def test_python_constants_not_exposed(self):
"""Tests that only TensorFlow values are exposed to users."""
encoder_py = simple_encoder.StatefulSimpleEncoder(
core_encoder.EncoderComposer(
test_utils.SimpleLinearEncodingStage(2.0, 3.0)).make())
a_var = tf.get_variable('a_var', initializer=2.0)
b_var = tf.get_variable('b_var', initializer=3.0)
encoder_tf = simple_encoder.StatefulSimpleEncoder(
core_encoder.EncoderComposer(
test_utils.SimpleLinearEncodingStage(a_var, b_var)).make())
x = tf.constant(1.0)
encoder_py.initialize()
encoder_tf.initialize()
encoded_x_py, decode_fn_py = encoder_py.encode(x)
decoded_x_py = decode_fn_py(encoded_x_py)
encoded_x_tf, decode_fn_tf = encoder_tf.encode(x)
decoded_x_tf = decode_fn_tf(encoded_x_tf)
# The encoded_x_tf should have two elements that encoded_x_py does not.
# These correspond to the two variables created passed on to constructor of
# encoder_tf, which are exposed as params. For encoder_py, these are python
# integers, and should thus be hidden from users.
self.assertLen(encoded_x_tf, len(encoded_x_py) + 2)
# Make sure functionality is still the same.
self.evaluate(tf.global_variables_initializer())
x, decoded_x_py, decoded_x_tf = self.evaluate(
[x, decoded_x_py, decoded_x_tf])
self.assertAllClose(x, decoded_x_tf)
self.assertAllClose(x, decoded_x_py)
def as_stateful_simple_encoder(encoder):
"""Wraps an `Encoder` object as a `StatefulSimpleEncoder`."""
if isinstance(encoder, simple_encoder.StatefulSimpleEncoder):
return encoder
if not isinstance(encoder, core_encoder.Encoder):
raise TypeError('The encoder must be an instance of `Encoder`.')
return simple_encoder.StatefulSimpleEncoder(encoder)
def test_composite_encoder(self):
"""Tests functionality with a general, composite `Encoder`."""
encoder = core_encoder.EncoderComposer(
test_utils.SignIntFloatEncodingStage())
encoder.add_child(test_utils.TimesTwoEncodingStage(), SIF_SIGNS)
encoder.add_child(test_utils.PlusOneEncodingStage(), SIF_INTS)
encoder.add_child(test_utils.TimesTwoEncodingStage(), SIF_FLOATS).add_child(
test_utils.PlusOneOverNEncodingStage(), T2_VALS)
encoder = simple_encoder.StatefulSimpleEncoder(encoder.make())
x = tf.constant(1.2)
encoder.initialize()
encoded_x, decode_fn = encoder.encode(x)
decoded_x = decode_fn(encoded_x)
self.evaluate(tf.global_variables_initializer())
for i in range(1, 10):
x_py, encoded_x_py, decoded_x_py = self.evaluate(
[x, encoded_x, decoded_x])
self.assertAllClose(x_py, decoded_x_py)
self.assertAllClose(
2.0, _encoded_x_field(encoded_x_py, [TENSORS, SIF_SIGNS, T2_VALS]))
self.assertAllClose(
2.0, _encoded_x_field(encoded_x_py, [TENSORS, SIF_INTS, P1_VALS]))
self.assertAllClose(
0.4 + 1 / i,
_encoded_x_field(encoded_x_py,
[TENSORS, SIF_FLOATS, T2_VALS, PN_VALS]))
def test_uninitialized_encode_raises(self):
"""Tests uninitialized stateful encoder cannot perform encode."""
encoder = simple_encoder.StatefulSimpleEncoder(
core_encoder.EncoderComposer(
test_utils.PlusOneOverNEncodingStage()).make())
x = tf.constant(1.0)
with self.assertRaisesRegex(RuntimeError, 'not been initialized'):
encoder.encode(x)
def test_multiple_initialize_raises(self):
"""Tests encoder can be initialized only once."""
encoder = simple_encoder.StatefulSimpleEncoder(
core_encoder.EncoderComposer(
test_utils.PlusOneOverNEncodingStage()).make())
encoder.initialize()
with self.assertRaisesRegex(RuntimeError, 'already initialized'):
encoder.initialize()
def test_multiple_encode_raises(self):
"""Tests the encode method of stateful encoder can only be called once."""
encoder = simple_encoder.StatefulSimpleEncoder(
core_encoder.EncoderComposer(
test_utils.PlusOneOverNEncodingStage()).make())
encoder.initialize()
x = tf.constant(1.0)
encoder.encode(x)
with self.assertRaisesRegex(RuntimeError, 'only once'):
encoder.encode(x)
def test_decode_needs_input_shape_dynamic(self):
"""Tests that mechanism for passing input shape works with dynamic shape."""
encoder = simple_encoder.StatefulSimpleEncoder(
core_encoder.EncoderComposer(
test_utils.ReduceMeanEncodingStage()).make())
x = test_utils.get_tensor_with_random_shape()
encoder.initialize()
encoded_x, decode_fn = encoder.encode(x)
decoded_x = decode_fn(encoded_x)
self.evaluate(tf.global_variables_initializer())
x, decoded_x = self.evaluate([x, decoded_x])
self.assertAllEqual(x.shape, decoded_x.shape)
def test_decode_needs_input_shape_static(self):
"""Tests that mechanism for passing input shape works with static shape."""
encoder = simple_encoder.StatefulSimpleEncoder(
core_encoder.EncoderComposer(
test_utils.ReduceMeanEncodingStage()).make())
x = tf.reshape(list(range(15)), [3, 5])
encoder.initialize()
encoded_x, decode_fn = encoder.encode(x)
decoded_x = decode_fn(encoded_x)
self.evaluate(tf.global_variables_initializer())
decoded_x = self.evaluate(decoded_x)
self.assertAllEqual([[7.0] * 5] * 3, decoded_x)
def test_basic_encode_decode(self):
"""Tests basic encoding and decoding works as expected."""
encoder = simple_encoder.StatefulSimpleEncoder(
core_encoder.EncoderComposer(
test_utils.PlusOneOverNEncodingStage()).make())
x = tf.constant(1.0)
encoder.initialize()
encoded_x, decode_fn = encoder.encode(x)
decoded_x = decode_fn(encoded_x)
self.evaluate(tf.global_variables_initializer())
for i in range(1, 10):
x_py, encoded_x_py, decoded_x_py = self.evaluate(
[x, encoded_x, decoded_x])
self.assertAllClose(x_py, decoded_x_py)
self.assertAllClose(1.0 + 1 / i,
_encoded_x_field(encoded_x_py, [TENSORS, PN_VALS]))
def test_initializer_raises(self, not_an_encoder):
"""Tests invalid encoder argument."""
with self.assertRaisesRegex(TypeError, 'Encoder'):
simple_encoder.StatefulSimpleEncoder(not_an_encoder)
