update

x2paddle/op_mapper/tf_op_mapper_nhwc.py

@@ -199,17 +199,21 @@ class TFOpMapperNHWC(OpMapper):
     def Fill(self, node):
         dims = self.graph.get_node(node.layer.input[0])
         input_value = self.graph.get_node(node.layer.input[1])
+        inputs = dict()
+        attr = dict()
         assert input_value.layer_type == "Const", "Value of fill OP should be Const"
+        if dims.layer_type == "Const":
+            attr["shape"] = dims.value.tolist()
+        else:
+            inputs["shape"] = dims.name
+        attr["dtype"] = string(input_value.dtype)
+        attr["value"] = input_value.value
 
-        input_value = input_value.value
-        input_dtype = string(input_value.dtype)
         program.add_layer(
             "fluid.layers.fill_constant",
-            inputs={},
+            inputs=inputs,
             outputs=[node.name],
-            shape=dims,
-            dtype=string(input_dtype),
-            value=input_value)
+            **attr)
 
     def DepthToSpace(self, node):
         input = self.graph.get_node(node.layer.input[0])
@@ -251,8 +255,8 @@ class TFOpMapperNHWC(OpMapper):
             shape=[0, c, h, w])
 
         program.add_layer(
-            kernel="fluid.layers.pixed_shuffle",
-            inputs={"input": reshape_name},
+            kernel="fluid.layers.pixel_shuffle",
+            inputs={"x": reshape_name},
             outputs=[node.name],
             upscale_factor=block_size)
 
@@ -309,9 +313,9 @@ class TFOpMapperNHWC(OpMapper):
         data_format = node.get_attr("data_format").decode()
         pad_mode = node.get_attr("padding").decode()
         if data_format == "NHWC":
-            n, c, h, w = input.out_shapes[0]
-        else:
             n, h, w, c = input.out_shapes[0]
+        else:
+            n, c, h, w = input.out_shapes[0]
 
         if kernel.layer_type == 'Const':
             kernel_value = kernel.value