def __init__(self,
num_point: int,
radii: List[float],
sample_nums: List[int],
mlp_channels: List[List[int]],
fps_mod: List[str] = ['D-FPS'],
fps_sample_range_list: List[int] = [-1],
dilated_group: bool = False,
norm_cfg: dict = dict(type='BN2d'),
use_xyz: bool = True,
pool_mod='max',
normalize_xyz: bool = False,
bias='auto'):
super().__init__()
assert len(radii) == len(sample_nums) == len(mlp_channels)
assert pool_mod in ['max', 'avg']
assert isinstance(fps_mod, list) or isinstance(fps_mod, tuple)
assert isinstance(fps_sample_range_list, list) or isinstance(
fps_sample_range_list, tuple)
assert len(fps_mod) == len(fps_sample_range_list)
if isinstance(mlp_channels, tuple):
mlp_channels = list(map(list, mlp_channels))
if isinstance(num_point, int):
self.num_point = [num_point]
elif isinstance(num_point, list) or isinstance(num_point, tuple):
self.num_point = num_point
else:
raise NotImplementedError('Error type of num_point!')
self.pool_mod = pool_mod
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
self.fps_mod_list = fps_mod
self.fps_sample_range_list = fps_sample_range_list
self.points_sampler = Points_Sampler(self.num_point, self.fps_mod_list,
self.fps_sample_range_list)
for i in range(len(radii)):
radius = radii[i]
sample_num = sample_nums[i]
if num_point is not None:
if dilated_group and i != 0:
min_radius = radii[i - 1]
else:
min_radius = 0
grouper = QueryAndGroup(radius,
sample_num,
min_radius=min_radius,
use_xyz=use_xyz,
normalize_xyz=normalize_xyz)
else:
grouper = GroupAll(use_xyz)
self.groupers.append(grouper)
mlp_spec = mlp_channels[i]
if use_xyz:
mlp_spec[0] += 3
mlp = nn.Sequential()
for i in range(len(mlp_spec) - 1):
mlp.add_module(
f'layer{i}',
ConvModule(mlp_spec[i],
mlp_spec[i + 1],
kernel_size=(1, 1),
stride=(1, 1),
conv_cfg=dict(type='Conv2d'),
act_cfg=dict(type='LeakyReLU',
negative_slope=0.2),
norm_cfg=norm_cfg,
bias=bias))
self.mlps.append(mlp)
def __init__(self,
num_point,
radii,
sample_nums,
mlp_channels,
fps_mod=['D-FPS'],
fps_sample_range_list=[-1],
dilated_group=False,
use_xyz=True,
pool_mod='max',
normalize_xyz=False,
grouper_return_grouped_xyz=False,
grouper_return_grouped_idx=False):
super(BasePointSAModule, self).__init__()
assert len(radii) == len(sample_nums) == len(mlp_channels)
assert pool_mod in ['max', 'avg']
assert isinstance(fps_mod, list) or isinstance(fps_mod, tuple)
assert isinstance(fps_sample_range_list, list) or isinstance(
fps_sample_range_list, tuple)
assert len(fps_mod) == len(fps_sample_range_list)
if isinstance(mlp_channels, tuple):
mlp_channels = list(map(list, mlp_channels))
self.mlp_channels = mlp_channels
if isinstance(num_point, int):
self.num_point = [num_point]
elif isinstance(num_point, list) or isinstance(num_point, tuple):
self.num_point = num_point
else:
raise NotImplementedError('Error type of num_point!')
self.pool_mod = pool_mod
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
self.fps_mod_list = fps_mod
self.fps_sample_range_list = fps_sample_range_list
self.points_sampler = Points_Sampler(self.num_point, self.fps_mod_list,
self.fps_sample_range_list)
for i in range(len(radii)):
radius = radii[i]
sample_num = sample_nums[i]
if num_point is not None:
if dilated_group and i != 0:
min_radius = radii[i - 1]
else:
min_radius = 0
grouper = QueryAndGroup(
radius,
sample_num,
min_radius=min_radius,
use_xyz=use_xyz,
normalize_xyz=normalize_xyz,
return_grouped_xyz=grouper_return_grouped_xyz,
return_grouped_idx=grouper_return_grouped_idx)
else:
grouper = GroupAll(use_xyz)
self.groupers.append(grouper)