# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['roi_align_rotated_forward', 'roi_align_rotated_backward'])
class RoIAlignRotatedFunction(Function):
@staticmethod
def symbolic(g, features, rois, out_size, spatial_scale, sample_num,
aligned, clockwise):
if isinstance(out_size, int):
out_h = out_size
out_w = out_size
elif isinstance(out_size, tuple):
assert len(out_size) == 2
assert isinstance(out_size[0], int)
assert isinstance(out_size[1], int)
out_h, out_w = out_size
else:
raise TypeError(
'"out_size" must be an integer or tuple of integers')
return g.op(
'mmcv::MMCVRoIAlignRotated',
features,
rois,
output_height_i=out_h,
output_width_i=out_h,
spatial_scale_f=spatial_scale,
sampling_ratio_i=sample_num,
aligned_i=aligned,
clockwise_i=clockwise)
@staticmethod
def forward(ctx,
features,
rois,
out_size,
spatial_scale,
sample_num=0,
aligned=True,
clockwise=False):
if isinstance(out_size, int):
out_h = out_size
out_w = out_size
elif isinstance(out_size, tuple):
assert len(out_size) == 2
assert isinstance(out_size[0], int)
assert isinstance(out_size[1], int)
out_h, out_w = out_size
else:
raise TypeError(
'"out_size" must be an integer or tuple of integers')
ctx.spatial_scale = spatial_scale
ctx.sample_num = sample_num
ctx.aligned = aligned
ctx.clockwise = clockwise
ctx.save_for_backward(rois)
ctx.feature_size = features.size()
batch_size, num_channels, data_height, data_width = features.size()
num_rois = rois.size(0)
output = features.new_zeros(num_rois, num_channels, out_h, out_w)
ext_module.roi_align_rotated_forward(
features,
rois,
output,
pooled_height=out_h,
pooled_width=out_w,
spatial_scale=spatial_scale,
sample_num=sample_num,
aligned=aligned,
clockwise=clockwise)
return output
@staticmethod
def backward(ctx, grad_output):
feature_size = ctx.feature_size
spatial_scale = ctx.spatial_scale
aligned = ctx.aligned
clockwise = ctx.clockwise
sample_num = ctx.sample_num
rois = ctx.saved_tensors[0]
assert feature_size is not None
batch_size, num_channels, data_height, data_width = feature_size
out_w = grad_output.size(3)
out_h = grad_output.size(2)
grad_input = grad_rois = None
if ctx.needs_input_grad[0]:
grad_input = rois.new_zeros(batch_size, num_channels, data_height,
data_width)
ext_module.roi_align_rotated_backward(
grad_output.contiguous(),
rois,
grad_input,
pooled_height=out_h,
pooled_width=out_w,
spatial_scale=spatial_scale,
sample_num=sample_num,
aligned=aligned,
clockwise=clockwise)
return grad_input, grad_rois, None, None, None, None, None
roi_align_rotated = RoIAlignRotatedFunction.apply
[docs]class RoIAlignRotated(nn.Module):
"""RoI align pooling layer for rotated proposals.
It accepts a feature map of shape (N, C, H, W) and rois with shape
(n, 6) with each roi decoded as (batch_index, center_x, center_y,
w, h, angle). The angle is in radian.
Args:
out_size (tuple): h, w
spatial_scale (float): scale the input boxes by this number
sample_num (int): number of inputs samples to take for each
output sample. 0 to take samples densely for current models.
aligned (bool): if False, use the legacy implementation in
MMDetection. If True, align the results more perfectly.
Default: True.
clockwise (bool): If True, the angle in each proposal follows a
clockwise fashion in image space, otherwise, the angle is
counterclockwise. Default: False.
Note:
The implementation of RoIAlign when aligned=True is modified from
https://github.com/facebookresearch/detectron2/
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;
The difference does not make a difference to the model's
performance if ROIAlign is used together with conv layers.
"""
def __init__(self,
out_size,
spatial_scale,
sample_num=0,
aligned=True,
clockwise=False):
super(RoIAlignRotated, self).__init__()
self.out_size = out_size
self.spatial_scale = float(spatial_scale)
self.sample_num = int(sample_num)
self.aligned = aligned
self.clockwise = clockwise
[docs] def forward(self, features, rois):
return RoIAlignRotatedFunction.apply(features, rois, self.out_size,
self.spatial_scale,
self.sample_num, self.aligned,
self.clockwise)