fiftyone.zoo#

Module contents#

The FiftyOne Zoo.

Classes:

`OrderedDict`	Dictionary that remembers insertion order
`GitHubRepository`(repo[, safe])	Class for interacting with a GitHub repository.
`ZooDatasetInfo`(zoo_dataset, dataset_type, ...)	Class containing info about a dataset in the FiftyOne Dataset Zoo.
`ZooDatasetSplitInfo`(split, num_samples)	Class containing info about a split of a dataset in the FiftyOne Dataset Zoo.
`ZooDataset`()	Base class for datasets made available in the FiftyOne Dataset Zoo.
`RemoteZooDataset`(dataset_dir[, url])	Class for working with remotely-sourced datasets that are compatible with the FiftyOne Dataset Zoo.
`DeprecatedZooDataset`()	Class representing a zoo dataset that no longer exists in the FiftyOne Dataset Zoo.
`defaultdict`	defaultdict(default_factory=None, /, [...]) --> dict with default factory
`HasZooModel`()	Mixin class for Config classes of `fiftyone.core.models.Model` instances whose models are stored in the FiftyOne Model Zoo.
`ZooModel`(base_name[, base_filename, subdir, ...])	Class describing a model in the FiftyOne Model Zoo.
`RemoteZooModel`(args, *kwargs)
`RemoteModelManagerConfig`(d)
`RemoteModelManager`(config)
`ZooModelsManifest`([models, name, url])	Class that describes the collection of models in the FiftyOne Model Zoo.
`RemoteZooModelsManifest`([models, name, url])	Class that describes the collection of remotely-sourced models in the FiftyOne Model Zoo.

Functions:

`list_zoo_datasets`([tags, source, license])	Lists the available datasets in the FiftyOne Dataset Zoo.
`list_zoo_dataset_sources`()	Returns the list of available zoo dataset sources.
`list_downloaded_zoo_datasets`()	Returns information about the zoo datasets that have been downloaded.
`download_zoo_dataset`(name_or_url[, split, ...])	Downloads the specified dataset from the FiftyOne Dataset Zoo.
`load_zoo_dataset`(name_or_url[, split, ...])	Loads the specified dataset from the FiftyOne Dataset Zoo.
`find_zoo_dataset`(name_or_url[, split])	Returns the directory containing the given zoo dataset.
`load_zoo_dataset_info`(name_or_url)	Loads the `ZooDatasetInfo` for the specified zoo dataset.
`get_zoo_dataset`(name_or_url[, overwrite])	Returns the `ZooDataset` instance for the given dataset.
`delete_zoo_dataset`(name_or_url[, split])	Deletes the zoo dataset from local disk, if necessary.
`deepcopy`(x[, memo, _nil])	Deep copy operation on arbitrary Python objects.
`list_zoo_models`([tags, source, license])	Returns the list of available models in the FiftyOne Model Zoo.
`list_downloaded_zoo_models`()	Returns information about the zoo models that have been downloaded.
`is_zoo_model_downloaded`(name)	Determines whether the zoo model of the given name is downloaded.
`download_zoo_model`(name_or_url[, ...])	Downloads the specified model from the FiftyOne Model Zoo.
`install_zoo_model_requirements`(name[, ...])	Installs any package requirements for the specified zoo model.
`ensure_zoo_model_requirements`(name[, ...])	Ensures that the package requirements for the specified zoo model are satisfied.
`load_zoo_model`(name_or_url[, model_name, ...])	Loads the specified model from the FiftyOne Model Zoo.
`find_zoo_model`(name)	Returns the path to the zoo model on disk.
`get_zoo_model`(name)	Returns the `ZooModel` instance for the specified zoo model.
`delete_zoo_model`(name)	Deletes the zoo model from local disk, if necessary.
`list_zoo_model_sources`()	Returns the list of remote model sources that are registered locally.
`register_zoo_model_source`(url_or_gh_repo[, ...])	Registers a remote source of models, if necessary.
`delete_zoo_model_source`(url_or_gh_repo)	Deletes the specified remote source and all downloaded models associated with it.

Exceptions:

ConfigError

Exception raised when an invalid Config instance is encountered.

class fiftyone.zoo.OrderedDict#

Bases: dict

Dictionary that remembers insertion order

Methods:

`clear`()
`popitem`([last])	Remove and return a (key, value) pair from the dictionary.
`move_to_end`(key[, last])	Move an existing element to the end (or beginning if last is false).
`update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`keys`()
`items`()
`values`()
`pop`(key[,default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
`setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`copy`()
`fromkeys`([value])	Create a new ordered dictionary with keys from iterable and values set to value.
`get`(key[, default])	Return the value for key if key is in the dictionary, else default.

clear() → None. Remove all items from od.#

popitem(last=True)#

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

move_to_end(key, last=True)#

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

update([E, ]**F) → None. Update D from dict/iterable E and F.#: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

keys() → a set-like object providing a view on D's keys#

items() → a set-like object providing a view on D's items#

values() → an object providing a view on D's values#

pop(key[, default]) → v, remove specified key and return the corresponding value.#: If the key is not found, return the default if given; otherwise, raise a KeyError.

setdefault(key, default=None)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

copy() → a shallow copy of od#

fromkeys(value=None)#: Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)#: Return the value for key if key is in the dictionary, else default.

class fiftyone.zoo.GitHubRepository(repo, safe=False)#

Bases: object

Class for interacting with a GitHub repository.

Note

To interact with private GitHub repositories that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

Parameters:

repo –
the GitHub repository or identifier, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
safe (False) – whether to allow repo to contain a tree path like https://github.com/<user>/<repo>/tree/<branch>/<path>. If safe=True and a <path> is found, it is extracted and stored in the safe_path() property

Attributes:

`user`	The username of the repo.
`repo`	The name of the repo.
`ref`	The ref (e.g. branch, tag, commit hash), if any.
`safe_path`	The path that was extracted from the provided ref, if any.
`identifier`	The repository identifier string.

Methods:

`get_repo_info`()	Returns a dict of info about the repository.
`get_file`(path[, outpath])	Downloads the file at the given path.
`list_path_contents`([path])	Returns the contents of the repo rooted at the given path.
`list_repo_contents`([recursive])	Returns a flat list of the repository's contents.
`download`(outdir)	Downloads the repository to the specified root directory.
`parse_url`(url)
`parse_identifier`(identifier)

property user#: The username of the repo.

property repo#: The name of the repo.

property ref#: The ref (e.g. branch, tag, commit hash), if any.

property safe_path#: The path that was extracted from the provided ref, if any.

property identifier#: The repository identifier string.

get_repo_info()#

Returns a dict of info about the repository.

Returns:: an info dict

get_file(path, outpath=None)#

Downloads the file at the given path.

Parameters:

path – the filepath in the repository
outpath (None) – a path on disk to write the file

Returns:

the file bytes, if no outpath is provided

list_path_contents(path=None)#

Returns the contents of the repo rooted at the given path.

Note

This method has a limit of 1,000 files. Documentation.

Parameters:: path (None) – an optional root path to start the search from
Returns:: a list of file info dicts

list_repo_contents(recursive=True)#

Returns a flat list of the repository’s contents.

Note

This method has a limit of 100,000 entries and 7MB response size. Documentation.

Parameters:: recursive (True) – whether to recursively traverse subdirectories
Returns:: a list of file info dicts

download(outdir)#

Downloads the repository to the specified root directory.

Parameters:: outdir – the output directory

static parse_url(url)#

static parse_identifier(identifier)#

fiftyone.zoo.list_zoo_datasets(tags=None, source=None, license=None)#

Lists the available datasets in the FiftyOne Dataset Zoo.

Also includes any remotely-sourced zoo datasets that you’ve downloaded.

Example usage:

import fiftyone as fo
import fiftyone.zoo as foz

#
# List all zoo datasets
#

names = foz.list_zoo_datasets()
print(names)

#
# List all zoo datasets with (both of) the specified tags
#

names = foz.list_zoo_datasets(tags=["image", "detection"])
print(names)

#
# List all zoo datasets available via the given source
#

names = foz.list_zoo_datasets(source="torch")
print(names)

Parameters:

tags (None) – only include datasets that have the specified tag or list of tags
source (None) – only include datasets available via the given source or list of sources
license (None) – only include datasets that are distributed under the specified license or any of the specified list of licenses. Run fiftyone zoo datasets list to see the available licenses

Returns:

a sorted list of dataset names

fiftyone.zoo.list_zoo_dataset_sources()#

Returns the list of available zoo dataset sources.

Returns:: a list of sources

fiftyone.zoo.list_downloaded_zoo_datasets()#

Returns information about the zoo datasets that have been downloaded.

Returns:: a dict mapping dataset names to (dataset_dir, ZooDatasetInfo) tuples

fiftyone.zoo.download_zoo_dataset(name_or_url, split=None, splits=None, overwrite=False, cleanup=True, **kwargs)#

Downloads the specified dataset from the FiftyOne Dataset Zoo.

Any dataset splits that have already been downloaded are not re-downloaded, unless overwrite == True is specified.

Note

To download from a private GitHub repository that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

Parameters:

name_or_url –
the name of the zoo dataset to download, or the remote source to download it from, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
split (None) – ("train", "validation", "test"). If neither split nor splits are provided, all available splits are downloaded. Consult the documentation for the ZooDataset you specified to see the supported splits
splits (None) – a list of splits to download, if applicable. Typical values are ("train", "validation", "test"). If neither split nor splits are provided, all available splits are downloaded. Consult the documentation for the ZooDataset you specified to see the supported splits
overwrite (False) – whether to overwrite any existing files
cleanup (True) – whether to cleanup any temporary files generated during download
**kwargs – optional arguments for the ZooDataset constructor or the remote dataset’s download_and_prepare() method

Returns:

a tuple of

info: the ZooDatasetInfo for the dataset
dataset_dir: the directory containing the dataset

fiftyone.zoo.load_zoo_dataset(name_or_url, split=None, splits=None, label_field=None, dataset_name=None, download_if_necessary=True, drop_existing_dataset=False, persistent=False, overwrite=False, cleanup=True, progress=None, **kwargs)#

Loads the specified dataset from the FiftyOne Dataset Zoo.

By default, the dataset will be downloaded if necessary.

Note

To download from a private GitHub repository that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

If you do not specify a custom dataset_name and you have previously loaded the same zoo dataset and split(s) into FiftyOne, the existing dataset will be returned.

Parameters:

name_or_url –
the name of the zoo dataset to load, or the remote source to load it from, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
split (None) – ("train", "validation", "test"). If neither split nor splits are provided, all available splits are loaded. Consult the documentation for the ZooDataset you specified to see the supported splits
splits (None) – a list of splits to load, if applicable. Typical values are ("train", "validation", "test"). If neither split nor splits are provided, all available splits are loaded. Consult the documentation for the ZooDataset you specified to see the supported splits
label_field (None) – the label field (or prefix, if the dataset contains multiple label fields) in which to store the dataset’s labels. By default, this is "ground_truth" if the dataset contains a single label field. If the dataset contains multiple label fields and this value is not provided, the labels will be stored under dataset-specific field names
dataset_name (None) – an optional name to give the returned fiftyone.core.dataset.Dataset. By default, a name will be constructed based on the dataset and split(s) you are loading
download_if_necessary (True) – whether to download the dataset if it is not found in the specified dataset directory
drop_existing_dataset (False) – whether to drop an existing dataset with the same name if it exists
persistent (False) – whether the dataset should persist in the database after the session terminates
overwrite (False) – whether to overwrite any existing files if the dataset is to be downloaded
cleanup (True) – whether to cleanup any temporary files generated during download
progress (None) – whether to render a progress bar (True/False), use the default value fiftyone.config.show_progress_bars (None), or a progress callback function to invoke instead
**kwargs – optional arguments to pass to the fiftyone.utils.data.importers.DatasetImporter constructor or the remote dataset’s load_dataset()` method. If ``download_if_necessary == True, then kwargs can also contain arguments for download_zoo_dataset()

Returns:

a fiftyone.core.dataset.Dataset

fiftyone.zoo.find_zoo_dataset(name_or_url, split=None)#

Returns the directory containing the given zoo dataset.

If a split is provided, the path to the dataset split is returned; otherwise, the path to the root directory is returned.

The dataset must be downloaded. Use download_zoo_dataset() to download datasets.

Parameters:

name_or_url –
the name of the zoo dataset or its remote source, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
split (None) – a specific split to locate

Returns:

the directory containing the dataset or split

Raises:

ValueError – if the dataset or split does not exist or has not been downloaded

fiftyone.zoo.load_zoo_dataset_info(name_or_url)#

Loads the ZooDatasetInfo for the specified zoo dataset.

The dataset must be downloaded. Use download_zoo_dataset() to download datasets.

Parameters:

name_or_url –

the name of the zoo dataset or its remote source, which can be:

a GitHub repo URL like https://github.com/<user>/<repo>
a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
a GitHub ref string like <user>/<repo>[/<ref>]
a publicly accessible URL of an archive (eg zip or tar) file

Returns:

the ZooDatasetInfo for the dataset

Raises:

ValueError – if the dataset has not been downloaded

fiftyone.zoo.get_zoo_dataset(name_or_url, overwrite=False, **kwargs)#

Returns the ZooDataset instance for the given dataset.

If the dataset is available from multiple sources, the default source is used.

Parameters:

name_or_url –
the name of the zoo dataset, or its remote source, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
overwrite (False) – whether to overwrite existing metadata if it has already been downloaded. Only applicable when name_or_url is a remote source
**kwargs – optional arguments for ZooDataset

Returns:

the ZooDataset instance

fiftyone.zoo.delete_zoo_dataset(name_or_url, split=None)#

Deletes the zoo dataset from local disk, if necessary.

If a split is provided, only that split is deleted.

Parameters:

name_or_url –
the name of the zoo dataset, or its remote source, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
split (None)

class fiftyone.zoo.ZooDatasetInfo(zoo_dataset, dataset_type, num_samples, downloaded_splits=None, parameters=None, classes=None)#

Bases: Serializable

Class containing info about a dataset in the FiftyOne Dataset Zoo.

Parameters:

zoo_dataset – the ZooDataset instance for the dataset
dataset_type – the fiftyone.types.Dataset type of the dataset
num_samples – the total number of samples in all downloaded splits of the dataset
downloaded_splits (None) – a dict of ZooDatasetSplitInfo instances describing the downloaded splits of the dataset, if applicable
parameters (None) – a dict of parameters for the dataset
classes (None) – a list of class label strings

Attributes:

`name`	The name of the dataset.
`zoo_dataset`	The fully-qualified class string for the `ZooDataset` of the dataset.
`dataset_type`	The fully-qualified class string of the `fiftyone.types.Dataset` type, if any.
`supported_splits`	A tuple of supported splits for the dataset, or None if the dataset does not have splits.
`url`	The dataset's URL, or None if it is not remotely-sourced.

Methods:

`get_zoo_dataset`()	Returns the `ZooDataset` instance for the dataset.
`get_dataset_type`()	Returns the `fiftyone.types.Dataset` type instance for the dataset.
`is_split_downloaded`(split)	Whether the given dataset split is downloaded.
`add_split`(split_info)	Adds the split to the dataset.
`remove_split`(split)	Removes the split from the dataset.
`attributes`()	Returns a list of class attributes to be serialized.
`from_dict`(d)	Loads a `ZooDatasetInfo` from a JSON dictionary.
`from_json`(json_path[, zoo_dataset, upgrade, ...])	Loads a `ZooDatasetInfo` from a JSON file on disk.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.

property name#: The name of the dataset.

property zoo_dataset#: The fully-qualified class string for the ZooDataset of the dataset.

property dataset_type#: The fully-qualified class string of the fiftyone.types.Dataset type, if any.

property supported_splits#: A tuple of supported splits for the dataset, or None if the dataset does not have splits.

property url#: The dataset’s URL, or None if it is not remotely-sourced.

get_zoo_dataset()#

Returns the ZooDataset instance for the dataset.

Returns:: a ZooDataset instance

get_dataset_type()#

Returns the fiftyone.types.Dataset type instance for the dataset.

Returns:: a fiftyone.types.Dataset instance

is_split_downloaded(split)#

Whether the given dataset split is downloaded.

Parameters:: split – the dataset split
Returns:: True/False

add_split(split_info)#

Adds the split to the dataset.

Parameters:: split_info – a ZooDatasetSplitInfo

remove_split(split)#

Removes the split from the dataset.

Parameters:: split – the name of the split

attributes()#

Returns a list of class attributes to be serialized.

Returns:: a list of class attributes

classmethod from_dict(d)#

Loads a ZooDatasetInfo from a JSON dictionary.

Parameters:: d – a JSON dictionary
Returns:: a ZooDatasetInfo

classmethod from_json(json_path, zoo_dataset=None, upgrade=False, warn_deprecated=False)#

Loads a ZooDatasetInfo from a JSON file on disk.

Parameters:

json_path – path to JSON file
zoo_dataset (None) – an existing ZooDataset instance
upgrade (False) – whether to upgrade the JSON file on disk if any migrations were necessary
warn_deprecated (False) – whether to issue a warning if the dataset has a deprecated format

Returns:

a ZooDatasetInfo

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.ZooDatasetSplitInfo(split, num_samples)#

Bases: Serializable

Class containing info about a split of a dataset in the FiftyOne Dataset Zoo.

Parameters:

split – the name of the split
num_samples – the number of samples in the split

Methods:

`attributes`()	Returns a list of class attributes to be serialized.
`from_dict`(d)	Loads a `ZooDatasetSplitInfo` from a JSON dictionary.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`from_json`(path, args, *kwargs)	Constructs a Serializable object from a JSON file.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.

attributes()#

Returns a list of class attributes to be serialized.

Returns:: a list of class attributes

classmethod from_dict(d)#

Loads a ZooDatasetSplitInfo from a JSON dictionary.

Parameters:: d – a JSON dictionary
Returns:: a ZooDatasetSplitInfo

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

path – the path to the JSON file on disk
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.ZooDataset#

Bases: object

Base class for datasets made available in the FiftyOne Dataset Zoo.

Attributes:

`name`	The name of the dataset.
`is_remote`	Whether the dataset is remotely-sourced.
`license`	The license or list,of,licenses under which the dataset is distributed, or None if unknown.
`tags`	A tuple of tags for the dataset.
`has_tags`	Whether the dataset has tags.
`parameters`	An optional dict of parameters describing the configuration of the zoo dataset when it was downloaded.
`supported_splits`	A tuple of supported splits for the dataset, or None if the dataset does not have splits.
`has_splits`	Whether the dataset has splits.
`has_patches`	Whether the dataset has patches that may need to be applied to already downloaded files.
`supports_partial_downloads`	Whether the dataset supports downloading partial subsets of its splits.
`requires_manual_download`	Whether this dataset requires some files to be manually downloaded by the user before the dataset can be loaded.
`importer_kwargs`	A dict of default kwargs to pass to this dataset's `fiftyone.utils.data.importers.DatasetImporter`.

Methods:

`has_tag`(tag)	Whether the dataset has the given tag.
`has_split`(split)	Whether the dataset has the given split.
`get_split_dir`(dataset_dir, split)	Returns the directory for the given split of the dataset.
`has_info`(dataset_dir)	Determines whether the directory contains `ZooDatasetInfo`.
`load_info`(dataset_dir[, upgrade, ...])	Loads the `ZooDatasetInfo` from the given dataset directory.
`get_info_path`(dataset_dir)	Returns the path to the `ZooDatasetInfo` for the dataset.
`download_and_prepare`(dataset_dir[, split, ...])	Downloads the dataset and prepares it for use.

property name#: The name of the dataset.

property is_remote#: Whether the dataset is remotely-sourced.

property license#: The license or list,of,licenses under which the dataset is distributed, or None if unknown.

property tags#: A tuple of tags for the dataset.

property has_tags#: Whether the dataset has tags.

property parameters#: An optional dict of parameters describing the configuration of the zoo dataset when it was downloaded.

property supported_splits#: A tuple of supported splits for the dataset, or None if the dataset does not have splits.

property has_splits#: Whether the dataset has splits.

property has_patches#: Whether the dataset has patches that may need to be applied to already downloaded files.

property supports_partial_downloads#: Whether the dataset supports downloading partial subsets of its splits.

property requires_manual_download#: Whether this dataset requires some files to be manually downloaded by the user before the dataset can be loaded.

property importer_kwargs#: A dict of default kwargs to pass to this dataset’s fiftyone.utils.data.importers.DatasetImporter.

has_tag(tag)#

Whether the dataset has the given tag.

Parameters:: tag – the tag
Returns:: True/False

has_split(split)#

Whether the dataset has the given split.

Parameters:: split – the dataset split
Returns:: True/False

get_split_dir(dataset_dir, split)#

Returns the directory for the given split of the dataset.

Parameters:

dataset_dir – the dataset directory
split – the dataset split

Returns:

the directory that will/does hold the specified split

static has_info(dataset_dir)#

Determines whether the directory contains ZooDatasetInfo.

Parameters:: dataset_dir – the dataset directory
Returns:: True/False

static load_info(dataset_dir, upgrade=True, warn_deprecated=False)#

Loads the ZooDatasetInfo from the given dataset directory.

Parameters:

dataset_dir – the directory in which to construct the dataset
upgrade (True) – whether to upgrade the JSON file on disk if any migrations were necessary
warn_deprecated (False) – whether to issue a warning if the dataset has a deprecated format

Returns:

the ZooDatasetInfo for the dataset

static get_info_path(dataset_dir)#

Returns the path to the ZooDatasetInfo for the dataset.

Parameters:: dataset_dir – the dataset directory
Returns:: the path to the ZooDatasetInfo

download_and_prepare(dataset_dir, split=None, splits=None, cleanup=True)#

Downloads the dataset and prepares it for use.

If the requested splits have already been downloaded, they are not re-downloaded.

Parameters:

dataset_dir – the directory in which to construct the dataset
split (None) – split nor splits are provided, the full dataset is downloaded
splits (None) – a list of splits to download, if applicable. If neither split nor splits are provided, the full dataset is downloaded
cleanup (True) – whether to cleanup any temporary files generated during download

Returns:

the ZooDatasetInfo for the dataset

class fiftyone.zoo.RemoteZooDataset(dataset_dir, url=None, **kwargs)#

Bases: ZooDataset

Class for working with remotely-sourced datasets that are compatible with the FiftyOne Dataset Zoo.

Parameters:

dataset_dir – the dataset’s local directory, which must contain a valid dataset YAML file
url (None) –
the dataset’s remote source, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
This is explicitly provided rather than relying on the YAML file’s url property in case the caller has specified a particular branch or commit
**kwargs – optional keyword arguments for the dataset’s download_and_prepare() and/or load_dataset() methods

Attributes:

`metadata`
`name`	The name of the dataset.
`url`
`is_remote`	Whether the dataset is remotely-sourced.
`author`
`version`
`source`
`license`	The license or list,of,licenses under which the dataset is distributed, or None if unknown.
`description`
`fiftyone_version`
`tags`	A tuple of tags for the dataset.
`supported_splits`	A tuple of supported splits for the dataset, or None if the dataset does not have splits.
`supports_partial_downloads`	Whether the dataset supports downloading partial subsets of its splits.
`size_samples`
`has_patches`	Whether the dataset has patches that may need to be applied to already downloaded files.
`has_splits`	Whether the dataset has splits.
`has_tags`	Whether the dataset has tags.
`importer_kwargs`	A dict of default kwargs to pass to this dataset's `fiftyone.utils.data.importers.DatasetImporter`.
`parameters`	An optional dict of parameters describing the configuration of the zoo dataset when it was downloaded.
`requires_manual_download`	Whether this dataset requires some files to be manually downloaded by the user before the dataset can be loaded.

Methods:

`download_and_prepare`(dataset_dir[, split, ...])	Downloads the dataset and prepares it for use.
`get_info_path`(dataset_dir)	Returns the path to the `ZooDatasetInfo` for the dataset.
`get_split_dir`(dataset_dir, split)	Returns the directory for the given split of the dataset.
`has_info`(dataset_dir)	Determines whether the directory contains `ZooDatasetInfo`.
`has_split`(split)	Whether the dataset has the given split.
`has_tag`(tag)	Whether the dataset has the given tag.
`load_info`(dataset_dir[, upgrade, ...])	Loads the `ZooDatasetInfo` from the given dataset directory.

property metadata#

property name#: The name of the dataset.

property url#

property is_remote#: Whether the dataset is remotely-sourced.

property author#

property version#

property source#

property license#: The license or list,of,licenses under which the dataset is distributed, or None if unknown.

property description#

property fiftyone_version#

property tags#: A tuple of tags for the dataset.

property supported_splits#: A tuple of supported splits for the dataset, or None if the dataset does not have splits.

property supports_partial_downloads#: Whether the dataset supports downloading partial subsets of its splits.

property size_samples#

download_and_prepare(dataset_dir, split=None, splits=None, cleanup=True)#

Downloads the dataset and prepares it for use.

If the requested splits have already been downloaded, they are not re-downloaded.

Parameters:

dataset_dir – the directory in which to construct the dataset
split (None) – split nor splits are provided, the full dataset is downloaded
splits (None) – a list of splits to download, if applicable. If neither split nor splits are provided, the full dataset is downloaded
cleanup (True) – whether to cleanup any temporary files generated during download

Returns:

the ZooDatasetInfo for the dataset

static get_info_path(dataset_dir)#

Returns the path to the ZooDatasetInfo for the dataset.

Parameters:: dataset_dir – the dataset directory
Returns:: the path to the ZooDatasetInfo

get_split_dir(dataset_dir, split)#

Returns the directory for the given split of the dataset.

Parameters:

dataset_dir – the dataset directory
split – the dataset split

Returns:

the directory that will/does hold the specified split

static has_info(dataset_dir)#

Determines whether the directory contains ZooDatasetInfo.

Parameters:: dataset_dir – the dataset directory
Returns:: True/False

property has_patches#: Whether the dataset has patches that may need to be applied to already downloaded files.

has_split(split)#

Whether the dataset has the given split.

Parameters:: split – the dataset split
Returns:: True/False

property has_splits#: Whether the dataset has splits.

has_tag(tag)#

Whether the dataset has the given tag.

Parameters:: tag – the tag
Returns:: True/False

property has_tags#: Whether the dataset has tags.

property importer_kwargs#: A dict of default kwargs to pass to this dataset’s fiftyone.utils.data.importers.DatasetImporter.

static load_info(dataset_dir, upgrade=True, warn_deprecated=False)#

Loads the ZooDatasetInfo from the given dataset directory.

Parameters:

dataset_dir – the directory in which to construct the dataset
upgrade (True) – whether to upgrade the JSON file on disk if any migrations were necessary
warn_deprecated (False) – whether to issue a warning if the dataset has a deprecated format

Returns:

the ZooDatasetInfo for the dataset

property parameters#: An optional dict of parameters describing the configuration of the zoo dataset when it was downloaded.

property requires_manual_download#: Whether this dataset requires some files to be manually downloaded by the user before the dataset can be loaded.

class fiftyone.zoo.DeprecatedZooDataset#

Bases: ZooDataset

Class representing a zoo dataset that no longer exists in the FiftyOne Dataset Zoo.

Attributes:

`name`	The name of the dataset.
`supported_splits`	A tuple of supported splits for the dataset, or None if the dataset does not have splits.
`has_patches`	Whether the dataset has patches that may need to be applied to already downloaded files.
`has_splits`	Whether the dataset has splits.
`has_tags`	Whether the dataset has tags.
`importer_kwargs`	A dict of default kwargs to pass to this dataset's `fiftyone.utils.data.importers.DatasetImporter`.
`is_remote`	Whether the dataset is remotely-sourced.
`license`	The license or list,of,licenses under which the dataset is distributed, or None if unknown.
`parameters`	An optional dict of parameters describing the configuration of the zoo dataset when it was downloaded.
`requires_manual_download`	Whether this dataset requires some files to be manually downloaded by the user before the dataset can be loaded.
`supports_partial_downloads`	Whether the dataset supports downloading partial subsets of its splits.
`tags`	A tuple of tags for the dataset.

Methods:

`download_and_prepare`(dataset_dir[, split, ...])	Downloads the dataset and prepares it for use.
`get_info_path`(dataset_dir)	Returns the path to the `ZooDatasetInfo` for the dataset.
`get_split_dir`(dataset_dir, split)	Returns the directory for the given split of the dataset.
`has_info`(dataset_dir)	Determines whether the directory contains `ZooDatasetInfo`.
`has_split`(split)	Whether the dataset has the given split.
`has_tag`(tag)	Whether the dataset has the given tag.
`load_info`(dataset_dir[, upgrade, ...])	Loads the `ZooDatasetInfo` from the given dataset directory.

property name#: The name of the dataset.

property supported_splits#: A tuple of supported splits for the dataset, or None if the dataset does not have splits.

download_and_prepare(dataset_dir, split=None, splits=None, cleanup=True)#

Downloads the dataset and prepares it for use.

If the requested splits have already been downloaded, they are not re-downloaded.

Parameters:

dataset_dir – the directory in which to construct the dataset
split (None) – split nor splits are provided, the full dataset is downloaded
splits (None) – a list of splits to download, if applicable. If neither split nor splits are provided, the full dataset is downloaded
cleanup (True) – whether to cleanup any temporary files generated during download

Returns:

the ZooDatasetInfo for the dataset

static get_info_path(dataset_dir)#

Returns the path to the ZooDatasetInfo for the dataset.

Parameters:: dataset_dir – the dataset directory
Returns:: the path to the ZooDatasetInfo

get_split_dir(dataset_dir, split)#

Returns the directory for the given split of the dataset.

Parameters:

dataset_dir – the dataset directory
split – the dataset split

Returns:

the directory that will/does hold the specified split

static has_info(dataset_dir)#

Determines whether the directory contains ZooDatasetInfo.

Parameters:: dataset_dir – the dataset directory
Returns:: True/False

property has_patches#: Whether the dataset has patches that may need to be applied to already downloaded files.

has_split(split)#

Whether the dataset has the given split.

Parameters:: split – the dataset split
Returns:: True/False

property has_splits#: Whether the dataset has splits.

has_tag(tag)#

Whether the dataset has the given tag.

Parameters:: tag – the tag
Returns:: True/False

property has_tags#: Whether the dataset has tags.

property importer_kwargs#: A dict of default kwargs to pass to this dataset’s fiftyone.utils.data.importers.DatasetImporter.

property is_remote#: Whether the dataset is remotely-sourced.

property license#: The license or list,of,licenses under which the dataset is distributed, or None if unknown.

static load_info(dataset_dir, upgrade=True, warn_deprecated=False)#

Loads the ZooDatasetInfo from the given dataset directory.

Parameters:

dataset_dir – the directory in which to construct the dataset
upgrade (True) – whether to upgrade the JSON file on disk if any migrations were necessary
warn_deprecated (False) – whether to issue a warning if the dataset has a deprecated format

Returns:

the ZooDatasetInfo for the dataset

property parameters#: An optional dict of parameters describing the configuration of the zoo dataset when it was downloaded.

property requires_manual_download#: Whether this dataset requires some files to be manually downloaded by the user before the dataset can be loaded.

property supports_partial_downloads#: Whether the dataset supports downloading partial subsets of its splits.

property tags#: A tuple of tags for the dataset.

class fiftyone.zoo.defaultdict#

Bases: dict

defaultdict(default_factory=None, /, […]) –> dict with default factory

The default factory is called without arguments to produce a new value when a key is not present, in __getitem__ only. A defaultdict compares equal to a dict with the same items. All remaining arguments are treated the same as if they were passed to the dict constructor, including keyword arguments.

Methods:

`clear`()
`copy`()
`fromkeys`([value])	Create a new dictionary with keys from iterable and values set to value.
`get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`items`()
`keys`()
`pop`(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
`popitem`()	Remove and return a (key, value) pair as a 2-tuple.
`setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`values`()

Attributes:

default_factory

Factory for default value called by __missing__().

clear() → None. Remove all items from D.#

copy() → a shallow copy of D.#

default_factory#: Factory for default value called by __missing__().

fromkeys(value=None, /)#: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#: Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items#

keys() → a set-like object providing a view on D's keys#

pop(k[, d]) → v, remove specified key and return the corresponding value.#: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.#: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values#

fiftyone.zoo.deepcopy(x, memo=None, _nil=[])#

Deep copy operation on arbitrary Python objects.

See the module’s __doc__ string for more info.

exception fiftyone.zoo.ConfigError#

Bases: Exception

Exception raised when an invalid Config instance is encountered.

Methods:

`add_note`	Exception.add_note(note) -- add a note to the exception
`with_traceback`	Exception.with_traceback(tb) -- set self.__traceback__ to tb and return self.

Attributes:

args

add_note()#: Exception.add_note(note) – add a note to the exception

args#

with_traceback()#: Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

fiftyone.zoo.list_zoo_models(tags=None, source=None, license=None)#

Returns the list of available models in the FiftyOne Model Zoo.

Also includes models from any remote sources that you’ve registered.

Example usage:

import fiftyone as fo
import fiftyone.zoo as foz

#
# List all zoo models
#

names = foz.list_zoo_models()
print(names)

#
# List all zoo models with the specified tag(s)
#

names = foz.list_zoo_models(tags="torch")
print(names)

Parameters:

tags (None) – only include models that have the specified tag or list of tags
source (None) – only include models available via the given remote source
license (None) – only include models that are distributed under the specified license or any of the specified list of licenses. Run fiftyone zoo models list to see the available licenses

Returns:

a list of model names

fiftyone.zoo.list_downloaded_zoo_models()#

Returns information about the zoo models that have been downloaded.

Returns:: a dict mapping model names to (model path, ZooModel) tuples

fiftyone.zoo.is_zoo_model_downloaded(name)#

Determines whether the zoo model of the given name is downloaded.

Parameters:: name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model
Returns:: True/False

fiftyone.zoo.download_zoo_model(name_or_url, model_name=None, overwrite=False)#

Downloads the specified model from the FiftyOne Model Zoo.

If the model is already downloaded, it is not re-downloaded unless overwrite == True is specified.

Note

To download from a private GitHub repository that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

Parameters:

name_or_url –
the name of the zoo model to download, which can have @<ver> appended to refer to a specific version of the model, or the remote source to download it from, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
model_name (None) – the specific model to download, if name_or_url is a remote source
overwrite (False) – whether to overwrite any existing files

Returns:

tuple of

model: the ZooModel for the model
model_path: the path to the downloaded model on disk

fiftyone.zoo.install_zoo_model_requirements(name, error_level=None)#

Installs any package requirements for the specified zoo model.

Parameters:

name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model
error_level (None) –
the error level to use, defined as:
- 0: raise error if a requirement install fails
- 1: log warning if a requirement install fails
- 2: ignore install fails requirements
By default, fo.config.requirement_error_level is used

fiftyone.zoo.ensure_zoo_model_requirements(name, error_level=None, log_success=True)#

Ensures that the package requirements for the specified zoo model are satisfied.

Parameters:

name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model
error_level (None) –
the error level to use when installing/ensuring requirements, defined as:
- 0: raise error if a requirement is not satisfied
- 1: log warning if a requirement is not satisfied
- 2: ignore unsatisfied requirements
By default, fo.config.requirement_error_level is used
log_success (True) – whether to generate a log message when a requirement is satisfied

fiftyone.zoo.load_zoo_model(name_or_url, model_name=None, download_if_necessary=True, ensure_requirements=True, install_requirements=False, error_level=None, cache=True, **kwargs)#

Loads the specified model from the FiftyOne Model Zoo.

By default, the model will be downloaded if necessary, and any documented package requirements will be checked to ensure that they are installed.

Note

To download from a private GitHub repository that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

Parameters:

name_or_url –
the name of the zoo model to load, which can have @<ver> appended to refer to a specific version of the model, or the remote source to load it from, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
model_name (None) – the specific model to load, if name_or_url is a remote source
download_if_necessary (True) – whether to download the model if necessary
ensure_requirements (True) – whether to ensure any requirements are installed before loading the model
install_requirements (False) – whether to install any requirements before loading the model
error_level (None) –
the error level to use when installing/ensuring requirements, defined as:
- 0: raise error if a requirement is not satisfied
- 1: log warning if a requirement is not satisfied
- 2: ignore unsatisfied requirements
By default, fo.config.requirement_error_level is used
cache (True) – whether to store a weak reference to the model so that running this method again will return the same instance while the model is still in use
**kwargs – keyword arguments to inject into the model’s Config instance

Returns:

a fiftyone.core.models.Model

fiftyone.zoo.find_zoo_model(name)#

Returns the path to the zoo model on disk.

The model must be downloaded. Use download_zoo_model() to download models.

Parameters:: name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model
Returns:: the path to the model on disk
Raises:: ValueError – if the model does not exist or has not been downloaded

fiftyone.zoo.get_zoo_model(name)#

Returns the ZooModel instance for the specified zoo model.

Parameters:: name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model
Returns:: a ZooModel

fiftyone.zoo.delete_zoo_model(name)#

Deletes the zoo model from local disk, if necessary.

Parameters:: name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model

fiftyone.zoo.list_zoo_model_sources()#

Returns the list of remote model sources that are registered locally.

Returns:: the list of remote sources

fiftyone.zoo.register_zoo_model_source(url_or_gh_repo, overwrite=False)#

Registers a remote source of models, if necessary.

Note

To download from a private GitHub repository that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

Parameters:

url_or_gh_repo –
the remote source to register, which can be:
- a GitHub repo URL like https://github.com/<user>/<repo>
- a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
- a GitHub ref string like <user>/<repo>[/<ref>]
- a publicly accessible URL of an archive (eg zip or tar) file
overwrite (False) – whether to overwrite any existing files

fiftyone.zoo.delete_zoo_model_source(url_or_gh_repo)#

Deletes the specified remote source and all downloaded models associated with it.

Parameters:

url_or_gh_repo –

the remote source to delete, which can be:

a GitHub repo URL like https://github.com/<user>/<repo>
a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>
a GitHub ref string like <user>/<repo>[/<ref>]
a publicly accessible URL of an archive (eg zip or tar) file

class fiftyone.zoo.HasZooModel#

Bases: HasPublishedModel

Mixin class for Config classes of fiftyone.core.models.Model instances whose models are stored in the FiftyOne Model Zoo.

This class provides the following functionality:

The model to load can be specified either by:
1. providing a model_name, which specifies the zoo model to load. The model will be downloaded, if necessary
2. providing a model_path, which directly specifies the path to the model to load
fiftyone.core.models.ModelConfig definitions that use zoo models with default deployments will have default values for any unspecified parameters loaded and applied at runtime

Parameters:

model_name – the name of the zoo model to load. If this value is provided, model_path does not need to be
model_path – the path to an already downloaded zoo model on disk to load. If this value is provided, model_name does not need to be

Methods:

`download_model_if_necessary`()	Downloads the published model specified by the config, if necessary.
`init`(d)	Initializes the published model config.

download_model_if_necessary()#

Downloads the published model specified by the config, if necessary.

After this method is called, the model_path attribute will always contain the path to the model on disk.

init(d)#

Initializes the published model config.

This method should be called by ModelConfig.__init__(), and it performs the following tasks:

Parses the model_name and model_path parameters
Populates any default parameters in the provided ModelConfig dict

Parameters:: d – a ModelConfig dict
Returns:: a ModelConfig dict with any default parameters populated

class fiftyone.zoo.ZooModel(base_name, base_filename=None, subdir=None, manager=None, author=None, version=None, url=None, source=None, license=None, description=None, size_bytes=None, default_deployment_config_dict=None, requirements=None, tags=None, date_added=None, training_data=None)#

Bases: Model

Class describing a model in the FiftyOne Model Zoo.

Parameters:

base_name – the base name of the model (no version info)
base_filename (None) – the base filename or directory of the model (no version info), if applicable
author (None) – the author of the model
version (None) – the version of the model
url (None) – the URL at which the model is hosted
license (None) – the license under which the model is distributed
source (None) – the source of the model
description (None) – the description of the model
tags (None) – a list of tags for the model
size_bytes (None) – the size of the model on disk
date_added (None) – the datetime that the model was added to the zoo
requirements (None) – the eta.core.models.ModelRequirements for the model
manager (None) – the fiftyone.core.models.ModelManager instance that describes the remote storage location of the model, if applicable
default_deployment_config_dict (None) – a fiftyone.core.models.ModelConfig dict describing the recommended settings for deploying the model
training_data (None) – the training data information for the model, if applicable

Methods:

`attributes`()	Returns a list of class attributes to be serialized.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`download_model`(model_path[, force])	Downloads the model to the given local path.
`ensure_requirements`([error_level, log_success])	Ensures that any requirement(s) for this model are satisfied.
`flush_model`(model_path)	Flushes the copy of the model at the given local path, if necessary.
`flush_model_from_dir`(models_dir)	Flushes the copy of the model in the given models directory, if necessary.
`from_dict`(d[, subdir])	Constructs a Model from a JSON dictionary.
`from_json`(path, args, *kwargs)	Constructs a Serializable object from a JSON file.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`get_path_in_dir`(models_dir)	Gets the model path for the model in the given models directory.
`has_tag`(tag)	Whether this model has the given tag.
`has_version_str`(name)	Determines whether the given model name has a version string.
`install_requirements`([error_level])	Installs any necessary requirement(s) for this model.
`is_in_dir`(models_dir)	Determines whether a copy of the model exists in the given models directory.
`is_model_downloaded`(model_path)	Determines whether the model is downloaded to the given location.
`parse_name`(name)	Parses the model name, returning the base name and the version, if any.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.

Attributes:

`comp_version`	The version of this model expressed as a distutils.version.LooseVersion intended for comparison operations.
`filename`	The version-aware filename of the model.
`has_manager`	Whether this model has a manager instance.
`has_requirements`	Whether this model has requirements in order to be used.
`has_tags`	Whether this model has tags.
`has_version`	Determines whether the model has a version.
`name`	The version-aware name of the model.
`supports_cpu`	Whether the model supports CPU (True), or not (False), or unknown (None).
`supports_gpu`	Whether the model supports GPU (True), or not (False), or unknown (None).

attributes()#

Returns a list of class attributes to be serialized.

Returns:: a list of class attributes

property comp_version#

The version of this model expressed as a distutils.version.LooseVersion intended for comparison operations.

Models with no version are given a version of 0.0.0.

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

download_model(model_path, force=False)#

Downloads the model to the given local path.

If the download is forced, any existing model is overwritten. If the download is not forced, the model will only be downloaded if it does not already exist locally.

If the model has no manager, nothing is downloaded.

Parameters:

model_path – the path to which to download the model
force – whether to force download the model. If True, the model is always downloaded. If False, the model is only downloaded if necessary. The default is False

Raises:

ModelError – if model downloading is not currently allowed

ensure_requirements(error_level=0, log_success=False)#

Ensures that any requirement(s) for this model are satisfied.

Parameters:

error_level –
the error level to use, defined as:

0: raise error if a requirement is not satisfied 1: log warning if a requirement is not satisifed 2: ignore unsatisifed requirements
log_success – whether to generate a log message when a requirement is satisifed

property filename#: The version-aware filename of the model.

flush_model(model_path)#

Flushes the copy of the model at the given local path, if necessary.

Parameters:: model_path – the path on disk for the model

flush_model_from_dir(models_dir)#

Flushes the copy of the model in the given models directory, if necessary.

Parameters:: models_dir – the models directory

classmethod from_dict(d, subdir=None)#

Constructs a Model from a JSON dictionary.

Parameters:

d – a JSON dictionary
subdir (optional) – a subdirectory for the model

Returns:

a Model instance

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

path – the path to the JSON file on disk
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

get_path_in_dir(models_dir)#

Gets the model path for the model in the given models directory.

Parameters:: models_dir – the models directory
Returns:: the model path, or None if the model has no manager

property has_manager#: Whether this model has a manager instance.

property has_requirements#: Whether this model has requirements in order to be used.

has_tag(tag)#

Whether this model has the given tag.

Parameters:: tag – a tag
Returns:: True/False

property has_tags#: Whether this model has tags.

property has_version#: Determines whether the model has a version.

static has_version_str(name)#

Determines whether the given model name has a version string.

Parameters:: name – the model name
Returns:: True/False

install_requirements(error_level=0)#

Installs any necessary requirement(s) for this model.

Parameters:

error_level –

the error level to use, defined as:

0: raise error if an install fails 1: log warning if an install fails 2: ignore install fails

is_in_dir(models_dir)#

Determines whether a copy of the model exists in the given models directory.

Parameters:: models_dir – the models directory
Returns:: True/False, or None if the model has no manager

is_model_downloaded(model_path)#

Determines whether the model is downloaded to the given location.

If model_path is an archive, this method will also return True if a directory with the same basename as model_path exists.

Parameters:: model_path – the path on disk for the model
Returns:: True/False, or None if the model has no manager

property name#: The version-aware name of the model.

static parse_name(name)#

Parses the model name, returning the base name and the version, if any.

Parameters:: name – the name of the model, which can have “@<ver>” appended to refer to a specific version of the model
Returns:: the base name of the model version: the version of the model, or None if no version was found
Return type:: base_name

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

property supports_cpu#: Whether the model supports CPU (True), or not (False), or unknown (None).

property supports_gpu#: Whether the model supports GPU (True), or not (False), or unknown (None).

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.RemoteZooModel(*args, **kwargs)#

Bases: ZooModel

Methods:

`load_model`(**kwargs)
`resolve_input`(ctx)
`parse_parameters`(ctx, params)
`attributes`()	Returns a list of class attributes to be serialized.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`download_model`(model_path[, force])	Downloads the model to the given local path.
`ensure_requirements`([error_level, log_success])	Ensures that any requirement(s) for this model are satisfied.
`flush_model`(model_path)	Flushes the copy of the model at the given local path, if necessary.
`flush_model_from_dir`(models_dir)	Flushes the copy of the model in the given models directory, if necessary.
`from_dict`(d[, subdir])	Constructs a Model from a JSON dictionary.
`from_json`(path, args, *kwargs)	Constructs a Serializable object from a JSON file.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`get_path_in_dir`(models_dir)	Gets the model path for the model in the given models directory.
`has_tag`(tag)	Whether this model has the given tag.
`has_version_str`(name)	Determines whether the given model name has a version string.
`install_requirements`([error_level])	Installs any necessary requirement(s) for this model.
`is_in_dir`(models_dir)	Determines whether a copy of the model exists in the given models directory.
`is_model_downloaded`(model_path)	Determines whether the model is downloaded to the given location.
`parse_name`(name)	Parses the model name, returning the base name and the version, if any.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.

Attributes:

`comp_version`	The version of this model expressed as a distutils.version.LooseVersion intended for comparison operations.
`filename`	The version-aware filename of the model.
`has_manager`	Whether this model has a manager instance.
`has_requirements`	Whether this model has requirements in order to be used.
`has_tags`	Whether this model has tags.
`has_version`	Determines whether the model has a version.
`name`	The version-aware name of the model.
`supports_cpu`	Whether the model supports CPU (True), or not (False), or unknown (None).
`supports_gpu`	Whether the model supports GPU (True), or not (False), or unknown (None).

load_model(**kwargs)#

resolve_input(ctx)#

parse_parameters(ctx, params)#

attributes()#

Returns a list of class attributes to be serialized.

Returns:: a list of class attributes

property comp_version#

The version of this model expressed as a distutils.version.LooseVersion intended for comparison operations.

Models with no version are given a version of 0.0.0.

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

download_model(model_path, force=False)#

Downloads the model to the given local path.

If the download is forced, any existing model is overwritten. If the download is not forced, the model will only be downloaded if it does not already exist locally.

If the model has no manager, nothing is downloaded.

Parameters:

model_path – the path to which to download the model
force – whether to force download the model. If True, the model is always downloaded. If False, the model is only downloaded if necessary. The default is False

Raises:

ModelError – if model downloading is not currently allowed

ensure_requirements(error_level=0, log_success=False)#

Ensures that any requirement(s) for this model are satisfied.

Parameters:

error_level –
the error level to use, defined as:

0: raise error if a requirement is not satisfied 1: log warning if a requirement is not satisifed 2: ignore unsatisifed requirements
log_success – whether to generate a log message when a requirement is satisifed

property filename#: The version-aware filename of the model.

flush_model(model_path)#

Flushes the copy of the model at the given local path, if necessary.

Parameters:: model_path – the path on disk for the model

flush_model_from_dir(models_dir)#

Flushes the copy of the model in the given models directory, if necessary.

Parameters:: models_dir – the models directory

classmethod from_dict(d, subdir=None)#

Constructs a Model from a JSON dictionary.

Parameters:

d – a JSON dictionary
subdir (optional) – a subdirectory for the model

Returns:

a Model instance

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

path – the path to the JSON file on disk
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

get_path_in_dir(models_dir)#

Gets the model path for the model in the given models directory.

Parameters:: models_dir – the models directory
Returns:: the model path, or None if the model has no manager

property has_manager#: Whether this model has a manager instance.

property has_requirements#: Whether this model has requirements in order to be used.

has_tag(tag)#

Whether this model has the given tag.

Parameters:: tag – a tag
Returns:: True/False

property has_tags#: Whether this model has tags.

property has_version#: Determines whether the model has a version.

static has_version_str(name)#

Determines whether the given model name has a version string.

Parameters:: name – the model name
Returns:: True/False

install_requirements(error_level=0)#

Installs any necessary requirement(s) for this model.

Parameters:

error_level –

the error level to use, defined as:

0: raise error if an install fails 1: log warning if an install fails 2: ignore install fails

is_in_dir(models_dir)#

Determines whether a copy of the model exists in the given models directory.

Parameters:: models_dir – the models directory
Returns:: True/False, or None if the model has no manager

is_model_downloaded(model_path)#

Determines whether the model is downloaded to the given location.

If model_path is an archive, this method will also return True if a directory with the same basename as model_path exists.

Parameters:: model_path – the path on disk for the model
Returns:: True/False, or None if the model has no manager

property name#: The version-aware name of the model.

static parse_name(name)#

Parses the model name, returning the base name and the version, if any.

Parameters:: name – the name of the model, which can have “@<ver>” appended to refer to a specific version of the model
Returns:: the base name of the model version: the version of the model, or None if no version was found
Return type:: base_name

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

property supports_cpu#: Whether the model supports CPU (True), or not (False), or unknown (None).

property supports_gpu#: Whether the model supports GPU (True), or not (False), or unknown (None).

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.RemoteModelManagerConfig(d)#

Bases: ModelManagerConfig

Methods:

`attributes`()	Returns a list of attributes to be serialized.
`builder`()	Returns a ConfigBuilder instance for this class.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`default`()	Returns the default config instance.
`from_dict`(d)	Constructs a Config object from a JSON dictionary.
`from_json`(path, args, *kwargs)	Constructs a Serializable object from a JSON file.
`from_kwargs`(**kwargs)	Constructs a Config object from keyword arguments.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`load_default`()	Loads the default config instance from file.
`parse_array`(d, key[, default])	Parses a raw array attribute.
`parse_bool`(d, key[, default])	Parses a boolean value.
`parse_categorical`(d, key, choices[, default])	Parses a categorical JSON field, which must take a value from among the given choices.
`parse_dict`(d, key[, default])	Parses a dictionary attribute.
`parse_int`(d, key[, default])	Parses an integer attribute.
`parse_mutually_exclusive_fields`(fields)	Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value.
`parse_number`(d, key[, default])	Parses a number attribute.
`parse_object`(d, key, cls[, default])	Parses an object attribute.
`parse_object_array`(d, key, cls[, default])	Parses an array of objects.
`parse_object_dict`(d, key, cls[, default])	Parses a dictionary whose values are objects.
`parse_path`(d, key[, default])	Parses a path attribute.
`parse_raw`(d, key[, default])	Parses a raw (arbitrary) JSON field.
`parse_string`(d, key[, default])	Parses a string attribute.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`validate_all_or_nothing_fields`(fields)	Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.

attributes()#

Returns a list of attributes to be serialized.

Returns:: a list of attributes

classmethod builder()#: Returns a ConfigBuilder instance for this class.

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

classmethod default()#

Returns the default config instance.

By default, this method instantiates the class from an empty dictionary, which will only succeed if all attributes are optional. Otherwise, subclasses should override this method to provide the desired default configuration.

classmethod from_dict(d)#

Constructs a Config object from a JSON dictionary.

Config subclass constructors accept JSON dictionaries, so this method simply passes the dictionary to cls().

Parameters:: d – a dict of fields expected by cls
Returns:: an instance of cls

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

path – the path to the JSON file on disk
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_kwargs(**kwargs)#

Constructs a Config object from keyword arguments.

Parameters:: **kwargs – keyword arguments that define the fields expected by cls
Returns:: an instance of cls

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

classmethod load_default()#

Loads the default config instance from file.

Subclasses must implement this method if they intend to support default instances.

static parse_array(d, key, default=<eta.core.config.NoDefault object>)#

Parses a raw array attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default list to return if key is not present

Returns:

a list of raw (untouched) values

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_bool(d, key, default=<eta.core.config.NoDefault object>)#

Parses a boolean value.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default bool to return if key is not present

Returns:

True/False

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_categorical(d, key, choices, default=<eta.core.config.NoDefault object>)#

Parses a categorical JSON field, which must take a value from among the given choices.

Parameters:

d – a JSON dictionary
key – the key to parse
choices – either an iterable of possible values or an enum-like class whose attributes define the possible values
default – a default value to return if key is not present

Returns:

the raw (untouched) value of the given field, which is equal to a value from choices

Raises:

ConfigError – if the key was present in the dictionary but its value was not an allowed choice, or if no default value was provided and the key was not found in the dictionary

static parse_dict(d, key, default=<eta.core.config.NoDefault object>)#

Parses a dictionary attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default dict to return if key is not present

Returns:

a dictionary

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_int(d, key, default=<eta.core.config.NoDefault object>)#

Parses an integer attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default integer value to return if key is not present

Returns:

an int

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_mutually_exclusive_fields(fields)#

Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value.

Parameters:: fields – a dictionary of pre-parsed fields
Returns:: the (field, value) that was set
Raises:: ConfigError – if zero or more than one truthy value was found

static parse_number(d, key, default=<eta.core.config.NoDefault object>)#

Parses a number attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default numeric value to return if key is not present

Returns:

a number (e.g. int, float)

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses an object attribute.

The value of d[key] can be either an instance of cls or a serialized dict from an instance of cls.

Parameters:

d – a JSON dictionary
key – the key to parse
cls – the class of d[key]
default – a default cls instance to return if key is not present

Returns:

an instance of cls

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object_array(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses an array of objects.

The values in d[key] can be either instances of cls or serialized dicts from instances of cls.

Parameters:

d – a JSON dictionary
key – the key to parse
cls – the class of the elements of list d[key]
default – the default list to return if key is not present

Returns:

a list of cls instances

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object_dict(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses a dictionary whose values are objects.

The values in d[key] can be either instances of cls or serialized dicts from instances of cls.

Parameters:

d – a JSON dictionary
key – the key to parse
cls – the class of the values of dictionary d[key]
default – the default dict of cls instances to return if key is not present

Returns:

a dictionary whose values are cls instances

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_path(d, key, default=<eta.core.config.NoDefault object>)#

Parses a path attribute.

The path is converted to an absolute path if necessary via os.path.abspath(os.path.expanduser(value)).

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default string to return if key is not present

Returns:

a path string

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_raw(d, key, default=<eta.core.config.NoDefault object>)#

Parses a raw (arbitrary) JSON field.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default value to return if key is not present

Returns:

the raw (untouched) value of the given field

Raises:

ConfigError – if no default value was provided and the key was not found in the dictionary

static parse_string(d, key, default=<eta.core.config.NoDefault object>)#

Parses a string attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default string to return if key is not present

Returns:

a string

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

static validate_all_or_nothing_fields(fields)#

Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.

Parameters:: fields – a dictionary of pre-parsed fields
Raises:: ConfigError – if some values are truth and some are not

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.RemoteModelManager(config)#

Bases: ModelManager

Methods:

`attributes`()	Returns a list of attributes to be serialized.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`delete_model`()	Deletes the model from remote storage.
`download_model`(model_path[, force])	Downloads the model to the given local path.
`flush_model`(model_path)	Flushes the copy of the model at the given local path, if necessary.
`from_config`(config)	Instantiates a Configurable class from a <cls>Config instance.
`from_dict`(d)	Builds the ModelManager subclass from a JSON dictionary.
`from_json`(json_path)	Instantiates a Configurable class from a <cls>Config JSON file.
`from_kwargs`(**kwargs)	Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`is_model_downloaded`(model_path)	Determines whether the model is downloaded to the given location.
`parse`(class_name[, module_name])	Parses a Configurable subclass name string.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`upload_model`(model_path, args, *kwargs)
`validate`(config)	Validates that the given config is an instance of <cls>Config.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.

attributes()#

Returns a list of attributes to be serialized.

Returns:: a list of attributes

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

delete_model()#: Deletes the model from remote storage.

download_model(model_path, force=False)#

Downloads the model to the given local path.

If the download is forced, any existing model is overwritten. If the download is not forced, the model will only be downloaded if it does not already exist locally.

Parameters:

model_path – the path to which to download the model
force – whether to force download the model. If True, the model is always downloaded. If False, the model is only downloaded if necessary. The default is False

Raises:

ModelError – if model downloading is not currently allowed

flush_model(model_path)#

Flushes the copy of the model at the given local path, if necessary.

Parameters:: model_path – the path on disk for the model

classmethod from_config(config)#: Instantiates a Configurable class from a <cls>Config instance.

classmethod from_dict(d)#

Builds the ModelManager subclass from a JSON dictionary.

Parameters:: d – a JSON dictionary
Returns:: a ModelManager instance

classmethod from_json(json_path)#

Instantiates a Configurable class from a <cls>Config JSON file.

Parameters:: json_path – path to a JSON file for type <cls>Config
Returns:: an instance of cls

classmethod from_kwargs(**kwargs)#

Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config.

Parameters:: **kwargs – keyword arguments that define the fields of a <cls>Config dict
Returns:: an instance of cls

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

is_model_downloaded(model_path)#

Determines whether the model is downloaded to the given location.

If model_path is an archive, this method will also return True if a directory with the same basename as model_path exists.

Parameters:: model_path – the path on disk for the model
Returns:: True/False

static parse(class_name, module_name=None)#

Parses a Configurable subclass name string.

Assumes both the Configurable class and the Config class are defined in the same module. The module containing the classes will be loaded if necessary.

Parameters:

class_name – a string containing the name of the Configurable class, e.g. “ClassName”, or a fully-qualified class name, e.g. “eta.core.config.ClassName”
module_name – a string containing the fully-qualified module name, e.g. “eta.core.config”, or None if class_name includes the module name. Set module_name = __name__ to load a class from the calling module

Returns:

the Configurable class config_cls: the Config class associated with cls

Return type:

cls

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

static upload_model(model_path, *args, **kwargs)#

classmethod validate(config)#

Validates that the given config is an instance of <cls>Config.

Raises:: ConfigurableError – if config is not an instance of <cls>Config

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.ZooModelsManifest(models=None, name=None, url=None)#

Bases: ModelsManifest

Class that describes the collection of models in the FiftyOne Model Zoo.

Parameters:: models – a list of ZooModel instances

Methods:

`add_model`(model[, error_level])	Adds the given model to the manifest.
`attributes`()	Returns a list of class attributes to be serialized.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`dir_has_manifest`(models_dir)	Determines whether the given directory has a models manifest.
`from_dict`(d)	Constructs a ModelsManifest from a JSON dictionary.
`from_dir`(models_dir)	Loads the ModelsManifest from the given models directory.
`from_json`(path, args, *kwargs)	Constructs a Serializable object from a JSON file.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`get_latest_model_with_base_name`(base_name)	Gets the Model instance for the latest version of the model with the given base name.
`get_model_with_name`(name)	Gets the model with the given name.
`has_model_with_filename`(model)	Determines whether this manifest contains a model with a conflicting filename.
`has_model_with_name`(name)	Determines whether this manifest contains the model with the given name.
`make_manifest_path`(models_dir)	Makes the manifest path for the given models directory.
`merge`(models_manifest[, error_level])	Merges the models manifest into this one.
`remove_model`(name[, error_level])	Removes the model with the given name from the ModelsManifest.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.
`write_to_dir`(models_dir)	Writes the ModelsManifest to the given models directory.

Attributes:

subdir

add_model(model, error_level=0)#

Adds the given model to the manifest.

Parameters:

model – a Model instance
error_level –
the error level to use, defined as:

0: raise error if the model cannot be added 1: log warning if the model cannot be added 2: ignore models that cannot be added

attributes()#

Returns a list of class attributes to be serialized.

This method is called internally by serialize() to determine the class attributes to serialize.

Subclasses can override this method, but, by default, all attributes in vars(self) are returned, minus private attributes, i.e., those starting with “_”. The order of the attributes in this list is preserved when serializing objects, so a common pattern is for subclasses to override this method if they want their JSON files to be organized in a particular way.

Returns:: a list of class attributes to be serialized

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

static dir_has_manifest(models_dir)#

Determines whether the given directory has a models manifest.

Parameters:: models_dir – the models directory
Returns:: True/False

classmethod from_dict(d)#

Constructs a ModelsManifest from a JSON dictionary.

Parameters:: d – a JSON dictionary
Returns:: a ModelsManifest

classmethod from_dir(models_dir)#

Loads the ModelsManifest from the given models directory.

Parameters:: models_dir – the models directory
Returns:: a ModelsManifest

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

path – the path to the JSON file on disk
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

get_latest_model_with_base_name(base_name)#

Gets the Model instance for the latest version of the model with the given base name.

Parameters:: base_name – the base name of the model
Returns:: the Model instance
Raises:: ModelError – if the model was not found

get_model_with_name(name)#

Gets the model with the given name.

Parameters:: name – the name of the model
Returns:: the Model instance
Raises:: ModelError – if the model was not found

has_model_with_filename(model)#

Determines whether this manifest contains a model with a conflicting filename.

Parameters:: model – a Model instance
Returns:: True/False

has_model_with_name(name)#

Determines whether this manifest contains the model with the given name.

Parameters:: name – the model name
Returns:: True/False

static make_manifest_path(models_dir)#

Makes the manifest path for the given models directory.

Parameters:: models_dir – the models directory
Returns:: the manifest path

merge(models_manifest, error_level=0)#

Merges the models manifest into this one.

Parameters:

models_manifest – a ModelsManifest
error_level –
the error level to use, defined as:

0: raise error if a model cannot be added 1: log warning if a model cannot be added 2: ignore models that cannot be added

remove_model(name, error_level=0)#

Removes the model with the given name from the ModelsManifest.

Parameters:

name – the name of the model
error_level –
the error level to use, defined as:

0: raise error if the model cannot be added 1: log warning if the model cannot be added 2: ignore models that cannot be added

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

property subdir#

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

write_to_dir(models_dir)#

Writes the ModelsManifest to the given models directory.

Parameters:: models_dir – the models directory

class fiftyone.zoo.RemoteZooModelsManifest(models=None, name=None, url=None)#

Bases: ZooModelsManifest

Class that describes the collection of remotely-sourced models in the FiftyOne Model Zoo.

Parameters:: models – a list of RemoteZooModel instances

Methods:

`add_model`(model[, error_level])	Adds the given model to the manifest.
`attributes`()	Returns a list of class attributes to be serialized.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`dir_has_manifest`(models_dir)	Determines whether the given directory has a models manifest.
`from_dict`(d)	Constructs a ModelsManifest from a JSON dictionary.
`from_dir`(models_dir)	Loads the ModelsManifest from the given models directory.
`from_json`(path, args, *kwargs)	Constructs a Serializable object from a JSON file.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`get_latest_model_with_base_name`(base_name)	Gets the Model instance for the latest version of the model with the given base name.
`get_model_with_name`(name)	Gets the model with the given name.
`has_model_with_filename`(model)	Determines whether this manifest contains a model with a conflicting filename.
`has_model_with_name`(name)	Determines whether this manifest contains the model with the given name.
`make_manifest_path`(models_dir)	Makes the manifest path for the given models directory.
`merge`(models_manifest[, error_level])	Merges the models manifest into this one.
`remove_model`(name[, error_level])	Removes the model with the given name from the ModelsManifest.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.
`write_to_dir`(models_dir)	Writes the ModelsManifest to the given models directory.

Attributes:

subdir

add_model(model, error_level=0)#

Adds the given model to the manifest.

Parameters:

model – a Model instance
error_level –
the error level to use, defined as:

0: raise error if the model cannot be added 1: log warning if the model cannot be added 2: ignore models that cannot be added

attributes()#

Returns a list of class attributes to be serialized.

This method is called internally by serialize() to determine the class attributes to serialize.

Subclasses can override this method, but, by default, all attributes in vars(self) are returned, minus private attributes, i.e., those starting with “_”. The order of the attributes in this list is preserved when serializing objects, so a common pattern is for subclasses to override this method if they want their JSON files to be organized in a particular way.

Returns:: a list of class attributes to be serialized

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

static dir_has_manifest(models_dir)#

Determines whether the given directory has a models manifest.

Parameters:: models_dir – the models directory
Returns:: True/False

classmethod from_dict(d)#

Constructs a ModelsManifest from a JSON dictionary.

Parameters:: d – a JSON dictionary
Returns:: a ModelsManifest

classmethod from_dir(models_dir)#

Loads the ModelsManifest from the given models directory.

Parameters:: models_dir – the models directory
Returns:: a ModelsManifest

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

path – the path to the JSON file on disk
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

get_latest_model_with_base_name(base_name)#

Gets the Model instance for the latest version of the model with the given base name.

Parameters:: base_name – the base name of the model
Returns:: the Model instance
Raises:: ModelError – if the model was not found

get_model_with_name(name)#

Gets the model with the given name.

Parameters:: name – the name of the model
Returns:: the Model instance
Raises:: ModelError – if the model was not found

has_model_with_filename(model)#

Determines whether this manifest contains a model with a conflicting filename.

Parameters:: model – a Model instance
Returns:: True/False

has_model_with_name(name)#

Determines whether this manifest contains the model with the given name.

Parameters:: name – the model name
Returns:: True/False

static make_manifest_path(models_dir)#

Makes the manifest path for the given models directory.

Parameters:: models_dir – the models directory
Returns:: the manifest path

merge(models_manifest, error_level=0)#

Merges the models manifest into this one.

Parameters:

models_manifest – a ModelsManifest
error_level –
the error level to use, defined as:

0: raise error if a model cannot be added 1: log warning if a model cannot be added 2: ignore models that cannot be added

remove_model(name, error_level=0)#

Removes the model with the given name from the ModelsManifest.

Parameters:

name – the name of the model
error_level –
the error level to use, defined as:

0: raise error if the model cannot be added 1: log warning if the model cannot be added 2: ignore models that cannot be added

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

property subdir#

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

write_to_dir(models_dir)#

Writes the ModelsManifest to the given models directory.

Parameters:: models_dir – the models directory