Kernel size can't be greater than actual input size

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I have a data with depth = 3 and I want to pass it through 3 convolution layers with 3x3x3 kernels each. My current code is below. The first input is [batch_size=10, in_channels=1, depth=3, height=128, width=256] and I notice after the first conv3d layer the output is [10,8,1,126,254]. Obviously it has now depth 1 and doesn't accept it for another 3x3x3 layer. How can I achieve this?

class CNet(nn.Module):
    def __init__(self, **kwargs):
        super().__init__()
        self.conv1 = nn.Conv3d(1, 8, kernel_size=3, stride=1, padding=0)
        self.conv2 = nn.Conv3d(8, 16, kernel_size=3, stride=1, padding=0)
        self.conv3 = nn.Conv3d(16, 32, kernel_size=3, stride=1, padding=0)
        self.fc1 = nn.Linear(value, 2)

    def forward(self, X):
        X = F.relu(self.conv1(X))
        X = F.relu(self.conv2(X))
        X = F.max_pool2d(X,2)
        X = self.conv3(X)
        X = F.max_pool2d(X,2)
        X = self.fc1(X)
        return F.softmax(X,dim =1)
conv-neural-network pytorch deep-learning python
1 Answers

You need to use padding. If you only want to pad the input for the convolutions after the first one and only in the depth dimensions to get the minimum dimension of 3, you would use padding=(1, 0, 0) (it's 1 because the same padding is applied to both sides, i.e. (padding, input, padding) along that dimension).

self.conv2 = nn.Conv3d(8, 16, kernel_size=3, stride=1, padding=(1, 0, 0))
self.conv3 = nn.Conv3d(16, 32, kernel_size=3, stride=1, padding=(1, 0, 0))

However, it is common to use padding=1 for all dimensions when using kernel_size=3, because that keeps the dimensions unchanged, which makes it much easier to build deeper network, as you don't need to worry about the sizes suddenly getting too small, as it happened already for your depth dimension. Also when no padding is used, the corners are only included in a single calculation, whereas all other elements contribute to multiple calculations. It is recommended to use kernel_size=3 and padding=1 for all your convolutions.

self.conv1 = nn.Conv3d(1, 8, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv3d(8, 16, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv3d(16, 32, kernel_size=3, stride=1, padding=1)
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