123 lines
4.7 KiB
Python
123 lines
4.7 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import numpy as np
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import os
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import random
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import paddle
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from paddle.io import Dataset
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import time
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from .imaug import transform, create_operators
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from ppocr.utils.logging import get_logger
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logger = get_logger()
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class SimpleDataSet(Dataset):
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def __init__(self, config, mode):
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super(SimpleDataSet, self).__init__()
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global_config = config['Global']
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dataset_config = config[mode]['dataset']
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loader_config = config[mode]['loader']
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batch_size = loader_config['batch_size_per_card']
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self.delimiter = dataset_config.get('delimiter', '\t')
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label_file_list = dataset_config.pop('label_file_list')
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data_source_num = len(label_file_list)
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if data_source_num == 1:
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ratio_list = [1.0]
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else:
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ratio_list = dataset_config.pop('ratio_list')
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assert sum(ratio_list) == 1, "The sum of the ratio_list should be 1."
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assert len(ratio_list) == data_source_num, "The length of ratio_list should be the same as the file_list."
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self.data_dir = dataset_config['data_dir']
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self.do_shuffle = loader_config['shuffle']
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logger.info("Initialize indexs of datasets:%s" % label_file_list)
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self.data_lines_list, data_num_list = self.get_image_info_list(
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label_file_list)
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self.data_idx_order_list = self.dataset_traversal(
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data_num_list, ratio_list, batch_size)
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self.shuffle_data_random()
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self.ops = create_operators(dataset_config['transforms'], global_config)
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def get_image_info_list(self, file_list):
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if isinstance(file_list, str):
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file_list = [file_list]
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data_lines_list = []
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data_num_list = []
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for file in file_list:
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with open(file, "rb") as f:
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lines = f.readlines()
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data_lines_list.append(lines)
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data_num_list.append(len(lines))
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return data_lines_list, data_num_list
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def dataset_traversal(self, data_num_list, ratio_list, batch_size):
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select_num_list = []
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dataset_num = len(data_num_list)
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for dno in range(dataset_num):
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select_num = round(batch_size * ratio_list[dno])
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select_num = max(select_num, 1)
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select_num_list.append(select_num)
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data_idx_order_list = []
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cur_index_sets = [0] * dataset_num
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while True:
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finish_read_num = 0
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for dataset_idx in range(dataset_num):
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cur_index = cur_index_sets[dataset_idx]
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if cur_index >= data_num_list[dataset_idx]:
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finish_read_num += 1
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else:
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select_num = select_num_list[dataset_idx]
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for sno in range(select_num):
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cur_index = cur_index_sets[dataset_idx]
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if cur_index >= data_num_list[dataset_idx]:
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break
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data_idx_order_list.append((
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dataset_idx, cur_index))
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cur_index_sets[dataset_idx] += 1
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if finish_read_num == dataset_num:
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break
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return data_idx_order_list
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def shuffle_data_random(self):
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if self.do_shuffle:
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for dno in range(len(self.data_lines_list)):
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random.shuffle(self.data_lines_list[dno])
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return
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def __getitem__(self, idx):
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dataset_idx, file_idx = self.data_idx_order_list[idx]
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data_line = self.data_lines_list[dataset_idx][file_idx]
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data_line = data_line.decode('utf-8')
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substr = data_line.strip("\n").split(self.delimiter)
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file_name = substr[0]
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label = substr[1]
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img_path = os.path.join(self.data_dir, file_name)
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data = {'img_path': img_path, 'label': label}
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with open(data['img_path'], 'rb') as f:
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img = f.read()
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data['image'] = img
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outs = transform(data, self.ops)
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if outs is None:
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return self.__getitem__(np.random.randint(self.__len__()))
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return outs
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def __len__(self):
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return len(self.data_idx_order_list)
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