def test_default_config_round_trip(self):
tempdir = tempfile.mkdtemp()
filepath = os.path.join(tempdir, 'config.json')
config = config_lib.get_config()
config_utils.save_config(config, filepath)
restored_config = config_utils.load_config(filepath)
self.assertTrue(config_utils.equals(config, restored_config))
def test_simple_round_trip(self):
tempdir = tempfile.mkdtemp()
filepath = os.path.join(tempdir, 'config.json')
config = Config()
config.int = 1
config.float = 2.0
config.str = 'value'
config.unicode = u'value'
config_utils.save_config(config, filepath)
restored_config = config_utils.load_config(filepath)
self.assertTrue(config_utils.equals(config, restored_config))
config.str = 'different'
self.assertFalse(config_utils.equals(config, restored_config))
def configure(data_dir, run_dir, config, override_values, xm_parameters=None):
"""Sets up the Learned Interpreter code with the specified configuration."""
seed()
# Apply any overrides set at the command line or in the launcher.
if config.overrides != config.default_overrides:
logging.info('Applying overrides set at command line: %s',
config.overrides)
overrides_lib.apply_overrides(
config, override_names=config.overrides.split(','))
config.update_from_flattened_dict(override_values)
# If a checkpoint is specified, it determines the "original run."
# Otherwise the run_dir, if already present, determines the "original run."
config_filepath = os.path.join(run_dir, 'config.json')
if checkpoint_utils.is_checkpoint_specified(config.checkpoint):
original_checkpoint_path = checkpoint_utils.get_specified_checkpoint_path(
run_dir, config.checkpoint)
original_run_dir = checkpoint_utils.get_run_dir(
original_checkpoint_path)
original_config_filepath = os.path.join(original_run_dir,
'config.json')
else:
checkpoint_dir = checkpoint_utils.build_checkpoint_dir(run_dir)
original_checkpoint_path = checkpoint_utils.latest_checkpoint(
checkpoint_dir)
original_config_filepath = config_filepath
original_config_exists = gfile.exists(original_config_filepath)
# Handle any existing configs.
if original_config_exists:
original_config = config_utils.load_config(original_config_filepath)
# Handle the model config.
if config.runner.model_config == 'load':
logging.info('Loading the model config from %s',
original_config_filepath)
config.model.update(original_config.model)
config.dataset.representation = original_config.dataset.representation
elif config.runner.model_config == 'assert':
same_config = config_utils.equals(config.model,
original_config.model)
# Resolution:
# Either use a new run_dir, or set model_config to 'load' or 'keep'.
assert same_config, 'Model config has changed.'
else:
assert config.runner.model_config == 'keep'
# Handle the dataset config.
if config.runner.dataset_config == 'load':
logging.info('Loading the data config from %s',
original_config_filepath)
config.dataset.update(original_config.dataset)
elif config.runner.dataset_config == 'assert':
same_config = config_utils.equals(config.dataset,
original_config.dataset)
assert same_config, 'Dataset config has changed.'
else:
assert config.runner.dataset_config == 'keep'
elif (config.runner.model_config == 'load'
or config.runner.dataset_config == 'load'):
raise ValueError('Original model config not found.')
# In interactive mode, force batch size 1.
if config.runner.mode == 'interact':
config.dataset.batch_size = 1
config_exists = gfile.exists(config_filepath)
if not config_exists and config.runner.mode in 'train':
gfile.makedirs(run_dir)
config_utils.save_config(config, config_filepath)
# Load dataset.
if config.setup.setup_dataset:
dataset_info = dataset_utils.get_dataset(data_dir, config)
info = dataset_info.info
else:
dataset_info = None
info = None
# Create model.
if config.setup.setup_model:
model = models_lib.get_model(info, config)
else:
model = None
adapter = adapters_lib.get_default_adapter(info, config)
return RunConfiguration(mode=config.runner.mode,
method=config.runner.method,
run_dir=run_dir,
data_dir=data_dir,
original_checkpoint_path=original_checkpoint_path,
model=model,
info=info,
config=config,
adapter=adapter,
dataset_info=dataset_info)
