Dimensions of an input image

I am new with Pytorch, and will be glad if someone will be able to help me understand the following (and correct me if I am wrong), regarding the meaning of the command x.view in Pytorch first tutorial:

https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html

( def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x)

  1. As far as I understand, an input 256X256 image to a convolutional layer is inserted in its 2D form (i.e. - a 256X256 matrix, or a 256X256X3 in the case of a color image). Nevertheless, when we insert an image to a fully-connected linear layer, we need to first reshape the 2D image into a 1D vector. Is this why we use the command “x = x.view(-1, 16 * 5 * 5)” before inserting x into the fully connected layers?

  2. If the input image x would be 3D (e.g. 256X256X256), would the syntax of the given above “forward” function remain the same?

Thanks a lot in advance

1 Like

Your explanation is right in general.
Just some minor issues:
In PyTorch, images are represented as [channels, height, width], so a color image would be [3, 256, 256].
During the training you will get batches of images, so your shape in the forward method will get an additional batch dimension at dim0: [batch_size, channels, height, width].

The .view() operation gives you a new view on the tensor without copying any data.
This means view is cheap to call and you don’t think too much about potential performance issues.
In your example, x is reshaped into a tensor of shape 16*5*5 in dim1 and all remaining values in dim0. That’s what the -1 stands for.
In the case of a 256 -channel image, the view operation would resume the same, since the spatial dimensions of the image are the same and the channels are defined by the number of kernels of conv2.

18 Likes

Thanks a lot ptrbick!

1 Like

Sorry for revisiting this.

But this particular line seems to suggest the order is n, b, w, h i.e., width comes before height. Probably a mistake but better to clarify.

I’m not familiar enough with the repository so cannot comment on the correctness or if internal permutations are applied to the tensor.
The standard is [N, C, H, W] for channels-last and [N, H, W, C] for channels last.
Here is a small code snippet which draws a vertical line in the H dimension at W=20:

# create input image
N, C, H, W = 1, 3, 200, 300
x = torch.zeros(N, C, H, W).to(torch.uint8)
# draw line in H dimension at W=30
x[:, :, :, 20] = 255

img = transforms.ToPILImage()(x[0])
arr = np.array(img)
print(arr.shape)
# (200, 300, 3) # H, W, C
plt.imshow(arr)

Output:
image

Given the output and the dimension of the output image, the format in PyTorch is [N, C, H, W] as the default while arr uses the channels-last format as [H, W, C] (the batch dimension is missing as imshow expects a single image).