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import os |
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import cv2 |
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import lmdb |
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import numpy as np |
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from torch.utils.data import Dataset |
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from openrec.preprocess import create_operators, transform |
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class STRLMDBDataSet(Dataset): |
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def __init__(self, config, mode, logger, seed=None, epoch=1, gpu_i=0): |
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super(STRLMDBDataSet, 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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loader_config['batch_size_per_card'] |
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data_dir = '../training_aug_lmdb_noerror/ep' + str( |
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epoch % 20 if epoch % 20 != 0 else 20) |
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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'], |
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global_config) |
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self.ext_op_transform_idx = dataset_config.get('ext_op_transform_idx', |
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1) |
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dataset_config.get('ratio_list', [1.0]) |
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self.need_reset = True |
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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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) |
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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] = { |
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'dirpath': dirpath, |
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'env': env, |
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'txn': txn, |
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'num_samples': num_samples, |
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} |
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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, |
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1] = 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_ext_data(self): |
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ext_data_num = 0 |
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for op in self.ops: |
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if hasattr(op, 'ext_data_num'): |
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ext_data_num = getattr(op, 'ext_data_num') |
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break |
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load_data_ops = self.ops[:self.ext_op_transform_idx] |
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ext_data = [] |
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while len(ext_data) < ext_data_num: |
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lmdb_idx, file_idx = self.data_idx_order_list[np.random.randint( |
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len(self))] |
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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( |
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self.lmdb_sets[lmdb_idx]['txn'], file_idx) |
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if sample_info is None: |
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continue |
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img, label = sample_info |
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data = {'image': img, 'label': label} |
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data = transform(data, load_data_ops) |
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if data is None: |
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continue |
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ext_data.append(data) |
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return ext_data |
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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( |
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self.lmdb_sets[lmdb_idx]['txn'], 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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data['ext_data'] = self.get_ext_data() |
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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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