torchvision.transforms.functional. resized_crop (img: torch.Tensor, top: int, left: int, height: int, width: int, size: List[int], interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.BILINEAR: 'bilinear'>) → torch.Tensor [source] ¶ Crop the given image and resize it to desired size.

Torchvision supports common computer vision transformations in the torchvision.transforms and torchvision.transforms.v2 modules. Transforms can be used to transform or augment data for training or inference of different tasks (image classification, …

torchvision.transforms¶ Transforms are common image transformations. They can be chained together using Compose. Additionally, there is the torchvision.transforms.functional module. Functional transforms give fine-grained control over the transformations.

class torchvision.transforms. Resize (size, interpolation = InterpolationMode.BILINEAR, max_size = None, antialias = True) [source] ¶ Resize the input image to the given size. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means a maximum of two leading dimensions. Parameters: size (sequence or int ...

class torchvision.transforms. Compose (transforms) [source] ¶ Composes several transforms together. This transform does not support torchscript. Please, see the note below. Parameters: transforms (list of Transform objects) – list of transforms to compose. Example >>>

If degrees is a number instead of sequence like (min, max), the range of degrees will be (-degrees, +degrees). interpolation (InterpolationMode): Desired interpolation enum defined by:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.

This example illustrates all of what you need to know to get started with the new torchvision.transforms.v2 API. We’ll cover simple tasks like image classification, and more advanced ones like object detection / segmentation.

The torchvision.transforms.v2 API supports images, videos, bounding boxes, and instance and segmentation masks. Thus, it offers native support for many Computer Vision tasks, like image and video classification, object detection or instance and semantic segmentation.

This guide explains how to write transforms that are compatible with the torchvision transforms V2 API.

class torchvision.transforms. Normalize (mean, std, inplace = False) [source] ¶ Normalize a tensor image with mean and standard deviation. This transform does not support PIL Image.