diff --git a/mace/python/tools/converter_tool/onnx_converter.py b/mace/python/tools/converter_tool/onnx_converter.py
index 805cbd272731e1548a11dc23a284b305f2a2ee14..99ae2a791cac9c0862e0d4ed2fc21e76ce7a6cc9 100644
--- a/mace/python/tools/converter_tool/onnx_converter.py
+++ b/mace/python/tools/converter_tool/onnx_converter.py
@@ -120,9 +120,8 @@ OnnxSupportedOps = [
     # 'OneHot',
     # 'Or',
     'PRelu',
-    # 'Pad',
+    'Pad',
     'PadContext',
-    'Padding',
     'PNorm',
     'Pow',
     # 'RNN',
@@ -346,8 +345,8 @@ class OnnxConverter(base_converter.ConverterInterface):
             OnnxOpType.Neg.name: self.convert_eltwise,
             OnnxOpType.Normalize: self.convert_normalize,
             OnnxOpType.Offset.name: self.convert_identity,
+            OnnxOpType.Pad.name: self.convert_pad,
             OnnxOpType.PadContext.name: self.convert_pad_context,
-            OnnxOpType.Padding.name: self.convert_identity,
             OnnxOpType.PNorm.name: self.convert_pnorm,
             OnnxOpType.Pow.name: self.convert_eltwise,
             OnnxOpType.PRelu.name: self.convert_activation,
@@ -982,6 +981,29 @@ class OnnxConverter(base_converter.ConverterInterface):
         op = self.convert_general_op(node)
         op.type = MaceOp.BatchNorm.name
 
+    def convert_pad(self, node):
+        op = self.convert_general_op(node)
+        op.type = MaceOp.Pad.name
+        if 'mode' in node.attrs:
+            mode = node.attrs['mode']
+            padding_type_arg = op.arg.add()
+            padding_type_arg.name = MaceKeyword.mace_padding_type_str
+            if mode == 'reflect':
+                padding_type_arg.i = PadType.REFLECT
+            elif mode == 'edge':
+                padding_type_arg.i = PadType.SYMMETRIC
+            else:
+                padding_type_arg.i = PadType.CONSTANT
+        if 'pads' in node.attrs:
+            paddings_arg = op.arg.add()
+            paddings_arg.name = MaceKeyword.mace_paddings_str
+            paddings_value = node.attrs['pads']
+            paddings_arg.ints.extend(paddings_value)
+        if 'value' in node.attrs:
+            constant_value_arg = op.arg.add()
+            constant_value_arg.name = MaceKeyword.mace_constant_value_str
+            constant_value_arg.f = node.attrs['value']
+
     def convert_pad_context(self, node):
         op = self.convert_general_op(node)
         op.type = MaceOp.PadContext.name
diff --git a/mace/python/tools/converter_tool/tensorflow_converter.py b/mace/python/tools/converter_tool/tensorflow_converter.py
index 53d57151f02ecd82f4a1d0504fc957542c1011e7..59bf9d88037d2cc2ca8838236b6c8e95ed1f3a50 100644
--- a/mace/python/tools/converter_tool/tensorflow_converter.py
+++ b/mace/python/tools/converter_tool/tensorflow_converter.py
@@ -1004,19 +1004,22 @@ class TensorflowConverter(base_converter.ConverterInterface):
 
     def convert_split(self, tf_op):
         op = self.convert_general_op(tf_op)
-        op.type = MaceOp.Split.name
-        axis = tf_op.inputs[0].eval().astype(np.int32)
-        axis = len(op.output_shape[0].dims) + axis if axis < 0 else axis
-        del op.input[0]
-
-        axis_arg = op.arg.add()
-        axis_arg.name = MaceKeyword.mace_axis_str
-        axis_arg.i = axis
+        num_or_size_splits = tf_op.get_attr('num_split')
+        if num_or_size_splits == 1:
+            op.type = MaceOp.Identity.name
+        else:
+            op.type = MaceOp.Split.name
+            axis = tf_op.inputs[0].eval().astype(np.int32)
+            axis = len(op.output_shape[0].dims) + axis if axis < 0 else axis
 
-        num_split_arg = op.arg.add()
-        num_split_arg.name = MaceKeyword.mace_num_split_str
-        num_split_arg.i = tf_op.get_attr('num_split')
+            axis_arg = op.arg.add()
+            axis_arg.name = MaceKeyword.mace_axis_str
+            axis_arg.i = axis
 
+            num_split_arg = op.arg.add()
+            num_split_arg.name = MaceKeyword.mace_num_split_str
+            num_split_arg.i = num_or_size_splits
+        del op.input[0]
         self._skip_tensor.add(tf_op.inputs[0].name)
 
     def convert_fake_quantize(self, tf_op):