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# Copyright 2019, 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_model_optimization.python.core.internal.tensor_encoding.stages.research import clipping
from tensorflow_model_optimization.python.core.internal.tensor_encoding.testing import test_utils
if tf.executing_eagerly():
tf.compat.v1.disable_eager_execution()
class ClipByNormEncodingStageTest(test_utils.BaseEncodingStageTest):
def default_encoding_stage(self):
"""See base class."""
return clipping.ClipByNormEncodingStage(1.0)
def default_input(self):
"""See base class."""
return tf.random.normal([20])
@property
def is_lossless(self):
"""See base class."""
return False
def common_asserts_for_test_data(self, data):
"""See base class."""
encoded_x = data.encoded_x[
clipping.ClipByNormEncodingStage.ENCODED_VALUES_KEY]
# The encoding should not change the shape...
self.assertAllEqual(data.x.shape, encoded_x.shape)
# The decoding should be identity.
self.assertAllEqual(encoded_x, data.decoded_x)
def test_clipping_effective(self):
stage = clipping.ClipByNormEncodingStage(1.0)
test_data = self.run_one_to_many_encode_decode(
stage, lambda: tf.constant([1.0, 1.0, 1.0, 1.0]))
self.common_asserts_for_test_data(test_data)
self.assertAllEqual([1.0, 1.0, 1.0, 1.0], test_data.x)
# The decoded values should have norm 1.
self.assertAllClose([0.5, 0.5, 0.5, 0.5], test_data.decoded_x)
def test_clipping_large_norm_identity(self):
stage = clipping.ClipByNormEncodingStage(1000.0)
test_data = self.run_one_to_many_encode_decode(
stage, lambda: tf.constant([1.0, 1.0, 1.0, 1.0]))
self.common_asserts_for_test_data(test_data)
# The encoding should act as an identity, if input value has smaller norm.
self.assertAllEqual(test_data.x, test_data.decoded_x)
@parameterized.parameters(([2,],), ([2, 3],), ([2, 3, 4],))
def test_different_shapes(self, shape):
stage = clipping.ClipByNormEncodingStage(1.0)
test_data = self.run_one_to_many_encode_decode(
stage, lambda: tf.random.uniform(shape) + 1.0)
self.common_asserts_for_test_data(test_data)
self.assertAllClose(1.0, np.linalg.norm(test_data.decoded_x))
@parameterized.parameters(
itertools.product([tf.float32, tf.float64], [tf.float32, tf.float64]))
def test_input_types(self, x_dtype, clip_norm_dtype):
# Tests combinations of input dtypes.
stage = clipping.ClipByNormEncodingStage(
tf.constant(1.0, clip_norm_dtype))
x = tf.constant([1.0, 1.0, 1.0, 1.0], dtype=x_dtype)
encode_params, decode_params = stage.get_params()
encoded_x, decoded_x = self.encode_decode_x(stage, x, encode_params,
decode_params)
test_data = test_utils.TestData(x, encoded_x, decoded_x)
test_data = self.evaluate_test_data(test_data)
self.assertAllEqual([1.0, 1.0, 1.0, 1.0], test_data.x)
# The decoded values should have norm 1.
self.assertAllClose([0.5, 0.5, 0.5, 0.5], test_data.decoded_x)
class ClipByValueEncodingStageTest(test_utils.BaseEncodingStageTest):
def default_encoding_stage(self):
"""See base class."""
return clipping.ClipByValueEncodingStage(-1.0, 1.0)
def default_input(self):
"""See base class."""
return tf.random.normal([20])
@property
def is_lossless(self):
"""See base class."""
return False
def common_asserts_for_test_data(self, data):
"""See base class."""
encoded_x = data.encoded_x[
clipping.ClipByValueEncodingStage.ENCODED_VALUES_KEY]
# The encoding should not change the shape...
self.assertAllEqual(data.x.shape, encoded_x.shape)
# The decoding should be identity.
self.assertAllEqual(encoded_x, data.decoded_x)
def test_clipping_effective(self):
stage = clipping.ClipByValueEncodingStage(-1.0, 1.0)
test_data = self.run_one_to_many_encode_decode(
stage, lambda: tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0]))
self.common_asserts_for_test_data(test_data)
self.assertAllEqual([-2.0, -1.0, 0.0, 1.0, 2.0], test_data.x)
self.assertAllClose([-1.0, -1.0, 0.0, 1.0, 1.0], test_data.decoded_x)
def test_clipping_large_min_max_identity(self):
stage = clipping.ClipByValueEncodingStage(-1000.0, 1000.0)
test_data = self.run_one_to_many_encode_decode(stage, self.default_input)
self.common_asserts_for_test_data(test_data)
# The encoding should act as an identity, if input has smaller values.
self.assertAllEqual(test_data.x, test_data.decoded_x)
@parameterized.parameters(([2,],), ([2, 3],), ([2, 3, 4],))
def test_different_shapes(self, shape):
stage = clipping.ClipByValueEncodingStage(-1.0, 1.0)
test_data = self.run_one_to_many_encode_decode(
stage, lambda: tf.random.normal(shape))
self.common_asserts_for_test_data(test_data)
self.assertGreaterEqual(1.0, np.amax(test_data.decoded_x))
self.assertLessEqual(-1.0, np.amin(test_data.decoded_x))
@parameterized.parameters(
itertools.product([tf.float32, tf.float64], [tf.float32, tf.float64],
[tf.float32, tf.float64]))
def test_input_types(self, x_dtype, clip_value_min_dtype,
clip_value_max_dtype):
# Tests combinations of input dtypes.
stage = clipping.ClipByValueEncodingStage(
tf.constant(-1.0, clip_value_min_dtype),
tf.constant(1.0, clip_value_max_dtype))
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=x_dtype)
encode_params, decode_params = stage.get_params()
encoded_x, decoded_x = self.encode_decode_x(stage, x, encode_params,
decode_params)
test_data = test_utils.TestData(x, encoded_x, decoded_x)
test_data = self.evaluate_test_data(test_data)
self.common_asserts_for_test_data(test_data)
self.assertAllEqual([-2.0, -1.0, 0.0, 1.0, 2.0], test_data.x)
self.assertAllClose([-1.0, -1.0, 0.0, 1.0, 1.0], test_data.decoded_x)
if __name__ == '__main__':
tf.test.main()
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