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add rec pact to tipc
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Global:
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use_gpu: true
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epoch_num: 500
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec_chinese_lite_v2.0
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save_epoch_step: 3
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [0, 2000]
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cal_metric_during_train: True
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: doc/imgs_words/ch/word_1.jpg
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# for data or label process
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character_dict_path: ppocr/utils/ppocr_keys_v1.txt
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max_text_length: 25
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infer_mode: False
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use_space_char: True
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save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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name: Cosine
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learning_rate: 0.001
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regularizer:
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name: 'L2'
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factor: 0.00001
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Architecture:
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model_type: rec
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algorithm: CRNN
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Transform:
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Backbone:
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name: MobileNetV3
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scale: 0.5
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model_name: small
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small_stride: [1, 2, 2, 2]
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Neck:
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name: SequenceEncoder
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encoder_type: rnn
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hidden_size: 48
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Head:
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name: CTCHead
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fc_decay: 0.00001
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Loss:
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name: CTCLoss
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PostProcess:
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name: CTCLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/ic15_data
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label_file_list: ["train_data/ic15_data/rec_gt_train.txt"]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- RecAug:
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- CTCLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 320]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: True
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batch_size_per_card: 256
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drop_last: True
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num_workers: 8
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/ic15_data
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label_file_list: ["train_data/ic15_data/rec_gt_test.txt"]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- CTCLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 320]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: False
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drop_last: False
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batch_size_per_card: 256
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num_workers: 8
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===========================train_params===========================
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model_name:ch_ppocr_mobile_v2.0_rec_PACT
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python:python3.7
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gpu_list:0
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Global.use_gpu:True|True
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Global.auto_cast:null
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Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
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Global.save_model_dir:./output/
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Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
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Global.checkpoints:null
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train_model_name:latest
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train_infer_img_dir:./train_data/ic15_data/test/word_1.png
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null:null
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##
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trainer:pact_train
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norm_train:null
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pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml -o
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fpgm_train:null
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distill_train:null
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null:null
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null:null
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##
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===========================eval_params===========================
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eval:null
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null:null
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##
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===========================infer_params===========================
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Global.save_inference_dir:./output/
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Global.checkpoints:
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norm_export:null
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quant_export:deploy/slim/quantization/export_model.py -ctest_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml -o
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fpgm_export:null
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distill_export:null
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export1:null
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export2:null
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inference_dir:null
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train_model:null
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infer_export:null
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infer_quant:False
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inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --rec_image_shape="3,32,100"
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--use_gpu:True|False
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--enable_mkldnn:True|False
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--cpu_threads:1|6
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--rec_batch_num:1|6
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--use_tensorrt:False|True
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--precision:fp32|fp16|int8
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--rec_model_dir:
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--image_dir:./inference/rec_inference
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--save_log_path:./test/output/
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--benchmark:True
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null:null
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