forked from mrq/DL-Art-School
40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
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from torch.autograd import Function, Variable
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from torch.nn.modules.module import Module
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import channelnorm_cuda
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class ChannelNormFunction(Function):
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@staticmethod
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def forward(ctx, input1, norm_deg=2):
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assert input1.is_contiguous()
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b, _, h, w = input1.size()
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output = input1.new(b, 1, h, w).zero_()
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channelnorm_cuda.forward(input1, output, norm_deg)
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ctx.save_for_backward(input1, output)
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ctx.norm_deg = norm_deg
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return output
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@staticmethod
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def backward(ctx, grad_output):
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input1, output = ctx.saved_tensors
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grad_input1 = Variable(input1.new(input1.size()).zero_())
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channelnorm_cuda.backward(input1, output, grad_output.data,
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grad_input1.data, ctx.norm_deg)
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return grad_input1, None
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class ChannelNorm(Module):
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def __init__(self, norm_deg=2):
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super(ChannelNorm, self).__init__()
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self.norm_deg = norm_deg
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def forward(self, input1):
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return ChannelNormFunction.apply(input1, self.norm_deg)
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