def test_score_image_dicom_mock_none(
test_output_dirs: OutputFolderForTests) -> None:
"""
Test that dicom in and dicom-rt out works.
Finally there is no mocking and full image scoring is run using the PassThroughModel.
:param test_output_dirs: Test output directories.
"""
model_config = PassThroughModel()
model_config.set_output_to(test_output_dirs.root_dir)
checkpoint_path = model_config.checkpoint_folder / "checkpoint.ckpt"
create_model_and_store_checkpoint(model_config, checkpoint_path)
azure_config = AzureConfig()
project_root = Path(__file__).parent.parent
ml_runner = MLRunner(model_config=model_config,
azure_config=azure_config,
project_root=project_root)
model_folder = test_output_dirs.root_dir / "final"
ml_runner.copy_child_paths_to_folder(model_folder=model_folder,
checkpoint_paths=[checkpoint_path])
zipped_dicom_series_path = zip_dicom_series(model_folder)
score_pipeline_config = ScorePipelineConfig(
data_folder=zipped_dicom_series_path.parent,
model_folder=str(model_folder),
image_files=[str(zipped_dicom_series_path)],
result_image_name=HNSEGMENTATION_FILE.name,
use_gpu=False,
use_dicom=True)
segmentation = score_image(score_pipeline_config)
assert_zip_file_contents(segmentation, HN_DICOM_RT_ZIPPED, model_folder)
def test_copy_child_paths_to_folder(is_ensemble: bool,
extra_code_directory: str,
test_output_dirs: OutputFolderForTests) -> None:
azure_config = AzureConfig(extra_code_directory=extra_code_directory)
fake_model = SegmentationModelBase(should_validate=False)
fake_model.set_output_to(test_output_dirs.root_dir)
# To simulate ensemble models, there are two checkpoints, one in the root dir and one in a folder
checkpoints_absolute, checkpoints_relative = create_checkpoints(fake_model, is_ensemble)
# Simulate a project root: We can't derive that from the repository root because that might point
# into Python's package folder
project_root = Path(__file__).parent.parent
ml_runner = MLRunner(model_config=fake_model, azure_config=azure_config, project_root=project_root)
model_folder = test_output_dirs.root_dir / "final"
ml_runner.copy_child_paths_to_folder(model_folder=model_folder, checkpoint_paths=checkpoints_absolute)
expected_files = [
fixed_paths.ENVIRONMENT_YAML_FILE_NAME,
fixed_paths.MODEL_INFERENCE_JSON_FILE_NAME,
"InnerEye/ML/runner.py",
"InnerEye/ML/model_testing.py",
"InnerEye/Common/fixed_paths.py",
"InnerEye/Common/common_util.py",
]
for r in checkpoints_relative:
expected_files.append(f"{CHECKPOINT_FOLDER}/{r}")
for expected_file in expected_files:
assert (model_folder / expected_file).is_file(), f"File missing: {expected_file}"
trm = model_folder / "TestsOutsidePackage/test_register_model.py"
if extra_code_directory:
assert trm.is_file()
else:
assert not trm.is_file()
def test_score_image_dicom_mock_run_store(
test_output_dirs: OutputFolderForTests) -> None:
"""
Test that dicom in and dicom-rt out works, by mocking out run and store functions.
This mocks out run_inference and store_as_ubyte_nifti so that init_from_model_inference_json
is tested in addition to the tests in test_score_image_dicom_mock_all.
:param test_output_dirs: Test output directories.
"""
mock_segmentation = {'mock_segmentation': True}
model_config = DummyModel()
model_config.set_output_to(test_output_dirs.root_dir)
checkpoint_path = model_config.checkpoint_folder / "checkpoint.ckpt"
create_model_and_store_checkpoint(model_config, checkpoint_path)
azure_config = AzureConfig()
project_root = Path(__file__).parent.parent
ml_runner = MLRunner(model_config=model_config,
azure_config=azure_config,
project_root=project_root)
model_folder = test_output_dirs.root_dir / "final"
ml_runner.copy_child_paths_to_folder(model_folder=model_folder,
checkpoint_paths=[checkpoint_path])
zipped_dicom_series_path = test_output_dirs.root_dir / "temp_pack_dicom_series" / "dicom_series.zip"
zip_known_dicom_series(zipped_dicom_series_path)
score_pipeline_config = ScorePipelineConfig(
data_folder=zipped_dicom_series_path.parent,
model_folder=str(model_folder),
image_files=[str(zipped_dicom_series_path)],
result_image_name=HNSEGMENTATION_FILE.name,
use_gpu=False,
use_dicom=True,
model_id="Dummy:1")
with mock.patch('score.run_inference',
return_value=mock_segmentation) as mock_run_inference:
with mock.patch(
'score.store_as_ubyte_nifti',
return_value=HNSEGMENTATION_FILE) as mock_store_as_ubyte_nifti:
segmentation = score_image(score_pipeline_config)
assert_zip_file_contents(segmentation, HN_DICOM_RT_ZIPPED,
model_folder)
mock_run_inference.assert_called()
mock_store_as_ubyte_nifti.assert_called()
def test_score_image_dicom_mock_run(
test_output_dirs: OutputFolderForTests) -> None:
"""
Test that dicom in and dicom-rt out works, by mocking out only the run scoring function.
This mocks out run_inference so that store_as_ubyte_nifti
is tested in addition to the tests in test_score_image_dicom_mock_run_store.
:param test_output_dirs: Test output directories.
"""
model_config = DummyModel()
model_config.set_output_to(test_output_dirs.root_dir)
checkpoint_path = model_config.checkpoint_folder / "checkpoint.ckpt"
create_model_and_store_checkpoint(model_config, checkpoint_path)
azure_config = AzureConfig()
project_root = Path(__file__).parent.parent
ml_runner = MLRunner(model_config=model_config,
azure_config=azure_config,
project_root=project_root)
model_folder = test_output_dirs.root_dir / "final"
ml_runner.copy_child_paths_to_folder(model_folder=model_folder,
checkpoint_paths=[checkpoint_path])
zipped_dicom_series_path = zip_dicom_series(model_folder)
score_pipeline_config = ScorePipelineConfig(
data_folder=zipped_dicom_series_path.parent,
model_folder=str(model_folder),
image_files=[str(zipped_dicom_series_path)],
result_image_name=HNSEGMENTATION_FILE.name,
use_gpu=False,
use_dicom=True)
image_with_header = io_util.load_nifti_image(HNSEGMENTATION_FILE)
with mock.patch(
'score.run_inference',
return_value=image_with_header.image) as mock_run_inference:
segmentation = score_image(score_pipeline_config)
assert_zip_file_contents(segmentation, HN_DICOM_RT_ZIPPED,
model_folder)
mock_run_inference.assert_called()
