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from torch import nn as nn
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', ['roipoint_pool3d_forward'])
class RoIPointPool3d(nn.Module):
"""Encode the geometry-specific features of each 3D proposal.
Please refer to `Paper of PartA2 <https://arxiv.org/pdf/1907.03670.pdf>`_
for more details.
Args:
num_sampled_points (int, optional): Number of samples in each roi.
Default: 512.
"""
def __init__(self, num_sampled_points=512):
super().__init__()
self.num_sampled_points = num_sampled_points
def forward(self, points, point_features, boxes3d):
"""
Args:
points (torch.Tensor): Input points whose shape is (B, N, C).
point_features (torch.Tensor): Features of input points whose shape
is (B, N, C).
boxes3d (B, M, 7), Input bounding boxes whose shape is (B, M, 7).
Returns:
pooled_features (torch.Tensor): The output pooled features whose
shape is (B, M, 512, 3 + C).
pooled_empty_flag (torch.Tensor): Empty flag whose shape is (B, M).
"""
return RoIPointPool3dFunction.apply(points, point_features, boxes3d,
self.num_sampled_points)
class RoIPointPool3dFunction(Function):
@staticmethod
def forward(ctx, points, point_features, boxes3d, num_sampled_points=512):
"""
Args:
points (torch.Tensor): Input points whose shape is (B, N, C).
point_features (torch.Tensor): Features of input points whose shape
is (B, N, C).
boxes3d (B, M, 7), Input bounding boxes whose shape is (B, M, 7).
num_sampled_points (int, optional): The num of sampled points.
Default: 512.
Returns:
pooled_features (torch.Tensor): The output pooled features whose
shape is (B, M, 512, 3 + C).
pooled_empty_flag (torch.Tensor): Empty flag whose shape is (B, M).
"""
assert len(points.shape) == 3 and points.shape[2] == 3
batch_size, boxes_num, feature_len = points.shape[0], boxes3d.shape[
1], point_features.shape[2]
pooled_boxes3d = boxes3d.view(batch_size, -1, 7)
pooled_features = point_features.new_zeros(
(batch_size, boxes_num, num_sampled_points, 3 + feature_len))
pooled_empty_flag = point_features.new_zeros(
(batch_size, boxes_num)).int()
ext_module.roipoint_pool3d_forward(points.contiguous(),
pooled_boxes3d.contiguous(),
point_features.contiguous(),
pooled_features, pooled_empty_flag)
return pooled_features, pooled_empty_flag
@staticmethod
def backward(ctx, grad_out):
raise NotImplementedError
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