remove det_mv3_db_v1.1.yml & add code annotation

configs/det/det_mv3_db.yml

@@ -24,6 +24,7 @@ Backbone:
   function: ppocr.modeling.backbones.det_mobilenet_v3,MobileNetV3
   scale: 0.5
   model_name: large
+  disable_se: true
 
 Head:
   function: ppocr.modeling.heads.det_db_head,DBHead

configs/det/det_mv3_db_v1.1.yml

-Global:
-  algorithm: DB
-  use_gpu: true
-  epoch_num: 1200
-  log_smooth_window: 20
-  print_batch_step: 2
-  save_model_dir: ./output/det_db/
-  save_epoch_step: 200
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: [4000, 5000]
-  train_batch_size_per_card: 16
-  test_batch_size_per_card: 16
-  image_shape: [3, 640, 640]
-  reader_yml: ./configs/det/det_db_icdar15_reader.yml
-  pretrain_weights: ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
-  checkpoints:
-  save_res_path: ./output/det_db/predicts_db.txt
-  save_inference_dir:
-  
-Architecture:
-  function: ppocr.modeling.architectures.det_model,DetModel
-
-Backbone:
-  function: ppocr.modeling.backbones.det_mobilenet_v3,MobileNetV3
-  scale: 0.5
-  model_name: large
-  disable_se: true
-
-Head:
-  function: ppocr.modeling.heads.det_db_head,DBHead
-  model_name: large
-  k: 50
-  inner_channels: 96
-  out_channels: 2
-
-Loss:
-  function: ppocr.modeling.losses.det_db_loss,DBLoss
-  balance_loss: true
-  main_loss_type: DiceLoss
-  alpha: 5
-  beta: 10
-  ohem_ratio: 3
-
-Optimizer:
-  function: ppocr.optimizer,AdamDecay
-  base_lr: 0.001
-  beta1: 0.9
-  beta2: 0.999
-
-PostProcess:
-  function: ppocr.postprocess.db_postprocess,DBPostProcess
-  thresh: 0.3
-  box_thresh: 0.6
-  max_candidates: 1000
-  unclip_ratio: 1.5

tools/program.py

@@ -204,6 +204,15 @@ def build(config, main_prog, startup_prog, mode):
 
 def build_export(config, main_prog, startup_prog):
     """
+    Build input and output for exporting a checkpoints model to an inference model
+    Args:
+        config(dict): config
+        main_prog(): main program
+        startup_prog(): startup program
+    Returns:
+        feeded_var_names(list[str]): var names of input for exported inference model
+        target_vars(list[Variable]): output vars for exported inference model
+        fetches_var_name: dict of checkpoints model outputs(included loss and measures)
     """
     with fluid.program_guard(main_prog, startup_prog):
         with fluid.unique_name.guard():
@@ -246,6 +255,9 @@ def train_eval_det_run(config,
                        train_info_dict,
                        eval_info_dict,
                        is_pruning=False):
+    '''
+    main program of evaluation for detection
+    '''
     train_batch_id = 0
     log_smooth_window = config['Global']['log_smooth_window']
     epoch_num = config['Global']['epoch_num']
@@ -337,6 +349,9 @@ def train_eval_det_run(config,
 
 
 def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
+    '''
+    main program of evaluation for recognition
+    '''
     train_batch_id = 0
     log_smooth_window = config['Global']['log_smooth_window']
     epoch_num = config['Global']['epoch_num']
@@ -513,6 +528,7 @@ def train_eval_cls_run(config, exe, train_info_dict, eval_info_dict):
 
 
 def preprocess():
+    # load config from yml file
     FLAGS = ArgsParser().parse_args()
     config = load_config(FLAGS.config)
     merge_config(FLAGS.opt)
@@ -522,6 +538,7 @@ def preprocess():
     use_gpu = config['Global']['use_gpu']
     check_gpu(use_gpu)
 
+    # check whether the set algorithm belongs to the supported algorithm list
     alg = config['Global']['algorithm']
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'

tools/train.py

@@ -46,6 +46,7 @@ from paddle.fluid.contrib.model_stat import summary
 
 
 def main():
+    # build train program
     train_build_outputs = program.build(
         config, train_program, startup_program, mode='train')
     train_loader = train_build_outputs[0]
@@ -54,6 +55,7 @@ def main():
     train_opt_loss_name = train_build_outputs[3]
     model_average = train_build_outputs[-1]
 
+    # build eval program
     eval_program = fluid.Program()
     eval_build_outputs = program.build(
         config, eval_program, startup_program, mode='eval')
@@ -61,9 +63,11 @@ def main():
     eval_fetch_varname_list = eval_build_outputs[2]
     eval_program = eval_program.clone(for_test=True)
 
+    # initialize train reader
     train_reader = reader_main(config=config, mode="train")
     train_loader.set_sample_list_generator(train_reader, places=place)
 
+    # initialize eval reader
     eval_reader = reader_main(config=config, mode="eval")
 
     exe = fluid.Executor(place)