.ext_test_case¶

The module contains the main class ExtTestCase which adds specific functionalities to this project.

class experimental_experiment.ext_test_case.ExtTestCase(methodName='runTest')[source]¶

Inherits from unittest.TestCase and adds specific comprison functions and other helpers.

assertAlmostEqual(expected: ndarray, value: ndarray, atol: float = 0, rtol: float = 0)[source]¶: In the name

assertEmpty(value: Any)[source]¶: In the name

assertEqual(expected: Any, value: Any, msg: str = '')[source]¶: Overwrites the error message to get a more explicit message about what is what.

assertEqualArray(expected: ndarray, value: ndarray, atol: float = 0, rtol: float = 0, msg: str | None = None)[source]¶: In the name

assertEqualArrays(expected: Sequence[ndarray], value: Sequence[ndarray], atol: float = 0, rtol: float = 0, msg: str | None = None)[source]¶: In the name

assertEqualOr(value: Any, expected: Tuple[Any, ...])[source]¶: Checks if value is one or the expected values.

assertExists(name)[source]¶: Checks the existing of a file.

assertGreaterOrEqual(a, b, msg=None)[source]¶: In the name

assertIn(tofind: str, text: str, msg: str = '')[source]¶: Just like self.assertTrue(a in b), but with a nicer default message.

assertNotEmpty(value: Any)[source]¶: In the name

assertRaise(fct: Callable, exc_type: type[Exception], msg: str | None = None)[source]¶: In the name

assertSetContained(set1, set2)[source]¶: Checks that set1 is contained in set2.

assertStartsWith(prefix: str, full: str)[source]¶: In the name

capture(fct: Callable)[source]¶

Runs a function and capture standard output and error.

Parameters:: fct – function to run
Returns:: result of fct, output, error

dump_onnx(name: str, proto: Any, folder: str | None = None) → str[source]¶: Dumps an onnx file.

get_dump_file(name: str, folder: str | None = None) → str[source]¶: Returns a filename to dump a model.

print_model(model: ModelProto)[source]¶: Prints a ModelProto

print_onnx(model: ModelProto)[source]¶: Prints a ModelProto

classmethod setUpClass()[source]¶: Hook method for setting up class fixture before running tests in the class.

classmethod tearDownClass()[source]¶: Hook method for deconstructing the class fixture after running all tests in the class.

classmethod todo(f: Callable, msg: str)[source]¶: Adds a todo printed when all test are run.

tryCall(fct: Callable, msg: str | None = None, none_if: str | None = None) → Any | None[source]¶

Calls the function, catch any error.

Parameters:

fct – function to call
msg – error message to display if failing
none_if – returns None if this substring is found in the error message

Returns:

output of fct

experimental_experiment.ext_test_case.dump_dort_onnx(fn)[source]¶: Context manager to dump onnx model created by dort.

experimental_experiment.ext_test_case.get_figure(ax)[source]¶: Returns the figure of a matplotlib figure.

experimental_experiment.ext_test_case.has_cuda() → bool[source]¶: Returns torch.cuda.device_count() > 0.

experimental_experiment.ext_test_case.has_executorch(version: str = '', msg: str = '') → Callable[source]¶: Tells if ExecuTorch is installed.

experimental_experiment.ext_test_case.has_onnxruntime_training(push_back_batch: bool = False)[source]¶: Tells if onnxruntime_training is installed.

experimental_experiment.ext_test_case.has_onnxscript(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnxscript is not recent enough.

experimental_experiment.ext_test_case.has_torch(version: str) → bool[source]¶: Returns True if torch verions is higher.

experimental_experiment.ext_test_case.hide_stdout(f: Callable | None = None) → Callable[source]¶

Catches warnings, hides standard output. The function may be disabled by setting UNHIDE=1 before running the unit test.

Parameters:: f – the function is called with the stdout as an argument

experimental_experiment.ext_test_case.ignore_warnings(warns: List[Warning]) → Callable[source]¶

Catches warnings.

Parameters:: warns – warnings to ignore

experimental_experiment.ext_test_case.is_azure() → bool[source]¶: Tells if the job is running on Azure DevOps.

experimental_experiment.ext_test_case.long_test(msg: str = '') → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Windows.

experimental_experiment.ext_test_case.measure_time(stmt: str | Callable, context: Dict[str, Any] | None = None, repeat: int = 10, number: int = 50, warmup: int = 1, div_by_number: bool = True, max_time: float | None = None) → Dict[str, str | int | float][source]¶

Measures a statement and returns the results as a dictionary.

Parameters:

stmt – string or callable
context – variable to know in a dictionary
repeat – average over repeat experiment
number – number of executions in one row
warmup – number of iteration to do before starting the real measurement
div_by_number – divide by the number of executions
max_time – execute the statement until the total goes beyond this time (approximatively), repeat is ignored, div_by_number must be set to True

Returns:

dictionary

<<<

from pprint import pprint
from math import cos
from experimental_experiment.ext_test_case import measure_time

res = measure_time(lambda: cos(0.5))
pprint(res)

>>>

    {'average': np.float64(6.213200049387524e-08),
     'context_size': 64,
     'deviation': np.float64(5.660813960705014e-09),
     'max_exec': np.float64(7.783999990351731e-08),
     'min_exec': np.float64(5.8120003814110535e-08),
     'number': 50,
     'repeat': 10,
     'ttime': np.float64(6.213200049387524e-07),
     'warmup_time': 2.4139999368344434e-06}

See Timer.repeat for a better understanding of parameter repeat and number. The function returns a duration corresponding to number times the execution of the main statement.

experimental_experiment.ext_test_case.never_test(msg: str = '') → Callable[source]¶: Skips a unit test.

experimental_experiment.ext_test_case.requires_cuda(msg: str = '', version: str = '', memory: int = 0)[source]¶

Skips a test if cuda is not available.

Parameters:

msg – to overwrite the message
version – minimum version
memory – minimun number of Gb to run the test

experimental_experiment.ext_test_case.requires_diffusers(version: str, msg: str = '', or_older_than: str | None = None) → Callable[source]¶: Skips a unit test if transformers is not recent enough.

experimental_experiment.ext_test_case.requires_executorch(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if executorch is not recent enough.

experimental_experiment.ext_test_case.requires_monai(version: str = '', msg: str = '') → Callable[source]¶: Skips a unit test if monai is not recent enough.

experimental_experiment.ext_test_case.requires_numpy(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if numpy is not recent enough.

experimental_experiment.ext_test_case.requires_onnx(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnx is not recent enough.

experimental_experiment.ext_test_case.requires_onnx_array_api(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnx-array-api is not recent enough.

experimental_experiment.ext_test_case.requires_onnx_diagnostic(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnx-diagnostic is not recent enough.

experimental_experiment.ext_test_case.requires_onnxruntime(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnxruntime is not recent enough.

experimental_experiment.ext_test_case.requires_onnxruntime_training(push_back_batch: bool = False, ortmodule: bool = False, msg: str = '') → Callable[source]¶: Skips a unit test if onnxruntime is not onnxruntime_training.

experimental_experiment.ext_test_case.requires_onnxscript(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnxscript is not recent enough.

experimental_experiment.ext_test_case.requires_pyinstrument(version: str = '', msg: str = '') → Callable[source]¶: Skips a unit test if pyinstrument is not recent enough.

experimental_experiment.ext_test_case.requires_sklearn(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if scikit-learn is not recent enough.

experimental_experiment.ext_test_case.requires_torch(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if pytorch is not recent enough.

experimental_experiment.ext_test_case.requires_transformers(version: str, msg: str = '', or_older_than: str | None = None) → Callable[source]¶: Skips a unit test if transformers is not recent enough.

experimental_experiment.ext_test_case.requires_vocos(version: str = '', msg: str = '') → Callable[source]¶: Skips a unit test if vocos is not recent enough.

experimental_experiment.ext_test_case.requires_zoo(msg: str = '') → Callable[source]¶: Skips a unit test if environment variable ZOO is not equal to 1.

experimental_experiment.ext_test_case.skipif_ci_apple(msg) → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Windows.

experimental_experiment.ext_test_case.skipif_ci_linux(msg) → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Linux.

experimental_experiment.ext_test_case.skipif_ci_windows(msg) → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Windows.

experimental_experiment.ext_test_case.skipif_not_onnxrt(msg) → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Windows.

experimental_experiment.ext_test_case.skipif_transformers(version_to_skip: str | Set[str], msg: str) → Callable[source]¶: Skips a unit test if transformers has a specific version.

experimental_experiment.ext_test_case.statistics_on_file(filename: str) → Dict[str, str | int | float][source]¶

Computes statistics on a file.

<<<

import pprint
from experimental_experiment.ext_test_case import statistics_on_file, __file__

pprint.pprint(statistics_on_file(__file__))

>>>

    {'chars': 27170, 'ext': '.py', 'lines': 912}

experimental_experiment.ext_test_case.statistics_on_folder(folder: str | List[str], pattern: str = '.*[.]((py|rst))$', aggregation: int = 0) → List[Dict[str, str | int | float]][source]¶

Computes statistics on files in a folder.

Parameters:

folder – folder or folders to investigate
pattern – file pattern
aggregation – show the first subfolders

Returns:

list of dictionaries

<<<

import os
import pprint
from experimental_experiment.ext_test_case import statistics_on_folder, __file__

pprint.pprint(statistics_on_folder(os.path.dirname(__file__)))

>>>

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Aggregated:

<<<

import os
import pprint
from experimental_experiment.ext_test_case import statistics_on_folder, __file__

pprint.pprint(statistics_on_folder(os.path.dirname(__file__), aggregation=1))

>>>

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experimental_experiment.ext_test_case.unit_test_going()[source]¶: Enables a flag telling the script is running while testing it. Avois unit tests to be very long.

experimental_experiment.ext_test_case.with_path_append(path_to_add: str | List[str]) → Callable[source]¶: Adds a path to sys.path to check.