You can use it for 4-D and 5-D data (your case, if you unsqueeze a fake channel dimension). However, the grid shape should be [N, H_out, W_out, 2], while you are passing 4 values. Here is a small example of the usage: N, C, H, W = 1, 24, 24, 24 x = torch.arange(N*C*H*W).view(N, 1, C, H, W).float(… Answer from ptrblck on discuss.pytorch.org
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PyTorch Forums
discuss.pytorch.org › vision
Pytorch resize 3d numpy array - vision - PyTorch Forums
February 19, 2020 - Dear all, I have 3d image and I would like to write a dataloader with a rescale trasformation . What is the best way to do the rescaling? I have [240,240,180] I would like to trasform [128,128,128]. Any suggestion on function for dataloader. Thanks so much class Resize(object): def __init__(self, output_size): assert isinstance(output_size, (int, tuple)) self.output_size = output_size def __call__(self, sample): image, label = sample["data"], sample["l...
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Reddit
reddit.com › r/pytorch › resize 3d tensor
r/pytorch on Reddit: Resize 3d tensor
July 22, 2023 -

I want to resize my [512,512,96] tensor to [64,64,64]. Torchvision.transoforms.Resize only works with the last two dimensions

image = T.Resize(64)(image) turns torch.Size([512, 512, 96]) to torch.Size([512, 341, 64]) ???(no idea where the 341)

image = T.Resize((64,64))(image) turns torch.Size([512, 512, 96]) to torch.Size([512, 64, 64]) .

image = T.Resize((64,64,64))(image) throws this error

If size is a sequence, it should have 1 or 2 values

Any ideas, besides iterating over all the images in a for loop and concatenating them

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https://pytorch.org/docs/stable/generated/torch.Tensor.view.html
Discussions
[Info Need] How resize 3D volumetric data?
For 3D volumetric data, how we can resample (upsampling and downsampling) in pytorch? For example: [ format: (bs, h, w, depth, channel) ] original_input = (None, 256, 256, 40, 3) upsample_output = (None, 512, 512, 80, 3) dnsample_output = (None, 128, 128, 20, 3) I’m trying to use it for 3D ... More on discuss.pytorch.org
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May 6, 2022
Scaling 3D images in pytorch
As a preprocessing step, I need to scale 3D images. Does pytorch have a 3D bilinear interpolation tool or any other useful upsample/downsample tools for this purpose? More on discuss.pytorch.org
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May 23, 2017
Resizing the ground truth of 3D CT scan
How can I resize a 3D image tensor of size 143 x 512 x 512 to 143 x 256 x 256? More on discuss.pytorch.org
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April 5, 2021
Bug with transforms.Resize when used with transforms.ConvertImageDtype
🐛 Describe the bug Recent releases of Torchvision and the documentations that support it seem to suggest that we can use io.read_image + transforms.ConvertImageDtype instead of the traditional PIL.... More on github.com
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Stack Overflow
stackoverflow.com › questions › 75586961 › how-to-resize-a-5d-pytorch-tensor-using-torchvision-transforms
python - How to resize a 5D PyTorch tensor using TorchVision transforms? - Stack Overflow
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One approach using TF.resize is to flatten the batch and depth dimensions, perform the resize, then recover the initial depth dimension:

>>> a_flat = a.swapaxes(1,2).flatten(0,1)     # (B*D, C,  H,   W)
>>> c = TF.resize(a_flat , (200, 200))        # (B*D, C, 200, 200)
>>> b = b.view(*a.shape[:3], *c.shape[-2:])   # (B, D, C, 200, 200)
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PyTorch Forums
discuss.pytorch.org › vision
[Info Need] How resize 3D volumetric data? - vision - PyTorch Forums
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This is what you need to decide when you give the mode parameter. The default value (if you do not specify) is nearest. For the case where you divide the size by 2, means that every even (or uneven) row/column will be dropped and the value will be taken from the nearest values. Which would be the a…
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You can use torch.nn.Upsample. But your data has to be in one of the following formats [image] Here is the documentation. Here you can see HOW you want to do the interpolation, since it will change if you use nearest, bilinear or something else. Hope this helps :smile:
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DEV Community
dev.to › hyperkai › resize-in-pytorch-47j7
Resize in PyTorch - DEV Community
May 16, 2025 - A tensor must be 3D or more D. Don't use img=. v2 is recommended to use according to V1 or V2? Which one should I use?. from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import Resize from torchvision.transforms.functional import InterpolationMode r = Resize(size=100) r = Resize(size=100, interpolation=InterpolationMode.BILINEAR, max_size=None, antialias=True) r # Resize(size=[100], interpolation=InterpolationMode.BILINEAR, # antialias=True) r.size # [100] r.interpolation # <InterpolationMode.BILINEAR: 'bilinear'> print(r.max_size) # None r.antialias # True origin_data = OxfordIIITPet( root="data", transform=None ) s1000_data = OxfordIIITPet( # `s` is size.
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PyTorch
docs.pytorch.org › vision › main › generated › torchvision.transforms.functional.resize.html
resize — Torchvision main documentation
resize · Shortcuts · torchvision.transforms.functional.resize(img: Tensor, size: list[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR, max_size: Optional[int] = None, antialias: Optional[bool] = True) → Tensor[source]¶ · Resize the input image to the given size.
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GitHub
github.com › kenshohara › 3D-ResNets-PyTorch › blob › master › spatial_transforms.py
3D-ResNets-PyTorch/spatial_transforms.py at master · kenshohara/3D-ResNets-PyTorch
from torchvision.transforms import transforms · from torchvision.transforms import functional as F · from PIL import Image · · · class Compose(transforms.Compose): · def randomize_parameters(self): for t in self.transforms: t.randomize_parameters() ·
Author: kenshohara
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PyTorch Forums
discuss.pytorch.org › t › scaling-3d-images-in-pytorch › 3373
Scaling 3D images in pytorch - PyTorch Forums
May 23, 2017 - As a preprocessing step, I need to scale 3D images. Does pytorch have a 3D bilinear interpolation tool or any other useful upsample/downsample tools for this purpose?
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discuss.pytorch.org › t › resizing-the-ground-truth-of-3d-ct-scan › 117068
Resizing the ground truth of 3D CT scan - PyTorch Forums
April 5, 2021 - How can I resize a 3D image tensor of size 143 x 512 x 512 to 143 x 256 x 256?
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Python Guides
pythonguides.com › pytorch-resize-images
How To Resize Images In PyTorch - Python Guides
June 19, 2025 - 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') ...
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PyTorch
docs.pytorch.org › vision › 0.13 › generated › torchvision.transforms.functional.resize.html
resize — Torchvision 0.13 documentation
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]¶ ·
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PyTorch
docs.pytorch.org › vision › 0.17 › generated › torchvision.transforms.functional.resize.html
resize — Torchvision 0.17 documentation
resize · Shortcuts · torchvision.transforms.functional.resize(img: Tensor, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR, max_size: Optional[int] = None, antialias: Optional[bool] = True) → Tensor[source]¶ · Resize the input image to the given size.
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PyTorch
docs.pytorch.org › vision › 0.16 › generated › torchvision.transforms.functional.resize.html
resize — Torchvision 0.16 documentation
resize · Shortcuts · torchvision.transforms.functional.resize(img: Tensor, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR, max_size: Optional[int] = None, antialias: Optional[Union[str, bool]] = 'warn') → Tensor[source]¶ ·
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PyTorch
docs.pytorch.org › vision › stable › generated › torchvision.transforms.Resize.html
Resize — Torchvision 0.29 documentation
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, W] shape, where …
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PyTorch
docs.pytorch.org › vision › 0.22 › generated › torchvision.transforms.Resize.html
Resize — Torchvision 0.22 documentation
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, W] shape, where …
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PyTorch
docs.pytorch.org › vision › stable › generated › torchvision.transforms.v2.Resize.html
Resize — Torchvision 0.28 documentation
class torchvision.transforms.v2.Resize(size: Optional[Union[int, Sequence[int]]], interpolation: Union[str, InterpolationMode, int] = 'bilinear', max_size: Optional[int] = None, antialias: Optional[bool] = True)[source]¶
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GitHub
github.com › pytorch › vision › issues › 4880
Bug with transforms.Resize when used with transforms.ConvertImageDtype · Issue #4880 · pytorch/vision
November 6, 2021 - Bug with transforms.Resize when used with transforms.ConvertImageDtype#4880 · #4979 · Copy link · Assignees · Labels · module: transforms · numpee · opened · on Nov 6, 2021 · Issue body actions · Recent releases of Torchvision and the documentations that support it seem to suggest that we can use io.read_image + transforms.ConvertImageDtype instead of the traditional PIL.Image.read_fn + transforms.ToTensor.
Author: pytorch
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PyTorch
docs.pytorch.org › vision › main › models › generated › torchvision.models.video.swin3d_s.html
swin3d_s — Torchvision main documentation
The frames are resized to resize_size=[256] using interpolation=InterpolationMode.BILINEAR, followed by a central crop of crop_size=[224, 224]. Finally the values are first rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225]. Finally the ...
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MONAI
docs.monai.io › en › stable › transforms.html
monai.transforms.Resize
Both are then typically augmented through the use of random rotations, flips, and deformations. Compose can be used with a series of transforms that take a dictionary that contains ‘image’ and ‘label’ entries. This might require wrapping torchvision transforms before passing them to compose.
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Stack Overflow
stackoverflow.com › questions › 76166919 › torchvision-resize-not-recognising-second-dimension-of-pytorch-tensor-input › 76167024
python - Torchvision resize not recognising second dimension of pytorch tensor input - Stack Overflow
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Torchvision transforms expect the tensor to have a colour channel dimension (1 for monochrome or 3 for RGB for example) and optionally batch dimension. In other words, you'll need a shape of at least (1, 512, 512) rather than (512, 512). The easiest way to add a dimension is likely using unsqueeze():

resized  =  T.Resize(size=(256,256))(img.unsqueeze(0))