def forward(
ctx, input, offset, mask, weight, bias, stride, padding, dilation, deformable_groups
):
ctx.stride = _pair(stride)
ctx.padding = _pair(padding)
ctx.dilation = _pair(dilation)
ctx.kernel_size = _pair(weight.shape[2:4])
ctx.deformable_groups = deformable_groups
output = _backend.dcn_v2_forward(
input,
weight,
bias,
offset,
mask,
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,
)
ctx.save_for_backward(input, offset, mask, weight, bias)
return output
def forward(ctx, input, offset_mask, weight, bias, stride, padding,
dilation, deformable_groups):
ctx.stride = _pair(stride)
ctx.padding = _pair(padding)
ctx.dilation = _pair(dilation)
ctx.kernel_size = _pair(weight.shape[2:4])
ctx.deformable_groups = deformable_groups
o1, o2, mask = torch.chunk(offset_mask, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
mask = torch.sigmoid(mask)
# @staticmethod
# def forward(ctx, input, offset, mask, weight, bias,
# stride, padding, dilation, deformable_groups):
# ctx.stride = _pair(stride)
# ctx.padding = _pair(padding)
# ctx.dilation = _pair(dilation)
# ctx.kernel_size = _pair(weight.shape[2:4])
# ctx.deformable_groups = deformable_groups
output = _backend.dcn_v2_forward(
input, weight, bias, offset, mask, 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)
ctx.save_for_backward(input, offset, mask, weight, bias)
return output
def forward(self, input, offset, mask):
# assert 2 * self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == \
# offset.shape[1]
# assert self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == \
# mask.shape[1]
output = _backend.dcn_v2_forward(
input, self.weight, self.bias, offset, mask, self.weight.shape[2],
self.weight.shape[3], self.stride[0], self.stride[1],
self.padding[0], self.padding[1], self.dilation[0],
self.dilation[1], self.deformable_groups)
return output
def forward(ctx, input, offset, mask, weight, bias, stride, padding,
dilation, deformable_groups):
ctx.stride = _pair(stride)
ctx.padding = _pair(padding)
ctx.dilation = _pair(dilation)
ctx.kernel_size = _pair(weight.shape[2:4])
ctx.deformable_groups = deformable_groups
# input, offset, mask为输入数据的类型,可能是半精度,apex混合精度转为float
# if input.dtype == torch.float16:
# weight, bias = weight.half(), bias.half()
output = _backend.dcn_v2_forward(
input, weight, bias, offset, mask, 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)
ctx.save_for_backward(input, offset, mask, weight, bias)
return output
def forward(
ctx,
input,
offset,
mask,
weight,
bias,
stride,
padding,
dilation,
deformable_groups,
):
if use_amp:
input = input.float()
offset = offset.float()
mask = mask.float()
weight = weight.float()
bias = bias.float()
ctx.stride = _pair(stride)
ctx.padding = _pair(padding)
ctx.dilation = _pair(dilation)
ctx.kernel_size = _pair(weight.shape[2:4])
ctx.deformable_groups = deformable_groups
output = _backend.dcn_v2_forward(
input,
weight,
bias,
offset,
mask,
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,
)
ctx.save_for_backward(input, offset, mask, weight, bias)
if use_amp:
return output.half()
return output
