CODEWITHSUNDEEP
pythonpytorch
Data Mastery
Data Mastery

Reputation: 2105

Scale down image represented in a tensor

I use the MNIST dataset to learn Pytorch.

This is from the documentation to get a picture.

import torch.nn.functional as F
import torch
from torchvision import datasets, transforms

Tensor comes from the torchvision dataset.

# Create prediction
images, labels = next(iter(trainloader))
images[0].shape

This is the Tensor:

tensor([[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -0.2549, -0.5059, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -0.8667, -0.3961,  0.2471,  0.6784,  0.9922,  0.8824,  0.3882,
         -0.8353, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -0.5922,
          0.3490,  0.6235,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,
         -0.2235, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -0.3647,  0.9451,
          0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.8510,  0.9922,
          0.8745, -0.3725, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -0.0118,  0.9922,
          0.9922,  0.9922,  0.9843,  0.8902,  0.3255, -0.1922, -0.6314,  0.7725,
          0.9922,  0.7804, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,  0.4431,  0.9922,
          0.9922,  0.7725, -0.1608, -1.0000, -1.0000, -1.0000, -1.0000,  0.6235,
          0.9922,  0.7804, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -0.0745,  0.9922,
          0.7333, -0.7020, -1.0000, -1.0000, -1.0000, -1.0000, -0.3020,  0.9843,
          0.9922,  0.7804, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -0.8980,  0.2078,
         -0.0902, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -0.2863,  0.9922,
          0.9922,  0.7804, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,  0.0039,  0.9922,
          0.9922,  0.7804, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -0.8275,  0.8275,  0.9922,
          0.9922, -0.0118, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,  0.5765,  0.9922,  1.0000,
          0.9529, -0.3725, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -0.9922, -0.3804,  0.9765,  0.9922,  0.9922,
          0.0039, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -0.8667,  0.0275,  0.9922,  0.9922,  0.9922,  0.9922,
         -0.4667, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
          0.0039,  0.8980,  0.9137,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,
          0.9216,  0.8980,  0.8980,  0.8980,  0.8980,  0.8980,  0.6314, -0.1843,
         -0.6941, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
          0.6784,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,
          0.9922,  1.0000,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,
          0.8353,  0.1451, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
          0.6784,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,
          0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.9922,  0.8745,  0.5059,
          0.4196, -0.5686, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -0.1765,  0.9216,  0.9922,  0.9765,  0.6706,  0.6706,  0.2863, -0.4118,
         -0.4118, -0.4118, -0.4118, -0.4118, -0.4118, -0.4118, -0.5529, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -0.4275, -0.2471, -0.2863, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000],
        [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
         -1.0000, -1.0000, -1.0000, -1.0000]])


Out[181]: torch.Size([1, 28, 28])

I want to scale the image down to a 14x14 picture, so I guess I need a torch.Size([1, 14, 14])

I tried this, but it results in a different format:

F.interpolate(images[0], 14).shape

Out[184]: torch.Size([1, 28, 14])

I expected this to work, but it results in an error:

F.interpolate(images[0], (14, 14))

ValueError: size shape must match input shape. Input is 1D, size is 2

Does anyone how to to get my desired result?

Upvotes: 2

Views: 1661

Answers (1)

gspr
gspr

Reputation: 11227

From the docs:

The input dimensions are interpreted in the form: mini-batch x channels x [optional depth] x [optional height] x width.

Currently the first 28 in your shape, and the 14 in the output size, are interpreted as the number of channels/colors, not the image height. Therefore the scaling does not happen in that dimension. Unsqueeze your input to be of shape (1, 1, 28, 28) to get the correct behavior.

Upvotes: 4

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