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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| from torch import nn | |
| from torchvision.ops import roi_align | |
| # NOTE: torchvision's RoIAlign has a different default aligned=False | |
| class ROIAlign(nn.Module): | |
| def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True): | |
| """ | |
| 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. | |
| aligned (bool): if False, use the legacy implementation in | |
| Detectron. If True, align the results more perfectly. | |
| Note: | |
| The meaning of aligned=True: | |
| 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). But the original | |
| roi_align (aligned=False) does not subtract the 0.5 when computing neighboring | |
| pixel indices and therefore it uses pixels with a slightly incorrect alignment | |
| (relative to our pixel model) when performing bilinear interpolation. | |
| With `aligned=True`, | |
| we first appropriately scale the ROI and then shift it by -0.5 | |
| prior to calling roi_align. This produces the correct neighbors; see | |
| detectron2/tests/test_roi_align.py for verification. | |
| The difference does not make a difference to the model's performance if | |
| ROIAlign is used together with conv layers. | |
| """ | |
| super().__init__() | |
| self.output_size = output_size | |
| self.spatial_scale = spatial_scale | |
| self.sampling_ratio = sampling_ratio | |
| self.aligned = aligned | |
| from torchvision import __version__ | |
| version = tuple(int(x) for x in __version__.split(".")[:2]) | |
| # https://github.com/pytorch/vision/pull/2438 | |
| assert version >= (0, 7), "Require torchvision >= 0.7" | |
| def forward(self, input, rois): | |
| """ | |
| Args: | |
| input: NCHW images | |
| rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy. | |
| """ | |
| assert rois.dim() == 2 and rois.size(1) == 5 | |
| if input.is_quantized: | |
| input = input.dequantize() | |
| return roi_align( | |
| input, | |
| rois.to(dtype=input.dtype), | |
| self.output_size, | |
| self.spatial_scale, | |
| self.sampling_ratio, | |
| self.aligned, | |
| ) | |
| 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 += ", aligned=" + str(self.aligned) | |
| tmpstr += ")" | |
| return tmpstr | |