Torchvision Transforms Functional Resize. 1. resize(inpt: Tensor, size: Optional[list[int]], interpolation

1. resize(inpt: Tensor, size: Optional[list[int]], interpolation: Union[InterpolationMode, int] = InterpolationMode. decomposition import PCA from scipy import signal DINOV3_GITHUB_LOCATION = "facebookresearch/dinov3" if os. BILINEAR, max_size: Optional[int] = None, antialias: Optional[bool] = True) → Tensor [source] Resize the input image to the given size. Default is InterpolationMode. For further information on the compatible versions, check GitHub - pytorch/vision: Datasets, Transforms and Models specific to Computer Vision for the compatibility matrix. Resize(size: Optional[Union[int, Sequence[int]]], interpolation: Union[InterpolationMode, int] = InterpolationMode. InterpolationMode = <InterpolationMode. 04, Cuda 11. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of Resize class torchvision.

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