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