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105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
# copyright (c) 2021 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 numpy as np
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import os
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from paddle.io import Dataset
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from .imaug import transform, create_operators
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import random
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class PGDataSet(Dataset):
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def __init__(self, config, mode, logger, seed=None):
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super(PGDataSet, self).__init__()
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self.logger = logger
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self.seed = seed
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self.mode = mode
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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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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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ratio_list = dataset_config.get("ratio_list", [1.0])
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if isinstance(ratio_list, (float, int)):
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ratio_list = [float(ratio_list)] * int(data_source_num)
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assert len(
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ratio_list
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) == 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 = self.get_image_info_list(label_file_list, ratio_list)
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self.data_idx_order_list = list(range(len(self.data_lines)))
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if mode.lower() == "train":
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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 shuffle_data_random(self):
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if self.do_shuffle:
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random.seed(self.seed)
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random.shuffle(self.data_lines)
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return
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def get_image_info_list(self, file_list, ratio_list):
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if isinstance(file_list, str):
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file_list = [file_list]
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data_lines = []
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for idx, file in enumerate(file_list):
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with open(file, "rb") as f:
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lines = f.readlines()
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if self.mode == "train" or ratio_list[idx] < 1.0:
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random.seed(self.seed)
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lines = random.sample(lines,
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round(len(lines) * ratio_list[idx]))
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data_lines.extend(lines)
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return data_lines
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def __getitem__(self, idx):
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file_idx = self.data_idx_order_list[idx]
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data_line = self.data_lines[file_idx]
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img_id = 0
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try:
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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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if self.mode.lower() == 'eval':
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try:
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img_id = int(data_line.split(".")[0][7:])
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except:
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img_id = 0
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data = {'img_path': img_path, 'label': label, 'img_id': img_id}
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if not os.path.exists(img_path):
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raise Exception("{} does not exist!".format(img_path))
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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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except Exception as e:
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self.logger.error(
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"When parsing line {}, error happened with msg: {}".format(
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self.data_idx_order_list[idx], e))
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outs = None
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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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