def test_simpler_classifier():
# Serves as integration test with
# feature extraction and multiple concurrent visualisations
SimplerClassifier = SimpleClassifier.with_dataset(
DummyClassificationDataLoader)
m = Main(SimplerClassifier)
parser = m.argparse(run=False)
args, _ = parser.parse_known_args()
args.batch_size = 10
args.train = True
args.test = True
args.max_epochs = 1
args.limit_train_batches = 100
args.limit_val_batches = 10
args.limit_test_batches = 10
args.test_confusion_matrix = True
args.visualise_features = "pca"
args.extract_features_after_layer = "l1"
args.loss = "cross_entropy"
m.main(args)
assert "loss" in m.runner.trainer.model.metrics()
assert "top1acc" in m.runner.trainer.model.metrics()
assert "top3acc" in m.runner.trainer.model.metrics()
assert (Path(m.log_dir) / "figures" / "test" / "l1_pca.png").is_file()
assert (Path(m.log_dir) / "figures" / "test" /
"confusion_matrix.png").is_file()
def main_and_args() -> Tuple[Main, AttributeDict]:
m = Main(ExDummyModule)
parser = m.argparse(run=False)
args, _ = parser.parse_known_args()
args.max_epochs = 1
args.gpus = 0
args.checkpoint_callback = True
args.optimization_metric = "loss"
args.id = "automated_test"
args.test_ensemble = 0
args.checkpoint_every_n_steps = 0
args.monitor_lr = 0
args.auto_lr_find = 0
args.auto_scale_batch_size = 0
args.num_workers = 1
args.batch_size = 4
return m, args
def test_simple_classifier():
m = Main(SimpleClassifier)
parser = m.argparse(run=False)
args, _ = parser.parse_known_args()
args.train = True
args.test = True
args.max_epochs = 1
args.limit_train_batches = 100
args.limit_val_batches = 10
args.limit_test_batches = 10
args.test_confusion_matrix = True
args.logging_backend = "wandb"
m.main(args)
assert "loss" in m.runner.trainer.model.metrics()
assert "top1acc" in m.runner.trainer.model.metrics()
assert "top3acc" in m.runner.trainer.model.metrics()
assert (Path(m.log_dir) / "figures" / "test" /
"confusion_matrix.png").is_file()
def test_default_id(self):
"""Test that a default id is given"""
m = Main(DummyModule)
parser = m.argparse(run=False)
args, _ = parser.parse_known_args()
assert args.id == "unnamed"