move test to separate file

Author: RandySheriffH

tests/test_backend.py

@@ -16,7 +16,7 @@
 import tensorflow as tf
 
 from tensorflow.python.ops import lookup_ops
-from tensorflow.python.ops import init_ops
+
 from backend_test_base import Tf2OnnxBackendTestBase
 # pylint reports unused-wildcard-import which is false positive, __all__ is defined in common
 from common import *  # pylint: disable=wildcard-import,unused-wildcard-import
@@ -2933,27 +2933,6 @@ def func(input_holder):
         for input_val in input_vals:
             self._run_test_case(func, [_OUTPUT], {_INPUT: input_val})
 
-    @skip_tf_cpu("only tf_gpu can run CudnnGPU")
-    @check_opset_min_version(11, "GRU")
-    def test_cudnngru(self):
-        def func():
-            seq_length = 3
-            batch_size = 5
-            input_size = 2
-            num_layers = 2
-            num_units = 2
-            num_dirs = 2
-            initializer = init_ops.constant_initializer(0.5)
-            x = np.random.randint(0, 100, [seq_length, batch_size, input_size]).astype(np.float32)
-            h = np.random.randint(0, 100, [num_layers * num_dirs, batch_size, num_units]).astype(np.float32).reshape(
-                [num_layers * num_dirs, batch_size, num_units])
-            cudnngru = tf.contrib.cudnn_rnn.CudnnGRU(num_layers, num_units, 'linear_input', 'bidirectional',
-                                                     kernel_initializer=initializer, bias_initializer=initializer)
-            cudnngru.build([seq_length, batch_size, input_size])
-            outputs = cudnngru.call(x, tuple([h]))
-            _ = tf.identity(outputs[0], name=_TFOUTPUT)
-        self.run_test_case(func, {}, [], [_OUTPUT], rtol=1e-05, atol=1e-04)
-
 
 if __name__ == '__main__':
     unittest_main()

tests/test_cudnn.py

@@ -0,0 +1,51 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT license.
+
+"""Unit Tests for cudnn."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import variable_scope
+from backend_test_base import Tf2OnnxBackendTestBase
+from common import *
+from tf2onnx.tf_loader import is_tf2
+
+
+class CudnnTests(Tf2OnnxBackendTestBase):
+    @skip_tf2()
+    @skip_tf_cpu("only tf_gpu can run CudnnGPU")
+    @check_opset_min_version(11, "CudnnGRU")
+    def test_cudnngru(self):
+        seq_length = 3
+        batch_size = 5
+        input_size = 2
+        num_layers = 2
+        num_units = 2
+        num_dirs = 2
+        x_val = np.random.randint(0, 100, [seq_length, batch_size, input_size]).astype(np.float32)
+        h_val = np.random.randint(0, 100, [num_layers * num_dirs, batch_size, num_units]).astype(np.float32).reshape(
+            [num_layers * num_dirs, batch_size, num_units])
+
+        def func(x, h):
+            initializer = init_ops.constant_initializer(0.5)
+            cudnngru = tf.contrib.cudnn_rnn.CudnnGRU(num_layers, num_units, 'linear_input', 'bidirectional',
+                                                     kernel_initializer=initializer, bias_initializer=initializer)
+            cudnngru.build([seq_length, batch_size, input_size])
+            outputs = cudnngru.call(x, tuple([h]))
+            _ = tf.identity(outputs[0], name='output')
+
+        feed_dict = {"input_1:0": x_val, "input_2:0": h_val}
+        input_names_with_port = ["input_1:0", "input_2:0"]
+        output_names_with_port = ["output:0"]
+        self.run_test_case(func, feed_dict, input_names_with_port, output_names_with_port, rtol=1e-05, atol=1e-04)
+
+
+if __name__ == '__main__':
+    unittest_main()