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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for FFT via the XLA JIT."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
import scipy.signal as sps
from tensorflow.compiler.tests import xla_test
from tensorflow.contrib.signal.python.ops import spectral_ops as signal
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import spectral_ops
from tensorflow.python.platform import googletest
BATCH_DIMS = (3, 5)
RTOL = 0.02 # Eigen/cuFFT differ widely from np, especially for FFT3D
ATOL = 1e-3
def pick_10(x):
x = list(x)
np.random.seed(123)
np.random.shuffle(x)
return x[:10]
def to_32bit(x):
if x.dtype == np.complex128:
return x.astype(np.complex64)
if x.dtype == np.float64:
return x.astype(np.float32)
return x
POWS_OF_2 = 2**np.arange(3, 12)
INNER_DIMS_1D = list((x,) for x in POWS_OF_2)
POWS_OF_2 = 2**np.arange(3, 8) # To avoid OOM on GPU.
INNER_DIMS_2D = pick_10(itertools.product(POWS_OF_2, POWS_OF_2))
INNER_DIMS_3D = pick_10(itertools.product(POWS_OF_2, POWS_OF_2, POWS_OF_2))
class FFTTest(xla_test.XLATestCase):
def _VerifyFftMethod(self, inner_dims, complex_to_input, input_to_expected,
tf_method):
for indims in inner_dims:
print("nfft =", indims)
shape = BATCH_DIMS + indims
data = np.arange(np.prod(shape) * 2) / np.prod(indims)
np.random.seed(123)
np.random.shuffle(data)
data = np.reshape(data.astype(np.float32).view(np.complex64), shape)
data = to_32bit(complex_to_input(data))
expected = to_32bit(input_to_expected(data))
with self.cached_session() as sess:
with self.test_scope():
ph = array_ops.placeholder(
dtypes.as_dtype(data.dtype), shape=data.shape)
out = tf_method(ph)
value = sess.run(out, {ph: data})
self.assertAllClose(expected, value, rtol=RTOL, atol=ATOL)
def testContribSignalSTFT(self):
ws = 512
hs = 128
dims = (ws * 20,)
shape = BATCH_DIMS + dims
data = np.arange(np.prod(shape)) / np.prod(dims)
np.random.seed(123)
np.random.shuffle(data)
data = np.reshape(data.astype(np.float32), shape)
window = sps.get_window("hann", ws)
expected = sps.stft(
data, nperseg=ws, noverlap=ws - hs, boundary=None, window=window)[2]
expected = np.swapaxes(expected, -1, -2)
expected *= window.sum() # scipy divides by window sum
with self.cached_session() as sess:
with self.test_scope():
ph = array_ops.placeholder(
dtypes.as_dtype(data.dtype), shape=data.shape)
out = signal.stft(ph, ws, hs)
grad = gradients_impl.gradients(out, ph,
grad_ys=array_ops.ones_like(out))
# For gradients, we simply verify that they compile & execute.
value, _ = sess.run([out, grad], {ph: data})
self.assertAllClose(expected, value, rtol=RTOL, atol=ATOL)
def testFFT(self):
self._VerifyFftMethod(INNER_DIMS_1D, lambda x: x, np.fft.fft,
spectral_ops.fft)
def testFFT2D(self):
self._VerifyFftMethod(INNER_DIMS_2D, lambda x: x, np.fft.fft2,
spectral_ops.fft2d)
def testFFT3D(self):
self._VerifyFftMethod(INNER_DIMS_3D, lambda x: x,
lambda x: np.fft.fftn(x, axes=(-3, -2, -1)),
spectral_ops.fft3d)
def testIFFT(self):
self._VerifyFftMethod(INNER_DIMS_1D, lambda x: x, np.fft.ifft,
spectral_ops.ifft)
def testIFFT2D(self):
self._VerifyFftMethod(INNER_DIMS_2D, lambda x: x, np.fft.ifft2,
spectral_ops.ifft2d)
def testIFFT3D(self):
self._VerifyFftMethod(INNER_DIMS_3D, lambda x: x,
lambda x: np.fft.ifftn(x, axes=(-3, -2, -1)),
spectral_ops.ifft3d)
def testRFFT(self):
self._VerifyFftMethod(
INNER_DIMS_1D, np.real, lambda x: np.fft.rfft(x, n=x.shape[-1]),
lambda x: spectral_ops.rfft(x, fft_length=[x.shape[-1].value]))
def testRFFT2D(self):
def _tf_fn(x):
return spectral_ops.rfft2d(
x, fft_length=[x.shape[-2].value, x.shape[-1].value])
self._VerifyFftMethod(
INNER_DIMS_2D, np.real,
lambda x: np.fft.rfft2(x, s=[x.shape[-2], x.shape[-1]]), _tf_fn)
def testRFFT3D(self):
def _to_expected(x):
return np.fft.rfftn(
x, axes=(-3, -2, -1), s=[x.shape[-3], x.shape[-2], x.shape[-1]])
def _tf_fn(x):
return spectral_ops.rfft3d(
x,
fft_length=[x.shape[-3].value, x.shape[-2].value, x.shape[-1].value])
self._VerifyFftMethod(INNER_DIMS_3D, np.real, _to_expected, _tf_fn)
def testIRFFT(self):
def _tf_fn(x):
return spectral_ops.irfft(x, fft_length=[2 * (x.shape[-1].value - 1)])
self._VerifyFftMethod(
INNER_DIMS_1D, lambda x: np.fft.rfft(np.real(x), n=x.shape[-1]),
lambda x: np.fft.irfft(x, n=2 * (x.shape[-1] - 1)), _tf_fn)
def testIRFFT2D(self):
def _tf_fn(x):
return spectral_ops.irfft2d(
x, fft_length=[x.shape[-2].value, 2 * (x.shape[-1].value - 1)])
self._VerifyFftMethod(
INNER_DIMS_2D,
lambda x: np.fft.rfft2(np.real(x), s=[x.shape[-2], x.shape[-1]]),
lambda x: np.fft.irfft2(x, s=[x.shape[-2], 2 * (x.shape[-1] - 1)]),
_tf_fn)
def testIRFFT3D(self):
def _to_input(x):
return np.fft.rfftn(
np.real(x),
axes=(-3, -2, -1),
s=[x.shape[-3], x.shape[-2], x.shape[-1]])
def _to_expected(x):
return np.fft.irfftn(
x,
axes=(-3, -2, -1),
s=[x.shape[-3], x.shape[-2], 2 * (x.shape[-1] - 1)])
def _tf_fn(x):
return spectral_ops.irfft3d(
x,
fft_length=[
x.shape[-3].value, x.shape[-2].value, 2 * (x.shape[-1].value - 1)
])
self._VerifyFftMethod(INNER_DIMS_3D, _to_input, _to_expected, _tf_fn)
if __name__ == "__main__":
googletest.main()
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