diff --git a/x2paddle/op_mapper/onnx2paddle/onnx_op_mapper.py b/x2paddle/op_mapper/onnx2paddle/onnx_op_mapper.py
index ae80b2d2a0409706ad483802ca8dcb8a990c9920..bbb960899f79e95a5b4276e6a179acc8a7a02b5a 100644
--- a/x2paddle/op_mapper/onnx2paddle/onnx_op_mapper.py
+++ b/x2paddle/op_mapper/onnx2paddle/onnx_op_mapper.py
@@ -53,21 +53,14 @@ class ONNXOpMapper(OpMapper):
 
     def op_checker(self):
         unsupported_ops = set()
-        contain_ops = set()
         for node_name in self.graph.topo_sort:
             node = self.graph.get_node(node_name)
             op = node.layer_type
-            contain_ops.add(op)
             if not hasattr(self.opset, op) and \
                 op not in self.opset.default_op_mapping and \
                 op not in custom_layers and \
                 op not in self.opset.elementwise_ops:
                 unsupported_ops.add(op)
-
-        print("There are {} ops need converted , list as below".format(
-            len(contain_ops)))
-        for op in contain_ops:
-            print(op)
         if len(unsupported_ops) == 0:
             return True
         else:
diff --git a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
index 58dfa77b4c668d731a4a02abd65480ff40e8d955..74f9f5a6bc97fccab0afee35ecd870f356bd659e 100644
--- a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
+++ b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
@@ -46,7 +46,7 @@ def _is_static_shape(shape):
     for dim in shape:
         if dim < 0:
             negtive_dims += 1
-        if dim != -1:
+        if dim < -1:
             error_dims += 1
     if negtive_dims > 1:
         return False
@@ -513,8 +513,21 @@ class OpSet9():
                     output=node,
                     param_attr={'shape': [1]})
         else:
-            node.fluid_code.add_layer(
-                'unsqueeze', inputs=val_x, output=node, param_attr=attr)
+            if str(val_x.dtype) == 'bool':
+                val_x_cast = val_x.layer_name + '_cast'
+                node.fluid_code.add_layer(
+                    'cast',
+                    inputs=val_x,
+                    output=val_x_cast,
+                    param_attr={'dtype': string('int64')})
+                node.fluid_code.add_layer(
+                    'unsqueeze',
+                    inputs=val_x_cast,
+                    output=node,
+                    param_attr=attr)
+            else:
+                node.fluid_code.add_layer(
+                    'unsqueeze', inputs=val_x, output=node, param_attr=attr)
 
     @print_mapping_info
     def Shrink(self, node):
@@ -783,9 +796,6 @@ class OpSet9():
                 param_attr=None)
         else:
             input_inner_indices = node.layer_name + '_input_inner_indices'
-            print('val_x shape:', val_x.out_shapes[0])
-            print('indices shape:', indices.out_shapes[0])
-            print('updates shape:', updates.out_shapes[0])
             node.fluid_code.add_layer(
                 'scatter_nd',
                 inputs={
@@ -1037,28 +1047,11 @@ class OpSet9():
         node.fluid_code.add_layer(
             'cast', inputs=val_input, output=node, param_attr=attr)
 
-    @print_mapping_info
-    def Cast(self, node):
-        val_input = self.graph.get_input_node(node, idx=0, copy=True)
-        val_output = self.graph.get_node(node.layer.output[0], copy=True)
-
-        dtype = node.get_attr('to')
-        if not isinstance(dtype, np.dtype):
-            dtype = TENSOR_TYPE_TO_NP_TYPE[dtype]
-
-        output_dtype = val_output.dtype
-        if output_dtype:
-            assert dtype == output_dtype, 'dtype of to unmatches output'
-        attr = {'dtype': string(dtype)}
-        node.fluid_code.add_layer(
-            'cast', inputs=val_input, output=node, param_attr=attr)
-
     @print_mapping_info
     def Not(self, node):
         val_input = self.graph.get_input_node(node, idx=0, copy=True)
         node.fluid_code.add_layer('logical_not', inputs=val_input, output=node)
 
-        val_output = self.graph.get_node(node.layer.output[0], copy=True)
         node.fluid_code.add_layer(
             'cast',
             inputs=node,