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# flake8: noqa
from torch.nn.modules.module import Module
from torch.autograd import Function, Variable
from torch.cuda.amp import autocast
import resample2d_cuda
class Resample2dFunction(Function):
@staticmethod
# def forward(ctx, input1, input2, kernel_size=1, bilinear=True):
def forward(ctx, input1, input2, kernel_size=1):
assert input1.is_contiguous()
assert input2.is_contiguous()
ctx.save_for_backward(input1, input2)
ctx.kernel_size = kernel_size
ctx.bilinear = True
_, d, _, _ = input1.size()
b, _, h, w = input2.size()
output = input1.new(b, d, h, w).zero_()
resample2d_cuda.forward(input1, input2, output, kernel_size)
return output
@staticmethod
def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
assert grad_output.is_contiguous()
input1, input2 = ctx.saved_tensors
grad_input1 = Variable(input1.new(input1.size()).zero_())
grad_input2 = Variable(input1.new(input2.size()).zero_())
# resample2d_cuda.backward(input1, input2, grad_output.data,
# grad_input1.data, grad_input2.data,
# ctx.kernel_size, ctx.bilinear)
resample2d_cuda.backward(input1, input2, grad_output.data,
grad_input1.data, grad_input2.data,
ctx.kernel_size)
return grad_input1, grad_input2, None, None
class Resample2d(Module):
def __init__(self, kernel_size=1, bilinear=True):
super(Resample2d, self).__init__()
self.kernel_size = kernel_size
self.bilinear = bilinear
@autocast(False)
def forward(self, input1, input2):
input1, input2 = input1.float(), input2.float()
input1_c = input1.contiguous()
# return Resample2dFunction.apply(
# input1_c, input2, self.kernel_size, self.bilinear)
return Resample2dFunction.apply(
input1_c, input2, self.kernel_size) |