Pytorch:Conv2d卷积前后尺寸详解

# m = nn.Conv2d(16, 33, 3, stride=2)
# non-square kernels and unequal stride and with padding
m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
# non-square kernels and unequal stride and with padding and dilation
# m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
input = torch.randn(20, 16, 50, 100)
print(input.size())
output = m(input)
print(output.size())

The size of the input feature map: (N, N)
Conv2dTranspose(kernel_size=k, padding, strides=s)

padding=‘same' ，输出尺寸 = N × s
padding=‘valid'，输出尺寸 = (N-1) × s + k