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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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