How to calculate kernel dimensions from original image dimensions?

https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py

From reading https://www.cs.toronto.edu/~kriz/cifar.html the cifar dataset consists of images each with 32x32 dimension.

My understanding of code :

self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1   = nn.Linear(16*5*5, 120)

Is :

self.conv1 = nn.Conv2d(3, 6, 5) # 3 channels in, 6 channels out ,  kernel size of 5
self.conv2 = nn.Conv2d(6, 16, 5) # 6 channels in, 16 channels out ,  kernel size of 5
self.fc1   = nn.Linear(16*5*5, 120) # 16*5*5 in features , 120 ouot feature

From resnet.py the following :

self.fc1   = nn.Linear(16*5*5, 120)

From http://cs231n.github.io/convolutional-networks/ the following is stated :

Summary. To summarize, the Conv Layer:

Accepts a volume of size W1×H1×D1 Requires four hyperparameters: Number of filters K, their spatial extent F, the stride S, the amount of zero padding P. Produces a volume of size W2×H2×D2 where: W2=(W1−F+2P)/S+1 H2=(H1−F+2P)/S+1 (i.e. width and height are computed equally by symmetry) D2=K With parameter sharing, it introduces F⋅F⋅D1 weights per filter, for a total of (F⋅F⋅D1)⋅K weights and K biases. In the output volume, the d-th depth slice (of size W2×H2) is the result of performing a valid convolution of the d-th filter over the input volume with a stride of S, and then offset by d-th bias.

From this I'm attempting to understand how the training image dimension 32x32 (1024 pixels) is transformed to feature map (16*5*5 -> 400) as aprt of nn.Linear(16*5*5, 120)

From https://pytorch.org/docs/stable/nn.html#torch.nn.Conv2d can see default stride is 1 and padding is 0.

What are steps to arrive at 16*5*5 from image dimension of 32*32 and can 16*5*5 be derived from above steps ?

From above steps how to calculate spatial extent ?

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