def load_archive(archive_file: str,
device=None,
weights_file: str = None) -> Archive:
"""
Instantiates an Archive from an archived `tar.gz` file.
Parameters
----------
archive_file: ``str``
The archive file to load the model from.
weights_file: ``str``, optional (default = None)
The weights file to use. If unspecified, weights.th in the archive_file will be used.
device: ``None`` or PyTorch device object.
"""
# redirect to the cache, if necessary
resolved_archive_file = cached_path(archive_file)
if resolved_archive_file == archive_file:
logger.info(f"loading archive file {archive_file}")
else:
logger.info(
f"loading archive file {archive_file} from cache at {resolved_archive_file}"
)
tempdir = None
if os.path.isdir(resolved_archive_file):
serialization_dir = resolved_archive_file
else:
# Extract archive to temp dir
tempdir = tempfile.mkdtemp()
logger.info(
f"extracting archive file {resolved_archive_file} to temp dir {tempdir}"
)
with tarfile.open(resolved_archive_file, 'r:gz') as archive:
archive.extractall(tempdir)
serialization_dir = tempdir
# Load config
config = Params.from_file(os.path.join(serialization_dir, CONFIG_NAME))
config.loading_from_archive = True
if weights_file:
weights_path = weights_file
else:
weights_path = os.path.join(serialization_dir, _WEIGHTS_NAME)
# Instantiate model. Use a duplicate of the config, as it will get consumed.
model = Model.load(config,
weights_file=weights_path,
serialization_dir=serialization_dir,
device=device)
if tempdir:
# Clean up temp dir
shutil.rmtree(tempdir)
return Archive(model=model, config=config)
def create_serialization_dir(params: Params) -> None:
"""
This function creates the serialization directory if it doesn't exist. If it already exists
and is non-empty, then it verifies that we're recovering from a training with an identical configuration.
Parameters
----------
params: ``Params``
A parameter object specifying an AllenNLP Experiment.
serialization_dir: ``str``
The directory in which to save results and logs.
recover: ``bool``
If ``True``, we will try to recover from an existing serialization directory, and crash if
the directory doesn't exist, or doesn't match the configuration we're given.
"""
serialization_dir = params['environment']['serialization_dir']
recover = params['environment']['recover']
if os.path.exists(serialization_dir) and os.listdir(serialization_dir):
if not recover:
raise ConfigurationError(f"Serialization directory ({serialization_dir}) already exists and is "
f"not empty. Specify --recover to recover training from existing output.")
logger.info(f"Recovering from prior training at {serialization_dir}.")
recovered_config_file = os.path.join(serialization_dir, CONFIG_NAME)
if not os.path.exists(recovered_config_file):
raise ConfigurationError("The serialization directory already exists but doesn't "
"contain a config.json. You probably gave the wrong directory.")
else:
loaded_params = Params.from_file(recovered_config_file)
if params != loaded_params:
raise ConfigurationError("Training configuration does not match the configuration we're "
"recovering from.")
# In the recover mode, we don't need to reload the pre-trained embeddings.
remove_pretrained_embedding_params(params)
else:
if recover:
raise ConfigurationError(f"--recover specified but serialization_dir ({serialization_dir}) "
"does not exist. There is nothing to recover from.")
os.makedirs(serialization_dir, exist_ok=True)
params.to_file(os.path.join(serialization_dir, CONFIG_NAME))
# if we have completed an epoch, try to create a model archive.
if os.path.exists(os.path.join(serialization_dir, _DEFAULT_WEIGHTS)):
logger.info("Training interrupted by the user. Attempting to create "
"a model archive using the current best epoch weights.")
archive_model(serialization_dir)
raise
# Now tar up results
archive_model(serialization_dir)
logger.info("Loading the best epoch weights.")
best_model_state_path = os.path.join(serialization_dir, 'best.th')
best_model_state = torch.load(best_model_state_path)
best_model = model
if not isinstance(best_model, torch.nn.DataParallel):
best_model_state = {re.sub(r'^module\.', '', k):v for k, v in best_model_state.items()}
best_model.load_state_dict(best_model_state)
return best_model
if __name__ == "__main__":
parser = argparse.ArgumentParser('train.py')
parser.add_argument('params', help='Parameters YAML file.')
args = parser.parse_args()
params = Params.from_file(args.params)
logger.info(params)
train_model(params)