eval.py

# -*- coding: UTF-8 -*-
#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
from functools import partial

import paddle
from data import convert_example, load_dataset, load_vocab
from model import BiGruCrf

from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.metrics import ChunkEvaluator

# fmt: off
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.")
parser.add_argument("--init_checkpoint", type=str, default=None, help="Path to init model.")
parser.add_argument("--batch_size", type=int, default=300, help="The number of sequences contained in a mini-batch.")
parser.add_argument("--max_seq_len", type=int, default=64, help="Number of words of the longest seqence.")
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.")
parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.")
parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.")
args = parser.parse_args()
# fmt: on


def evaluate(args):
    paddle.set_device(args.device)

    # create dataset.
    test_ds = load_dataset(datafiles=(os.path.join(args.data_dir, "test.tsv")))
    word_vocab = load_vocab(os.path.join(args.data_dir, "word.dic"))
    label_vocab = load_vocab(os.path.join(args.data_dir, "tag.dic"))
    # q2b.dic is used to replace DBC case to SBC case
    normlize_vocab = load_vocab(os.path.join(args.data_dir, "q2b.dic"))

    trans_func = partial(
        convert_example,
        max_seq_len=args.max_seq_len,
        word_vocab=word_vocab,
        label_vocab=label_vocab,
        normlize_vocab=normlize_vocab,
    )
    test_ds.map(trans_func)

    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=0, dtype="int64"),  # word_ids
        Stack(dtype="int64"),  # length
        Pad(axis=0, pad_val=0, dtype="int64"),  # label_ids
    ): fn(samples)

    # Create sampler for dataloader
    test_sampler = paddle.io.BatchSampler(dataset=test_ds, batch_size=args.batch_size, shuffle=False, drop_last=False)
    test_loader = paddle.io.DataLoader(
        dataset=test_ds, batch_sampler=test_sampler, return_list=True, collate_fn=batchify_fn
    )

    # Define the model network and metric evaluator
    model = BiGruCrf(args.emb_dim, args.hidden_size, len(word_vocab), len(label_vocab))
    chunk_evaluator = ChunkEvaluator(label_list=label_vocab.keys(), suffix=True)

    # Load the model and start predicting
    model_dict = paddle.load(args.init_checkpoint)
    model.load_dict(model_dict)

    model.eval()
    chunk_evaluator.reset()
    for batch in test_loader:
        token_ids, length, labels = batch
        preds = model(token_ids, length)
        num_infer_chunks, num_label_chunks, num_correct_chunks = chunk_evaluator.compute(length, preds, labels)
        chunk_evaluator.update(num_infer_chunks.numpy(), num_label_chunks.numpy(), num_correct_chunks.numpy())
        precision, recall, f1_score = chunk_evaluator.accumulate()
    print("eval precision: %f, recall: %f, f1: %f" % (precision, recall, f1_score))


if __name__ == "__main__":
    args = parser.parse_args()
    evaluate(args)