120 lines
4.3 KiB
Python
120 lines
4.3 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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import lmdb
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import cv2
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from .imaug import transform, create_operators
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class LMDBDateSet(Dataset):
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def __init__(self, config, mode, logger):
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super(LMDBDateSet, 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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data_dir = dataset_config['data_dir']
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self.do_shuffle = loader_config['shuffle']
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self.lmdb_sets = self.load_hierarchical_lmdb_dataset(data_dir)
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logger.info("Initialize indexs of datasets:%s" % data_dir)
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self.data_idx_order_list = self.dataset_traversal()
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if self.do_shuffle:
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np.random.shuffle(self.data_idx_order_list)
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self.ops = create_operators(dataset_config['transforms'], global_config)
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def load_hierarchical_lmdb_dataset(self, data_dir):
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lmdb_sets = {}
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dataset_idx = 0
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for dirpath, dirnames, filenames in os.walk(data_dir + '/'):
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if not dirnames:
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env = lmdb.open(
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dirpath,
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max_readers=32,
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readonly=True,
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lock=False,
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readahead=False,
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meminit=False)
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txn = env.begin(write=False)
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num_samples = int(txn.get('num-samples'.encode()))
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lmdb_sets[dataset_idx] = {"dirpath":dirpath, "env":env, \
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"txn":txn, "num_samples":num_samples}
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dataset_idx += 1
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return lmdb_sets
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def dataset_traversal(self):
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lmdb_num = len(self.lmdb_sets)
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total_sample_num = 0
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for lno in range(lmdb_num):
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total_sample_num += self.lmdb_sets[lno]['num_samples']
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data_idx_order_list = np.zeros((total_sample_num, 2))
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beg_idx = 0
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for lno in range(lmdb_num):
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tmp_sample_num = self.lmdb_sets[lno]['num_samples']
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end_idx = beg_idx + tmp_sample_num
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data_idx_order_list[beg_idx:end_idx, 0] = lno
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data_idx_order_list[beg_idx:end_idx, 1] \
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= list(range(tmp_sample_num))
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data_idx_order_list[beg_idx:end_idx, 1] += 1
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beg_idx = beg_idx + tmp_sample_num
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return data_idx_order_list
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def get_img_data(self, value):
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"""get_img_data"""
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if not value:
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return None
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imgdata = np.frombuffer(value, dtype='uint8')
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if imgdata is None:
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return None
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imgori = cv2.imdecode(imgdata, 1)
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if imgori is None:
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return None
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return imgori
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def get_lmdb_sample_info(self, txn, index):
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label_key = 'label-%09d'.encode() % index
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label = txn.get(label_key)
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if label is None:
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return None
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label = label.decode('utf-8')
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img_key = 'image-%09d'.encode() % index
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imgbuf = txn.get(img_key)
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return imgbuf, label
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def __getitem__(self, idx):
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lmdb_idx, file_idx = self.data_idx_order_list[idx]
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lmdb_idx = int(lmdb_idx)
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file_idx = int(file_idx)
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sample_info = self.get_lmdb_sample_info(self.lmdb_sets[lmdb_idx]['txn'],
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file_idx)
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if sample_info is None:
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return self.__getitem__(np.random.randint(self.__len__()))
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img, label = sample_info
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data = {'image': img, 'label': label}
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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 self.data_idx_order_list.shape[0]
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