本次源码解读的地址为：https://github.com/yanx27/Pointnet_Pointnet2_pytorch，这一版本的源码易读性高，主要是封装程度较低，注释较全，安装额外的库也比较少。

Pipeline

前向过程：数据加载、数据增强

 for epoch in range(start_epoch, args.epoch):log_string('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch))mean_correct = []classifier = classifier.train()scheduler.step()for batch_id, (points, target) in tqdm(enumerate(trainDataLoader, 0), total=len(trainDataLoader),smoothing=0.9):optimizer.zero_grad()# b x n x cpoints = points.data.numpy()# b x n x c：并不缩减point的数量，而是设定某个阈值，将小于某个阈值的点覆盖为第一个点的信息points = provider.random_point_dropout(points)# 针对不同的batch进行随机坐标缩放，将某batch所有的pc乘某个缩放系数points[:, :, 0:3] = provider.random_scale_point_cloud(points[:, :, 0:3])# 针对不同的batch进行随机坐标缩放，将某batch所有的pc同步进行坐标移动points[:, :, 0:3] = provider.shift_point_cloud(points[:, :, 0:3])points = torch.Tensor(points)points = points.transpose(2, 1) # b,c,n：conv1d是channel first因此要转换if not args.use_cpu:points, target = points.cuda(), target.cuda()# 喂入模型数据pred, trans_feat = classifier(points)

model

Classification Model和Segmentation Model，区别在于PointNetEncoder的参数global_feature=true | false
这里以Classification Model为例：

class get_model(nn.Module):def __init__(self, k=40, normal_channel=True):super(get_model, self).__init__()if normal_channel:channel = 6else:channel = 3self.feat = PointNetEncoder(global_feat=True, feature_transform=True, channel=channel)    # classification modelself.fc1 = nn.Linear(1024, 512)self.fc2 = nn.Linear(512, 256)self.fc3 = nn.Linear(256, k)self.dropout = nn.Dropout(p=0.4)self.bn1 = nn.BatchNorm1d(512)self.bn2 = nn.BatchNorm1d(256)self.relu = nn.ReLU()def forward(self, x):x, trans, trans_feat = self.feat(x)   # 这里计算出x = F.relu(self.bn1(self.fc1(x)))x = F.relu(self.bn2(self.dropout(self.fc2(x))))x = self.fc3(x)# 每个点云计算对应的类别损失x = F.log_softmax(x, dim=1)   # bs, kreturn x, trans_feat

如果是segmentation model，只需对如上代码做如下修改

    def forward(self, x):batchsize = x.size()[0]n_pts = x.size()[2]x, trans, trans_feat = self.feat(x)x = F.relu(self.bn1(self.conv1(x)))x = F.relu(self.bn2(self.conv2(x)))x = F.relu(self.bn3(self.conv3(x)))x = self.conv4(x)x = x.transpose(2,1).contiguous()x = F.log_softmax(x.view(-1,self.k), dim=-1)x = x.view(batchsize, n_pts, self.k)return x, trans_feat

PointNetEncoder

class PointNetEncoder(nn.Module):def __init__(self, global_feat=True, feature_transform=False, channel=3):super(PointNetEncoder, self).__init__()self.stn = STN3d(channel)   # 计算第一个3x3的transformation矩阵self.conv1 = torch.nn.Conv1d(channel, 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_featself.feature_transform = feature_transformif self.feature_transform:self.fstn = STNkd(k=64)  # 计算第二个transformation矩阵def forward(self, x):B, D, N = x.size()trans = self.stn(x) # bs, 3, 3x = x.transpose(2, 1)   # bs, n, 3if D > 3:feature = x[:, :, 3:]x = x[:, :, :3]x = torch.bmm(x, trans) #if D > 3:x = torch.cat([x, feature], dim=2)x = x.transpose(2, 1)   # bs, c, nx = F.relu(self.bn1(self.conv1(x))) # [bs, 3, n]->[bs, 64, n]if self.feature_transform:trans_feat = self.fstn(x)   # bs, 64， 64x = x.transpose(2, 1)x = torch.bmm(x, trans_feat)    # bs, n, 64x = x.transpose(2, 1)else:trans_feat = Nonepointfeat = xx = F.relu(self.bn2(self.conv2(x)))x = self.bn3(self.conv3(x))x = torch.max(x, 2, keepdim=True)[0]x = x.view(-1, 1024)    # bs, 1024：这里是global feature# 如果global_feat=true, 返回的x是global featureif self.global_feat:return x, trans, trans_feat # 分别是global feature, 3x3的转换矩阵， transform feature，用于instance-wise的classfication# 返回的x为每个点加入global feature之后的特征，用于segmentationelse:x = x.view(-1, 1024, 1).repeat(1, 1, N)# x:[bs, c, n] + pointfeat:[bs, c, n] -> [bs, c+c, n]return torch.cat([x, pointfeat], 1), trans, trans_feat  # 这里是point-wise的classfication

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PointNet源码解读

Pipeline

model

PointNetEncoder

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