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from datetime import datetime |
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from parser import Model |
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from parser.cmds.cmd import CMD |
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from parser.utils.corpus import Corpus |
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from parser.utils.data import TextDataset, batchify |
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class Predict(CMD): |
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def add_subparser(self, name, parser): |
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subparser = parser.add_parser( |
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name, help='Use a trained model to make predictions.' |
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) |
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subparser.add_argument('--batch-size', default=1000, type=int, |
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help='batch size') |
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subparser.add_argument('--fdata', default='data/ptb/tamtest.conllx', |
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help='path to dataset') |
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subparser.add_argument('--fpred', default='pred.conllx', |
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help='path to predicted result') |
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return subparser |
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def __call__(self, args): |
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super(Predict, self).__call__(args) |
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print("Load the dataset") |
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corpus = Corpus.load(args.fdata, self.fields) |
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dataset = TextDataset(corpus, [self.WORD, self.FEAT]) |
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dataset.loader = batchify(dataset, args.batch_size) |
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print(type(dataset)) |
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print(f"{len(dataset)} sentences, " |
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f"{len(dataset.loader)} batches") |
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print("Load the model") |
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self.model = Model.load(args.model) |
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print(f"{self.model}\n") |
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print("Make predictions on the dataset") |
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start = datetime.now() |
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corpus.heads, corpus.rels = self.predict(dataset.loader) |
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print(f"Save the predicted result to {args.fpred}") |
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corpus.save(args.fpred) |
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total_time = datetime.now() - start |
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print(f"{total_time}s elapsed, " |
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f"{len(dataset) / total_time.total_seconds():.2f} Sents/s") |
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