open3d.ml.torch.datasets.SemanticKITTI

class open3d.ml.torch.datasets.SemanticKITTI(dataset_path, name='SemanticKITTI', cache_dir='./logs/cache', use_cache=False, class_weights=[55437630, 320797, 541736, 2578735, 3274484, 552662, 184064, 78858, 240942562, 17294618, 170599734, 6369672, 230413074, 101130274, 476491114, 9833174, 129609852, 4506626, 1168181], ignored_label_inds=[0], test_result_folder='./test', test_split=['11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21'], training_split=['00', '01', '02', '03', '04', '05', '06', '07', '09', '10'], validation_split=['08'], all_split=['00', '01', '02', '03', '04', '05', '06', '07', '09', '08', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21'], **kwargs)

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

The dataset is best for semantic scene understanding.

__init__(dataset_path, name='SemanticKITTI', cache_dir='./logs/cache', use_cache=False, class_weights=[55437630, 320797, 541736, 2578735, 3274484, 552662, 184064, 78858, 240942562, 17294618, 170599734, 6369672, 230413074, 101130274, 476491114, 9833174, 129609852, 4506626, 1168181], ignored_label_inds=[0], test_result_folder='./test', test_split=['11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21'], training_split=['00', '01', '02', '03', '04', '05', '06', '07', '09', '10'], validation_split=['08'], all_split=['00', '01', '02', '03', '04', '05', '06', '07', '09', '08', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21'], **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.

• 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 dictonary 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 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.

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.

save_test_result_kpconv(results, inputs)