File size: 5,446 Bytes
fc8c192
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
import json
import os
import random
import traceback

import numpy as np
from paddle.io import Dataset

from .imaug import create_operators, transform


class SimpleDataSet(Dataset):
    def __init__(self, config, mode, logger, seed=None):
        super(SimpleDataSet, self).__init__()
        self.logger = logger
        self.mode = mode.lower()

        global_config = config["Global"]
        dataset_config = config[mode]["dataset"]
        loader_config = config[mode]["loader"]

        self.delimiter = dataset_config.get("delimiter", "\t")
        label_file_list = dataset_config.pop("label_file_list")
        data_source_num = len(label_file_list)
        ratio_list = dataset_config.get("ratio_list", 1.0)
        if isinstance(ratio_list, (float, int)):
            ratio_list = [float(ratio_list)] * int(data_source_num)

        assert (
            len(ratio_list) == data_source_num
        ), "The length of ratio_list should be the same as the file_list."
        self.data_dir = dataset_config["data_dir"]
        self.do_shuffle = loader_config["shuffle"]
        self.seed = seed
        logger.info("Initialize indexs of datasets:%s" % label_file_list)
        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
        self.data_idx_order_list = list(range(len(self.data_lines)))
        if self.mode == "train" and self.do_shuffle:
            self.shuffle_data_random()
        self.ops = create_operators(dataset_config["transforms"], global_config)
        self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx", 2)
        self.need_reset = True in [x < 1 for x in ratio_list]

    def get_image_info_list(self, file_list, ratio_list):
        if isinstance(file_list, str):
            file_list = [file_list]
        data_lines = []
        for idx, file in enumerate(file_list):
            with open(file, "rb") as f:
                lines = f.readlines()
                if self.mode == "train" or ratio_list[idx] < 1.0:
                    random.seed(self.seed)
                    lines = random.sample(lines, round(len(lines) * ratio_list[idx]))
                data_lines.extend(lines)
        return data_lines

    def shuffle_data_random(self):
        random.seed(self.seed)
        random.shuffle(self.data_lines)
        return

    def _try_parse_filename_list(self, file_name):
        # multiple images -> one gt label
        if len(file_name) > 0 and file_name[0] == "[":
            try:
                info = json.loads(file_name)
                file_name = random.choice(info)
            except:
                pass
        return file_name

    def get_ext_data(self):
        ext_data_num = 0
        for op in self.ops:
            if hasattr(op, "ext_data_num"):
                ext_data_num = getattr(op, "ext_data_num")
                break
        load_data_ops = self.ops[: self.ext_op_transform_idx]
        ext_data = []

        while len(ext_data) < ext_data_num:
            file_idx = self.data_idx_order_list[np.random.randint(self.__len__())]
            data_line = self.data_lines[file_idx]
            data_line = data_line.decode("utf-8")
            substr = data_line.strip("\n").split(self.delimiter)
            file_name = substr[0]
            file_name = self._try_parse_filename_list(file_name)
            label = substr[1]
            img_path = os.path.join(self.data_dir, file_name)
            data = {"img_path": img_path, "label": label}
            if not os.path.exists(img_path):
                continue
            with open(data["img_path"], "rb") as f:
                img = f.read()
                data["image"] = img
            data = transform(data, load_data_ops)

            if data is None:
                continue
            if "polys" in data.keys():
                if data["polys"].shape[1] != 4:
                    continue
            ext_data.append(data)
        return ext_data

    def __getitem__(self, idx):
        file_idx = self.data_idx_order_list[idx]
        data_line = self.data_lines[file_idx]
        try:
            data_line = data_line.decode("utf-8")
            substr = data_line.strip("\n").split(self.delimiter)
            file_name = substr[0]
            file_name = self._try_parse_filename_list(file_name)
            label = substr[1]
            img_path = os.path.join(self.data_dir, file_name)
            data = {"img_path": img_path, "label": label}
            if not os.path.exists(img_path):
                raise Exception("{} does not exist!".format(img_path))
            with open(data["img_path"], "rb") as f:
                img = f.read()
                data["image"] = img
            data["ext_data"] = self.get_ext_data()
            outs = transform(data, self.ops)
        except:
            self.logger.error(
                "When parsing line {}, error happened with msg: {}".format(
                    data_line, traceback.format_exc()
                )
            )
            outs = None
        if outs is None:
            # during evaluation, we should fix the idx to get same results for many times of evaluation.
            rnd_idx = (
                np.random.randint(self.__len__())
                if self.mode == "train"
                else (idx + 1) % self.__len__()
            )
            return self.__getitem__(rnd_idx)
        return outs

    def __len__(self):
        return len(self.data_idx_order_list)