TensorFlow: some more python3 compatibility test fixes

Change-Id: I5678cbdfb45757e2218494f542c9b0a0d5cc16a4

Author: Vijay Vasudevan

tensorflow/python/kernel_tests/parsing_ops_test.py

@@ -610,7 +610,7 @@ def testSequenceExampleWithMultipleSizeFeatureLists(self):
             int64_feature([5, 6, 7]),
             int64_feature([8, 9, 10]),]),
         "b": feature_list([
-            bytes_feature(["r00", "r01", "r10", "r11"])]),
+            bytes_feature([b"r00", b"r01", b"r10", b"r11"])]),
         "c": feature_list([
             float_feature([3, 4]),
             float_feature([-1, 2])]),
@@ -625,7 +625,7 @@ def testSequenceExampleWithMultipleSizeFeatureLists(self):
             [[5, 6, 7]],
             [[8, 9, 10]]], dtype=np.int64),
         "b": np.array([  # outer dimension is time, inside are 2x2 matrices
-            [["r00", "r01"], ["r10", "r11"]]], dtype=np.str),
+            [[b"r00", b"r01"], [b"r10", b"r11"]]], dtype=np.str),
         "c": np.array([  # outer dimension is time, inside are 2-vectors
             [3, 4],
             [-1, 2]], dtype=np.float32),

tensorflow/python/kernel_tests/sparse_serialization_ops_test.py

@@ -95,7 +95,7 @@ def testSerializeManyDeserializeManyRoundTrip(self):
     with self.test_session(use_gpu=False) as sess:
       # N == 4 because shape_value == [4, 5]
       indices_value = np.array([[0, 0], [0, 1], [2, 0]], dtype=np.int64)
-      values_value = np.array(["a", "b", "c"])
+      values_value = np.array([b"a", b"b", b"c"])
       shape_value = np.array([4, 5], dtype=np.int64)
       sparse_tensor = self._SparseTensorPlaceholder(dtype=tf.string)
       serialized = tf.serialize_many_sparse(sparse_tensor)

tensorflow/python/ops/image_ops_test.py

@@ -49,8 +49,8 @@ def testBatch(self):
       batch1 = image_ops.rgb_to_hsv(batch0)
       batch2 = image_ops.hsv_to_rgb(batch1)
       split0 = array_ops.unpack(batch0)
-      split1 = map(image_ops.rgb_to_hsv, split0)
-      split2 = map(image_ops.hsv_to_rgb, split1)
+      split1 = list(map(image_ops.rgb_to_hsv, split0))
+      split2 = list(map(image_ops.hsv_to_rgb, split1))
       join1 = array_ops.pack(split1)
       join2 = array_ops.pack(split2)
       batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2])