fiftyone.utils.activitynet

module documentation

(source)

Utilities for working with the ActivityNet dataset.

Class	`ActivityNet100DatasetInfo`	ActivityNet 100 dataset info.
Class	`ActivityNet200DatasetInfo`	ActivityNet 200 dataset info.
Class	`ActivityNetDatasetImporter`	Class for importing AcitivityNet dataset splits downloaded via `download_activitynet_split`.
Class	`ActivityNetDatasetInfo`	Class that stores information related to paths, labels, and sample IDs for an ActivityNet dataset download.
Class	`ActivityNetDatasetManager`	Class that manages the sample IDs and labels that need to be downloaded to load the specified subset of an ActivityNet dataset.
Class	`ActivityNetDownloadConfig`	Configuration class for downloading full or partial splits from the ActivityNet dataset.
Class	`ActivityNetInfo`	Necessary information used to parse and format annotations.
Class	`ActivityNetSplitInfo`	Class that contains information related to paths, labels, and sample IDs of a single ActivityNet split.
Function	`download_activitynet_split`	Utility that downloads full or partial splits of the ActivityNet dataset.
Variable	`logger`	Undocumented
Function	`_flatten_list`	Undocumented
Function	`_get_all_classes`	Undocumented
Constant	`_ANNOTATION_DOWNLOAD_LINKS`	Undocumented
Constant	`_NUM_TOTAL_SAMPLES`	Undocumented
Constant	`_SOURCE_DIR_NAMES`	Undocumented
Constant	`_SOURCE_ZIPS`	Undocumented
Constant	`_SPLIT_MAP`	Undocumented
Constant	`_SPLIT_MAP_REV`	Undocumented

def download_activitynet_split(dataset_dir, split, source_dir=None, classes=None, max_duration=None, copy_files=True, num_workers=None, shuffle=None, seed=None, max_samples=None, version='200'): (source) ¶

Utility that downloads full or partial splits of the ActivityNet dataset.

Parameters
dataset_dir	the directory to download the dataset
split	the split to download. Supported values are `("train", "validation", "test")`
source_dir:`None`	the directory containing the manually downloaded ActivityNet files
classes:`None`	a string or list of strings specifying required classes to load. If provided, only samples containing at least one instance of a specified class will be loaded
max_duration:`None`	only videos with a duration in seconds that is less than or equal to the `max_duration` will be downloaded. By default, all videos are downloaded
copy_files:`True`	whether to move (False) or create copies (True) of the source files when populating `dataset_dir`. This is only relevant when a `source_dir` is provided
num_workers:`None`	a suggested number of threads to use when downloading individual videos
shuffle:`False`	whether to randomly shuffle the order in which samples are chosen for partial downloads
seed:`None`	a random seed to use when shuffling
max_samples:`None`	a maximum number of samples to load per split. If `classes` are also specified, only up to the number of samples that contain at least one specified class will be loaded. By default, all matching samples are loaded
version:"200"	the ActivityNet dataset version to download. The supported values are `("100", "200")`
Returns
a tuple of	num_samples: the total number of downloaded videos, or `None` if everything was already downloaded classes: the list of all classes, or `None` if everything was already downloaded did_download: whether any content was downloaded (True) or if all necessary files were already downloaded (False)

logger = (source) ¶

Undocumented

def _flatten_list(l): (source) ¶

Undocumented

def _get_all_classes(taxonomy): (source) ¶

Undocumented

_ANNOTATION_DOWNLOAD_LINKS: dict[str, str] = (source) ¶

Undocumented

Value

{'100': 'http://ec2-52-25-205-214.us-west-2.compute.amazonaws.com/files/activity↵
_net.v1-2.min.json',
 '200': 'http://ec2-52-25-205-214.us-west-2.compute.amazonaws.com/files/activity↵
_net.v1-3.min.json'}

_NUM_TOTAL_SAMPLES: dict = (source) ¶

Undocumented

Value

{'100': {'train': 4819, 'test': 2480, 'validation': 2383},
 '200': {'train': 10024, 'test': 5044, 'validation': 4926}}

_SOURCE_DIR_NAMES: dict = (source) ¶

Undocumented

Value

{'missing_files': [],
 'missing_files_v1-2_test': [],
 'missing_files_v1-3_test': [],
 'v1-2': ['test', 'train', 'val'],
 'v1-3': ['test', 'train_val']}

_SOURCE_ZIPS: list[str] = (source) ¶

Undocumented

Value

['missing_files.zip',
 'missing_files_v1-2_test.zip',
 'missing_files_v1-3_test.zip',
 'v1-2_test.tar.gz',
 'v1-2_train.tar.gz',
 'v1-2_val.tar.gz',
 'v1-3_test.tar.gz',
...

_SPLIT_MAP: dict[str, str] = (source) ¶

Undocumented

Value

{'train': 'training', 'test': 'testing', 'validation': 'validation'}

_SPLIT_MAP_REV = (source) ¶

Undocumented

Value

{v: k for k, v in _SPLIT_MAP.items()}