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