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import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from maskrcnn_benchmark import _C
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(
ctx,
data,
rois,
offset,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
sample_per_part=4,
trans_std=.0
):
ctx.spatial_scale = spatial_scale
ctx.out_size = out_size
ctx.out_channels = out_channels
ctx.no_trans = no_trans
ctx.group_size = group_size
ctx.part_size = out_size if part_size is None else part_size
ctx.sample_per_part = sample_per_part
ctx.trans_std = trans_std
assert 0.0 <= ctx.trans_std <= 1.0
if not data.is_cuda:
raise NotImplementedError
n = rois.shape[0]
output = data.new_empty(n, out_channels, out_size, out_size)
output_count = data.new_empty(n, out_channels, out_size, out_size)
_C.deform_psroi_pooling_forward(
data,
rois,
offset,
output,
output_count,
ctx.no_trans,
ctx.spatial_scale,
ctx.out_channels,
ctx.group_size,
ctx.out_size,
ctx.part_size,
ctx.sample_per_part,
ctx.trans_std
)
if data.requires_grad or rois.requires_grad or offset.requires_grad:
ctx.save_for_backward(data, rois, offset)
ctx.output_count = output_count
return output
@staticmethod
@once_differentiable
def backward(ctx, grad_output):
if not grad_output.is_cuda:
raise NotImplementedError
data, rois, offset = ctx.saved_tensors
output_count = ctx.output_count
grad_input = torch.zeros_like(data)
grad_rois = None
grad_offset = torch.zeros_like(offset)
_C.deform_psroi_pooling_backward(
grad_output,
data,
rois,
offset,
output_count,
grad_input,
grad_offset,
ctx.no_trans,
ctx.spatial_scale,
ctx.out_channels,
ctx.group_size,
ctx.out_size,
ctx.part_size,
ctx.sample_per_part,
ctx.trans_std
)
return (grad_input, grad_rois, grad_offset, None, None, None, None, None, None, None, None)
deform_roi_pooling = DeformRoIPoolingFunction.apply
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