| |
| from torch import nn |
| from torch.autograd import Function |
| from torch.autograd.function import once_differentiable |
| from torch.nn.modules.utils import _pair |
|
|
| from ..utils import ext_loader |
|
|
| ext_module = ext_loader.load_ext( |
| '_ext', ['deform_roi_pool_forward', 'deform_roi_pool_backward']) |
|
|
|
|
| class DeformRoIPoolFunction(Function): |
|
|
| @staticmethod |
| def symbolic(g, input, rois, offset, output_size, spatial_scale, |
| sampling_ratio, gamma): |
| return g.op( |
| 'mmcv::MMCVDeformRoIPool', |
| input, |
| rois, |
| offset, |
| pooled_height_i=output_size[0], |
| pooled_width_i=output_size[1], |
| spatial_scale_f=spatial_scale, |
| sampling_ratio_f=sampling_ratio, |
| gamma_f=gamma) |
|
|
| @staticmethod |
| def forward(ctx, |
| input, |
| rois, |
| offset, |
| output_size, |
| spatial_scale=1.0, |
| sampling_ratio=0, |
| gamma=0.1): |
| if offset is None: |
| offset = input.new_zeros(0) |
| ctx.output_size = _pair(output_size) |
| ctx.spatial_scale = float(spatial_scale) |
| ctx.sampling_ratio = int(sampling_ratio) |
| ctx.gamma = float(gamma) |
|
|
| assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' |
|
|
| output_shape = (rois.size(0), input.size(1), ctx.output_size[0], |
| ctx.output_size[1]) |
| output = input.new_zeros(output_shape) |
|
|
| ext_module.deform_roi_pool_forward( |
| input, |
| rois, |
| offset, |
| output, |
| pooled_height=ctx.output_size[0], |
| pooled_width=ctx.output_size[1], |
| spatial_scale=ctx.spatial_scale, |
| sampling_ratio=ctx.sampling_ratio, |
| gamma=ctx.gamma) |
|
|
| ctx.save_for_backward(input, rois, offset) |
| return output |
|
|
| @staticmethod |
| @once_differentiable |
| def backward(ctx, grad_output): |
| input, rois, offset = ctx.saved_tensors |
| grad_input = grad_output.new_zeros(input.shape) |
| grad_offset = grad_output.new_zeros(offset.shape) |
|
|
| ext_module.deform_roi_pool_backward( |
| grad_output, |
| input, |
| rois, |
| offset, |
| grad_input, |
| grad_offset, |
| pooled_height=ctx.output_size[0], |
| pooled_width=ctx.output_size[1], |
| spatial_scale=ctx.spatial_scale, |
| sampling_ratio=ctx.sampling_ratio, |
| gamma=ctx.gamma) |
| if grad_offset.numel() == 0: |
| grad_offset = None |
| return grad_input, None, grad_offset, None, None, None, None |
|
|
|
|
| deform_roi_pool = DeformRoIPoolFunction.apply |
|
|
|
|
| class DeformRoIPool(nn.Module): |
|
|
| def __init__(self, |
| output_size, |
| spatial_scale=1.0, |
| sampling_ratio=0, |
| gamma=0.1): |
| super(DeformRoIPool, self).__init__() |
| self.output_size = _pair(output_size) |
| self.spatial_scale = float(spatial_scale) |
| self.sampling_ratio = int(sampling_ratio) |
| self.gamma = float(gamma) |
|
|
| def forward(self, input, rois, offset=None): |
| return deform_roi_pool(input, rois, offset, self.output_size, |
| self.spatial_scale, self.sampling_ratio, |
| self.gamma) |
|
|
|
|
| class DeformRoIPoolPack(DeformRoIPool): |
|
|
| def __init__(self, |
| output_size, |
| output_channels, |
| deform_fc_channels=1024, |
| spatial_scale=1.0, |
| sampling_ratio=0, |
| gamma=0.1): |
| super(DeformRoIPoolPack, self).__init__(output_size, spatial_scale, |
| sampling_ratio, gamma) |
|
|
| self.output_channels = output_channels |
| self.deform_fc_channels = deform_fc_channels |
|
|
| self.offset_fc = nn.Sequential( |
| nn.Linear( |
| self.output_size[0] * self.output_size[1] * |
| self.output_channels, self.deform_fc_channels), |
| nn.ReLU(inplace=True), |
| nn.Linear(self.deform_fc_channels, self.deform_fc_channels), |
| nn.ReLU(inplace=True), |
| nn.Linear(self.deform_fc_channels, |
| self.output_size[0] * self.output_size[1] * 2)) |
| self.offset_fc[-1].weight.data.zero_() |
| self.offset_fc[-1].bias.data.zero_() |
|
|
| def forward(self, input, rois): |
| assert input.size(1) == self.output_channels |
| x = deform_roi_pool(input, rois, None, self.output_size, |
| self.spatial_scale, self.sampling_ratio, |
| self.gamma) |
| rois_num = rois.size(0) |
| offset = self.offset_fc(x.view(rois_num, -1)) |
| offset = offset.view(rois_num, 2, self.output_size[0], |
| self.output_size[1]) |
| return deform_roi_pool(input, rois, offset, self.output_size, |
| self.spatial_scale, self.sampling_ratio, |
| self.gamma) |
|
|
|
|
| class ModulatedDeformRoIPoolPack(DeformRoIPool): |
|
|
| def __init__(self, |
| output_size, |
| output_channels, |
| deform_fc_channels=1024, |
| spatial_scale=1.0, |
| sampling_ratio=0, |
| gamma=0.1): |
| super(ModulatedDeformRoIPoolPack, |
| self).__init__(output_size, spatial_scale, sampling_ratio, gamma) |
|
|
| self.output_channels = output_channels |
| self.deform_fc_channels = deform_fc_channels |
|
|
| self.offset_fc = nn.Sequential( |
| nn.Linear( |
| self.output_size[0] * self.output_size[1] * |
| self.output_channels, self.deform_fc_channels), |
| nn.ReLU(inplace=True), |
| nn.Linear(self.deform_fc_channels, self.deform_fc_channels), |
| nn.ReLU(inplace=True), |
| nn.Linear(self.deform_fc_channels, |
| self.output_size[0] * self.output_size[1] * 2)) |
| self.offset_fc[-1].weight.data.zero_() |
| self.offset_fc[-1].bias.data.zero_() |
|
|
| self.mask_fc = nn.Sequential( |
| nn.Linear( |
| self.output_size[0] * self.output_size[1] * |
| self.output_channels, self.deform_fc_channels), |
| nn.ReLU(inplace=True), |
| nn.Linear(self.deform_fc_channels, |
| self.output_size[0] * self.output_size[1] * 1), |
| nn.Sigmoid()) |
| self.mask_fc[2].weight.data.zero_() |
| self.mask_fc[2].bias.data.zero_() |
|
|
| def forward(self, input, rois): |
| assert input.size(1) == self.output_channels |
| x = deform_roi_pool(input, rois, None, self.output_size, |
| self.spatial_scale, self.sampling_ratio, |
| self.gamma) |
| rois_num = rois.size(0) |
| offset = self.offset_fc(x.view(rois_num, -1)) |
| offset = offset.view(rois_num, 2, self.output_size[0], |
| self.output_size[1]) |
| mask = self.mask_fc(x.view(rois_num, -1)) |
| mask = mask.view(rois_num, 1, self.output_size[0], self.output_size[1]) |
| d = deform_roi_pool(input, rois, offset, self.output_size, |
| self.spatial_scale, self.sampling_ratio, |
| self.gamma) |
| return d * mask |
|
|
