def test_generate_classification_crossval_report_glaucoma(
test_output_dirs: OutputFolderForTests) -> None:
"""
Test creation of the cross validation report if there are no metrics available for the test set.
"""
config = GlaucomaPublic(number_of_cross_validation_splits=2)
config.inference_on_test_set = False
generate_crossval_notebook(
config,
metrics_file="Glaucoma_MetricsAcrossAllRuns.csv",
temp_folder=test_output_dirs.root_dir)
def test_inference_required_single_runs() -> None:
"""
Test the flags for running full inference on the test set, for models in single training runs.
"""
model = GlaucomaPublic()
assert not model.perform_cross_validation
# Normal training run without cross validation: it should not matter if the model is a classification model or not
assert model.is_inference_required(model_proc=ModelProcessing.DEFAULT,
data_split=ModelExecutionMode.TEST)
# If a flag is set explicitly, use that.
model.inference_on_test_set = False
assert not model.is_inference_required(model_proc=ModelProcessing.DEFAULT,
data_split=ModelExecutionMode.TEST)
def test_inference_required_crossval_runs() -> None:
"""
Test the flags for running full inference on the test set, for models that are trained in crossval mode.
"""
classification_model = GlaucomaPublic()
classification_model.number_of_cross_validation_splits = 2
segmentation_model = SegmentationModelBase(should_validate=False)
segmentation_model.number_of_cross_validation_splits = 2
assert classification_model.perform_cross_validation
assert segmentation_model.perform_cross_validation
# Cross validation child runs for classification models need test set inference to ensure that the report works
# correctly.
assert classification_model.is_inference_required(
model_proc=ModelProcessing.DEFAULT, data_split=ModelExecutionMode.TEST)
# For models other than classification models, there is by default no inference on the test set.
assert not segmentation_model.is_inference_required(
model_proc=ModelProcessing.DEFAULT, data_split=ModelExecutionMode.TEST)
classification_model.inference_on_test_set = False
# If a flag is set explicitly, use that.
assert not classification_model.is_inference_required(
model_proc=ModelProcessing.DEFAULT, data_split=ModelExecutionMode.TEST)
def test_model_summary_on_classification1() -> None:
model = create_model_with_temperature_scaling(GlaucomaPublic())
ModelSummary(model).generate_summary(input_sizes=[(1, 6, 64, 60)])
def test_is_classification_model() -> None:
assert is_classification_model(GlaucomaPublic())
assert is_classification_model(ClassificationModelForTesting())
assert not is_classification_model(BasicModel2Epochs())