mirror of
https://github.com/QData/TextAttack.git
synced 2021-10-13 00:05:06 +03:00
89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
import argparse
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import textattack
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import torch
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import sys
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from run_attack_args_helper import *
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import textattack.models as models
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def _cb(s): return textattack.shared.utils.color_text(str(s), color='blue', method='stdout')
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def _cg(s): return textattack.shared.utils.color_text(str(s), color='green', method='stdout')
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def _cr(s): return textattack.shared.utils.color_text(str(s), color='red', method='stdout')
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def _pb(): print(_cg('-' * 60))
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from collections import Counter
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def get_num_successes(model, ids, true_labels):
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id_dim = torch.tensor(ids).ndim
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if id_dim == 2:
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# For models where the input is a single vector.
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ids = torch.tensor(ids).to(textattack.shared.utils.get_device())
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preds = model(ids)
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elif id_dim == 3:
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# For models that take multiple vectors per input.
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ids = map(torch.tensor, zip(*ids))
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ids = (x.to(textattack.shared.utils.get_device()) for x in ids)
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preds = model(*ids)
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else:
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raise TypeError(f'Error: malformed id_dim ({id_dim})')
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true_labels = torch.tensor(true_labels).to(textattack.shared.utils.get_device())
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guess_labels = preds.argmax(dim=1)
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successes = (guess_labels == true_labels).sum().item()
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return successes, true_labels, guess_labels
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def test_model_on_dataset(model, dataset, batch_size=16, num_examples=100):
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succ = 0
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fail = 0
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batch_ids = []
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batch_labels = []
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all_true_labels = []
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all_guess_labels = []
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for i, (text, label) in enumerate(dataset):
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if i >= num_examples: break
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ids = model.tokenizer.encode(text)
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batch_ids.append(ids)
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batch_labels.append(label)
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if len(batch_ids) == batch_size:
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batch_succ, true_labels, guess_labels = get_num_successes(model, batch_ids, batch_labels)
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batch_fail = batch_size - batch_succ
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succ += batch_succ
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fail += batch_fail
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batch_ids = []
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batch_labels = []
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all_true_labels.extend(true_labels.tolist())
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all_guess_labels.extend(guess_labels.tolist())
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if len(batch_ids) > 0:
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batch_succ, true_labels, guess_labels = get_num_successes(model, batch_ids, batch_labels)
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batch_fail = len(batch_ids) - batch_succ
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succ += batch_succ
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fail += batch_fail
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all_true_labels.extend(true_labels.tolist())
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all_guess_labels.extend(guess_labels.tolist())
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perc = float(succ)/(succ+fail)*100.0
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perc = '{:.2f}%'.format(perc)
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print(f'Successes {succ}/{succ+fail} ({_cb(perc)})')
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return perc
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def test_all_models(num_examples):
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_pb()
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for model_name in MODEL_CLASS_NAMES:
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model = eval(MODEL_CLASS_NAMES[model_name])()
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dataset = DATASET_BY_MODEL[model_name]()
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print(f'Testing {_cr(model_name)} on {_cr(type(dataset))}...')
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test_model_on_dataset(model, dataset, num_examples=num_examples)
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_pb()
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# @TODO print the grid of models/dataset names with results in a nice table :)
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--n', type=int, default=100,
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help="number of examples to test on")
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return parser.parse_args()
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if __name__ == '__main__':
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args = parse_args()
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with torch.no_grad():
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test_all_models(args.n)
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