Add tf.lite tests for tf.signal.frame.

PiperOrigin-RevId: 283624804
Change-Id: I6fd2f5234fb474de3cc5490e926938f76ffe3b5b

tensorflow/python/kernel_tests/signal/shape_ops_test.py

@@ -18,9 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import itertools
-
-from absl.testing import parameterized
 import numpy as np
 
 from tensorflow.python.eager import context
@@ -28,7 +25,6 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_spec
 from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.kernel_tests.signal import test_util
 from tensorflow.python.ops import array_ops
@@ -38,7 +34,7 @@ from tensorflow.python.platform import test
 
 
 @tf_test_util.run_all_in_graph_and_eager_modes
-class FrameTest(test.TestCase, parameterized.TestCase):
+class FrameTest(test.TestCase):
 
   def test_mapping_of_indices_without_padding(self):
     tensor = constant_op.constant(np.arange(9152), dtypes.int32)
@@ -356,46 +352,6 @@ class FrameTest(test.TestCase, parameterized.TestCase):
         rewritten_graph = test_util.grappler_optimize(g, [frames])
         self.assertEqual(1, len(rewritten_graph.node))
 
-  @parameterized.parameters(
-      itertools.product(
-          # length % step == 0
-          ((32, 16),
-           # gcd(length, step) == 1
-           (32, 15),
-           # gcd(length, step) == 5
-           (25, 15),
-           # length == step
-           (32, 32)),
-          (False, True),  # pad_end
-          (False, True),  # use_mlir
-          (False, True)))  # known_batch
-  def test_tflite_convert(self, length_step, pad_end, use_mlir, known_batch):
-    """Check for tf.lite compatibility in a variety of settings."""
-    def fn(signal):
-      return shape_ops.frame(
-          signal, length_step[0], length_step[1], pad_end=pad_end)
-
-    # TODO(b/144998258): unknown batch does not currently work with padding.
-    if not known_batch and pad_end:
-      return
-
-    signal_length, dtype = 8001, dtypes.float32
-    # If batch size is unknown, tf.lite assumes it's 1. Test batch_size > 1
-    # only when batch size is known.
-    batch_size = 2 if known_batch else 1
-    static_batch_size = batch_size if known_batch else None
-    tflite_model = test_util.tflite_convert(
-        fn, [tensor_spec.TensorSpec(
-            shape=[static_batch_size, signal_length], dtype=dtype)],
-        use_mlir)
-    signal = np.random.normal(size=(batch_size, signal_length)).astype(
-        dtype.as_numpy_dtype)
-    actual_output, = test_util.evaluate_tflite_model(
-        tflite_model, [signal])
-
-    expected_output = self.evaluate(fn(signal))
-    self.assertAllClose(actual_output, expected_output)
-
 
 if __name__ == "__main__":
   test.main()