mirror of
https://github.com/QData/TextAttack.git
synced 2021-10-13 00:05:06 +03:00
317 lines
14 KiB
Python
317 lines
14 KiB
Python
import argparse
|
|
import numpy as np
|
|
import os
|
|
import random
|
|
import sys
|
|
import textattack
|
|
import time
|
|
import torch
|
|
|
|
RECIPE_NAMES = {
|
|
'alzantot': 'textattack.attack_recipes.Alzantot2018',
|
|
'alz-adjusted': 'textattack.attack_recipes.Alzantot2018Adjusted',
|
|
'deepwordbug': 'textattack.attack_recipes.DeepWordBugGao2018',
|
|
'hotflip': 'textattack.attack_recipes.HotFlipEbrahimi2017',
|
|
'kuleshov': 'textattack.attack_recipes.Kuleshov2017',
|
|
'seq2sick': 'textattack.attack_recipes.Seq2SickCheng2018BlackBox',
|
|
'textfooler': 'textattack.attack_recipes.TextFoolerJin2019',
|
|
'tf-adjusted': 'textattack.attack_recipes.TextFoolerJin2019Adjusted',
|
|
}
|
|
|
|
MODEL_CLASS_NAMES = {
|
|
#
|
|
# Text classification models
|
|
#
|
|
|
|
# BERT models - default uncased
|
|
'bert-ag-news': 'textattack.models.classification.bert.BERTForAGNewsClassification',
|
|
'bert-imdb': 'textattack.models.classification.bert.BERTForIMDBSentimentClassification',
|
|
'bert-mr': 'textattack.models.classification.bert.BERTForMRSentimentClassification',
|
|
'bert-yelp-sentiment': 'textattack.models.classification.bert.BERTForYelpSentimentClassification',
|
|
# CNN models
|
|
'cnn-ag-news': 'textattack.models.classification.cnn.WordCNNForAGNewsClassification',
|
|
'cnn-imdb': 'textattack.models.classification.cnn.WordCNNForIMDBSentimentClassification',
|
|
'cnn-mr': 'textattack.models.classification.cnn.WordCNNForMRSentimentClassification',
|
|
'cnn-yelp-sentiment': 'textattack.models.classification.cnn.WordCNNForYelpSentimentClassification',
|
|
# LSTM models
|
|
'lstm-ag-news': 'textattack.models.classification.lstm.LSTMForAGNewsClassification',
|
|
'lstm-imdb': 'textattack.models.classification.lstm.LSTMForIMDBSentimentClassification',
|
|
'lstm-mr': 'textattack.models.classification.lstm.LSTMForMRSentimentClassification',
|
|
'lstm-yelp-sentiment': 'textattack.models.classification.lstm.LSTMForYelpSentimentClassification',
|
|
#
|
|
# Textual entailment models
|
|
#
|
|
# BERT models
|
|
'bert-mnli': 'textattack.models.entailment.bert.BERTForMNLI',
|
|
'bert-snli': 'textattack.models.entailment.bert.BERTForSNLI',
|
|
#
|
|
# Translation models
|
|
#
|
|
't5-en2fr': 'textattack.models.translation.t5.T5EnglishToFrench',
|
|
't5-en2de': 'textattack.models.translation.t5.T5EnglishToGerman',
|
|
't5-en2ro': 'textattack.models.translation.t5.T5EnglishToRomanian',
|
|
#
|
|
# Summarization models
|
|
#
|
|
't5-summ': 'textattack.models.summarization.T5Summarization',
|
|
}
|
|
|
|
DATASET_BY_MODEL = {
|
|
#
|
|
# Text classification datasets
|
|
#
|
|
# AG News
|
|
'bert-ag-news': textattack.datasets.classification.AGNews,
|
|
'cnn-ag-news': textattack.datasets.classification.AGNews,
|
|
'lstm-ag-news': textattack.datasets.classification.AGNews,
|
|
# IMDB
|
|
'bert-imdb': textattack.datasets.classification.IMDBSentiment,
|
|
'cnn-imdb': textattack.datasets.classification.IMDBSentiment,
|
|
'lstm-imdb': textattack.datasets.classification.IMDBSentiment,
|
|
# MR
|
|
'bert-mr': textattack.datasets.classification.MovieReviewSentiment,
|
|
'cnn-mr': textattack.datasets.classification.MovieReviewSentiment,
|
|
'lstm-mr': textattack.datasets.classification.MovieReviewSentiment,
|
|
# Yelp
|
|
'bert-yelp-sentiment': textattack.datasets.classification.YelpSentiment,
|
|
'cnn-yelp-sentiment': textattack.datasets.classification.YelpSentiment,
|
|
'lstm-yelp-sentiment': textattack.datasets.classification.YelpSentiment,
|
|
#
|
|
# Textual entailment datasets
|
|
#
|
|
'bert-mnli': textattack.datasets.entailment.MNLI,
|
|
'bert-snli': textattack.datasets.entailment.SNLI,
|
|
#
|
|
# Translation datasets
|
|
#
|
|
't5-en2de': textattack.datasets.translation.NewsTest2013EnglishToGerman,
|
|
}
|
|
|
|
TRANSFORMATION_CLASS_NAMES = {
|
|
'word-swap-wordnet': 'textattack.transformations.WordSwapWordNet',
|
|
'word-swap-embedding': 'textattack.transformations.WordSwapEmbedding',
|
|
'word-swap-homoglyph': 'textattack.transformations.WordSwapHomoglyph',
|
|
'word-swap-neighboring-char-swap': 'textattack.transformations.WordSwapNeighboringCharacterSwap',
|
|
}
|
|
|
|
CONSTRAINT_CLASS_NAMES = {
|
|
'embedding': 'textattack.constraints.semantics.WordEmbeddingDistance',
|
|
'goog-lm': 'textattack.constraints.semantics.language_models.GoogleLanguageModel',
|
|
'bert': 'textattack.constraints.semantics.sentence_encoders.BERT',
|
|
'infer-sent': 'textattack.constraints.semantics.sentence_encoders.InferSent',
|
|
'use': 'textattack.constraints.semantics.sentence_encoders.UniversalSentenceEncoder',
|
|
'lang-tool': 'textattack.constraints.syntax.LanguageTool',
|
|
}
|
|
|
|
SEARCH_CLASS_NAMES = {
|
|
'beam-search': 'textattack.search_methods.BeamSearch',
|
|
'greedy-word': 'textattack.search_methods.GreedyWordSwap',
|
|
'ga-word': 'textattack.search_methods.GeneticAlgorithm',
|
|
'greedy-word-wir': 'textattack.search_methods.GreedyWordSwapWIR',
|
|
}
|
|
|
|
GOAL_FUNCTION_CLASS_NAMES = {
|
|
'untargeted-classification': 'textattack.goal_functions.UntargetedClassification',
|
|
'targeted-classification': 'textattack.goal_functions.TargetedClassification',
|
|
}
|
|
|
|
def set_seed(random_seed):
|
|
random.seed(random_seed)
|
|
np.random.seed(random_seed)
|
|
torch.manual_seed(random_seed)
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser(
|
|
description='A commandline parser for TextAttack',
|
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
|
|
|
parser.add_argument('--transformation', type=str, required=False,
|
|
default='word-swap-embedding', choices=TRANSFORMATION_CLASS_NAMES.keys(),
|
|
help='The transformations to apply.')
|
|
|
|
parser.add_argument('--model', type=str, required=False, default='bert-yelp-sentiment',
|
|
choices=MODEL_CLASS_NAMES.keys(), help='The classification model to attack.')
|
|
|
|
parser.add_argument('--constraints', type=str, required=False, nargs='*',
|
|
default=[], choices=CONSTRAINT_CLASS_NAMES.keys(),
|
|
help=('Constraints to add to the attack. Usage: "--constraints {constraint}:{arg_1}={value_1},{arg_3}={value_3}"'))
|
|
|
|
parser.add_argument('--out-dir', type=str, required=False, default=None,
|
|
help='A directory to output results to.')
|
|
|
|
parser.add_argument('--enable-visdom', action='store_true',
|
|
help='Enable logging to visdom.')
|
|
|
|
parser.add_argument('--enable-wandb', action='store_true',
|
|
help='Enable logging to Weights & Biases.')
|
|
|
|
parser.add_argument('--disable-stdout', action='store_true',
|
|
help='Disable logging to stdout')
|
|
|
|
parser.add_argument('--enable-csv', nargs='?', default=None, const='fancy', type=str,
|
|
help='Enable logging to csv. Use --enable_csv plain to remove [[]] around words.')
|
|
|
|
parser.add_argument('--num-examples', '-n', type=int, required=False,
|
|
default='5', help='The number of examples to process.')
|
|
|
|
parser.add_argument('--num-examples-offset', '-o', type=int, required=False,
|
|
default=0, help='The offset to start at in the dataset.')
|
|
|
|
parser.add_argument('--shuffle', action='store_true', required=False,
|
|
default=False, help='Randomly shuffle the data before attacking')
|
|
|
|
parser.add_argument('--interactive', action='store_true', default=False,
|
|
help='Whether to run attacks interactively.')
|
|
|
|
parser.add_argument('--attack-n', action='store_true', default=False,
|
|
help='Whether to run attack until `n` examples have been attacked (not skipped).')
|
|
|
|
parser.add_argument('--parallel', action='store_true', default=False,
|
|
help='Run attack using multiple GPUs.')
|
|
|
|
goal_function_choices = ', '.join(GOAL_FUNCTION_CLASS_NAMES.keys())
|
|
parser.add_argument('--goal_function', '-g', default='untargeted-classification',
|
|
help=f'The goal function to use. choices: {goal_function_choices}')
|
|
|
|
def str_to_int(s): return sum((ord(c) for c in s))
|
|
parser.add_argument('--random_seed', default=str_to_int('TEXTATTACK'))
|
|
|
|
attack_group = parser.add_mutually_exclusive_group(required=False)
|
|
|
|
search_choices = ', '.join(SEARCH_CLASS_NAMES.keys())
|
|
attack_group.add_argument('--attack', '--attack_method', type=str,
|
|
required=False, default='greedy-word-wir',
|
|
help=f'The type of attack to run. choices: {search_choices}')
|
|
|
|
attack_group.add_argument('--recipe', type=str, required=False, default=None,
|
|
help='full attack recipe (overrides provided goal function, transformation & constraints)',
|
|
choices=RECIPE_NAMES.keys())
|
|
|
|
command_line_args = None if sys.argv[1:] else ['-h'] # Default to help with empty arguments.
|
|
args = parser.parse_args(command_line_args)
|
|
|
|
set_seed(args.random_seed)
|
|
|
|
return args
|
|
|
|
def parse_transformation_from_args(args):
|
|
# Transformations
|
|
transformation = args.transformation
|
|
if ':' in transformation:
|
|
transformation_name, params = transformation.split(':')
|
|
if transformation_name not in TRANSFORMATION_CLASS_NAMES:
|
|
raise ValueError(f'Error: unsupported transformation {transformation_name}')
|
|
transformation = eval(f'{TRANSFORMATION_CLASS_NAMES[transformation_name]}({params})')
|
|
elif transformation in TRANSFORMATION_CLASS_NAMES:
|
|
transformation = eval(f'{TRANSFORMATION_CLASS_NAMES[transformation]}()')
|
|
else:
|
|
raise ValueError(f'Error: unsupported transformation {transformation}')
|
|
return transformation
|
|
|
|
def parse_goal_function_from_args(args, model):
|
|
# Goal Functions
|
|
goal_function = args.goal_function
|
|
if ':' in goal_function:
|
|
goal_function_name, params = goal_function.split(':')
|
|
if goal_function_name not in GOAL_FUNCTION_CLASS_NAMES:
|
|
raise ValueError(f'Error: unsupported goal_function {goal_function_name}')
|
|
goal_function = eval(f'{GOAL_FUNCTION_CLASS_NAMES[goal_function_name]}(model, {params})')
|
|
elif goal_function in GOAL_FUNCTION_CLASS_NAMES:
|
|
goal_function = eval(f'{GOAL_FUNCTION_CLASS_NAMES[goal_function]}(model)')
|
|
else:
|
|
raise ValueError(f'Error: unsupported goal_function {goal_function}')
|
|
return goal_function
|
|
|
|
def parse_constraints_from_args(args):
|
|
# Constraints
|
|
if not args.constraints:
|
|
return []
|
|
|
|
_constraints = []
|
|
for constraint in args.constraints:
|
|
if ':' in constraint:
|
|
constraint_name, params = constraint.split(':')
|
|
if constraint_name not in CONSTRAINT_CLASS_NAMES:
|
|
raise ValueError(f'Error: unsupported constraint {constraint_name}')
|
|
_constraints.append(eval(f'{CONSTRAINT_CLASS_NAMES[constraint_name]}({params})'))
|
|
elif constraint in CONSTRAINT_CLASS_NAMES:
|
|
_constraints.append(eval(f'{CONSTRAINT_CLASS_NAMES[constraint]}()'))
|
|
else:
|
|
raise ValueError(f'Error: unsupported constraint {constraint}')
|
|
|
|
return _constraints
|
|
|
|
def parse_recipe_from_args(model, args):
|
|
if ':' in args.recipe:
|
|
recipe_name, params = args.recipe.split(':')
|
|
if recipe_name not in RECIPE_NAMES:
|
|
raise ValueError(f'Error: unsupported recipe {recipe_name}')
|
|
recipe = eval(f'{RECIPE_NAMES[recipe_name]}(model, {params})')
|
|
elif args.recipe in RECIPE_NAMES:
|
|
recipe = eval(f'{RECIPE_NAMES[args.recipe]}(model)')
|
|
else:
|
|
raise ValueError('Invalid recipe {args.recipe}')
|
|
return recipe
|
|
|
|
def parse_goal_function_and_attack_from_args(args):
|
|
if ':' in args.model:
|
|
model_name, params = args.model.split(':')
|
|
if model_name not in MODEL_CLASS_NAMES:
|
|
raise ValueError(f'Error: unsupported model {model_name}')
|
|
model = eval(f'{MODEL_CLASS_NAMES[model_name]}({params})')
|
|
elif args.model in MODEL_CLASS_NAMES:
|
|
model = eval(f'{MODEL_CLASS_NAMES[args.model]}()')
|
|
else:
|
|
raise ValueError(f'Error: unsupported model {args.model}')
|
|
if args.recipe:
|
|
attack = parse_recipe_from_args(model, args)
|
|
goal_function = attack.goal_function
|
|
else:
|
|
goal_function = parse_goal_function_from_args(args, model)
|
|
transformation = parse_transformation_from_args(args)
|
|
constraints = parse_constraints_from_args(args)
|
|
if ':' in args.attack:
|
|
attack_name, params = args.attack.split(':')
|
|
if attack_name not in SEARCH_CLASS_NAMES:
|
|
raise ValueError(f'Error: unsupported attack {attack_name}')
|
|
attack = eval(f'{SEARCH_CLASS_NAMES[attack_name]}(goal_function, transformation, constraints=constraints, {params})')
|
|
elif args.attack in SEARCH_CLASS_NAMES:
|
|
attack = eval(f'{SEARCH_CLASS_NAMES[args.attack]}(goal_function, transformation, constraints=constraints)')
|
|
else:
|
|
raise ValueError(f'Error: unsupported attack {args.attack}')
|
|
return goal_function, attack
|
|
|
|
def parse_logger_from_args(args):# Create logger
|
|
attack_log_manager = textattack.loggers.AttackLogManager()
|
|
# Set default output directory to `textattack/outputs`.
|
|
if not args.out_dir:
|
|
current_dir = os.path.dirname(os.path.realpath(__file__))
|
|
outputs_dir = os.path.join(current_dir, os.pardir, os.pardir, os.pardir, 'outputs')
|
|
args.out_dir = outputs_dir
|
|
|
|
# Output file.
|
|
out_time = int(time.time()*1000) # Output file
|
|
outfile_name = 'attack-{}.txt'.format(out_time)
|
|
attack_log_manager.add_output_file(os.path.join(args.out_dir, outfile_name))
|
|
|
|
# CSV
|
|
if args.enable_csv:
|
|
outfile_name = 'attack-{}.csv'.format(out_time)
|
|
color_method = None if args.enable_csv == 'plain' else 'file'
|
|
csv_path = os.path.join(args.out_dir, outfile_name)
|
|
attack_log_manager.add_output_csv(csv_path, color_method)
|
|
print('Logging to CSV at path {}.'.format(csv_path))
|
|
|
|
# Visdom
|
|
if args.enable_visdom:
|
|
attack_log_manager.enable_visdom()
|
|
|
|
# Weights & Biases
|
|
if args.enable_wandb:
|
|
attack_log_manager.enable_wandb()
|
|
|
|
# Stdout
|
|
if not args.disable_stdout:
|
|
attack_log_manager.enable_stdout()
|
|
return attack_log_manager
|