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import torch
import _ext.cunnex
class SeparableConvolution(torch.autograd.Function):
def __init__(self):
super(SeparableConvolution, self).__init__()
# end
def forward(self, input, vertical, horizontal):
intBatches = input.size(0)
intInputDepth = input.size(1)
intInputHeight = input.size(2)
intInputWidth = input.size(3)
intFilterSize = min(vertical.size(1), horizontal.size(1))
intOutputHeight = min(vertical.size(2), horizontal.size(2))
intOutputWidth = min(vertical.size(3), horizontal.size(3))
assert(intInputHeight - 51 == intOutputHeight - 1)
assert(intInputWidth - 51 == intOutputWidth - 1)
assert(intFilterSize == 51)
assert(input.is_contiguous() == True)
assert(vertical.is_contiguous() == True)
assert(horizontal.is_contiguous() == True)
output = input.new().resize_(intBatches, intInputDepth, intOutputHeight, intOutputWidth).zero_()
if input.is_cuda == True:
_ext.cunnex.SeparableConvolution_cuda_forward(
input,
vertical,
horizontal,
output
)
elif input.is_cuda == False:
raise NotImplementedError() # CPU VERSION NOT IMPLEMENTED
# end
return output
# end
def backward(self, gradOutput):
raise NotImplementedError() # BACKPROPAGATION NOT IMPLEMENTED
# end
# end
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