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Update tf.signal.overlap_and_add tests to be parameterized and enable V2 numerical gradient tests.

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PiperOrigin-RevId: 269343900
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RJ Skerry-Ryan 2019-09-16 08:51:42 -07:00 committed by TensorFlower Gardener
parent 9d8beea892
commit 9a97b1ee2e
2 changed files with 49 additions and 59 deletions
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1
tensorflow/python/kernel_tests/signal/BUILD
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@ -86,6 +86,7 @@ cuda_py_tests(
name = "reconstruction_ops_test",
srcs = ["reconstruction_ops_test.py"],
additional_deps = [
"@absl_py//absl/testing:parameterized",
"//third_party/py/numpy",
"//tensorflow/python:array_ops",
"//tensorflow/python:gradients",

107
tensorflow/python/kernel_tests/signal/reconstruction_ops_test.py
View File

@ -18,6 +18,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import numpy as np
from tensorflow.python.eager import context
@ -25,6 +26,7 @@ from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.signal import reconstruction_ops
@ -32,7 +34,7 @@ from tensorflow.python.platform import test
@test_util.run_all_in_graph_and_eager_modes
class ReconstructionOpsTest(test.TestCase):
class ReconstructionOpsTest(test.TestCase, parameterized.TestCase):
def __init__(self, *args, **kwargs):
super(ReconstructionOpsTest, self).__init__(*args, **kwargs)
@ -87,8 +89,6 @@ class ReconstructionOpsTest(test.TestCase):
self.assertAllClose(reconstruction, expected_output)
def test_fast_path(self):
if context.executing_eagerly():
return
# This test uses tensor names and does not work in eager mode.
if context.executing_eagerly():
return
@ -99,35 +99,31 @@ class ReconstructionOpsTest(test.TestCase):
expected_output = np.ones([15])
self.assertAllClose(reconstruction, expected_output)
def test_simple(self):
@parameterized.parameters(
# All hop lengths on a frame length of 2.
(2, [1, 5, 9, 6], 1),
(2, [1, 2, 3, 4, 5, 6], 2),
# All hop lengths on a frame length of 3.
(3, [1, 6, 15, 14, 9], 1),
(3, [1, 2, 7, 5, 13, 8, 9], 2),
(3, [1, 2, 3, 4, 5, 6, 7, 8, 9], 3),
# All hop lengths on a frame length of 4.
(4, [1, 7, 18, 21, 19, 12], 1),
(4, [1, 2, 8, 10, 16, 18, 11, 12], 2),
(4, [1, 2, 3, 9, 6, 7, 17, 10, 11, 12], 3),
(4, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 4))
def test_simple(self, frame_length, expected, frame_hop):
def make_input(frame_length, num_frames=3):
"""Generate a tensor of num_frames frames of frame_length."""
return np.reshape(np.arange(1, num_frames * frame_length + 1),
(-1, frame_length))
# List of (signal, expected_result, frame_hop).
configurations = [
# All hop lengths on a frame length of 2.
(make_input(2), [1, 5, 9, 6], 1),
(make_input(2), [1, 2, 3, 4, 5, 6], 2),
# All hop lengths on a frame length of 3.
(make_input(3), [1, 6, 15, 14, 9], 1),
(make_input(3), [1, 2, 7, 5, 13, 8, 9], 2),
(make_input(3), [1, 2, 3, 4, 5, 6, 7, 8, 9], 3),
# All hop lengths on a frame length of 4.
(make_input(4), [1, 7, 18, 21, 19, 12], 1),
(make_input(4), [1, 2, 8, 10, 16, 18, 11, 12], 2),
(make_input(4), [1, 2, 3, 9, 6, 7, 17, 10, 11, 12], 3),
(make_input(4), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 4),
]
for signal, expected, frame_hop in configurations:
reconstruction = reconstruction_ops.overlap_and_add(
np.array(signal), frame_hop)
expected_output = np.array(expected)
self.assertAllClose(reconstruction, expected_output)
signal = make_input(frame_length)
reconstruction = reconstruction_ops.overlap_and_add(
np.array(signal), frame_hop)
expected_output = np.array(expected)
self.assertAllClose(reconstruction, expected_output)
def test_powers(self):
signal = constant_op.constant(np.squeeze(self.powers[0, :, :]),
@ -165,28 +161,25 @@ class ReconstructionOpsTest(test.TestCase):
self.assertEqual(output.shape, (1, 9))
self.assertEqual(string_output, self.expected_string)
def test_gradient(self):
@parameterized.parameters(
((1, 128), 1),
((5, 35), 17),
((10, 128), 128),
((2, 10, 128), 127),
((2, 2, 10, 128), 126),
((2, 2, 2, 10, 128), 125))
def test_gradient(self, shape, frame_hop):
# TODO(rjryan): Eager gradient tests.
if context.executing_eagerly():
return
configurations = [
((1, 128), 1),
((5, 35), 17),
((10, 128), 128),
((2, 10, 128), 127),
((2, 2, 10, 128), 126),
((2, 2, 2, 10, 128), 125),
]
for shape, frame_hop in configurations:
signal = array_ops.zeros(shape)
reconstruction = reconstruction_ops.overlap_and_add(signal, frame_hop)
loss = math_ops.reduce_sum(reconstruction)
# Increasing any sample in the input frames by one will increase the sum
# of all the samples in the reconstruction by 1, so the gradient should
# be all ones, no matter the shape or hop.
gradient = self.evaluate(gradients_impl.gradients([loss], [signal])[0])
self.assertTrue((gradient == 1.0).all())
signal = array_ops.zeros(shape)
reconstruction = reconstruction_ops.overlap_and_add(signal, frame_hop)
loss = math_ops.reduce_sum(reconstruction)
# Increasing any sample in the input frames by one will increase the sum
# of all the samples in the reconstruction by 1, so the gradient should
# be all ones, no matter the shape or hop.
gradient = self.evaluate(gradients_impl.gradients([loss], [signal])[0])
self.assertTrue((gradient == 1.0).all())
def test_gradient_batch(self):
# TODO(rjryan): Eager gradient tests.
@ -214,18 +207,14 @@ class ReconstructionOpsTest(test.TestCase):
self.assertAllEqual(expected_gradient, gradient)
def test_gradient_numerical(self):
# TODO(rjryan): Eager gradient tests.
if context.executing_eagerly():
return
with self.session(use_gpu=True):
shape = (2, 10, 10)
framed_signal = array_ops.zeros(shape)
frame_hop = 10
reconstruction = reconstruction_ops.overlap_and_add(
framed_signal, frame_hop)
error = test.compute_gradient_error(
framed_signal, shape, reconstruction, [2, 100])
self.assertLess(error, 2e-5)
shape = (2, 10, 10)
framed_signal = array_ops.zeros(shape)
frame_hop = 10
def f(signal):
return reconstruction_ops.overlap_and_add(signal, frame_hop)
((jacob_t,), (jacob_n,)) = gradient_checker_v2.compute_gradient(
f, [framed_signal])
self.assertAllClose(jacob_t, jacob_n)
if __name__ == "__main__":