Open3D面向机器学习的扩展库

# make sure you have the latest pip versionpip install --upgrade pip# install open3dpip install open3d

# To install a compatible version of TensorFlowpip install -r requirements-tensorflow.txt# To install a compatible version of PyTorch with CUDApip install -r requirements-torch-cuda.txt

# with PyTorch$ python -c "import open3d.ml.torch as ml3d"# or with TensorFlow$ python -c "import open3d.ml.tf as ml3d"

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))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))

import open3d.ml as _ml3dimport open3d.ml.torch as ml3d # or open3d.ml.tf as ml3d  
framework = "torch" # or tfcfg_file = "ml3d/configs/randlanet_semantickitti.yml"cfg = _ml3d.utils.Config.load_from_file(cfg_file)
# fetch the classes by the namePipeline = _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)

import osimport open3d.ml as _ml3dimport 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()

# 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()