set mode of DB head as 'export' when export model

ppocr/modeling/architectures/det_model.py

@@ -109,7 +109,10 @@ class DetModel(object):
         """
         image, labels, loader = self.create_feed(mode)
         conv_feas = self.backbone(image)
-        predicts = self.head(conv_feas)
+        if self.algorithm == "DB":
+            predicts = self.head(conv_feas, mode)
+        else:
+            predicts = self.head(conv_feas)
         if mode == "train":
             losses = self.loss(predicts, labels)
             return loader, losses

ppocr/modeling/heads/det_db_head.py

@@ -196,7 +196,7 @@ class DBHead(object):
         fuse = fluid.layers.concat(input=[p5, p4, p3, p2], axis=1)
         shrink_maps = self.binarize(fuse)
         if mode != "train":
-            return {"maps", shrink_maps}
+            return {"maps": shrink_maps}
         threshold_maps = self.thresh(fuse)
         binary_maps = self.step_function(shrink_maps, threshold_maps)
         y = fluid.layers.concat(

tools/program.py

@@ -191,7 +191,7 @@ def build_export(config, main_prog, startup_prog):
             func_infor = config['Architecture']['function']
             model = create_module(func_infor)(params=config)
             image, outputs = model(mode='export')
-            fetches_var_name = sorted([name for name in outputs])
+            fetches_var_name = sorted([name for name in outputs.keys()])
             fetches_var = [outputs[name] for name in fetches_var_name]
     feeded_var_names = [image.name]
     target_vars = fetches_var
@@ -271,7 +271,7 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
             train_loader.reset()
         if epoch == 0 and save_epoch_step == 1:
             save_path = save_model_dir + "/iter_epoch_0"
-            save_model(train_info_dict['train_program'],save_path)
+            save_model(train_info_dict['train_program'], save_path)
         if epoch > 0 and epoch % save_epoch_step == 0:
             save_path = save_model_dir + "/iter_epoch_%d" % (epoch)
             save_model(train_info_dict['train_program'], save_path)
@@ -350,7 +350,7 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
             train_loader.reset()
         if epoch == 0 and save_epoch_step == 1:
             save_path = save_model_dir + "/iter_epoch_0"
-            save_model(train_info_dict['train_program'],save_path)
+            save_model(train_info_dict['train_program'], save_path)
         if epoch > 0 and epoch % save_epoch_step == 0:
             save_path = save_model_dir + "/iter_epoch_%d" % (epoch)
             save_model(train_info_dict['train_program'], save_path)