def backward(ctx, grad_output):
input, offset, mask, weight, bias = ctx.saved_tensors
grad_input, grad_offset, grad_mask, grad_weight, grad_bias = _backend.dcn_v2_backward(
input,
weight,
bias,
offset,
mask,
grad_output,
ctx.kernel_size[0],
ctx.kernel_size[1],
ctx.stride[0],
ctx.stride[1],
ctx.padding[0],
ctx.padding[1],
ctx.dilation[0],
ctx.dilation[1],
ctx.deformable_groups,
)
return (
grad_input,
grad_offset,
grad_mask,
grad_weight,
grad_bias,
None,
None,
None,
None,
)
def backward(ctx, grad_output):
if use_amp:
grad_output = grad_output.float()
input, offset, mask, weight, bias = ctx.saved_tensors
(
grad_input,
grad_offset,
grad_mask,
grad_weight,
grad_bias,
) = _backend.dcn_v2_backward(
input,
weight,
bias,
offset,
mask,
grad_output,
ctx.kernel_size[0],
ctx.kernel_size[1],
ctx.stride[0],
ctx.stride[1],
ctx.padding[0],
ctx.padding[1],
ctx.dilation[0],
ctx.dilation[1],
ctx.deformable_groups,
)
if use_amp:
grad_input = grad_input.half()
grad_offset = grad_offset.half()
grad_mask = grad_mask.half()
grad_weight = grad_weight.half()
grad_bias = grad_bias.half()
return (
grad_input,
grad_offset,
grad_mask,
grad_weight,
grad_bias,
None,
None,
None,
None,
)
