Correct UT

Author: RandySheriffH

tests/test_backend.py

@@ -1370,18 +1370,13 @@ def func():
             return tf.identity(x_, name=_TFOUTPUT)
         # since results are random, compare the shapes only
         self._run_test_case(func, [_OUTPUT], {}, check_value=False, check_shape=True)
-
+    '''
     @skip_caffe2_backend()
     def test_randomuniform_dyn_shape(self):
         # test for dynamic shape coming from a shape op
-        x_val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
+        x_val = np.array([0,1,2,3,5], dtype=np.int64)
         def func(x):
-            x_ = tf.stack([x, x])
-            x_ = tf.identity(x_)
-            x_ = tf.shape(x_, name="shape")
-            x_ = random_uniform(x_, name="rand", dtype=tf.float32)
-            x_ = tf.identity(x_)
-            return tf.identity(x_, name=_TFOUTPUT)
+            return random_uniform(x[3:], name=_TFOUTPUT, dtype=tf.float32)
         # since results are random, compare the shapes only
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, check_value=False, check_shape=True)
 

tf2onnx/onnx_opset/generator.py

@@ -39,16 +39,22 @@ def version_1(cls, ctx, node, **kwargs):
         seed = node.get_attr("seed")
         node.set_attr("seed", float(seed.f))
         if len(node.input) > 0:
-            if node.inputs[0].is_const():
-                shape = node.inputs[0].get_tensor_value()
-                ctx.remove_input(node, node.input[0])
-                node.set_attr("shape", shape)
-                ctx.set_shape(node.output[0], shape)
-            else:
-                cast_node = ctx.make_node("Cast", node.input, attr={'to': onnx_pb.TensorProto.INT64})
-                const_node = ctx.make_node("ConstantOfShape", cast_node.output)
-                node.input = const_node.output
-                node.type = node.type + 'Like'
+            shape = node.inputs[0].get_tensor_value()
+            ctx.remove_input(node, node.input[0])
+            node.set_attr("shape", shape)
+            ctx.set_shape(node.output[0], shape)
+
+    @classmethod
+    def version_9(cls, ctx, node, **kwargs):
+        if node.inputs[0].is_const():
+            version_1(cls, ctx, node, **kwargs)
+        else:
+            seed = node.get_attr("seed")
+            node.set_attr("seed", float(seed.f))
+            cast_node = ctx.make_node("Cast", node.input, attr={'to': onnx_pb.TensorProto.INT64})
+            const_node = ctx.make_node("ConstantOfShape", cast_node.output)
+            node.input = const_node.output
+            node.type = node.type + 'Like'
 
 
 @tf_op(["RandomNormalLike", "RandomUniformLike"])