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# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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 numpy as np
import os
import random
from paddle.io import Dataset
import json
from copy import deepcopy
from .imaug import transform, create_operators
class PubTabDataSet(Dataset):
def __init__(self, config, mode, logger, seed=None):
super(PubTabDataSet, self).__init__()
self.logger = logger
global_config = config['Global']
dataset_config = config[mode]['dataset']
loader_config = config[mode]['loader']
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
self.mode = mode.lower()
logger.info("Initialize indexs of datasets:%s" % label_file_list)
self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
# self.check(config['Global']['max_text_length'])
if mode.lower() == "train" and self.do_shuffle:
self.shuffle_data_random()
self.ops = create_operators(dataset_config['transforms'], global_config)
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 check(self, max_text_length):
data_lines = []
for line in self.data_lines:
data_line = line.decode('utf-8').strip("\n")
info = json.loads(data_line)
file_name = info['filename']
cells = info['html']['cells'].copy()
structure = info['html']['structure']['tokens'].copy()
img_path = os.path.join(self.data_dir, file_name)
if not os.path.exists(img_path):
self.logger.warning("{} does not exist!".format(img_path))
continue
if len(structure) == 0 or len(structure) > max_text_length:
continue
# data = {'img_path': img_path, 'cells': cells, 'structure':structure,'file_name':file_name}
data_lines.append(line)
self.data_lines = data_lines
def shuffle_data_random(self):
if self.do_shuffle:
random.seed(self.seed)
random.shuffle(self.data_lines)
return
def __getitem__(self, idx):
try:
data_line = self.data_lines[idx]
data_line = data_line.decode('utf-8').strip("\n")
info = json.loads(data_line)
file_name = info['filename']
cells = info['html']['cells'].copy()
structure = info['html']['structure']['tokens'].copy()
img_path = os.path.join(self.data_dir, file_name)
if not os.path.exists(img_path):
raise Exception("{} does not exist!".format(img_path))
data = {
'img_path': img_path,
'cells': cells,
'structure': structure,
'file_name': file_name
}
with open(data['img_path'], 'rb') as f:
img = f.read()
data['image'] = img
outs = transform(data, self.ops)
except:
import traceback
err = traceback.format_exc()
self.logger.error(
"When parsing line {}, error happened with msg: {}".format(
data_line, err))
outs = None
if outs is None:
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_lines)
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