def test_default_kwargs_throw_error_on_compression(self):
noisy = uniform_noise.NoisyNormal(loc=.25, scale=10.)
em = ContinuousBatchedEntropyModel(noisy, 1)
x = tf.zeros(10)
with self.assertRaises(RuntimeError):
em.compress(x)
s = tf.zeros(10, dtype=tf.string)
with self.assertRaises(RuntimeError):
em.decompress(s, [10])
def test_compression_works_after_serialization_no_offset(self):
noisy = uniform_noise.NoisyNormal(loc=0, scale=5.)
em = ContinuousBatchedEntropyModel(noisy, 1, compression=True)
self.assertIs(em._quantization_offset, None)
json = tf.keras.utils.serialize_keras_object(em)
weights = em.get_weights()
x = noisy.base.sample([100])
x_quantized = em.quantize(x)
x_compressed = em.compress(x)
em = tf.keras.utils.deserialize_keras_object(json)
em.set_weights(weights)
self.assertAllEqual(em.compress(x), x_compressed)
self.assertAllEqual(em.decompress(x_compressed, [100]), x_quantized)
def test_compression_consistent_with_quantization(self):
noisy = uniform_noise.NoisyNormal(loc=.25, scale=10.)
em = ContinuousBatchedEntropyModel(noisy, 1, compression=True)
x = noisy.base.sample([100])
x_quantized = em.quantize(x)
x_decompressed = em.decompress(em.compress(x), [100])
self.assertAllEqual(x_decompressed, x_quantized)
class Compressor:
def compress(self, values):
if not hasattr(self, "em"):
self.em = ContinuousBatchedEntropyModel(noisy,
1,
compression=True)
compressed = self.em.compress(values)
return self.em.decompress(compressed, [100])
def test_high_entropy_bounds(self): # For high entropy distributions, the training bound should be very tight, # and the overhead of range coding manageable. noisy = uniform_noise.NoisyNormal(loc=0., scale=100.) em = ContinuousBatchedEntropyModel(noisy, 1, compression=True) x = noisy.base.sample([10000]) bits_eval = em.bits(x, training=False) bits_training = em.bits(x, training=True) bits_compressed = 8 * len(em.compress(x).numpy()) self.assertAllClose(bits_training, bits_eval, atol=0, rtol=5e-5) self.assertAllClose(bits_compressed, bits_eval, atol=0, rtol=5e-3)
def test_information_bounds(self):
# `bits(training=True)` should be greater than `bits(training=False)`
# because it is defined as an upper bound (albeit for infinite data). The
# actual length of the bit string should always be greater than
# `bits(training=False)` because range coding is only asymptotically
# optimal, and because it operates on quantized probabilities.
for scale in 2 ** tf.linspace(-2., 7., 10):
noisy = uniform_noise.NoisyNormal(loc=0., scale=scale)
em = ContinuousBatchedEntropyModel(noisy, 1, compression=True)
x = noisy.base.sample([10000])
bits_eval = em.bits(x, training=False)
bits_training = em.bits(x, training=True)
bits_compressed = 8 * len(em.compress(x).numpy())
self.assertGreater(bits_training, .9975 * bits_eval)
self.assertGreater(bits_compressed, bits_eval)
def test_small_bitcost_for_dirac_prior(self):
prior = uniform_noise.NoisyNormal(loc=100 * tf.range(16.0), scale=1e-10)
em = ContinuousBatchedEntropyModel(
prior, coding_rank=2, compression=True)
num_symbols = 1000
source = prior.base
x = source.sample((3, num_symbols))
_, bits_estimate = em(x, training=True)
bitstring = em.compress(x)
x_decoded = em.decompress(bitstring, (num_symbols,))
bitstring_bits = tf.reshape(
[len(b) * 8 for b in bitstring.numpy().flatten()], bitstring.shape)
# Max 2 bytes.
self.assertAllLessEqual(bits_estimate, 16)
self.assertAllLessEqual(bitstring_bits, 16)
# Quantization noise should be between -.5 and .5
self.assertAllLessEqual(tf.abs(x - x_decoded), 0.5)
def test_information_bounds(self, scale):
# Off-center prior to test quantization offset heuristic. Without it, it
# should be harder to achieve the bounds below.
prior = uniform_noise.NoisyNormal(loc=.5, scale=scale)
em = ContinuousBatchedEntropyModel(prior,
coding_rank=1,
compression=True)
x = prior.base.sample([1000000])
_, bits_eval = em(x, training=False)
_, bits_training = em(x, training=True)
bits_compressed = 8 * len(em.compress(x).numpy())
# Asymptotically, the entropy estimate with `training=True` is an upper
# bound on the entropy estimate with `training=False`. (With limited data,
# fluctuations are possible.)
with self.subTest("training bits > eval bits"):
# Sample size is too small for the bound to be asymptotic. Increasing it
# would make tests run too long.
self.assertGreater(bits_training, 0.999999 * bits_eval)
# Asymptotically, the length of the bit string should be greater than the
# entropy estimate with `training=False` because range coding is only
# asymptotically optimal, and because it operates on quantized
# probabilities.
with self.subTest("compressed bits > eval bits"):
self.assertGreater(bits_compressed, bits_eval)
# For low entropy distributions, the training bound can be very loose.
if scale <= .5:
with self.subTest("training bound loose"):
self.assertAllClose(bits_training,
bits_eval,
atol=0,
rtol=1.25)
self.assertNotAllClose(bits_training,
bits_eval,
atol=0,
rtol=1e-2)
# For high entropy distributions, the training bound should be tight.
if scale >= 64:
with self.subTest("training bound tight"):
self.assertAllClose(bits_training,
bits_eval,
atol=0,
rtol=1e-5)
# The overhead of range coding should always be manageable.
with self.subTest("range coding overhead"):
self.assertAllClose(bits_compressed, bits_eval, atol=0, rtol=5e-3)
def test_dtypes_are_correct_with_mixed_precision(self):
tf.keras.mixed_precision.set_global_policy("mixed_float16")
try:
noisy = uniform_noise.NoisyNormal(
loc=tf.constant(0, dtype=tf.float64),
scale=tf.constant(1, dtype=tf.float64))
em = ContinuousBatchedEntropyModel(noisy, 1, compression=True)
self.assertEqual(em.bottleneck_dtype, tf.float16)
self.assertEqual(em.prior.dtype, tf.float64)
x = tf.random.stateless_normal((2, 5),
seed=(0, 1),
dtype=tf.float16)
x_tilde, bits = em(x)
bitstring = em.compress(x)
x_hat = em.decompress(bitstring, (5, ))
self.assertEqual(x_hat.dtype, tf.float16)
self.assertAllClose(x, x_hat, rtol=0, atol=.5)
self.assertEqual(x_tilde.dtype, tf.float16)
self.assertAllClose(x, x_tilde, rtol=0, atol=.5)
self.assertEqual(bits.dtype, tf.float64)
self.assertEqual(bits.shape, (2, ))
self.assertAllGreaterEqual(bits, 0.)
finally:
tf.keras.mixed_precision.set_global_policy(None)