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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 detectron2 import _C |
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class _ROIAlign(Function): |
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@staticmethod |
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def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio, aligned): |
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ctx.save_for_backward(roi) |
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ctx.output_size = _pair(output_size) |
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ctx.spatial_scale = spatial_scale |
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ctx.sampling_ratio = sampling_ratio |
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ctx.input_shape = input.size() |
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ctx.aligned = aligned |
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output = _C.roi_align_forward( |
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input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned |
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) |
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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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(rois,) = ctx.saved_tensors |
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output_size = ctx.output_size |
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spatial_scale = ctx.spatial_scale |
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sampling_ratio = ctx.sampling_ratio |
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bs, ch, h, w = ctx.input_shape |
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grad_input = _C.roi_align_backward( |
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grad_output, |
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rois, |
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spatial_scale, |
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output_size[0], |
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output_size[1], |
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bs, |
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ch, |
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h, |
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w, |
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sampling_ratio, |
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ctx.aligned, |
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) |
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return grad_input, None, None, None, None, None |
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roi_align = _ROIAlign.apply |
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class ROIAlign(nn.Module): |
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def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True): |
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""" |
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Args: |
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output_size (tuple): h, w |
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spatial_scale (float): scale the input boxes by this number |
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sampling_ratio (int): number of inputs samples to take for each output |
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sample. 0 to take samples densely. |
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aligned (bool): if False, use the legacy implementation in |
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Detectron. If True, align the results more perfectly. |
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Note: |
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The meaning of aligned=True: |
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Given a continuous coordinate c, its two neighboring pixel indices (in our |
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pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, |
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c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled |
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from the underlying signal at continuous coordinates 0.5 and 1.5). But the original |
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roi_align (aligned=False) does not subtract the 0.5 when computing neighboring |
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pixel indices and therefore it uses pixels with a slightly incorrect alignment |
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(relative to our pixel model) when performing bilinear interpolation. |
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With `aligned=True`, |
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we first appropriately scale the ROI and then shift it by -0.5 |
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prior to calling roi_align. This produces the correct neighbors; see |
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detectron2/tests/test_roi_align.py for verification. |
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The difference does not make a difference to the model's performance if |
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ROIAlign is used together with conv layers. |
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""" |
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super(ROIAlign, self).__init__() |
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self.output_size = output_size |
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self.spatial_scale = spatial_scale |
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self.sampling_ratio = sampling_ratio |
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self.aligned = aligned |
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def forward(self, input, rois): |
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""" |
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Args: |
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input: NCHW images |
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rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy. |
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""" |
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assert rois.dim() == 2 and rois.size(1) == 5 |
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return roi_align( |
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input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, self.aligned |
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) |
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def __repr__(self): |
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tmpstr = self.__class__.__name__ + "(" |
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tmpstr += "output_size=" + str(self.output_size) |
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tmpstr += ", spatial_scale=" + str(self.spatial_scale) |
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tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) |
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tmpstr += ", aligned=" + str(self.aligned) |
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tmpstr += ")" |
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return tmpstr |
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