modify caffe

x2paddle/op_mapper/caffe_op_mapper.py

@@ -27,34 +27,12 @@ class CaffeOpMapper(OpMapper):
         self.weights = dict()
         resolver = decoder.resolver
         self.used_custom_layers = {}
-        self.inputs = self.graph.input_nodes
-        self.outputs = self.graph.output_nodes
         if resolver.has_pycaffe():
             self.did_use_pb = False
         else:
             self.did_use_pb = True
 
-    def op_checker(self):
-        unsupported_ops = set()
-        for node_name in self.graph.topo_sort:
-            node = self.graph.get_node(node_name)
-            op = node.layer_type
-            if not hasattr(self, op) and op not in custom_layers:
-                unsupported_ops.add(op)
-        if len(unsupported_ops) == 0:
-            return True
-        else:
-            print("There are {} ops not supported yet, list as below".format(
-                len(unsupported_ops)))
-            for op in unsupported_ops:
-                print(op)
-            return False
-
-    def run(self):
         print("Total nodes: {}".format(len(self.graph.topo_sort)))
-        # check if ops in model are all supported
-        if not self.op_checker():
-            raise Exception("Model are not supported yet.")
         for node_name in self.graph.topo_sort:
             node = self.graph.get_node(node_name)
             op = node.layer_type
@@ -67,13 +45,22 @@ class CaffeOpMapper(OpMapper):
                 self.deal_custom_layer(node)
             else:
                 raise Exception("Model are not supported yet.")
-        for key in self.used_custom_layers:
-            self.net_code.append(self.used_custom_layers[key])
 
-        for i in range(len(self.graph.topo_sort)):
-            node_name = self.graph.topo_sort[i]
+    def op_checker(self):
+        unsupported_ops = set()
+        for node_name in self.graph.topo_sort:
             node = self.graph.get_node(node_name)
-            self.net_code += node.fluid_code.gen_codes()
+            op = node.layer_type
+            if not hasattr(self, op) and op not in custom_layers:
+                unsupported_ops.add(op)
+        if len(unsupported_ops) == 0:
+            return True
+        else:
+            print("There are {} ops not supported yet, list as below".format(
+                len(unsupported_ops)))
+            for op in unsupported_ops:
+                print(op)
+            return False
 
     def set_shape(self, node, is_fluid_op=True):
         inputs = node.inputs