matrice.exported_model module#

class matrice.exported_model.ExportedModel(session, model_export_id=None, model_export_name='')[source]#

Bases: object

A class to handle operations related to model export within a project.

The ExportedModel class facilitates managing model export processes, including fetching summaries, listing available exported models, and performing evaluation tasks on optimized inferences.

Parameters:

session (Session) – An active session object that holds project information such as the project ID and RPC client.
model_export_id (str, optional) – A unique identifier for the model export or inference optimization. Defaults to None.
model_export_name (str, optional) – The name of the model export or inference optimization. Defaults to an empty string.

project_id#

The project ID associated with the current session.

Type:: str

model_export_id#

The unique identifier for the model export, provided at initialization or set later.

Type:: str or None

model_export_name#

The name of the model export, provided at initialization or set later.

Type:: str

rpc#

The RPC client used to make API requests.

Type:: object

Example

>>> session = Session(account_number=account_number)
>>> exported_model = ExportedModel(session=session, model_export_id="12345", model_export_name="sample_export")
>>> print(exported_model.model_export_name)  # Output: "sample_export"

__init__(session, model_export_id=None, model_export_name='')[source]#

add_evaluation(id_dataset, dataset_version, split_types, is_gpu_required=True, is_pruned=False)[source]#

Add a new model evaluation using specified parameters.

Parameters:

is_pruned (bool) – Whether the model is pruned.
id_dataset (str) – The ID of the dataset used for evaluation.
id_experiment (str) – The ID of the experiment associated with the model.
dataset_version (str) – The version of the dataset.
is_gpu_required (bool) – Whether the model requires GPU for inference.
split_types (list) – A list of split types used in the evaluation.

Returns:

A tuple containing: - resp (dict): The API response object. - error (str or None): Error message if the API call failed, otherwise None. - message (str): Success or error message.

Return type:

tuple

Example

>>> eval_result, err, msg = exported_model.add_evaluation(
        is_pruned=False,
        id_dataset="dataset123", id_experiment="experiment123", dataset_version="v1.0",
        is_gpu_required=True, split_types=["train", "test"])
>>> if err:
>>>     print(f"Error: {err}")
>>> else:
>>>     print(f"Evaluation added: {eval_result}")

delete()[source]#

Delete a model export.

Returns:: A tuple containing: - resp (dict): The API response object. - error (str or None): Error message if the API call failed, otherwise None. - message (str): Success or error message.
Return type:: tuple

Example

>>> result, err, msg = exported_model.delete()
>>> if err:
>>>     print(f"Error: {err}")
>>> else:
>>>     print(f"Model Export Deleted: {result}")

download_model(file_name)[source]#

Download the specified model type to a local file. There are 2 types of model types: trained and exported.

Parameters:

file_name (str) – The name of the file to save the downloaded model.
model_type (str) – The type of the model to download. Default is “trained”.

Returns:

A tuple with the download status, error message, and status message.

Return type:

tuple

Example

>>> result, error, message = exported_model.download_model("model.pth")
>>> if error:
>>>     print(f"Error: {error}")
>>> else:
>>>     print(f"Model downloaded: {result}")

get_details()[source]#

Retrieve details of the model export based on the model export ID or name.

This method fetches details by ID if available; otherwise, it attempts to fetch by name. Raises a ValueError if neither identifier is provided.

Returns:: A tuple containing the model export details, error message (if any), and a status message.
Return type:: tuple
Raises:: ValueError – If neither ‘model_export_id’ nor ‘model_export_name’ is provided.

Example

>>> details, err, msg = exported_model.get_details()
>>> if err:
>>>     print(f"Error: {err}")
>>> else:
>>>     print(f"Model Export Details: {details}")

get_download_path()[source]#

Get the download path for the specified model type. There are 2 types of model types: trained and exported.

Parameters:: model_type (str) – The type of the model to download.
Returns:: A tuple with the download path, error message, and status message.
Return type:: tuple

Example

>>> download_path, error, message = exported_model.get_model_download_path()
>>> if error:
>>>     print(f"Error: {error}")
>>> else:
>>>     print(f"Download path: {download_path}")

get_evaluation_result(dataset_id, dataset_version, split_types)[source]#

Fetch the evaluation result of a trained model using a specific dataset version and split type.

Parameters:

dataset_id (str) – The ID of the dataset.
dataset_version (str) – The version of the dataset.
split_type (list) – The types of splits used for the evaluation.

Returns:

A tuple with the evaluation result, error message, and status message.

Return type:

tuple

Example

>>> eval_result, error, message = exported_model.get_evaluation_result("dataset123", "v1.0", ["train"])
>>> if error:
>>>     print(f"Error: {error}")
>>> else:
>>>     print(f"Evaluation result: {eval_result}")

get_prediction(image_path)[source]#

Tests a exported model for a given image.

Parameters:#

image_pathstr: The path to the image for testing.

Returns:#

: tuple:

A tuple consisting of (result, error, message) with the test results.

Example:#

>>> result, error, message = exported_model.get_prediction("/path/to/test_image.jpg")
>>> print(result)
{'test_result': 'success', 'confidence': 0.85}

get_trained_model()[source]#

Fetch details of a model training associated with a specific export ID.

Returns:: A tuple containing: - resp (dict): The API response object. - error (str or None): Error message if the API call failed, otherwise None. - message (str): Success or error message.
Return type:: tuple

Example

>>> training_data, err, msg = exported_model.get_model_train_of_the_export()
>>> if err:
>>>     print(f"Error: {err}")
>>> else:
>>>     print(f"Model Training Data: {training_data}")

refresh()[source]#: Refresh the instance by reinstantiating it with the previous values.

rename(updated_name)[source]#

Update the name of a model export.

Parameters:: updated_name (str) – The new name for the model export.
Returns:: A tuple containing: - resp (dict): The API response object. - error (str or None): Error message if the API call failed, otherwise None. - message (str): Success or error message.
Return type:: tuple

Example

>>> result, err, msg = exported_model.rename("NewModelExportName")
>>> if err:
>>>     print(f"Error: {err}")
>>> else:
>>>     print(f"Model Export Name Updated: {result}")