dataloader.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 yaml 
import argparse 
from pprint import pprint
from attrdict import AttrDict

import paddle
from paddle.io import DataLoader  

import paddle
from paddlenlp.transformers import LinearDecayWithWarmup
from paddlenlp.transformers import TransformerModel, InferTransformerModel, CrossEntropyCriterion, position_encoding_init
from paddlenlp.utils.log import logger
from paddlenlp.datasets import DatasetBuilder
from paddlenlp.transformers import ElectraForTokenClassification, ElectraTokenizer
from paddlenlp.data import Stack, Tuple, Pad, Dict

import os
import pandas as pd
from sklearn.metrics import classification_report
from functools import partial


class TEDTalk(DatasetBuilder):
    '''
    构建针对TEDTalk数据集的dataset的类
    '''

    SPLITS = {
        'train': 'train.tsv',
        'dev':'dev.tsv',
        'test': 'test.tsv'
    }

    def _get_data(self, mode, **kwargs):
        default_root='.'
        self.mode=mode
        filename = self.SPLITS[mode]
        fullname = os.path.join(default_root, filename)

        return fullname

    def _read(self, filename, *args):
        df=pd.read_csv(filename,sep='\t')
        for idx,row in df.iterrows():
            text=row['text_a']
            if(type(text)==float):
                print(text)
                continue
            tokens=row['text_a'].split()
            tags=row['label'].split()
            # if(self.mode=='train'):
            #     tags=row['label'].split()
            # else:
            #     tags = []
            yield {"tokens": tokens, "labels": tags}

    def get_labels(self):

        return ["0", "1", "2", "3"]
 
def load_dataset(path_or_read_func,
                 name=None,
                 data_files=None,
                 splits=None,
                 lazy=None,
                 **kwargs):

    '''
    根据需要的数据集类型，加载相应TEDTalk dataset
    '''
    
    reader_cls = TEDTalk
    print(reader_cls)
    if not name:
        reader_instance = reader_cls(lazy=lazy, **kwargs)
    else:
        reader_instance = reader_cls(lazy=lazy, name=name, **kwargs)

    datasets = reader_instance.read_datasets(data_files=data_files, splits=splits)
    return datasets
 
def tokenize_and_align_labels(example, tokenizer, no_entity_id, max_seq_len=512):
    labels = example['labels']
    example = example['tokens']
    # print(labels)
    tokenized_input = tokenizer(
        example,
        return_length=True,
        is_split_into_words=True,
        max_seq_len=max_seq_len)

    # -2 for [CLS] and [SEP]
    if len(tokenized_input['input_ids']) - 2 < len(labels):
        labels = labels[:len(tokenized_input['input_ids']) - 2]
    tokenized_input['labels'] = [no_entity_id] + labels + [no_entity_id]
    tokenized_input['labels'] += [no_entity_id] * (
        len(tokenized_input['input_ids']) - len(tokenized_input['labels']))
    # print(tokenized_input)
    return tokenized_input

def create_train_dataloader(args):
    '''
    构建用于训练的dataloader
    Create dataset, tokenizer and dataloader.

    input:
        args: 配置文件提供的参数借口 
    return:
        train_data_loader：训练数据data loader
        valid_data_loader：验证数据data loader
    '''
    
    # 加载dataset   
    train_ds, valid_ds = load_dataset('TEDTalk', splits=('train', 'dev'), lazy=False)

    label_list = train_ds.label_list
    label_num = len(label_list)
    # no_entity_id = label_num - 1
    no_entity_id=0
    
    print(label_list)

    # 构建dataloader
    model_name_or_path=args.model_name_or_path
    tokenizer = ElectraTokenizer.from_pretrained(model_name_or_path)

    trans_func = partial(
            tokenize_and_align_labels,
            tokenizer=tokenizer,
            no_entity_id=no_entity_id,
            max_seq_len=args.max_seq_length)
    train_ds = train_ds.map(trans_func)
 
    batchify_fn = lambda samples, fn=Dict({
            'input_ids': Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype='int32'),  # input
            'token_type_ids': Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype='int32'),  # segment
            'seq_len': Stack(dtype='int64'),  # seq_len
            'labels': Pad(axis=0, pad_val=args.ignore_label, dtype='int64')  # label
        }): fn(samples)
    
    train_batch_sampler = paddle.io.DistributedBatchSampler(
            train_ds, batch_size=args.batch_size, shuffle=True, drop_last=True)
    
    train_data_loader = DataLoader(
            dataset=train_ds,
            collate_fn=batchify_fn,
            num_workers=0,
            batch_sampler=train_batch_sampler,
            return_list=True) 

    valid_ds = valid_ds.map(trans_func)

    valid_data_loader = DataLoader(
            dataset=valid_ds,
            collate_fn=batchify_fn,
            num_workers=0,
            batch_size=args.batch_size,
            return_list=True)
    
    # 测试
    # for index,data in enumerate(train_data_loader):
    #     # print(len(data))
    #     print(index)
    #     print(data)
    #     break

    return train_data_loader, valid_data_loader  
 
def create_test_dataloader(args):
    '''
    构建测试用的dataloader
    Create dataset, tokenizer and dataloader.

    input:
        args: 配置文件提供的参数借口 
    return: 
        test_data_loader 
    '''
    no_entity_id=0
    
    # 加载dataset    
    test_ds = load_dataset('TEDTalk', splits=('test'), lazy=False)

    # 构建dataloader
    model_name_or_path=args.model_name_or_path
    tokenizer = ElectraTokenizer.from_pretrained(model_name_or_path)

    trans_func = partial(
            tokenize_and_align_labels,
            tokenizer=tokenizer,
            no_entity_id=no_entity_id,
            max_seq_len=args.max_seq_length)
 
    batchify_fn = lambda samples, fn=Dict({
            'input_ids': Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype='int32'),  # input
            'token_type_ids': Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype='int32'),  # segment
            'seq_len': Stack(dtype='int64'),  # seq_len
            'labels': Pad(axis=0, pad_val=args.ignore_label, dtype='int64')  # label
        }): fn(samples)

    test_ds = test_ds.map(trans_func)

    test_data_loader = DataLoader(
            dataset=test_ds,
            collate_fn=batchify_fn,
            num_workers=0,
            batch_size=args.batch_size,
            return_list=True) 

    return test_data_loader