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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import torch | |
| from torch import nn | |
| from torch.autograd import Function | |
| from torch.autograd.function import once_differentiable | |
| from torch.nn.modules.utils import _pair | |
| from detectron2 import _C | |
| class _ROIAlignRotated(Function): | |
| def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio): | |
| ctx.save_for_backward(roi) | |
| ctx.output_size = _pair(output_size) | |
| ctx.spatial_scale = spatial_scale | |
| ctx.sampling_ratio = sampling_ratio | |
| ctx.input_shape = input.size() | |
| output = _C.roi_align_rotated_forward( | |
| input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio | |
| ) | |
| return output | |
| def backward(ctx, grad_output): | |
| (rois,) = ctx.saved_tensors | |
| output_size = ctx.output_size | |
| spatial_scale = ctx.spatial_scale | |
| sampling_ratio = ctx.sampling_ratio | |
| bs, ch, h, w = ctx.input_shape | |
| grad_input = _C.roi_align_rotated_backward( | |
| grad_output, | |
| rois, | |
| spatial_scale, | |
| output_size[0], | |
| output_size[1], | |
| bs, | |
| ch, | |
| h, | |
| w, | |
| 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: | |
| output_size (tuple): h, w | |
| spatial_scale (float): scale the input boxes by this number | |
| sampling_ratio (int): number of inputs samples to take for each output | |
| 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 | |
| self.spatial_scale = spatial_scale | |
| self.sampling_ratio = sampling_ratio | |
| def forward(self, input, rois): | |
| """ | |
| Args: | |
| input: NCHW images | |
| rois: Bx6 boxes. First column is the index into N. | |
| 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() | |
| return roi_align_rotated( | |
| input, rois, self.output_size, self.spatial_scale, self.sampling_ratio | |
| ).to(dtype=orig_dtype) | |
| def __repr__(self): | |
| tmpstr = self.__class__.__name__ + "(" | |
| tmpstr += "output_size=" + str(self.output_size) | |
| tmpstr += ", spatial_scale=" + str(self.spatial_scale) | |
| tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) | |
| tmpstr += ")" | |
| return tmpstr | |