def test_download_azureml_dataset(test_output_dirs: OutputFolderForTests) -> None: dataset_name = "test-dataset" config = DummyModel() config.local_dataset = None config.azure_dataset_id = "" azure_config = get_default_azure_config() runner = MLRunner(config, azure_config=azure_config) # If the model has neither local_dataset or azure_dataset_id, mount_or_download_dataset should fail. # This mounting call must happen before any other operations on the container, because already the model # creation may need access to the dataset. with pytest.raises(ValueError) as ex: runner.setup() assert ex.value.args[0] == "The model must contain either local_dataset or azure_dataset_id." runner.project_root = test_output_dirs.root_dir # Pointing the model to a dataset folder that does not exist should raise an Exception fake_folder = runner.project_root / "foo" runner.container.local_dataset = fake_folder with pytest.raises(FileNotFoundError): runner.mount_or_download_dataset(runner.container.azure_dataset_id, runner.container.local_dataset) # If the local dataset folder exists, mount_or_download_dataset should not do anything. fake_folder.mkdir() local_dataset = runner.mount_or_download_dataset(runner.container.azure_dataset_id, runner.container.local_dataset) assert local_dataset == fake_folder # Pointing the model to a dataset in Azure should trigger a download runner.container.local_dataset = None runner.container.azure_dataset_id = dataset_name with logging_section("Starting download"): result_path = runner.mount_or_download_dataset(runner.container.azure_dataset_id, runner.container.local_dataset) # Download goes into <project_root> / "datasets" / "test_dataset" expected_path = runner.project_root / fixed_paths.DATASETS_DIR_NAME / dataset_name assert result_path == expected_path assert result_path.is_dir() dataset_csv = Path(result_path) / DATASET_CSV_FILE_NAME assert dataset_csv.is_file() # Check that each individual file in the dataset is present for folder in [1, *range(10, 20)]: sub_folder = result_path / str(folder) sub_folder.is_dir() for file in ["ct", "esophagus", "heart", "lung_l", "lung_r", "spinalcord"]: f = (sub_folder / file).with_suffix(".nii.gz") assert f.is_file()
def _test_mount_for_lightning_container(test_output_dirs: OutputFolderForTests, is_offline_run: bool, local_dataset: Optional[Path], azure_dataset: str, is_lightning_model: bool) -> LightningContainer: config: Optional[DeepLearningConfig] = None container: Optional[LightningContainer] = None if is_lightning_model: container = DummyContainerWithDatasets() container.azure_dataset_id = azure_dataset container.local_dataset = local_dataset else: config = DummyModel() config.azure_dataset_id = azure_dataset config.local_dataset = local_dataset # The legacy InnerEye models require an existing dataset_csv file present in the dataset folder. Create that. download_path = test_output_dirs.root_dir / "downloaded" mount_path = test_output_dirs.root_dir / "mounted" if not is_lightning_model: train_and_test_data = "train_and_test_data" for path in [download_path, mount_path, test_output_dirs.root_dir]: # If destination folder exists, delete content to ensure consistency and avoid 'FileExistsError' if (path / train_and_test_data).is_dir(): shutil.rmtree(path / train_and_test_data) # Creates directory structure and copy data shutil.copytree(full_ml_test_data_path(train_and_test_data), path / train_and_test_data) # Copy 'data.csv' file shutil.copy(full_ml_test_data_path(DATASET_CSV_FILE_NAME), path / DATASET_CSV_FILE_NAME) with mock.patch("InnerEye.ML.run_ml.MLRunner.is_offline_run", is_offline_run): with mock.patch("InnerEye.ML.run_ml.download_dataset", return_value=download_path): with mock.patch("InnerEye.ML.run_ml.try_to_mount_input_dataset", return_value=mount_path): runner = MLRunner(config, container=container, azure_config=None, project_root=test_output_dirs.root_dir) runner.setup() return runner.container
def test_model_test(test_output_dirs: OutputFolderForTests) -> None: train_and_test_data_dir = full_ml_test_data_path("train_and_test_data") config = DummyModel() config.set_output_to(test_output_dirs.root_dir) epoch = 1 config.num_epochs = epoch assert config.get_test_epochs() == [epoch] placeholder_dataset_id = "place_holder_dataset_id" config.azure_dataset_id = placeholder_dataset_id transform = config.get_full_image_sample_transforms().test df = pd.read_csv(full_ml_test_data_path(DATASET_CSV_FILE_NAME)) df = df[df.subject.isin([1, 2])] # noinspection PyTypeHints config._datasets_for_inference = \ {ModelExecutionMode.TEST: FullImageDataset(config, df, full_image_sample_transforms=transform)} # type: ignore execution_mode = ModelExecutionMode.TEST checkpoint_handler = get_default_checkpoint_handler(model_config=config, project_root=test_output_dirs.root_dir) # Mimic the behaviour that checkpoints are downloaded from blob storage into the checkpoints folder. stored_checkpoints = full_ml_test_data_path("checkpoints") shutil.copytree(str(stored_checkpoints), str(config.checkpoint_folder)) checkpoint_handler.additional_training_done() inference_results = model_testing.segmentation_model_test(config, data_split=execution_mode, checkpoint_handler=checkpoint_handler) epoch_dir = config.outputs_folder / get_epoch_results_path(epoch, execution_mode) assert inference_results.epochs[epoch] == pytest.approx(0.66606902, abs=1e-6) assert config.outputs_folder.is_dir() assert epoch_dir.is_dir() patient1 = io_util.load_nifti_image(train_and_test_data_dir / "id1_channel1.nii.gz") patient2 = io_util.load_nifti_image(train_and_test_data_dir / "id2_channel1.nii.gz") assert_file_contains_string(epoch_dir / DATASET_ID_FILE, placeholder_dataset_id) assert_file_contains_string(epoch_dir / GROUND_TRUTH_IDS_FILE, "region") assert_text_files_match(epoch_dir / model_testing.METRICS_FILE_NAME, train_and_test_data_dir / model_testing.METRICS_FILE_NAME) assert_text_files_match(epoch_dir / model_testing.METRICS_AGGREGATES_FILE, train_and_test_data_dir / model_testing.METRICS_AGGREGATES_FILE) # Plotting results vary between platforms. Can only check if the file is generated, but not its contents. assert (epoch_dir / model_testing.BOXPLOT_FILE).exists() assert_nifti_content(epoch_dir / "001" / "posterior_region.nii.gz", get_image_shape(patient1), patient1.header, [136], np.ubyte) assert_nifti_content(epoch_dir / "002" / "posterior_region.nii.gz", get_image_shape(patient2), patient2.header, [136], np.ubyte) assert_nifti_content(epoch_dir / "001" / DEFAULT_RESULT_IMAGE_NAME, get_image_shape(patient1), patient1.header, [1], np.ubyte) assert_nifti_content(epoch_dir / "002" / DEFAULT_RESULT_IMAGE_NAME, get_image_shape(patient2), patient2.header, [1], np.ubyte) assert_nifti_content(epoch_dir / "001" / "posterior_background.nii.gz", get_image_shape(patient1), patient1.header, [118], np.ubyte) assert_nifti_content(epoch_dir / "002" / "posterior_background.nii.gz", get_image_shape(patient2), patient2.header, [118], np.ubyte) thumbnails_folder = epoch_dir / model_testing.THUMBNAILS_FOLDER assert thumbnails_folder.is_dir() png_files = list(thumbnails_folder.glob("*.png")) overlays = [f for f in png_files if "_region_slice_" in str(f)] assert len(overlays) == len(df.subject.unique()), "There should be one overlay/contour file per subject" # Writing dataset.csv normally happens at the beginning of training, # but this test reads off a saved checkpoint file. # Dataset.csv must be present for plot_cross_validation. config.write_dataset_files() # Test if the metrics files can be picked up correctly by the cross validation code config_and_files = get_config_and_results_for_offline_runs(config) result_files = config_and_files.files assert len(result_files) == 1 for file in result_files: assert file.execution_mode == execution_mode assert file.dataset_csv_file is not None assert file.dataset_csv_file.exists() assert file.metrics_file is not None assert file.metrics_file.exists()
def test_model_test(test_output_dirs: OutputFolderForTests, use_partial_ground_truth: bool, allow_partial_ground_truth: bool) -> None: """ Check the CSVs (and image files) output by InnerEye.ML.model_testing.segmentation_model_test :param test_output_dirs: The fixture in conftest.py :param use_partial_ground_truth: Whether to remove some ground truth labels from some test users :param allow_partial_ground_truth: What to set the allow_incomplete_labels flag to """ train_and_test_data_dir = full_ml_test_data_path("train_and_test_data") seed_everything(42) config = DummyModel() config.allow_incomplete_labels = allow_partial_ground_truth config.set_output_to(test_output_dirs.root_dir) placeholder_dataset_id = "place_holder_dataset_id" config.azure_dataset_id = placeholder_dataset_id transform = config.get_full_image_sample_transforms().test df = pd.read_csv(full_ml_test_data_path(DATASET_CSV_FILE_NAME)) if use_partial_ground_truth: config.check_exclusive = False config.ground_truth_ids = ["region", "region_1"] # As in Tests.ML.pipelines.test.inference.test_evaluate_model_predictions patients 3, 4, # and 5 are in the test dataset with: # Patient 3 has one missing ground truth channel: "region" df = df[df["subject"].ne(3) | df["channel"].ne("region")] # Patient 4 has all missing ground truth channels: "region", "region_1" df = df[df["subject"].ne(4) | df["channel"].ne("region")] df = df[df["subject"].ne(4) | df["channel"].ne("region_1")] # Patient 5 has no missing ground truth channels. config.dataset_data_frame = df df = df[df.subject.isin([3, 4, 5])] config.train_subject_ids = ['1', '2'] config.test_subject_ids = ['3', '4', '5'] config.val_subject_ids = ['6', '7'] else: df = df[df.subject.isin([1, 2])] if use_partial_ground_truth and not allow_partial_ground_truth: with pytest.raises(ValueError) as value_error: # noinspection PyTypeHints config._datasets_for_inference = { ModelExecutionMode.TEST: FullImageDataset(config, df, full_image_sample_transforms=transform) } # type: ignore assert "Patient 3 does not have channel 'region'" in str( value_error.value) return else: # noinspection PyTypeHints config._datasets_for_inference = { ModelExecutionMode.TEST: FullImageDataset(config, df, full_image_sample_transforms=transform) } # type: ignore execution_mode = ModelExecutionMode.TEST checkpoint_handler = get_default_checkpoint_handler( model_config=config, project_root=test_output_dirs.root_dir) # Mimic the behaviour that checkpoints are downloaded from blob storage into the checkpoints folder. create_model_and_store_checkpoint( config, config.checkpoint_folder / LAST_CHECKPOINT_FILE_NAME_WITH_SUFFIX) checkpoint_handler.additional_training_done() inference_results = model_testing.segmentation_model_test( config, execution_mode=execution_mode, checkpoint_paths=checkpoint_handler.get_checkpoints_to_test()) epoch_dir = config.outputs_folder / get_best_epoch_results_path( execution_mode) total_num_patients_column_name = f"total_{MetricsFileColumns.Patient.value}".lower( ) if not total_num_patients_column_name.endswith("s"): total_num_patients_column_name += "s" if use_partial_ground_truth: num_subjects = len(pd.unique(df["subject"])) if allow_partial_ground_truth: assert csv_column_contains_value( csv_file_path=epoch_dir / METRICS_AGGREGATES_FILE, column_name=total_num_patients_column_name, value=num_subjects, contains_only_value=True) assert csv_column_contains_value( csv_file_path=epoch_dir / SUBJECT_METRICS_FILE_NAME, column_name=MetricsFileColumns.Dice.value, value='', contains_only_value=False) else: aggregates_df = pd.read_csv(epoch_dir / METRICS_AGGREGATES_FILE) assert total_num_patients_column_name not in aggregates_df.columns # Only added if using partial ground truth assert not csv_column_contains_value( csv_file_path=epoch_dir / SUBJECT_METRICS_FILE_NAME, column_name=MetricsFileColumns.Dice.value, value='', contains_only_value=False) assert inference_results.metrics == pytest.approx(0.66606902, abs=1e-6) assert config.outputs_folder.is_dir() assert epoch_dir.is_dir() patient1 = io_util.load_nifti_image(train_and_test_data_dir / "id1_channel1.nii.gz") patient2 = io_util.load_nifti_image(train_and_test_data_dir / "id2_channel1.nii.gz") assert_file_contains_string(epoch_dir / DATASET_ID_FILE, placeholder_dataset_id) assert_file_contains_string(epoch_dir / GROUND_TRUTH_IDS_FILE, "region") assert_text_files_match( epoch_dir / model_testing.SUBJECT_METRICS_FILE_NAME, train_and_test_data_dir / model_testing.SUBJECT_METRICS_FILE_NAME) assert_text_files_match( epoch_dir / model_testing.METRICS_AGGREGATES_FILE, train_and_test_data_dir / model_testing.METRICS_AGGREGATES_FILE) # Plotting results vary between platforms. Can only check if the file is generated, but not its contents. assert (epoch_dir / model_testing.BOXPLOT_FILE).exists() assert_nifti_content(epoch_dir / "001" / "posterior_region.nii.gz", get_image_shape(patient1), patient1.header, [137], np.ubyte) assert_nifti_content(epoch_dir / "002" / "posterior_region.nii.gz", get_image_shape(patient2), patient2.header, [137], np.ubyte) assert_nifti_content(epoch_dir / "001" / DEFAULT_RESULT_IMAGE_NAME, get_image_shape(patient1), patient1.header, [1], np.ubyte) assert_nifti_content(epoch_dir / "002" / DEFAULT_RESULT_IMAGE_NAME, get_image_shape(patient2), patient2.header, [1], np.ubyte) assert_nifti_content(epoch_dir / "001" / "posterior_background.nii.gz", get_image_shape(patient1), patient1.header, [117], np.ubyte) assert_nifti_content(epoch_dir / "002" / "posterior_background.nii.gz", get_image_shape(patient2), patient2.header, [117], np.ubyte) thumbnails_folder = epoch_dir / model_testing.THUMBNAILS_FOLDER assert thumbnails_folder.is_dir() png_files = list(thumbnails_folder.glob("*.png")) overlays = [f for f in png_files if "_region_slice_" in str(f)] assert len(overlays) == len(df.subject.unique( )), "There should be one overlay/contour file per subject" # Writing dataset.csv normally happens at the beginning of training, # but this test reads off a saved checkpoint file. # Dataset.csv must be present for plot_cross_validation. config.write_dataset_files() # Test if the metrics files can be picked up correctly by the cross validation code config_and_files = get_config_and_results_for_offline_runs(config) result_files = config_and_files.files assert len(result_files) == 1 for file in result_files: assert file.execution_mode == execution_mode assert file.dataset_csv_file is not None assert file.dataset_csv_file.exists() assert file.metrics_file is not None assert file.metrics_file.exists()