def train_model(params: Union[Params, Dict[str, Any]], cuda_device: int,
serialization_dir: str, filtering: str) -> Model:
"""
This function can be used as an entry point to running models in AllenNLP
directly from a JSON specification using a :class:`Driver`. Note that if
you care about reproducibility, you should avoid running code using Pytorch
or numpy which affect the reproducibility of your experiment before you
import and use this function, these libraries rely on random seeds which
can be set in this function via a JSON specification file. Note that this
function performs training and will also evaluate the trained model on
development and test sets if provided in the parameter json.
Parameters
----------
params: Params, required.
A parameter object specifying an AllenNLP Experiment.
serialization_dir: str, required
The directory in which to save results and logs.
"""
SimpleRandom.set_seeds()
os.makedirs(serialization_dir, exist_ok=True)
try:
sys.stdout = TeeLogger(os.path.join(serialization_dir, "stdout.log"),
sys.stdout, True) # type: ignore
sys.stderr = TeeLogger(os.path.join(serialization_dir, "stderr.log"),
sys.stderr, True) # type: ignore
except TypeError:
sys.stdout = TeeLogger(os.path.join(serialization_dir, "stdout.log"),
sys.stdout) # type: ignore
sys.stderr = TeeLogger(os.path.join(serialization_dir, "stderr.log"),
sys.stderr) # type: ignore
handler = logging.FileHandler(
os.path.join(serialization_dir, "python_logging.log"))
handler.setLevel(logging.INFO)
handler.setFormatter(
logging.Formatter(
'%(asctime)s - %(levelname)s - %(name)s - %(message)s'))
logging.getLogger().addHandler(handler)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, "model_params.json"),
"w") as param_file:
json.dump(serialization_params, param_file, indent=4)
# Now we begin assembling the required parts for the Trainer.
ds_params = params.pop('dataset_reader', {})
read_settings = ds_params.pop('read_settings', {})
dataset_reader = FEVERReader.from_params(ds_params)
train_data_path = params.pop('train_data_path')
logger.info("Reading training data from %s", train_data_path)
train_data = dataset_reader.read(
train_data_path,
include_metadata=True,
replace_with_gold=read_settings.pop('replace_gold', False),
pad_with_nearest=read_settings.pop('pad_with_nearest', 0))
validation_data_path = params.pop('validation_data_path', None)
if validation_data_path is not None:
logger.info("Reading validation data from %s", validation_data_path)
validation_data = dataset_reader.read(validation_data_path,
include_metadata=True)
else:
validation_data = None
vocab_params = params.pop("vocabulary", {})
dataset = None
print(dict(vocab_params), 'directory_path' not in vocab_params)
assert ('directory_path' in vocab_params)
vocab = Vocabulary.from_params(vocab_params, dataset)
print(vocab)
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
model = Model.from_params(vocab=vocab, params=params.pop('model'))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
trainer_params = params.pop("trainer")
if cuda_device is not None:
trainer_params["cuda_device"] = cuda_device
trainer = Trainer.from_params(model, serialization_dir, iterator,
train_data, validation_data, trainer_params)
trainer.train()
# Now tar up results
archive_model(serialization_dir)
return model
def train_model(db: FeverDocDB, params: Union[Params, Dict[str, Any]],
cuda_device: int, serialization_dir: str,
filtering: str) -> Model:
"""
This function can be used as an entry point to running models in AllenNLP
directly from a JSON specification using a :class:`Driver`. Note that if
you care about reproducibility, you should avoid running code using Pytorch
or numpy which affect the reproducibility of your experiment before you
import and use this function, these libraries rely on random seeds which
can be set in this function via a JSON specification file. Note that this
function performs training and will also evaluate the trained model on
development and test sets if provided in the parameter json.
Parameters
----------
params: Params, required.
A parameter object specifying an AllenNLP Experiment.
serialization_dir: str, required
The directory in which to save results and logs.
"""
SimpleRandom.set_seeds()
os.makedirs(serialization_dir, exist_ok=True)
sys.stdout = TeeLogger(os.path.join(serialization_dir, "stdout.log"),
sys.stdout) # type: ignore
sys.stderr = TeeLogger(os.path.join(serialization_dir, "stderr.log"),
sys.stderr) # type: ignore
handler = logging.FileHandler(
os.path.join(serialization_dir, "python_logging.log"))
handler.setLevel(logging.INFO)
handler.setFormatter(
logging.Formatter(
'%(asctime)s - %(levelname)s - %(name)s - %(message)s'))
logging.getLogger().addHandler(handler)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, "model_params.json"),
"w") as param_file:
json.dump(serialization_params, param_file, indent=4)
# Now we begin assembling the required parts for the Trainer.
ds_params = params.pop('dataset_reader', {})
dataset_reader = FEVERReader(db,
sentence_level=ds_params.pop(
"sentence_level", False),
wiki_tokenizer=Tokenizer.from_params(
ds_params.pop('wiki_tokenizer', {})),
claim_tokenizer=Tokenizer.from_params(
ds_params.pop('claim_tokenizer', {})),
token_indexers=TokenIndexer.dict_from_params(
ds_params.pop('token_indexers', {})),
filtering=filtering)
train_data_path = params.pop('train_data_path')
logger.info("Reading training data from %s", train_data_path)
train_data = dataset_reader.read(train_data_path)
all_datasets = [train_data]
datasets_in_vocab = ["train"]
validation_data_path = params.pop('validation_data_path', None)
if validation_data_path is not None:
logger.info("Reading validation data from %s", validation_data_path)
validation_data = dataset_reader.read(validation_data_path)
all_datasets.append(validation_data)
datasets_in_vocab.append("validation")
else:
validation_data = None
logger.info("Creating a vocabulary using %s data.",
", ".join(datasets_in_vocab))
vocab = Vocabulary.from_params(
params.pop("vocabulary", {}),
Dataset([
instance for dataset in all_datasets
for instance in dataset.instances
]))
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
model = Model.from_params(vocab, params.pop('model'))
iterator = DataIterator.from_params(params.pop("iterator"))
train_data.index_instances(vocab)
if validation_data:
validation_data.index_instances(vocab)
trainer_params = params.pop("trainer")
if cuda_device is not None:
trainer_params["cuda_device"] = cuda_device
trainer = Trainer.from_params(model, serialization_dir, iterator,
train_data, validation_data, trainer_params)
trainer.train()
# Now tar up results
archive_model(serialization_dir)
return model
def fine_tune_model(model: Model,
params: Params,
serialization_dir: str,
extend_vocab: bool = False,
file_friendly_logging: bool = False,
batch_weight_key: str = "",
embedding_sources_mapping: Dict[str, str] = None,
in_fold = None,
num_folds = None,
ewc_weight=None) -> Model:
"""
Fine tunes the given model, using a set of parameters that is largely identical to those used
for :func:`~allennlp.commands.train.train_model`, except that the ``model`` section is ignored,
if it is present (as we are already given a ``Model`` here).
The main difference between the logic done here and the logic done in ``train_model`` is that
here we do not worry about vocabulary construction or creating the model object. Everything
else is the same.
Parameters
----------
model : ``Model``
A model to fine tune.
params : ``Params``
A parameter object specifying an AllenNLP Experiment
serialization_dir : ``str``
The directory in which to save results and logs.
extend_vocab: ``bool``, optional (default=False)
If ``True``, we use the new instances to extend your vocabulary.
file_friendly_logging : ``bool``, optional (default=False)
If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
down tqdm's output to only once every 10 seconds.
batch_weight_key : ``str``, optional (default="")
If non-empty, name of metric used to weight the loss on a per-batch basis.
embedding_sources_mapping: ``Dict[str, str]``, optional (default=None)
mapping from model paths to the pretrained embedding filepaths
used during fine-tuning.
"""
prepare_environment(params)
if os.path.exists(serialization_dir) and os.listdir(serialization_dir):
raise ConfigurationError(f"Serialization directory ({serialization_dir}) "
f"already exists and is not empty.")
os.makedirs(serialization_dir, exist_ok=True)
prepare_global_logging(serialization_dir, file_friendly_logging)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, CONFIG_NAME), "w") as param_file:
json.dump(serialization_params, param_file, indent=4)
if params.pop('model', None):
logger.warning("You passed parameters for the model in your configuration file, but we "
"are ignoring them, using instead the model parameters in the archive.")
vocabulary_params = params.pop('vocabulary', {})
if vocabulary_params.get('directory_path', None):
logger.warning("You passed `directory_path` in parameters for the vocabulary in "
"your configuration file, but it will be ignored. ")
all_datasets = datasets_from_params(params)
vocab = model.vocab
if extend_vocab:
datasets_for_vocab_creation = set(params.pop("datasets_for_vocab_creation", all_datasets))
for dataset in datasets_for_vocab_creation:
if dataset not in all_datasets:
raise ConfigurationError(f"invalid 'dataset_for_vocab_creation' {dataset}")
logger.info("Extending model vocabulary using %s data.", ", ".join(datasets_for_vocab_creation))
vocab.extend_from_instances(vocabulary_params,
(instance for key, dataset in all_datasets.items()
for instance in dataset
if key in datasets_for_vocab_creation))
model.extend_embedder_vocab(embedding_sources_mapping)
trainer_params = params.pop("trainer")
no_grad_regexes = trainer_params.pop("no_grad", ())
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad_regexes):
parameter.requires_grad_(False)
frozen_parameter_names, tunable_parameter_names = \
get_frozen_and_tunable_parameter_names(model)
logger.info("Following parameters are Frozen (without gradient):")
for name in frozen_parameter_names:
logger.info(name)
logger.info("Following parameters are Tunable (with gradient):")
for name in tunable_parameter_names:
logger.info(name)
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
dl_params = params.pop("data_loader")
if test_data is not None:
rand = random.Random(1234)
test_data.index_with(vocab)
shuffled_test = copy(test_data.instances)
rand.shuffle(shuffled_test)
extra_test = shuffled_test[:2000]
keys = deepcopy(dl_params.as_dict())
keys.update({"dataset": AllennlpDataset(extra_test, vocab)})
extra_test_loader = DataLoader.from_params(params.pop("test_data_loader", keys))
keys = deepcopy(dl_params.as_dict())
keys.update({"dataset": test_data})
test_loader = DataLoader.from_params(params.pop("test_data_loader", keys))
master_model = model
global_metrics = {}
training_metrics = []
final_metrics = {}
master_trainer = trainer_params.as_dict()
if num_folds is not None:
rand = random.Random(1234)
fold_train = []
fold_test = []
fold_train_loader = []
fold_test_loader = []
shuffled_instances = copy(train_data.instances)
rand.shuffle(shuffled_instances)
kfold = KFold(n_splits=num_folds, random_state=None, shuffle=False)
computed_folds = list(kfold.split(shuffled_instances))
for fold in range(num_folds):
train_indexes, test_indexes = computed_folds[fold]
new_train = [shuffled_instances[i] for i in train_indexes]
new_test = [shuffled_instances[i] for i in test_indexes]
fold_train.append(AllennlpDataset(new_train, vocab=vocab))
fold_test.append(AllennlpDataset(new_test, vocab=vocab))
keys = deepcopy(dl_params.as_dict())
keys.update({"dataset": fold_test[-1]})
fold_test_loader.append(DataLoader.from_params(params.pop("fold_test_data_loader",keys)))
keys = deepcopy(dl_params.as_dict())
keys.update({"dataset": fold_train[-1]})
fold_train_loader.append(DataLoader.from_params(params.pop("fold_train_data_loader", keys)))
for fold in ([in_fold] if in_fold is not None else range(num_folds)):
fold_model = deepcopy(master_model)
eval_epoch_callback = EvalEpochCallback(fold, fold_test_loader[fold], test_loader, global_metrics)
callbacks = [eval_epoch_callback]
if ewc_weight is not None:
ewc = EWC(extra_test_loader)
def ewc_forward(*args, **kwargs) -> Dict[str, torch.Tensor]:
ewc_loss = 0
if ewc.model.training:
ewc_loss = ewc.penalty(ewc.model)
ret = ewc.model.old_forward(*args, **kwargs)
ret["loss"] += ewc_weight * ewc_loss
return ret
fold_model.old_forward = fold_model.forward
fold_model.forward = ewc_forward
callbacks.append(CallLossCallback(ewc))
trainer = Trainer.from_params(model=fold_model,
serialization_dir=serialization_dir,
data_loader=fold_train_loader[fold],
train_data=train_data,
validation_data=None,
params=Params(deepcopy(master_trainer)),
validation_data_loader=None,
epoch_callbacks=callbacks)
training_metrics.append(trainer.train())
del fold_model
del trainer
del eval_epoch_callback
state = glob(serialization_dir+"/*.th")
for file in state:
logger.info("deleting state - {}".format(file))
os.unlink(file)
else:
callbacks = []
if ewc_weight is not None:
ewc = EWC(extra_test_loader)
def ewc_forward(*args, **kwargs) -> Dict[str, torch.Tensor]:
ewc_loss = 0
if ewc.model.training:
ewc_loss = ewc.penalty(ewc.model)
ret = ewc.model.old_forward(*args, **kwargs)
ret["loss"] += ewc_weight * ewc_loss
return ret
model.old_forward = model.forward
model.forward = ewc_forward
callbacks.append(CallLossCallback(ewc))
keys = deepcopy(dl_params.as_dict())
keys.update({"dataset": train_data})
train_data.index_with(vocab)
train_data_loader = DataLoader.from_params(params.pop("train_loader",keys))
if validation_data is not None:
validation_data.index_with(vocab)
keys = deepcopy(dl_params.as_dict())
keys.update({"dataset": validation_data})
validation_data_loader = DataLoader.from_params(params.pop("validation_loader", keys))
else:
validation_data_loader = None
if "finetune" in dir(model):
model.finetune()
logger.info("Fine tuning model")
trainer = Trainer.from_params(model=model,
serialization_dir=serialization_dir,
data_loader=train_data_loader,
train_data=train_data,
validation_data=None,
params=Params(deepcopy(master_trainer)),
validation_data_loader=validation_data_loader,
epoch_callbacks=callbacks)
training_metrics = trainer.train()
archive_model(serialization_dir)
final_metrics["fine_tune"] = global_metrics
final_metrics["training"] = training_metrics
metrics_json = json.dumps(final_metrics, indent=2)
with open(os.path.join(serialization_dir, "metrics.json"), "w") as metrics_file:
metrics_file.write(metrics_json)
logger.info("Metrics: %s", metrics_json)
return model
def fine_tune_model(model: Model,
params: Params,
serialization_dir: str,
extend_vocab: bool = False,
file_friendly_logging: bool = False,
batch_weight_key: str = "") -> Model:
"""
Fine tunes the given model, using a set of parameters that is largely identical to those used
for :func:`~allennlp.commands.train.train_model`, except that the ``model`` section is ignored,
if it is present (as we are already given a ``Model`` here).
The main difference between the logic done here and the logic done in ``train_model`` is that
here we do not worry about vocabulary construction or creating the model object. Everything
else is the same.
Parameters
----------
archive : ``Archive``
A saved model archive that is the result of running the ``train`` command.
train_data_path : ``str``
Path to the training data to use for fine-tuning.
serialization_dir : ``str``
The directory in which to save results and logs.
validation_data_path : ``str``, optional
Path to the validation data to use while fine-tuning.
extend_vocab: ``bool``, optional (default=False)
If ``True``, we use the new instances to extend your vocabulary.
file_friendly_logging : ``bool``, optional (default=False)
If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
down tqdm's output to only once every 10 seconds.
"""
prepare_environment(params)
if os.path.exists(serialization_dir) and os.listdir(serialization_dir):
raise ConfigurationError(
f"Serialization directory ({serialization_dir}) "
f"already exists and is not empty.")
os.makedirs(serialization_dir, exist_ok=True)
prepare_global_logging(serialization_dir, file_friendly_logging)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, CONFIG_NAME), "w") as param_file:
json.dump(serialization_params, param_file, indent=4)
if params.pop('model', None):
logger.warning(
"You passed parameters for the model in your configuration file, but we "
"are ignoring them, using instead the model parameters in the archive."
)
vocabulary_params = params.pop('vocabulary', {})
if vocabulary_params.get('directory_path', None):
logger.warning(
"You passed `directory_path` in parameters for the vocabulary in "
"your configuration file, but it will be ignored. ")
all_datasets = datasets_from_params(params)
vocab = model.vocab
if extend_vocab:
datasets_for_vocab_creation = set(
params.pop("datasets_for_vocab_creation", all_datasets))
for dataset in datasets_for_vocab_creation:
if dataset not in all_datasets:
raise ConfigurationError(
f"invalid 'dataset_for_vocab_creation' {dataset}")
logger.info("Extending model vocabulary using %s data.",
", ".join(datasets_for_vocab_creation))
vocab.extend_from_instances(
vocabulary_params,
(instance for key, dataset in all_datasets.items()
for instance in dataset if key in datasets_for_vocab_creation))
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(model.vocab)
validation_iterator_params = params.pop("validation_iterator", None)
if validation_iterator_params:
validation_iterator = DataIterator.from_params(
validation_iterator_params)
validation_iterator.index_with(vocab)
else:
validation_iterator = None
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
trainer_params = params.pop("trainer")
no_grad_regexes = trainer_params.pop("no_grad", ())
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad_regexes):
parameter.requires_grad_(False)
frozen_parameter_names, tunable_parameter_names = \
get_frozen_and_tunable_parameter_names(model)
logger.info("Following parameters are Frozen (without gradient):")
for name in frozen_parameter_names:
logger.info(name)
logger.info("Following parameters are Tunable (with gradient):")
for name in tunable_parameter_names:
logger.info(name)
trainer_type = trainer_params.pop("type", "default")
if trainer_type == "default":
trainer = Trainer.from_params(model=model,
serialization_dir=serialization_dir,
iterator=iterator,
train_data=train_data,
validation_data=validation_data,
params=trainer_params,
validation_iterator=validation_iterator)
else:
raise ConfigurationError(
"currently fine-tune only works with the default Trainer")
evaluate_on_test = params.pop_bool("evaluate_on_test", False)
params.assert_empty('base train command')
try:
metrics = trainer.train()
except KeyboardInterrupt:
# if we have completed an epoch, try to create a model archive.
if os.path.exists(os.path.join(serialization_dir, _DEFAULT_WEIGHTS)):
logging.info(
"Fine-tuning interrupted by the user. Attempting to create "
"a model archive using the current best epoch weights.")
archive_model(serialization_dir,
files_to_archive=params.files_to_archive)
raise
# Evaluate
if test_data and evaluate_on_test:
logger.info(
"The model will be evaluated using the best epoch weights.")
test_metrics = evaluate(
model,
test_data,
validation_iterator or iterator,
cuda_device=trainer._cuda_devices[0], # pylint: disable=protected-access,
batch_weight_key=batch_weight_key)
for key, value in test_metrics.items():
metrics["test_" + key] = value
elif test_data:
logger.info(
"To evaluate on the test set after training, pass the "
"'evaluate_on_test' flag, or use the 'allennlp evaluate' command.")
# Now tar up results
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
metrics_json = json.dumps(metrics, indent=2)
with open(os.path.join(serialization_dir, "metrics.json"),
"w") as metrics_file:
metrics_file.write(metrics_json)
logger.info("Metrics: %s", metrics_json)
return model
def fine_tune_model(model: Model,
params: Params,
serialization_dir: str,
file_friendly_logging: bool = False) -> Model:
"""
Fine tunes the given model, using a set of parameters that is largely identical to those used
for :func:`~allennlp.commands.train.train_model`, except that the ``model`` section is ignored,
if it is present (as we are already given a ``Model`` here).
The main difference between the logic done here and the logic done in ``train_model`` is that
here we do not worry about vocabulary construction or creating the model object. Everything
else is the same.
Parameters
----------
archive : ``Archive``
A saved model archive that is the result of running the ``train`` command.
train_data_path : ``str``
Path to the training data to use for fine-tuning.
serialization_dir : ``str``
The directory in which to save results and logs.
validation_data_path : ``str``, optional
Path to the validation data to use while fine-tuning.
file_friendly_logging : ``bool``, optional (default=False)
If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
down tqdm's output to only once every 10 seconds.
"""
prepare_environment(params)
os.makedirs(serialization_dir)
# TODO(mattg): pull this block out into a separate function (maybe just add this to
# `prepare_environment`?)
Tqdm.set_slower_interval(file_friendly_logging)
sys.stdout = TeeLogger(
os.path.join(serialization_dir, "stdout.log"), # type: ignore
sys.stdout,
file_friendly_logging)
sys.stderr = TeeLogger(
os.path.join(serialization_dir, "stderr.log"), # type: ignore
sys.stderr,
file_friendly_logging)
handler = logging.FileHandler(
os.path.join(serialization_dir, "python_logging.log"))
handler.setLevel(logging.INFO)
handler.setFormatter(
logging.Formatter(
'%(asctime)s - %(levelname)s - %(name)s - %(message)s'))
logging.getLogger().addHandler(handler)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, CONFIG_NAME), "w") as param_file:
json.dump(serialization_params, param_file, indent=4)
if params.pop('model', None):
logger.warning(
"You passed parameters for the model in your configuration file, but we "
"are ignoring them, using instead the model parameters in the archive."
)
if params.pop('vocabulary', None):
logger.warning(
"You passed parameters for the vocabulary in your configuration file, but "
"we are ignoring them, using instead the vocabulary from the saved model."
)
vocab = model.vocab
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
all_datasets = datasets_from_params(params)
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
trainer_params = params.pop("trainer")
trainer = Trainer.from_params(model, serialization_dir, iterator,
train_data, validation_data, trainer_params)
evaluate_on_test = params.pop_bool("evaluate_on_test", False)
params.assert_empty('base train command')
metrics = trainer.train()
# Now tar up results
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
if test_data and evaluate_on_test:
test_metrics = evaluate(model,
test_data,
iterator,
cuda_device=trainer._cuda_devices[0]) # pylint: disable=protected-access
for key, value in test_metrics.items():
metrics["test_" + key] = value
elif test_data:
logger.info(
"To evaluate on the test set after training, pass the "
"'evaluate_on_test' flag, or use the 'allennlp evaluate' command.")
metrics_json = json.dumps(metrics, indent=2)
with open(os.path.join(serialization_dir, "metrics.json"),
"w") as metrics_file:
metrics_file.write(metrics_json)
logger.info("Metrics: %s", metrics_json)
return model
def fine_tune_model(model: Model,
params: Params,
serialization_dir: str,
file_friendly_logging: bool = False) -> Model:
"""
Fine tunes the given model, using a set of parameters that is largely identical to those used
for :func:`~allennlp.commands.train.train_model`, except that the ``model`` section is ignored,
if it is present (as we are already given a ``Model`` here).
The main difference between the logic done here and the logic done in ``train_model`` is that
here we do not worry about vocabulary construction or creating the model object. Everything
else is the same.
Parameters
----------
archive : ``Archive``
A saved model archive that is the result of running the ``train`` command.
train_data_path : ``str``
Path to the training data to use for fine-tuning.
serialization_dir : ``str``
The directory in which to save results and logs.
validation_data_path : ``str``, optional
Path to the validation data to use while fine-tuning.
file_friendly_logging : ``bool``, optional (default=False)
If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
down tqdm's output to only once every 10 seconds.
"""
prepare_environment(params)
os.makedirs(serialization_dir)
prepare_global_logging(serialization_dir, file_friendly_logging)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, CONFIG_NAME), "w") as param_file:
json.dump(serialization_params, param_file, indent=4)
if params.pop('model', None):
logger.warning(
"You passed parameters for the model in your configuration file, but we "
"are ignoring them, using instead the model parameters in the archive."
)
if params.pop('vocabulary', None):
logger.warning(
"You passed parameters for the vocabulary in your configuration file, but "
"we are ignoring them, using instead the vocabulary from the saved model."
)
vocab = model.vocab
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
all_datasets = datasets_from_params(params)
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
trainer_params = params.pop("trainer")
trainer = Trainer.from_params(model, serialization_dir, iterator,
train_data, validation_data, trainer_params)
evaluate_on_test = params.pop_bool("evaluate_on_test", False)
params.assert_empty('base train command')
try:
metrics = trainer.train()
except KeyboardInterrupt:
# if we have completed an epoch, try to create a model archive.
if os.path.exists(os.path.join(serialization_dir, _DEFAULT_WEIGHTS)):
logging.info(
"Fine-tuning interrupted by the user. Attempting to create "
"a model archive using the current best epoch weights.")
archive_model(serialization_dir,
files_to_archive=params.files_to_archive)
raise
# Now tar up results
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
if test_data and evaluate_on_test:
test_metrics = evaluate(model,
test_data,
iterator,
cuda_device=trainer._cuda_devices[0]) # pylint: disable=protected-access
for key, value in test_metrics.items():
metrics["test_" + key] = value
elif test_data:
logger.info(
"To evaluate on the test set after training, pass the "
"'evaluate_on_test' flag, or use the 'allennlp evaluate' command.")
metrics_json = json.dumps(metrics, indent=2)
with open(os.path.join(serialization_dir, "metrics.json"),
"w") as metrics_file:
metrics_file.write(metrics_json)
logger.info("Metrics: %s", metrics_json)
return model
def save_best_model(self):
"""Packages the best model as tar.gz archive"""
archive_model(self._output_dir)
def main(file, embeddings, model, emb_wt_key, namespace, output_dir):
archive = load_archive(model)
config = archive.config
os.makedirs(output_dir, exist_ok=True)
config.to_file(os.path.join(output_dir, CONFIG_NAME))
model = archive.model
# first expand the vocabulary
dataset_reader = DatasetReader.from_params(config.pop('dataset_reader'))
instances = dataset_reader.read(file)
vocab = model.vocab
# get all the tokens in the new file
namespace_token_counts: Dict[str, Dict[str, int]] = defaultdict(
lambda: defaultdict(int))
for instance in Tqdm.tqdm(instances):
instance.count_vocab_items(namespace_token_counts)
old_token_size = vocab.get_vocab_size(namespace)
print("Before expansion: Number of instances in {} namespace: {}".format(
namespace, old_token_size))
if namespace not in namespace_token_counts:
logger.error(
"No tokens found for namespace: {} in the new input file".format(
namespace))
# identify the new tokens in the new instances
token_to_add = set()
token_hits = 0
for token, count in namespace_token_counts[namespace].items():
if token not in vocab._token_to_index[namespace]:
# new token, must add
token_to_add.add(token)
else:
token_hits += 1
print("Found {} existing tokens and {} new tokens in {}".format(
token_hits, len(token_to_add), file))
# add the new tokens to the vocab
for token in token_to_add:
vocab.add_token_to_namespace(token=token, namespace=namespace)
archived_parameters = dict(model.named_parameters())
# second, expand the embedding matrix
for name, weights in archived_parameters.items():
# find the wt matrix for the embeddings
if name == emb_wt_key:
if weights.dim() != 2:
logger.error(
"Expected an embedding matrix for the parameter: {} instead"
"found {} tensor".format(emb_wt_key, weights.shape))
emb_dim = weights.shape[-1]
print("Before expansion: Size of emb matrix: {}".format(
weights.shape))
# Loading embeddings for old and new tokens since that is cleaner than copying all
# the embedding loading logic here
all_embeddings = _read_pretrained_embeddings_file(
embeddings, emb_dim, vocab, namespace)
# concatenate the new entries i.e last token_to_add embeddings to the original weights
if len(token_to_add) > 0:
weights.data = torch.cat(
[weights.data, all_embeddings[-len(token_to_add):, :]])
print("After expansion: Size of emb matrix: {}".format(
weights.shape))
# save the files needed by the model archiver
model_path = os.path.join(output_dir, "weight.th")
model_state = model.state_dict()
torch.save(model_state, model_path)
vocab.save_to_files(os.path.join(output_dir, "vocabulary"))
archive_model(output_dir, weights="weight.th")
# more debug messages
new_token_size = vocab.get_vocab_size(namespace)
for name, weights in archived_parameters.items():
if name == emb_wt_key:
print("Size of emb matrix: {}".format(weights.shape))
print("After expansion: Number of instances in {} namespace: {}".format(
namespace, new_token_size))
def fine_tune_model(model: Model,
params: Params,
serialization_dir: str,
file_friendly_logging: bool = False) -> Model:
"""
Fine tunes the given model, using a set of parameters that is largely identical to those used
for :func:`~allennlp.commands.train.train_model`, except that the ``model`` section is ignored,
if it is present (as we are already given a ``Model`` here).
The main difference between the logic done here and the logic done in ``train_model`` is that
here we do not worry about vocabulary construction or creating the model object. Everything
else is the same.
Parameters
----------
archive : ``Archive``
A saved model archive that is the result of running the ``train`` command.
train_data_path : ``str``
Path to the training data to use for fine-tuning.
serialization_dir : ``str``
The directory in which to save results and logs.
validation_data_path : ``str``, optional
Path to the validation data to use while fine-tuning.
file_friendly_logging : ``bool``, optional (default=False)
If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
down tqdm's output to only once every 10 seconds.
"""
prepare_environment(params)
os.makedirs(serialization_dir)
prepare_global_logging(serialization_dir, file_friendly_logging)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, CONFIG_NAME), "w") as param_file:
json.dump(serialization_params, param_file, indent=4)
if params.pop('model', None):
logger.warning("You passed parameters for the model in your configuration file, but we "
"are ignoring them, using instead the model parameters in the archive.")
vocabulary_params = params.pop('vocabulary', {})
if vocabulary_params.get('directory_path', None):
logger.warning("You passed `directory_path` in parameters for the vocabulary in "
"your configuration file, but it will be ignored. "
"Vocabulary from the saved model will be extended with current data.")
all_datasets = datasets_from_params(params)
datasets_for_vocab_creation = set(params.pop("datasets_for_vocab_creation", all_datasets))
for dataset in datasets_for_vocab_creation:
if dataset not in all_datasets:
raise ConfigurationError(f"invalid 'dataset_for_vocab_creation' {dataset}")
logger.info("Extending model vocabulary using %s data.", ", ".join(datasets_for_vocab_creation))
vocab = model.vocab
vocab.extend_from_instances(vocabulary_params,
(instance for key, dataset in all_datasets.items()
for instance in dataset
if key in datasets_for_vocab_creation))
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
trainer_params = params.pop("trainer")
no_grad_regexes = trainer_params.pop("no_grad", ())
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad_regexes):
parameter.requires_grad_(False)
frozen_parameter_names, tunable_parameter_names = \
get_frozen_and_tunable_parameter_names(model)
logger.info("Following parameters are Frozen (without gradient):")
for name in frozen_parameter_names:
logger.info(name)
logger.info("Following parameters are Tunable (with gradient):")
for name in tunable_parameter_names:
logger.info(name)
trainer = Trainer.from_params(model,
serialization_dir,
iterator,
train_data,
validation_data,
trainer_params)
evaluate_on_test = params.pop_bool("evaluate_on_test", False)
params.assert_empty('base train command')
try:
metrics = trainer.train()
except KeyboardInterrupt:
# if we have completed an epoch, try to create a model archive.
if os.path.exists(os.path.join(serialization_dir, _DEFAULT_WEIGHTS)):
logging.info("Fine-tuning interrupted by the user. Attempting to create "
"a model archive using the current best epoch weights.")
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
raise
# Now tar up results
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
if test_data and evaluate_on_test:
test_metrics = evaluate(model, test_data, iterator, cuda_device=trainer._cuda_devices[0]) # pylint: disable=protected-access
for key, value in test_metrics.items():
metrics["test_" + key] = value
elif test_data:
logger.info("To evaluate on the test set after training, pass the "
"'evaluate_on_test' flag, or use the 'allennlp evaluate' command.")
metrics_json = json.dumps(metrics, indent=2)
with open(os.path.join(serialization_dir, "metrics.json"), "w") as metrics_file:
metrics_file.write(metrics_json)
logger.info("Metrics: %s", metrics_json)
return model
def train_model(data_path,
params,
serialization_dir,
cuda_device=-1,
use_validation_data=True):
os.makedirs(serialization_dir, exist_ok=True)
handler = logging.FileHandler(
os.path.join(serialization_dir, "python_logging.log"))
handler.setLevel(logging.INFO)
handler.setFormatter(
logging.Formatter(
'%(asctime)s - %(levelname)s - %(name)s - %(message)s'))
logging.getLogger().addHandler(handler)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, "model_params.json"),
"w") as param_file:
json.dump(serialization_params, param_file, indent=4)
ds_params = params.pop('dataset_reader', {})
data_params = ds_params.pop('data', {})
dataset_reader = CMVReader.from_params(ds_params)
'''
dataset_reader = CMVReader(data_path,
tokenizer=Tokenizer.from_params(ds_params.pop('tokenizer', {})),
token_indexers=TokenIndexer.from_params(ds_params.pop('token_indexers', {})))
'''
logger.info('Reading training data...')
train_data = dataset_reader.read('train', **data_params)
#train_data_response_only_for_vocab = dataset_reader.read('train', response_only=True)
#train_data_op_only_for_vocab = dataset_reader.read('train', op_only=True)
#all_datasets = [train_data_response_only_for_vocab, train_data_op_only_for_vocab]
all_datasets = [train_data]
datasets_in_vocab = ['train'] #_response_only_for_vocab']
if use_validation_data:
logger.info('Reading validation data...')
validation_data = dataset_reader.read('val', **data_params)
all_datasets.append(validation_data)
datasets_in_vocab.append('val')
else:
validation_data = None
logger.info('Creating a vocabulary using %s data.',
', '.join(datasets_in_vocab))
vocab_params = params.pop('vocabulary', {})
dataset = None
if 'directory_path' not in vocab_params:
dataset = Batch([
instance for dataset in all_datasets
for instance in dataset.instances
])
vocab = Vocabulary.from_params(vocab_params, dataset)
vocab.save_to_files(os.path.join(serialization_dir, 'vocabulary'))
model = Model.from_params(params=params.pop('model'), vocab=vocab)
iterator = DataIterator.from_params(params.pop('iterator'))
train_data.index_instances(vocab)
if validation_data:
validation_data.index_instances(vocab)
trainer_params = params.pop("trainer")
if cuda_device is not None:
trainer_params["cuda_device"] = cuda_device
trainer = Trainer.from_params(model, serialization_dir, iterator,
train_data, validation_data, trainer_params)
trainer.train()
# Now tar up results
archive_model(serialization_dir)
return model
def train_model(params: Params,
serialization_dir: str,
file_friendly_logging: bool = False,
recover: bool = False,
force: bool = False) -> Model:
prepare_environment(params)
create_serialization_dir(params, serialization_dir, recover, force)
prepare_global_logging(serialization_dir, file_friendly_logging)
cuda_device = params.params.get('trainer').get('cuda_device', -1)
if isinstance(cuda_device, list):
for device in cuda_device:
check_for_gpu(device)
else:
check_for_gpu(cuda_device)
params.to_file(os.path.join(serialization_dir, CONFIG_NAME))
all_datasets = datasets_from_params(params)
datasets_for_vocab_creation = set(
params.pop("datasets_for_vocab_creation", all_datasets))
for dataset in datasets_for_vocab_creation:
if dataset not in all_datasets:
raise ConfigurationError(
f"invalid 'dataset_for_vocab_creation' {dataset}")
logger.info(
"From dataset instances, %s will be considered for vocabulary creation.",
", ".join(datasets_for_vocab_creation))
if recover and os.path.exists(os.path.join(serialization_dir,
"vocabulary")):
vocab = Vocabulary.from_files(
os.path.join(serialization_dir, "vocabulary"))
else:
vocab = Vocabulary.from_params(
params.pop("vocabulary", {}),
(instance for key, dataset in all_datasets.items()
for instance in dataset if key in datasets_for_vocab_creation))
model = Model.from_params(vocab=vocab, params=params.pop('model'))
# Initializing the model can have side effect of expanding the vocabulary
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
validation_iterator_params = params.pop("validation_iterator", None)
if validation_iterator_params:
validation_iterator = DataIterator.from_params(
validation_iterator_params)
validation_iterator.index_with(vocab)
else:
validation_iterator = None
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
trainer_params = params.pop("trainer")
no_grad_regexes = trainer_params.pop("no_grad", ())
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad_regexes):
parameter.requires_grad_(False)
frozen_parameter_names, tunable_parameter_names = \
get_frozen_and_tunable_parameter_names(model)
logger.info("Following parameters are Frozen (without gradient):")
for name in frozen_parameter_names:
logger.info(name)
logger.info("Following parameters are Tunable (with gradient):")
for name in tunable_parameter_names:
logger.info(name)
trainer_choice = trainer_params.pop_choice("type",
Trainer.list_available(),
default_to_first_choice=True)
trainer = Trainer.by_name(trainer_choice).from_params(
model=model,
serialization_dir=serialization_dir,
iterator=iterator,
train_data=train_data,
validation_data=validation_data,
params=trainer_params,
validation_iterator=validation_iterator)
evaluate_on_test = params.pop_bool("evaluate_on_test", False)
params.assert_empty('base train command')
try:
metrics = trainer.train()
except KeyboardInterrupt:
# if we have completed an epoch, try to create a model archive.
if os.path.exists(os.path.join(serialization_dir, DEFAULT_WEIGHTS)):
logging.info(
"Training interrupted by the user. Attempting to create "
"a model archive using the current best epoch weights.")
archive_model(serialization_dir,
files_to_archive=params.files_to_archive)
raise
# Now tar up results
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
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
best_model.load_state_dict(best_model_state)
if test_data and evaluate_on_test:
logger.info(
"The model will be evaluated using the best epoch weights.")
test_metrics = evaluate(
best_model,
test_data,
validation_iterator or iterator,
cuda_device=trainer._cuda_devices[0] # pylint: disable=protected-access
)
for key, value in test_metrics.items():
metrics["test_" + key] = value
elif test_data:
logger.info(
"To evaluate on the test set after training, pass the "
"'evaluate_on_test' flag, or use the 'allennlp evaluate' command.")
dump_metrics(os.path.join(serialization_dir, "metrics.json"),
metrics,
log=True)
return best_model
if 'vocabulary' in params:
vocab_params = params['vocabulary']
vocab = Vocabulary.from_params(
params=vocab_params, instances=train_instances.extend(valid_instances))
else:
vocab = Vocabulary.from_instances(train_instances.extend(valid_instances))
dep_model = Model.from_params(vocab=vocab, params=params['model'])
print(dep_model)
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
train_dataset = Batch(train_instances)
train_dataset.index_instances(vocab)
valid_dataset = Batch(valid_instances)
valid_dataset.index_instances(vocab)
# dep_trainer = Trainer(dep_model, dep_file_path, dep_iterator, dep_train_data, dep_valid_data)
trainer_params = params.pop("trainer")
trainer = Trainer.from_params(model=dep_model,
serialization_dir='',
iterator=iterator,
train_data=train_dataset,
validation_data=valid_dataset,
params=trainer_params,
validation_iterator=iterator)
metrics = trainer.train()
archive_model('data/output')
def train_model(data_path,
params,
serialization_dir,
cuda_device=-1,
use_validation_data=True):
os.makedirs(serialization_dir, exist_ok=True)
handler = logging.FileHandler(
os.path.join(serialization_dir, "python_logging.log"))
handler.setLevel(logging.INFO)
handler.setFormatter(
logging.Formatter(
'%(asctime)s - %(levelname)s - %(name)s - %(message)s'))
logging.getLogger().addHandler(handler)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, "model_params.json"),
"w") as param_file:
json.dump(serialization_params, param_file, indent=4)
ds_params = params.pop('dataset_reader', {})
data_params = ds_params.pop('data', {})
dataset_reader = CMVReader.from_params(ds_params)
logger.info('Reading training data...')
train_data = dataset_reader.read('train', **data_params)
#train_data_response_only_for_vocab = dataset_reader.read('train', response_only=True)
#all_datasets = [train_data_response_only_for_vocab]
all_datasets = [train_data]
datasets_in_vocab = ['train'] #_response_only_for_vocab']
if use_validation_data:
logger.info('Reading validation data...')
data_params['weakpoints_only'] = False
validation_data = dataset_reader.read('val', **data_params)
all_datasets.append(validation_data)
datasets_in_vocab.append('val')
else:
validation_data = None
logger.info('Creating a vocabulary using %s data.',
', '.join(datasets_in_vocab))
vocab_params = params.pop('vocabulary', {})
dataset = None
if 'directory_path' not in vocab_params:
dataset = Batch([
instance for dataset in all_datasets
for instance in dataset.instances
])
vocab = Vocabulary.from_params(vocab_params, dataset)
vocab.save_to_files(os.path.join(serialization_dir, 'vocabulary'))
iterator = DataIterator.from_params(params.pop('iterator'))
cmv_predictor_params = params.pop('cmv_predictor')
predictor_pretrained_params = cmv_predictor_params.pop(
'predictor_pretrained_params', None)
cmv_predictor = Model.from_params(params=cmv_predictor_params, vocab=vocab)
model_state = torch.load(predictor_pretrained_params['filename'],
map_location=util.device_mapping(
predictor_pretrained_params['cuda_device']))
cmv_predictor.load_state_dict(model_state)
if params.pop('shared_embedder', False):
print('using shared embedder')
document_embedder = HierarchicalDocumentEmbedder(
vocab, cmv_predictor._response_embedder,
cmv_predictor._response_word_attention,
cmv_predictor._response_encoder)
else:
document_embedder = Model.from_params(
params=params.pop('document_embedder'), vocab=vocab)
cmv_extractor = Model.from_params(params=params.pop('cmv_extractor'))
cmv_discriminator = Model.from_params(
params=params.pop('cmv_discriminator'))
cmv_actor_critic_params = params.pop('cmv_actor_critic', None)
cmv_actor_critic = None
if cmv_actor_critic_params is not None:
cmv_actor_critic = Model.from_params(params=cmv_actor_critic_params)
train_data.index_instances(vocab)
if validation_data:
validation_data.index_instances(vocab)
trainer_params = params.pop("trainer", None)
if trainer_params is not None:
if cuda_device is not None:
trainer_params["cuda_device"] = cuda_device
trainer = Trainer.from_params(cmv_predictor, serialization_dir,
iterator, train_data, validation_data,
trainer_params)
compress_response = params.pop('compress_response', False)
generator_iterator = DataIterator.from_params(
params.pop('generator_iterator'))
cmv_actor_critic_trainer_params = params.pop('actor_critic_trainer', None)
if cmv_actor_critic_trainer_params is not None:
cmv_actor_critic_pretrainer = CMVActorCriticTrainer(
document_embedder, cmv_predictor, cmv_extractor, cmv_actor_critic,
cmv_actor_critic_trainer_params.pop('train_predictor', False),
cmv_actor_critic_trainer_params.pop('train_fake_predictor', False),
compress_response)
cmv_actor_critic_serialization_dir = os.path.join(
serialization_dir, 'actor_critic')
cmv_actor_critic_trainer = Trainer.from_params(
cmv_actor_critic_pretrainer, cmv_actor_critic_serialization_dir,
generator_iterator, train_data, validation_data,
cmv_actor_critic_trainer_params)
else:
ac_pretrained_params = params.pop('pretrained_actor_critic', None)
if ac_pretrained_params is not None:
cmv_actor_critic_pretrainer = CMVActorCriticTrainer(
document_embedder, cmv_predictor, cmv_extractor, None)
model_state = torch.load(ac_pretrained_params['filename'],
map_location=util.device_mapping(
ac_pretrained_params['cuda_device']))
cmv_actor_critic_pretrainer.load_state_dict(model_state)
document_embedder = cmv_actor_critic_pretrainer._document_embedder
cmv_predictor = cmv_actor_critic_pretrainer._cmv_predictor
cmv_extractor = cmv_actor_critic_pretrainer._cmv_extractor
generator = CMVGeneratorTrainer(
document_embedder,
cmv_predictor,
cmv_extractor,
cmv_discriminator,
cmv_actor_critic,
update_extractor=True, #cmv_actor_critic_trainer_params is None,
update_gold_extractor=False, #True,
compress_response=compress_response) #False)
discriminator = CMVDiscriminatorTrainer(document_embedder, cmv_predictor,
cmv_extractor, cmv_discriminator,
compress_response)
generator_serialization_dir = os.path.join(serialization_dir, 'generator')
os.makedirs(generator_serialization_dir, exist_ok=True)
generator_trainer = GANTrainer.from_params(generator,
generator_serialization_dir,
generator_iterator, train_data,
validation_data,
params.pop('generator_trainer'))
discriminator_serialization_dir = os.path.join(serialization_dir,
'discriminator')
os.makedirs(discriminator_serialization_dir, exist_ok=True)
discriminator_trainer = GANTrainer.from_params(
discriminator, discriminator_serialization_dir, iterator, train_data,
validation_data, params.pop('discriminator_trainer'))
#first train predictor for N steps
if trainer_params is not None:
trainer._num_epochs = 5 #hacky
trainer.train()
#TODO? then train actor critic for M steps
#if we are using separate predictors, use the full CMV to train the extractor based on maximizing persuasiveness prediction
if cmv_actor_critic_trainer_params is not None:
cmv_actor_critic_trainer.train()
#then alternate training between discriminator and generator for E epochs
generator_trainer._num_epochs = 1 #hacky
discriminator_trainer._num_epochs = 1 #hacky
gan_epochs = params.pop("gan_epochs")
for i in range(gan_epochs):
discriminator_trainer.train()
generator_trainer.train()
discriminator_trainer._num_epochs += 1 #very hacky
generator_trainer._num_epochs += 1 #also hacky
#if cmv_actor_critic_trainer_params is not None:
# cmv_actor_critic_trainer._num_epochs += 1
# cmv_actor_critic_trainer.train()
# Now tar up results
archive_model(serialization_dir)
archive_model(generator_serialization_dir)
archive_model(discriminator_serialization_dir)
return generator
