giantmonkeyTC
2344
34d1f8b
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet3d.models.layers import SparseBasicBlock
from mmdet3d.models.layers.spconv import IS_SPCONV2_AVAILABLE
if IS_SPCONV2_AVAILABLE:
from spconv.pytorch import (SparseConvTensor, SparseInverseConv3d,
SubMConv3d)
else:
from mmcv.ops import SparseConvTensor, SparseInverseConv3d, SubMConv3d
def test_SparseBasicBlock():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
voxel_features = torch.tensor(
[[6.56126, 0.9648336, -1.7339306, 0.315],
[6.8162713, -2.480431, -1.3616394, 0.36],
[11.643568, -4.744306, -1.3580885, 0.16],
[23.482342, 6.5036807, 0.5806964, 0.35]],
dtype=torch.float32).cuda() # n, point_features
coordinates = torch.tensor(
[[0, 12, 819, 131], [0, 16, 750, 136], [1, 16, 705, 232],
[1, 35, 930, 469]],
dtype=torch.int32).cuda() # n, 4(batch, ind_x, ind_y, ind_z)
# test
input_sp_tensor = SparseConvTensor(voxel_features, coordinates,
[41, 1600, 1408], 2)
self = SparseBasicBlock(
4,
4,
conv_cfg=dict(type='SubMConv3d', indice_key='subm1'),
norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01)).cuda()
# test conv and bn layer
assert isinstance(self.conv1, SubMConv3d)
assert self.conv1.in_channels == 4
assert self.conv1.out_channels == 4
assert isinstance(self.conv2, SubMConv3d)
assert self.conv2.out_channels == 4
assert self.conv2.out_channels == 4
assert self.bn1.eps == 1e-3
assert self.bn1.momentum == 0.01
out_features = self(input_sp_tensor)
assert out_features.features.shape == torch.Size([4, 4])
def test_make_sparse_convmodule():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
from mmdet3d.models.layers import make_sparse_convmodule
voxel_features = torch.tensor(
[[6.56126, 0.9648336, -1.7339306, 0.315],
[6.8162713, -2.480431, -1.3616394, 0.36],
[11.643568, -4.744306, -1.3580885, 0.16],
[23.482342, 6.5036807, 0.5806964, 0.35]],
dtype=torch.float32).cuda() # n, point_features
coordinates = torch.tensor(
[[0, 12, 819, 131], [0, 16, 750, 136], [1, 16, 705, 232],
[1, 35, 930, 469]],
dtype=torch.int32).cuda() # n, 4(batch, ind_x, ind_y, ind_z)
# test
input_sp_tensor = SparseConvTensor(voxel_features, coordinates,
[41, 1600, 1408], 2)
sparse_block0 = make_sparse_convmodule(
4,
16,
3,
'test0',
stride=1,
padding=0,
conv_type='SubMConv3d',
norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
order=('conv', 'norm', 'act')).cuda()
assert isinstance(sparse_block0[0], SubMConv3d)
assert sparse_block0[0].in_channels == 4
assert sparse_block0[0].out_channels == 16
assert isinstance(sparse_block0[1], torch.nn.BatchNorm1d)
assert sparse_block0[1].eps == 0.001
assert sparse_block0[1].momentum == 0.01
assert isinstance(sparse_block0[2], torch.nn.ReLU)
# test forward
out_features = sparse_block0(input_sp_tensor)
assert out_features.features.shape == torch.Size([4, 16])
sparse_block1 = make_sparse_convmodule(
4,
16,
3,
'test1',
stride=1,
padding=0,
conv_type='SparseInverseConv3d',
norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
order=('norm', 'act', 'conv'))
assert isinstance(sparse_block1[0], torch.nn.BatchNorm1d)
assert isinstance(sparse_block1[1], torch.nn.ReLU)
assert isinstance(sparse_block1[2], SparseInverseConv3d)