evaluate.py

# Copyright (c) 2021 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.

from functools import partial
import argparse
import os
import random
import time

import numpy as np
import pandas as pd
from tqdm import tqdm
import paddle
import paddle.nn.functional as F
from paddlenlp.data import Stack, Tuple, Pad
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import LinearDecayWithWarmup, AutoModelForSequenceClassification, AutoTokenizer

from data import create_dataloader
from data import convert_example, read_data

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--test_file", type=str, required=True, help="The full path of test file")
parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. "
    "Sequences longer than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument('--model_name_or_path', default="rocketqa-base-cross-encoder", help="The pretrained model used for training")
parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization.")
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
args = parser.parse_args()
# yapf: enable


def set_seed(seed):
    """sets random seed"""
    random.seed(seed)
    np.random.seed(seed)
    paddle.seed(seed)


@paddle.no_grad()
def evaluate(model, metric, data_loader, phase="dev"):
    """
    Given a dataset, it evals model and computes the metric.

    Args:
        model(obj:`paddle.nn.Layer`): A model to classify texts.
        data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
        metric(obj:`paddle.metric.Metric`): The evaluation metric.
    """
    model.eval()
    metric.reset()

    for idx, batch in enumerate(data_loader):
        input_ids, token_type_ids, labels = batch

        pos_probs = model(input_ids=input_ids, token_type_ids=token_type_ids)

        sim_score = F.softmax(pos_probs)
        metric.update(preds=sim_score.numpy(), labels=labels)

    print("eval_{} auc:{:.3}".format(phase, metric.accumulate()))
    metric.reset()
    model.train()


def main():
    paddle.set_device(args.device)
    rank = paddle.distributed.get_rank()
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()

    set_seed(args.seed)

    dev_ds = load_dataset(read_data, data_path=args.test_file, lazy=False)

    model = AutoModelForSequenceClassification.from_pretrained(args.model_name_or_path, num_classes=2)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)

    trans_func_eval = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length, is_pair=True)

    batchify_fn_eval = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # pair_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"),  # pair_segment
        Stack(dtype="int64"),  # label
    ): [data for data in fn(samples)]

    dev_data_loader = create_dataloader(
        dev_ds, mode="dev", batch_size=args.batch_size, batchify_fn=batchify_fn_eval, trans_fn=trans_func_eval
    )

    if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
        state_dict = paddle.load(args.init_from_ckpt)
        model.set_dict(state_dict)
    else:
        raise ValueError("Please set --params_path with correct pretrained model file")

    metric = paddle.metric.Auc()
    evaluate(model, metric, dev_data_loader, "dev")


if __name__ == "__main__":
    main()