test_backbones.py

import numpy as np
import pytest
import torch

from mmdet3d.models import build_backbone


def test_pointnet2_sa_ssg():
    if not torch.cuda.is_available():
        pytest.skip()

    cfg = dict(
        type='PointNet2SASSG',
        in_channels=6,
        num_points=(32, 16),
        radius=(0.8, 1.2),
        num_samples=(16, 8),
        sa_channels=((8, 16), (16, 16)),
        fp_channels=((16, 16), (16, 16)))
    self = build_backbone(cfg)
    self.cuda()
    assert self.SA_modules[0].mlps[0].layer0.conv.in_channels == 6
    assert self.SA_modules[0].mlps[0].layer0.conv.out_channels == 8
    assert self.SA_modules[0].mlps[0].layer1.conv.out_channels == 16
    assert self.SA_modules[1].mlps[0].layer1.conv.out_channels == 16
    assert self.FP_modules[0].mlps.layer0.conv.in_channels == 32
    assert self.FP_modules[0].mlps.layer0.conv.out_channels == 16
    assert self.FP_modules[1].mlps.layer0.conv.in_channels == 19

    xyz = np.load('tests/data/sunrgbd/sunrgbd_trainval/lidar/000001.npy')
    xyz = torch.from_numpy(xyz).view(1, -1, 6).cuda()  # (B, N, 6)
    # test forward
    ret_dict = self(xyz)
    fp_xyz = ret_dict['fp_xyz']
    fp_features = ret_dict['fp_features']
    fp_indices = ret_dict['fp_indices']
    assert len(fp_xyz) == len(fp_features) == len(fp_indices) == 3
    assert fp_xyz[0].shape == torch.Size([1, 16, 3])
    assert fp_xyz[1].shape == torch.Size([1, 32, 3])
    assert fp_xyz[2].shape == torch.Size([1, 100, 3])
    assert fp_features[2].shape == torch.Size([1, 16, 100])
    assert fp_indices[2].shape == torch.Size([1, 100])