首先是train_semseg.py文件中,修改类别,当然我们还需要根据硬件配置来修改batch等信息 classes = [ "tower", "wire", "ground_wire", "insulator", "drainage_thread", "tower_distributing", "wire_distributing", "plant", "road", ] 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 然后是数据集的地...
对于代码部分,我们拿一个训练的过程来分析,在文件结构中,可以看到有train_partseg.py和train_semseg.py,前者是对shapenet进行训练的代码,后者是针对于S3DIS这种场景分割的,我们拿后者来看。 首先,在train_semseg.py的大约111行处,注释着MODEL LOADING,我们就在这里开始看,前面的部分就是一些参数的设置和数据集加载部...
执行训练命令: python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg 1 2 可视化结果保存在 log/sem_seg/pointnet2_sem_seg/visual/ 1 执行测试命令: python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual 1版权...
# 初始化模型 model = PointNet2SemSeg(num_classes=dataset.num_classes) model = model.cuda() # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # 训练模型 num_epochs = 20 for epoch in range(num_epochs): model.train() runn...
pointnet2_ssg python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual Visualization results will save in log/sem_seg/pointnet2_sem_seg/visual/ and you can visualize these ....
python pointnet2/train.py task=semseg Multi-GPU training can be enabled by passing a list of GPU ids to use, for instance python pointnet2/train.py task=cls gpus=[0,1,2,3] Building only the CUDA kernels pip install pointnet2_ops_lib/. # Or if you would like to install them dir...
python pointnet2/train.py task=semseg Multi-GPU training can be enabled by passing a list of GPU ids to use, for instance python pointnet2/train.py task=cls gpus=[0,1,2,3] Building only the CUDA kernels pip install pointnet2_ops_lib/. ...
执行训练命令: python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg 1 2 可视化结果保存在 log/sem_seg/pointnet2_sem_seg/visual/ 1 执行测试命令: python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual 1版权...
## Check model in ./models ## e.g., pointnet2_ssg python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual Visualization results will save in log/sem_seg/pointnet2_sem...
## Check model in ./models ## e.g., pointnet2_ssg python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual Visualization results will save inlog/sem_seg/pointnet2_sem_...