concrete.ml.pytest.utils.md

module concrete.ml.pytest.utils

Common functions or lists for test files, which can't be put in fixtures.

Global Variables

• sklearn_models_and_datasets

---

function get_random_extract_of_sklearn_models_and_datasets

get_random_extract_of_sklearn_models_and_datasets()

Return a random sublist of sklearn_models_and_datasets.

The sublist contains exactly one model of each kind.

Returns: the sublist

---

function instantiate_model_generic

instantiate_model_generic(model_class, n_bits, **parameters)

Instantiate any Concrete ML model type.

Args:

• model_class (class): The type of the model to instantiate.

• n_bits (int): The number of quantization to use when initializing the model. For QNNs, default parameters are used based on whether n_bits is greater or smaller than 8.

• parameters (dict): Hyper-parameters for the model instantiation. For QNNs, these parameters will override the matching default ones.

Returns:

• model_name (str): The type of the model as a string.

• model (object): The model instance.

---

function get_torchvision_dataset

get_torchvision_dataset(param: Dict, train_set: bool)

Get train or testing data-set.

Args:

• param (Dict): Set of hyper-parameters to use based on the selected torchvision data-set.

• It must contain: data-set transformations (torchvision.transforms.Compose), and the data-set_size (Optional[int]).

• train_set (bool): Use train data-set if True, else testing data-set

Returns: A torchvision data-sets.

---

function data_calibration_processing

data_calibration_processing(data, n_sample: int, targets=None)

Reduce size of the given data-set.

Args:

• data: The input container to consider

• n_sample (int): Number of samples to keep if the given data-set

• targets: If dataset is a torch.utils.data.Dataset, it typically contains both the data and the corresponding targets. In this case, targets must be set to None. If data is instance of torch.Tensor or 'numpy.ndarray, targets` is expected.

Returns:

• Tuple[numpy.ndarray, numpy.ndarray]: The input data and the target (respectively x and y).

Raises:

• TypeError: If the 'data-set' does not match any expected type.

---

function load_torch_model

load_torch_model(
    model_class: Module,
    state_dict_or_path: Optional[str, Path, Dict[str, Any]],
    params: Dict,
    device: str = 'cpu'
) → Module

Load an object saved with torch.save() from a file or dict.

Args:

• model_class (torch.nn.Module): A PyTorch or Brevitas network.

• state_dict_or_path (Optional[Union[str, Path, Dict[str, Any]]]): Path or state_dict

• params (Dict): Model's parameters

• device (str): Device type.

Returns:

• torch.nn.Module: A PyTorch or Brevitas network.

---

function values_are_equal

values_are_equal(value_1: Any, value_2: Any) → bool

Indicate if two values are equal.

This method takes into account objects of type None, numpy.ndarray, numpy.floating, numpy.integer, numpy.random.RandomState or any instance that provides a __eq__ method.

Args:

• value_2 (Any): The first value to consider.

• value_1 (Any): The second value to consider.

Returns:

• bool: If the two values are equal.

---

function check_serialization

check_serialization(
    object_to_serialize: Any,
    expected_type: Type,
    equal_method: Optional[Callable] = None,
    check_str: bool = True
)

Check that the given object can properly be serialized.

This function serializes all objects using the dump, dumps, load and loads functions from Concrete ML. If the given object provides a dump and dumps method, they are also serialized using these.

Args:

• object_to_serialize (Any): The object to serialize.

• expected_type (Type): The object's expected type.

• equal_method (Optional[Callable]): The function to use to compare the two loaded objects. Default to values_are_equal.

• check_str (bool): If the JSON strings should also be checked. Default to True.