Merge pull request #473 from lucienwang1009/bug

fix create_onnx_random_uniform_op shape

Author: nbcsm

tf2onnx/rewriter/random_uniform.py

@@ -87,7 +87,7 @@ def create_onnx_random_uniform_op(g, tmax, tmin, ru_op, output, to_delete):
             # to make that work for onnx we just need to remove the shape op.
             new_node = g.make_node("RandomUniformLike", inputs=[shape_node.input[0]], name=op_name,
                                    attr={"low": tmin, "high": tmax, "dtype": dtype},
-                                   shapes=shape, dtypes=[dtype])
+                                   shapes=[shape], dtypes=[dtype])
         else:
             # if the shape is calculated we need to create a tensor so RandomUniformLike
             # can take the shape from there. Pre opset9 this is somewhat hacky because there is
@@ -99,11 +99,11 @@ def create_onnx_random_uniform_op(g, tmax, tmin, ru_op, output, to_delete):
             # create a fill op with the shape of the value of the input tensor
             zero = g.make_const(utils.make_name("zero"), np.zeros((), dtype=np.float32))
             fill_node = g.make_node("Fill", inputs=[shape_node.output[0], zero.name],
-                                    shapes=shape, dtypes=[dtype])
+                                    shapes=[shape], dtypes=[dtype])
             func, _ = handler.tf_op.find_effective_op("Fill")
             func(g, fill_node)
             # and use RandomUniformLike to create the random tensor
             new_node = g.make_node("RandomUniformLike", inputs=[fill_node.output[0]], name=op_name,
                                    attr={"low": tmin, "high": tmax, "dtype": dtype},
-                                   shapes=shape, dtypes=[dtype])
+                                   shapes=[shape], dtypes=[dtype])
     return new_node