update

x2paddle/op_mapper/tf_op_mapper_nhwc.py

@@ -197,8 +197,8 @@ class TFOpMapperNHWC(OpMapper):
             perm=perm)
 
     def Fill(self, node):
-        dims = self.graph.get_node(node.layer.input[0], copy=True)
-        input_value = self.graph.get_node(node.layer.input[1], copy=True)
+        dims = self.graph.get_node(node.layer.input[0])
+        input_value = self.graph.get_node(node.layer.input[1])
         assert input_value.layer_type == "Const", "Value of fill OP should be Const"
 
         input_value = input_value.value
@@ -212,7 +212,7 @@ class TFOpMapperNHWC(OpMapper):
             value=input_value)
 
     def DepthToSpace(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
+        input = self.graph.get_node(node.layer.input[0])
 
         block_size = node.get_attr("block_size")
         data_format = node.get_attr("data_format").decode()
@@ -264,7 +264,7 @@ class TFOpMapperNHWC(OpMapper):
                 perm=[0, 2, 3, 1])
 
     def MaxPool(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
+        input = self.graph.get_node(node.layer.input[0])
 
         k_size = node.get_attr("ksize")
         strides = node.get_attr("strides")
@@ -592,7 +592,7 @@ class TFOpMapperNHWC(OpMapper):
                 perm=[0, 2, 3, 1])
 
     def AvgPool(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
+        input = self.graph.get_node(node.layer.input[0])
 
         k_size = node.get_attr("ksize")
         strides = node.get_attr("strides")
@@ -904,7 +904,7 @@ class TFOpMapperNHWC(OpMapper):
             keep_dim=keep_dims)
 
     def RandomUniform(self, node):
-        shape = self.graph.get_node(node.layer.input[0], copy=True)
+        shape = self.graph.get_node(node.layer.input[0])
         if shape.layer_type == "Const":
             shape = shape.value.tolist()
             program.add_layer(
@@ -984,8 +984,8 @@ class TFOpMapperNHWC(OpMapper):
                 perm=[0, 2, 3, 1])
 
     def Tile(self, node):
-        input = self.graph.get_node(node.layer.input[0], copy=True)
-        expand_times = self.graph.get_node(node.layer.input[1], copy=True)
+        input = self.graph.get_node(node.layer.input[0])
+        expand_times = self.graph.get_node(node.layer.input[1])
         inputs = {"x": input.name}
         attr = dict()
         if expand_times.layer_type == "Const":
@@ -1001,9 +1001,9 @@ class TFOpMapperNHWC(OpMapper):
             **attr)
 
     def Range(self, node):
-        start = self.graph.get_node(node.layer.input[0], copy=True)
-        limit = self.graph.get_node(node.layer.input[1], copy=True)
-        delta = self.graph.get_node(node.layer.input[2], copy=True)
+        start = self.graph.get_node(node.layer.input[0])
+        limit = self.graph.get_node(node.layer.input[1])
+        delta = self.graph.get_node(node.layer.input[2])
         inputs = dict()
         attr = dict()
 
@@ -1028,8 +1028,8 @@ class TFOpMapperNHWC(OpMapper):
             **attr)
 
     def SquaredDifference(self, node):
-        x = self.graph.get_node(node.layer.input[0], copy=True)
-        y = self.graph.get_node(node.layer.input[1], copy=True)
+        x = self.graph.get_node(node.layer.input[0])
+        y = self.graph.get_node(node.layer.input[1])
         inputs = {"x": x.name, "y": y.name}
         program.add_layer(
             "fluid.layers.elementwise_sub", inputs=inputs, outputs=[node.name])