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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, input1, input2, input3):
intBatches = input1.size(0)
intInputDepth = input1.size(1)
intInputHeight = input1.size(2)
intInputWidth = input1.size(3)
intFilterSize = min(input2.size(1), input3.size(1))
intOutputHeight = min(input2.size(2), input3.size(2))
intOutputWidth = min(input2.size(3), input3.size(3))
assert(intInputHeight - 51 == intOutputHeight - 1)
assert(intInputWidth - 51 == intOutputWidth - 1)
assert(intFilterSize == 51)
assert(input1.is_contiguous() == True)
assert(input2.is_contiguous() == True)
assert(input3.is_contiguous() == True)
output = input1.new().resize_(intBatches, intInputDepth, intOutputHeight, intOutputWidth).zero_()
if input1.is_cuda == True:
_ext.cunnex.SeparableConvolution_cuda_forward(
input1,
input2,
input3,
output
)
elif input1.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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