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pointnet.pytorch代码解析
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pointnet.pytorch代码解析
代码运行
Training
cd utils
python train_classification.py --dataset <dataset path> --nepoch=<number epochs> --dataset_type <modelnet40 | shapenet>
python train_segmentation.py --dataset <dataset path> --nepoch=<number epochs>
运行结果
-
Classification on ShapeNet
epoch = 10 Overall Acc Original implementation N/A this implementation(无 feature transform) 95.6 this implementation(有 feature transform) 92.97 -
Segmentation on ShapeNet
dataset代码
-
读取的数据格式
ShapeNetDataset()
：默认读取分割数据，返回值d：点云个数*（点云数据ps，标签seg）
数据ps
：torch.Size([2500, 3]) torch.FloatTensor ，一个点云有2500个点，每个点3个特征
标签seg
：torch.Size([2500]) torch.LongTensor，每个点都有一个标签
代码及注释如下：
if __name__ == '__main__':
dataset = sys.argv[1] # 运行命令中传入的第一个参数
datapath = sys.argv[2] # 运行命令中传入的第二个参数
if dataset == 'shapenet':
# 读取标签为Chair的分割数据
d = ShapeNetDataset(root = datapath, class_choice = ['Chair'])
print(len(d)) #2658，共有2658个Chair点云
ps, seg = d[0]
print(ps.size(), ps.type(), seg.size(),seg.type())
# torch.Size([2500, 3]) torch.FloatTensor ，第一个点云有2500个点，每个点3个特征
# torch.Size([2500]) torch.LongTensor，每个点都有一个标签
d = ShapeNetDataset(root = datapath, classification = True)
print(len(d))
ps, cls = d[0]
print(ps.size(), ps.type(), cls.size(),cls.type())
# torch.Size([2500, 3]) torch.FloatTensor torch.Size([1]) torch.LongTensor，每个点云一个标签
# get_segmentation_classes(datapath)
-
数据读取
model代码
-
网络整体结构
if __name__ == '__main__':
# input transform
sim_data = Variable(torch.rand(32,3,2500)) # 32个点云，3个特征，2500个点
trans = STN3d()
out = trans(sim_data) # stn torch.Size([32, 3, 3])，返回3x3的输入变换矩阵
print('stn', out.size())
print('loss', feature_transform_regularizer(out))
# feature transform
sim_data_64d = Variable(torch.rand(32, 64, 2500))
trans = STNkd(k=64)
out = trans(sim_data_64d) # stn64d torch.Size([32, 64, 64])，返回64x64的特征变换矩阵
print('stn64d', out.size())
print('loss', feature_transform_regularizer(out))
# global feat
pointfeat = PointNetfeat(global_feat=True)
out, _, _ = pointfeat(sim_data) # global feat torch.Size([32, 1024])，32个点云，每个有1024维全局特征
print('global feat', out.size())
# point feat
pointfeat = PointNetfeat(global_feat=False)
out, _, _ = pointfeat(sim_data) # point feat torch.Size([32, 1088, 2500])，2500个点，每个点有1024+64维特征
print('point feat', out.size())
# Classification
cls = PointNetCls(k = 5)
out, _, _ = cls(sim_data) # class torch.Size([32, 5])，global feat经过全连接层，得到在5个类别上的概率信息
print('class', out.size())
# Segmentation
seg = PointNetDenseCls(k = 3)
out, _, _ = seg(sim_data) # seg torch.Size([32, 2500, 3])，point feat经过一维卷积，得到在3个类别上概率信息
print('seg', out.size())
-
PointNetfeat特征提取网络
class PointNetfeat(nn.Module):
'''
点云的特征提取网络：global feature 和 point features
'''
def __init__(self, global_feat = True, feature_transform = False):
super(PointNetfeat, self).__init__()
self.stn = STN3d()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
self.global_feat = global_feat
self.feature_transform = feature_transform
if self.feature_transform:
self.fstn = STNkd(k=64)
def forward(self, x):
n_pts = x.size()[2]
trans = self.stn(x)
x = x.transpose(2, 1)
x = torch.bmm(x, trans) # 乘以3x3变换矩阵
x = x.transpose(2, 1)
x = F.relu(self.bn1(self.conv1(x)))
if self.feature_transform: # 特征变换，64x64矩阵
trans_feat = self.fstn(x)
x = x.transpose(2,1)
x = torch.bmm(x, trans_feat)
x = x.transpose(2,1)
else:
trans_feat = None
pointfeat = x # nx64的点特征
x = F.relu(self.bn2(self.conv2(x)))
x = self.bn3(self.conv3(x))
x = torch.max(x, 2, keepdim=True)[0] # Maxpool
x = x.view(-1, 1024)
if self.global_feat:
return x, trans, trans_feat # x：mx1x1024的global feature，两个变换矩阵
else:
x = x.view(-1, 1024, 1).repeat(1, 1, n_pts)
return torch.cat([x, pointfeat], 1), trans, trans_feat # global feature+point features = nx1088的点特征矩阵
读取的数据格式
ShapeNetDataset()
：默认读取分割数据，返回值d：点云个数*（点云数据ps，标签seg）
数据ps
：torch.Size([2500, 3]) torch.FloatTensor ，一个点云有2500个点，每个点3个特征
标签seg
：torch.Size([2500]) torch.LongTensor，每个点都有一个标签
代码及注释如下：
if __name__ == '__main__':
dataset = sys.argv[1] # 运行命令中传入的第一个参数
datapath = sys.argv[2] # 运行命令中传入的第二个参数
if dataset == 'shapenet':
# 读取标签为Chair的分割数据
d = ShapeNetDataset(root = datapath, class_choice = ['Chair'])
print(len(d)) #2658，共有2658个Chair点云
ps, seg = d[0]
print(ps.size(), ps.type(), seg.size(),seg.type())
# torch.Size([2500, 3]) torch.FloatTensor ，第一个点云有2500个点，每个点3个特征
# torch.Size([2500]) torch.LongTensor，每个点都有一个标签
d = ShapeNetDataset(root = datapath, classification = True)
print(len(d))
ps, cls = d[0]
print(ps.size(), ps.type(), cls.size(),cls.type())
# torch.Size([2500, 3]) torch.FloatTensor torch.Size([1]) torch.LongTensor，每个点云一个标签
# get_segmentation_classes(datapath)
数据读取
-
网络整体结构
if __name__ == '__main__':
# input transform
sim_data = Variable(torch.rand(32,3,2500)) # 32个点云，3个特征，2500个点
trans = STN3d()
out = trans(sim_data) # stn torch.Size([32, 3, 3])，返回3x3的输入变换矩阵
print('stn', out.size())
print('loss', feature_transform_regularizer(out)) # feature transform
sim_data_64d = Variable(torch.rand(32, 64, 2500))
trans = STNkd(k=64)
out = trans(sim_data_64d) # stn64d torch.Size([32, 64, 64])，返回64x64的特征变换矩阵
print('stn64d', out.size())
print('loss', feature_transform_regularizer(out)) # global feat
pointfeat = PointNetfeat(global_feat=True)
out, _, _ = pointfeat(sim_data) # global feat torch.Size([32, 1024])，32个点云，每个有1024维全局特征
print('global feat', out.size()) # point feat
pointfeat = PointNetfeat(global_feat=False)
out, _, _ = pointfeat(sim_data) # point feat torch.Size([32, 1088, 2500])，2500个点，每个点有1024+64维特征
print('point feat', out.size()) # Classification
cls = PointNetCls(k = 5)
out, _, _ = cls(sim_data) # class torch.Size([32, 5])，global feat经过全连接层，得到在5个类别上的概率信息
print('class', out.size()) # Segmentation
seg = PointNetDenseCls(k = 3)
out, _, _ = seg(sim_data) # seg torch.Size([32, 2500, 3])，point feat经过一维卷积，得到在3个类别上概率信息
print('seg', out.size())
-
PointNetfeat特征提取网络
class PointNetfeat(nn.Module):
'''
点云的特征提取网络：global feature 和 point features
'''
def __init__(self, global_feat = True, feature_transform = False):
super(PointNetfeat, self).__init__()
self.stn = STN3d()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
self.global_feat = global_feat
self.feature_transform = feature_transform
if self.feature_transform:
self.fstn = STNkd(k=64) def forward(self, x):
n_pts = x.size()[2]
trans = self.stn(x)
x = x.transpose(2, 1)
x = torch.bmm(x, trans) # 乘以3x3变换矩阵
x = x.transpose(2, 1)
x = F.relu(self.bn1(self.conv1(x))) if self.feature_transform: # 特征变换，64x64矩阵
trans_feat = self.fstn(x)
x = x.transpose(2,1)
x = torch.bmm(x, trans_feat)
x = x.transpose(2,1)
else:
trans_feat = None pointfeat = x # nx64的点特征
x = F.relu(self.bn2(self.conv2(x)))
x = self.bn3(self.conv3(x))
x = torch.max(x, 2, keepdim=True)[0] # Maxpool
x = x.view(-1, 1024)
if self.global_feat:
return x, trans, trans_feat # x：mx1x1024的global feature，两个变换矩阵
else:
x = x.view(-1, 1024, 1).repeat(1, 1, n_pts)
return torch.cat([x, pointfeat], 1), trans, trans_feat # global feature+point features = nx1088的点特征矩阵
