fix pylint

Author: RandySheriffH

tests/test_backend.py

@@ -16,13 +16,13 @@
 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
 from tf2onnx import constants, utils
 from tf2onnx.graph_matcher import OpTypePattern, GraphMatcher
 from tf2onnx.tf_loader import is_tf2
-from tensorflow.python.ops import init_ops
 
 # pylint: disable=missing-docstring,invalid-name,unused-argument,function-redefined,cell-var-from-loop
 
@@ -2919,24 +2919,6 @@ def func(query_holder):
         self._run_test_case(func, [_OUTPUT], {_INPUT: query}, constant_fold=False)
         os.remove(filnm)
 
-    @check_opset_min_version(11, "GRU")
-    def test_cudnngru(self):
-        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]))
-        self.run_test_case({}, [], [outputs[0].name], rtol=1e-05, atol=1e-04)
-
     @check_opset_min_version(11)
     def test_matrix_diag_part(self):
         input_vals = [
@@ -2951,6 +2933,26 @@ def func(input_holder):
         for input_val in input_vals:
             self._run_test_case(func, [_OUTPUT], {_INPUT: input_val})
 
+    @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()

tf2onnx/onnx_opset/rnn.py

@@ -233,14 +233,14 @@ def NM(nm):
             suffix = '_' + str(i*num_dirs)
             ctx.make_node('GRU', [XNF, NM('W' + suffix), NM('R' + suffix), NM('B' + suffix), '', NM('H'+ suffix)],
                           outputs=[NM('Y' + suffix), NM('YH' + suffix)],
-                          attr={'direction':'forward', 'hidden_size':num_units})
+                          attr={'direction': 'forward', 'hidden_size': num_units})
             XNF = NM(X + suffix)
             ctx.make_node('Squeeze', [NM('Y' + suffix)], outputs=[XNF], attr={'axes': [1]})
             if num_dirs == 2:
                 suffix = '_' + str(i*2+1)
                 ctx.make_node('GRU', [XNB, NM('W' + suffix), NM('R' + suffix), NM('B' + suffix), '', NM('H'+ suffix)],
                               outputs=[NM('Y' + suffix), NM('YH' + suffix)],
-                              attr={'direction':'reverse', 'hidden_size':num_units})
+                              attr={'direction': 'reverse', 'hidden_size': num_units})
                 XNB = NM(X + suffix)
                 ctx.make_node('Squeeze', [NM('Y' + suffix)], outputs=[XNB], attr={'axes': [1]})
         ctx.remove_node(node.name)