def test_predict_ensemble(batch_size: int) -> None:
config_returns_0 = ConstantScalarConfig(0.)
model_and_info_returns_0 = ModelAndInfo(config=config_returns_0, model_execution_mode=ModelExecutionMode.TEST,
is_mean_teacher=False, checkpoint_path=None)
model_loaded = model_and_info_returns_0.try_create_model_load_from_checkpoint_and_adjust()
assert model_loaded
model_returns_0 = model_and_info_returns_0.model
config_returns_1 = ConstantScalarConfig(1.)
model_and_info_returns_1 = ModelAndInfo(config=config_returns_1, model_execution_mode=ModelExecutionMode.TEST,
is_mean_teacher=False, checkpoint_path=None)
model_loaded = model_and_info_returns_1.try_create_model_load_from_checkpoint_and_adjust()
assert model_loaded
model_returns_1 = model_and_info_returns_1.model
pipeline_0 = ScalarInferencePipeline(model_returns_0, config_returns_0, 0, 0)
pipeline_1 = ScalarInferencePipeline(model_returns_0, config_returns_0, 0, 1)
pipeline_2 = ScalarInferencePipeline(model_returns_0, config_returns_0, 0, 2)
pipeline_3 = ScalarInferencePipeline(model_returns_1, config_returns_1, 0, 3)
pipeline_4 = ScalarInferencePipeline(model_returns_1, config_returns_1, 0, 4)
ensemble_pipeline = ScalarEnsemblePipeline([pipeline_0, pipeline_1, pipeline_2, pipeline_3, pipeline_4],
config_returns_0, EnsembleAggregationType.Average)
data = {"metadata": [GeneralSampleMetadata(id='2')] * batch_size,
"label": torch.zeros((batch_size, 1)),
"images": torch.zeros(((batch_size, 1) + config_returns_0.expected_image_size_zyx)),
"numerical_non_image_features": torch.tensor([]),
"categorical_non_image_features": torch.tensor([]),
"segmentations": torch.tensor([])}
results = ensemble_pipeline.predict(data)
ids, labels, predicted = results.subject_ids, results.labels, results.model_outputs
assert ids == ['2'] * batch_size
assert torch.equal(labels, torch.zeros((batch_size, 1)))
# 3 models return 0, 2 return 1, so predicted should be ((sigmoid(0)*3)+(sigmoid(1)*2))/5
assert torch.allclose(predicted, torch.full((batch_size, 1), 0.592423431))
def test_predict_ensemble(batch_size: int) -> None:
config_returns_0 = ConstantScalarConfig(0.)
model_returns_0 = create_lightning_model(config_returns_0,
set_optimizer_and_scheduler=False)
assert isinstance(model_returns_0, ScalarLightning)
config_returns_1 = ConstantScalarConfig(1.)
model_returns_1 = create_lightning_model(config_returns_1,
set_optimizer_and_scheduler=False)
assert isinstance(model_returns_1, ScalarLightning)
pipeline_0 = ScalarInferencePipeline(model_returns_0, config_returns_0, 0)
pipeline_1 = ScalarInferencePipeline(model_returns_0, config_returns_0, 1)
pipeline_2 = ScalarInferencePipeline(model_returns_0, config_returns_0, 2)
pipeline_3 = ScalarInferencePipeline(model_returns_1, config_returns_1, 3)
pipeline_4 = ScalarInferencePipeline(model_returns_1, config_returns_1, 4)
ensemble_pipeline = ScalarEnsemblePipeline(
[pipeline_0, pipeline_1, pipeline_2, pipeline_3, pipeline_4],
config_returns_0, EnsembleAggregationType.Average)
data = {
"metadata": [GeneralSampleMetadata(id='2')] * batch_size,
"label":
torch.zeros((batch_size, 1)),
"images":
torch.zeros(
((batch_size, 1) + config_returns_0.expected_image_size_zyx)),
"numerical_non_image_features":
torch.tensor([]),
"categorical_non_image_features":
torch.tensor([]),
"segmentations":
torch.tensor([])
}
results = ensemble_pipeline.predict(data)
ids, labels, predicted = results.subject_ids, results.labels, results.posteriors
assert ids == ['2'] * batch_size
assert torch.equal(labels, torch.zeros((batch_size, 1)))
# 3 models return 0, 2 return 1, so predicted should be ((sigmoid(0)*3)+(sigmoid(1)*2))/5
assert torch.allclose(predicted, torch.full((batch_size, 1), 0.592423431))
def test_predict_ensemble(batch_size: int) -> None:
config = ClassificationModelForTesting()
model_returns_0: Any = ScalarOnesModel(config.expected_image_size_zyx, 0.)
model_returns_1: Any = ScalarOnesModel(config.expected_image_size_zyx, 1.)
model_and_opt_0 = update_model_for_multiple_gpus(
ModelAndInfo(model_returns_0),
args=config,
execution_mode=ModelExecutionMode.TEST)
model_returns_0 = model_and_opt_0.model
model_and_opt_1 = update_model_for_multiple_gpus(
ModelAndInfo(model_returns_1),
args=config,
execution_mode=ModelExecutionMode.TEST)
model_returns_1 = model_and_opt_1.model
pipeline_0 = ScalarInferencePipeline(model_returns_0, config, 0, 0)
pipeline_1 = ScalarInferencePipeline(model_returns_0, config, 0, 1)
pipeline_2 = ScalarInferencePipeline(model_returns_0, config, 0, 2)
pipeline_3 = ScalarInferencePipeline(model_returns_1, config, 0, 3)
pipeline_4 = ScalarInferencePipeline(model_returns_1, config, 0, 4)
ensemble_pipeline = ScalarEnsemblePipeline(
[pipeline_0, pipeline_1, pipeline_2, pipeline_3, pipeline_4], config,
EnsembleAggregationType.Average)
data = {
"metadata": [GeneralSampleMetadata(id='2')] * batch_size,
"label": torch.zeros((batch_size, 1)),
"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 = ensemble_pipeline.predict(data)
ids, labels, predicted = results.subject_ids, results.labels, results.model_outputs
assert ids == ['2'] * batch_size
assert torch.equal(labels, torch.zeros((batch_size, 1)))
# 3 models return 0, 2 return 1, so predicted should be ((sigmoid(0)*3)+(sigmoid(1)*2))/5
assert torch.allclose(predicted, torch.full((batch_size, 1), 0.592423431))