Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_multi_backbone.py 4.14 KB
Newer Older
VVsssssk's avatar
[Fix] Fix tests dir (#1704)  
VVsssssk committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117 
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch

from mmdet3d.models import build_backbone


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

    # test list config
    cfg_list = dict(
        type='MultiBackbone',
        num_streams=4,
        suffixes=['net0', 'net1', 'net2', 'net3'],
        backbones=[
            dict(
                type='PointNet2SASSG',
                in_channels=4,
                num_points=(256, 128, 64, 32),
                radius=(0.2, 0.4, 0.8, 1.2),
                num_samples=(64, 32, 16, 16),
                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
                             (128, 128, 256)),
                fp_channels=((256, 256), (256, 256)),
                norm_cfg=dict(type='BN2d')),
            dict(
                type='PointNet2SASSG',
                in_channels=4,
                num_points=(256, 128, 64, 32),
                radius=(0.2, 0.4, 0.8, 1.2),
                num_samples=(64, 32, 16, 16),
                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
                             (128, 128, 256)),
                fp_channels=((256, 256), (256, 256)),
                norm_cfg=dict(type='BN2d')),
            dict(
                type='PointNet2SASSG',
                in_channels=4,
                num_points=(256, 128, 64, 32),
                radius=(0.2, 0.4, 0.8, 1.2),
                num_samples=(64, 32, 16, 16),
                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
                             (128, 128, 256)),
                fp_channels=((256, 256), (256, 256)),
                norm_cfg=dict(type='BN2d')),
            dict(
                type='PointNet2SASSG',
                in_channels=4,
                num_points=(256, 128, 64, 32),
                radius=(0.2, 0.4, 0.8, 1.2),
                num_samples=(64, 32, 16, 16),
                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
                             (128, 128, 256)),
                fp_channels=((256, 256), (256, 256)),
                norm_cfg=dict(type='BN2d'))
        ])

    self = build_backbone(cfg_list)
    self.cuda()

    assert len(self.backbone_list) == 4

    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin', dtype=np.float32)
    xyz = torch.from_numpy(xyz).view(1, -1, 6).cuda()  # (B, N, 6)
    # test forward
    ret_dict = self(xyz[:, :, :4])

    assert ret_dict['hd_feature'].shape == torch.Size([1, 256, 128])
    assert ret_dict['fp_xyz_net0'][-1].shape == torch.Size([1, 128, 3])
    assert ret_dict['fp_features_net0'][-1].shape == torch.Size([1, 256, 128])

    # test dict config
    cfg_dict = dict(
        type='MultiBackbone',
        num_streams=2,
        suffixes=['net0', 'net1'],
        aggregation_mlp_channels=[512, 128],
        backbones=dict(
            type='PointNet2SASSG',
            in_channels=4,
            num_points=(256, 128, 64, 32),
            radius=(0.2, 0.4, 0.8, 1.2),
            num_samples=(64, 32, 16, 16),
            sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
                         (128, 128, 256)),
            fp_channels=((256, 256), (256, 256)),
            norm_cfg=dict(type='BN2d')))

    self = build_backbone(cfg_dict)
    self.cuda()

    assert len(self.backbone_list) == 2

    # test forward
    ret_dict = self(xyz[:, :, :4])

    assert ret_dict['hd_feature'].shape == torch.Size([1, 128, 128])
    assert ret_dict['fp_xyz_net0'][-1].shape == torch.Size([1, 128, 3])
    assert ret_dict['fp_features_net0'][-1].shape == torch.Size([1, 256, 128])

    # Length of backbone configs list should be equal to num_streams
    with pytest.raises(AssertionError):
        cfg_list['num_streams'] = 3
        build_backbone(cfg_list)

    # Length of suffixes list should be equal to num_streams
    with pytest.raises(AssertionError):
        cfg_dict['suffixes'] = ['net0', 'net1', 'net2']
        build_backbone(cfg_dict)

    # Type of 'backbones' should be Dict or List[Dict].
    with pytest.raises(AssertionError):
        cfg_dict['backbones'] = 'PointNet2SASSG'
        build_backbone(cfg_dict)