def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
if not grad_output.is_cuda:
raise NotImplementedError
input, offset, weight, bias = ctx.saved_tensors
grad_input = torch.zeros_like(input)
grad_offset = torch.zeros_like(offset)
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
MDCONV_CUDA.deform_conv2d_backward_cuda(
input, weight, bias, offset, grad_input, grad_weight, grad_bias,
grad_offset, grad_output, weight.shape[2], weight.shape[3],
ctx.stride[0], ctx.stride[1], ctx.padding[0], ctx.padding[1],
ctx.dilation[0], ctx.dilation[1], ctx.groups,
ctx.deformable_groups, ctx.in_step, ctx.with_bias)
'''
int deform_conv2d_backward_cuda(
at::Tensor input, at::Tensor weight, at::Tensor bias,at::Tensor offset,
at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
at::Tensor grad_offset, at::Tensor grad_output,
const int kernel_h,const int kernel_w,const int stride_h,const int stride_w,
const int pad_h,const int pad_w,const int dilation_h,const int dilation_w,
const int group,const int deformable_group, const int in_step,const bool with_bias);
'''
if not ctx.with_bias:
grad_bias = None
# print(grad_input,grad_offset,grad_weight,grad_bias)
return grad_input, grad_offset, grad_weight, grad_bias, None, None, None, None, None, None
def forward(ctx, input, offset, mask, weight, bias=None, stride=1, padding=0, dilation=1,
groups=1, deformable_groups=1 , in_step=64):
ctx.stride = _pair(stride)
ctx.padding = _pair(padding)
ctx.dilation = _pair(dilation)
ctx.groups = groups
ctx.deformable_groups = deformable_groups
ctx.in_step = in_step
ctx.with_bias = bias is not None
if not ctx.with_bias:
bias = input.new_empty(0) # fake tensor
if not input.is_cuda:
raise NotImplementedError
if weight.requires_grad or mask.requires_grad or offset.requires_grad or input.requires_grad:
ctx.save_for_backward(input, offset, mask, weight, bias)
output = input.new_empty(ModulatedDeformConv2dFunction._infer_shape(ctx, input, weight))
MDCONV_CUDA.modulated_deform_conv2d_forward_cuda(
input, weight, bias, offset, mask, output,
weight.shape[2],weight.shape[3],
ctx.stride[0], ctx.stride[1],
ctx.padding[0], ctx.padding[1],
ctx.dilation[0],ctx.dilation[1],
ctx.groups, ctx.deformable_groups,ctx.in_step, ctx.with_bias)
'''
int modulated_deform_conv2d_forward_cuda(
at::Tensor input, at::Tensor weight, at::Tensor bias,
at::Tensor offset, at::Tensor mask, at::Tensor output,
const int kernel_h, const int kernel_w, const int stride_h, const int stride_w,
const int pad_h, const int pad_w, const int dilation_h,
const int dilation_w, const int group, const int deformable_group,
const int in_step,const bool with_bias);
'''
return output