fix the unpack

x2paddle/core/program.py

@@ -76,6 +76,7 @@ class PaddleGraph(object):
         self.source_type = source_type
         self.custom_code = None
         self.inputs_info = None
+        self.has_unpack = False
 
     def set_name(self, name):
         self.name = name.replace("-", "_").replace("/", "_")
@@ -112,6 +113,8 @@ class PaddleGraph(object):
                                          layer_id)
         layer = PaddleLayer(layer_id, kernel, inputs, outputs, scope_name=scope_name, **kwargs)
         self.layers[layer_id] = layer
+        if layer.kernel in ["prim.list_unpack" or "prim.tuple_unpack"]:
+            self.has_unpack = True
         return layer_id
 
     def del_layer(self, layer_id):
@@ -272,12 +275,16 @@ class PaddleGraph(object):
                 
     def gen_dygraph_model(self, save_dir, jit_type=None):
         if jit_type == "trace":
-            from x2paddle.optimizer.pytorch_code_optimizer import HierarchicalTree
-            hierarchical_tree = HierarchicalTree(self)
-            for layer_id, layer in self.layers.items():
-                hierarchical_tree.insert(layer)
-            hierarchical_tree.save_source_files(save_dir)
-            self.dump_dygraph_parameter(save_dir)
+            if not self.has_unpack:
+                from x2paddle.optimizer.pytorch_code_optimizer import HierarchicalTree
+                hierarchical_tree = HierarchicalTree(self)
+                for layer_id, layer in self.layers.items():
+                    hierarchical_tree.insert(layer)
+                hierarchical_tree.save_source_files(save_dir)
+                self.dump_dygraph_parameter(save_dir)
+            else:
+                self.gen_dygraph_code(save_dir)
+                self.dump_dygraph_parameter(save_dir)
         else:
             if self.source_type == "pytorch":
                 from x2paddle.optimizer.pytorch_code_optimizer import ModuleGraph

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -313,6 +313,9 @@ class OpSet9():
                 inputs['size'] = var_hw
                 attrs = {"align_corners": False,
                          "mode": string(node.get_attr('mode', 'nearest'))}
+                val_x_shape = val_x.out_shapes[0]
+                if len(val_x_shape) == 4:
+                    attrs["mode"] = string("bilinear")
                 self.paddle_graph.add_layer(
                     kernel="paddle.nn.functional.interpolate",
                     inputs=inputs,