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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet3d.registry import MODELS
def test_pointnet2_sa_msg():
if not torch.cuda.is_available():
pytest.skip()
# PN2MSG used in 3DSSD
cfg = dict(
type='PointNet2SAMSG',
in_channels=4,
num_points=(256, 64, (32, 32)),
radii=((0.2, 0.4, 0.8), (0.4, 0.8, 1.6), (1.6, 3.2, 4.8)),
num_samples=((8, 8, 16), (8, 8, 16), (8, 8, 8)),
sa_channels=(((8, 8, 16), (8, 8, 16),
(8, 8, 16)), ((16, 16, 32), (16, 16, 32), (16, 24, 32)),
((32, 32, 64), (32, 24, 64), (32, 64, 64))),
aggregation_channels=(16, 32, 64),
fps_mods=(('D-FPS'), ('FS'), ('F-FPS', 'D-FPS')),
fps_sample_range_lists=((-1), (-1), (64, -1)),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(
type='PointSAModuleMSG',
pool_mod='max',
use_xyz=True,
normalize_xyz=False))
self = MODELS.build(cfg)
self.cuda()
assert self.SA_modules[0].mlps[0].layer0.conv.in_channels == 4
assert self.SA_modules[0].mlps[0].layer0.conv.out_channels == 8
assert self.SA_modules[0].mlps[1].layer1.conv.out_channels == 8
assert self.SA_modules[2].mlps[2].layer2.conv.out_channels == 64
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])
sa_xyz = ret_dict['sa_xyz'][-1]
sa_features = ret_dict['sa_features'][-1]
sa_indices = ret_dict['sa_indices'][-1]
assert sa_xyz.shape == torch.Size([1, 64, 3])
assert sa_features.shape == torch.Size([1, 64, 64])
assert sa_indices.shape == torch.Size([1, 64])
# out_indices should smaller than the length of SA Modules.
with pytest.raises(AssertionError):
MODELS.build(
dict(
type='PointNet2SAMSG',
in_channels=4,
num_points=(256, 64, (32, 32)),
radii=((0.2, 0.4, 0.8), (0.4, 0.8, 1.6), (1.6, 3.2, 4.8)),
num_samples=((8, 8, 16), (8, 8, 16), (8, 8, 8)),
sa_channels=(((8, 8, 16), (8, 8, 16), (8, 8, 16)),
((16, 16, 32), (16, 16, 32), (16, 24, 32)),
((32, 32, 64), (32, 24, 64), (32, 64, 64))),
aggregation_channels=(16, 32, 64),
fps_mods=(('D-FPS'), ('FS'), ('F-FPS', 'D-FPS')),
fps_sample_range_lists=((-1), (-1), (64, -1)),
out_indices=(2, 3),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(
type='PointSAModuleMSG',
pool_mod='max',
use_xyz=True,
normalize_xyz=False)))
# PN2MSG used in segmentation
cfg = dict(
type='PointNet2SAMSG',
in_channels=6, # [xyz, rgb]
num_points=(1024, 256, 64, 16),
radii=((0.05, 0.1), (0.1, 0.2), (0.2, 0.4), (0.4, 0.8)),
num_samples=((16, 32), (16, 32), (16, 32), (16, 32)),
sa_channels=(((16, 16, 32), (32, 32, 64)), ((64, 64, 128), (64, 96,
128)),
((128, 196, 256), (128, 196, 256)), ((256, 256, 512),
(256, 384, 512))),
aggregation_channels=(None, None, None, None),
fps_mods=(('D-FPS'), ('D-FPS'), ('D-FPS'), ('D-FPS')),
fps_sample_range_lists=((-1), (-1), (-1), (-1)),
dilated_group=(False, False, False, False),
out_indices=(0, 1, 2, 3),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(
type='PointSAModuleMSG',
pool_mod='max',
use_xyz=True,
normalize_xyz=False))
self = MODELS.build(cfg)
self.cuda()
ret_dict = self(xyz)
sa_xyz = ret_dict['sa_xyz']
sa_features = ret_dict['sa_features']
sa_indices = ret_dict['sa_indices']
assert len(sa_xyz) == len(sa_features) == len(sa_indices) == 5
assert sa_xyz[0].shape == torch.Size([1, 100, 3])
assert sa_xyz[1].shape == torch.Size([1, 1024, 3])
assert sa_xyz[2].shape == torch.Size([1, 256, 3])
assert sa_xyz[3].shape == torch.Size([1, 64, 3])
assert sa_xyz[4].shape == torch.Size([1, 16, 3])
assert sa_features[0].shape == torch.Size([1, 3, 100])
assert sa_features[1].shape == torch.Size([1, 96, 1024])
assert sa_features[2].shape == torch.Size([1, 256, 256])
assert sa_features[3].shape == torch.Size([1, 512, 64])
assert sa_features[4].shape == torch.Size([1, 1024, 16])
assert sa_indices[0].shape == torch.Size([1, 100])
assert sa_indices[1].shape == torch.Size([1, 1024])
assert sa_indices[2].shape == torch.Size([1, 256])
assert sa_indices[3].shape == torch.Size([1, 64])
assert sa_indices[4].shape == torch.Size([1, 16])
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