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import torch
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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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)
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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 = 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,
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)
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return grad_input, None, None, None, None, None
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roi_align_rotated = _ROIAlignRotated.apply
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class ROIAlignRotated(nn.Module):
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def __init__(self, output_size, spatial_scale, sampling_ratio):
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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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Note:
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ROIAlignRotated supports continuous coordinate by default:
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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).
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"""
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super(ROIAlignRotated, 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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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: 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).
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"""
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assert rois.dim() == 2 and rois.size(1) == 6
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orig_dtype = input.dtype
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if orig_dtype == torch.float16:
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input = input.float()
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rois = rois.float()
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output_size = _pair(self.output_size)
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if torch.jit.is_scripting() or torch.jit.is_tracing():
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return torch.ops.detectron2.roi_align_rotated_forward(
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input, rois, self.spatial_scale, output_size[0], output_size[1], self.sampling_ratio
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).to(dtype=orig_dtype)
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return roi_align_rotated(
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input, rois, self.output_size, self.spatial_scale, self.sampling_ratio
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).to(dtype=orig_dtype)
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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 += ")"
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return tmpstr
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