little modify

x2paddle/core/util.py

@@ -38,9 +38,3 @@ def run_net(param_dir="./"):
                        param_dir,
                        fluid.default_main_program(),
                        predicate=if_exist)
-
-    fluid.io.save_inference_model(dirname='inference_model',
-                                  feeded_var_names=[i.name for i in inputs],
-                                  target_vars=outputs,
-                                  executor=exe,
-                                  params_filename="__params__")

x2paddle/decoder/tf_decoder.py

+#coding:utf-8
 #   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"
@@ -181,24 +182,6 @@ class TFGraph(Graph):
 
             self.identity_map[node_name] = input_node.layer_name
 
-            #            node = self.get_node(node_name)
-            #            # Remind: Only 1 input for Identity node
-            #            input_node = self.get_node(node.inputs[0])
-            #
-            #            # remove identity node from graph
-            #            self.identity_map[node_name] = input_node.layer_name
-            #            idx = input_node.outputs.index(node_name)
-            #            del input_node.outputs[idx]
-            #
-            #            output_names = node.outputs
-            #            for output_name in output_names:
-            #                output_node = self.get_node(output_name)
-            #                idx = output_node.inputs.index(node_name)
-            #                output_node.inputs[idx] = input_node.layer_name
-            #
-            #            idx = self.topo_sort.index(node_name)
-            #            del self.topo_sort[idx]
-
             if node_name in self.output_nodes:
                 idx = self.output_nodes.index(node_name)
                 self.output_nodes[idx] = input_node.layer_name
@@ -227,10 +210,6 @@ class TFDecoder(object):
             self.sess.graph.as_default()
             tf.import_graph_def(graph_def, name='', input_map=input_map)
 
-
-#        for node in graph_def.node:
-#            print(node.name, node.op, node.input)
-
         self.sess.run(tf.global_variables_initializer())
 
         self.tf_graph = TFGraph(
@@ -264,13 +243,17 @@ class TFDecoder(object):
 
             if need_define_shape > 0:
                 if need_define_shape == 1:
-                    print(
-                        "\nUnknown shape for input tensor[tensor name: \"{}\"]".
-                        format(layer.name))
+                    print("无法获取到输入结点\"{}\"的shape".format(layer.name))
+                    print("Unknown shape for input tensor[tensor name: \"{}\"]".
+                          format(layer.name))
                 else:
+                    print(
+                        "输入结点\"{}\"的shape为{}，但我们现仅支持batch维为不定长，所以需要你重新设定shape".
+                        format(layer.name, shape))
                     print(
                         "\nShape[now is {}] for input tensor[tensor name: \"{}\"] not support yet"
                         .format(shape, layer.name))
+                print("需要你手动在下面输入对应这个输入结点的shape:)")
                 print(
                     "Use your keyboard type the shape of input tensor below :)")
 
@@ -293,13 +276,11 @@ class TFDecoder(object):
                                                     layer.name))
                 input_map["{}:0".format(layer.name)] = x2paddle_input
                 shape[shape.index(None)] = -1
-                #                self.input_example_data["x2paddle_{}".format(layer.name)] = numpy.random.random_sample(shape).astype(dtype)
                 self.input_info["x2paddle_{}".format(layer.name)] = (shape,
                                                                      dtype)
             else:
                 value = graph_node.layer.attr["shape"].shape
                 shape = [dim.size for dim in value.dim]
-                #                self.input_example_data[graph_node.layer_name] = numpy.random.random_sample(shape).astype(dtype)
                 self.input_info[graph_node.layer_name] = (shape, dtype)
 
         return input_map