add pse to benchmark

test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml

-Global:
-  use_gpu: true
-  epoch_num: 600
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output/det_r50_vd_pse/
-  save_epoch_step: 600
-  # evaluation is run every 125 iterations
-  eval_batch_step: [ 0,1000 ]
-  cal_metric_during_train: False
-  pretrained_model: 
-  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
-  save_inference_dir:
-  use_visualdl: False
-  infer_img: doc/imgs_en/img_10.jpg
-  save_res_path: ./output/det_pse/predicts_pse.txt
-
-Architecture:
-  model_type: det
-  algorithm: PSE
-  Transform:
-  Backbone:
-    name: ResNet
-    layers: 50
-  Neck:
-    name: FPN
-    out_channels: 256
-  Head:
-    name: PSEHead
-    hidden_dim: 256
-    out_channels: 7
-
-Loss:
-  name: PSELoss
-  alpha: 0.7
-  ohem_ratio: 3
-  kernel_sample_mask: pred
-  reduction: none
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  lr:
-    name: Step
-    learning_rate: 0.0001
-    step_size: 200
-    gamma: 0.1
-  regularizer:
-    name: 'L2'
-    factor: 0.0005
-
-PostProcess:
-  name: PSEPostProcess
-  thresh: 0
-  box_thresh: 0.85
-  min_area: 16
-  box_type: box # 'box' or 'poly'
-  scale: 1
-
-Metric:
-  name: DetMetric
-  main_indicator: hmean
-
-Train:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./train_data/icdar2015/text_localization/
-    label_file_list:
-      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
-    ratio_list: [ 1.0 ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - DetLabelEncode: # Class handling label
-      - ColorJitter:
-          brightness: 0.12549019607843137
-          saturation: 0.5
-      - IaaAugment:
-          augmenter_args:
-            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
-            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
-            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
-      - MakePseGt:
-          kernel_num: 7
-          min_shrink_ratio: 0.4
-          size: 640
-      - RandomCropImgMask:
-          size: [ 640,640 ]
-          main_key: gt_text
-          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
-      - NormalizeImage:
-          scale: 1./255.
-          mean: [ 0.485, 0.456, 0.406 ]
-          std: [ 0.229, 0.224, 0.225 ]
-          order: 'hwc'
-      - ToCHWImage:
-      - KeepKeys:
-          keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
-  loader:
-    shuffle: True
-    drop_last: False
-    batch_size_per_card: 8
-    num_workers: 8
-
-Eval:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./train_data/icdar2015/text_localization/
-    label_file_list:
-      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
-    ratio_list: [ 1.0 ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - DetLabelEncode: # Class handling label
-      - DetResizeForTest:
-          limit_side_len: 736
-          limit_type: min
-      - NormalizeImage:
-          scale: 1./255.
-          mean: [ 0.485, 0.456, 0.406 ]
-          std: [ 0.229, 0.224, 0.225 ]
-          order: 'hwc'
-      - ToCHWImage:
-      - KeepKeys:
-          keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 1 # must be 1
-    num_workers: 8
\ No newline at end of file

test_tipc/configs/det_r50_vd_pse_v2.0/train_infer_python.txt

-===========================train_params===========================
-model_name:det_r50_vd_pse_v2.0
-python:python3.7
-gpu_list:0
-Global.use_gpu:True|True
-Global.auto_cast:fp32
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
-Global.pretrained_model:null
-train_model_name:latest
-train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
-null:null
-##
-trainer:norm_train
-norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
-pact_train:null
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params=========================== 
-eval:null
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.checkpoints:
-norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
-quant_export:null 
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-##
-train_model:./inference/det_r50_vd_pse_v2.0_train/best_accuracy
-infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml  -o
-infer_quant:False
-inference:tools/infer/predict_det.py
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False
---precision:fp32|fp16|int8
---det_model_dir:
---image_dir:./inference/ch_det_data_50/all-sum-510/
---save_log_path:null
---benchmark:True
---det_algorithm:PSE

test_tipc/prepare.sh

@@ -223,7 +223,7 @@ elif [ ${MODE} = "whole_infer" ];then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar --no-check-certificate
         cd ./inference/ && tar xf det_mv3_pse_v2.0_train.tar & cd ../
     fi
-    if [ ${model_name} == "det_r50_vd_pse_v2.0" ]; then
+    if [ ${model_name} == "det_r50_vd_pse_v2_0" ]; then
         wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar  --no-check-certificate
         cd ./inference/ && tar xf det_r50_vd_pse_v2.0_train.tar & cd ../
     fi