def test_submit_for_inference(test_output_dirs: OutputFolderForTests) -> None:
"""
Execute the submit_for_inference script on the model that was recently trained. This starts an AzureML job,
and downloads the segmentation. Then check if the segmentation was actually produced.
:return:
"""
model = get_most_recent_model(
fallback_run_id_for_local_execution=FALLBACK_SINGLE_RUN)
image_file = fixed_paths_for_tests.full_ml_test_data_path(
) / "train_and_test_data" / "id1_channel1.nii.gz"
assert image_file.exists(), f"Image file not found: {image_file}"
settings_file = fixed_paths.SETTINGS_YAML_FILE
assert settings_file.exists(), f"Settings file not found: {settings_file}"
# Read the name of the branch from environment, so that the inference experiment is also listed alongside
# all other AzureML runs that belong to the current PR.
build_branch = os.environ.get("BUILD_BRANCH", None)
experiment_name = to_azure_friendly_string(
build_branch) if build_branch else "model_inference"
args = [
"--image_file",
str(image_file), "--model_id", model.id, "--settings",
str(settings_file), "--download_folder",
str(test_output_dirs.root_dir), "--cluster", "training-nc12",
"--experiment", experiment_name
]
seg_path = test_output_dirs.root_dir / DEFAULT_RESULT_IMAGE_NAME
assert not seg_path.exists(
), f"Result file {seg_path} should not yet exist"
submit_for_inference.main(
args, project_root=fixed_paths.repository_root_directory())
assert seg_path.exists(), f"Result file {seg_path} was not created"
def test_submit_for_inference(test_output_dirs: OutputFolderForTests) -> None:
"""
Execute the submit_for_inference script on the model that was recently trained. This starts an AzureML job,
and downloads the segmentation. Then check if the segmentation was actually produced.
:param test_output_dirs: Test output directories.
"""
model = get_most_recent_model(fallback_run_id_for_local_execution=FALLBACK_SINGLE_RUN)
assert PYTHON_ENVIRONMENT_NAME in model.tags, "Environment name not present in model properties"
# Both parts of this test rely on the same model that was trained in a previous run. If these tests are executed
# independently (via pytest.mark.parametrize), get_most_recent_model would pick up the AML run that the
# previously executed part of this test submitted.
for use_dicom in [False, True]:
if use_dicom:
size = (64, 64, 64)
spacing = (1., 1., 2.5)
image_file = test_output_dirs.root_dir / "temp_pack_dicom_series" / "dicom_series.zip"
scratch_folder = test_output_dirs.root_dir / "temp_dicom_series"
zip_random_dicom_series(size, spacing, image_file, scratch_folder)
else:
image_file = fixed_paths_for_tests.full_ml_test_data_path() / "train_and_test_data" / "id1_channel1.nii.gz"
assert image_file.exists(), f"Image file not found: {image_file}"
settings_file = fixed_paths.SETTINGS_YAML_FILE
assert settings_file.exists(), f"Settings file not found: {settings_file}"
args = ["--image_file", str(image_file),
"--model_id", model.id,
"--settings", str(settings_file),
"--download_folder", str(test_output_dirs.root_dir),
"--cluster", "training-nc12",
"--experiment", get_experiment_name_from_environment() or "model_inference",
"--use_dicom", str(use_dicom)]
download_file = DEFAULT_RESULT_ZIP_DICOM_NAME if use_dicom else DEFAULT_RESULT_IMAGE_NAME
seg_path = test_output_dirs.root_dir / download_file
assert not seg_path.exists(), f"Result file {seg_path} should not yet exist"
submit_for_inference.main(args, project_root=fixed_paths.repository_root_directory())
assert seg_path.exists(), f"Result file {seg_path} was not created"
def test_submit_for_inference() -> None:
args = [
"--image_file",
"Tests/ML/test_data/train_and_test_data/id1_channel1.nii.gz",
"--model_id", DEFAULT_MODEL_ID_NUMERIC, "--settings",
"InnerEye/settings.yml", "--download_folder", "."
]
seg_path = Path(DEFAULT_RESULT_IMAGE_NAME)
if seg_path.exists():
seg_path.unlink()
main(args)
assert seg_path.exists()
def test_submit_for_inference(use_dicom: bool,
test_output_dirs: OutputFolderForTests) -> None:
"""
Execute the submit_for_inference script on the model that was recently trained. This starts an AzureML job,
and downloads the segmentation. Then check if the segmentation was actually produced.
:param use_dicom: True to test DICOM in/out, False otherwise.
:param test_output_dirs: Test output directories.
"""
model = get_most_recent_model(
fallback_run_id_for_local_execution=FALLBACK_SINGLE_RUN)
assert PYTHON_ENVIRONMENT_NAME in model.tags, "Environment name not present in model properties"
if use_dicom:
size = (64, 64, 64)
spacing = (1., 1., 2.5)
image_file = test_output_dirs.root_dir / "temp_pack_dicom_series" / "dicom_series.zip"
scratch_folder = test_output_dirs.root_dir / "temp_dicom_series"
zip_random_dicom_series(size, spacing, image_file, scratch_folder)
else:
image_file = fixed_paths_for_tests.full_ml_test_data_path(
) / "train_and_test_data" / "id1_channel1.nii.gz"
assert image_file.exists(), f"Image file not found: {image_file}"
settings_file = fixed_paths.SETTINGS_YAML_FILE
assert settings_file.exists(), f"Settings file not found: {settings_file}"
# Read the name of the branch from environment, so that the inference experiment is also listed alongside
# all other AzureML runs that belong to the current PR.
build_branch = os.environ.get("BUILD_BRANCH", None)
experiment_name = to_azure_friendly_string(
build_branch) if build_branch else "model_inference"
args = [
"--image_file",
str(image_file), "--model_id", model.id, "--settings",
str(settings_file), "--download_folder",
str(test_output_dirs.root_dir), "--cluster", "training-nc12",
"--experiment", experiment_name, "--use_dicom",
str(use_dicom)
]
download_file = DEFAULT_RESULT_ZIP_DICOM_NAME if use_dicom else DEFAULT_RESULT_IMAGE_NAME
seg_path = test_output_dirs.root_dir / download_file
assert not seg_path.exists(
), f"Result file {seg_path} should not yet exist"
submit_for_inference.main(
args, project_root=fixed_paths.repository_root_directory())
assert seg_path.exists(), f"Result file {seg_path} was not created"