def testUpsampleAndDownsampleAreTransposes(self):
"""Tests that _downsample() is the transpose of _upsample()."""
n = 8
x = np.random.uniform(size=(1, n, 1))
for f_len in range(1, 5):
f = np.random.uniform(size=f_len)
for shift in [0, 1]:
# We're only testing the resampling operators away from the boundaries,
# as this test appears to fail in the presences of boundary conditions.
# TODO(barron): Figure out what's happening and make this test more
# thorough, and then set range1 = range(d), range2 = range(d//2) and
# have this code depend on util.compute_jacobian().
range1 = np.arange(f_len // 2 + 1, n - (f_len // 2 + 1))
range2 = np.arange(f_len // 4, n // 2 - (f_len // 4))
y = wavelet._downsample(x, f, 0, shift)
# Construct the jacobian of _downsample().
x_ph = tf.placeholder(tf.float32, x.shape)
jacobian_down = []
vec = lambda x: tf.reshape(x, [-1])
for d in range2:
jacobian_down.append(
vec(
tf.gradients(
vec(wavelet._downsample(x_ph, f, 0, shift))[d],
x_ph)[0]))
jacobian_down = tf.stack(jacobian_down, 1)
with tf.Session() as sess:
jacobian_down, y = sess.run([jacobian_down, y], {x_ph: x})
# Construct the jacobian of _upsample().
y_ph = tf.placeholder(tf.float32, y.shape)
jacobian_up = []
vec = lambda x: tf.reshape(x, [-1])
for d in range1:
jacobian_up.append(
vec(
tf.gradients(
vec(
wavelet._upsample(y_ph, x.shape[1:], f, 0,
shift))[d], y_ph)[0]))
jacobian_up = tf.stack(jacobian_up, 1)
with tf.Session() as sess:
jacobian_up = sess.run(jacobian_up, {y_ph: y})
# Test that the jacobian of _downsample() is close to the transpose of
# the jacobian of _upsample().
self.assertAllClose(jacobian_down[range1, :],
np.transpose(jacobian_up[range2, :]),
atol=1e-6,
rtol=1e-6)
def testUpsampleAndDownsampleAreTransposes(self):
"""Tests that _downsample() is the transpose of _upsample()."""
n = 8
x = tf.convert_to_tensor(np.random.uniform(size=(1, n, 1)))
for f_len in range(1, 5):
f = np.random.uniform(size=f_len)
for shift in [0, 1]:
# We're only testing the resampling operators away from the boundaries,
# as this test appears to fail in the presences of boundary conditions.
# TODO(barron): Figure out what's happening and make this test more
# thorough, and then set range1 = range(d), range2 = range(d//2) and
# have this code depend on util.compute_jacobian().
range1 = np.arange(f_len // 2 + 1, n - (f_len // 2 + 1))
range2 = np.arange(f_len // 4, n // 2 - (f_len // 4))
y = wavelet._downsample(x, f, 0, shift)
vec = lambda z: tf.reshape(z, [-1])
jacobian_down = []
with tf.GradientTape(persistent=True) as tape:
tape.watch(x)
for d in range2:
yd = vec(wavelet._downsample(x, f, 0, shift))[d]
jacobian_down.append(vec(tape.gradient(yd, x)))
jacobian_down = tf.stack(jacobian_down, 1).numpy()
jacobian_up = []
with tf.GradientTape(persistent=True) as tape:
tape.watch(y)
for d in range1:
xd = vec(wavelet._upsample(y, x.shape[1:], f, 0, shift))[d]
jacobian_up.append(vec(tape.gradient(xd, y)))
jacobian_up = tf.stack(jacobian_up, 1).numpy()
# Test that the jacobian of _downsample() is close to the transpose of
# the jacobian of _upsample().
self.assertAllClose(
jacobian_down[range1, :],
np.transpose(jacobian_up[range2, :]),
atol=1e-6,
rtol=1e-6)