Adjust error message of tf.debugging.assert_type

This PR tries to address the issue raised in 45975 where
the error message of tf.debugging.assert_type could be
misleading when the tf_type is not passed with a DType.

This PR adds additional check so that tf_type arg can be guarded
if non-DType value (e.g., list, tuple etc) is passed.

This PR fixes 45975.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

tensorflow/python/kernel_tests/check_ops_test.py

@@ -1557,6 +1557,14 @@ class AssertTypeTest(test.TestCase):
     with self.assertRaisesRegexp(TypeError, "must be of type.*float32"):
       check_ops.assert_type(sparse_float16, dtypes.float32)
 
+  def test_raise_when_tf_type_is_not_dtype(self):
+    # Test case for GitHub issue:
+    # https://github.com/tensorflow/tensorflow/issues/45975
+    value = constant_op.constant(0.0)
+    with self.assertRaisesRegexp(
+        TypeError, "Cannot convert.*to a TensorFlow DType"):
+      check_ops.assert_type(value, (dtypes.float32,))
+
 
 class AssertShapesTest(test.TestCase):
 

tensorflow/python/ops/check_ops.py

@@ -1552,6 +1552,7 @@ def assert_type(tensor, tf_type, message=None, name=None):
     A `no_op` that does nothing.  Type can be determined statically.
   """
   message = message or ''
+  tf_type = dtypes.as_dtype(tf_type)
   with ops.name_scope(name, 'assert_type', [tensor]):
     if not isinstance(tensor, sparse_tensor.SparseTensor):
       tensor = ops.convert_to_tensor(tensor, name='tensor')