Deep-Learning-with-PyTorch

198 CHAPTER 8 Using convolutions to generalize
the bias conv.weight are initialized randomly, so the output image will not be particularly
meaningful. As usual, we need to add the zeroth batch dimension with
unsqueeze if we want to call the conv module with one input image, since nn.Conv2d
expects a B × C × H × W shaped tensor as input:
# In[13]:
img, _ = cifar2[0]
output = conv(img.unsqueeze(0))
img.unsqueeze(0).shape, output.shape
# Out[13]:
(torch.Size([1, 3, 32, 32]), torch.Size([1, 16, 30, 30]))
We’re curious, so we can display the output, shown in figure 8.2:
# In[15]:
plt.imshow(output[0, 0].detach(), cmap='gray')
plt.show()
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output
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input
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Figure 8.2 Our bird after a random convolution treatment. (We cheated a little with the code
to show you the input, too.)
Wait a minute. Let’s take a look a the size of output: it’s torch.Size([1, 16, 30,
30]). Huh; we lost a few pixels in the process. How did that happen?
8.2.1 Padding the boundary
The fact that our output image is smaller than the input is a side effect of deciding what
to do at the boundary of the image. Applying a convolution kernel as a weighted sum
of pixels in a 3 × 3 neighborhood requires that there are neighbors in all directions. If
we are at i00, we only have pixels to the right of and below us. By default, PyTorch will
slide the convolution kernel within the input picture, getting width - kernel_width +1
horizontal and vertical positions. For odd-sized kernels, this results in images that are