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# Copyright 2023 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.
"""Tests for multitask.task_sampler."""
import tensorflow as tf, tf_keras
from official.modeling.multitask import configs
from official.modeling.multitask import task_sampler as sampler
class TaskSamplerTest(tf.test.TestCase):
def setUp(self):
super(TaskSamplerTest, self).setUp()
self._task_weights = {'A': 1.0, 'B': 2.0, 'C': 3.0}
def test_uniform_sample_distribution(self):
uniform_sampler = sampler.get_task_sampler(
configs.TaskSamplingConfig(type='uniform'), self._task_weights)
for step in range(5):
cumulative_distribution = uniform_sampler.task_cumulative_distribution(
tf.constant(step, dtype=tf.int64))
self.assertAllClose([0.333333, 0.666666, 1.0],
cumulative_distribution.numpy())
def test_proportional_sample_distribution(self):
prop_sampler = sampler.get_task_sampler(
configs.TaskSamplingConfig(
type='proportional',
proportional=configs.ProportionalSampleConfig(alpha=2.0)),
self._task_weights)
# CucmulativeOf(Normalize([1.0^2, 2.0^2, 3.0^2]))
for step in range(5):
cumulative_distribution = prop_sampler.task_cumulative_distribution(
tf.constant(step, dtype=tf.int64))
self.assertAllClose([0.07142857, 0.35714286, 1.0],
cumulative_distribution.numpy())
def test_annealing_sample_distribution(self):
num_epoch = 3
step_per_epoch = 6
annel_sampler = sampler.get_task_sampler(
configs.TaskSamplingConfig(
type='annealing',
annealing=configs.AnnealingSampleConfig(
steps_per_epoch=step_per_epoch,
total_steps=step_per_epoch * num_epoch)), self._task_weights)
global_step = tf.Variable(
0, dtype=tf.int64, name='global_step', trainable=False)
expected_cumulative_epochs = [[0.12056106, 0.4387236, 1.0],
[0.16666667, 0.5, 1.0],
[0.22477472, 0.5654695, 1.0]]
for epoch in range(num_epoch):
for _ in range(step_per_epoch):
cumulative_distribution = annel_sampler.task_cumulative_distribution(
tf.constant(global_step, dtype=tf.int64))
global_step.assign_add(1)
self.assertAllClose(expected_cumulative_epochs[epoch],
cumulative_distribution.numpy())
if __name__ == '__main__':
tf.test.main()
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