Model/MultimodelNER/VLSP2021/dataset_roberta.py

import torch
import logging
import os

logger = logging.getLogger(__name__)
from torchvision import transforms
from PIL import Image


class SBInputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b, img_id, label=None, auxlabel=None):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.img_id = img_id
        self.label = label
        # Please note that the auxlabel is not used in SB
        # it is just kept in order not to modify the original code
        self.auxlabel = auxlabel


class SBInputFeatures(object):
    """A single set of features of data"""

    def __init__(self, input_ids, input_mask, added_input_mask, segment_ids, img_feat, label_id, auxlabel_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.added_input_mask = added_input_mask
        self.segment_ids = segment_ids
        self.img_feat = img_feat
        self.label_id = label_id
        self.auxlabel_id = auxlabel_id


def sbreadfile(filename):
    '''
    Đọc dữ liệu từ tệp và trả về dưới dạng danh sách các cặp từ và nhãn, cùng với danh sách hình ảnh và nhãn phụ.
    '''
    print("Chuẩn bị dữ liệu cho ", filename)
    f = open(filename, encoding='utf8')
    data = []
    imgs = []
    auxlabels = []
    sentence = []
    label = []
    auxlabel = []
    imgid = ''

    for line in f:
        line = line.strip()  # Loại bỏ các dấu cách thừa ở đầu và cuối dòng
        if line.startswith('IMGID:'):
            imgid = line.split('IMGID:')[1] + '.jpg'
            continue
        if line == '':
            if len(sentence) > 0:
                data.append((sentence, label))
                imgs.append(imgid)
                auxlabels.append(auxlabel)
                sentence = []
                label = []
                auxlabel = []
                imgid = ''
            continue
        splits = line.split('\t')
        if len(splits) == 2:  # Đảm bảo dòng có ít nhất một từ và một nhãn
            word, cur_label = splits
            sentence.append(word)
            label.append(cur_label)
            auxlabel.append(cur_label[0])  # Lấy ký tự đầu tiên của nhãn làm nhãn phụ

    if len(sentence) > 0:  # Xử lý dữ liệu cuối cùng trong tệp
        data.append((sentence, label))
        imgs.append(imgid)
        auxlabels.append(auxlabel)

    print("Số lượng mẫu: " + str(len(data)))
    print("Số lượng hình ảnh: " + str(len(imgs)))
    return data, imgs, auxlabels


# def sbreadfile(filename): #code gốc
#     '''
#     read file
#     return format :
#     [ ['EU', 'B-ORG'], ['rejects', 'O'], ['German', 'B-MISC'], ['call', 'O'], ['to', 'O'], ['boycott', 'O'], ['British', 'B-MISC'], ['lamb', 'O'], ['.', 'O'] ]
#     '''
#     print("prepare data for ",filename)
#     f = open(filename,encoding='utf8')
#     data = []
#     imgs = []
#     auxlabels = []
#     sentence = []
#     label = []
#     auxlabel = []
#     imgid = ''
#     a = 0
#     for line in f:
#         if line.startswith('IMGID:'):
#             imgid = line.strip().split('IMGID:')[1] + '.jpg'
#             continue
#         if line[0] == "\n":
#             if len(sentence) > 0:
#                 data.append((sentence, label))
#                 imgs.append(imgid)
#                 auxlabels.append(auxlabel)
#                 sentence = []
#                 label = []
#                 imgid = ''
#                 auxlabel = []
#             continue
#         splits = line.split('\t')
#         sentence.append(splits[0])
#         cur_label = splits[-1][:-1]
#         # if cur_label == 'B-OTHER':
#         #     cur_label = 'B-MISC'
#         # elif cur_label == 'I-OTHER':
#         #     cur_label = 'I-MISC'
#         label.append(cur_label)
#         auxlabel.append(cur_label[0])

#     if len(sentence) > 0:
#         data.append((sentence, label))
#         imgs.append(imgid)
#         auxlabels.append(auxlabel)
#         sentence = []
#         label = []
#         auxlabel = []

#     print("The number of samples: " + str(len(data)))
#     print("The number of images: " + str(len(imgs)))
#     return data, imgs, auxlabels

class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_sbtsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        return sbreadfile(input_file)


class MNERProcessor_2021(DataProcessor):
    """Processor for the CoNLL-2003 data set."""

    def get_train_examples(self, data_dir):
        """See base class."""
        data, imgs, auxlabels = self._read_sbtsv(os.path.join(data_dir, "train.txt"))
        return self._create_examples(data, imgs, auxlabels, "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        data, imgs, auxlabels = self._read_sbtsv(os.path.join(data_dir, "dev.txt"))
        return self._create_examples(data, imgs, auxlabels, "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        data, imgs, auxlabels = self._read_sbtsv(os.path.join(data_dir, "test.txt"))
        return self._create_examples(data, imgs, auxlabels, "test")

    def get_labels(self):
        return [
            "O",  # 1
            "I-PRODUCT-AWARD",  # 2
            "B-MISCELLANEOUS",  # 3
            "B-QUANTITY-NUM",  # 4
            "B-ORGANIZATION-SPORTS",  # 5
            "B-DATETIME",  # 6
            "I-ADDRESS",  # 7
            "I-PERSON",  # 8
            "I-EVENT-SPORT",  # 9
            "B-ADDRESS",  # 10
            "B-EVENT-NATURAL",  # 11
            "I-LOCATION-GPE",  # 12
            "B-EVENT-GAMESHOW",  # 13
            "B-DATETIME-TIMERANGE",  # 14
            "I-QUANTITY-NUM",  # 15
            "I-QUANTITY-AGE",  # 16
            "B-EVENT-CUL",  # 17
            "I-QUANTITY-TEM",  # 18
            "I-PRODUCT-LEGAL",  # 19
            "I-LOCATION-STRUC",  # 20
            "I-ORGANIZATION",  # 21
            "B-PHONENUMBER",  # 22
            "B-IP",  # 23
            "B-QUANTITY-AGE",  # 24
            "I-DATETIME-TIME",  # 25
            "I-DATETIME",  # 26
            "B-ORGANIZATION-MED",  # 27
            "B-DATETIME-SET",  # 28
            "I-EVENT-CUL",  # 29
            "B-QUANTITY-DIM",  # 30
            "I-QUANTITY-DIM",  # 31
            "B-EVENT",  # 32
            "B-DATETIME-DATERANGE",  # 33
            "I-EVENT-GAMESHOW",  # 34
            "B-PRODUCT-AWARD",  # 35
            "B-LOCATION-STRUC",  # 36
            "B-LOCATION",  # 37
            "B-PRODUCT",  # 38
            "I-MISCELLANEOUS",  # 39
            "B-SKILL",  # 40
            "I-QUANTITY-ORD",  # 41
            "I-ORGANIZATION-STOCK",  # 42
            "I-LOCATION-GEO",  # 43
            "B-PERSON",  # 44
            "B-PRODUCT-COM",  # 45
            "B-PRODUCT-LEGAL",  # 46
            "I-LOCATION",  # 47
            "B-QUANTITY-TEM",  # 48
            "I-PRODUCT",  # 49
            "B-QUANTITY-CUR",  # 50
            "I-QUANTITY-CUR",  # 51
            "B-LOCATION-GPE",  # 52
            "I-PHONENUMBER",  # 53
            "I-ORGANIZATION-MED",  # 54
            "I-EVENT-NATURAL",  # 55
            "I-EMAIL",  # 56
            "B-ORGANIZATION",  # 57
            "B-URL",  # 58
            "I-DATETIME-TIMERANGE",  # 59
            "I-QUANTITY",  # 60
            "I-IP",  # 61
            "B-EVENT-SPORT",  # 62
            "B-PERSONTYPE",  # 63
            "B-QUANTITY-PER",  # 64
            "I-QUANTITY-PER",  # 65
            "I-PRODUCT-COM",  # 66
            "I-DATETIME-DURATION",  # 67
            "B-LOCATION-GPE-GEO",  # 68
            "B-QUANTITY-ORD",  # 69
            "I-EVENT",  # 70
            "B-DATETIME-TIME",  # 71
            "B-QUANTITY",  # 72
            "I-DATETIME-SET",  # 73
            "I-LOCATION-GPE-GEO",  # 74
            "B-ORGANIZATION-STOCK",  # 75
            "I-ORGANIZATION-SPORTS",  # 76
            "I-SKILL",  # 77
            "I-URL",  # 78
            "B-DATETIME-DURATION",  # 79
            "I-DATETIME-DATE",  # 80
            "I-PERSONTYPE",  # 81
            "B-DATETIME-DATE",  # 82
            "I-DATETIME-DATERANGE",  # 83
            "B-LOCATION-GEO",  # 84
            "B-EMAIL",  # 85
            "X",  # 86
            "<s>",  # 87
            "</s>"  # 88
        ]

        # vlsp2016


        # vlsp2018
        # return [
        #         "O","I-ORGANIZATION",
        #         "B-ORGANIZATION",
        #         "I-LOCATION",
        #         "B-MISCELLANEOUS",
        #         "I-PERSON",
        #         "B-PERSON",
        #         "I-MISCELLANEOUS",
        #         "B-LOCATION",
        #         "X",
        #         "<s>",
        #         "</s>"]

    def get_auxlabels(self):
        return ["O", "B", "I", "X", "<s>", "</s>"]

    def get_start_label_id(self):
        label_list = self.get_labels()
        label_map = {label: i for i, label in enumerate(label_list, 1)}
        return label_map['<s>']

    def get_stop_label_id(self):
        label_list = self.get_labels()
        label_map = {label: i for i, label in enumerate(label_list, 1)}
        return label_map['</s>']

    def _create_examples(self, lines, imgs, auxlabels, set_type):
        examples = []
        for i, (sentence, label) in enumerate(lines):
            guid = "%s-%s" % (set_type, i)
            text_a = ' '.join(sentence)
            text_b = None
            img_id = imgs[i]
            label = label
            auxlabel = auxlabels[i]
            examples.append(
                SBInputExample(guid=guid, text_a=text_a, text_b=text_b, img_id=img_id, label=label, auxlabel=auxlabel))
        return examples


def image_process(image_path, transform):
    image = Image.open(image_path).convert('RGB')
    image = transform(image)
    return image


def convert_mm_examples_to_features(examples, label_list, auxlabel_list,
                                    max_seq_length, tokenizer, crop_size, path_img):
    label_map = {label: i for i, label in enumerate(label_list, 1)}
    auxlabel_map = {label: i for i, label in enumerate(auxlabel_list, 1)}

    features = []
    count = 0

    transform = transforms.Compose([
        transforms.Resize([256, 256]),
        transforms.RandomCrop(crop_size),  # args.crop_size, by default it is set to be 224
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406),
                             (0.229, 0.224, 0.225))])

    for (ex_index, example) in enumerate(examples):
        textlist = example.text_a.split(' ')
        labellist = example.label
        auxlabellist = example.auxlabel
        tokens = []
        labels = []
        auxlabels = []
        for i, word in enumerate(textlist):
            token = tokenizer.tokenize(word)
            tokens.extend(token)
            label_1 = labellist[i]
            auxlabel_1 = auxlabellist[i]
            for m in range(len(token)):
                if m == 0:
                    labels.append(label_1)
                    auxlabels.append(auxlabel_1)
                else:
                    labels.append("X")
                    auxlabels.append("X")
        if len(tokens) >= max_seq_length - 1:
            tokens = tokens[0:(max_seq_length - 2)]
            labels = labels[0:(max_seq_length - 2)]
            auxlabels = auxlabels[0:(max_seq_length - 2)]
        ntokens = []
        segment_ids = []
        label_ids = []
        auxlabel_ids = []
        ntokens.append("<s>")
        segment_ids.append(0)
        label_ids.append(label_map["<s>"])
        auxlabel_ids.append(auxlabel_map["<s>"])
        for i, token in enumerate(tokens):
            ntokens.append(token)
            segment_ids.append(0)
            label_ids.append(label_map[labels[i]])
            auxlabel_ids.append(auxlabel_map[auxlabels[i]])
        ntokens.append("</s>")
        segment_ids.append(0)
        label_ids.append(label_map["</s>"])
        auxlabel_ids.append(auxlabel_map["</s>"])
        input_ids = tokenizer.convert_tokens_to_ids(ntokens)
        input_mask = [1] * len(input_ids)
        added_input_mask = [1] * (len(input_ids) + 49)  # 1 or 49 is for encoding regional image representations

        while len(input_ids) < max_seq_length:
            input_ids.append(0)
            input_mask.append(0)
            added_input_mask.append(0)
            segment_ids.append(0)
            label_ids.append(0)
            auxlabel_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length
        assert len(label_ids) == max_seq_length
        assert len(auxlabel_ids) == max_seq_length

        image_name = example.img_id
        image_path = os.path.join(path_img, image_name)

        if not os.path.exists(image_path):
            if 'NaN' not in image_path:
                print(image_path)
        try:
            image = image_process(image_path, transform)
        except:
            count += 1
            image_path_fail = os.path.join(path_img, 'background.jpg')
            image = image_process(image_path_fail, transform)

        else:
            if ex_index < 2:
                logger.info("*** Example ***")
                logger.info("guid: %s" % (example.guid))
                logger.info("tokens: %s" % " ".join(
                    [str(x) for x in tokens]))
                logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
                logger.info(
                    "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
                logger.info("label: %s" % " ".join([str(x) for x in label_ids]))
                logger.info("auxlabel: %s" % " ".join([str(x) for x in auxlabel_ids]))

            features.append(
                SBInputFeatures(input_ids=input_ids, input_mask=input_mask, added_input_mask=added_input_mask,
                                segment_ids=segment_ids, img_feat=image, label_id=label_ids, auxlabel_id=auxlabel_ids))

    print('the number of problematic samples: ' + str(count))
    return features


# if __name__ == "__main__":
#     processor = MNERProcessor_2016()
#     label_list = processor.get_labels()
#     auxlabel_list = processor.get_auxlabels()
#     num_labels = len(label_list) + 1  # label 0 corresponds to padding, label in label_list starts from 1
#
#     start_label_id = processor.get_start_label_id()
#     stop_label_id = processor.get_stop_label_id()
#
#     data_dir = r'sample_data'
#     train_examples = processor.get_train_examples(data_dir)
#     print(train_examples[0].img_id)