wehub-resource-sync
93 lines
3.8 KiB
Python
93 lines
3.8 KiB
Python
# -*- coding: UTF-8 -*-
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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from functools import partial
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import paddle
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from data import convert_example, load_dataset, load_vocab
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from model import BiGruCrf
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from paddlenlp.data import Pad, Stack, Tuple
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from paddlenlp.metrics import ChunkEvaluator
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# fmt: off
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.")
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parser.add_argument("--init_checkpoint", type=str, default=None, help="Path to init model.")
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parser.add_argument("--batch_size", type=int, default=300, help="The number of sequences contained in a mini-batch.")
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parser.add_argument("--max_seq_len", type=int, default=64, help="Number of words of the longest sequence.")
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parser.add_argument("--device", default="gpu", type=str, choices=["cpu", "gpu"], help="The device to select to train the model, is must be cpu/gpu.")
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parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.")
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parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.")
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args = parser.parse_args()
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# fmt: on
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def evaluate(args):
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paddle.set_device(args.device)
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# create dataset.
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test_ds = load_dataset(datafiles=(os.path.join(args.data_dir, "test.tsv")))
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word_vocab = load_vocab(os.path.join(args.data_dir, "word.dic"))
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label_vocab = load_vocab(os.path.join(args.data_dir, "tag.dic"))
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# q2b.dic is used to replace DBC case to SBC case
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normlize_vocab = load_vocab(os.path.join(args.data_dir, "q2b.dic"))
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trans_func = partial(
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convert_example,
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max_seq_len=args.max_seq_len,
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word_vocab=word_vocab,
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label_vocab=label_vocab,
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normlize_vocab=normlize_vocab,
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)
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test_ds.map(trans_func)
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=0, dtype="int64"), # word_ids
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Stack(dtype="int64"), # length
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Pad(axis=0, pad_val=0, dtype="int64"), # label_ids
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): fn(samples)
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# Create sampler for dataloader
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test_sampler = paddle.io.BatchSampler(dataset=test_ds, batch_size=args.batch_size, shuffle=False, drop_last=False)
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test_loader = paddle.io.DataLoader(
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dataset=test_ds, batch_sampler=test_sampler, return_list=True, collate_fn=batchify_fn
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)
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# Define the model network and metric evaluator
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model = BiGruCrf(args.emb_dim, args.hidden_size, len(word_vocab), len(label_vocab))
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chunk_evaluator = ChunkEvaluator(label_list=label_vocab.keys(), suffix=True)
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# Load the model and start predicting
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model_dict = paddle.load(args.init_checkpoint)
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model.load_dict(model_dict)
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model.eval()
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chunk_evaluator.reset()
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for batch in test_loader:
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token_ids, length, labels = batch
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preds = model(token_ids, length)
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num_infer_chunks, num_label_chunks, num_correct_chunks = chunk_evaluator.compute(length, preds, labels)
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chunk_evaluator.update(num_infer_chunks.numpy(), num_label_chunks.numpy(), num_correct_chunks.numpy())
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precision, recall, f1_score = chunk_evaluator.accumulate()
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print("eval precision: %f, recall: %f, f1: %f" % (precision, recall, f1_score))
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if __name__ == "__main__":
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args = parser.parse_args()
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evaluate(args)
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