def parse_model_from_args(args):
if args.model_from_file:
colored_model_name = textattack.shared.utils.color_text(
args.model_from_file, color="blue", method="ansi")
textattack.shared.logger.info(
f"Loading model and tokenizer from file: {colored_model_name}")
if ":" in args.model_from_file:
model_file, model_name, tokenizer_name = args.model_from_file.split(
":")
else:
model_file, model_name, tokenizer_name = (
args.model_from_file,
"model",
"tokenizer",
)
try:
model_module = load_module_from_file(args.model_from_file)
except:
raise ValueError(f"Failed to import file {args.model_from_file}")
try:
model = getattr(model_module, model_name)
except AttributeError:
raise AttributeError(
f"``{model_name}`` not found in module {args.model_from_file}")
try:
tokenizer = getattr(model_module, tokenizer_name)
except AttributeError:
raise AttributeError(
f"``{tokenizer_name}`` not found in module {args.model_from_file}"
)
model = model.to(textattack.shared.utils.device)
setattr(model, "tokenizer", tokenizer)
elif (args.model
in HUGGINGFACE_DATASET_BY_MODEL) or args.model_from_huggingface:
import transformers
model_name = (HUGGINGFACE_DATASET_BY_MODEL[args.model][0] if
(args.model in HUGGINGFACE_DATASET_BY_MODEL) else
args.model_from_huggingface)
if ":" in model_name:
model_class, model_name = model_name
model_class = eval(f"transformers.{model_class}")
else:
model_class, model_name = (
transformers.AutoModelForSequenceClassification,
model_name,
)
colored_model_name = textattack.shared.utils.color_text(model_name,
color="blue",
method="ansi")
textattack.shared.logger.info(
f"Loading pre-trained model from HuggingFace model repository: {colored_model_name}"
)
model = model_class.from_pretrained(model_name)
model = model.to(textattack.shared.utils.device)
try:
tokenizer = textattack.models.tokenizers.AutoTokenizer(model_name)
except OSError:
textattack.shared.logger.warn(
f"AutoTokenizer {args.model_from_huggingface} not found. Defaulting to `bert-base-uncased`"
)
tokenizer = textattack.models.tokenizers.AutoTokenizer(
"bert-base-uncased")
setattr(model, "tokenizer", tokenizer)
else:
if args.model in TEXTATTACK_DATASET_BY_MODEL:
model_path, _ = TEXTATTACK_DATASET_BY_MODEL[args.model]
model = textattack.shared.utils.load_textattack_model_from_path(
args.model, model_path)
elif args.model and os.path.exists(args.model):
# If `args.model` is a path/directory, let's assume it was a model
# trained with textattack, and try and load it.
model_args_json_path = os.path.join(args.model, "train_args.json")
if not os.path.exists(model_args_json_path):
raise FileNotFoundError(
f"Tried to load model from path {args.model} - could not find train_args.json."
)
model_train_args = json.loads(open(model_args_json_path).read())
model_train_args["model"] = args.model
num_labels = model_train_args["num_labels"]
from textattack.commands.train_model.train_args_helpers import (
model_from_args, )
model = model_from_args(argparse.Namespace(**model_train_args),
num_labels)
else:
raise ValueError(
f"Error: unsupported TextAttack model {args.model}")
return model
def parse_model_from_args(args):
if args.model_from_file:
# Support loading the model from a .py file where a model wrapper
# is instantiated.
colored_model_name = textattack.shared.utils.color_text(
args.model_from_file, color="blue", method="ansi")
textattack.shared.logger.info(
f"Loading model and tokenizer from file: {colored_model_name}")
if ARGS_SPLIT_TOKEN in args.model_from_file:
model_file, model_name = args.model_from_file.split(
ARGS_SPLIT_TOKEN)
else:
_, model_name = args.model_from_file, "model"
try:
model_module = load_module_from_file(args.model_from_file)
except Exception:
raise ValueError(f"Failed to import file {args.model_from_file}")
try:
model = getattr(model_module, model_name)
except AttributeError:
raise AttributeError(
f"``{model_name}`` not found in module {args.model_from_file}")
if not isinstance(model, textattack.models.wrappers.ModelWrapper):
raise TypeError(
"Model must be of type "
f"``textattack.models.ModelWrapper``, got type {type(model)}")
elif (args.model
in HUGGINGFACE_DATASET_BY_MODEL) or args.model_from_huggingface:
# Support loading models automatically from the HuggingFace model hub.
import transformers
model_name = (HUGGINGFACE_DATASET_BY_MODEL[args.model][0] if
(args.model in HUGGINGFACE_DATASET_BY_MODEL) else
args.model_from_huggingface)
if ARGS_SPLIT_TOKEN in model_name:
model_class, model_name = model_name
model_class = eval(f"transformers.{model_class}")
else:
model_class, model_name = (
transformers.AutoModelForSequenceClassification,
model_name,
)
colored_model_name = textattack.shared.utils.color_text(model_name,
color="blue",
method="ansi")
textattack.shared.logger.info(
f"Loading pre-trained model from HuggingFace model repository: {colored_model_name}"
)
model = model_class.from_pretrained(model_name)
tokenizer = textattack.models.tokenizers.AutoTokenizer(model_name)
model = textattack.models.wrappers.HuggingFaceModelWrapper(
model, tokenizer, batch_size=args.model_batch_size)
elif args.model in TEXTATTACK_DATASET_BY_MODEL:
# Support loading TextAttack pre-trained models via just a keyword.
model_path, _ = TEXTATTACK_DATASET_BY_MODEL[args.model]
model = textattack.shared.utils.load_textattack_model_from_path(
args.model, model_path)
# Choose the approprate model wrapper (based on whether or not this is
# a HuggingFace model).
if isinstance(
model,
textattack.models.helpers.BERTForClassification) or isinstance(
model, textattack.models.helpers.T5ForTextToText):
model = textattack.models.wrappers.HuggingFaceModelWrapper(
model, model.tokenizer, batch_size=args.model_batch_size)
else:
model = textattack.models.wrappers.PyTorchModelWrapper(
model, model.tokenizer, batch_size=args.model_batch_size)
elif args.model and os.path.exists(args.model):
# Support loading TextAttack-trained models via just their folder path.
# If `args.model` is a path/directory, let's assume it was a model
# trained with textattack, and try and load it.
model_args_json_path = os.path.join(args.model, "train_args.json")
if not os.path.exists(model_args_json_path):
raise FileNotFoundError(
f"Tried to load model from path {args.model} - could not find train_args.json."
)
model_train_args = json.loads(open(model_args_json_path).read())
if model_train_args["model"] not in {"cnn", "lstm"}:
# for huggingface models, set args.model to the path of the model
model_train_args["model"] = args.model
num_labels = model_train_args["num_labels"]
from textattack.commands.train_model.train_args_helpers import model_from_args
model = model_from_args(
argparse.Namespace(**model_train_args),
num_labels,
model_path=args.model,
)
model = textattack.models.wrappers.PyTorchModelWrapper(
model, model.tokenizer, batch_size=args.model_batch_size)
else:
raise ValueError(f"Error: unsupported TextAttack model {args.model}")
return model