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- import os
- total_path = '/data2/liudan/ocr/'
- # 删除已存在文件路径
- path_data = os.path.join(total_path, 'data')
- # 备份total_data路径
- old_file_path = os.path.join(total_path, 'field_data')
- new_file_path = os.path.join(total_path, 'data/total_data')
- # 划分数据集路径
- dataset_path = os.path.join(total_path, 'data/total_data')
- train_set_save_path = os.path.join(total_path, 'data/train')
- val_set_save_path = os.path.join(total_path, 'data/val')
- # batch_process路径
- dir = os.path.join(total_path, 'data')
- # multigpu_train.py路径
- train_path = '/data2/liudan/ocr/model_saved/cscs/'
- # pretrained_model_path = os.path.join(train_path, 'pretrain_model/model.ckpt-103336')
- train_save_path = os.path.join(train_path, '09-01') # train得到的数据位置(.index, .meta, .data),这里表示save_pb_model的输入
- pretrained_model_path1 = os.path.join(train_path, '09-01')
- save_pb_path = os.path.join(train_path, '0902/') # .pb文件位置
- save_str_hegd_path = os.path.join(train_path, '/evalsave/0902')
- # score_the_ocr_model中测试数据位置
- testdat_msg = os.path.join(total_path, 'data/ocr_txt/')
- eval_saved = os.path.join(train_path, '/evalsave/0902')
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