add db++

configs/det/det_r50_db++_ic15.yml

@@ -18,7 +18,7 @@ Global:
   save_res_path: ./checkpoints/det_db/predicts_db.txt
 Architecture:
   model_type: det
-  algorithm: DB
+  algorithm: DB++
   Transform: null
   Backbone:
     name: ResNet

configs/det/det_r50_db++_td_tr.yml

@@ -18,7 +18,7 @@ Global:
   save_res_path: ./checkpoints/det_db/predicts_db.txt
 Architecture:
   model_type: det
-  algorithm: DB
+  algorithm: DB++
   Transform: null
   Backbone:
     name: ResNet

tools/infer/predict_det.py

@@ -67,6 +67,23 @@ class TextDetector(object):
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
             postprocess_params["use_dilation"] = args.use_dilation
             postprocess_params["score_mode"] = args.det_db_score_mode
+        elif self.det_algorithm == "DB++":
+            postprocess_params['name'] = 'DBPostProcess'
+            postprocess_params["thresh"] = args.det_db_thresh
+            postprocess_params["box_thresh"] = args.det_db_box_thresh
+            postprocess_params["max_candidates"] = 1000
+            postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
+            postprocess_params["use_dilation"] = args.use_dilation
+            postprocess_params["score_mode"] = args.det_db_score_mode
+            pre_process_list[1] = {
+                'NormalizeImage': {
+                    'std': [1.0, 1.0, 1.0],
+                    'mean':
+                    [0.48109378172549, 0.45752457890196, 0.40787054090196],
+                    'scale': '1./255.',
+                    'order': 'hwc'
+                }
+            }
         elif self.det_algorithm == "EAST":
             postprocess_params['name'] = 'EASTPostProcess'
             postprocess_params["score_thresh"] = args.det_east_score_thresh
@@ -231,7 +248,7 @@ class TextDetector(object):
             preds['f_score'] = outputs[1]
             preds['f_tco'] = outputs[2]
             preds['f_tvo'] = outputs[3]
-        elif self.det_algorithm in ['DB', 'PSE']:
+        elif self.det_algorithm in ['DB', 'PSE', 'DB++']:
             preds['maps'] = outputs[0]
         elif self.det_algorithm == 'FCE':
             for i, output in enumerate(outputs):

tools/program.py

@@ -307,7 +307,8 @@ def train(config,
             train_stats.update(stats)
 
             if log_writer is not None and dist.get_rank() == 0:
-                log_writer.log_metrics(metrics=train_stats.get(), prefix="TRAIN", step=global_step)
+                log_writer.log_metrics(
+                    metrics=train_stats.get(), prefix="TRAIN", step=global_step)
 
             if dist.get_rank() == 0 and (
                 (global_step > 0 and global_step % print_batch_step == 0) or
@@ -354,7 +355,8 @@ def train(config,
 
                 # logger metric
                 if log_writer is not None:
-                    log_writer.log_metrics(metrics=cur_metric, prefix="EVAL", step=global_step)
+                    log_writer.log_metrics(
+                        metrics=cur_metric, prefix="EVAL", step=global_step)
 
                 if cur_metric[main_indicator] >= best_model_dict[
                         main_indicator]:
@@ -377,11 +379,18 @@ def train(config,
                 logger.info(best_str)
                 # logger best metric
                 if log_writer is not None:
-                    log_writer.log_metrics(metrics={
-                        "best_{}".format(main_indicator): best_model_dict[main_indicator]
-                        }, prefix="EVAL", step=global_step)
-                    
-                    log_writer.log_model(is_best=True, prefix="best_accuracy", metadata=best_model_dict)
+                    log_writer.log_metrics(
+                        metrics={
+                            "best_{}".format(main_indicator):
+                            best_model_dict[main_indicator]
+                        },
+                        prefix="EVAL",
+                        step=global_step)
+
+                    log_writer.log_model(
+                        is_best=True,
+                        prefix="best_accuracy",
+                        metadata=best_model_dict)
 
             reader_start = time.time()
         if dist.get_rank() == 0:
@@ -413,7 +422,8 @@ def train(config,
                 epoch=epoch,
                 global_step=global_step)
             if log_writer is not None:
-                log_writer.log_model(is_best=False, prefix='iter_epoch_{}'.format(epoch))
+                log_writer.log_model(
+                    is_best=False, prefix='iter_epoch_{}'.format(epoch))
 
     best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
@@ -564,7 +574,7 @@ def preprocess(is_train=False):
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
-        'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR'
+        'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR', 'DB++'
     ]
 
     if use_xpu:
@@ -585,7 +595,8 @@ def preprocess(is_train=False):
         vdl_writer_path = '{}/vdl/'.format(save_model_dir)
         log_writer = VDLLogger(save_model_dir)
         loggers.append(log_writer)
-    if ('use_wandb' in config['Global'] and config['Global']['use_wandb']) or 'wandb' in config:
+    if ('use_wandb' in config['Global'] and
+            config['Global']['use_wandb']) or 'wandb' in config:
         save_dir = config['Global']['save_model_dir']
         wandb_writer_path = "{}/wandb".format(save_dir)
         if "wandb" in config: