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