modify code

ppcls/arch/backbone/base/theseus_layer.py

@@ -32,7 +32,7 @@ class TheseusLayer(nn.Layer):
                     stop_layer_name)
         return after_stop
 
-    def _update_res(self, return_patterns):
+    def update_res(self, return_patterns):
         if not return_patterns:
             return
         for layer_i in self._sub_layers:
@@ -47,7 +47,7 @@ class TheseusLayer(nn.Layer):
                         self._sub_layers[layer_i]._save_sub_res_hook)
                     self._sub_layers[layer_i].res_dict = self.res_dict
             if isinstance(self._sub_layers[layer_i], TheseusLayer):
-                self._sub_layers[layer_i]._update_res(return_patterns)
+                self._sub_layers[layer_i].update_res(return_patterns)
 
     def _save_sub_res_hook(self, layer, input, output):
         if self.res_dict is not None:

ppcls/arch/backbone/legendary_models/vgg.py

@@ -137,7 +137,7 @@ class VGGNet(TheseusLayer):
         self.fc1 = Linear(7 * 7 * 512, 4096)
         self.fc2 = Linear(4096, 4096)
         self.fc3 = Linear(4096, class_num)
-        self._update_res(return_patterns)
+        self.update_res(return_patterns)
 
     def forward(self, inputs, res_dict=None):
         x = self.conv_block_1(inputs)

ppcls/engine/trainer.py

@@ -396,11 +396,7 @@ class Trainer(object):
         self.model.train()
         return eval_result
 
-    def forward(self, batch):
-        if self.return_inter:
-            res_dict = {}
-        else:
-            res_dict = None
+    def forward(self, batch, res_dict=None):
         if not self.is_rec:
             out = self.model(batch[0], res_dict=res_dict)
         else:
@@ -662,7 +658,11 @@ 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)
-                out = self.forward([batch_tensor])
+                if self.return_inter:
+                    res_dict = {}
+                else:
+                    res_dict = None
+                out = self.forward([batch_tensor], res_dict)
                 if isinstance(out, list):
                     out = out[0]
                 result = postprocess_func(out, image_file_list)