - mean) * contrast_factor + mean
# Random saturation (convert to HSV, modify S, convert back)
# For simplicity, we'll just apply a random scaling to the color channels
# Use tensor operations instead of .item() for torch.compile compatibility
saturation_factor = 0.5 + torch.rand(1, device=image.device) * 1.0 # Random factor between 0.5 and 1.5
gray = image.mean(dim=-1, keepdim=True)
image = gray + (image - gray) * saturation_factor
# 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
# obtain mask
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]
# 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)
return SimpleProcessedObservation(
images=out_images,
image_masks=out_masks,
state=observation.state,
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,
)
def _preprocess_observation(self, observation, *, train=True):
"""Helper method to preprocess observation."""
observation = _preprocessing.preprocess_observation_pytorch(observation, train=train)
return (
list(observation.images.values()),
list(observation.image_masks.values()),
observation.tokenized_prompt,
observation.tokenized_prompt_mask,
observation.state,
)
images, img_masks, lang_tokens, lang_masks, state = self._preprocess_observation(observation, train=False)
prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
# prefill
prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks)
_, past_key_values = self.paligemma_with_expert.forward(
attention_mask=prefix_att_2d_masks_4d,
position_ids=prefix_position_ids,
past_key_values=None,
inputs_embeds=[prefix_embs, None],
use_cache=True,
)
|
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