# Copyright 2017 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 object_detection.utils.learning_schedules."""
import numpy as np
import tensorflow as tf

from object_detection.utils import learning_schedules
from object_detection.utils import test_case


class LearningSchedulesTest(test_case.TestCase):

  def testExponentialDecayWithBurnin(self):
    def graph_fn(global_step):
      learning_rate_base = 1.0
      learning_rate_decay_steps = 3
      learning_rate_decay_factor = .1
      burnin_learning_rate = .5
      burnin_steps = 2
      min_learning_rate = .05
      learning_rate = learning_schedules.exponential_decay_with_burnin(
          global_step, learning_rate_base, learning_rate_decay_steps,
          learning_rate_decay_factor, burnin_learning_rate, burnin_steps,
          min_learning_rate)
      assert learning_rate.op.name.endswith('learning_rate')
      return (learning_rate,)

    output_rates = [
        self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(9)
    ]

    exp_rates = [.5, .5, 1, 1, 1, .1, .1, .1, .05]
    self.assertAllClose(output_rates, exp_rates, rtol=1e-4)

  def testCosineDecayWithWarmup(self):
    def graph_fn(global_step):
      learning_rate_base = 1.0
      total_steps = 100
      warmup_learning_rate = 0.1
      warmup_steps = 9
      learning_rate = learning_schedules.cosine_decay_with_warmup(
          global_step, learning_rate_base, total_steps,
          warmup_learning_rate, warmup_steps)
      assert learning_rate.op.name.endswith('learning_rate')
      return (learning_rate,)
    exp_rates = [0.1, 0.5, 0.9, 1.0, 0]
    input_global_steps = [0, 4, 8, 9, 100]
    output_rates = [
        self.execute(graph_fn, [np.array(step).astype(np.int64)])
        for step in input_global_steps
    ]
    self.assertAllClose(output_rates, exp_rates)

  def testCosineDecayAfterTotalSteps(self):
    def graph_fn(global_step):
      learning_rate_base = 1.0
      total_steps = 100
      warmup_learning_rate = 0.1
      warmup_steps = 9
      learning_rate = learning_schedules.cosine_decay_with_warmup(
          global_step, learning_rate_base, total_steps,
          warmup_learning_rate, warmup_steps)
      assert learning_rate.op.name.endswith('learning_rate')
      return (learning_rate,)
    exp_rates = [0]
    input_global_steps = [101]
    output_rates = [
        self.execute(graph_fn, [np.array(step).astype(np.int64)])
        for step in input_global_steps
    ]
    self.assertAllClose(output_rates, exp_rates)

  def testCosineDecayWithHoldBaseLearningRateSteps(self):
    def graph_fn(global_step):
      learning_rate_base = 1.0
      total_steps = 120
      warmup_learning_rate = 0.1
      warmup_steps = 9
      hold_base_rate_steps = 20
      learning_rate = learning_schedules.cosine_decay_with_warmup(
          global_step, learning_rate_base, total_steps,
          warmup_learning_rate, warmup_steps, hold_base_rate_steps)
      assert learning_rate.op.name.endswith('learning_rate')
      return (learning_rate,)
    exp_rates = [0.1, 0.5, 0.9, 1.0, 1.0, 1.0, 0.999702, 0.874255, 0.577365,
                 0.0]
    input_global_steps = [0, 4, 8, 9, 10, 29, 30, 50, 70, 120]
    output_rates = [
        self.execute(graph_fn, [np.array(step).astype(np.int64)])
        for step in input_global_steps
    ]
    self.assertAllClose(output_rates, exp_rates)

  def testManualStepping(self):
    def graph_fn(global_step):
      boundaries = [2, 3, 7]
      rates = [1.0, 2.0, 3.0, 4.0]
      learning_rate = learning_schedules.manual_stepping(
          global_step, boundaries, rates)
      assert learning_rate.op.name.endswith('learning_rate')
      return (learning_rate,)

    output_rates = [
        self.execute(graph_fn, [np.array(i).astype(np.int64)])
        for i in range(10)
    ]
    exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]
    self.assertAllClose(output_rates, exp_rates)

  def testManualSteppingWithWarmup(self):
    def graph_fn(global_step):
      boundaries = [4, 6, 8]
      rates = [0.02, 0.10, 0.01, 0.001]
      learning_rate = learning_schedules.manual_stepping(
          global_step, boundaries, rates, warmup=True)
      assert learning_rate.op.name.endswith('learning_rate')
      return (learning_rate,)

    output_rates = [
        self.execute(graph_fn, [np.array(i).astype(np.int64)])
        for i in range(9)
    ]
    exp_rates = [0.02, 0.04, 0.06, 0.08, 0.10, 0.10, 0.01, 0.01, 0.001]
    self.assertAllClose(output_rates, exp_rates)

  def testManualSteppingWithZeroBoundaries(self):
    def graph_fn(global_step):
      boundaries = []
      rates = [0.01]
      learning_rate = learning_schedules.manual_stepping(
          global_step, boundaries, rates)
      return (learning_rate,)

    output_rates = [
        self.execute(graph_fn, [np.array(i).astype(np.int64)])
        for i in range(4)
    ]
    exp_rates = [0.01] * 4
    self.assertAllClose(output_rates, exp_rates)


if __name__ == '__main__':
  tf.test.main()
