ModelContributor

class medigan.contribute_model.model_contributor.ModelContributor(model_id: str, init_py_path: str)

Bases: object

ModelContributor class: Contributes a user’s local model to the public medigan library

Parameters

• model_id (str) – The generative model’s unique id

• init_py_path (str) – The path to the local model’s __init__.py file needed for importing and running this model.

model_id

The generative model’s unique id

Type

str

init_py_path

The path to the local model’s __init__.py file needed for importing and running this model.

Type

str

package_path

Path as string to the generative model’s python package

Type

str

package_name

Name of the model’s python package i.e. the name of the model’s zip file and unzipped package folder

Type

str

metadata_file_path

Path as string to the generative model’s metadata file e.g. default is relative path to package root.

Type

str

zenodo_model_uploader

An instance of the ZenodoModelUploader class

Type

str

github_model_uploader

An instance of the GithubModelUploader class.

Type

str

Methods Summary

add_metadata_from_file(metadata_file_path)	Read and parse the metadata of a local model, identified by model_id, from a metadata file in json format.
add_metadata_from_input([...])	Create a metadata dict for a local model, identified by model_id, given the necessary minimum metadata contents.
is_value_for_key_already_set(key, metadata, ...)	Check if the value of a key in a metadata dictionary is already set and e.g.
load_metadata_template()	Loads and parses (json to dict) a default medigan metadata template.
push_to_github(access_token[, package_link, ...])	Upload the model's metadata inside a github issue to the medigan github repository.
push_to_zenodo(access_token, creator_name, ...)	Upload the model files as zip archive to a public Zenodo repository where the model will be persistently stored.
validate_and_update_model_weights_path()	Check if the model files can be found in the package_path or based on the path_to_metadata.
validate_init_py_path(init_py_path)	Asserts whether the init_py_path exists and points to a valid __init__.py correct file.
validate_local_model_import()	Check if the model package in the package_path can be imported as python library using importlib.
validate_model_id(model_id[, max_chars, ...])	Asserts if the model_id is in the correct format and has a valid length

Methods Documentation

add_metadata_from_file(metadata_file_path) → dict

Read and parse the metadata of a local model, identified by model_id, from a metadata file in json format.

Parameters

• model_id (str) – The generative model’s unique id

• metadata_file_path (str) – the path pointing to the metadata file

Returns

Returns a dict containing the contents of parsed metadata json file.

Return type

dict

add_metadata_from_input(model_weights_name: Optional[str] = None, model_weights_extension: Optional[str] = None, generate_method_name: Optional[str] = None, dependencies: list = [], fill_more_fields_interactively: bool = True, output_path: str = 'config')

Create a metadata dict for a local model, identified by model_id, given the necessary minimum metadata contents.

Parameters

• model_id (str) – The generative model’s unique id

• model_weights_name (str) – the name of the checkpoint file containing the model’s weights

• model_weights_extension (str) – the extension (e.g. .pt) of the checkpoint file containing the model’s weights

• generate_method_name (str) – the name of the sample generation method inside the models __init__.py file

• dependencies (list) – the list of dependencies that need to be installed via pip to run the model.

• fill_more_fields_interactively (bool) – flag indicating whether a user will be interactively asked via command line for further input to fill out missing metadata content

• output_path (str) – the path where the created metadata json file will be stored.

Returns

Returns a dict containing the contents of the metadata json file.

Return type

dict

is_value_for_key_already_set(key: str, metadata: dict, nested_key) → bool

Check if the value of a key in a metadata dictionary is already set and e.g. not an empty string, dict or list.

Parameters

• key (str) – The key in the currently traversed part of the model’s metadata dictionary

• metadata (dict) – The currently traversed part of the model’s metadata dictionary

• nested_key (str) – the nested_key indicates which subpart of the model’s metadata we are currently traversing

Returns

Flag indicating whether a value exists for the key in the dict

Return type

bool

load_metadata_template() → dict

Loads and parses (json to dict) a default medigan metadata template.

Returns

Returns the metadata template as dict

Return type

dict

push_to_github(access_token: str, package_link: Optional[str] = None, creator_name: str = '', creator_affiliation: str = '', model_description: str = '')

Upload the model’s metadata inside a github issue to the medigan github repository.

To add your model to medigan, your metadata will be reviewed on Github and added to medigan’s official model metadata

The medigan repository issues page: https://github.com/RichardObi/medigan/issues

Get your Github access token here: https://github.com/settings/tokens

Parameters

• access_token (str) – a personal access token linked to your github user account, used as means of authentication

• package_link – a package link

• creator_name (str) – the creator name that will appear on the corresponding github issue

• creator_affiliation (str) – the creator affiliation that will appear on the corresponding github issue

• model_description (list) – the model_description that will appear on the corresponding github issue

Returns

Returns the url pointing to the corresponding issue on github

Return type

str

push_to_zenodo(access_token: str, creator_name: str, creator_affiliation: str, model_description: str = '')

Upload the model files as zip archive to a public Zenodo repository where the model will be persistently stored.

Get your Zenodo access token here: https://zenodo.org/account/settings/applications/tokens/new/ (Enable scopes deposit:actions and deposit:write)

Parameters

• access_token (str) – a personal access token in Zenodo linked to a user account for authentication

• creator_name (str) – the creator name that will appear on the corresponding Zenodo model upload homepage

• creator_affiliation (str) – the creator affiliation that will appear on the corresponding Zenodo model upload homepage

• model_description (list) – the model_description that will appear on the corresponding Zenodo model upload homepage

Returns

Returns the url pointing to the corresponding Zenodo model upload homepage

Return type

str

validate_and_update_model_weights_path() → dict

Check if the model files can be found in the package_path or based on the path_to_metadata.

Ideally, the user provided package_path and the path_to_metadata should both point to the same model package containing weights, config, license, etc. Here we check both of these paths to find the model weights.

Returns

Returns the metadata after updating the path to the model’s checkpoint’s weights

Return type

dict

validate_init_py_path(init_py_path) → bool

Asserts whether the init_py_path exists and points to a valid __init__.py correct file.

Parameters

init_py_path (str) – The path to the local model’s __init__.py file needed for importing and running this model.

validate_local_model_import()

Check if the model package in the package_path can be imported as python library using importlib.

validate_model_id(model_id: str, max_chars: int = 30, min_chars: int = 13) → bool

Asserts if the model_id is in the correct format and has a valid length

Parameters

• model_id (str) – The generative model’s unique id

• max_chars (int) – the maximum of chars allowed in the model_id

• min_chars (int) – the minimum of chars allowed in the model_id

Returns

Returns flag indicating whether the model_id is correctly formatted.

Return type

bool