Demystify Transposed Convolutional Layer

dilation.py

# Create a TCL layer with kernel_size=2, stride=2, dilation=2
transConv5 = nn.ConvTranspose2d(1, 1, 2, stride=2, dilation=2, bias=False)
# Set kernel weights to be 1
transConv5.weight.data = torch.ones(1,1,2,2)
# Calculate
transConv5(input_data)

# Output:
# tensor([[[[ 1.,  0.,  3.,  0.,  2.],
#           [ 0.,  0.,  0.,  0.,  0.],
#           [ 4.,  0., 10.,  0.,  6.],
#           [ 0.,  0.,  0.,  0.,  0.],
#           [ 3.,  0.,  7.,  0.,  4.]]]])