|
|
|
import torch
|
|
from torch import nn as nn
|
|
from torch.autograd import Function
|
|
|
|
import annotator.uniformer.mmcv as mmcv
|
|
from ..utils import ext_loader
|
|
|
|
ext_module = ext_loader.load_ext(
|
|
'_ext', ['roiaware_pool3d_forward', 'roiaware_pool3d_backward'])
|
|
|
|
|
|
class RoIAwarePool3d(nn.Module):
|
|
"""Encode the geometry-specific features of each 3D proposal.
|
|
|
|
Please refer to `PartA2 <https://arxiv.org/pdf/1907.03670.pdf>`_ for more
|
|
details.
|
|
|
|
Args:
|
|
out_size (int or tuple): The size of output features. n or
|
|
[n1, n2, n3].
|
|
max_pts_per_voxel (int, optional): The maximum number of points per
|
|
voxel. Default: 128.
|
|
mode (str, optional): Pooling method of RoIAware, 'max' or 'avg'.
|
|
Default: 'max'.
|
|
"""
|
|
|
|
def __init__(self, out_size, max_pts_per_voxel=128, mode='max'):
|
|
super().__init__()
|
|
|
|
self.out_size = out_size
|
|
self.max_pts_per_voxel = max_pts_per_voxel
|
|
assert mode in ['max', 'avg']
|
|
pool_mapping = {'max': 0, 'avg': 1}
|
|
self.mode = pool_mapping[mode]
|
|
|
|
def forward(self, rois, pts, pts_feature):
|
|
"""
|
|
Args:
|
|
rois (torch.Tensor): [N, 7], in LiDAR coordinate,
|
|
(x, y, z) is the bottom center of rois.
|
|
pts (torch.Tensor): [npoints, 3], coordinates of input points.
|
|
pts_feature (torch.Tensor): [npoints, C], features of input points.
|
|
|
|
Returns:
|
|
pooled_features (torch.Tensor): [N, out_x, out_y, out_z, C]
|
|
"""
|
|
|
|
return RoIAwarePool3dFunction.apply(rois, pts, pts_feature,
|
|
self.out_size,
|
|
self.max_pts_per_voxel, self.mode)
|
|
|
|
|
|
class RoIAwarePool3dFunction(Function):
|
|
|
|
@staticmethod
|
|
def forward(ctx, rois, pts, pts_feature, out_size, max_pts_per_voxel,
|
|
mode):
|
|
"""
|
|
Args:
|
|
rois (torch.Tensor): [N, 7], in LiDAR coordinate,
|
|
(x, y, z) is the bottom center of rois.
|
|
pts (torch.Tensor): [npoints, 3], coordinates of input points.
|
|
pts_feature (torch.Tensor): [npoints, C], features of input points.
|
|
out_size (int or tuple): The size of output features. n or
|
|
[n1, n2, n3].
|
|
max_pts_per_voxel (int): The maximum number of points per voxel.
|
|
Default: 128.
|
|
mode (int): Pooling method of RoIAware, 0 (max pool) or 1 (average
|
|
pool).
|
|
|
|
Returns:
|
|
pooled_features (torch.Tensor): [N, out_x, out_y, out_z, C], output
|
|
pooled features.
|
|
"""
|
|
|
|
if isinstance(out_size, int):
|
|
out_x = out_y = out_z = out_size
|
|
else:
|
|
assert len(out_size) == 3
|
|
assert mmcv.is_tuple_of(out_size, int)
|
|
out_x, out_y, out_z = out_size
|
|
|
|
num_rois = rois.shape[0]
|
|
num_channels = pts_feature.shape[-1]
|
|
num_pts = pts.shape[0]
|
|
|
|
pooled_features = pts_feature.new_zeros(
|
|
(num_rois, out_x, out_y, out_z, num_channels))
|
|
argmax = pts_feature.new_zeros(
|
|
(num_rois, out_x, out_y, out_z, num_channels), dtype=torch.int)
|
|
pts_idx_of_voxels = pts_feature.new_zeros(
|
|
(num_rois, out_x, out_y, out_z, max_pts_per_voxel),
|
|
dtype=torch.int)
|
|
|
|
ext_module.roiaware_pool3d_forward(rois, pts, pts_feature, argmax,
|
|
pts_idx_of_voxels, pooled_features,
|
|
mode)
|
|
|
|
ctx.roiaware_pool3d_for_backward = (pts_idx_of_voxels, argmax, mode,
|
|
num_pts, num_channels)
|
|
return pooled_features
|
|
|
|
@staticmethod
|
|
def backward(ctx, grad_out):
|
|
ret = ctx.roiaware_pool3d_for_backward
|
|
pts_idx_of_voxels, argmax, mode, num_pts, num_channels = ret
|
|
|
|
grad_in = grad_out.new_zeros((num_pts, num_channels))
|
|
ext_module.roiaware_pool3d_backward(pts_idx_of_voxels, argmax,
|
|
grad_out.contiguous(), grad_in,
|
|
mode)
|
|
|
|
return None, None, grad_in, None, None, None
|
|
|
