109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
|
import mmcv
|
||
|
|
import torch
|
||
|
|
from torch import nn as nn
|
||
|
|
from torch.autograd import Function
|
||
|
|
|
||
|
|
from . import roiaware_pool3d_ext
|
||
|
|
|
||
|
|
|
||
|
|
class RoIAwarePool3d(nn.Module):
|
||
|
|
def __init__(self, out_size, max_pts_per_voxel=128, mode="max"):
|
||
|
|
super().__init__()
|
||
|
|
"""RoIAwarePool3d module
|
||
|
|
|
||
|
|
Args:
|
||
|
|
out_size (int or tuple): n or [n1, n2, n3]
|
||
|
|
max_pts_per_voxel (int): m
|
||
|
|
mode (str): 'max' or 'avg'
|
||
|
|
"""
|
||
|
|
self.out_size = out_size
|
||
|
|
self.max_pts_per_voxel = max_pts_per_voxel
|
||
|
|
assert mode in ["max", "avg"]
|
||
|
|
pool_method_map = {"max": 0, "avg": 1}
|
||
|
|
self.mode = pool_method_map[mode]
|
||
|
|
|
||
|
|
def forward(self, rois, pts, pts_feature):
|
||
|
|
"""RoIAwarePool3d module forward.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
rois (torch.Tensor): [N, 7],in LiDAR coordinate,
|
||
|
|
(x, y, z) is the bottom center of rois
|
||
|
|
pts (torch.Tensor): [npoints, 3]
|
||
|
|
pts_feature (torch.Tensor): [npoints, C]
|
||
|
|
|
||
|
|
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):
|
||
|
|
"""RoIAwarePool3d function forward.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
rois (torch.Tensor): [N, 7], in LiDAR coordinate,
|
||
|
|
(x, y, z) is the bottom center of rois
|
||
|
|
pts (torch.Tensor): [npoints, 3]
|
||
|
|
pts_feature (torch.Tensor): [npoints, C]
|
||
|
|
out_size (int or tuple): n or [n1, n2, n3]
|
||
|
|
max_pts_per_voxel (int): m
|
||
|
|
mode (int): 0 (max pool) or 1 (average pool)
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
pooled_features (torch.Tensor): [N, out_x, out_y, out_z, C]
|
||
|
|
"""
|
||
|
|
|
||
|
|
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
|
||
|
|
)
|
||
|
|
|
||
|
|
roiaware_pool3d_ext.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):
|
||
|
|
"""RoIAwarePool3d function forward.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
grad_out (torch.Tensor): [N, out_x, out_y, out_z, C]
|
||
|
|
Returns:
|
||
|
|
grad_in (torch.Tensor): [npoints, C]
|
||
|
|
"""
|
||
|
|
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))
|
||
|
|
roiaware_pool3d_ext.backward(
|
||
|
|
pts_idx_of_voxels, argmax, grad_out.contiguous(), grad_in, mode
|
||
|
|
)
|
||
|
|
|
||
|
|
return None, None, grad_in, None, None, None
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
pass
|