def test_predict_non_ensemble(batch_size: int, empty_labels: bool) -> None:
config = ConstantScalarConfig(1.)
model_and_info = ModelAndInfo(config=config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=None)
model_loaded = model_and_info.try_create_model_load_from_checkpoint_and_adjust(
)
assert model_loaded
model = model_and_info.model
pipeline = ScalarInferencePipeline(model, config, 0, 0)
actual_labels = torch.zeros(
(batch_size, 1)) * np.nan if empty_labels else torch.zeros(
(batch_size, 1))
data = {
"metadata": [GeneralSampleMetadata(id='2')] * batch_size,
"label": actual_labels,
"images": torch.zeros(
((batch_size, 1) + config.expected_image_size_zyx)),
"numerical_non_image_features": torch.tensor([]),
"categorical_non_image_features": torch.tensor([]),
"segmentations": torch.tensor([])
}
results = pipeline.predict(data)
ids, labels, predicted = results.subject_ids, results.labels, results.model_outputs
assert ids == ['2'] * batch_size
assert torch.allclose(labels, actual_labels, equal_nan=True)
# The model always returns 1, so predicted should be sigmoid(1)
assert torch.allclose(predicted, torch.full((batch_size, 1), 0.731058578))
def test_predict_non_ensemble(batch_size: int, empty_labels: bool) -> None:
config = ConstantScalarConfig(1.)
model = create_lightning_model(config, set_optimizer_and_scheduler=False)
assert isinstance(model, ScalarLightning)
pipeline = ScalarInferencePipeline(model, config, 0)
actual_labels = torch.zeros(
(batch_size, 1)) * np.nan if empty_labels else torch.zeros(
(batch_size, 1))
data = {
"metadata": [GeneralSampleMetadata(id='2')] * batch_size,
"label": actual_labels,
"images": torch.zeros(
((batch_size, 1) + config.expected_image_size_zyx)),
"numerical_non_image_features": torch.tensor([]),
"categorical_non_image_features": torch.tensor([]),
"segmentations": torch.tensor([])
}
results = pipeline.predict(data)
ids, labels, predicted = results.subject_ids, results.labels, results.posteriors
assert ids == ['2'] * batch_size
assert torch.allclose(labels, actual_labels, equal_nan=True)
# The model always returns 1, so predicted should be sigmoid(1)
assert torch.allclose(predicted, torch.full((batch_size, 1), 0.731058578))
def test_predict_non_ensemble(batch_size: int, empty_labels: bool) -> None:
config = ClassificationModelForTesting()
model: Any = ScalarOnesModel(config.expected_image_size_zyx, 1.)
update_model_for_multiple_gpus(ModelAndInfo(model),
args=config,
execution_mode=ModelExecutionMode.TEST)
pipeline = ScalarInferencePipeline(model, config, 0, 0)
actual_labels = torch.zeros(
(batch_size, 1)) * np.nan if empty_labels else torch.zeros(
(batch_size, 1))
data = {
"metadata": [GeneralSampleMetadata(id='2')] * batch_size,
"label": actual_labels,
"images": torch.zeros(
((batch_size, 1) + config.expected_image_size_zyx)),
"numerical_non_image_features": torch.tensor([]),
"categorical_non_image_features": torch.tensor([]),
"segmentations": torch.tensor([])
}
results = pipeline.predict(data)
ids, labels, predicted = results.subject_ids, results.labels, results.model_outputs
assert ids == ['2'] * batch_size
assert torch.allclose(labels, actual_labels, equal_nan=True)
# The model always returns 1, so predicted should be sigmoid(1)
assert torch.allclose(predicted, torch.full((batch_size, 1), 0.731058578))