yml文件去除个人路径

configs/det/det_r50_vd_db.yml

@@ -3,15 +3,15 @@ Global:
   epoch_num: 1200
   log_smooth_window: 20
   print_batch_step: 2
-  save_model_dir: ./output/20201015_r50/
+  save_model_dir: ./output/det_r50_vd/
   save_epoch_step: 1200
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: 8
   # if pretrained_model is saved in static mode, load_static_weights must set to True
   load_static_weights: True
   cal_metric_during_train: False
-  pretrained_model: /home/zhoujun20/pretrain_models/ResNet50_vd_ssld_pretrained/
-  checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
+  checkpoints:
   save_inference_dir:
   use_visualdl: True
   infer_img: doc/imgs_en/img_10.jpg
@@ -65,9 +65,9 @@ Metric:
 TRAIN:
   dataset:
     name: SimpleDataSet
-    data_dir: /home/zhoujun20/detection/
+    data_dir: ./detection/
     file_list:
-      - /home/zhoujun20/detection/train_icdar2015_label.txt # dataset1
+      - ./detection/train_icdar2015_label.txt # dataset1
     ratio_list: [1.0]
     transforms:
       - DecodeImage: # load image
@@ -107,9 +107,9 @@ TRAIN:
 EVAL:
   dataset:
     name: SimpleDataSet
-    data_dir: /home/zhoujun20/detection/
+    data_dir: ./detection/
     file_list:
-      - /home/zhoujun20/detection/test_icdar2015_label.txt
+      - ./detection/test_icdar2015_label.txt
     transforms:
       - DecodeImage: # load image
           img_mode: BGR

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -68,7 +68,7 @@ TRAIN:
     name: SimpleDataSet
     data_dir: ./rec
     file_list:
-      - ./rec/real_data.txt # dataset1
+      - ./rec/train.txt # dataset1
     ratio_list: [ 0.4,0.6 ]
     transforms:
       - DecodeImage: # load image
@@ -91,7 +91,7 @@ EVAL:
     name: SimpleDataSet
     data_dir: ./rec
     file_list:
-      - ./rec/label_val_all.txt
+      - ./rec/val.txt
     transforms:
       - DecodeImage: # load image
           img_mode: BGR

configs/rec/rec_mv3_none_none_ctc_lmdb.yml

 Global:
   use_gpu: false
-  epoch_num: 500
+  epoch_num: 72
   log_smooth_window: 20
-  print_batch_step: 1
-  save_model_dir: ./output/rec/test/
+  print_batch_step: 10
+  save_model_dir: ./output/rec/mv3_none_none_ctc/
   save_epoch_step: 500
   # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 1016
+  eval_batch_step: 2000
   # if pretrained_model is saved in static mode, load_static_weights must set to True
   load_static_weights: True
   cal_metric_during_train: True
   pretrained_model:
-  checkpoints: #output/rec/rec_crnn/best_accuracy
+  checkpoints:
   save_inference_dir:
   use_visualdl: True
   infer_img: doc/imgs_words/ch/word_1.jpg
   # for data or label process
-  max_text_length: 80
-  character_dict_path: /home/zhoujun20/rec/lmdb/dict.txt
+  max_text_length: 25
+  character_dict_path:
   character_type: 'en'
-  use_space_char: True
+  use_space_char: False
   infer_mode: False
   use_tps: False
 
@@ -29,9 +29,9 @@ Optimizer:
   beta1: 0.9
   beta2: 0.999
   learning_rate:
-    name: Cosine
+#    name: Cosine
     lr: 0.0005
-    warmup_epoch: 1
+#    warmup_epoch: 1
   regularizer:
     name: 'L2'
     factor: 0.00001
@@ -43,7 +43,7 @@ Architecture:
   Backbone:
     name: MobileNetV3
     scale: 0.5
-    model_name: small
+    model_name: large
     small_stride: [ 1, 2, 2, 2 ]
   Neck:
     name: SequenceEncoder
@@ -66,7 +66,7 @@ TRAIN:
   dataset:
     name: LMDBDateSet
     file_list:
-      - /Users/zhoujun20/Downloads/evaluation_new # dataset1
+      - ./rec/train # dataset1
     ratio_list: [ 0.4,0.6 ]
     transforms:
       - DecodeImage: # load image
@@ -75,7 +75,7 @@ TRAIN:
       - CTCLabelEncode: # Class handling label
       - RecAug:
       - RecResizeImg:
-          image_shape: [ 3,32,320 ]
+          image_shape: [ 3,32,100 ]
       - keepKeys:
           keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
   loader:
@@ -88,14 +88,14 @@ EVAL:
   dataset:
     name: LMDBDateSet
     file_list:
-      - /home/zhoujun20/rec/lmdb/val
+      - ./rec/val/
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
       - CTCLabelEncode: # Class handling label
       - RecResizeImg:
-          image_shape: [ 3,32,320 ]
+          image_shape: [ 3,32,100 ]
       - keepKeys:
           keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
   loader:

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -64,9 +64,9 @@ Metric:
 TRAIN:
   dataset:
     name: SimpleDataSet
-    data_dir: /home/zhoujun20/rec
+    data_dir: ./rec
     file_list:
-      - /home/zhoujun20/rec/real_data.txt # dataset1
+      - ./rec/train.txt # dataset1
     ratio_list: [ 0.4,0.6 ]
     transforms:
       - DecodeImage: # load image
@@ -87,9 +87,9 @@ TRAIN:
 EVAL:
   dataset:
     name: SimpleDataSet
-    data_dir: /home/zhoujun20/rec
+    data_dir: ./rec
     file_list:
-      - /home/zhoujun20/rec/label_val_all.txt
+      - ./rec/val.txt
     transforms:
       - DecodeImage: # load image
           img_mode: BGR

configs/rec/rec_r34_vd_none_none_ctc.yml

+Global:
+  use_gpu: false
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/res34_none_none_ctc/
+  save_epoch_step: 500
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: 127
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  load_static_weights: True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  max_text_length: 80
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  character_type: 'ch'
+  use_space_char: False
+  infer_mode: False
+  use_tps: False
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  learning_rate:
+    name: Cosine
+    lr: 0.001
+    warmup_epoch: 4
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 34
+  Neck:
+    name: SequenceEncoder
+    encoder_type: reshape
+  Head:
+    name: CTC
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+TRAIN:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./rec
+    file_list:
+      - ./rec/train.txt # dataset1
+    ratio_list: [ 0.4,0.6 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecAug:
+      - RecResizeImg:
+          image_shape: [ 3,32,320 ]
+      - keepKeys:
+          keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
+  loader:
+    batch_size: 256
+    shuffle: True
+    drop_last: True
+    num_workers: 8
+
+EVAL:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./rec
+    file_list:
+      - ./rec/val.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [ 3,32,320 ]
+      - keepKeys:
+          keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size: 256
+    num_workers: 8