Open3D-ML是Open3D的一个扩展，用于3D机器学习任务。它建立在Open3D核心库之上，并通过机器学习工具对其进行扩展，以进行3D数据处理。此repo集中于语义点云分割等应用程序，并提供可应用于常见任务的预训练模型以及用于训练的流程。
Open3D-ML与TensorFlow和PyTorch一起工作，可以轻松地集成到现有项目中，还可以提供独立于ML框架的通用功能，如数据可视化。
安装
Open3D-ML集成在Open3D v0.11 python发行版中，并与以下版本的ML框架兼容
* PyTorch 1.6
* TensorFlow 2.3
* CUDA 10.1 (On GNU/Linux x86_64, optional)
安装Open3D
代码语言：javascript复制# make sure you have the latest pip version
pip install --upgrade pip
# install open3d
pip install open3d要安装Pythorch或TensorFlow的兼容版本，需要使用相应的需求文件：
代码语言：javascript复制# To install a compatible version of TensorFlow
pip install -r requirements-tensorflow.txt
# To install a compatible version of PyTorch with CUDA
pip install -r requirements-torch-cuda.txt测试安装
代码语言：javascript复制# with PyTorch
$ python -c "import open3d.ml.torch as ml3d"# or with TensorFlow
$ python -c "import open3d.ml.tf as ml3d"如果需要使用不同版本的ML框架或CUDA，可以从源代码重新构建Open3D。
使用教程
读取数据集
dataset命名空间包含用于读取公共数据集的类。这里我们读取SemanticKITTI数据集并将其可视化。
代码语言：javascript复制import open3d.ml.torch as ml3d # or open3d.ml.tf as ml3d
# construct a dataset by specifying dataset_path
dataset = ml3d.datasets.SemanticKITTI(dataset_path='/path/to/SemanticKITTI/')
# get the 'all' split that combines training, validation and test set
all_split = dataset.get_split('all')
# print the attributes of the first datum
print(all_split.get_attr(0))
# print the shape of the first point cloud
print(all_split.get_data(0)['point'].shape)
# show the first 100 frames using the visualizer
vis = ml3d.vis.Visualizer()
vis.visualize_dataset(dataset, 'all', indices=range(100))import open3d.ml.torch as ml3d # or open3d.ml.tf as ml3d
# construct a dataset by specifying dataset_pathdataset = ml3d.datasets.SemanticKITTI(dataset_path='/path/to/SemanticKITTI/')
# get the 'all' split that combines training, validation and test setall_split = dataset.get_split('all')
# print the attributes of the first datumprint(all_split.get_attr(0))
# print the shape of the first point cloudprint(all_split.get_data(0)['point'].shape)
# show the first 100 frames using the visualizervis = ml3d.vis.Visualizer()
vis.visualize_dataset(dataset, 'all', indices=range(100))
加载配置文件
模型、数据集和流程的配置存储在ml3d/Configs中。用户还可以构建自己的yaml文件来记录他们的定制配置。下面是一个读取配置文件并从中构造模块的示例。
代码语言：javascript复制import open3d.ml as _ml3d
import open3d.ml.torch as ml3d # or open3d.ml.tf as ml3d
framework = "torch" # or tf
cfg_file = "ml3d/configs/randlanet_semantickitti.yml"
cfg = _ml3d.utils.Config.load_from_file(cfg_file)
# fetch the classes by the name
Pipeline = _ml3d.utils.get_module("pipeline", cfg.pipeline.name, framework)
Model = _ml3d.utils.get_module("model", cfg.model.name, framework)
Dataset = _ml3d.utils.get_module("dataset", cfg.dataset.name)
# use the arguments in the config file to construct the instances
cfg.dataset['dataset_path'] = "/path/to/your/dataset"
dataset = Dataset(cfg.dataset.pop('dataset_path', None), **cfg.dataset)
model = Model(**cfg.model)
pipeline = Pipeline(model, dataset, **cfg.pipeline)运行一个预先训练过的模型
在上一个例子的基础上，我们可以用一个预先训练的语义分割模型实例化一个算法，并在数据集的点云上运行它。查看模型集合以获取预训练模型的权重。
代码语言：javascript复制import os
import open3d.ml as _ml3d
import open3d.ml.torch as ml3d
cfg_file = "ml3d/configs/randlanet_semantickitti.yml"
cfg = _ml3d.utils.Config.load_from_file(cfg_file)
model = ml3d.models.RandLANet(**cfg.model)
cfg.dataset['dataset_path'] = "/path/to/your/dataset"
dataset = ml3d.datasets.SemanticKITTI(cfg.dataset.pop('dataset_path', None), **cfg.dataset)
pipeline = ml3d.pipelines.SemanticSegmentation(model, dataset=dataset, device="gpu", **cfg.pipeline)
# download the weights.
ckpt_folder = "./logs/"
os.makedirs(ckpt_folder, exist_ok=True)
ckpt_path = ckpt_folder "randlanet_semantickitti_202009090354utc.pth"
randlanet_url = "https://storage.googleapis.com/open3d-releases/model-zoo/randlanet_semantickitti_202009090354utc.pth"
if not os.path.exists(ckpt_path):
cmd = "wget {} -O {}".format(randlanet_url, ckpt_path)
os.system(cmd)
# load the parameters.
pipeline.load_ckpt(ckpt_path=ckpt_path)
test_split = dataset.get_split("test")
data = test_split.get_data(0)
# run inference on a single example.
# returns dict with 'predict_labels' and 'predict_scores'.
result = pipeline.run_inference(data)
# evaluate performance on the test set; this will write logs to './logs'.
pipeline.run_test()用户还可以使用预定义的脚本来加载预先训练的权重并运行测试。
训练模型
与推理类似，流程中提供了一个在数据集上训练模型的接口。
代码语言：javascript复制# use a cache for storing the results of the preprocessing (default path is './logs/cache')
dataset = ml3d.datasets.SemanticKITTI(dataset_path='/path/to/SemanticKITTI/', use_cache=True)
# create the model with random initialization.
model = RandLANet()
pipeline = SemanticSegmentation(model=model, dataset=dataset, max_epoch=100)
# prints training progress in the console.
pipeline.run_train()有关更多示例，请参见examples/和scripts/目录。
使用预定义脚本
scripts/semseg.py 提供了一个简单的数据集评估接口。准确地定义模型，避免了定义具体模型的麻烦。
python scripts/semseg.py {tf/torch} -c <path-to-config> --<extra args>
注意， extra args 将优先于配置文件中的相同参数。因此，在启动脚本时，可以将其作为命令行参数传递，而不是更改配置文件中的param。
例如：
代码语言：javascript复制# Launch training for RandLANet on SemanticKITTI with torch.
python scripts/semseg.py torch -c ml3d/configs/randlanet_semantickitti.yml --dataset.dataset_path <path-to-dataset> --dataset.use_cache True
# Launch testing for KPConv on Toronto3D with tensorflow.
python scripts/semseg.py tf -c ml3d/configs/kpconv_toronto3d.yml --split test --dataset.dataset_path <path-to-dataset> --model.ckpt_path <path-to-checkpoint>要获得进一步的帮助，可运行python脚本 python scripts/semseg.py --help
ML库结构
Open3D-ML的核心部分位于ml3d子文件夹中，该子文件夹被集成到ML命名空间中的Open3D中。除了核心部分之外，目录示例和脚本还提供了支持脚本，用于开始在数据集上设置训练流程或运行网络。
代码语言：javascript复制├─ docs # Markdown and rst files for documentation
├─ examples # Place for example scripts and notebooks
├─ ml3d # Package root dir that is integrated in open3d
├─ configs # Model configuration files
├─ datasets # Generic dataset code; will be integratede as open3d.ml.{tf,torch}.datasets
├─ utils # Framework independent utilities; available as open3d.ml.{tf,torch}.utils
├─ vis # ML specific visualization functions
├─ tf # Directory for TensorFlow specific code. same structure as ml3d/torch.
│ # This will be available as open3d.ml.tf
├─ torch # Directory for PyTorch specific code; available as open3d.ml.torch
├─ dataloaders # Framework specific dataset code, e.g. wrappers that can make use of the
│ # generic dataset code.
├─ models # Code for models
├─ modules # Smaller modules, e.g., metrics and losses
├─ pipelines # Pipelines for tasks like semantic segmentation
├─ scripts # Demo scripts for training and dataset download scripts任务和算法
分割
对于语义分割的任务，我们使用mIoU（mean intersection-over-union）来衡量不同方法在所有类上的性能。下表显示了分段任务的可用模型和数据集以及各自的分数。每个分数链接到各自的权重文件。
模型集合
有关所有权重文件的完整列表，请参见模型文件 model_weights.txt 以及MD5校验model_weights.md5.
数据集集合
下面是我们为其提供数据集读取器类的数据集列表。
SemanticKITTI
Toronto 3D
Semantic 3D
S3DIS
Paris-Lille 3D
要下载这些数据集，请访问相应的网页，可查看scripts/download_datasets中的脚本。
