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Remove PIL fallback from GR00T preprocessing

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This commit is contained in:
Andy Wrenn
2026-06-20 07:02:00 -07:00
parent ee41109d35
commit 5a83db89de
1 changed files with 7 additions and 13 deletions
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src/lerobot/policies/groot/processor_groot.py
+3 -9
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@@ -24,7 +24,6 @@ import numpy as np
import torch import torch
import torchvision.transforms.v2.functional as tv_functional import torchvision.transforms.v2.functional as tv_functional
from einops import rearrange from einops import rearrange
from PIL import Image
from torchvision.transforms import InterpolationMode from torchvision.transforms import InterpolationMode
from lerobot.utils.import_utils import _transformers_available, require_package from lerobot.utils.import_utils import _transformers_available, require_package
@@ -750,13 +749,13 @@ def _build_n1_7_processor(model_name: str = GROOT_N1_7_BACKBONE_MODEL) -> Proces
def _transform_n1_7_image_for_vlm_albumentations( def _transform_n1_7_image_for_vlm_albumentations(
image: np.ndarray | Image.Image, image: np.ndarray,
*, *,
image_crop_size: list[int] | None, image_crop_size: list[int] | None,
image_target_size: list[int] | None, image_target_size: list[int] | None,
shortest_image_edge: int | None, shortest_image_edge: int | None,
crop_fraction: float | None, crop_fraction: float | None,
) -> np.ndarray | Image.Image: ) -> np.ndarray:
"""cv2/INTER_AREA eval transform mirroring Isaac-GR00T's albumentations preprocessing. """cv2/INTER_AREA eval transform mirroring Isaac-GR00T's albumentations preprocessing.
Used only for checkpoints saved with ``use_albumentations=True``. cv2 is Used only for checkpoints saved with ``use_albumentations=True``. cv2 is
@@ -778,15 +777,10 @@ def _transform_n1_7_image_for_vlm_albumentations(
"GR00T N1.7 checkpoints with use_albumentations=True require opencv-python-headless." "GR00T N1.7 checkpoints with use_albumentations=True require opencv-python-headless."
) from exc ) from exc
if isinstance(image, Image.Image):
if image.mode != "RGB":
image = image.convert("RGB")
image_np = np.asarray(image)
else:
image_np = np.asarray(image) image_np = np.asarray(image)
if image_np.ndim == 2: if image_np.ndim == 2:
image_np = np.repeat(image_np[:, :, None], 3, axis=2) image_np = np.repeat(image_np[:, :, None], 3, axis=2)
elif image_np.shape[-1] == 4: elif image_np.ndim == 3 and image_np.shape[-1] == 4:
image_np = image_np[:, :, :3] image_np = image_np[:, :, :3]
if not image_np.flags.c_contiguous: if not image_np.flags.c_contiguous: