def run_in_situ(self, azure_run_info: AzureRunInfo) -> None:
"""
Actually run the AzureML job; this method will typically run on an Azure VM.
:param azure_run_info: Contains all information about the present run in AzureML, in particular where the
datasets are mounted.
"""
# Only set the logging level now. Usually, when we set logging to DEBUG, we want diagnostics about the model
# build itself, but not the tons of debug information that AzureML submissions create.
# Suppress the logging from all processes but the one for GPU 0 on each node, to make log files more readable
logging_to_stdout(
self.azure_config.log_level if is_local_rank_zero() else "ERROR")
package_setup_and_hacks()
if is_global_rank_zero():
self.print_git_tags()
# For the PR build in AzureML, we can either pytest, or the training of the simple PR model. Running both
# only works when using DDP_spawn, but that has as a side-effect that it messes up memory consumption of the
# large models.
if self.azure_config.pytest_mark:
outputs_folder = Path.cwd() / fixed_paths.DEFAULT_AML_UPLOAD_DIR
pytest_passed, results_file_path = run_pytest(
self.azure_config.pytest_mark, outputs_folder)
if not pytest_passed:
# Terminate if pytest has failed. This makes the smoke test in
# PR builds fail if pytest fails.
pytest_failures = f"Not all PyTest tests passed. See {results_file_path}"
raise ValueError(pytest_failures)
else:
# Set environment variables for multi-node training if needed. This function will terminate early
# if it detects that it is not in a multi-node environment.
set_environment_variables_for_multi_node()
self.ml_runner = self.create_ml_runner()
self.ml_runner.setup(azure_run_info)
self.ml_runner.run()
def test_rnn_classifier_via_config_2(test_output_dirs: TestOutputDirectories) -> None:
"""
Test if we can build an RNN classifier that learns sequences, of the same kind as in
test_rnn_classifier_toy_problem, but built via the config.
"""
expected_max_train_loss = 0.71
expected_max_val_loss = 0.71
num_sequences = 100
ml_util.set_random_seed(123)
dataset_contents = "subject,index,feature,label\n"
for subject in range(num_sequences):
# Sequences have variable length
sequence_length = np.random.choice([9, 10, 11, 12])
# Each sequence is a series of 0 and 1
inputs = np.random.choice([0, 1], size=(sequence_length,), p=[1. / 3, 2. / 3])
label = np.sum(inputs) > (sequence_length // 2)
for i, value in enumerate(inputs.tolist()):
dataset_contents += f"S{subject},{i},{value},{label}\n"
logging_to_stdout()
config = ToySequenceModel2(should_validate=False)
config.num_epochs = 2
config.set_output_to(test_output_dirs.root_dir)
config.dataset_data_frame = _get_mock_sequence_dataset(dataset_contents)
results = model_train(config)
actual_train_loss = results.train_results_per_epoch[-1].values()[MetricType.LOSS.value][0]
actual_val_loss = results.val_results_per_epoch[-1].values()[MetricType.LOSS.value][0]
print(f"Training loss after {config.num_epochs} epochs: {actual_train_loss}")
print(f"Validation loss after {config.num_epochs} epochs: {actual_val_loss}")
assert actual_train_loss <= expected_max_train_loss, "Training loss too high"
assert actual_val_loss <= expected_max_val_loss, "Validation loss too high"
assert len(results.optimal_temperature_scale_values_per_checkpoint_epoch) \
== config.get_total_number_of_save_epochs()
assert np.allclose(results.optimal_temperature_scale_values_per_checkpoint_epoch, [0.97], rtol=0.1)
def test_train_2d_classification_model(test_output_dirs: OutputFolderForTests,
use_mixed_precision: bool) -> None:
"""
Test training and testing of 2d classification models.
"""
logging_to_stdout(logging.DEBUG)
config = ClassificationModelForTesting2D()
config.set_output_to(test_output_dirs.root_dir)
# Train for 4 epochs, checkpoints at epochs 2 and 4
config.num_epochs = 4
config.use_mixed_precision = use_mixed_precision
model_training_result, checkpoint_handler = model_train_unittest(
config, dirs=test_output_dirs)
assert model_training_result is not None
expected_learning_rates = [0.0001, 9.99971e-05, 9.99930e-05, 9.99861e-05]
expected_train_loss = [0.705931, 0.698664, 0.694489, 0.693151]
expected_val_loss = [1.078517, 1.140510, 1.199026, 1.248595]
actual_train_loss = model_training_result.get_metric(
is_training=True, metric_type=MetricType.LOSS.value)
actual_val_loss = model_training_result.get_metric(
is_training=False, metric_type=MetricType.LOSS.value)
actual_lr = model_training_result.get_metric(
is_training=True, metric_type=MetricType.LEARNING_RATE.value)
assert actual_train_loss == pytest.approx(expected_train_loss, abs=1e-6)
assert actual_val_loss == pytest.approx(expected_val_loss, abs=1e-6)
assert actual_lr == pytest.approx(expected_learning_rates, rel=1e-5)
test_results = model_testing.model_test(
config,
ModelExecutionMode.TRAIN,
checkpoint_handler=checkpoint_handler)
assert isinstance(test_results, InferenceMetricsForClassification)
def test_rnn_classifier_via_config_1(
use_combined_model: bool, imaging_feature_type: ImagingFeatureType,
combine_hidden_state: bool, use_encoder_layer_norm: bool,
use_mean_teacher_model: bool,
test_output_dirs: OutputFolderForTests) -> None:
"""
Test if we can build a simple RNN model that only feeds off non-image features.
This just tests the mechanics of training, but not if the model learned.
"""
logging_to_stdout()
config = ToySequenceModel(use_combined_model,
imaging_feature_type=imaging_feature_type,
combine_hidden_states=combine_hidden_state,
use_encoder_layer_norm=use_encoder_layer_norm,
use_mean_teacher_model=use_mean_teacher_model,
should_validate=False)
config.use_mixed_precision = True
config.set_output_to(test_output_dirs.root_dir)
config.dataset_data_frame = _get_mock_sequence_dataset()
# Patch the load_images function that will be called once we access a dataset item
image_and_seg = ImageAndSegmentations[np.ndarray](
images=np.random.uniform(0, 1, SCAN_SIZE),
segmentations=np.random.randint(0, 2, SCAN_SIZE))
with mock.patch('InnerEye.ML.utils.io_util.load_image_in_known_formats',
return_value=image_and_seg):
model_train(
config,
get_default_checkpoint_handler(
model_config=config, project_root=test_output_dirs.root_dir))
def test_rnn_classifier_via_config_1(
use_combined_model: bool, imaging_feature_type: ImagingFeatureType,
combine_hidden_state: bool, use_encoder_layer_norm: bool,
use_mean_teacher_model: bool,
test_output_dirs: OutputFolderForTests) -> None:
"""
Test if we can build a simple RNN model that only feeds off non-image features.
This just tests the mechanics of training, but not if the model learned.
"""
logging_to_stdout()
config = ToySequenceModel(use_combined_model,
imaging_feature_type=imaging_feature_type,
combine_hidden_states=combine_hidden_state,
use_encoder_layer_norm=use_encoder_layer_norm,
use_mean_teacher_model=use_mean_teacher_model,
should_validate=False)
# This fails with 16bit precision, saying "torch.nn.functional.binary_cross_entropy and torch.nn.BCELoss are
# unsafe to autocast. Many models use a sigmoid layer right before the binary cross entropy layer. In this case,
# combine the two layers using torch.nn.functional.binary_cross_entropy_with_logits or
# torch.nn.BCEWithLogitsLoss. binary_cross_entropy_with_logits and BCEWithLogits are safe to autocast."
config.use_mixed_precision = False
config.set_output_to(test_output_dirs.root_dir)
config.dataset_data_frame = _get_mock_sequence_dataset()
# Patch the load_images function that will be called once we access a dataset item
image_and_seg = ImageAndSegmentations[np.ndarray](
images=np.random.uniform(0, 1, SCAN_SIZE),
segmentations=np.random.randint(0, 2, SCAN_SIZE))
with mock.patch('InnerEye.ML.utils.io_util.load_image_in_known_formats',
return_value=image_and_seg):
model_train(
config,
get_default_checkpoint_handler(
model_config=config, project_root=test_output_dirs.root_dir))
def test_run_ml_with_classification_model(
test_output_dirs: OutputFolderForTests,
number_of_offline_cross_validation_splits: int,
model_name: str) -> None:
"""
Test training and testing of classification models, when it is started together via run_ml.
"""
logging_to_stdout()
azure_config = get_default_azure_config()
azure_config.train = True
config: ScalarModelBase = ModelConfigLoader[ScalarModelBase]() \
.create_model_config_from_name(model_name)
config.number_of_cross_validation_splits = number_of_offline_cross_validation_splits
config.set_output_to(test_output_dirs.root_dir)
# Trying to run DDP from the test suite hangs, hence restrict to single GPU.
config.max_num_gpus = 1
MLRunner(config, azure_config).run()
_check_offline_cross_validation_output_files(config)
if config.perform_cross_validation:
# Test that the result files can be correctly picked up by the cross validation routine.
# For that, we point the downloader to the local results folder. The core download method
# recognizes run_recovery_id == None as the signal to read from the local_run_results folder.
config_and_files = get_config_and_results_for_offline_runs(config)
result_files = config_and_files.files
# One file for VAL and one for TRAIN for each child run
assert len(result_files
) == config.get_total_number_of_cross_validation_runs() * 2
for file in result_files:
assert file.execution_mode != ModelExecutionMode.TEST
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_rnn_classifier_via_config_2(test_output_dirs: OutputFolderForTests) -> None:
"""
Test if we can build an RNN classifier that learns sequences, of the same kind as in
test_rnn_classifier_toy_problem, but built via the config.
"""
expected_max_train_loss = 0.71
expected_max_val_loss = 0.71
num_sequences = 100
ml_util.set_random_seed(123)
dataset_contents = "subject,index,feature,label\n"
for subject in range(num_sequences):
# Sequences have variable length
sequence_length = np.random.choice([9, 10, 11, 12])
# Each sequence is a series of 0 and 1
inputs = np.random.choice([0, 1], size=(sequence_length,), p=[1. / 3, 2. / 3])
label = np.sum(inputs) > (sequence_length // 2)
for i, value in enumerate(inputs.tolist()):
dataset_contents += f"S{subject},{i},{value},{label}\n"
logging_to_stdout()
config = ToySequenceModel2(should_validate=False)
config.num_epochs = 2
config.set_output_to(test_output_dirs.root_dir)
config.dataset_data_frame = _get_mock_sequence_dataset(dataset_contents)
results, _ = model_train_unittest(config, dirs=test_output_dirs)
actual_train_loss = results.get_metric(is_training=True, metric_type=MetricType.LOSS.value)[-1]
actual_val_loss = results.get_metric(is_training=False, metric_type=MetricType.LOSS.value)[-1]
print(f"Training loss after {config.num_epochs} epochs: {actual_train_loss}")
print(f"Validation loss after {config.num_epochs} epochs: {actual_val_loss}")
assert actual_train_loss <= expected_max_train_loss, "Training loss too high"
assert actual_val_loss <= expected_max_val_loss, "Validation loss too high"
def main() -> None:
"""
Main function.
"""
logging_to_stdout()
config = ReportStructureExtremesConfig.parse_args()
report_structure_extremes(config.dataset, config.yaml_file)
def test_rnn_classifier_via_config_1(use_combined_model: bool,
imaging_feature_type: ImagingFeatureType,
combine_hidden_state: bool,
use_encoder_layer_norm: bool,
use_mean_teacher_model: bool,
test_output_dirs: TestOutputDirectories) -> None:
"""
Test if we can build a simple RNN model that only feeds off non-image features.
This just tests the mechanics of training, but not if the model learned.
"""
logging_to_stdout()
config = ToySequenceModel(use_combined_model,
imaging_feature_type=imaging_feature_type,
combine_hidden_states=combine_hidden_state,
use_encoder_layer_norm=use_encoder_layer_norm,
use_mean_teacher_model=use_mean_teacher_model,
should_validate=False)
config.set_output_to(test_output_dirs.root_dir)
config.dataset_data_frame = _get_mock_sequence_dataset()
# Patch the load_images function that will be called once we access a dataset item
image_and_seg = ImageAndSegmentations[np.ndarray](images=np.random.uniform(0, 1, SCAN_SIZE),
segmentations=np.random.randint(0, 2, SCAN_SIZE))
with mock.patch('InnerEye.ML.utils.io_util.load_image_in_known_formats', return_value=image_and_seg):
results = model_train(config)
assert len(results.optimal_temperature_scale_values_per_checkpoint_epoch) \
== config.get_total_number_of_save_epochs()
def run(self) -> Tuple[Optional[DeepLearningConfig], Optional[Run]]:
"""
The main entry point for training and testing models from the commandline. This chooses a model to train
via a commandline argument, runs training or testing, and writes all required info to disk and logs.
:return: If submitting to AzureML, returns the model configuration that was used for training,
including commandline overrides applied (if any).
"""
# Usually, when we set logging to DEBUG, we want diagnostics about the model
# build itself, but not the tons of debug information that AzureML submissions create.
logging_to_stdout(logging.INFO if is_local_rank_zero() else "ERROR")
initialize_rpdb()
user_agent.append(azure_util.INNEREYE_SDK_NAME, azure_util.INNEREYE_SDK_VERSION)
self.parse_and_load_model()
if self.lightning_container.perform_cross_validation:
if self.model_config is None:
raise NotImplementedError("Cross validation for LightingContainer models is not yet supported.")
# force hyperdrive usage if performing cross validation
self.azure_config.hyperdrive = True
run_object: Optional[Run] = None
if self.azure_config.azureml:
run_object = self.submit_to_azureml()
else:
self.run_in_situ()
if self.model_config is None:
return self.lightning_container, run_object
return self.model_config, run_object
def test_run_ml_with_sequence_model(use_combined_model: bool,
imaging_feature_type: ImagingFeatureType,
test_output_dirs: TestOutputDirectories) -> None:
"""
Test training and testing of sequence models, when it is started together via run_ml.
"""
logging_to_stdout()
config = ToySequenceModel(use_combined_model, imaging_feature_type,
should_validate=False, sequence_target_positions=[2, 10])
config.set_output_to(test_output_dirs.root_dir)
config.dataset_data_frame = _get_mock_sequence_dataset()
config.num_epochs = 1
config.max_batch_grad_cam = 1
# make sure we are testing with at least one sequence position that will not exist
# to ensure correct handling of sequences that do not contain all the expected target positions
assert max(config.sequence_target_positions) > config.dataset_data_frame[config.sequence_column].astype(float).max()
# Patch the load_images function that will be called once we access a dataset item
image_and_seg = ImageAndSegmentations[np.ndarray](images=np.random.uniform(0, 1, SCAN_SIZE),
segmentations=np.random.randint(0, 2, SCAN_SIZE))
with mock.patch('InnerEye.ML.utils.io_util.load_image_in_known_formats', return_value=image_and_seg):
azure_config = get_default_azure_config()
azure_config.train = True
MLRunner(config, azure_config).run()
def test_compare_folder_against_run(
test_output_dirs: OutputFolderForTests) -> None:
"""
Test if we can compare that a set of files exists in an AML run.
"""
logging_to_stdout(log_level=logging.DEBUG)
run = get_most_recent_run(
fallback_run_id_for_local_execution=FALLBACK_SINGLE_RUN)
file1 = test_output_dirs.root_dir / REGRESSION_TEST_AZUREML_FOLDER / \
FINAL_MODEL_FOLDER / MODEL_INFERENCE_JSON_FILE_NAME
create_folder_and_write_text(
file1, '{"model_name": "BasicModel2Epochs", "checkpoint_paths": ['
'"checkpoints/last.ckpt"], '
'"model_configs_namespace": "InnerEye.ML.configs.segmentation.BasicModel2Epochs"}'
)
with mock.patch("InnerEye.ML.baselines_util.RUN_CONTEXT", run):
# First comparison only on the .json file should pass
compare_folders_and_run_outputs(expected=test_output_dirs.root_dir,
actual=Path.cwd(),
csv_relative_tolerance=0.0)
# Now add a file to the set of expected files that does not exist in the run: comparison should now fail
no_such_file = "no_such_file.txt"
file2 = test_output_dirs.root_dir / REGRESSION_TEST_AZUREML_FOLDER / no_such_file
create_folder_and_write_text(file2, "foo")
with pytest.raises(ValueError) as ex:
compare_folders_and_run_outputs(expected=test_output_dirs.root_dir,
actual=Path.cwd(),
csv_relative_tolerance=0.0)
message = ex.value.args[0].splitlines()
assert f"{baselines_util.MISSING_FILE}: {no_such_file}" in message
# Now run the same comparison that failed previously, without mocking the RUN_CONTEXT. This should now
# realize that the present run is an offline run, and skip the comparison
compare_folders_and_run_outputs(expected=test_output_dirs.root_dir,
actual=Path.cwd(),
csv_relative_tolerance=0.0)
def test_is_completed_single_run() -> None:
"""
Test if we can correctly check run status for a single run.
:return:
"""
logging_to_stdout()
workspace = get_default_workspace()
get_run_and_check(get_most_recent_run_id(), True, workspace)
def test_model_config_loader() -> None:
logging_to_stdout(log_level=logging.DEBUG)
default_loader = get_model_loader()
assert default_loader.create_model_config_from_name("BasicModel2Epochs") is not None
with pytest.raises(ValueError):
default_loader.create_model_config_from_name("DummyModel")
loader_including_tests = get_model_loader(namespace="Tests.ML.configs")
assert loader_including_tests.create_model_config_from_name("BasicModel2Epochs") is not None
assert loader_including_tests.create_model_config_from_name("DummyModel") is not None
def test_create_ml_runner_args(is_default_namespace: bool,
test_output_dirs: TestOutputDirectories,
is_offline_run: bool) -> None:
"""Test round trip parsing of commandline arguments:
From arguments to the Azure runner to the arguments of the ML runner, checking that
whatever is passed on can be correctly parsed."""
logging_to_stdout()
model_name = "Lung"
outputs_folder = Path(test_output_dirs.root_dir)
project_root = fixed_paths.repository_root_directory()
if is_default_namespace:
model_configs_namespace = None
else:
model_configs_namespace = "Tests.ML.configs"
model_name = "DummyModel"
args_list = [
f"--model={model_name}", "--train=True", "--l_rate=100.0",
"--norm_method=Simple Norm", "--subscription_id", "Test1",
"--tenant_id=Test2", "--application_id", "Test3",
"--datasets_storage_account=Test4", "--datasets_container", "Test5",
"--pytest_mark", "gpu", f"--output_to={outputs_folder}"
]
if not is_default_namespace:
args_list.append(
f"--model_configs_namespace={model_configs_namespace}")
with mock.patch("sys.argv", [""] + args_list):
with mock.patch(
"InnerEye.ML.deep_learning_config.is_offline_run_context",
return_value=is_offline_run):
runner = Runner(project_root=project_root,
yaml_config_file=fixed_paths.SETTINGS_YAML_FILE)
runner.parse_and_load_model()
azure_config = runner.azure_config
model_config = runner.model_config
assert azure_config.datasets_storage_account == "Test4"
assert azure_config.model == model_name
assert model_config.l_rate == 100.0
assert model_config.norm_method == PhotometricNormalizationMethod.SimpleNorm
if is_offline_run:
# The actual output folder must be a subfolder of the folder given on the commandline. The folder will contain
# a timestamp, that will start with the year number, hence will start with 20...
assert str(model_config.outputs_folder).startswith(
str(outputs_folder / "20"))
assert model_config.logs_folder == (model_config.outputs_folder /
DEFAULT_LOGS_DIR_NAME)
else:
# For runs inside AzureML, the output folder is the project root (the root of the folders that are
# included in the snapshot). The "outputs_to" argument will be ignored.
assert model_config.outputs_folder == (project_root /
DEFAULT_AML_UPLOAD_DIR)
assert model_config.logs_folder == (project_root /
DEFAULT_LOGS_DIR_NAME)
assert not hasattr(model_config, "datasets_storage_account")
assert azure_config.pytest_mark == "gpu"
def test_config_loader_on_lightning_container() -> None:
"""
Test if the config loader can load an model that is neither classification nor segmentation.
"""
# First test if the container can be instantiated at all (it is tricky to get that right when inheritance change)
DummyContainerWithParameters()
logging_to_stdout(log_level=logging.DEBUG)
model = model_loader_including_tests.create_model_config_from_name("DummyContainerWithParameters")
assert model is not None
def test_is_completed_ensemble_run() -> None:
"""
Test if we can correctly check run status and status of child runs for an ensemble run.
:return:
"""
logging_to_stdout()
workspace = get_default_workspace()
run_id = get_most_recent_run_id(
fallback_run_id_for_local_execution=FALLBACK_ENSEMBLE_RUN)
get_run_and_check(run_id, True, workspace)
def main(settings_yaml_file: Optional[Path] = None,
project_root: Optional[Path] = None) -> None:
"""
Main function.
"""
logging_to_stdout()
config = ReportStructureExtremesConfig.parse_args()
azure_config = AzureConfig.from_yaml(yaml_file_path=settings_yaml_file or config.settings,
project_root=project_root)
report_structure_extremes(config.dataset, azure_config)
def main(args: Optional[List[str]] = None, project_root: Optional[Path] = None) -> None:
"""
Main function.
"""
logging_to_stdout()
inference_config = SubmitForInferenceConfig.parse_args(args)
settings = inference_config.settings or fixed_paths.SETTINGS_YAML_FILE
azure_config = AzureConfig.from_yaml(settings, project_root=project_root)
if inference_config.cluster:
azure_config.cluster = inference_config.cluster
submit_for_inference(inference_config, azure_config)
def get_configs(
default_model_config: SegmentationModelBase, yaml_file_path: Path
) -> Tuple[SegmentationModelBase, AzureConfig, Dict]:
parser_result = create_parser(yaml_file_path)
args = parser_result.args
runner_config = AzureConfig(**args)
logging_to_stdout(args["log_level"])
config = default_model_config or ModelConfigLoader(
).create_model_config_from_name(runner_config.model)
config.apply_overrides(parser_result.overrides, should_validate=False)
return config, runner_config, args
def plot_cross_validation(config: PlotCrossValidationConfig) -> Path:
"""
Collects results from an AzureML cross validation run, and writes aggregate metrics files.
:param config: The settings for plotting cross validation results.
:return:
"""
logging_to_stdout(logging.INFO)
with logging_section("Downloading cross-validation results"):
result_files, root_folder = download_crossval_result_files(config)
config_and_files = OfflineCrossvalConfigAndFiles(config=config, files=result_files)
with logging_section("Plotting cross-validation results"):
plot_cross_validation_from_files(config_and_files, root_folder)
return root_folder
def run_in_situ(self) -> None:
"""
Actually run the AzureML job; this method will typically run on an Azure VM.
"""
# Only set the logging level now. Usually, when we set logging to DEBUG, we want diagnostics about the model
# build itself, but not the tons of debug information that AzureML submissions create.
logging_to_stdout(self.azure_config.log_level)
suppress_logging_noise()
pytest_failed = False
training_failed = False
pytest_passed = True
# Ensure that both model training and pytest both get executed in all cases, so that we see a full set of
# test results in each PR
outputs_folder = self.model_config.outputs_folder
try:
logging_to_file(self.model_config.logs_folder / LOG_FILE_NAME)
try:
self.create_ml_runner().run()
except Exception as ex:
print_exception(ex, "Model training/testing failed.")
training_failed = True
if self.azure_config.pytest_mark:
try:
pytest_passed, results_file_path = run_pytest(
self.azure_config.pytest_mark, outputs_folder)
if not pytest_passed:
logging.error(
f"Not all PyTest tests passed. See {results_file_path}"
)
except Exception as ex:
print_exception(ex, "Unable to run PyTest.")
pytest_failed = True
finally:
# wait for aggregation if required, and only if the training actually succeeded.
if not training_failed and self.model_config.should_wait_for_other_cross_val_child_runs(
):
self.wait_for_cross_val_runs_to_finish_and_aggregate()
disable_logging_to_file()
message = []
if training_failed:
message.append("Training failed")
if pytest_failed:
message.append("Unable to run Pytest")
if not pytest_passed:
message.append("At least 1 test in Pytest failed")
# Terminate if pytest or model training has failed. This makes the smoke test in
# PR builds fail if pytest fails.
if message:
raise ValueError(
f"One component of the training pipeline failed: {'. '.join(message)}"
)
def test_compare_folder_against_parent_run(
test_output_dirs: OutputFolderForTests) -> None:
"""
Test if we can compare that a set of files exists in an AML run.
"""
logging_to_stdout(log_level=logging.DEBUG)
parent_run = get_most_recent_run(
fallback_run_id_for_local_execution=FALLBACK_ENSEMBLE_RUN)
file1 = test_output_dirs.root_dir / REGRESSION_TEST_AZUREML_PARENT_FOLDER / \
CROSSVAL_RESULTS_FOLDER / "Test_outliers.txt"
create_folder_and_write_text(
file1, """
=== METRIC: Dice ===
No outliers found
=== METRIC: HausdorffDistance_mm ===
No outliers found""")
with mock.patch("InnerEye.ML.baselines_util.PARENT_RUN_CONTEXT",
parent_run):
# No plain files to compare. The file Test_outliers.txt should be compared and found to match.
compare_folders_and_run_outputs(expected=test_output_dirs.root_dir,
actual=Path.cwd(),
csv_relative_tolerance=0.0)
create_folder_and_write_text(file1, "foo")
with pytest.raises(ValueError) as ex:
compare_folders_and_run_outputs(expected=test_output_dirs.root_dir,
actual=Path.cwd(),
csv_relative_tolerance=0.0)
message = ex.value.args[0].splitlines()
assert f"{baselines_util.CONTENTS_MISMATCH}: {CROSSVAL_RESULTS_FOLDER}/{file1.name}" in message
# Now add a file to the set of expected files that does not exist in the run: comparison should now fail
no_such_file = "no_such_file.txt"
file2 = test_output_dirs.root_dir / REGRESSION_TEST_AZUREML_PARENT_FOLDER / no_such_file
create_folder_and_write_text(file2, "foo")
with pytest.raises(ValueError) as ex:
compare_folders_and_run_outputs(expected=test_output_dirs.root_dir,
actual=Path.cwd(),
csv_relative_tolerance=0.0)
message = ex.value.args[0].splitlines()
assert f"{baselines_util.MISSING_FILE}: {no_such_file}" in message
# Now run the same comparison without mocking the PARENT_RUN_CONTEXT. This should now
# realize that the present run is a crossval child run
with pytest.raises(ValueError) as ex:
compare_folders_and_run_outputs(expected=test_output_dirs.root_dir,
actual=Path.cwd(),
csv_relative_tolerance=0.0)
assert "no (parent) run to compare against" in str(ex)
def test_run_ml_with_multi_label_sequence_model(
test_output_dirs: OutputFolderForTests) -> None:
"""
Test training and testing of sequence models that predicts at multiple time points,
when it is started via run_ml.
"""
logging_to_stdout()
config = ToyMultiLabelSequenceModel(should_validate=False)
assert config.get_target_indices() == [1, 2, 3]
expected_prediction_targets = [
f"{SEQUENCE_POSITION_HUE_NAME_PREFIX} {x}" for x in ["01", "02", "03"]
]
_target_indices = config.get_target_indices()
assert _target_indices is not None
assert len(_target_indices) == len(expected_prediction_targets)
metrics_dict = create_metrics_dict_for_scalar_models(config)
assert metrics_dict.get_hue_names(
include_default=False) == expected_prediction_targets
config.set_output_to(test_output_dirs.root_dir)
# Create a fake dataset directory to make config validation pass
config.local_dataset = test_output_dirs.root_dir
config.dataset_data_frame = _get_multi_label_sequence_dataframe()
config.pre_process_dataset_dataframe()
config.num_epochs = 1
config.max_batch_grad_cam = 1
azure_config = get_default_azure_config()
azure_config.train = True
MLRunner(config, azure_config).run()
# The metrics file should have one entry per epoch per subject per prediction target,
# for all the 3 prediction targets.
metrics_file = config.outputs_folder / "Train" / SUBJECT_METRICS_FILE_NAME
assert metrics_file.exists()
metrics = pd.read_csv(metrics_file)
assert LoggingColumns.Patient.value in metrics
assert LoggingColumns.Epoch.value in metrics
assert LoggingColumns.Hue.value in metrics
assert metrics[LoggingColumns.Hue.value].unique().tolist(
) == expected_prediction_targets
group_by_subject = metrics.groupby(
by=[LoggingColumns.Patient.value, LoggingColumns.Epoch.value])
expected_prediction_target_lengths = [3, 2, 3, 3]
for i, x in enumerate(group_by_subject):
assert len(x[1]) == expected_prediction_target_lengths[i]
group_by_subject_and_target = metrics.groupby(by=[
LoggingColumns.Patient.value, LoggingColumns.Epoch.value,
LoggingColumns.Hue.value
])
for _, group in group_by_subject_and_target:
assert len(group) == 1
def test_train_2d_classification_model(test_output_dirs: OutputFolderForTests,
use_mixed_precision: bool) -> None:
"""
Test training and testing of 2d classification models.
"""
logging_to_stdout(logging.DEBUG)
config = ClassificationModelForTesting2D()
config.set_output_to(test_output_dirs.root_dir)
# Train for 4 epochs, checkpoints at epochs 2 and 4
config.num_epochs = 4
config.use_mixed_precision = use_mixed_precision
config.save_start_epoch = 2
config.save_step_epochs = 2
config.test_start_epoch = 2
config.test_step_epochs = 2
config.test_diff_epochs = 2
expected_epochs = [2, 4]
assert config.get_test_epochs() == expected_epochs
checkpoint_handler = get_default_checkpoint_handler(
model_config=config, project_root=Path(test_output_dirs.root_dir))
model_training_result = model_training.model_train(
config, checkpoint_handler=checkpoint_handler)
assert model_training_result is not None
expected_learning_rates = [0.0001, 9.99971e-05, 9.99930e-05, 9.99861e-05]
expected_train_loss = [0.705931, 0.698664, 0.694489, 0.693151]
expected_val_loss = [1.078517, 1.140510, 1.199026, 1.248595]
def extract_loss(results: List[MetricsDict]) -> List[float]:
return [d.values()[MetricType.LOSS.value][0] for d in results]
actual_train_loss = extract_loss(
model_training_result.train_results_per_epoch)
actual_val_loss = extract_loss(model_training_result.val_results_per_epoch)
actual_learning_rates = list(
flatten(model_training_result.learning_rates_per_epoch))
assert actual_train_loss == pytest.approx(expected_train_loss, abs=1e-6)
assert actual_val_loss == pytest.approx(expected_val_loss, abs=1e-6)
assert actual_learning_rates == pytest.approx(expected_learning_rates,
rel=1e-5)
test_results = model_testing.model_test(
config,
ModelExecutionMode.TRAIN,
checkpoint_handler=checkpoint_handler)
assert isinstance(test_results, InferenceMetricsForClassification)
assert list(test_results.epochs.keys()) == expected_epochs
def plot_cross_validation(config: PlotCrossValidationConfig) -> Path:
"""
Collects results from an AzureML cross validation run, and writes aggregate metrics files.
and assert that there are N+1 data files available. If false, this analysis only concerns the cross
validation runs, and check that the number of files is N.
:param config: The settings for plotting cross validation results.
:return: The path with all cross validation result files.
"""
logging_to_stdout(logging.INFO)
with logging_section("Downloading cross-validation results"):
result_files, root_folder = download_crossval_result_files(config)
config_and_files = OfflineCrossvalConfigAndFiles(config=config,
files=result_files)
with logging_section("Plotting cross-validation results"):
plot_cross_validation_from_files(config_and_files, root_folder)
return root_folder
def test_compare_files_text(test_output_dirs: OutputFolderForTests,
file_extension: str) -> None:
"""
Checks the basic code to compare the contents of two text files.
:param test_output_dirs:
:param file_extension: The extension of the file to create.
"""
logging_to_stdout(log_level=logging.DEBUG)
expected = test_output_dirs.root_dir / f"expected{file_extension}"
actual = test_output_dirs.root_dir / "actual.does_not_matter"
# Make sure that we test different line endings - the files should still match
create_folder_and_write_text(expected, "Line1\r\nLine2")
create_folder_and_write_text(actual, "Line1\nLine2")
assert compare_files(expected=expected, actual=actual) == ""
actual.write_text("does_not_match")
assert compare_files(expected=expected,
actual=actual) == baselines_util.CONTENTS_MISMATCH
def test_image_encoder_with_segmentation(
test_output_dirs: OutputFolderForTests, encode_channels_jointly: bool,
aggregation_type: AggregationType,
imaging_feature_type: ImagingFeatureType) -> None:
"""
Test if the image encoder networks can be trained on segmentations from HDF5.
"""
logging_to_stdout()
set_random_seed(0)
scan_size = (6, 64, 60)
dataset_contents = """subject,channel,path,label
S1,week0,scan1.h5,
S1,week1,scan2.h5,True
S2,week0,scan3.h5,
S2,week1,scan4.h5,False
S3,week0,scan5.h5,
S3,week1,scan6.h5,True
S4,week0,scan7.h5,
S4,week1,scan8.h5,True
"""
config = ImageEncoder(encode_channels_jointly=encode_channels_jointly,
imaging_feature_type=imaging_feature_type,
should_validate=False,
aggregation_type=aggregation_type,
scan_size=scan_size)
# This fails with 16bit precision, saying "torch.nn.functional.binary_cross_entropy and torch.nn.BCELoss are
# unsafe to autocast. Many models use a sigmoid layer right before the binary cross entropy layer. In this case,
# combine the two layers using torch.nn.functional.binary_cross_entropy_with_logits or
# torch.nn.BCEWithLogitsLoss. binary_cross_entropy_with_logits and BCEWithLogits are safe to autocast."
config.use_mixed_precision = False
config.set_output_to(test_output_dirs.root_dir)
config.num_epochs = 1
config.local_dataset = Path()
config.dataset_data_frame = pd.read_csv(StringIO(dataset_contents),
sep=",",
dtype=str)
# Patch the load_images function that will be called once we access a dataset item
image_and_seg = ImageAndSegmentations[np.ndarray](
images=np.zeros(scan_size, dtype=np.float32),
segmentations=np.ones(scan_size, dtype=np.uint8))
with mock.patch('InnerEye.ML.utils.io_util.load_image_in_known_formats',
return_value=image_and_seg):
azure_config = get_default_azure_config()
azure_config.train = True
MLRunner(config, azure_config).run()
def test_is_completed() -> None:
"""
Test if we can correctly check run status and status of child runs.
:return:
"""
logging_to_stdout()
workspace = get_default_workspace()
def get_run_and_check(run_id: str, expected: bool) -> None:
run = fetch_run(workspace, run_id)
status = is_run_and_child_runs_completed(run)
assert status == expected
get_run_and_check(DEFAULT_RUN_RECOVERY_ID, True)
get_run_and_check(DEFAULT_ENSEMBLE_RUN_RECOVERY_ID, True)
# This Hyperdrive run has 1 failing child run, the parent run completed successfully.
get_run_and_check(
"refs_pull_326_merge:HD_d123f042-ca58-4e35-9a64-48d71c5f63a7", False)
def test_compare_files_binary(test_output_dirs: OutputFolderForTests,
file_extension: str) -> None:
"""
Checks the comparison of files that are not recognized as text files, for example images.
:param test_output_dirs:
:param file_extension: The extension of the file to create.
"""
logging_to_stdout(log_level=logging.DEBUG)
expected = test_output_dirs.root_dir / f"expected{file_extension}"
actual = test_output_dirs.root_dir / "actual.does_not_matter"
data1 = bytes([1, 2, 3])
data2 = bytes([4, 5, 6])
expected.write_bytes(data1)
actual.write_bytes(data1)
assert compare_files(expected=expected, actual=actual) == ""
actual.write_bytes(data2)
assert compare_files(expected=expected,
actual=actual) == baselines_util.CONTENTS_MISMATCH