revert trainer

ppcls/engine/trainer.py

@@ -38,7 +38,6 @@ from ppcls.utils.config import print_config
 from ppcls.data import build_dataloader
 from ppcls.arch import build_model
 from ppcls.arch import apply_to_static
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
 from ppcls.loss import build_loss
 from ppcls.metric import build_metrics
 from ppcls.optimizer import build_optimizer
@@ -76,11 +75,6 @@ class Trainer(object):
             self.is_rec = False
 
         self.model = build_model(self.config["Arch"])
-        if "return_patterns" in self.config["Arch"] and isinstance(self.model, TheseusLayer):
-            self.model.update_res(self.config["Arch"]["return_patterns"])
-            self.return_inter = True
-        else:
-            self.return_inter = False
         # set @to_static for benchmark, skip this by default.
         apply_to_static(self.config, self.model)
 
@@ -397,11 +391,11 @@ class Trainer(object):
         self.model.train()
         return eval_result
 
-    def forward(self, batch, res_dict=None):
+    def forward(self, batch):
         if not self.is_rec:
-            out = self.model(batch[0], res_dict=res_dict)
+            out = self.model(batch[0])
         else:
-            out = self.model(batch[0], batch[1], return_dict=res_dict)
+            out = self.model(batch[0], batch[1])
         return out
 
     @paddle.no_grad()
@@ -659,11 +653,7 @@ class Trainer(object):
             image_file_list.append(image_file)
             if len(batch_data) >= batch_size or idx == len(image_list) - 1:
                 batch_tensor = paddle.to_tensor(batch_data)
-                if self.return_inter:
-                    res_dict = {}
-                else:
-                    res_dict = None
-                out = self.forward([batch_tensor], res_dict)
+                out = self.forward([batch_tensor])
                 if isinstance(out, list):
                     out = out[0]
                 result = postprocess_func(out, image_file_list)