# 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 from paddle.io import Dataset from .imaug import transform, create_operators import random class PGDataSet(Dataset): def __init__(self, config, mode, logger, seed=None): super(PGDataSet, self).__init__() self.logger = logger self.seed = seed self.mode = mode 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'] 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 mode.lower() == "train": self.shuffle_data_random() self.ops = create_operators(dataset_config['transforms'], global_config) def shuffle_data_random(self): if self.do_shuffle: random.seed(self.seed) random.shuffle(self.data_lines) return 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 __getitem__(self, idx): file_idx = self.data_idx_order_list[idx] data_line = self.data_lines[file_idx] img_id = 0 try: data_line = data_line.decode('utf-8') substr = data_line.strip("\n").split(self.delimiter) file_name = substr[0] label = substr[1] img_path = os.path.join(self.data_dir, file_name) if self.mode.lower() == 'eval': try: img_id = int(data_line.split(".")[0][7:]) except: img_id = 0 data = {'img_path': img_path, 'label': label, 'img_id': img_id} 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 outs = transform(data, self.ops) except Exception as e: self.logger.error( "When parsing line {}, error happened with msg: {}".format( self.data_idx_order_list[idx], e)) outs = None if outs is None: return self.__getitem__(np.random.randint(self.__len__())) return outs def __len__(self): return len(self.data_idx_order_list)