def __init__(self,
in_channels,
num_classes,
num_seg_classes,
stem_channels=(64, 128, 128),
local_channels=(512, 2048),
cls_channels=(256, 256),
seg_channels=(256, 256, 128),
dropout_prob_cls=0.3,
dropout_prob_seg=0.2,
with_transform=True):
"""
Args:
in_channels (int): the number of input channels
out_channels (int): the number of output channels
stem_channels (tuple of int): the numbers of channels in stem feature extractor
local_channels (tuple of int): the numbers of channels in local mlp
cls_channels (tuple of int): the numbers of channels in classification mlp
seg_channels (tuple of int): the numbers of channels in segmentation mlp
dropout_prob_cls (float): the probability to dropout in classification mlp
dropout_prob_seg (float): the probability to dropout in segmentation mlp
with_transform (bool): whether to use TNet to transform features.
"""
super(PointNetPartSeg, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.num_seg_classes = num_seg_classes
# stem
self.stem = Stem(in_channels,
stem_channels,
with_transform=with_transform)
self.mlp_local = SharedMLP(stem_channels[-1], local_channels)
# classification
# Notice that we apply dropout to each classification mlp.
self.mlp_cls = MLP(local_channels[-1],
cls_channels,
dropout=dropout_prob_cls)
self.cls_logit = nn.Linear(cls_channels[-1], num_classes, bias=True)
# part segmentation
# Notice that the original repo concatenates global feature, one hot class embedding,
# stem features and local features. However, the paper does not use last local feature.
# Here, we follow the released repo.
in_channels_seg = local_channels[-1] + num_classes + sum(
stem_channels) + sum(local_channels)
self.mlp_seg = SharedMLP(in_channels_seg,
seg_channels[:-1],
dropout=dropout_prob_seg)
self.conv_seg = Conv1d(seg_channels[-2], seg_channels[-1], 1)
self.seg_logit = nn.Conv1d(seg_channels[-1],
num_seg_classes,
1,
bias=True)
self.init_weights()
class EdgeConvBlock(nn.Module):
"""EdgeConv Block
Structure: point features -> [get_edge_feature] -> edge features -> [MLP(2d)]
-> [MaxPool] -> point features
"""
def __init__(self, in_channels, out_channels, k):
super(EdgeConvBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.k = k
self.mlp = SharedMLP(2 * in_channels, out_channels, ndim=2)
def forward(self, x):
x = get_edge_feature(x, self.k)
x = self.mlp(x)
x, _ = torch.max(x, 3)
return x
def init_weights(self, init_fn=None):
self.mlp.init_weights(init_fn)
def __init__(self, in_channels, out_channels, k):
super(EdgeConvBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.k = k
self.mlp = SharedMLP(2 * in_channels, out_channels, ndim=2)
class TNet(nn.Module):
"""Transformation Network. The structure is similar with PointNet"""
def __init__(self,
in_channels=3,
out_channels=3,
local_channels=(64, 128, 1024),
global_channels=(512, 256)):
super(TNet, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
# local features
self.mlp_local = SharedMLP(in_channels, local_channels)
# global features
self.mlp_global = MLP(local_channels[-1], global_channels)
# linear output
self.linear = nn.Linear(global_channels[-1],
in_channels * out_channels,
bias=True)
self.init_weights()
def forward(self, x):
"""TNet forward
Args:
x (torch.Tensor): (batch_size, in_channels, num_points)
Returns:
torch.Tensor: (batch_size, out_channels, in_channels)
"""
x = self.mlp_local(x) # (batch_size, local_channels[-1], num_points)
x, _ = torch.max(x, 2) # (batch_size, local_channels[-1])
x = self.mlp_global(x)
x = self.linear(x)
x = x.view(-1, self.out_channels, self.in_channels)
I = torch.eye(self.out_channels,
self.in_channels,
dtype=x.dtype,
device=x.device)
x = x.add(I) # broadcast add
return x
def init_weights(self):
# Default initialization in original implementation
self.mlp_local.init_weights(xavier_uniform)
self.mlp_global.init_weights(xavier_uniform)
# Initialize linear transform to 0
nn.init.zeros_(self.linear.weight)
nn.init.zeros_(self.linear.bias)
def __init__(self, in_channels, mlp_channels, num_centroids, radius,
num_neighbours, use_xyz):
super(PointNetSAModule, self).__init__()
self.in_channels = in_channels
self.out_channels = mlp_channels[-1]
if use_xyz:
in_channels += 3
self.mlp = SharedMLP(in_channels, mlp_channels, ndim=2, bn=True)
self.sampler = FarthestPointSampler(num_centroids)
self.grouper = QueryGrouper(radius, num_neighbours, use_xyz=use_xyz)
def __init__(self,
in_channels,
out_channels,
num_centroids=(512, 128),
radius_list=((0.1, 0.2, 0.4), (0.2, 0.4, 0.8)),
num_neighbours_list=((16, 32, 128), (32, 64, 128)),
sa_channels_list=(((32, 32, 64), (64, 64, 128), (64, 96,
128)),
((64, 64, 128), (128, 128, 256), (128, 128,
256))),
local_channels=(256, 512, 1024),
global_channels=(512, 256),
dropout_prob=0.5,
use_xyz=True):
"""Refer to PointNet2SSGCls. Major difference is that all the arguments will be a tuple of original types."""
super(PointNet2MSGCls, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.use_xyz = use_xyz
# sanity check
num_layers = len(num_centroids)
assert len(radius_list) == num_layers
assert len(num_neighbours_list) == num_layers
assert len(sa_channels_list) == num_layers
feature_channels = in_channels - 3
self.sa_modules = nn.ModuleList()
for ind in range(num_layers):
sa_module = PointNetSAModuleMSG(
in_channels=feature_channels,
mlp_channels_list=sa_channels_list[ind],
num_centroids=num_centroids[ind],
radius_list=radius_list[ind],
num_neighbours_list=num_neighbours_list[ind],
use_xyz=use_xyz)
self.sa_modules.append(sa_module)
feature_channels = sa_module.out_channels
if use_xyz:
feature_channels += 3
self.mlp_local = SharedMLP(feature_channels, local_channels, bn=True)
self.mlp_global = MLP(local_channels[-1],
global_channels,
dropout=dropout_prob)
self.classifier = nn.Linear(global_channels[-1],
out_channels,
bias=True)
self.init_weights()
def __init__(self, in_channels, mlp_channels_list, num_centroids,
radius_list, num_neighbours_list, use_xyz):
super(PointNetSAModuleMSG, self).__init__()
self.in_channels = in_channels
self.out_channels = sum(mlp_channels[-1]
for mlp_channels in mlp_channels_list)
num_scales = len(mlp_channels_list)
assert len(radius_list) == num_scales
assert len(num_neighbours_list) == num_scales
if use_xyz:
in_channels += 3
self.mlp = nn.ModuleList()
self.sampler = FarthestPointSampler(num_centroids)
self.grouper = nn.ModuleList()
for ind in range(num_scales):
self.mlp.append(
SharedMLP(in_channels, mlp_channels_list[ind], ndim=2,
bn=True))
self.grouper.append(
QueryGrouper(radius_list[ind],
num_neighbours_list[ind],
use_xyz=use_xyz))
class PointnetFPModule(nn.Module):
"""PointNet feature propagation module"""
def __init__(self, in_channels, mlp_channels, num_neighbors):
super(PointnetFPModule, self).__init__()
self.in_channels = in_channels
self.mlp = SharedMLP(in_channels, mlp_channels, ndim=1, bn=True)
self.interpolator = FeatureInterpolator(num_neighbors)
def forward(self, query_xyz, key_xyz, query_feature, key_feature):
new_feature = self.interpolator(query_xyz, key_xyz, query_feature,
key_feature)
new_feature = self.mlp(new_feature)
return new_feature
def init_weights(self, init_fn=None):
self.mlp.init_weights(init_fn)
class PointNetSAModule(nn.Module):
"""PointNet set abstraction module"""
def __init__(self, in_channels, mlp_channels, num_centroids, radius,
num_neighbours, use_xyz):
super(PointNetSAModule, self).__init__()
self.in_channels = in_channels
self.out_channels = mlp_channels[-1]
if use_xyz:
in_channels += 3
self.mlp = SharedMLP(in_channels, mlp_channels, ndim=2, bn=True)
self.sampler = FarthestPointSampler(num_centroids)
self.grouper = QueryGrouper(radius, num_neighbours, use_xyz=use_xyz)
def forward(self, xyz, feature=None):
"""
Args:
xyz (torch.Tensor): (batch_size, 3, num_points)
xyz coordinates of feature
feature (torch.Tensor, optional): (batch_size, in_channels, num_points)
Returns:
new_xyz (torch.Tensor): (batch_size, 3, num_centroids)
new_feature (torch.Tensor): (batch_size, out_channels, num_centroids)
"""
# sample new points
index = self.sampler(xyz)
# (batch_size, 3, num_centroids)
new_xyz = _F.gather_points(xyz, index)
# (batch_size, in_channels, num_centroids, num_neighbours)
new_feature = self.grouper(new_xyz, xyz, feature)
new_feature = self.mlp(new_feature)
new_feature, _ = torch.max(new_feature, 3)
return new_xyz, new_feature
def init_weights(self, init_fn=None):
self.mlp.init_weights(init_fn)
def __init__(self,
in_channels,
stem_channels=(64, 64),
with_transform=True,
bn=True):
super(Stem, self).__init__()
self.in_channels = in_channels
self.out_channels = stem_channels[-1]
self.with_transform = with_transform
# feature stem
self.mlp = SharedMLP(in_channels, stem_channels, bn=bn)
self.mlp.init_weights(xavier_uniform)
if self.with_transform:
# input transform
self.transform_input = TNet(in_channels, in_channels)
# feature transform
self.transform_feature = TNet(self.out_channels, self.out_channels)
def __init__(self,
in_channels=3,
out_channels=3,
conv_channels=(64, 128),
local_channels=(1024, ),
global_channels=(512, 256),
k=20):
super(TNet, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.k = k
self.edge_conv = SharedMLP(2 * in_channels, conv_channels, ndim=2)
self.mlp_local = SharedMLP(conv_channels[-1], local_channels)
self.mlp_global = MLP(local_channels[-1], global_channels)
self.linear = nn.Linear(global_channels[-1],
self.in_channels * out_channels,
bias=True)
self.init_weights()
def __init__(self,
in_channels=3,
out_channels=3,
local_channels=(64, 128, 1024),
global_channels=(512, 256)):
super(TNet, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
# local features
self.mlp_local = SharedMLP(in_channels, local_channels)
# global features
self.mlp_global = MLP(local_channels[-1], global_channels)
# linear output
self.linear = nn.Linear(global_channels[-1],
in_channels * out_channels,
bias=True)
self.init_weights()
def __init__(self,
in_channels,
out_channels,
stem_channels=(64, 64),
local_channels=(64, 128, 1024),
global_channels=(512, 256),
dropout_prob=0.3,
with_transform=True):
"""
Args:
in_channels (int): the number of input channels
out_channels (int): the number of output channels
stem_channels (tuple of int): the numbers of channels in stem feature extractor
local_channels (tuple of int): the numbers of channels in local mlp
global_channels (tuple of int): the numbers of channels in global mlp
dropout_prob (float): the probability to dropout
with_transform (bool): whether to use TNet to transform features.
"""
super(PointNetCls, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.stem = Stem(in_channels,
stem_channels,
with_transform=with_transform)
self.mlp_local = SharedMLP(stem_channels[-1], local_channels)
self.mlp_global = MLP(local_channels[-1],
global_channels,
dropout=dropout_prob)
# self.classifier = nn.Linear(global_channels[-1], out_channels, bias=True)
self.classifier = nn.Sequential(
FC(global_channels[-1], global_channels[-1]),
nn.Linear(global_channels[-1], out_channels, bias=True))
self.init_weights()
def __init__(self,
in_channels,
out_channels,
num_class,
num_seg_class,
edge_conv_channels=((64, 64), (64, 64), (64, 64)),
inter_channels=1024,
global_channels=(256, 256, 128),
k=20,
dropout_prob=0.4,
with_transform=True):
super(DGCNNPartSeg, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.k = k
self.with_transform = with_transform
self.num_gpu = torch.cuda.device_count()
self.num_class = num_class
# input transform
if self.with_transform:
self.transform_input = TNet(in_channels, in_channels, k=k)
self.mlp_edge_conv = nn.ModuleList()
for out in edge_conv_channels:
self.mlp_edge_conv.append(EdgeConvBlock(in_channels, out, k))
in_channels = out[-1]
out_channel = edge_conv_channels[0][0]
self.lable_conv = Conv2d(num_class, out_channel, [1, 1])
mlplocal_input = sum([item[-1] for item in edge_conv_channels])
self.mlp_local = Conv1d(mlplocal_input, inter_channels, 1)
mlp_in_channels = inter_channels + edge_conv_channels[-1][-1] + sum(
[item[-1] for item in edge_conv_channels])
self.mlp_seg = SharedMLP(mlp_in_channels,
global_channels[:-1],
dropout=dropout_prob)
self.conv_seg = Conv1d(global_channels[-2], global_channels[-1], 1)
self.seg_logit = nn.Conv1d(global_channels[-1],
num_seg_class,
1,
bias=True)
self.init_weights()
set_bn(self, momentum=0.01)
def __init__(self,
in_channels,
out_channels,
num_centroids=(512, 128),
radius=(0.2, 0.4),
num_neighbours=(32, 64),
sa_channels=((64, 64, 128), (128, 128, 256)),
local_channels=(256, 512, 1024),
global_channels=(512, 256),
dropout_prob=0.5,
use_xyz=True):
"""
Args:
in_channels (int): the number of input channels
out_channels (int): the number of semantics classes to predict over
num_centroids (tuple of int): the numbers of centroids to sample in each set abstraction module
radius (tuple of float): a tuple of radius to query neighbours in each set abstraction module
num_neighbours (tuple of int): the numbers of neighbours to query for each centroid
sa_channels (tuple of tuple of int): the numbers of channels to within each set abstraction module
local_channels (tuple of int): the numbers of channels to extract local features after set abstraction
global_channels (tuple of int): the numbers of channels to extract global features
dropout_prob (float): the probability to dropout input features
use_xyz (bool): whether or not to use the xyz position of a points as a feature
"""
super(PointNet2SSGCls, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.use_xyz = use_xyz
# sanity check
num_layers = len(num_centroids)
assert len(radius) == num_layers
assert len(num_neighbours) == num_layers
assert len(sa_channels) == num_layers
feature_channels = in_channels - 3
self.sa_modules = nn.ModuleList()
for ind in range(num_layers):
sa_module = PointNetSAModule(in_channels=feature_channels,
mlp_channels=sa_channels[ind],
num_centroids=num_centroids[ind],
radius=radius[ind],
num_neighbours=num_neighbours[ind],
use_xyz=use_xyz)
self.sa_modules.append(sa_module)
feature_channels = sa_channels[ind][-1]
if use_xyz:
feature_channels += 3
self.mlp_local = SharedMLP(feature_channels, local_channels, bn=True)
self.mlp_global = MLP(local_channels[-1],
global_channels,
dropout=dropout_prob)
self.classifier = nn.Linear(global_channels[-1],
out_channels,
bias=True)
self.init_weights()
def __init__(self, in_channels, mlp_channels, num_neighbors):
super(PointnetFPModule, self).__init__()
self.in_channels = in_channels
self.mlp = SharedMLP(in_channels, mlp_channels, ndim=1, bn=True)
self.interpolator = FeatureInterpolator(num_neighbors)
class PointNet2SSGCls(nn.Module):
"""PointNet2 with single-scale grouping for classification
Structure: input -> [PointNetSA]s -> [MLP]s -> [MaxPooling] -> [MLP]s -> [Linear] -> logits
Notice different with the original implementation, the last set abstraction is implemented as a local MLP.
"""
def __init__(self,
in_channels,
out_channels,
num_centroids=(512, 128),
radius=(0.2, 0.4),
num_neighbours=(32, 64),
sa_channels=((64, 64, 128), (128, 128, 256)),
local_channels=(256, 512, 1024),
global_channels=(512, 256),
dropout_prob=0.5,
use_xyz=True):
"""
Args:
in_channels (int): the number of input channels
out_channels (int): the number of semantics classes to predict over
num_centroids (tuple of int): the numbers of centroids to sample in each set abstraction module
radius (tuple of float): a tuple of radius to query neighbours in each set abstraction module
num_neighbours (tuple of int): the numbers of neighbours to query for each centroid
sa_channels (tuple of tuple of int): the numbers of channels to within each set abstraction module
local_channels (tuple of int): the numbers of channels to extract local features after set abstraction
global_channels (tuple of int): the numbers of channels to extract global features
dropout_prob (float): the probability to dropout input features
use_xyz (bool): whether or not to use the xyz position of a points as a feature
"""
super(PointNet2SSGCls, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.use_xyz = use_xyz
# sanity check
num_layers = len(num_centroids)
assert len(radius) == num_layers
assert len(num_neighbours) == num_layers
assert len(sa_channels) == num_layers
feature_channels = in_channels - 3
self.sa_modules = nn.ModuleList()
for ind in range(num_layers):
sa_module = PointNetSAModule(in_channels=feature_channels,
mlp_channels=sa_channels[ind],
num_centroids=num_centroids[ind],
radius=radius[ind],
num_neighbours=num_neighbours[ind],
use_xyz=use_xyz)
self.sa_modules.append(sa_module)
feature_channels = sa_channels[ind][-1]
if use_xyz:
feature_channels += 3
self.mlp_local = SharedMLP(feature_channels, local_channels, bn=True)
self.mlp_global = MLP(local_channels[-1],
global_channels,
dropout=dropout_prob)
self.classifier = nn.Linear(global_channels[-1],
out_channels,
bias=True)
self.init_weights()
def forward(self, data_batch):
points = data_batch["points"]
end_points = {}
# torch.Tensor.narrow; share same memory
xyz = points.narrow(1, 0, 3)
if points.size(1) > 3:
feature = points.narrow(1, 3, points.size(1) - 3)
else:
feature = None
for sa_module in self.sa_modules:
xyz, feature = sa_module(xyz, feature)
if self.use_xyz:
feature = torch.cat([xyz, feature], dim=1)
x = self.mlp_local(feature)
x, max_indices = torch.max(x, 2)
end_points['key_point_inds'] = max_indices
x = self.mlp_global(x)
cls_logit = self.classifier(x)
preds = {'cls_logit': cls_logit}
preds.update(end_points)
return preds
def init_weights(self):
for sa_module in self.sa_modules:
sa_module.init_weights(xavier_uniform)
self.mlp_local.init_weights(xavier_uniform)
self.mlp_global.init_weights(xavier_uniform)
nn.init.xavier_uniform_(self.classifier.weight)
nn.init.zeros_(self.classifier.bias)
set_bn(self, momentum=0.01)
class PointNet2MSGCls(nn.Module):
"""PointNet2 with multi-scale grouping for classification
Structure: input -> [PointNetSA(MSG)]s -> [MLP]s -> MaxPooling -> [MLP]s -> [Linear] -> logits
"""
def __init__(self,
in_channels,
out_channels,
num_centroids=(512, 128),
radius_list=((0.1, 0.2, 0.4), (0.2, 0.4, 0.8)),
num_neighbours_list=((16, 32, 128), (32, 64, 128)),
sa_channels_list=(((32, 32, 64), (64, 64, 128), (64, 96,
128)),
((64, 64, 128), (128, 128, 256), (128, 128,
256))),
local_channels=(256, 512, 1024),
global_channels=(512, 256),
dropout_prob=0.5,
use_xyz=True):
"""Refer to PointNet2SSGCls. Major difference is that all the arguments will be a tuple of original types."""
super(PointNet2MSGCls, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.use_xyz = use_xyz
# sanity check
num_layers = len(num_centroids)
assert len(radius_list) == num_layers
assert len(num_neighbours_list) == num_layers
assert len(sa_channels_list) == num_layers
feature_channels = in_channels - 3
self.sa_modules = nn.ModuleList()
for ind in range(num_layers):
sa_module = PointNetSAModuleMSG(
in_channels=feature_channels,
mlp_channels_list=sa_channels_list[ind],
num_centroids=num_centroids[ind],
radius_list=radius_list[ind],
num_neighbours_list=num_neighbours_list[ind],
use_xyz=use_xyz)
self.sa_modules.append(sa_module)
feature_channels = sa_module.out_channels
if use_xyz:
feature_channels += 3
self.mlp_local = SharedMLP(feature_channels, local_channels, bn=True)
self.mlp_global = MLP(local_channels[-1],
global_channels,
dropout=dropout_prob)
self.classifier = nn.Linear(global_channels[-1],
out_channels,
bias=True)
self.init_weights()
def forward(self, data_batch):
point = data_batch["points"]
end_points = {}
# torch.Tensor.narrow; share same memory
xyz = point.narrow(1, 0, 3)
if point.size(1) > 3:
feature = point.narrow(1, 3, point.size(1) - 3)
else:
feature = None
for sa_module in self.sa_modules:
xyz, feature = sa_module(xyz, feature)
if self.use_xyz:
x = torch.cat([xyz, feature], dim=1)
else:
x = feature
x = self.mlp_local(x)
x, max_indices = torch.max(x, 2)
end_points['key_point_inds'] = max_indices
x = self.mlp_global(x)
cls_logit = self.classifier(x)
preds = {'cls_logit': cls_logit}
preds.update(end_points)
return preds
def init_weights(self):
for sa_module in self.sa_modules:
sa_module.init_weights(xavier_uniform)
self.mlp_local.init_weights(xavier_uniform)
self.mlp_global.init_weights(xavier_uniform)
nn.init.xavier_uniform_(self.classifier.weight)
nn.init.zeros_(self.classifier.bias)
set_bn(self, momentum=0.01)
class PointNetPartSeg(nn.Module):
"""PointNet for part segmentation
References:
https://github.com/charlesq34/pointnet/blob/master/part_seg/pointnet_part_seg.py
"""
def __init__(self,
in_channels,
num_classes,
num_seg_classes,
stem_channels=(64, 128, 128),
local_channels=(512, 2048),
cls_channels=(256, 256),
seg_channels=(256, 256, 128),
dropout_prob_cls=0.3,
dropout_prob_seg=0.2,
with_transform=True):
"""
Args:
in_channels (int): the number of input channels
out_channels (int): the number of output channels
stem_channels (tuple of int): the numbers of channels in stem feature extractor
local_channels (tuple of int): the numbers of channels in local mlp
cls_channels (tuple of int): the numbers of channels in classification mlp
seg_channels (tuple of int): the numbers of channels in segmentation mlp
dropout_prob_cls (float): the probability to dropout in classification mlp
dropout_prob_seg (float): the probability to dropout in segmentation mlp
with_transform (bool): whether to use TNet to transform features.
"""
super(PointNetPartSeg, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.num_seg_classes = num_seg_classes
# stem
self.stem = Stem(in_channels,
stem_channels,
with_transform=with_transform)
self.mlp_local = SharedMLP(stem_channels[-1], local_channels)
# classification
# Notice that we apply dropout to each classification mlp.
self.mlp_cls = MLP(local_channels[-1],
cls_channels,
dropout=dropout_prob_cls)
self.cls_logit = nn.Linear(cls_channels[-1], num_classes, bias=True)
# part segmentation
# Notice that the original repo concatenates global feature, one hot class embedding,
# stem features and local features. However, the paper does not use last local feature.
# Here, we follow the released repo.
in_channels_seg = local_channels[-1] + num_classes + sum(
stem_channels) + sum(local_channels)
self.mlp_seg = SharedMLP(in_channels_seg,
seg_channels[:-1],
dropout=dropout_prob_seg)
self.conv_seg = Conv1d(seg_channels[-2], seg_channels[-1], 1)
self.seg_logit = nn.Conv1d(seg_channels[-1],
num_seg_classes,
1,
bias=True)
self.init_weights()
def forward(self, data_batch):
x = data_batch["points"]
cls_label = data_batch["cls_label"]
num_points = x.shape[2]
end_points = {}
# stem
stem_feature, end_points_stem = self.stem(x)
end_points["trans_input"] = end_points_stem["trans_input"]
end_points["trans_feature"] = end_points_stem["trans_feature"]
stem_features = end_points_stem["stem_features"]
# mlp for local features
local_features = []
x = stem_feature
for ind, mlp in enumerate(self.mlp_local):
x = mlp(x)
local_features.append(x)
# max pool over points
global_feature, max_indices = torch.max(
x, 2) # (batch_size, local_channels[-1])
end_points['key_point_inds'] = max_indices
# classification
x = global_feature
x = self.mlp_cls(x)
cls_logit = self.cls_logit(x)
# segmentation
global_feature_expand = global_feature.unsqueeze(2).expand(
-1, -1, num_points)
with torch.no_grad():
I = torch.eye(self.num_classes,
dtype=global_feature.dtype,
device=global_feature.device)
one_hot = I[cls_label] # (batch_size, num_classes)
one_hot_expand = one_hot.unsqueeze(2).expand(-1, -1, num_points)
x = torch.cat(stem_features + local_features +
[global_feature_expand, one_hot_expand],
dim=1)
x = self.mlp_seg(x)
x = self.conv_seg(x)
seg_logit = self.seg_logit(x)
preds = {"cls_logit": cls_logit, "seg_logit": seg_logit}
preds.update(end_points)
return preds
def init_weights(self):
self.mlp_local.init_weights(xavier_uniform)
self.mlp_cls.init_weights(xavier_uniform)
self.mlp_seg.init_weights(xavier_uniform)
self.conv_seg.init_weights(xavier_uniform)
nn.init.xavier_uniform_(self.cls_logit.weight)
nn.init.zeros_(self.cls_logit.bias)
nn.init.xavier_uniform_(self.seg_logit.weight)
nn.init.zeros_(self.seg_logit.bias)
# Set batch normalization to 0.01 as default
set_bn(self, momentum=0.01)
class PointNet2MSGPartSeg(nn.Module):
""" PointNet++ part segmentation with multi-scale grouping
Refer to PointNet2SSGPartSeg
"""
def __init__(self,
in_channels,
num_classes,
num_seg_classes,
num_centroids=(512, 128),
radius_list=((0.1, 0.2, 0.4), (0.4, 0.8)),
num_neighbours_list=((32, 64, 128), (64, 128)),
sa_channels_list=(((32, 32, 64), (64, 64, 128),
(64, 96, 128)), ((128, 128, 256),
(128, 196, 256))),
local_channels=(256, 512, 1024),
fp_local_channels=(256, 256),
fp_channels=((256, 128), (128, 128)),
num_fp_neighbours=(3, 3),
seg_channels=(128, ),
dropout_prob=0.5,
use_xyz=True):
super(PointNet2MSGPartSeg, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.num_seg_classes = num_seg_classes
self.use_xyz = use_xyz
# sanity check
num_sa_layers = len(num_centroids)
num_fp_layers = len(fp_channels)
assert len(radius_list) == num_sa_layers
assert len(num_neighbours_list) == num_sa_layers
assert len(sa_channels_list) == num_sa_layers
assert num_sa_layers == num_fp_layers
assert len(num_fp_neighbours) == num_fp_layers
# Set Abstraction Layers
feature_channels = in_channels - 3
self.sa_modules = nn.ModuleList()
for ind in range(num_sa_layers):
sa_module = PointNetSAModuleMSG(
in_channels=feature_channels,
mlp_channels_list=sa_channels_list[ind],
num_centroids=num_centroids[ind],
radius_list=radius_list[ind],
num_neighbours_list=num_neighbours_list[ind],
use_xyz=use_xyz)
self.sa_modules.append(sa_module)
feature_channels = sa_module.out_channels
# Local Set Abstraction Layer
if use_xyz:
feature_channels += 3
self.mlp_local = SharedMLP(feature_channels, local_channels, bn=True)
inter_channels = [in_channels if use_xyz else in_channels - 3]
inter_channels[0] += num_classes # concat with one-hot
inter_channels.extend(
[sa_module.out_channels for sa_module in self.sa_modules])
# Local Feature Propagation Layer
self.mlp_local_fp = SharedMLP(local_channels[-1] + inter_channels[-1],
fp_local_channels,
bn=True)
# Feature Propagation Layers
self.fp_modules = nn.ModuleList()
feature_channels = fp_local_channels[-1]
for ind in range(num_fp_layers):
fp_module = PointnetFPModule(in_channels=feature_channels +
inter_channels[-2 - ind],
mlp_channels=fp_channels[ind],
num_neighbors=num_fp_neighbours[ind])
self.fp_modules.append(fp_module)
feature_channels = fp_channels[ind][-1]
# MLP
self.mlp_seg = SharedMLP(feature_channels,
seg_channels,
ndim=1,
dropout=dropout_prob)
self.seg_logit = nn.Conv1d(seg_channels[-1],
num_seg_classes,
1,
bias=True)
self.init_weights()
def forward(self, data_batch):
points = data_batch["points"]
end_points = {}
xyz = points.narrow(1, 0, 3)
if points.size(1) > 3:
feature = points.narrow(1, 3, points.size(1) - 3)
else:
feature = None
# Save intermediate results
inter_xyz = [xyz]
inter_feature = [points if self.use_xyz else feature]
# Create one hot class label
num_points = points.size(2)
with torch.no_grad():
cls_label = data_batch["cls_label"]
I = torch.eye(self.num_classes,
dtype=points.dtype,
device=points.device)
one_hot = I[cls_label]
one_hot_expand = one_hot.unsqueeze(2).expand(-1, -1, num_points)
inter_feature[0] = torch.cat([inter_feature[0], one_hot_expand],
dim=1)
# Set Abstraction Layers
for sa_module in self.sa_modules:
xyz, feature = sa_module(xyz, feature)
inter_xyz.append(xyz)
inter_feature.append(feature)
# Local Set Abstraction Layer
if self.use_xyz:
feature = torch.cat([xyz, feature], dim=1)
feature = self.mlp_local(feature)
global_feature, _ = torch.max(feature, 2)
# Local Feature Propagation Layer
global_feature_expand = global_feature.unsqueeze(2).expand(
-1, -1, inter_xyz[-1].size(2))
feature = torch.cat([global_feature_expand, inter_feature[-1]], dim=1)
feature = self.mlp_local_fp(feature)
# Feature Propagation Layers
key_xyz = xyz
key_feature = feature
for fp_ind, fp_module in enumerate(self.fp_modules):
query_xyz = inter_xyz[-2 - fp_ind]
query_feature = inter_feature[-2 - fp_ind]
fp_feature = fp_module(query_xyz, key_xyz, query_feature,
key_feature)
key_xyz = query_xyz
key_feature = fp_feature
# MLP
x = self.mlp_seg(key_feature)
seg_logit = self.seg_logit(x)
preds = {"seg_logit": seg_logit}
preds.update(end_points)
return preds
def init_weights(self):
for sa_module in self.sa_modules:
sa_module.init_weights(xavier_uniform)
self.mlp_local.init_weights(xavier_uniform)
self.mlp_local_fp.init_weights(xavier_uniform)
for fp_module in self.fp_modules:
fp_module.init_weights(xavier_uniform)
self.mlp_seg.init_weights(xavier_uniform)
nn.init.xavier_uniform_(self.seg_logit.weight)
nn.init.zeros_(self.seg_logit.bias)
set_bn(self, momentum=0.01)
def __init__(self,
in_channels,
num_classes,
num_seg_classes,
num_centroids=(512, 128),
radius_list=((0.1, 0.2, 0.4), (0.4, 0.8)),
num_neighbours_list=((32, 64, 128), (64, 128)),
sa_channels_list=(((32, 32, 64), (64, 64, 128),
(64, 96, 128)), ((128, 128, 256),
(128, 196, 256))),
local_channels=(256, 512, 1024),
fp_local_channels=(256, 256),
fp_channels=((256, 128), (128, 128)),
num_fp_neighbours=(3, 3),
seg_channels=(128, ),
dropout_prob=0.5,
use_xyz=True):
super(PointNet2MSGPartSeg, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.num_seg_classes = num_seg_classes
self.use_xyz = use_xyz
# sanity check
num_sa_layers = len(num_centroids)
num_fp_layers = len(fp_channels)
assert len(radius_list) == num_sa_layers
assert len(num_neighbours_list) == num_sa_layers
assert len(sa_channels_list) == num_sa_layers
assert num_sa_layers == num_fp_layers
assert len(num_fp_neighbours) == num_fp_layers
# Set Abstraction Layers
feature_channels = in_channels - 3
self.sa_modules = nn.ModuleList()
for ind in range(num_sa_layers):
sa_module = PointNetSAModuleMSG(
in_channels=feature_channels,
mlp_channels_list=sa_channels_list[ind],
num_centroids=num_centroids[ind],
radius_list=radius_list[ind],
num_neighbours_list=num_neighbours_list[ind],
use_xyz=use_xyz)
self.sa_modules.append(sa_module)
feature_channels = sa_module.out_channels
# Local Set Abstraction Layer
if use_xyz:
feature_channels += 3
self.mlp_local = SharedMLP(feature_channels, local_channels, bn=True)
inter_channels = [in_channels if use_xyz else in_channels - 3]
inter_channels[0] += num_classes # concat with one-hot
inter_channels.extend(
[sa_module.out_channels for sa_module in self.sa_modules])
# Local Feature Propagation Layer
self.mlp_local_fp = SharedMLP(local_channels[-1] + inter_channels[-1],
fp_local_channels,
bn=True)
# Feature Propagation Layers
self.fp_modules = nn.ModuleList()
feature_channels = fp_local_channels[-1]
for ind in range(num_fp_layers):
fp_module = PointnetFPModule(in_channels=feature_channels +
inter_channels[-2 - ind],
mlp_channels=fp_channels[ind],
num_neighbors=num_fp_neighbours[ind])
self.fp_modules.append(fp_module)
feature_channels = fp_channels[ind][-1]
# MLP
self.mlp_seg = SharedMLP(feature_channels,
seg_channels,
ndim=1,
dropout=dropout_prob)
self.seg_logit = nn.Conv1d(seg_channels[-1],
num_seg_classes,
1,
bias=True)
self.init_weights()
class TNet(nn.Module):
"""Transformation Network for DGCNN
Structure: input -> [EdgeFeature] -> [EdgeConv]s -> [EdgePool] -> features -> [MLP] -> local features
-> [MaxPool] -> global features -> [MLP] -> [Linear] -> logits
Args:
conv_channels (tuple of int): the numbers of channels of edge convolution layers
k: the number of neareast neighbours for edge feature extractor
"""
def __init__(self,
in_channels=3,
out_channels=3,
conv_channels=(64, 128),
local_channels=(1024, ),
global_channels=(512, 256),
k=20):
super(TNet, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.k = k
self.edge_conv = SharedMLP(2 * in_channels, conv_channels, ndim=2)
self.mlp_local = SharedMLP(conv_channels[-1], local_channels)
self.mlp_global = MLP(local_channels[-1], global_channels)
self.linear = nn.Linear(global_channels[-1],
self.in_channels * out_channels,
bias=True)
self.init_weights()
def forward(self, x):
"""TNet forward
Args:
x (torch.Tensor): (batch_size, in_channels, num_points)
Returns:
torch.Tensor: (batch_size, out_channels, in_channels)
"""
x = get_edge_feature(
x, self.k) # (batch_size, 2 * in_channels, num_points, k)
x = self.edge_conv(x)
x, _ = torch.max(x, 3) # (batch_size, edge_channels[-1], num_points)
x = self.mlp_local(x)
x, _ = torch.max(x, 2) # (batch_size, local_channels[-1], num_points)
x = self.mlp_global(x)
x = self.linear(x)
x = x.view(-1, self.out_channels, self.in_channels)
I = torch.eye(self.out_channels, self.in_channels, device=x.device)
x = x.add(I) # broadcast first dimension
return x
def init_weights(self):
self.edge_conv.init_weights(xavier_uniform)
self.mlp_local.init_weights(xavier_uniform)
self.mlp_global.init_weights(xavier_uniform)
# Set linear transform be 0
nn.init.zeros_(self.linear.weight)
nn.init.zeros_(self.linear.bias)
class Stem(nn.Module):
"""Stem (main body or stalk). Extract features from raw point clouds
Structure: input (-> [TNet] -> transform_input) -> [MLP] -> features (-> [TNet] -> transform_feature)
Attributes:
with_transform: whether to use TNet
"""
def __init__(self,
in_channels,
stem_channels=(64, 64),
with_transform=True,
bn=True):
super(Stem, self).__init__()
self.in_channels = in_channels
self.out_channels = stem_channels[-1]
self.with_transform = with_transform
# feature stem
self.mlp = SharedMLP(in_channels, stem_channels, bn=bn)
self.mlp.init_weights(xavier_uniform)
if self.with_transform:
# input transform
self.transform_input = TNet(in_channels, in_channels)
# feature transform
self.transform_feature = TNet(self.out_channels, self.out_channels)
def forward(self, x):
"""PointNet Stem forward
Args:
x (torch.Tensor): (batch_size, in_channels, num_points)
Returns:
torch.Tensor: (batch_size, stem_channels[-1], num_points)
dict (optional non-empty):
trans_input: (batch_size, in_channels, in_channels)
trans_feature: (batch_size, stem_channels[-1], stem_channels[-1])
"""
end_points = {}
# input transform
if self.with_transform:
trans_input = self.transform_input(x)
x = torch.bmm(trans_input, x)
end_points["trans_input"] = trans_input
# feature
x = self.mlp(x)
# feature transform
if self.with_transform:
trans_feature = self.transform_feature(x)
x = torch.bmm(trans_feature, x)
end_points["trans_feature"] = trans_feature
return x, end_points
class PointNetCls(nn.Module):
"""PointNet for classification
Structure: input -> [Stem] -> features -> [SharedMLP] -> local features
-> [MaxPool] -> gloal features -> [MLP] -> [Linear] -> logits
"""
def __init__(self,
in_channels,
out_channels,
stem_channels=(64, 64),
local_channels=(64, 128, 1024),
global_channels=(512, 256),
dropout_prob=0.3,
with_transform=True):
"""
Args:
in_channels (int): the number of input channels
out_channels (int): the number of output channels
stem_channels (tuple of int): the numbers of channels in stem feature extractor
local_channels (tuple of int): the numbers of channels in local mlp
global_channels (tuple of int): the numbers of channels in global mlp
dropout_prob (float): the probability to dropout
with_transform (bool): whether to use TNet to transform features.
"""
super(PointNetCls, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.stem = Stem(in_channels,
stem_channels,
with_transform=with_transform)
self.mlp_local = SharedMLP(stem_channels[-1], local_channels)
self.mlp_global = MLP(local_channels[-1],
global_channels,
dropout=dropout_prob)
# self.classifier = nn.Linear(global_channels[-1], out_channels, bias=True)
self.classifier = nn.Sequential(
FC(global_channels[-1], global_channels[-1]),
nn.Linear(global_channels[-1], out_channels, bias=True))
self.init_weights()
def forward(self, data_batch):
x = data_batch["points"]
# stem
x, end_points = self.stem(x)
# mlp for local features
x = self.mlp_local(x)
# max pool over points
x, max_indices = torch.max(x, 2)
end_points["key_point_inds"] = max_indices
# mlp for global features
x = self.mlp_global(x)
x = self.classifier(x)
preds = {"cls_logit": x}
preds.update(end_points)
return preds
def init_weights(self):
# Default initialization in original implementation
self.mlp_local.init_weights(xavier_uniform)
self.mlp_global.init_weights(xavier_uniform)
nn.init.xavier_uniform_(self.classifier.weight)
nn.init.zeros_(self.classifier.bias)
# Set batch normalization to 0.01 as default
set_bn(self, momentum=0.01)
