mirror of https://github.com/JosephKJ/OWOD.git
118 lines
4.5 KiB
Python
118 lines
4.5 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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from torch import nn
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from torchvision.ops import roi_align as tv_roi_align
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try:
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from torchvision import __version__
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version = tuple(int(x) for x in __version__.split(".")[:2])
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USE_TORCHVISION = version >= (0, 7) # https://github.com/pytorch/vision/pull/2438
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except ImportError: # only open source torchvision has __version__
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USE_TORCHVISION = True
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if USE_TORCHVISION:
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roi_align = tv_roi_align
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else:
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from torch.nn.modules.utils import _pair
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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 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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# NOTE: torchvision's RoIAlign has a different default aligned=False
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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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