MNIST image size

1 
import torch
import torchvision
# !pip install torchvision
from torchvision import datasets, transforms
mnist2 =  torchvision.datasets.MNIST('mnist_data', train=True, download=True,
                        transform=transforms.Compose([
                            torchvision.transforms.Pad(18, fill=0, padding_mode='constant'),
                            transforms.ToTensor()
                        ]))

The following code block gives [28,28] for img2 shape.

import matplotlib.pyplot as plt
img2= mnist2.data[0]
print(img2.shape)
plt.imshow(img2)
plt.show()

The following one gives [1, 64, 64] for img shape. Why are they different?

img, digit = next(iter(mnist2))
print(img.shape)
plt.imshow(img.squeeze())
plt.show()
2

The first code snippet uses the internal raw data which has a spatial shape of 28x28.
The second code snippet uses the DataLoader which adds the Pad transformation to each sample and thus increases the spatial shape to 28 + 18 + 18 = 64.

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