Source code for detectron2.layers.roi_align_rotated

# Copyright (c) Facebook, Inc. and its affiliates.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair

class _ROIAlignRotated(Function):
    def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
        ctx.output_size = _pair(output_size)
        ctx.spatial_scale = spatial_scale
        ctx.sampling_ratio = sampling_ratio
        ctx.input_shape = input.size()
        output = torch.ops.detectron2.roi_align_rotated_forward(
            input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio
        return output

    def backward(ctx, grad_output):
        (rois,) = ctx.saved_tensors
        output_size = ctx.output_size
        spatial_scale = ctx.spatial_scale
        sampling_ratio = ctx.sampling_ratio
        bs, ch, h, w = ctx.input_shape
        grad_input = torch.ops.detectron2.roi_align_rotated_backward(
        return grad_input, None, None, None, None, None

roi_align_rotated = _ROIAlignRotated.apply

[docs]class ROIAlignRotated(nn.Module):
[docs] def __init__(self, output_size, spatial_scale, sampling_ratio): """ 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. Note: ROIAlignRotated supports continuous coordinate by default: 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). """ super(ROIAlignRotated, self).__init__() self.output_size = output_size self.spatial_scale = spatial_scale self.sampling_ratio = sampling_ratio
[docs] def forward(self, input, rois): """ Args: input: NCHW images rois: Bx6 boxes. First column is the index into N. The other 5 columns are (x_ctr, y_ctr, width, height, angle_degrees). """ assert rois.dim() == 2 and rois.size(1) == 6 orig_dtype = input.dtype if orig_dtype == torch.float16: input = input.float() rois = rois.float() output_size = _pair(self.output_size) # Scripting for Autograd is currently unsupported. # This is a quick fix without having to rewrite code on the C++ side if torch.jit.is_scripting() or torch.jit.is_tracing(): return torch.ops.detectron2.roi_align_rotated_forward( input, rois, self.spatial_scale, output_size[0], output_size[1], self.sampling_ratio ).to(dtype=orig_dtype) return roi_align_rotated( input, rois, self.output_size, self.spatial_scale, self.sampling_ratio ).to(dtype=orig_dtype)
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 += ")" return tmpstr