forked from mrq/DL-Art-School
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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from torch.nn.modules.module import Module
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from torch.autograd import Function, Variable
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import resample2d_cuda
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class Resample2dFunction(Function):
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@staticmethod
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def forward(ctx, input1, input2, kernel_size=1, bilinear= True):
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assert input1.is_contiguous()
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assert input2.is_contiguous()
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ctx.save_for_backward(input1, input2)
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ctx.kernel_size = kernel_size
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ctx.bilinear = bilinear
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_, d, _, _ = input1.size()
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b, _, h, w = input2.size()
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output = input1.new(b, d, h, w).zero_()
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resample2d_cuda.forward(input1, input2, output, kernel_size, bilinear)
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return output
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@staticmethod
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def backward(ctx, grad_output):
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grad_output = grad_output.contiguous()
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assert grad_output.is_contiguous()
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input1, input2 = ctx.saved_tensors
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grad_input1 = Variable(input1.new(input1.size()).zero_())
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grad_input2 = Variable(input1.new(input2.size()).zero_())
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resample2d_cuda.backward(input1, input2, grad_output.data,
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grad_input1.data, grad_input2.data,
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ctx.kernel_size, ctx.bilinear)
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return grad_input1, grad_input2, None, None
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class Resample2d(Module):
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def __init__(self, kernel_size=1, bilinear = True):
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super(Resample2d, self).__init__()
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self.kernel_size = kernel_size
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self.bilinear = bilinear
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def forward(self, input1, input2):
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input1_c = input1.contiguous()
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return Resample2dFunction.apply(input1_c, input2, self.kernel_size, self.bilinear)
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