reference/generated/torchrl.data.vla.OpenVLAImagePreprocessor

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OpenVLAImagePreprocessor

class torchrl.data.vla.OpenVLAImagePreprocessor(*, size: int = 224, jpeg_quality: int = 95, center_crop: bool = False, backend: Literal['torchvision', 'pil'] = 'torchvision', mean: Tensor | list[float] | tuple[float, ...] | None = None, std: Tensor | list[float] | tuple[float, ...] | None = None)

OpenVLA-style image resize, JPEG round-trip and optional center crop.

The operation order mirrors the OpenVLA-OFT evaluation path: resize to a square image, JPEG encode/decode at the requested quality, optionally apply a 0.9-area center crop, and resize back. The default "torchvision" backend keeps data as tensors and uses torchvision.io JPEG codecs; "pil" is the reference/debugging backend.

Parameters:

• size (int) – Square output size. Defaults to 224.

• jpeg_quality (int) – JPEG quality. Defaults to 95.

• center_crop (bool) – Whether to apply the OpenVLA 0.9-area center crop. Defaults to False.

• backend (str) – "torchvision" or "pil". Defaults to "torchvision".

• mean (torch.Tensor | sequence, optional) – Per-channel normalization mean.

• std (torch.Tensor | sequence, optional) – Per-channel normalization std.

Note

Floating-point inputs are ambiguous: this helper treats float images with maximum value at most 1 as normalized [0, 1] data and rescales them to uint8; other float images are interpreted as [0, 255] data.

Examples

>>> import torch
>>> from torchrl.data.vla import OpenVLAImagePreprocessor
>>> proc = OpenVLAImagePreprocessor(backend="pil")
>>> out = proc(torch.zeros(2, 3, 32, 32, dtype=torch.uint8))
>>> out.shape
torch.Size([2, 3, 224, 224])