mirror of
https://github.com/QData/TextAttack.git
synced 2021-10-13 00:05:06 +03:00
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
import argparse
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import collections
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import sys
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import torch
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from attack_args_helper import get_args, parse_dataset_from_args, parse_model_from_args
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import textattack
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def _cb(s):
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return textattack.shared.utils.color_text(str(s), color="blue", method="ansi")
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def get_num_successes(args, model, ids, true_labels):
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with torch.no_grad():
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preds = textattack.shared.utils.model_predict(model, **ids)
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true_labels = torch.tensor(true_labels).to(textattack.shared.utils.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(args, model, dataset, batch_size=128):
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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_input, label) in enumerate(dataset):
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text = textattack.shared.AttackedText(text_input).text
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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(
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args, model, batch_ids, batch_labels
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)
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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(
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args, model, batch_ids, batch_labels
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)
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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)
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