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', 'torch_reference', 'pil', 'tensorflow'] = 'torch_reference', mean: Tensor | Sequence[float] | Sequence[Sequence[float]] | None = None, std: Tensor | Sequence[float] | Sequence[Sequence[float]] | None = None)

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

The "tensorflow" backend mirrors the OpenVLA-OFT evaluation path: JPEG encode/decode at the requested quality, resize with Lanczos3, optionally apply a 0.9-area center crop, and resize back. The "torch_reference" backend follows the same order and interpolation semantics using PyTorch and torchvision only, and is the default. The "torchvision" backend keeps data as tensors but uses a faster bicubic path; "pil" is a lightweight 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", "torch_reference", "pil" or "tensorflow". Defaults to "torch_reference".

• mean (torch.Tensor | sequence, optional) – Per-channel normalization mean. A two-dimensional sequence applies multiple normalizations to the same image and concatenates the results along the channel axis, as required by fused OpenVLA vision backbones.

• 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])