first commit

test_tipc/configs/rec_resnet_rfl/rec_resnet_rfl.yml

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+Global:
+  use_gpu: True
+  epoch_num: 6
+  log_smooth_window: 20
+  print_batch_step: 50
+  save_model_dir: ./output/rec/rec_resnet_rfl/
+  save_epoch_step: 1
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 5000]
+  cal_metric_during_train: False
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/rec_resnet_rfl.txt
+
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  weight_decay: 0.0
+  clip_norm_global: 5.0
+  lr:
+    name: Piecewise
+    decay_epochs : [3, 4, 5]
+    values : [0.001, 0.0003, 0.00009, 0.000027]
+
+Architecture:
+  model_type: rec
+  algorithm: RFL
+  in_channels: 1
+  Transform:
+    name: TPS
+    num_fiducial: 20
+    loc_lr: 1.0
+    model_name: large
+  Backbone:
+    name: ResNetRFL
+    use_cnt: True
+    use_seq: True
+  Neck:
+    name: RFAdaptor
+    use_v2s: True
+    use_s2v: True
+  Head:
+    name: RFLHead  
+    in_channels: 512
+    hidden_size: 256
+    batch_max_legnth: 25
+    out_channels: 38
+    use_cnt: True
+    use_seq: True
+
+Loss:
+  name: RFLLoss
+
+PostProcess:
+  name: RFLLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RFLLabelEncode: # Class handling label
+      - RFLRecResizeImg:
+          image_shape: [1, 32, 100]
+          interpolation: 2
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length', 'cnt_label'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 64
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RFLLabelEncode: # Class handling label
+      - RFLRecResizeImg:
+          image_shape: [1, 32, 100]
+          interpolation: 2
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length', 'cnt_label'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

test_tipc/configs/rec_resnet_rfl/train_infer_python.txt

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+===========================train_params===========================
+model_name:rec_resnet_rfl
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=64
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_resnet_rfl/rec_resnet_rfl.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_resnet_rfl/rec_resnet_rfl.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_resnet_rfl/rec_resnet_rfl.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/rec_resnet_rfl_train/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/configs/rec_resnet_rfl/rec_resnet_rfl.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_image_shape="1,32,100" --rec_algorithm="RFL" --min_subgraph_size=5
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[1,32,100]}]