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Re-enable signal kernel tests on py38

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PiperOrigin-RevId: 312166420
Change-Id: Ie18cf2e29d8a05d57675ce3e75b06509205a4e61
This commit is contained in:
Jared Duke 2020-05-18 15:29:57 -07:00 committed by TensorFlower Gardener
parent 4001e3dad3
commit f5c5747f13
3 changed files with 9 additions and 13 deletions
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1
tensorflow/python/kernel_tests/signal/BUILD
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@ -149,7 +149,6 @@ cuda_py_tests(
python_version = "PY3",
shard_count = 4,
tags = [
"no_oss_py38", #TODO(b/151631881)
"no_windows_gpu",
],
deps = [

4
tensorflow/python/kernel_tests/signal/test_util.py
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@ -50,7 +50,7 @@ def grappler_optimize(graph, fetches=None, config_proto=None):
return tf_optimizer.OptimizeGraph(config_proto, metagraph)
def tflite_convert(fn, input_templates, use_mlir=False):
def tflite_convert(fn, input_templates):
"""Converts the provided fn to tf.lite model.
Args:
@ -59,7 +59,6 @@ def tflite_convert(fn, input_templates, use_mlir=False):
input_templates: A list of Tensors, ndarrays or TensorSpecs describing the
inputs that fn expects. The actual values of the Tensors or ndarrays are
unused.
use_mlir: Experimental. Whether to use the tf.lite MLIR converter.
Returns:
The serialized tf.lite model.
@ -67,7 +66,6 @@ def tflite_convert(fn, input_templates, use_mlir=False):
fn = def_function.function(fn)
concrete_func = fn.get_concrete_function(*input_templates)
converter = lite.TFLiteConverterV2([concrete_func])
converter.experimental_new_converter = use_mlir
return converter.convert()

17
tensorflow/python/kernel_tests/signal/window_ops_test.py
View File

@ -156,15 +156,14 @@ class WindowOpsTest(test.TestCase, parameterized.TestCase):
self.assertLen(rewritten_graph.node, 1)
@parameterized.parameters(
# Due to control flow, only MLIR is supported.
# Only float32 is supported.
(window_ops.hann_window, 10, False, dtypes.float32, True),
(window_ops.hann_window, 10, True, dtypes.float32, True),
(window_ops.hamming_window, 10, False, dtypes.float32, True),
(window_ops.hamming_window, 10, True, dtypes.float32, True),
(window_ops.vorbis_window, 12, None, dtypes.float32, True))
def test_tflite_convert(self, window_fn, window_length, periodic, dtype,
use_mlir):
(window_ops.hann_window, 10, False, dtypes.float32),
(window_ops.hann_window, 10, True, dtypes.float32),
(window_ops.hamming_window, 10, False, dtypes.float32),
(window_ops.hamming_window, 10, True, dtypes.float32),
(window_ops.vorbis_window, 12, None, dtypes.float32))
def test_tflite_convert(self, window_fn, window_length, periodic, dtype):
def fn(window_length):
try:
return window_fn(window_length, periodic=periodic, dtype=dtype)
@ -172,7 +171,7 @@ class WindowOpsTest(test.TestCase, parameterized.TestCase):
return window_fn(window_length, dtype=dtype)
tflite_model = test_util.tflite_convert(
fn, [tensor_spec.TensorSpec(shape=[], dtype=dtypes.int32)], use_mlir)
fn, [tensor_spec.TensorSpec(shape=[], dtype=dtypes.int32)])
window_length = np.array(window_length).astype(np.int32)
actual_output, = test_util.evaluate_tflite_model(
tflite_model, [window_length])