|
|
| import torch
|
| 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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|
|
|
|
| class _ROIAlignRotated(Function):
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| @staticmethod
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| def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
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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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| output = torch.ops.detectron2.roi_align_rotated_forward(
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| input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio
|
| )
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| return output
|
|
|
| @staticmethod
|
| @once_differentiable
|
| 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 = torch.ops.detectron2.roi_align_rotated_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,
|
| )
|
| return grad_input, None, None, None, None, None
|
|
|
|
|
| roi_align_rotated = _ROIAlignRotated.apply
|
|
|
|
|
| class ROIAlignRotated(nn.Module):
|
| def __init__(self, output_size, spatial_scale, sampling_ratio):
|
| """
|
| 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.
|
|
|
| Note:
|
| ROIAlignRotated supports continuous coordinate by default:
|
| Given a continuous coordinate c, its two neighboring pixel indices (in our
|
| pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
|
| c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
|
| from the underlying signal at continuous coordinates 0.5 and 1.5).
|
| """
|
| super(ROIAlignRotated, self).__init__()
|
| self.output_size = output_size
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| self.spatial_scale = spatial_scale
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| self.sampling_ratio = sampling_ratio
|
|
|
| def forward(self, input, rois):
|
| """
|
| Args:
|
| input: NCHW images
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| rois: Bx6 boxes. First column is the index into N.
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| The other 5 columns are (x_ctr, y_ctr, width, height, angle_degrees).
|
| """
|
| assert rois.dim() == 2 and rois.size(1) == 6
|
| orig_dtype = input.dtype
|
| if orig_dtype == torch.float16:
|
| input = input.float()
|
| rois = rois.float()
|
| output_size = _pair(self.output_size)
|
|
|
|
|
|
|
| if torch.jit.is_scripting() or torch.jit.is_tracing():
|
| return torch.ops.detectron2.roi_align_rotated_forward(
|
| input, rois, self.spatial_scale, output_size[0], output_size[1], self.sampling_ratio
|
| ).to(dtype=orig_dtype)
|
|
|
| return roi_align_rotated(
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| input, rois, self.output_size, self.spatial_scale, self.sampling_ratio
|
| ).to(dtype=orig_dtype)
|
|
|
| def __repr__(self):
|
| tmpstr = self.__class__.__name__ + "("
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| tmpstr += "output_size=" + str(self.output_size)
|
| tmpstr += ", spatial_scale=" + str(self.spatial_scale)
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| tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
|
| tmpstr += ")"
|
| return tmpstr
|
|
|
