fix2

x2paddle/core/program.py

@@ -285,14 +285,14 @@ class PaddleGraph(object):
             hierarchical_tree.save_source_files(save_dir)
             self.dump_dygraph_parameter(save_dir)
         else:
-#             if self.source_type == "pytorch":
-#                 from x2paddle.optimizer.pytorch_code_optimizer import ModuleGraph
-#                 module_graph = ModuleGraph(self)
-#                 module_graph.save_source_files(save_dir)
-#                 self.dump_dygraph_parameter(save_dir)
-#             else:
-            self.gen_dygraph_code(save_dir)
-            self.dump_dygraph_parameter(save_dir)
+            if self.source_type == "pytorch":
+                from x2paddle.optimizer.pytorch_code_optimizer import ModuleGraph
+                module_graph = ModuleGraph(self)
+                module_graph.save_source_files(save_dir)
+                self.dump_dygraph_parameter(save_dir)
+            else:
+                self.gen_dygraph_code(save_dir)
+                self.dump_dygraph_parameter(save_dir)
         # 动转静
         code_path = osp.join(osp.abspath(save_dir), "x2paddle_code.py")
         print("Exporting inference model from python code ('{}')... \n".format(code_path))

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -4511,10 +4511,10 @@ def aten_upsample_bilinear2d(mapper, graph, node):
                             current_outputs, scope_name)
         layer_inputs["align_corners"] = inputs_name[2]
         current_inputs.append(inputs_name[2])
-#     if "size" in layer_attrs and layer_attrs["size"] is None:
-#     mapper._check_input(graph, inputs_node[3], inputs_name[3],
-#                         current_outputs, scope_name)
-#     layer_inputs["scale_factor"] = inputs_name[3]
+    if "size" in layer_attrs and layer_attrs["size"] is None:
+        mapper._check_input(graph, inputs_node[3], inputs_name[3],
+                            current_outputs, scope_name)
+        layer_inputs["scale_factor"] = inputs_name[3]
     layer_attrs["align_mode"] = 0
     layer_attrs["mode"] = string("bilinear")
     graph.add_layer(

x2paddle/optimizer/optimizer.py

@@ -28,11 +28,11 @@ class GraphOptimizer(object):
                     "dygraph_constant_fuse_pass", 
                     "dygraph_batchnorm2d_fuse_pass",
                     "dygraph_interpolate_bilinear_fuse_pass", 
-#                     "dygraph_fc_fuse_pass",
-#                     "dygraph_adaptive_pool2d_fuse_pass", 
-#                     "dygraph_reshape_fuse_pass",
-#                     "dygraph_dropout_fuse_pass",
-#                     "dygraph_if_fuse_pass"
+                    "dygraph_fc_fuse_pass",
+                    "dygraph_adaptive_pool2d_fuse_pass", 
+                    "dygraph_reshape_fuse_pass",
+                    "dygraph_dropout_fuse_pass",
+                    "dygraph_if_fuse_pass"
                 ]
         elif source_frame == "caffe":
             if paddle_type == "dygraph":

x2paddle/optimizer/pattern_matcher.py

@@ -130,8 +130,6 @@ class PatternMatcher(object):
                                 if is_pop:
                                     subgraph_id2layers.pop(layer_id)
                                     continue
-                    if layer_id not in subgraph_id2layers:
-                        continue
                     # 当为控制流时的处理
                     if layer.kernel == "prim.if" or layer.kernel == "prim.loop":
                         if len(pattern_layer.blocks) != len(layer.blocks):
@@ -156,7 +154,6 @@ class PatternMatcher(object):
                             if pattern_index == 0 or is_subblock:
                                 return False
                             else:
-                                print(subgraph_id2layers.keys())
                                 index = list(subgraph_id2layers.keys()).index(
                                     layer_id)
                                 for key in list(subgraph_id2layers.keys())[