PyTorch
docs.pytorch.org › vision › main › generated › torchvision.transforms.Resize.html
Resize — Torchvision main documentation
Transforming images, videos, boxes and more > Resize · Shortcuts · 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 ...
ProgramCreek
programcreek.com › python › example › 104834 › torchvision.transforms.Resize
Python Examples of torchvision.transforms.Resize
def load_training(root_path, dir, batch_size, kwargs): transform = transforms.Compose( [transforms.Resize([256, 256]), transforms.RandomCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor()]) data = datasets.ImageFolder(root=root_path + dir, transform=transform) train_loader = ...
TutorialsPoint
tutorialspoint.com › pytorch-how-to-resize-an-image-to-a-given-size
PyTorch – How to resize an image to a given size?
January 6, 2022 - Apply the above-defined transform on the input image to resize the input image. ... Show the resized image. ... This image is used as the input file in all the following examples. The input image is resized to (300, 350).
PyTorch
docs.pytorch.org › vision › 0.11 › transforms.html
torchvision.transforms — Torchvision 0.11.0 documentation
Examples using RandomVerticalFlip: Illustration of transforms¶ · forward(img)[source]¶ · Parameters · img (PIL Image or Tensor) – Image to be flipped. Returns · Randomly flipped image. Return type · PIL Image or Tensor · class torchvision.transforms.Resize(size, interpolation=<InterpolationMode.BILINEAR: 'bilinear'>, max_size=None, antialias=None)[source]¶ ·
PyTorch
docs.pytorch.org › vision › 0.27 › generated › torchvision.transforms.Resize.html
Resize — Torchvision 0.27 documentation
Transforming images, videos, boxes and more > Resize · Shortcuts · 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 ...
PyTorch
docs.pytorch.org › vision › 0.16 › generated › torchvision.transforms.Resize.html
Resize — Torchvision 0.16 documentation
Transforming and augmenting images > Resize · Shortcuts · class torchvision.transforms.Resize(size, interpolation=InterpolationMode.BILINEAR, max_size=None, antialias='warn')[source]¶ · Resize the input image to the given size. If the image is torch Tensor, it is expected to have […, ...
APXML
apxml.com › courses › getting-started-with-pytorch › chapter-5-efficient-data-handling › data-transformations-torchvision-transforms
PyTorch Data Augmentation | torchvision.transforms
Here's an example of creating a ... transform pipeline for training train_transform = transforms.Compose([ transforms.Resize(256), # Resize smaller edge to 256 transforms.RandomCrop(224), # Randomly crop a 224x224 patch transforms.RandomHorizontalFlip(), # Randomly flip ...
PyTorch
docs.pytorch.org › vision › 0.15 › generated › torchvision.transforms.Resize.html
Resize — Torchvision 0.15 documentation
Transforming and augmenting images > Resize · Shortcuts · class torchvision.transforms.Resize(size, interpolation=InterpolationMode.BILINEAR, max_size=None, antialias='warn')[source]¶ · Resize the input image to the given size. If the image is torch Tensor, it is expected to have […, ...
Chsasank
chsasank.com › vision › transforms.html
Transforms — Torchvision master documentation
Example · >>> transforms.Compose([ >>> transforms.CenterCrop(10), >>> transforms.ToTensor(), >>> ]) class torchvision.transforms.Resize(size, interpolation=2)[source]¶ · Resize the input PIL Image to the given size. class torchvision.transforms.Scale(*args, **kwargs)[source]¶ ·
Medium
medium.com › @MarkAiCode › resize-images-with-pytorch-a-comprehensive-guide-b9025b8fe79d
Resize Images with PyTorch: A Comprehensive Guide | by Markaicode | Medium
August 5, 2024 - Here’s a basic example: # Load ... image resized_img.save('resized_image.jpg') This code snippet resizes the input image to a fixed size of 224×224 pixels, which is a common input size for many neural network archite...
PyTorch
docs.pytorch.org › vision › 0.28 › generated › torchvision.transforms.v2.Resize.html
Resize — Torchvision 0.28 documentation
class torchvision.transforms.v... Video, BoundingBoxes etc.) it can have arbitrary number of leading batch dimensions. For example, the image can have [..., C, H, W] shape....
PyTorch
docs.pytorch.org › vision › 0.22 › generated › torchvision.transforms.Resize.html
Resize — Torchvision 0.22 documentation
Transforming and augmenting images > Resize · Shortcuts · 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, ...
PyTorch
docs.pytorch.org › vision › 0.13 › generated › torchvision.transforms.functional.resize.html
resize — Torchvision 0.13 documentation
Transforming and augmenting images > resize · Shortcuts · torchvision.transforms.functional.resize(img: torch.Tensor, size: List[int], interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.BILINEAR: 'bilinear'>, max_size: Optional[int] = None, antialias: Optional[bool] = None) → torch.Tensor[source]¶ ·
PyTorch
docs.pytorch.org › vision › stable › generated › torchvision.transforms.Resize.html
torchvision.transforms. - Resize
Transforming images, videos, boxes and more > Resize · Shortcuts · 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 ...
Top answer 1 of 3
16
In order to automatically resize your input images you need to define a preprocessing pipeline all your images go through. This can be done with torchvision.transforms.Compose() (Compose docs). To resize Images you can use torchvision.transforms.Scale() (Scale docs) from the torchvision package.
See the documentation:
Note, in the documentation it says that .Scale() is deprecated and .Resize() should be used instead. Resize docs
This would be a minimal working example:
import torch
from torchvision import transforms
p = transforms.Compose([transforms.Scale((48,48))])
from PIL import Image
img = Image.open('img.jpg')
img.size
# (224, 224) <-- This will be the original dimensions of your image
p(img).size
# (48, 48) <-- This will be the rescaled/resized dimensions of your image
2 of 3
2
1)
If you are using transform you can simply use resize. For example, this code will convert MNIST dataloading into a 32*32 shape (in the resize line)
train_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('/files/', train=True, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Resize(32), # This line
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))
])),
batch_size=batch_size_train, shuffle=True)
2)
If you only want a function you can use torchvision.transforms.functional.resize(image, size, ...). The other answers seem deprecated.
In the Resize Docs is written
Resize the input image to the given size.
Parameters:
- img (PIL Image or Tensor) – Image to be resized.
- size Desired output size. If size is a sequence like (h, w), the output size will be matched to this. If size is an int, the smaller edge of the image will be matched to this number maintaining the aspect ratio
Return type:
- PIL Image or Tensor
PyTorch
docs.pytorch.org › vision › 0.24 › generated › torchvision.transforms.Resize.html
Resize — Torchvision 0.24 documentation
Transforming images, videos, boxes and more > Resize · Shortcuts · 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 ...
Mlverse
torchvision.mlverse.org › reference › transform_resize
Resize the input image to the given size — transform_resize • torchvision
Toggle navigation torchvision 0.9.0 · Examples · mnist-mlp · mnist-cnn · mnist-dcgan · tinyimagenet-alexnet · style-transfer · texture-nca · fcnresnet · keypoints · Reference · Changelog · Source: R/transforms-generics.R · transform_resize.Rd · The image can be a Magic Image or a torch Tensor, in which case it is expected to have [..., H, W] shape, where ...
Python Guides
pythonguides.com › pytorch-resize-images
How To Resize Images In PyTorch - Python Guides
June 19, 2025 - In this guide, I will share the exact methods I use to resize images in PyTorch, along with practical examples that you can implement right away. ... Now, I will explain methods to resize images in PyTorch. The easiest way to resize images in PyTorch is through the transforms module. I use this method in nearly 80% of my projects because of its simplicity. import torch from torchvision import transforms from PIL import Image # Load an image img = Image.open(r'C:\Users\Public\grand_canyon.jpg') print(f"Original size: {img.size}") # Resize transform resize_transform = transforms.Resize((224, 224)) resized_img = resize_transform(img) print(f"New size: {resized_img.size}") # Convert to tensor to_tensor = transforms.ToTensor() img_tensor = to_tensor(resized_img) print(f"Tensor shape: {img_tensor.shape}")
Top answer 1 of 3
23
It seems like you are looking for interpolate (a function in nn.functional):
import torch.nn.functional as nnf
x = torch.rand(5, 1, 44, 44)
out = nnf.interpolate(x, size=(224, 224), mode='bicubic', align_corners=False)
If you really care about the accuracy of the interpolation, you should have a look at ResizeRight: a pytorch/numpy package that accurately deals with all sorts of "edge cases" when resizing images. This can have an effect when directly merging features of different scales: inaccurate interpolation may result in misalignments.
2 of 3
4
The TorchVision transforms.functional.resize() function is what you're looking for:
import torchvision.transforms.functional as F
t = torch.randn([5, 1, 44, 44])
t_resized = F.resize(t, 224)
If you wish to use another interpolation mode than bilinear, you can specify this with the interpolation argument.
Stack Overflow
stackoverflow.com › questions › 76064717 › pytorch-resize-specific-dimension-while-keeping-aspect-ratio
deep learning - Pytorch resize specific dimension while keeping aspect ratio - Stack Overflow
This transform takes a fixed height value and resizes the input image. this is a roughly schema of how you should proceed. import math from torchvision.transforms import functional as F class FixedHeightResize: def __init__(self, size): self.size = size def __call__(self, img): w, h = img.size aspect_ratio = float(h) / float(w) new_w = math.ceil(self.size / aspect_ratio) return F.resize(img, (self.size, new_w))