| |
| 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 |
|
|
|
|
| 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 |
|
|
| def forward(self, features, rois): |
| return RoIAlignRotatedFunction.apply(features, rois, self.out_size, |
| self.spatial_scale, |
| self.sample_num, self.aligned, |
| self.clockwise) |
|
|
