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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
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