PaddleOCR_train_SVTR-T.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOBoHhhB9WDehcMiPPVFGHU"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"gpuClass":"standard"},"cells":[{"cell_type":"code","source":["#for gpu\n","#!python3 -m pip install paddlepaddle-gpu==2.3.1\n","#for cpu\n","!python3 -m pip install paddlepaddle"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xLGdCYFTghM7","executionInfo":{"status":"ok","timestamp":1669201765343,"user_tz":-180,"elapsed":22168,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}},"outputId":"80cb3e60-84f4-45f5-e609-d39b56434c3a"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting paddlepaddle\n","  Downloading paddlepaddle-2.4.0-cp37-cp37m-manylinux1_x86_64.whl (121.6 MB)\n","\u001b[K     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paddlepaddle-2.4.0\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","import torch\n","import random\n","import numpy as np\n","drive.mount('/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ANhoYt9Ig5Ue","executionInfo":{"status":"ok","timestamp":1669201793287,"user_tz":-180,"elapsed":22319,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}},"outputId":"89de5a29-a06f-455d-8372-bc63d5d48dd2"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /drive\n"]}]},{"cell_type":"code","source":["import os\n","os.chdir('/drive/My Drive/ocr/paddle_ocr/PaddleOCR')"],"metadata":{"id":"wbWcj9dShA8X","executionInfo":{"status":"ok","timestamp":1669201794609,"user_tz":-180,"elapsed":1325,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["#!git clone https://github.com/PaddlePaddle/PaddleOCR"],"metadata":{"id":"2a7AQsIfgnoK","executionInfo":{"status":"ok","timestamp":1669201794609,"user_tz":-180,"elapsed":4,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["!pip3 install -r requirements.txt"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B6HnGivhhToR","executionInfo":{"status":"ok","timestamp":1669201836558,"user_tz":-180,"elapsed":41952,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}},"outputId":"e39675b9-0e15-475a-ee17-c07db3c9f387"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: shapely in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (1.8.5.post1)\n","Requirement already satisfied: scikit-image in 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Бондарь","userId":"06919916406136291697"}},"outputId":"5282d81a-66c3-49b4-a0ba-71ab29e577fe"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting paddleocr>=2.0.1\n","  Downloading paddleocr-2.6.1.0-py3-none-any.whl (409 kB)\n","\u001b[K     |████████████████████████████████| 409 kB 12.1 MB/s \n","\u001b[?25hRequirement already satisfied: attrdict in /usr/local/lib/python3.7/dist-packages (from paddleocr>=2.0.1) (2.0.1)\n","Requirement already satisfied: PyMuPDF==1.19.0 in /usr/local/lib/python3.7/dist-packages (from paddleocr>=2.0.1) (1.19.0)\n","Requirement already satisfied: imgaug in /usr/local/lib/python3.7/dist-packages (from paddleocr>=2.0.1) (0.4.0)\n","Requirement already satisfied: opencv-contrib-python in /usr/local/lib/python3.7/dist-packages (from paddleocr>=2.0.1) (4.6.0.66)\n","Requirement already satisfied: opencv-python in 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Drive/ocr/paddle_ocr/PaddleOCR/train_data/scene_text_recognition.zip'"],"metadata":{"id":"WxMSlF0JtvqO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["os.chdir('/drive/My Drive/ocr/paddle_ocr/PaddleOCR')"],"metadata":{"id":"ICmljtkhuqNC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["os.listdir('../../../../../scene_text_recognition')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XZdfZt4CxSGN","executionInfo":{"status":"ok","timestamp":1669044202258,"user_tz":-180,"elapsed":21,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}},"outputId":"c97126af-84ed-4739-94a8-7acffb6c320a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['train_images', 'val_images', 'train.txt', 'val.txt']"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["!python3 tools/train.py -c configs/rec/rec_svtrnet_habr.yml -o Global.pretrained_model=./pretrain_models/rec_svtr_tiny_none_ctc_en_train_/best_accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4phpl9670jud","outputId":"c97d696a-2925-4439-b8f2-f9f1d36a5f52","executionInfo":{"status":"ok","timestamp":1669044514272,"user_tz":-180,"elapsed":312020,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[2022/11/21 15:24:36] ppocr INFO: Architecture : \n","[2022/11/21 15:24:36] ppocr INFO:     Backbone : \n","[2022/11/21 15:24:36] ppocr INFO:         depth : [3, 6, 3]\n","[2022/11/21 15:24:36] ppocr INFO:         embed_dim : [64, 128, 256]\n","[2022/11/21 15:24:36] ppocr INFO:         img_size : [32, 100]\n","[2022/11/21 15:24:36] ppocr INFO:         last_stage : True\n","[2022/11/21 15:24:36] ppocr INFO:         local_mixer : [[7, 11], [7, 11], [7, 11]]\n","[2022/11/21 15:24:36] ppocr INFO:         mixer : ['Local', 'Local', 'Local', 'Local', 'Local', 'Local', 'Global', 'Global', 'Global', 'Global', 'Global', 'Global']\n","[2022/11/21 15:24:36] ppocr INFO:         name : SVTRNet\n","[2022/11/21 15:24:36] ppocr INFO:         num_heads : [2, 4, 8]\n","[2022/11/21 15:24:36] ppocr INFO:         out_channels : 192\n","[2022/11/21 15:24:36] ppocr INFO:         out_char_num : 40\n","[2022/11/21 15:24:36] ppocr INFO:         patch_merging : Conv\n","[2022/11/21 15:24:36] ppocr INFO:         prenorm : False\n","[2022/11/21 15:24:36] ppocr INFO:     Head : \n","[2022/11/21 15:24:36] ppocr INFO:         name : CTCHead\n","[2022/11/21 15:24:36] ppocr INFO:     Neck : \n","[2022/11/21 15:24:36] ppocr INFO:         encoder_type : reshape\n","[2022/11/21 15:24:36] ppocr INFO:         name : SequenceEncoder\n","[2022/11/21 15:24:36] ppocr INFO:     Transform : \n","[2022/11/21 15:24:36] ppocr INFO:         name : STN_ON\n","[2022/11/21 15:24:36] ppocr INFO:         num_control_points : 20\n","[2022/11/21 15:24:36] ppocr INFO:         stn_activation : none\n","[2022/11/21 15:24:36] ppocr INFO:         tps_inputsize : [32, 64]\n","[2022/11/21 15:24:36] ppocr INFO:         tps_margins : [0.05, 0.05]\n","[2022/11/21 15:24:36] ppocr INFO:         tps_outputsize : [32, 100]\n","[2022/11/21 15:24:36] ppocr INFO:     algorithm : SVTR\n","[2022/11/21 15:24:36] ppocr INFO:     model_type : rec\n","[2022/11/21 15:24:36] ppocr INFO: Eval : \n","[2022/11/21 15:24:36] ppocr INFO:     dataset : \n","[2022/11/21 15:24:36] ppocr INFO:         data_dir : ../../../../../scene_text_recognition/val_images\n","[2022/11/21 15:24:36] ppocr INFO:         label_file_list : ['../../../../../scene_text_recognition/val.txt']\n","[2022/11/21 15:24:36] ppocr INFO:         name : SimpleDataSet\n","[2022/11/21 15:24:36] ppocr INFO:         transforms : \n","[2022/11/21 15:24:36] ppocr INFO:             DecodeImage : \n","[2022/11/21 15:24:36] ppocr INFO:                 channel_first : False\n","[2022/11/21 15:24:36] ppocr INFO:                 img_mode : BGR\n","[2022/11/21 15:24:36] ppocr INFO:             CTCLabelEncode : None\n","[2022/11/21 15:24:36] ppocr INFO:             SVTRRecResizeImg : \n","[2022/11/21 15:24:36] ppocr INFO:                 image_shape : [3, 64, 256]\n","[2022/11/21 15:24:36] ppocr INFO:                 padding : False\n","[2022/11/21 15:24:36] ppocr INFO:             KeepKeys : \n","[2022/11/21 15:24:36] ppocr INFO:                 keep_keys : ['image', 'label', 'length']\n","[2022/11/21 15:24:36] ppocr INFO:     loader : \n","[2022/11/21 15:24:36] ppocr INFO:         batch_size_per_card : 128\n","[2022/11/21 15:24:36] ppocr INFO:         drop_last : False\n","[2022/11/21 15:24:36] ppocr INFO:         num_workers : 2\n","[2022/11/21 15:24:36] ppocr INFO:         shuffle : False\n","[2022/11/21 15:24:36] ppocr INFO: Global : \n","[2022/11/21 15:24:36] ppocr INFO:     cal_metric_during_train : True\n","[2022/11/21 15:24:36] ppocr INFO:     character_dict_path : ./ppocr/utils/dict/rus_chars.txt\n","[2022/11/21 15:24:36] ppocr INFO:     character_type : en\n","[2022/11/21 15:24:36] ppocr INFO:     checkpoints : None\n","[2022/11/21 15:24:36] ppocr INFO:     distributed : False\n","[2022/11/21 15:24:36] ppocr INFO:     epoch_num : 25\n","[2022/11/21 15:24:36] ppocr INFO:     eval_batch_step : [0, 100]\n","[2022/11/21 15:24:36] ppocr INFO:     infer_img : doc/imgs_words_en/word_10.png\n","[2022/11/21 15:24:36] ppocr INFO:     infer_mode : False\n","[2022/11/21 15:24:36] ppocr INFO:     log_smooth_window : 20\n","[2022/11/21 15:24:36] ppocr INFO:     max_text_length : 40\n","[2022/11/21 15:24:36] ppocr INFO:     pretrained_model : ./pretrain_models/rec_svtr_tiny_none_ctc_en_train_/best_accuracy\n","[2022/11/21 15:24:36] ppocr INFO:     print_batch_step : 10\n","[2022/11/21 15:24:36] ppocr INFO:     save_epoch_step : 200\n","[2022/11/21 15:24:36] ppocr INFO:     save_inference_dir : None\n","[2022/11/21 15:24:36] ppocr INFO:     save_model_dir : ./output/svtr_habr/\n","[2022/11/21 15:24:36] ppocr INFO:     save_res_path : ./output/rec/predicts_svtr_tiny.txt\n","[2022/11/21 15:24:36] ppocr INFO:     use_gpu : True\n","[2022/11/21 15:24:36] ppocr INFO:     use_space_char : True\n","[2022/11/21 15:24:36] ppocr INFO:     use_visualdl : False\n","[2022/11/21 15:24:36] ppocr INFO: Loss : \n","[2022/11/21 15:24:36] ppocr INFO:     name : CTCLoss\n","[2022/11/21 15:24:36] ppocr INFO: Metric : \n","[2022/11/21 15:24:36] ppocr INFO:     main_indicator : acc\n","[2022/11/21 15:24:36] ppocr INFO:     name : RecMetric\n","[2022/11/21 15:24:36] ppocr INFO: Optimizer : \n","[2022/11/21 15:24:36] ppocr INFO:     beta1 : 0.9\n","[2022/11/21 15:24:36] ppocr INFO:     beta2 : 0.99\n","[2022/11/21 15:24:36] ppocr INFO:     epsilon : 8e-08\n","[2022/11/21 15:24:36] ppocr INFO:     lr : \n","[2022/11/21 15:24:36] ppocr INFO:         learning_rate : 0.0005\n","[2022/11/21 15:24:36] ppocr INFO:         name : Cosine\n","[2022/11/21 15:24:36] ppocr INFO:         warmup_epoch : 1\n","[2022/11/21 15:24:36] ppocr INFO:     name : AdamW\n","[2022/11/21 15:24:36] ppocr INFO:     no_weight_decay_name : norm pos_embed\n","[2022/11/21 15:24:36] ppocr INFO:     one_dim_param_no_weight_decay : True\n","[2022/11/21 15:24:36] ppocr INFO:     weight_decay : 0.05\n","[2022/11/21 15:24:36] ppocr INFO: PostProcess : \n","[2022/11/21 15:24:36] ppocr INFO:     name : CTCLabelDecode\n","[2022/11/21 15:24:36] ppocr INFO: Train : \n","[2022/11/21 15:24:36] ppocr INFO:     dataset : \n","[2022/11/21 15:24:36] ppocr INFO:         data_dir : ../../../../../scene_text_recognition/train_images\n","[2022/11/21 15:24:36] ppocr INFO:         label_file_list : ['../../../../../scene_text_recognition/train.txt']\n","[2022/11/21 15:24:36] ppocr INFO:         name : SimpleDataSet\n","[2022/11/21 15:24:36] ppocr INFO:         transforms : \n","[2022/11/21 15:24:36] ppocr INFO:             DecodeImage : \n","[2022/11/21 15:24:36] ppocr INFO:                 channel_first : False\n","[2022/11/21 15:24:36] ppocr INFO:                 img_mode : BGR\n","[2022/11/21 15:24:36] ppocr INFO:             CTCLabelEncode : None\n","[2022/11/21 15:24:36] ppocr INFO:             SVTRRecResizeImg : \n","[2022/11/21 15:24:36] ppocr INFO:                 image_shape : [3, 64, 256]\n","[2022/11/21 15:24:36] ppocr INFO:                 padding : False\n","[2022/11/21 15:24:36] ppocr INFO:             KeepKeys : \n","[2022/11/21 15:24:36] ppocr INFO:                 keep_keys : ['image', 'label', 'length']\n","[2022/11/21 15:24:36] ppocr INFO:     loader : \n","[2022/11/21 15:24:36] ppocr INFO:         batch_size_per_card : 256\n","[2022/11/21 15:24:36] ppocr INFO:         drop_last : True\n","[2022/11/21 15:24:36] ppocr INFO:         num_workers : 2\n","[2022/11/21 15:24:36] ppocr INFO:         shuffle : True\n","[2022/11/21 15:24:36] ppocr INFO: profiler_options : None\n","[2022/11/21 15:24:36] ppocr INFO: train with paddle 2.3.1 and device Place(gpu:0)\n","[2022/11/21 15:24:36] ppocr INFO: Initialize indexs of datasets:['../../../../../scene_text_recognition/train.txt']\n","[2022/11/21 15:24:36] ppocr INFO: Initialize indexs of datasets:['../../../../../scene_text_recognition/val.txt']\n","W1121 15:24:41.343456   455 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.2, Runtime API Version: 10.2\n","W1121 15:24:41.392942   455 gpu_resources.cc:91] device: 0, cuDNN Version: 8.1.\n","[2022/11/21 15:25:10] ppocr INFO: train dataloader has 15 iters\n","[2022/11/21 15:25:10] ppocr INFO: valid dataloader has 4 iters\n","[2022/11/21 15:25:11] ppocr WARNING: The shape of model params head.fc.weight [192, 157] not matched with loaded params head.fc.weight [192, 37] !\n","[2022/11/21 15:25:11] ppocr WARNING: The shape of model params head.fc.bias [157] not matched with loaded params head.fc.bias [37] !\n","[2022/11/21 15:25:11] ppocr INFO: load pretrain successful from ./pretrain_models/rec_svtr_tiny_none_ctc_en_train_/best_accuracy\n","[2022/11/21 15:25:11] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 100 iterations\n","[2022/11/21 15:25:23] ppocr INFO: epoch: [1/25], global_step: 10, lr: 0.000150, acc: 0.000000, norm_edit_dis: 0.017382, loss: 145.038315, avg_reader_cost: 0.05503 s, avg_batch_cost: 1.12090 s, avg_samples: 256.0, ips: 228.38740 samples/s, eta: 0:06:49\n","[2022/11/21 15:25:24] ppocr INFO: epoch: [1/25], global_step: 15, lr: 0.000233, acc: 0.000000, norm_edit_dis: 0.015063, loss: 97.441689, avg_reader_cost: 0.00020 s, avg_batch_cost: 0.18808 s, avg_samples: 128.0, ips: 680.54705 samples/s, eta: 0:05:14\n","[2022/11/21 15:25:25] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:25:28] ppocr INFO: epoch: [2/25], global_step: 20, lr: 0.000317, acc: 0.000000, norm_edit_dis: 0.006993, loss: 46.319504, avg_reader_cost: 0.08750 s, avg_batch_cost: 0.34853 s, avg_samples: 128.0, ips: 367.25216 samples/s, eta: 0:04:54\n","[2022/11/21 15:25:33] ppocr INFO: epoch: [2/25], global_step: 30, lr: 0.000499, acc: 0.000000, norm_edit_dis: 0.000000, loss: 34.437920, avg_reader_cost: 0.00356 s, avg_batch_cost: 0.46922 s, avg_samples: 256.0, ips: 545.58652 samples/s, eta: 0:04:04\n","[2022/11/21 15:25:33] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:25:38] ppocr INFO: epoch: [3/25], global_step: 40, lr: 0.000498, acc: 0.000000, norm_edit_dis: 0.000000, loss: 32.381828, avg_reader_cost: 0.11356 s, avg_batch_cost: 0.54314 s, avg_samples: 256.0, ips: 471.33213 samples/s, eta: 0:03:43\n","[2022/11/21 15:25:40] ppocr INFO: epoch: [3/25], global_step: 45, lr: 0.000497, acc: 0.000000, norm_edit_dis: 0.000000, loss: 31.879721, avg_reader_cost: 0.00030 s, avg_batch_cost: 0.19134 s, avg_samples: 128.0, ips: 668.96484 samples/s, eta: 0:03:29\n","[2022/11/21 15:25:40] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:25:43] ppocr INFO: epoch: [4/25], global_step: 50, lr: 0.000495, acc: 0.000000, norm_edit_dis: 0.000000, loss: 31.048038, avg_reader_cost: 0.08848 s, avg_batch_cost: 0.31681 s, avg_samples: 128.0, ips: 404.02791 samples/s, eta: 0:03:26\n","[2022/11/21 15:25:47] ppocr INFO: epoch: [4/25], global_step: 60, lr: 0.000490, acc: 0.000000, norm_edit_dis: 0.007530, loss: 29.522408, avg_reader_cost: 0.00067 s, avg_batch_cost: 0.40742 s, avg_samples: 256.0, ips: 628.34415 samples/s, eta: 0:03:08\n","[2022/11/21 15:25:48] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:25:53] ppocr INFO: epoch: [5/25], global_step: 70, lr: 0.000483, acc: 0.000000, norm_edit_dis: 0.082231, loss: 26.735985, avg_reader_cost: 0.10150 s, avg_batch_cost: 0.54732 s, avg_samples: 256.0, ips: 467.73580 samples/s, eta: 0:03:00\n","[2022/11/21 15:25:55] ppocr INFO: epoch: [5/25], global_step: 75, lr: 0.000479, acc: 0.000000, norm_edit_dis: 0.122521, loss: 25.270618, avg_reader_cost: 0.00030 s, avg_batch_cost: 0.19957 s, avg_samples: 128.0, ips: 641.37341 samples/s, eta: 0:02:53\n","[2022/11/21 15:25:55] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:25:58] ppocr INFO: epoch: [6/25], global_step: 80, lr: 0.000474, acc: 0.001953, norm_edit_dis: 0.193619, loss: 23.681295, avg_reader_cost: 0.09490 s, avg_batch_cost: 0.32801 s, avg_samples: 128.0, ips: 390.22901 samples/s, eta: 0:02:51\n","[2022/11/21 15:26:02] ppocr INFO: epoch: [6/25], global_step: 90, lr: 0.000464, acc: 0.019531, norm_edit_dis: 0.331194, loss: 19.916338, avg_reader_cost: 0.00066 s, avg_batch_cost: 0.40423 s, avg_samples: 256.0, ips: 633.29767 samples/s, eta: 0:02:40\n","[2022/11/21 15:26:02] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:26:07] ppocr INFO: epoch: [7/25], global_step: 100, lr: 0.000453, acc: 0.117187, norm_edit_dis: 0.494478, loss: 16.201275, avg_reader_cost: 0.08813 s, avg_batch_cost: 0.53374 s, avg_samples: 256.0, ips: 479.63428 samples/s, eta: 0:02:33\n","eval model:: 100% 4/4 [00:00<00:00,  5.08it/s]\n","[2022/11/21 15:26:08] ppocr INFO: cur metric, acc: 0.2804597636675917, norm_edit_dis: 0.6427993548719104, fps: 1091.9372028554603\n","[2022/11/21 15:26:09] ppocr INFO: save best model is to ./output/svtr_habr/best_accuracy\n","[2022/11/21 15:26:09] ppocr INFO: best metric, acc: 0.2804597636675917, is_float16: False, norm_edit_dis: 0.6427993548719104, fps: 1091.9372028554603, best_epoch: 7\n","[2022/11/21 15:26:10] ppocr INFO: epoch: [7/25], global_step: 105, lr: 0.000447, acc: 0.175781, norm_edit_dis: 0.550402, loss: 14.641998, avg_reader_cost: 0.00088 s, avg_batch_cost: 0.19666 s, avg_samples: 128.0, ips: 650.87025 samples/s, eta: 0:02:29\n","[2022/11/21 15:26:11] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:26:14] ppocr INFO: epoch: [8/25], global_step: 110, lr: 0.000440, acc: 0.222656, norm_edit_dis: 0.593875, loss: 13.093129, avg_reader_cost: 0.09262 s, avg_batch_cost: 0.32246 s, avg_samples: 128.0, ips: 396.95121 samples/s, eta: 0:02:27\n","[2022/11/21 15:26:18] ppocr INFO: epoch: [8/25], global_step: 120, lr: 0.000426, acc: 0.312500, norm_edit_dis: 0.669525, loss: 10.771172, avg_reader_cost: 0.00071 s, avg_batch_cost: 0.42600 s, avg_samples: 256.0, ips: 600.93796 samples/s, eta: 0:02:19\n","[2022/11/21 15:26:19] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:26:24] ppocr INFO: epoch: [9/25], global_step: 130, lr: 0.000410, acc: 0.367187, norm_edit_dis: 0.714848, loss: 9.348814, avg_reader_cost: 0.12886 s, avg_batch_cost: 0.58035 s, avg_samples: 256.0, ips: 441.11396 samples/s, eta: 0:02:14\n","[2022/11/21 15:26:26] ppocr INFO: epoch: [9/25], global_step: 135, lr: 0.000402, acc: 0.386719, norm_edit_dis: 0.732974, loss: 8.859810, avg_reader_cost: 0.00046 s, avg_batch_cost: 0.20043 s, avg_samples: 128.0, ips: 638.61408 samples/s, eta: 0:02:10\n","[2022/11/21 15:26:26] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:26:29] ppocr INFO: epoch: [10/25], global_step: 140, lr: 0.000394, acc: 0.400391, norm_edit_dis: 0.743802, loss: 8.127662, avg_reader_cost: 0.09140 s, avg_batch_cost: 0.32461 s, avg_samples: 128.0, ips: 394.31428 samples/s, eta: 0:02:08\n","[2022/11/21 15:26:33] ppocr INFO: epoch: [10/25], global_step: 150, lr: 0.000376, acc: 0.449219, norm_edit_dis: 0.776523, loss: 6.928028, avg_reader_cost: 0.00066 s, avg_batch_cost: 0.41286 s, avg_samples: 256.0, ips: 620.06830 samples/s, eta: 0:02:00\n","[2022/11/21 15:26:34] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:26:39] ppocr INFO: epoch: [11/25], global_step: 160, lr: 0.000357, acc: 0.484375, norm_edit_dis: 0.807794, loss: 5.956957, avg_reader_cost: 0.09177 s, avg_batch_cost: 0.53922 s, avg_samples: 256.0, ips: 474.76278 samples/s, eta: 0:01:55\n","[2022/11/21 15:26:41] ppocr INFO: epoch: [11/25], global_step: 165, lr: 0.000348, acc: 0.496094, norm_edit_dis: 0.823933, loss: 5.717906, avg_reader_cost: 0.00030 s, avg_batch_cost: 0.19848 s, avg_samples: 128.0, ips: 644.89409 samples/s, eta: 0:01:51\n","[2022/11/21 15:26:41] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:26:45] ppocr INFO: epoch: [12/25], global_step: 170, lr: 0.000338, acc: 0.505859, norm_edit_dis: 0.829174, loss: 5.340258, avg_reader_cost: 0.09957 s, avg_batch_cost: 0.40194 s, avg_samples: 128.0, ips: 318.45507 samples/s, eta: 0:01:50\n","[2022/11/21 15:26:49] ppocr INFO: epoch: [12/25], global_step: 180, lr: 0.000318, acc: 0.541016, norm_edit_dis: 0.846921, loss: 4.859314, avg_reader_cost: 0.00279 s, avg_batch_cost: 0.47300 s, avg_samples: 256.0, ips: 541.22665 samples/s, eta: 0:01:44\n","[2022/11/21 15:26:50] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:26:55] ppocr INFO: epoch: [13/25], global_step: 190, lr: 0.000298, acc: 0.564453, norm_edit_dis: 0.861895, loss: 4.281512, avg_reader_cost: 0.09058 s, avg_batch_cost: 0.55707 s, avg_samples: 256.0, ips: 459.54704 samples/s, eta: 0:01:39\n","[2022/11/21 15:26:57] ppocr INFO: epoch: [13/25], global_step: 195, lr: 0.000288, acc: 0.578125, norm_edit_dis: 0.864651, loss: 4.228631, avg_reader_cost: 0.00030 s, avg_batch_cost: 0.19977 s, avg_samples: 128.0, ips: 640.74481 samples/s, eta: 0:01:36\n","[2022/11/21 15:26:57] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:27:00] ppocr INFO: epoch: [14/25], global_step: 200, lr: 0.000277, acc: 0.585937, norm_edit_dis: 0.872929, loss: 4.086688, avg_reader_cost: 0.11150 s, avg_batch_cost: 0.35280 s, avg_samples: 128.0, ips: 362.81413 samples/s, eta: 0:01:34\n","eval model:: 100% 4/4 [00:00<00:00,  5.97it/s]\n","[2022/11/21 15:27:01] ppocr INFO: cur metric, acc: 0.5724137799445108, norm_edit_dis: 0.8472747894642093, fps: 1470.1108552403637\n","[2022/11/21 15:27:01] ppocr INFO: save best model is to ./output/svtr_habr/best_accuracy\n","[2022/11/21 15:27:01] ppocr INFO: best metric, acc: 0.5724137799445108, is_float16: False, norm_edit_dis: 0.8472747894642093, fps: 1470.1108552403637, best_epoch: 14\n","[2022/11/21 15:27:06] ppocr INFO: epoch: [14/25], global_step: 210, lr: 0.000256, acc: 0.613281, norm_edit_dis: 0.880200, loss: 3.792567, avg_reader_cost: 0.00191 s, avg_batch_cost: 0.43076 s, avg_samples: 256.0, ips: 594.29670 samples/s, eta: 0:01:28\n","[2022/11/21 15:27:06] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:27:11] ppocr INFO: epoch: [15/25], global_step: 220, lr: 0.000235, acc: 0.638672, norm_edit_dis: 0.887517, loss: 3.497077, avg_reader_cost: 0.08965 s, avg_batch_cost: 0.55407 s, avg_samples: 256.0, ips: 462.03413 samples/s, eta: 0:01:22\n","[2022/11/21 15:27:13] ppocr INFO: epoch: [15/25], global_step: 225, lr: 0.000225, acc: 0.650391, norm_edit_dis: 0.899167, loss: 3.204424, avg_reader_cost: 0.00034 s, avg_batch_cost: 0.20123 s, avg_samples: 128.0, ips: 636.08504 samples/s, eta: 0:01:19\n","[2022/11/21 15:27:14] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:27:17] ppocr INFO: epoch: [16/25], global_step: 230, lr: 0.000215, acc: 0.656250, norm_edit_dis: 0.904220, loss: 3.091755, avg_reader_cost: 0.09193 s, avg_batch_cost: 0.33119 s, avg_samples: 128.0, ips: 386.48027 samples/s, eta: 0:01:17\n","[2022/11/21 15:27:21] ppocr INFO: epoch: [16/25], global_step: 240, lr: 0.000194, acc: 0.667969, norm_edit_dis: 0.911064, loss: 2.806614, avg_reader_cost: 0.00134 s, avg_batch_cost: 0.42197 s, avg_samples: 256.0, ips: 606.67246 samples/s, eta: 0:01:11\n","[2022/11/21 15:27:21] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:27:26] ppocr INFO: epoch: [17/25], global_step: 250, lr: 0.000174, acc: 0.705078, norm_edit_dis: 0.921429, loss: 2.625690, avg_reader_cost: 0.08812 s, avg_batch_cost: 0.54409 s, avg_samples: 256.0, ips: 470.50859 samples/s, eta: 0:01:06\n","[2022/11/21 15:27:28] ppocr INFO: epoch: [17/25], global_step: 255, lr: 0.000164, acc: 0.712891, norm_edit_dis: 0.918210, loss: 2.677903, avg_reader_cost: 0.00099 s, avg_batch_cost: 0.20898 s, avg_samples: 128.0, ips: 612.49472 samples/s, eta: 0:01:03\n","[2022/11/21 15:27:29] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:27:33] ppocr INFO: epoch: [18/25], global_step: 260, lr: 0.000154, acc: 0.712891, norm_edit_dis: 0.921343, loss: 2.547332, avg_reader_cost: 0.14268 s, avg_batch_cost: 0.45875 s, avg_samples: 128.0, ips: 279.01759 samples/s, eta: 0:01:01\n","[2022/11/21 15:27:37] ppocr INFO: epoch: [18/25], global_step: 270, lr: 0.000135, acc: 0.720703, norm_edit_dis: 0.923998, loss: 2.557530, avg_reader_cost: 0.00157 s, avg_batch_cost: 0.43626 s, avg_samples: 256.0, ips: 586.80426 samples/s, eta: 0:00:55\n","[2022/11/21 15:27:38] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:27:43] ppocr INFO: epoch: [19/25], global_step: 280, lr: 0.000117, acc: 0.734375, norm_edit_dis: 0.931242, loss: 2.252641, avg_reader_cost: 0.09297 s, avg_batch_cost: 0.55194 s, avg_samples: 256.0, ips: 463.81872 samples/s, eta: 0:00:50\n","[2022/11/21 15:27:45] ppocr INFO: epoch: [19/25], global_step: 285, lr: 0.000108, acc: 0.744141, norm_edit_dis: 0.932357, loss: 2.156269, avg_reader_cost: 0.00031 s, avg_batch_cost: 0.20378 s, avg_samples: 128.0, ips: 628.12519 samples/s, eta: 0:00:47\n","[2022/11/21 15:27:45] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:27:48] ppocr INFO: epoch: [20/25], global_step: 290, lr: 0.000100, acc: 0.750000, norm_edit_dis: 0.933753, loss: 2.107620, avg_reader_cost: 0.08837 s, avg_batch_cost: 0.33414 s, avg_samples: 128.0, ips: 383.07617 samples/s, eta: 0:00:45\n","[2022/11/21 15:27:52] ppocr INFO: epoch: [20/25], global_step: 300, lr: 0.000083, acc: 0.759766, norm_edit_dis: 0.935848, loss: 2.067693, avg_reader_cost: 0.00066 s, avg_batch_cost: 0.42362 s, avg_samples: 256.0, ips: 604.32232 samples/s, eta: 0:00:39\n","eval model:: 100% 4/4 [00:00<00:00,  5.76it/s]\n","[2022/11/21 15:27:53] ppocr INFO: cur metric, acc: 0.6482758471660726, norm_edit_dis: 0.8734183984277089, fps: 1425.0361546284016\n","[2022/11/21 15:27:54] ppocr INFO: save best model is to ./output/svtr_habr/best_accuracy\n","[2022/11/21 15:27:54] ppocr INFO: best metric, acc: 0.6482758471660726, is_float16: False, norm_edit_dis: 0.8734183984277089, fps: 1425.0361546284016, best_epoch: 20\n","[2022/11/21 15:27:54] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:27:59] ppocr INFO: epoch: [21/25], global_step: 310, lr: 0.000068, acc: 0.769531, norm_edit_dis: 0.935959, loss: 2.042537, avg_reader_cost: 0.08939 s, avg_batch_cost: 0.54470 s, avg_samples: 256.0, ips: 469.98201 samples/s, eta: 0:00:34\n","[2022/11/21 15:28:01] ppocr INFO: epoch: [21/25], global_step: 315, lr: 0.000061, acc: 0.769531, norm_edit_dis: 0.939662, loss: 2.025022, avg_reader_cost: 0.00033 s, avg_batch_cost: 0.19881 s, avg_samples: 128.0, ips: 643.84568 samples/s, eta: 0:00:31\n","[2022/11/21 15:28:01] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:28:04] ppocr INFO: epoch: [22/25], global_step: 320, lr: 0.000055, acc: 0.765625, norm_edit_dis: 0.939662, loss: 1.935146, avg_reader_cost: 0.08862 s, avg_batch_cost: 0.33816 s, avg_samples: 128.0, ips: 378.52188 samples/s, eta: 0:00:29\n","[2022/11/21 15:28:09] ppocr INFO: epoch: [22/25], global_step: 330, lr: 0.000042, acc: 0.750000, norm_edit_dis: 0.940221, loss: 1.877116, avg_reader_cost: 0.00064 s, avg_batch_cost: 0.42315 s, avg_samples: 256.0, ips: 604.98298 samples/s, eta: 0:00:23\n","[2022/11/21 15:28:09] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:28:14] ppocr INFO: epoch: [23/25], global_step: 340, lr: 0.000031, acc: 0.769531, norm_edit_dis: 0.944099, loss: 1.869779, avg_reader_cost: 0.09604 s, avg_batch_cost: 0.54456 s, avg_samples: 256.0, ips: 470.10368 samples/s, eta: 0:00:18\n","[2022/11/21 15:28:16] ppocr INFO: epoch: [23/25], global_step: 345, lr: 0.000027, acc: 0.777344, norm_edit_dis: 0.945170, loss: 1.907090, avg_reader_cost: 0.00029 s, avg_batch_cost: 0.19846 s, avg_samples: 128.0, ips: 644.96901 samples/s, eta: 0:00:15\n","[2022/11/21 15:28:16] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:28:19] ppocr INFO: epoch: [24/25], global_step: 350, lr: 0.000022, acc: 0.777344, norm_edit_dis: 0.946255, loss: 1.942410, avg_reader_cost: 0.09144 s, avg_batch_cost: 0.32542 s, avg_samples: 128.0, ips: 393.33309 samples/s, eta: 0:00:13\n","[2022/11/21 15:28:24] ppocr INFO: epoch: [24/25], global_step: 360, lr: 0.000014, acc: 0.781250, norm_edit_dis: 0.947002, loss: 1.768162, avg_reader_cost: 0.00065 s, avg_batch_cost: 0.42316 s, avg_samples: 256.0, ips: 604.97473 samples/s, eta: 0:00:07\n","[2022/11/21 15:28:24] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:28:29] ppocr INFO: epoch: [25/25], global_step: 370, lr: 0.000008, acc: 0.781250, norm_edit_dis: 0.945956, loss: 1.768162, avg_reader_cost: 0.10195 s, avg_batch_cost: 0.56815 s, avg_samples: 256.0, ips: 450.58327 samples/s, eta: 0:00:02\n","[2022/11/21 15:28:31] ppocr INFO: epoch: [25/25], global_step: 375, lr: 0.000006, acc: 0.779297, norm_edit_dis: 0.945956, loss: 1.767037, avg_reader_cost: 0.00028 s, avg_batch_cost: 0.20048 s, avg_samples: 128.0, ips: 638.47805 samples/s, eta: 0:00:00\n","[2022/11/21 15:28:32] ppocr INFO: save model in ./output/svtr_habr/latest\n","[2022/11/21 15:28:32] ppocr INFO: best metric, acc: 0.6482758471660726, is_float16: False, norm_edit_dis: 0.8734183984277089, fps: 1425.0361546284016, best_epoch: 20\n"]}]},{"cell_type":"code","source":["!python3 tools/export_model.py -c configs/rec/rec_svtrnet_habr.yml -o  Global.pretrained_model=./output/svtr_habr/best_accuracy  Global.save_inference_dir=./inference/svtr_habr"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yWXDN4FHdpvk","executionInfo":{"status":"ok","timestamp":1669098885884,"user_tz":-180,"elapsed":321,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}},"outputId":"e5db2bf5-7d0d-4229-a29d-1163bfa01329"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[2022/11/22 06:34:30] ppocr INFO: load pretrain successful from ./output/svtr_habr/best_accuracy\n","[2022/11/22 06:34:38] ppocr INFO: inference model is saved to ./inference/svtr_habr/inference\n"]}]},{"cell_type":"code","source":["import sys\n","sys.path.append('/drive/MyDrive')"],"metadata":{"id":"AchoyL_ag6ro","executionInfo":{"status":"ok","timestamp":1669201908378,"user_tz":-180,"elapsed":2,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["import os\n","import sys\n","from PIL import Image\n","__dir__ = os.path.dirname(os.path.abspath(\"__file__\"))\n","sys.path.append(__dir__)\n","sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))"],"metadata":{"id":"vz7RVG6cg9sZ","executionInfo":{"status":"ok","timestamp":1669203399281,"user_tz":-180,"elapsed":1,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":43,"outputs":[]},{"cell_type":"code","source":["os.environ[\"FLAGS_allocator_strategy\"] = 'auto_growth'\n","\n","import cv2\n","import numpy as np\n","import math\n","import time\n","import traceback\n","import paddle\n","\n","import tools.infer.utility as utility\n","from ppocr.postprocess import build_post_process\n","from ppocr.utils.logging import get_logger\n","from ppocr.utils.utility import get_image_file_list, check_and_read\n","\n","logger = get_logger()"],"metadata":{"id":"5i3ZwN85hx-t","executionInfo":{"status":"ok","timestamp":1669203399738,"user_tz":-180,"elapsed":2,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":44,"outputs":[]},{"cell_type":"code","source":["import argparse\n","\n","class TextRecognizer(object):\n","    def __init__(self, args):\n","        self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(\",\")]\n","        self.rec_batch_num = args.rec_batch_num\n","        self.rec_algorithm = args.rec_algorithm\n","        postprocess_params = {\n","            'name': 'CTCLabelDecode',\n","            \"character_dict_path\": args.rec_char_dict_path,\n","            \"use_space_char\": args.use_space_char\n","        }\n","        if self.rec_algorithm == \"SRN\":\n","            postprocess_params = {\n","                'name': 'SRNLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == \"RARE\":\n","            postprocess_params = {\n","                'name': 'AttnLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == 'NRTR':\n","            postprocess_params = {\n","                'name': 'NRTRLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == \"SAR\":\n","            postprocess_params = {\n","                'name': 'SARLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == \"VisionLAN\":\n","            postprocess_params = {\n","                'name': 'VLLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == 'ViTSTR':\n","            postprocess_params = {\n","                'name': 'ViTSTRLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == 'ABINet':\n","            postprocess_params = {\n","                'name': 'ABINetLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == \"SPIN\":\n","            postprocess_params = {\n","                'name': 'SPINLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == \"RobustScanner\":\n","            postprocess_params = {\n","                'name': 'SARLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char,\n","                \"rm_symbol\": True\n","            }\n","        elif self.rec_algorithm == 'RFL':\n","            postprocess_params = {\n","                'name': 'RFLLabelDecode',\n","                \"character_dict_path\": None,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        elif self.rec_algorithm == \"PREN\":\n","            postprocess_params = {'name': 'PRENLabelDecode'}\n","        elif self.rec_algorithm == \"CAN\":\n","            self.inverse = args.rec_image_inverse\n","            postprocess_params = {\n","                'name': 'CANLabelDecode',\n","                \"character_dict_path\": args.rec_char_dict_path,\n","                \"use_space_char\": args.use_space_char\n","            }\n","        self.postprocess_op = build_post_process(postprocess_params)\n","        self.predictor, self.input_tensor, self.output_tensors, self.config = \\\n","            utility.create_predictor(args, 'rec', logger)\n","        self.benchmark = args.benchmark\n","        self.use_onnx = args.use_onnx\n","        if args.benchmark:\n","            import auto_log\n","            pid = os.getpid()\n","            gpu_id = utility.get_infer_gpuid()\n","            self.autolog = auto_log.AutoLogger(\n","                model_name=\"rec\",\n","                model_precision=args.precision,\n","                batch_size=args.rec_batch_num,\n","                data_shape=\"dynamic\",\n","                save_path=None,  #args.save_log_path,\n","                inference_config=self.config,\n","                pids=pid,\n","                process_name=None,\n","                gpu_ids=gpu_id if args.use_gpu else None,\n","                time_keys=[\n","                    'preprocess_time', 'inference_time', 'postprocess_time'\n","                ],\n","                warmup=0,\n","                logger=logger)\n","\n","    def resize_norm_img(self, img, max_wh_ratio):\n","        imgC, imgH, imgW = self.rec_image_shape\n","        if self.rec_algorithm == 'NRTR' or self.rec_algorithm == 'ViTSTR':\n","            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n","            # return padding_im\n","            image_pil = Image.fromarray(np.uint8(img))\n","            if self.rec_algorithm == 'ViTSTR':\n","                img = image_pil.resize([imgW, imgH], Image.BICUBIC)\n","            else:\n","                img = image_pil.resize([imgW, imgH], Image.ANTIALIAS)\n","            img = np.array(img)\n","            norm_img = np.expand_dims(img, -1)\n","            norm_img = norm_img.transpose((2, 0, 1))\n","            if self.rec_algorithm == 'ViTSTR':\n","                norm_img = norm_img.astype(np.float32) / 255.\n","            else:\n","                norm_img = norm_img.astype(np.float32) / 128. - 1.\n","            return norm_img\n","        elif self.rec_algorithm == 'RFL':\n","            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n","            resized_image = cv2.resize(\n","                img, (imgW, imgH), interpolation=cv2.INTER_CUBIC)\n","            resized_image = resized_image.astype('float32')\n","            resized_image = resized_image / 255\n","            resized_image = resized_image[np.newaxis, :]\n","            resized_image -= 0.5\n","            resized_image /= 0.5\n","            return resized_image\n","\n","        assert imgC == img.shape[2]\n","        imgW = int((imgH * max_wh_ratio))\n","        if self.use_onnx:\n","            w = self.input_tensor.shape[3:][0]\n","            if w is not None and w > 0:\n","                imgW = w\n","\n","        h, w = img.shape[:2]\n","        ratio = w / float(h)\n","        if math.ceil(imgH * ratio) > imgW:\n","            resized_w = imgW\n","        else:\n","            resized_w = int(math.ceil(imgH * ratio))\n","        if self.rec_algorithm == 'RARE':\n","            if resized_w > self.rec_image_shape[2]:\n","                resized_w = self.rec_image_shape[2]\n","            imgW = self.rec_image_shape[2]\n","        resized_image = cv2.resize(img, (resized_w, imgH))\n","        resized_image = resized_image.astype('float32')\n","        resized_image = resized_image.transpose((2, 0, 1)) / 255\n","        resized_image -= 0.5\n","        resized_image /= 0.5\n","        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)\n","        padding_im[:, :, 0:resized_w] = resized_image\n","        return padding_im\n","\n","    def resize_norm_img_vl(self, img, image_shape):\n","\n","        imgC, imgH, imgW = image_shape\n","        img = img[:, :, ::-1]  # bgr2rgb\n","        resized_image = cv2.resize(\n","            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)\n","        resized_image = resized_image.astype('float32')\n","        resized_image = resized_image.transpose((2, 0, 1)) / 255\n","        return resized_image\n","\n","    def resize_norm_img_srn(self, img, image_shape):\n","        imgC, imgH, imgW = image_shape\n","\n","        img_black = np.zeros((imgH, imgW))\n","        im_hei = img.shape[0]\n","        im_wid = img.shape[1]\n","\n","        if im_wid <= im_hei * 1:\n","            img_new = cv2.resize(img, (imgH * 1, imgH))\n","        elif im_wid <= im_hei * 2:\n","            img_new = cv2.resize(img, (imgH * 2, imgH))\n","        elif im_wid <= im_hei * 3:\n","            img_new = cv2.resize(img, (imgH * 3, imgH))\n","        else:\n","            img_new = cv2.resize(img, (imgW, imgH))\n","\n","        img_np = np.asarray(img_new)\n","        img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)\n","        img_black[:, 0:img_np.shape[1]] = img_np\n","        img_black = img_black[:, :, np.newaxis]\n","\n","        row, col, c = img_black.shape\n","        c = 1\n","\n","        return np.reshape(img_black, (c, row, col)).astype(np.float32)\n","\n","    def srn_other_inputs(self, image_shape, num_heads, max_text_length):\n","\n","        imgC, imgH, imgW = image_shape\n","        feature_dim = int((imgH / 8) * (imgW / 8))\n","\n","        encoder_word_pos = np.array(range(0, feature_dim)).reshape(\n","            (feature_dim, 1)).astype('int64')\n","        gsrm_word_pos = np.array(range(0, max_text_length)).reshape(\n","            (max_text_length, 1)).astype('int64')\n","\n","        gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))\n","        gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(\n","            [-1, 1, max_text_length, max_text_length])\n","        gsrm_slf_attn_bias1 = np.tile(\n","            gsrm_slf_attn_bias1,\n","            [1, num_heads, 1, 1]).astype('float32') * [-1e9]\n","\n","        gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(\n","            [-1, 1, max_text_length, max_text_length])\n","        gsrm_slf_attn_bias2 = np.tile(\n","            gsrm_slf_attn_bias2,\n","            [1, num_heads, 1, 1]).astype('float32') * [-1e9]\n","\n","        encoder_word_pos = encoder_word_pos[np.newaxis, :]\n","        gsrm_word_pos = gsrm_word_pos[np.newaxis, :]\n","\n","        return [\n","            encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,\n","            gsrm_slf_attn_bias2\n","        ]\n","\n","    def process_image_srn(self, img, image_shape, num_heads, max_text_length):\n","        norm_img = self.resize_norm_img_srn(img, image_shape)\n","        norm_img = norm_img[np.newaxis, :]\n","\n","        [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \\\n","            self.srn_other_inputs(image_shape, num_heads, max_text_length)\n","\n","        gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)\n","        gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)\n","        encoder_word_pos = encoder_word_pos.astype(np.int64)\n","        gsrm_word_pos = gsrm_word_pos.astype(np.int64)\n","\n","        return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,\n","                gsrm_slf_attn_bias2)\n","\n","    def resize_norm_img_sar(self, img, image_shape,\n","                            width_downsample_ratio=0.25):\n","        imgC, imgH, imgW_min, imgW_max = image_shape\n","        h = img.shape[0]\n","        w = img.shape[1]\n","        valid_ratio = 1.0\n","        # make sure new_width is an integral multiple of width_divisor.\n","        width_divisor = int(1 / width_downsample_ratio)\n","        # resize\n","        ratio = w / float(h)\n","        resize_w = math.ceil(imgH * ratio)\n","        if resize_w % width_divisor != 0:\n","            resize_w = round(resize_w / width_divisor) * width_divisor\n","        if imgW_min is not None:\n","            resize_w = max(imgW_min, resize_w)\n","        if imgW_max is not None:\n","            valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)\n","            resize_w = min(imgW_max, resize_w)\n","        resized_image = cv2.resize(img, (resize_w, imgH))\n","        resized_image = resized_image.astype('float32')\n","        # norm \n","        if image_shape[0] == 1:\n","            resized_image = resized_image / 255\n","            resized_image = resized_image[np.newaxis, :]\n","        else:\n","            resized_image = resized_image.transpose((2, 0, 1)) / 255\n","        resized_image -= 0.5\n","        resized_image /= 0.5\n","        resize_shape = resized_image.shape\n","        padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)\n","        padding_im[:, :, 0:resize_w] = resized_image\n","        pad_shape = padding_im.shape\n","\n","        return padding_im, resize_shape, pad_shape, valid_ratio\n","\n","    def resize_norm_img_spin(self, img):\n","        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n","        # return padding_im\n","        img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)\n","        img = np.array(img, np.float32)\n","        img = np.expand_dims(img, -1)\n","        img = img.transpose((2, 0, 1))\n","        mean = [127.5]\n","        std = [127.5]\n","        mean = np.array(mean, dtype=np.float32)\n","        std = np.array(std, dtype=np.float32)\n","        mean = np.float32(mean.reshape(1, -1))\n","        stdinv = 1 / np.float32(std.reshape(1, -1))\n","        img -= mean\n","        img *= stdinv\n","        return img\n","\n","    def resize_norm_img_svtr(self, img, image_shape):\n","\n","        imgC, imgH, imgW = image_shape\n","        resized_image = cv2.resize(\n","            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)\n","        resized_image = resized_image.astype('float32')\n","        resized_image = resized_image.transpose((2, 0, 1)) / 255\n","        resized_image -= 0.5\n","        resized_image /= 0.5\n","        return resized_image\n","\n","    def resize_norm_img_abinet(self, img, image_shape):\n","\n","        imgC, imgH, imgW = image_shape\n","\n","        resized_image = cv2.resize(\n","            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)\n","        resized_image = resized_image.astype('float32')\n","        resized_image = resized_image / 255.\n","\n","        mean = np.array([0.485, 0.456, 0.406])\n","        std = np.array([0.229, 0.224, 0.225])\n","        resized_image = (\n","            resized_image - mean[None, None, ...]) / std[None, None, ...]\n","        resized_image = resized_image.transpose((2, 0, 1))\n","        resized_image = resized_image.astype('float32')\n","\n","        return resized_image\n","\n","    def norm_img_can(self, img, image_shape):\n","\n","        img = cv2.cvtColor(\n","            img, cv2.COLOR_BGR2GRAY)  # CAN only predict gray scale image\n","\n","        if self.inverse:\n","            img = 255 - img\n","\n","        if self.rec_image_shape[0] == 1:\n","            h, w = img.shape\n","            _, imgH, imgW = self.rec_image_shape\n","            if h < imgH or w < imgW:\n","                padding_h = max(imgH - h, 0)\n","                padding_w = max(imgW - w, 0)\n","                img_padded = np.pad(img, ((0, padding_h), (0, padding_w)),\n","                                    'constant',\n","                                    constant_values=(255))\n","                img = img_padded\n","\n","        img = np.expand_dims(img, 0) / 255.0  # h,w,c -> c,h,w\n","        img = img.astype('float32')\n","\n","        return img\n","\n","    def __call__(self, img_list):\n","        img_num = len(img_list)\n","        # Calculate the aspect ratio of all text bars\n","        width_list = []\n","        for img in img_list:\n","            width_list.append(img.shape[1] / float(img.shape[0]))\n","        # Sorting can speed up the recognition process\n","        indices = np.argsort(np.array(width_list))\n","        rec_res = [['', 0.0]] * img_num\n","        batch_num = self.rec_batch_num\n","        st = time.time()\n","        if self.benchmark:\n","            self.autolog.times.start()\n","        for beg_img_no in range(0, img_num, batch_num):\n","            end_img_no = min(img_num, beg_img_no + batch_num)\n","            norm_img_batch = []\n","            if self.rec_algorithm == \"SRN\":\n","                encoder_word_pos_list = []\n","                gsrm_word_pos_list = []\n","                gsrm_slf_attn_bias1_list = []\n","                gsrm_slf_attn_bias2_list = []\n","            if self.rec_algorithm == \"SAR\":\n","                valid_ratios = []\n","            imgC, imgH, imgW = self.rec_image_shape[:3]\n","            max_wh_ratio = imgW / imgH\n","            # max_wh_ratio = 0\n","            for ino in range(beg_img_no, end_img_no):\n","                h, w = img_list[indices[ino]].shape[0:2]\n","                wh_ratio = w * 1.0 / h\n","                max_wh_ratio = max(max_wh_ratio, wh_ratio)\n","            for ino in range(beg_img_no, end_img_no):\n","                if self.rec_algorithm == \"SAR\":\n","                    norm_img, _, _, valid_ratio = self.resize_norm_img_sar(\n","                        img_list[indices[ino]], self.rec_image_shape)\n","                    norm_img = norm_img[np.newaxis, :]\n","                    valid_ratio = np.expand_dims(valid_ratio, axis=0)\n","                    valid_ratios.append(valid_ratio)\n","                    norm_img_batch.append(norm_img)\n","                elif self.rec_algorithm == \"SRN\":\n","                    norm_img = self.process_image_srn(\n","                        img_list[indices[ino]], self.rec_image_shape, 8, 25)\n","                    encoder_word_pos_list.append(norm_img[1])\n","                    gsrm_word_pos_list.append(norm_img[2])\n","                    gsrm_slf_attn_bias1_list.append(norm_img[3])\n","                    gsrm_slf_attn_bias2_list.append(norm_img[4])\n","                    norm_img_batch.append(norm_img[0])\n","                elif self.rec_algorithm == \"SVTR\":\n","                    norm_img = self.resize_norm_img_svtr(img_list[indices[ino]],\n","                                                         self.rec_image_shape)\n","                    norm_img = norm_img[np.newaxis, :]\n","                    norm_img_batch.append(norm_img)\n","                elif self.rec_algorithm in [\"VisionLAN\", \"PREN\"]:\n","                    norm_img = self.resize_norm_img_vl(img_list[indices[ino]],\n","                                                       self.rec_image_shape)\n","                    norm_img = norm_img[np.newaxis, :]\n","                    norm_img_batch.append(norm_img)\n","                elif self.rec_algorithm == 'SPIN':\n","                    norm_img = self.resize_norm_img_spin(img_list[indices[ino]])\n","                    norm_img = norm_img[np.newaxis, :]\n","                    norm_img_batch.append(norm_img)\n","                elif self.rec_algorithm == \"ABINet\":\n","                    norm_img = self.resize_norm_img_abinet(\n","                        img_list[indices[ino]], self.rec_image_shape)\n","                    norm_img = norm_img[np.newaxis, :]\n","                    norm_img_batch.append(norm_img)\n","                elif self.rec_algorithm == \"RobustScanner\":\n","                    norm_img, _, _, valid_ratio = self.resize_norm_img_sar(\n","                        img_list[indices[ino]],\n","                        self.rec_image_shape,\n","                        width_downsample_ratio=0.25)\n","                    norm_img = norm_img[np.newaxis, :]\n","                    valid_ratio = np.expand_dims(valid_ratio, axis=0)\n","                    valid_ratios = []\n","                    valid_ratios.append(valid_ratio)\n","                    norm_img_batch.append(norm_img)\n","                    word_positions_list = []\n","                    word_positions = np.array(range(0, 40)).astype('int64')\n","                    word_positions = np.expand_dims(word_positions, axis=0)\n","                    word_positions_list.append(word_positions)\n","                elif self.rec_algorithm == \"CAN\":\n","                    norm_img = self.norm_img_can(img_list[indices[ino]],\n","                                                 max_wh_ratio)\n","                    norm_img = norm_img[np.newaxis, :]\n","                    norm_img_batch.append(norm_img)\n","                    norm_image_mask = np.ones(norm_img.shape, dtype='float32')\n","                    word_label = np.ones([1, 36], dtype='int64')\n","                    norm_img_mask_batch = []\n","                    word_label_list = []\n","                    norm_img_mask_batch.append(norm_image_mask)\n","                    word_label_list.append(word_label)\n","                else:\n","                    norm_img = self.resize_norm_img(img_list[indices[ino]],\n","                                                    max_wh_ratio)\n","                    norm_img = norm_img[np.newaxis, :]\n","                    norm_img_batch.append(norm_img)\n","            norm_img_batch = np.concatenate(norm_img_batch)\n","            norm_img_batch = norm_img_batch.copy()\n","            if self.benchmark:\n","                self.autolog.times.stamp()\n","\n","            if self.rec_algorithm == \"SRN\":\n","                encoder_word_pos_list = np.concatenate(encoder_word_pos_list)\n","                gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)\n","                gsrm_slf_attn_bias1_list = np.concatenate(\n","                    gsrm_slf_attn_bias1_list)\n","                gsrm_slf_attn_bias2_list = np.concatenate(\n","                    gsrm_slf_attn_bias2_list)\n","\n","                inputs = [\n","                    norm_img_batch,\n","                    encoder_word_pos_list,\n","                    gsrm_word_pos_list,\n","                    gsrm_slf_attn_bias1_list,\n","                    gsrm_slf_attn_bias2_list,\n","                ]\n","                if self.use_onnx:\n","                    input_dict = {}\n","                    input_dict[self.input_tensor.name] = norm_img_batch\n","                    outputs = self.predictor.run(self.output_tensors,\n","                                                 input_dict)\n","                    preds = {\"predict\": outputs[2]}\n","                else:\n","                    input_names = self.predictor.get_input_names()\n","                    for i in range(len(input_names)):\n","                        input_tensor = self.predictor.get_input_handle(\n","                            input_names[i])\n","                        input_tensor.copy_from_cpu(inputs[i])\n","                    self.predictor.run()\n","                    outputs = []\n","                    for output_tensor in self.output_tensors:\n","                        output = output_tensor.copy_to_cpu()\n","                        outputs.append(output)\n","                    if self.benchmark:\n","                        self.autolog.times.stamp()\n","                    preds = {\"predict\": outputs[2]}\n","            elif self.rec_algorithm == \"SAR\":\n","                valid_ratios = np.concatenate(valid_ratios)\n","                inputs = [\n","                    norm_img_batch,\n","                    np.array(\n","                        [valid_ratios], dtype=np.float32),\n","                ]\n","                if self.use_onnx:\n","                    input_dict = {}\n","                    input_dict[self.input_tensor.name] = norm_img_batch\n","                    outputs = self.predictor.run(self.output_tensors,\n","                                                 input_dict)\n","                    preds = outputs[0]\n","                else:\n","                    input_names = self.predictor.get_input_names()\n","                    for i in range(len(input_names)):\n","                        input_tensor = self.predictor.get_input_handle(\n","                            input_names[i])\n","                        input_tensor.copy_from_cpu(inputs[i])\n","                    self.predictor.run()\n","                    outputs = []\n","                    for output_tensor in self.output_tensors:\n","                        output = output_tensor.copy_to_cpu()\n","                        outputs.append(output)\n","                    if self.benchmark:\n","                        self.autolog.times.stamp()\n","                    preds = outputs[0]\n","            elif self.rec_algorithm == \"RobustScanner\":\n","                valid_ratios = np.concatenate(valid_ratios)\n","                word_positions_list = np.concatenate(word_positions_list)\n","                inputs = [norm_img_batch, valid_ratios, word_positions_list]\n","\n","                if self.use_onnx:\n","                    input_dict = {}\n","                    input_dict[self.input_tensor.name] = norm_img_batch\n","                    outputs = self.predictor.run(self.output_tensors,\n","                                                 input_dict)\n","                    preds = outputs[0]\n","                else:\n","                    input_names = self.predictor.get_input_names()\n","                    for i in range(len(input_names)):\n","                        input_tensor = self.predictor.get_input_handle(\n","                            input_names[i])\n","                        input_tensor.copy_from_cpu(inputs[i])\n","                    self.predictor.run()\n","                    outputs = []\n","                    for output_tensor in self.output_tensors:\n","                        output = output_tensor.copy_to_cpu()\n","                        outputs.append(output)\n","                    if self.benchmark:\n","                        self.autolog.times.stamp()\n","                    preds = outputs[0]\n","            elif self.rec_algorithm == \"CAN\":\n","                norm_img_mask_batch = np.concatenate(norm_img_mask_batch)\n","                word_label_list = np.concatenate(word_label_list)\n","                inputs = [norm_img_batch, norm_img_mask_batch, word_label_list]\n","                if self.use_onnx:\n","                    input_dict = {}\n","                    input_dict[self.input_tensor.name] = norm_img_batch\n","                    outputs = self.predictor.run(self.output_tensors,\n","                                                 input_dict)\n","                    preds = outputs\n","                else:\n","                    input_names = self.predictor.get_input_names()\n","                    input_tensor = []\n","                    for i in range(len(input_names)):\n","                        input_tensor_i = self.predictor.get_input_handle(\n","                            input_names[i])\n","                        input_tensor_i.copy_from_cpu(inputs[i])\n","                        input_tensor.append(input_tensor_i)\n","                    self.input_tensor = input_tensor\n","                    self.predictor.run()\n","                    outputs = []\n","                    for output_tensor in self.output_tensors:\n","                        output = output_tensor.copy_to_cpu()\n","                        outputs.append(output)\n","                    if self.benchmark:\n","                        self.autolog.times.stamp()\n","                    preds = outputs\n","            else:\n","                if self.use_onnx:\n","                    input_dict = {}\n","                    input_dict[self.input_tensor.name] = norm_img_batch\n","                    outputs = self.predictor.run(self.output_tensors,\n","                                                 input_dict)\n","                    preds = outputs[0]\n","                else:\n","                    self.input_tensor.copy_from_cpu(norm_img_batch)\n","                    self.predictor.run()\n","                    outputs = []\n","                    for output_tensor in self.output_tensors:\n","                        output = output_tensor.copy_to_cpu()\n","                        outputs.append(output)\n","                    if self.benchmark:\n","                        self.autolog.times.stamp()\n","                    if len(outputs) != 1:\n","                        preds = outputs\n","                    else:\n","                        preds = outputs[0]\n","            rec_result = self.postprocess_op(preds)\n","            for rno in range(len(rec_result)):\n","                rec_res[indices[beg_img_no + rno]] = rec_result[rno]\n","            if self.benchmark:\n","                self.autolog.times.end(stamp=True)\n","        return rec_res, time.time() - st\n","\n","\n","\n","def str2bool(v):\n","    return v.lower() in (\"true\", \"t\", \"1\")\n","\n","parser = argparse.ArgumentParser()\n","# params for prediction engine\n","parser.add_argument(\"--use_gpu\", type=str2bool, default=True)\n","parser.add_argument(\"--use_xpu\", type=str2bool, default=False)\n","parser.add_argument(\"--use_npu\", type=str2bool, default=False)\n","parser.add_argument(\"--ir_optim\", type=str2bool, default=True)\n","parser.add_argument(\"--use_tensorrt\", type=str2bool, default=False)\n","parser.add_argument(\"--min_subgraph_size\", type=int, default=15)\n","parser.add_argument(\"--precision\", type=str, default=\"fp32\")\n","parser.add_argument(\"--gpu_mem\", type=int, default=500)\n","\n","# params for text detector\n","parser.add_argument(\"--image_dir\", type=str)\n","parser.add_argument(\"--page_num\", type=int, default=0)\n","parser.add_argument(\"--det_algorithm\", type=str, default='DB')\n","parser.add_argument(\"--det_model_dir\", type=str)\n","parser.add_argument(\"--det_limit_side_len\", type=float, default=960)\n","parser.add_argument(\"--det_limit_type\", type=str, default='max')\n","parser.add_argument(\"--det_box_type\", type=str, default='quad')\n","\n","# DB parmas\n","parser.add_argument(\"--det_db_thresh\", type=float, default=0.3)\n","parser.add_argument(\"--det_db_box_thresh\", type=float, default=0.6)\n","parser.add_argument(\"--det_db_unclip_ratio\", type=float, default=1.5)\n","parser.add_argument(\"--max_batch_size\", type=int, default=10)\n","parser.add_argument(\"--use_dilation\", type=str2bool, default=False)\n","parser.add_argument(\"--det_db_score_mode\", type=str, default=\"fast\")\n","\n","# EAST parmas\n","parser.add_argument(\"--det_east_score_thresh\", type=float, default=0.8)\n","parser.add_argument(\"--det_east_cover_thresh\", type=float, default=0.1)\n","parser.add_argument(\"--det_east_nms_thresh\", type=float, default=0.2)\n","\n","# SAST parmas\n","parser.add_argument(\"--det_sast_score_thresh\", type=float, default=0.5)\n","parser.add_argument(\"--det_sast_nms_thresh\", type=float, default=0.2)\n","\n","# PSE parmas\n","parser.add_argument(\"--det_pse_thresh\", type=float, default=0)\n","parser.add_argument(\"--det_pse_box_thresh\", type=float, default=0.85)\n","parser.add_argument(\"--det_pse_min_area\", type=float, default=16)\n","parser.add_argument(\"--det_pse_scale\", type=int, default=1)\n","\n","# FCE parmas\n","parser.add_argument(\"--scales\", type=list, default=[8, 16, 32])\n","parser.add_argument(\"--alpha\", type=float, default=1.0)\n","parser.add_argument(\"--beta\", type=float, default=1.0)\n","parser.add_argument(\"--fourier_degree\", type=int, default=5)\n","\n","# params for text recognizer\n","parser.add_argument(\"--rec_algorithm\", type=str, default='SVTR_LCNet')\n","parser.add_argument(\"--rec_model_dir\", type=str)\n","parser.add_argument(\"--rec_image_inverse\", type=str2bool, default=True)\n","parser.add_argument(\"--rec_image_shape\", type=str, default=\"3, 48, 320\")\n","parser.add_argument(\"--rec_batch_num\", type=int, default=6)\n","parser.add_argument(\"--max_text_length\", type=int, default=25)\n","parser.add_argument(\n","    \"--rec_char_dict_path\",\n","    type=str,\n","    default=\"./ppocr/utils/ppocr_keys_v1.txt\")\n","parser.add_argument(\"--use_space_char\", type=str2bool, default=True)\n","parser.add_argument(\n","    \"--vis_font_path\", type=str, default=\"./doc/fonts/simfang.ttf\")\n","parser.add_argument(\"--drop_score\", type=float, default=0.5)\n","\n","# params for e2e\n","parser.add_argument(\"--e2e_algorithm\", type=str, default='PGNet')\n","parser.add_argument(\"--e2e_model_dir\", type=str)\n","parser.add_argument(\"--e2e_limit_side_len\", type=float, default=768)\n","parser.add_argument(\"--e2e_limit_type\", type=str, default='max')\n","\n","# PGNet parmas\n","parser.add_argument(\"--e2e_pgnet_score_thresh\", type=float, default=0.5)\n","parser.add_argument(\n","    \"--e2e_char_dict_path\", type=str, default=\"./ppocr/utils/ic15_dict.txt\")\n","parser.add_argument(\"--e2e_pgnet_valid_set\", type=str, default='totaltext')\n","parser.add_argument(\"--e2e_pgnet_mode\", type=str, default='fast')\n","\n","# params for text classifier\n","parser.add_argument(\"--use_angle_cls\", type=str2bool, default=False)\n","parser.add_argument(\"--cls_model_dir\", type=str)\n","parser.add_argument(\"--cls_image_shape\", type=str, default=\"3, 48, 192\")\n","parser.add_argument(\"--label_list\", type=list, default=['0', '180'])\n","parser.add_argument(\"--cls_batch_num\", type=int, default=6)\n","parser.add_argument(\"--cls_thresh\", type=float, default=0.9)\n","\n","parser.add_argument(\"--enable_mkldnn\", type=str2bool, default=False)\n","parser.add_argument(\"--cpu_threads\", type=int, default=10)\n","parser.add_argument(\"--use_pdserving\", type=str2bool, default=False)\n","parser.add_argument(\"--warmup\", type=str2bool, default=False)\n","\n","# SR parmas\n","parser.add_argument(\"--sr_model_dir\", type=str)\n","parser.add_argument(\"--sr_image_shape\", type=str, default=\"3, 32, 128\")\n","parser.add_argument(\"--sr_batch_num\", type=int, default=1)\n","\n","#\n","parser.add_argument(\n","    \"--draw_img_save_dir\", type=str, default=\"./inference_results\")\n","parser.add_argument(\"--save_crop_res\", type=str2bool, default=False)\n","parser.add_argument(\"--crop_res_save_dir\", type=str, default=\"./output\")\n","\n","# multi-process\n","parser.add_argument(\"--use_mp\", type=str2bool, default=False)\n","parser.add_argument(\"--total_process_num\", type=int, default=1)\n","parser.add_argument(\"--process_id\", type=int, default=0)\n","\n","parser.add_argument(\"--benchmark\", type=str2bool, default=False)\n","parser.add_argument(\"--save_log_path\", type=str, default=\"./log_output/\")\n","\n","parser.add_argument(\"--show_log\", type=str2bool, default=True)\n","parser.add_argument(\"--use_onnx\", type=str2bool, default=False)\n","\n","args, unknown = parser.parse_known_args()"],"metadata":{"id":"ypqLzSqpeslZ","executionInfo":{"status":"ok","timestamp":1669203401278,"user_tz":-180,"elapsed":2,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":45,"outputs":[]},{"cell_type":"code","source":["args.rec_model_dir = './inference/svtr_habr/'\n","args.rec_algorithm = 'SVTR'\n","args.max_text_length = 40\n","args.rec_image_shape = '3,64,256'\n","args.rec_char_dict_path = './ppocr/utils/dict/rus_chars.txt'"],"metadata":{"id":"O2M8-kBjF_5Y","executionInfo":{"status":"ok","timestamp":1669203409630,"user_tz":-180,"elapsed":3,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":46,"outputs":[]},{"cell_type":"code","source":["text_recognizer = TextRecognizer(args)"],"metadata":{"id":"Xx9Kz1nn3u4h","executionInfo":{"status":"ok","timestamp":1669203410992,"user_tz":-180,"elapsed":2,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":47,"outputs":[]},{"cell_type":"code","source":["from natsort import natsorted\n","import matplotlib.pyplot as plt"],"metadata":{"id":"_upiZkQRsIc9","executionInfo":{"status":"ok","timestamp":1669204032055,"user_tz":-180,"elapsed":415,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}}},"execution_count":60,"outputs":[]},{"cell_type":"code","source":["all_images = os.listdir('./tvoy_dom_nochyu/')\n","all_images = natsorted(all_images)\n","print(len(all_images))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"71eBshzt5nkg","executionInfo":{"status":"ok","timestamp":1669204034517,"user_tz":-180,"elapsed":574,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}},"outputId":"02b9c1c4-65db-41f7-90cd-a8926099c848"},"execution_count":61,"outputs":[{"output_type":"stream","name":"stdout","text":["5\n"]}]},{"cell_type":"code","source":["all_images"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T40zJn_cTwoG","executionInfo":{"status":"ok","timestamp":1669204035899,"user_tz":-180,"elapsed":5,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}},"outputId":"e896706e-9524-473f-af26-497f31686770"},"execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['0.png', '1.png', '2.png', '3.png', '4.png']"]},"metadata":{},"execution_count":62}]},{"cell_type":"code","source":["for im in all_images:\n","\n","    opened_image = cv2.imread('./tvoy_dom_nochyu/' + im)\n","    opened_image = cv2.cvtColor(opened_image, cv2.COLOR_BGR2RGB)\n","    rec_res, _ = text_recognizer([opened_image])\n","    print(f'Text prediction: {rec_res[0][0]}', f'Probability: {rec_res[0][1]}', im)\n","    plt.imshow(opened_image)\n","    plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"FWE1qCLhSMTB","executionInfo":{"status":"ok","timestamp":1669204040857,"user_tz":-180,"elapsed":3363,"user":{"displayName":"Андрей Бондарь","userId":"06919916406136291697"}},"outputId":"fed412e8-824e-4e4e-fc2b-a489ca020b2c"},"execution_count":63,"outputs":[{"output_type":"stream","name":"stdout","text":["Text prediction: ТВОЙ Probability: 0.8311489820480347 0.png\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Text prediction: AOM Probability: 0.6917451024055481 1.png\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Text prediction: ночб Probability: 0.8348243236541748 2.png\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Text prediction: UII Probability: 0.4265989363193512 3.png\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Text prediction: gнем! Probability: 0.835499107837677 4.png\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"U6UpCSuayptN"},"execution_count":null,"outputs":[]}]}