def __init__(self,
*,
mlp: List[int],
npoint: int = None,
radius: float = None,
nsample: int = None,
bn: bool = True,
use_xyz: bool = True):
super().__init__()
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
if self.npoint is not None:
assert radius is not None
assert nsample is not None
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample,
use_xyz=use_xyz))
else:
self.groupers.append(pointnet2_utils.GroupAll(use_xyz=use_xyz))
if use_xyz:
mlp[0] += 3
self.mlps.append(pt_utils.SharedMLP(mlp, bn=bn))
def __init__(self,
*,
npoint: int,
radii: List[float],
nsamples: List[int],
mlps: List[List[int]],
bn: bool = True,
use_xyz: bool = True,
sample_uniformly: bool = False):
super().__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius,
nsample,
use_xyz=use_xyz,
sample_uniformly=sample_uniformly
)
if npoint is not None else pointnet2_utils.GroupAll(use_xyz))
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn))
def __init__(self, npoint, radii, nsamples, mlps, bn=True, use_xyz=True):
# type: (PointnetSAModuleMSGPN2, int, List[float], List[int], List[List[int]], bool, bool) -> None
super(PointnetSAModuleMSGPN2, self).__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
self.out_mlps = nn.ModuleList()
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None else pointnet2_utils.GroupAll(use_xyz))
mlp_spec = mlps[i]
self.out_mlps.append(
nn.Sequential(nn.Conv1d(mlp_spec[1], mlp_spec[-1], 1),
nn.BatchNorm1d(mlp_spec[-1]),
nn.ReLU(inplace=True)))
mlp_spec = mlp_spec[0:2]
if use_xyz:
mlp_spec[0] += 3
self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn))
def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True,
use_xyz: bool = True, pool_method='max_pool', instance_norm=False):
"""
:param npoint: int
:param radii: list of float, list of radii to group with
:param nsamples: list of int, number of samples in each ball query
:param mlps: list of list of int, spec of the pointnet before the global pooling for each scale
:param bn: whether to use batchnorm
:param use_xyz:
:param pool_method: max_pool / avg_pool
:param instance_norm: whether to use instance_norm
"""
super().__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
)
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm))
self.pool_method = pool_method
def __init__(self,
*,
npoint: int,
radii: List[float],
nsamples: List[int],
mlps: List[List[int]],
group_number=1,
use_xyz: bool = True,
pool: bool = False,
before_pool: bool = False,
after_pool: bool = False,
bias=True,
init=nn.init.kaiming_normal):
super().__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.pool = pool
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
if pool:
C_in = (mlps[0][0] + 3) if use_xyz else mlps[0][0]
C_out = mlps[0][1]
pconv = nn.Conv2d(in_channels=C_in,
out_channels=C_out,
kernel_size=(1, 1),
stride=(1, 1),
bias=bias)
init(pconv.weight)
if bias:
nn.init.constant(pconv.bias, 0)
convs = [pconv]
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None else pointnet2_utils.GroupAll(use_xyz))
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
if npoint is None:
self.mlps.append(
pt_utils.GloAvgConv(C_in=mlp_spec[0], C_out=mlp_spec[1]))
elif pool:
self.mlps.append(
pt_utils.PointConv(C_in=mlp_spec[0],
C_out=mlp_spec[1],
convs=convs))
else:
self.mlps.append(
pt_utils.EnhancedPointConv(C_in=mlp_spec[0],
C_out=mlp_spec[1],
group_number=group_number,
before_pool=before_pool,
after_pool=after_pool))
def __init__(
self,
*,
mlp: List[int],
npoint: int = None,
radius: float = None,
nsample: int = None,
bn: bool = True,
use_xyz: bool = True,
pooling: str = 'max',
sigma: float = None, # for RBF pooling
normalize_xyz: bool = False, # noramlize local XYZ with radius
sample_uniformly: bool = False,
ret_unique_cnt: bool = False,
binary='None'):
super().__init__()
self.npoint = npoint
self.radius = radius
self.nsample = nsample
self.pooling = pooling
self.mlp_module = None
self.use_xyz = use_xyz
self.sigma = sigma
if self.sigma is None:
self.sigma = self.radius / 2
self.normalize_xyz = normalize_xyz
self.ret_unique_cnt = ret_unique_cnt
if npoint is not None:
self.grouper = pointnet2_utils.QueryAndGroup(
radius,
nsample,
use_xyz=use_xyz,
ret_grouped_xyz=True,
normalize_xyz=normalize_xyz,
sample_uniformly=sample_uniformly,
ret_unique_cnt=ret_unique_cnt)
else:
self.grouper = pointnet2_utils.GroupAll(use_xyz,
ret_grouped_xyz=True)
mlp_spec = mlp
if use_xyz and len(mlp_spec) > 0:
mlp_spec[0] += 3
if binary == 'None':
self.mlp_module = pt_utils.SharedMLP(mlp_spec, bn=bn)
elif binary == 'BiMLP':
self.mlp_module = BiMLP(mlp_spec, BiLinear=BiLinearLSR)
elif binary == 'BiSharedMLP':
self.mlp_module = pt_utils.BiSharedMLP(mlp_spec, bn=bn, lsr=False)
elif binary == 'BiLSRSharedMLP':
self.mlp_module = pt_utils.BiSharedMLP(mlp_spec, bn=bn, lsr=True)
else:
raise NotImplementedError
def __init__(
self,
*,
mlp: List[int],
npoint: int = None,
split: int = 18,
radius: float = None,
nsample: int = None,
bn: bool = True,
use_xyz: bool = True,
pooling: str = 'max',
sigma: float = None, # for RBF pooling
normalize_xyz: bool = False, # noramlize local XYZ with radius
sample_uniformly: bool = False,
ret_unique_cnt: bool = False,
same_idx: bool = False,
use_feature: bool = True
):
super().__init__()
self.npoint = npoint
self.radius = radius
self.split = split
self.nsample = nsample
self.pooling = pooling
self.mlp_module = None
self.use_xyz = use_xyz
self.sigma = sigma
if self.sigma is None:
self.sigma = self.radius/2
self.normalize_xyz = normalize_xyz
self.ret_unique_cnt = ret_unique_cnt
self.same_idx = same_idx
if npoint is not None:
'''
self.grouper = pointnet2_utils.PairwiseGroup(radius, nsample,
use_xyz=use_xyz, ret_grouped_xyz=True, normalize_xyz=normalize_xyz,
sample_uniformly=sample_uniformly, ret_unique_cnt=ret_unique_cnt, use_feature=use_feature)
'''
self.grouper = pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz, ret_grouped_xyz=True, normalize_xyz=normalize_xyz,sample_uniformly=sample_uniformly, ret_unique_cnt=ret_unique_cnt, use_feature=use_feature, ret_idx=True)
else:
self.grouper = pointnet2_utils.GroupAll(use_xyz, ret_grouped_xyz=True)
mlp_spec = mlp
if use_feature and len(mlp_spec)>0:
mlp_spec[0] += mlp_spec[0]
if use_xyz and len(mlp_spec)>0:
mlp_spec[0] += 3
self.mlp_module = nn.ModuleList()
for i in range(split):
self.mlp_module.append(pt_utils.SharedMLP(mlp_spec, bn=bn))
def __init__(
self,
*,
mlp: List[int],
npoint: int = None,
radius: float = None,
nsample: int = None,
bn: bool = True,
use_xyz: bool = True,
pooling: str = 'max',
sigma: float = None, # for RBF pooling
normalize_xyz: bool = False, # noramlize local XYZ with radius
sample_uniformly: bool = False,
ret_unique_cnt: bool = False,
ret_idx: bool = False,
):
super().__init__()
self.npoint = npoint
self.radius = radius
self.nsample = nsample
self.pooling = pooling
self.mlp_module = None
self.use_xyz = use_xyz
self.sigma = sigma
if self.sigma is None:
self.sigma = self.radius / 2
self.normalize_xyz = normalize_xyz
self.ret_unique_cnt = ret_unique_cnt
self.ret_idx = ret_idx
if npoint is not None:
self.grouper = pointnet2_utils.QueryAndGroup(
radius,
nsample,
use_xyz=use_xyz,
ret_grouped_xyz=True,
normalize_xyz=normalize_xyz,
sample_uniformly=sample_uniformly,
ret_unique_cnt=ret_unique_cnt,
ret_idx=ret_idx)
else:
self.grouper = pointnet2_utils.GroupAll(use_xyz,
ret_grouped_xyz=True)
mlp_spec = mlp
if use_xyz and len(mlp_spec) > 0:
mlp_spec[0] += 3
self.mlp_module = pt_utils.SharedMLP(mlp_spec, bn=bn)
def __init__(
self,
*,
mlp: List[int],
npoint: int = None,
radius: float = None,
nsample: int = None,
bn: bool = True,
use_xyz: bool = True,
pooling: str = 'max',
normalize_xyz: bool = False, # noramlize local XYZ with radius
sample_uniformly: bool = False,
ret_unique_cnt: bool = False):
super().__init__()
self.npoint = npoint
self.nsample = nsample
self.pooling = pooling
self.mlp_module = None
self.use_xyz = use_xyz
self.normalize_xyz = normalize_xyz
self.ret_unique_cnt = ret_unique_cnt
'''
if npoint is not None:
print ("not used for plane")
else:
'''
self.grouper1 = pointnet2_utils.QueryAndGroup(
radius,
nsample,
use_xyz=use_xyz,
ret_grouped_xyz=True,
normalize_xyz=normalize_xyz,
sample_uniformly=sample_uniformly,
ret_unique_cnt=ret_unique_cnt)
self.grouper2 = pointnet2_utils.GroupAll(use_xyz, ret_grouped_xyz=True)
mlp_spec = mlp
if use_xyz and len(mlp_spec) > 0:
mlp_spec[0] += 3
self.mlp_module1 = pt_utils.SharedMLP(mlp_spec, bn=bn)
self.mlp_module2 = pt_utils.SharedMLP(mlp_spec, bn=bn)
def __init__(
self,
*,
npoint: int,
radii: List[float],
nsamples: List[int],
mlps: List[List[int]],
use_xyz: bool = True,
bias=True,
init=nn.init.kaiming_normal,
first_layer=False,
last_layer=False,
):
super().__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
mlp = MLP(mlps[0])
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
if npoint is not None:
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius,
nsample,
use_xyz=use_xyz))
self.mlps.append(
pt_utils.PointNetConv(mlp=mlp,
first_layer=first_layer,
last_layer=last_layer))
else:
# global pooling
self.groupers.append(pointnet2_utils.GroupAll(False))
self.mlps.append(pt_utils.GloAvgConv(mlp=MLP(mlps[i])))
def __init__(
self,
*,
npoint: int,
radii: List[float],
nsamples: List[int],
mlps: List[List[int]],
use_xyz: bool = True,
bias = True,
init = nn.init.kaiming_normal,
first_layer = False,
relation_prior = 1
):
super().__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
# initialize shared mapping functions
C_in = (mlps[0][0] + 3) if use_xyz else mlps[0][0]
C_out = mlps[0][1]
if relation_prior == 0:
in_channels = 1
elif relation_prior == 1 or relation_prior == 2:
in_channels = 10
else:
assert False, "relation_prior can only be 0, 1, 2."
if first_layer:
mapping_func1 = nn.Conv2d(in_channels = in_channels, out_channels = math.floor(C_out / 2), kernel_size = (1, 1),
stride = (1, 1), bias = bias)
mapping_func2 = nn.Conv2d(in_channels = math.floor(C_out / 2), out_channels = 16, kernel_size = (1, 1),
stride = (1, 1), bias = bias)
xyz_raising = nn.Conv2d(in_channels = C_in, out_channels = 16, kernel_size = (1, 1),
stride = (1, 1), bias = bias)
init(xyz_raising.weight)
if bias:
nn.init.constant(xyz_raising.bias, 0)
elif npoint is not None:
mapping_func1 = nn.Conv2d(in_channels = in_channels, out_channels = math.floor(C_out / 4), kernel_size = (1, 1),
stride = (1, 1), bias = bias)
mapping_func2 = nn.Conv2d(in_channels = math.floor(C_out / 4), out_channels = C_in, kernel_size = (1, 1),
stride = (1, 1), bias = bias)
if npoint is not None:
init(mapping_func1.weight)
init(mapping_func2.weight)
if bias:
nn.init.constant(mapping_func1.bias, 0)
nn.init.constant(mapping_func2.bias, 0)
# channel raising mapping
cr_mapping = nn.Conv1d(in_channels = C_in if not first_layer else 16, out_channels = C_out, kernel_size = 1,
stride = 1, bias = bias)
init(cr_mapping.weight)
nn.init.constant(cr_mapping.bias, 0)
if first_layer:
mapping = [mapping_func1, mapping_func2, cr_mapping, xyz_raising]
elif npoint is not None:
mapping = [mapping_func1, mapping_func2, cr_mapping]
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
)
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
if npoint is not None:
self.mlps.append(pt_utils.SharedRSConv(mlp_spec, mapping = mapping, relation_prior = relation_prior, first_layer = first_layer))
else: # global convolutional pooling
self.mlps.append(pt_utils.GloAvgConv(C_in = C_in, C_out = C_out))