open3d.ml.torch.datasets.Semantic3D#

class open3d.ml.torch.datasets.Semantic3D(dataset_path, name='Semantic3D', cache_dir='./logs/cache', use_cache=False, num_points=65536, class_weights=[5181602, 5012952, 6830086, 1311528, 10476365, 946982, 334860, 269353], ignored_label_inds=[0], val_files=['bildstein_station3_xyz_intensity_rgb', 'sg27_station2_intensity_rgb'], test_result_folder='./test', **kwargs)#

This class is used to create a dataset based on the Semantic3D dataset, and used in visualizer, training, or testing.

The dataset includes 8 semantic classes and covers a variety of urban outdoor scenes.

__init__(dataset_path, name='Semantic3D', cache_dir='./logs/cache', use_cache=False, num_points=65536, class_weights=[5181602, 5012952, 6830086, 1311528, 10476365, 946982, 334860, 269353], ignored_label_inds=[0], val_files=['bildstein_station3_xyz_intensity_rgb', 'sg27_station2_intensity_rgb'], test_result_folder='./test', **kwargs)#

Initialize the function by passing the dataset and other details.

Parameters:

  • dataset_path – The path to the dataset to use.

  • name – The name of the dataset (Semantic3D in this case).

  • cache_dir – The directory where the cache is stored.

  • use_cache – Indicates if the dataset should be cached.

  • num_points – The maximum number of points to use when splitting the dataset.

  • class_weights – The class weights to use in the dataset.

  • ignored_label_inds – A list of labels that should be ignored in the dataset.

  • val_files – The files with the data.

  • test_result_folder – The folder where the test results should be stored.

Returns:

The corresponding class.

Return type:

class

static get_label_to_names()#

Returns a label to names dictionary object.

Returns:

A dict where keys are label numbers and values are the corresponding names.

get_split(split)#

Returns a dataset split.

Parameters:

  • split – A string identifying the dataset split that is usually one of

  • 'training' –

  • 'test' –

  • 'validation' –

  • 'all'. (or) –

Returns:

A dataset split object providing the requested subset of the data.

get_split_list(split)#

Returns the list of data splits available.

Parameters:

  • split – A string identifying the dataset split that is usually one of

  • 'training' –

  • 'test' –

  • 'validation' –

  • 'all'. (or) –

Returns:

A dataset split object providing the requested subset of the data.

Raises:

  • ValueError – Indicates that the split name passed is incorrect. The split name should be one of

  • 'training', 'test', 'validation', or 'all'. –

is_tested(attr)#

Checks if a datum in the dataset has been tested.

Parameters:

attr – The attribute that needs to be checked.

Returns:

If the datum attribute is tested, then return the path where the

attribute is stored; else, returns false.

save_test_result(results, attr)#

Saves the output of a model.

Parameters:

  • results – The output of a model for the datum associated with the attribute passed.

  • attr – The attributes that correspond to the outputs passed in results.