def main() -> None:
try:
from InnerEye import ML # noqa: 411
except:
add_package_to_sys_path_if_needed()
from InnerEye.ML import runner
from InnerEye.Common import fixed_paths
print(f"Repository root: {repository_root}")
runner.run(project_root=repository_root,
yaml_config_file=fixed_paths.SETTINGS_YAML_FILE,
post_cross_validation_hook=None)
def test_runner1(test_output_dirs: OutputFolderForTests) -> None:
"""
Test starting a classification model via the commandline runner. Test if we can provide overrides
for parameters that live inside the DeepLearningConfig, and ones that are specific to classification models.
:return:
"""
set_from_commandline = 12345
scalar1 = '["label"]'
model_name = "DummyClassification"
initial_config = ModelConfigLoader[ScalarModelBase](
).create_model_config_from_name(model_name)
assert initial_config.non_image_feature_channels == []
output_root = str(test_output_dirs.root_dir)
args = [
"", "--model", model_name, "--train", "True", "--random_seed",
str(set_from_commandline), "--non_image_feature_channels", scalar1,
"--output_to", output_root, "--max_num_gpus", "1"
]
with mock.patch("sys.argv", args):
config, _ = runner.run(
project_root=fixed_paths.repository_root_directory(),
yaml_config_file=fixed_paths.SETTINGS_YAML_FILE)
assert isinstance(config, ScalarModelBase)
assert config.model_name == "DummyClassification"
assert config.get_effective_random_seed() == set_from_commandline
assert config.non_image_feature_channels == ["label"]
assert str(config.outputs_folder).startswith(output_root)
assert (config.logs_folder / runner.LOG_FILE_NAME).exists()
def test_runner1(test_output_dirs: OutputFolderForTests) -> None:
"""
Test starting a classification model via the commandline runner. Test if we can provide overrides
for parameters that live inside the DeepLearningConfig, and ones that are specific to classification models.
:return:
"""
set_from_commandline = 12345
scalar1 = '["label"]'
model_name = "DummyClassification"
initial_config = ModelConfigLoader().create_model_config_from_name(
model_name)
assert initial_config.non_image_feature_channels == []
output_root = str(test_output_dirs.root_dir)
args = [
"",
"--model",
model_name,
"--train",
"True",
"--random_seed",
str(set_from_commandline),
"--non_image_feature_channels",
scalar1,
"--output_to",
output_root,
"--max_num_gpus",
"1",
"--recovery_checkpoint_save_interval",
"2",
"--recovery_checkpoints_save_last_k",
"2",
"--num_epochs",
"6",
]
with mock.patch("sys.argv", args):
config, _ = runner.run(
project_root=fixed_paths.repository_root_directory(),
yaml_config_file=fixed_paths.SETTINGS_YAML_FILE)
assert isinstance(config, ScalarModelBase)
assert config.model_name == "DummyClassification"
assert config.get_effective_random_seed() == set_from_commandline
assert config.non_image_feature_channels == ["label"]
assert str(config.outputs_folder).startswith(output_root)
assert (config.logs_folder / LOG_FILE_NAME).exists()
# Check that we saved one checkpoint every second epoch and that we kept only that last 2 and that last.ckpt has
# been renamed to best.ckpt
assert len(os.listdir(config.checkpoint_folder)) == 3
assert (config.checkpoint_folder /
str(RECOVERY_CHECKPOINT_FILE_NAME + "_epoch=3" +
CHECKPOINT_SUFFIX)).exists()
assert (config.checkpoint_folder /
str(RECOVERY_CHECKPOINT_FILE_NAME + "_epoch=5" +
CHECKPOINT_SUFFIX)).exists()
assert (config.checkpoint_folder /
BEST_CHECKPOINT_FILE_NAME_WITH_SUFFIX).exists()
def test_runner2(test_output_dirs: OutputFolderForTests) -> None:
"""
Test starting a classification model via the commandline runner, and provide the same arguments
that would be passed in via the YAML files.
:return:
"""
output_root = str(test_output_dirs.root_dir)
args = [
"", "--model", "DummyClassification", "--train", "True", "--output_to",
output_root, "--max_num_gpus", "1"
]
with mock.patch("sys.argv", args):
config, _ = runner.run(
project_root=fixed_paths.repository_root_directory(),
yaml_config_file=fixed_paths.SETTINGS_YAML_FILE)
assert isinstance(config, ScalarModelBase)
assert config.name.startswith("DummyClassification")