fixed for CI

docs/inference_model_convertor/demo/tensorflow2paddle.ipynb

@@ -81,7 +81,7 @@
    "source": [
     "## 模型迁移\n",
     "### 1. 获取MobileNetV1的FrozenModel\n",
-    "由于X2Paddle只支持TensorFlow中FrozenModel的转换，如果为纯checkpoint模型，需要参考参考X2Paddle官方[文档](https://github.com/PaddlePaddle/X2Paddle/blob/develop/docs/user_guides/export_tf_model.md)，将其转换为FrozenModel，本示例中提供的模型为FrozenModel，所以无需转换。"
+    "由于X2Paddle只支持TensorFlow中FrozenModel的转换，如果为纯checkpoint模型，需要参考参考X2Paddle官方[文档](https://github.com/PaddlePaddle/X2Paddle/blob/release-1.1/docs/user_guides/export_tf_model.md)，将其转换为FrozenModel，本示例中提供的模型为FrozenModel，所以无需转换。"
    ]
   },
   {

x2paddle/core/program.py

@@ -532,9 +532,9 @@ class PaddleGraph(object):
         paddle.save(self.parameters, save_path)
 
     def dygraph2static(self, save_dir, input_shapes=[], input_types=[]):
-        sepc_list = list()
+        spec_list = list()
         for i, name in enumerate(self.inputs):
-            sepc_list.append(
+            spec_list.append(
                 paddle.static.InputSpec(
                     shape=input_shapes[i], name=name, dtype=input_types[i]))
         path = osp.abspath(save_dir)
@@ -548,7 +548,7 @@ class PaddleGraph(object):
         else:
             model.set_dict(restore)
         model.eval()
-        static_model = paddle.jit.to_static(model, input_spec=sepc_list)
+        static_model = paddle.jit.to_static(model, input_spec=spec_list)
         try:
             paddle.jit.save(static_model,
                             osp.join(save_dir, "inference_model/model"))

x2paddle/decoder/onnx_decoder.py

@@ -583,7 +583,8 @@ class ONNXDecoder(object):
             item.name = self.make_variable_name(item.name)
         for node in graph.node:
             node.name = node.output[0]
-            if ":" in node.name and len(node.output) > 1:
+            if ":" in node.name and len(
+                    node.output) > 1 and node.op_type != "LSTM":
                 node.name = node.name.split(':')[0]
             node.name = self.make_variable_name(node.name)
             for i in range(len(node.input)):