mirror of
https://github.com/QData/TextAttack.git
synced 2021-10-13 00:05:06 +03:00
80 lines
3.0 KiB
Python
80 lines
3.0 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 attack_args_helper import get_args, parse_model_from_args, parse_dataset_from_args
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def _cb(s): return textattack.shared.utils.color_text(str(s), color='blue', method='ansi')
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def _cg(s): return textattack.shared.utils.color_text(str(s), color='green', method='ansi')
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def _cr(s): return textattack.shared.utils.color_text(str(s), color='red', method='ansi')
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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(args, 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 = textattack.shared.utils.preprocess_ids(ids)
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ids = torch.tensor(ids).to(textattack.shared.utils.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.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.device)
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if isinstance(preds, tuple):
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preds = preds[0]
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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(args, model, dataset, batch_size=16):
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num_examples = args.num_examples
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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(args, model, batch_ids, batch_labels)
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# for t, g in zip(true_labels, guess_labels):
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# print(t, 'but guessed', g)
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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(args, 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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if __name__ == '__main__':
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args = get_args()
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model = parse_model_from_args(args)
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dataset = parse_dataset_from_args(args)
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with torch.no_grad():
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test_model_on_dataset(args, model, dataset) |