Merge pull request #4 from tink2123/develop
polish infer_rec and add ic15_dict
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commit
aa9bedf308
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@ -1,13 +1,13 @@
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TrainReader:
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reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
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num_workers: 8
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img_set_dir: .
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label_file_path: ./train_data/hard_label.txt
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img_set_dir: ./train_data
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label_file_path: ./train_data/rec_gt_train.txt
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EvalReader:
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reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
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img_set_dir: .
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label_file_path: ./train_data/label_val_all.txt
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img_set_dir: ./train_data
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label_file_path: ./train_data/rec_gt_test.txt
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TestReader:
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reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
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@ -4,7 +4,7 @@ Global:
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epoch_num: 300
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output
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save_model_dir: output_ic15
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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@ -12,11 +12,12 @@ Global:
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image_shape: [3, 32, 100]
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max_text_length: 25
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character_type: ch
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character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
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character_dict_path: ./ppocr/utils/ic15_dict.txt
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loss_type: ctc
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reader_yml: ./configs/rec/rec_chinese_reader.yml
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pretrain_weights:
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reader_yml: ./configs/rec/rec_icdar15_reader.yml
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pretrain_weights: ./pretrain_models/CRNN/best_accuracy
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -22,6 +22,7 @@ import string
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import lmdb
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from ppocr.utils.utility import initial_logger
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from ppocr.utils.utility import get_image_file_list
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logger = initial_logger()
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from .img_tools import process_image, get_img_data
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@ -143,8 +144,9 @@ class SimpleReader(object):
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self.num_workers = 1
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else:
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self.num_workers = params['num_workers']
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self.img_set_dir = params['img_set_dir']
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self.label_file_path = params['label_file_path']
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if params['mode'] != 'test':
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self.img_set_dir = params['img_set_dir']
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self.label_file_path = params['label_file_path']
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self.char_ops = params['char_ops']
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self.image_shape = params['image_shape']
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self.loss_type = params['loss_type']
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@ -164,29 +166,34 @@ class SimpleReader(object):
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def sample_iter_reader():
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if self.mode == 'test':
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print("infer_img:", self.infer_img)
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img = cv2.imread(self.infer_img)
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norm_img = process_image(img, self.image_shape)
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yield norm_img
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with open(self.label_file_path, "rb") as fin:
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label_infor_list = fin.readlines()
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img_num = len(label_infor_list)
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img_id_list = list(range(img_num))
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random.shuffle(img_id_list)
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for img_id in range(process_id, img_num, self.num_workers):
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label_infor = label_infor_list[img_id_list[img_id]]
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substr = label_infor.decode('utf-8').strip("\n").split("\t")
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img_path = self.img_set_dir + "/" + substr[0]
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img = cv2.imread(img_path)
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if img is None:
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continue
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label = substr[1]
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outs = process_image(img, self.image_shape, label,
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self.char_ops, self.loss_type,
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self.max_text_length)
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if outs is None:
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continue
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yield outs
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image_file_list = get_image_file_list(self.infer_img)
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for single_img in image_file_list:
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img = cv2.imread(single_img)
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if img.shape[-1]==1 or len(list(img.shape))==2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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norm_img = process_image(img, self.image_shape)
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yield norm_img
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else:
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with open(self.label_file_path, "rb") as fin:
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label_infor_list = fin.readlines()
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img_num = len(label_infor_list)
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img_id_list = list(range(img_num))
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random.shuffle(img_id_list)
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for img_id in range(process_id, img_num, self.num_workers):
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label_infor = label_infor_list[img_id_list[img_id]]
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substr = label_infor.decode('utf-8').strip("\n").split("\t")
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img_path = self.img_set_dir + "/" + substr[0]
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img = cv2.imread(img_path)
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if img is None:
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logger.info("{} does not exist!".format(img_path))
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continue
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label = substr[1]
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outs = process_image(img, self.image_shape, label,
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self.char_ops, self.loss_type,
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self.max_text_length)
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if outs is None:
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continue
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yield outs
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def batch_iter_reader():
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batch_outs = []
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@ -198,4 +205,6 @@ class SimpleReader(object):
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if len(batch_outs) != 0:
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yield batch_outs
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return batch_iter_reader
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if self.mode != 'test':
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return batch_iter_reader
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return sample_iter_reader
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12
set_env.sh
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set_env.sh
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@ -1,12 +0,0 @@
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#. /paddle/set_env.sh↩
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export CUDA_VISIBLE_DEVICES="0,1,2,3"↩
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export PYTHONPATH=$PYTHONPATH:.↩
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export FLAGS_fraction_of_gpu_memory_to_use=1.0↩
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↩
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python_bin_dir="/opt/_internal/cpython-3.7.0/bin/"↩
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alias python=$python_bin_dir"python3.7"↩
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alias pip=$python_bin_dir"pip3.7"↩
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alias ipython=$python_bin_dir"ipython3"↩
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export LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib:$LD_LIBRARY_PATH↩
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export PYTHONPATH=$PYTHONPATH:.↩
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ldconfig↩
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@ -80,7 +80,7 @@ def main():
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metrics = eval_det_run(exe, config, eval_info_dict, "test")
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else:
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reader_type = config['Global']['reader_yml']
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if "chinese" in reader_type:
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if "benchmark" not in reader_type:
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eval_reader = reader_main(config=config, mode="eval")
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eval_info_dict = {'program': eval_program, \
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'reader': eval_reader, \
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@ -21,7 +21,6 @@ import time
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import multiprocessing
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import numpy as np
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def set_paddle_flags(**kwargs):
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for key, value in kwargs.items():
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if os.environ.get(key, None) is None:
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@ -47,7 +46,7 @@ from ppocr.data.reader_main import reader_main
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from ppocr.utils.save_load import init_model
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from ppocr.utils.character import CharacterOps
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from ppocr.utils.utility import create_module
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from ppocr.utils.utility import get_image_file_list
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logger = initial_logger()
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@ -79,9 +78,15 @@ def main():
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init_model(config, eval_prog, exe)
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blobs = reader_main(config, 'test')
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imgs = next(blobs())
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for img in imgs:
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blobs = reader_main(config, 'test')()
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infer_img = config['TestReader']['infer_img']
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infer_list = get_image_file_list(infer_img)
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max_img_num = len(infer_list)
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if len(infer_list) == 0:
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logger.info("Can not find img in infer_img dir.")
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for i in range(max_img_num):
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print("infer_img:",infer_list[i])
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img = next(blobs)
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predict = exe.run(program=eval_prog,
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feed={"image": img},
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fetch_list=fetch_varname_list,
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@ -101,8 +106,8 @@ def main():
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preds_text = preds_text.reshape(-1)
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preds_text = char_ops.decode(preds_text)
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print(preds)
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print(preds_text)
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print("\t index:",preds)
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print("\t word :",preds_text)
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# save for inference model
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target_var = []
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