def test_graph_classifier_call(
epoch_type: epoch.Type,
logger: logging.Logger,
graph_classification_graph_db: graph_tuple_database.Database,
):
"""Test running a graph classifier."""
run_id = run_id_lib.RunId.GenerateUnique(
f"mock{random.randint(0, int(1e6)):06}"
)
# Create and initialize an untrained model.
model = zero_r.ZeroR(logger, graph_classification_graph_db, run_id=run_id)
model.Initialize()
# Run the model over some random graphs.
batch_iterator = batch_iterator_lib.MakeBatchIterator(
model=model,
graph_db=graph_classification_graph_db,
splits={epoch.Type.TRAIN: [0], epoch.Type.VAL: [1], epoch.Type.TEST: [2],},
epoch_type=epoch_type,
)
results = model(
epoch_type=epoch_type, batch_iterator=batch_iterator, logger=logger,
)
assert isinstance(results, epoch.Results)
assert results.batch_count
def test_node_classifier_call(
epoch_type: epoch.Type,
node_classification_graph_db: graph_tuple_database.Database,
logger: logging.Logger,
):
"""Test running a node classifier."""
# Create and initialize an untrained model.
model = zero_r.ZeroR(logger, node_classification_graph_db)
model.Initialize()
# Run the model over some random graphs.
batch_iterator = batch_iterator_lib.MakeBatchIterator(
model=model,
graph_db=node_classification_graph_db,
splits={
epoch.Type.TRAIN: [0],
epoch.Type.VAL: [1],
epoch.Type.TEST: [2],
},
epoch_type=epoch_type,
)
results = model(
epoch_type=epoch_type,
batch_iterator=batch_iterator,
logger=logger,
)
assert isinstance(results, epoch.Results)
assert results.batch_count
def test_load_restore_model_from_checkpoint_smoke_test( logger: logging.Logger, node_classification_graph_db: graph_tuple_database.Database, ): """Test creating and restoring model from checkpoint.""" # Create and initialize an untrained model. model = zero_r.ZeroR(logger, node_classification_graph_db) model.Initialize() # Smoke test save and restore. checkpoint_ref = model.SaveCheckpoint() model.RestoreFrom(checkpoint_ref)
def test_load_restore_model_from_checkpoint_smoke_test(
logger: logging.Logger,
node_classification_graph_db: graph_tuple_database.Database,
):
"""Test creating and restoring model from checkpoint."""
run_id = run_id_lib.RunId.GenerateUnique(
f"mock{random.randint(0, int(1e6)):06}"
)
# Create and initialize an untrained model.
model = zero_r.ZeroR(logger, node_classification_graph_db, run_id=run_id)
model.Initialize()
# Smoke test save and restore.
checkpoint_ref = model.SaveCheckpoint()
model.RestoreFrom(checkpoint_ref)
def test_load_restore_model_from_checkpoint_smoke_test(
<<<<<<< HEAD:deeplearning/ml4pl/models/zero_r/zero_r_test.py
logger: logging.Logger,
node_classification_graph_db: graph_tuple_database.Database,
=======
logger: logging.Logger, node_y_graph_db: graph_tuple_database.Database,
>>>>>>> de933d07a... Add a node text embedding enum.:deeplearning/ml4pl/models/ggnn/ggnn_test.py
):
"""Test creating and restoring model from checkpoint."""
run_id = run_id_lib.RunId.GenerateUnique(
f"mock{random.randint(0, int(1e6)):06}"
)
# Create and initialize an untrained model.
<<<<<<< HEAD:deeplearning/ml4pl/models/zero_r/zero_r_test.py
model = zero_r.ZeroR(logger, node_classification_graph_db, run_id=run_id)
=======
model = ggnn.Ggnn(logger, node_y_graph_db, run_id=run_id)
>>>>>>> de933d07a... Add a node text embedding enum.:deeplearning/ml4pl/models/ggnn/ggnn_test.py
model.Initialize()
# Smoke test save and restore.
checkpoint_ref = model.SaveCheckpoint()
model.RestoreFrom(checkpoint_ref)
def test_node_classifier_call(
<<<<<<< HEAD:deeplearning/ml4pl/models/zero_r/zero_r_test.py
epoch_type: epoch.Type,
node_classification_graph_db: graph_tuple_database.Database,
logger: logging.Logger,