def _test_shift(self, x, tf_shift_fn, np_shift_fn):
def tf_fn():
return tf_shift_fn(tf.constant(x))
x_shift_actual = test_utils.exec_tf(tf_fn)
x_shift_expect = np_shift_fn(x)
assert_array_equal(x_shift_actual, x_shift_expect)
def _test_convolution(self, d, k, mode, actual=None):
def tf_fn():
dt = tf.constant(d, dtype=tf.float32)
kt = tf.constant(k, dtype=tf.float32)
# Determine FFT dimensions and functions
pad_dims = fft_utils_tf.get_fft_pad_dims(dt, kt)
optim_dims = fft_utils_tf.optimize_dims(pad_dims, mode)
fft_fwd, fft_rev = fft_utils_tf.get_fft_tf_fns(dt.shape.ndims)
# Run convolution of data 'd' with kernel 'k'
dk_fft = fft_fwd(kt, fft_length=optim_dims)
dconv = fft_utils_tf.convolve(dt, dk_fft, optim_dims, fft_fwd,
fft_rev)
# Extract patch from result matching dimensions of original data array
return fft_utils_tf.extract(dconv, tf.shape(dt), pad_dims)
tf_res = test_utils.exec_tf(tf_fn)
np_res = fft_utils_np.convolve(d, k)
assert_almost_equal(tf_res, np_res, decimal=3)
self.assertEqual(tf_res.shape, np_res.shape)
if actual is not None:
assert_almost_equal(tf_res, actual, decimal=6)
def _test_padding(self, x, **kwargs):
def tf_fn():
xt = tf.constant(x)
yt = tf_ops.pad_around_center(xt, **kwargs)
zt = tf_ops.unpad_around_center(yt, tf.shape(xt))
return zt
x_actual = test_utils.exec_tf(tf_fn)
assert_array_equal(x, x_actual, 'Padded vs un-padded array not equal')
def _test_padding(self, d, k, actual):
def tf_fn():
dt = tf.constant(d, dtype=tf.float32)
kt = tf.constant(k, dtype=tf.float32)
return fft_utils_tf.get_fft_pad_dims(dt, kt)
tf_res = test_utils.exec_tf(tf_fn)
np_res = fft_utils_np.get_fft_pad_dims(d, k)
self.assertTrue(type(tf_res) is np.ndarray)
self.assertTrue(type(np_res) is np.ndarray)
self.assertTrue(np.array_equal(tf_res, np_res))
self.assertTrue(np.array_equal(tf_res, actual))
def _test_optimize_padding(self, d, k, mode, actual):
def tf_fn():
dt = tf.constant(d, dtype=tf.float32)
kt = tf.constant(k, dtype=tf.float32)
pad_dims = fft_utils_tf.get_fft_pad_dims(dt, kt)
return fft_utils_tf.optimize_dims(pad_dims, mode)
tf_res = test_utils.exec_tf(tf_fn)
np_res = fft_utils_np.optimize_dims(fft_utils_np.get_fft_pad_dims(d, k), mode)
self.assertTrue(type(tf_res) is np.ndarray)
self.assertTrue(type(np_res) is np.ndarray)
assert_array_equal(tf_res, np_res)
assert_array_equal(tf_res, actual)