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mmcv.ops.roiaware_pool3d 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Tuple, Union

import mmengine
import torch
from torch import nn as nn
from torch.autograd import Function

from ..utils import ext_loader

ext_module = ext_loader.load_ext(
    '_ext', ['roiaware_pool3d_forward', 'roiaware_pool3d_backward'])


[文档]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: Union[int, tuple], max_pts_per_voxel: int = 128, mode: str = '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]
[文档] def forward(self, rois: torch.Tensor, pts: torch.Tensor, pts_feature: torch.Tensor) -> torch.Tensor: """ 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: torch.Tensor: Pooled features whose shape is [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: Any, rois: torch.Tensor, pts: torch.Tensor, pts_feature: torch.Tensor, out_size: Union[int, tuple], max_pts_per_voxel: int, mode: int) -> torch.Tensor: """ 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: torch.Tensor: Pooled features whose shape is [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 mmengine.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, pool_method=mode) ctx.roiaware_pool3d_for_backward = (pts_idx_of_voxels, argmax, mode, num_pts, num_channels) return pooled_features @staticmethod def backward( ctx: Any, grad_out: torch.Tensor ) -> Tuple[None, None, torch.Tensor, None, None, None]: 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, pool_method=mode) return None, None, grad_in, None, None, None
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