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How to use reset_stats method in locust

Best Python code snippet using locust

get_bounds_test.py

1# coding=utf-82# Copyright 2021 The Google Research Authors.3#4# Licensed under the Apache License, Version 2.0 (the "License");5# you may not use this file except in compliance with the License.6# You may obtain a copy of the License at7#8#     http://www.apache.org/licenses/LICENSE-2.09#10# Unless required by applicable law or agreed to in writing, software11# distributed under the License is distributed on an "AS IS" BASIS,12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.13# See the License for the specific language governing permissions and14# limitations under the License.15"""Tests for aqt.jax.get_bounds."""16from absl.testing import absltest17from absl.testing import parameterized18import flax19from jax import random20import jax.numpy as jnp21import numpy as onp22from aqt.jax import get_bounds23from aqt.jax import quant_config24from aqt.jax import test_utils25test_utils.configure_jax()26class GetBoundsTest(parameterized.TestCase):27  def setUp(self):28    super(GetBoundsTest, self).setUp()29    self.rng = random.PRNGKey(0)30    key1, key2 = random.split(self.rng)31    self.key2 = key232    self.x = random.normal(key1, (4, 3, 2))33    self.x2 = jnp.ones((4, 3, 2))34    self.hyperparam = get_bounds.GetBounds.Hyper(35        initial_bound=6.0,36        stddev_coeff=2.0,37        absdev_coeff=1.5,38        mix_coeff=0.7,39        reset_stats=False,40        granularity=quant_config.QuantGranularity.per_channel)41  def init_model(self,42                 update_bounds,43                 update_stats=True,44                 reset_stats=False,45                 use_cams=False,46                 granularity=quant_config.QuantGranularity.per_tensor,47                 ema_coeff=None):48    self.hyperparam = get_bounds.GetBounds.Hyper(49        initial_bound=self.hyperparam.initial_bound,50        stddev_coeff=self.hyperparam.stddev_coeff,51        absdev_coeff=self.hyperparam.absdev_coeff,52        mix_coeff=self.hyperparam.mix_coeff,53        reset_stats=reset_stats,54        use_cams=use_cams,55        ema_coeff=ema_coeff,56        granularity=granularity)57    gb_bounds_params = get_bounds.GetBounds.Params(58        update_bounds=update_bounds, update_stats=update_stats)59    bounds_module = get_bounds.GetBounds(hyper=self.hyperparam)60    init_state = bounds_module.init(61        self.key2, self.x, bounds_params=gb_bounds_params)62    return bounds_module, init_state, gb_bounds_params63  # TODO(shivaniagrawal): parametrize test for different values of axis64  @parameterized.named_parameters(65      dict(testcase_name='update_bound', update_bound=True),66      dict(testcase_name='do_not_update', update_bound=False),67  )68  def test_get_bounds_init(self, update_bound):69    _, init_state, _ = self.init_model(update_bound)70    init_state_stats = init_state['get_bounds']['stats']71    onp.testing.assert_array_equal(init_state_stats.n, 0)72    onp.testing.assert_array_equal(init_state_stats.mean, 0)73    onp.testing.assert_array_equal(init_state_stats.mean_abs, 0)74    onp.testing.assert_array_equal(init_state_stats.mean_sq, 0)75    onp.testing.assert_array_equal(init_state['get_bounds']['bounds'], 6.)76  # TODO(shivaniagrawal): more elaborate testing here as follows:77  # - run with (update_stats, update_bounds) = (False, False)78  # check that neither state changed79  # - run with (update_stats, update_bounds) = (True, False)80  # check that stats.n increased, bound unchanged81  # - run with (update_stats, update_bounds) = (False, True)82  # check that stats.n unchanged but bound updated.83  # - run again with (update_stats, update_bounds) = (False, True)84  # check that both unchanged (update_bounds is idempotent)85  # - run again with (update_stats, update_bounds) = (True, True)86  # check that both changed.87  @parameterized.named_parameters(88      dict(89          testcase_name='update_bound_reset_stats',90          update_bound=True,91          reset_stats=True),92      dict(93          testcase_name='no_update_bound_reset_stats',94          update_bound=False,95          reset_stats=True),96      dict(97          testcase_name='update_bound_no_reset_stats',98          update_bound=True,99          reset_stats=False),100      dict(101          testcase_name='no_update_bound_no_reset_stats',102          update_bound=False,103          reset_stats=False),104  )105  def test_update_stats(self, update_bound, reset_stats):106    model, init_state, params = self.init_model(107        update_bound,108        reset_stats=reset_stats,109        granularity=quant_config.QuantGranularity.per_tensor)110    _, state_0 = model.apply(111        init_state, self.x, bounds_params=params, mutable='get_bounds')112    stats_0_stats = state_0['get_bounds']['stats']113    if reset_stats and update_bound:114      onp.testing.assert_array_equal(stats_0_stats.n, 0)115    else:116      onp.testing.assert_array_equal(stats_0_stats.n, 1)117    _, state = model.apply(118        state_0, self.x2, bounds_params=params, mutable='get_bounds')119    stats = state['get_bounds']['stats']120    if reset_stats and update_bound:121      onp.testing.assert_array_equal(stats.n, 0)122      expected_updated_mean = 0.123      onp.testing.assert_array_equal(expected_updated_mean, stats.mean)124    else:125      onp.testing.assert_array_equal(stats.n, 2)126      expected_updated_mean = 1 / 2 * (1 + (stats_0_stats.mean))127      onp.testing.assert_array_equal(expected_updated_mean, stats.mean)128  @parameterized.named_parameters(129      dict(130          testcase_name='update_bound_reset_stats',131          update_bound=True,132          reset_stats=True),133      dict(134          testcase_name='no_update_bound_reset_stats',135          update_bound=False,136          reset_stats=True),137      dict(138          testcase_name='update_bound_no_reset_stats',139          update_bound=True,140          reset_stats=False),141      dict(142          testcase_name='no_update_bound_no_reset_stats',143          update_bound=False,144          reset_stats=False),145  )146  def test_update_stats_false(self, update_bound, reset_stats):147    model, init_state, params = self.init_model(148        update_bound, update_stats=False, reset_stats=reset_stats)149    _, state_0 = model.apply(150        init_state, self.x, bounds_params=params, mutable='get_bounds')151    stats_0_stats = state_0['get_bounds']['stats']152    onp.testing.assert_array_equal(stats_0_stats.n, 0)153    _, state = model.apply(154        state_0, self.x2, bounds_params=params, mutable='get_bounds')155    onp.testing.assert_array_equal(state['get_bounds']['stats'].n, 0)156    expected_updated_mean = 0.157    onp.testing.assert_array_equal(expected_updated_mean,158                                   state['get_bounds']['stats'].mean)159  @parameterized.named_parameters(160      dict(161          testcase_name='update_bounds_true',162          update_stats=False,163          update_bounds=True),164      dict(165          testcase_name='update_stats_true',166          update_stats=True,167          update_bounds=False),168      dict(testcase_name='both_true', update_stats=True, update_bounds=True),169  )170  def test_update_state_with_mutable_false_context_raises_error(171      self, update_stats, update_bounds):172    model, init_state, _ = self.init_model(True)173    with self.assertRaises(flax.errors.ModifyScopeVariableError):174      model.apply(175          init_state,176          self.x2,177          bounds_params=get_bounds.GetBounds.Params(178              update_stats=update_stats, update_bounds=update_bounds),179          mutable=False)180  @parameterized.named_parameters(181      dict(182          testcase_name='update_bound_no_ucb',183          update_bound=True,184          use_cams=False),185      dict(186          testcase_name='update_bound_with_ucb',187          update_bound=True,188          use_cams=True),189      dict(testcase_name='do_not_update', update_bound=False, use_cams=False),190  )191  def test_get_bounds_update_bounds(self, update_bound, use_cams=False):192    model, init_state, params = self.init_model(update_bound, use_cams=use_cams)193    y, state_0 = model.apply(194        init_state, self.x, bounds_params=params, mutable='get_bounds')195    if not update_bound:196      onp.testing.assert_array_equal(state_0['get_bounds']['bounds'], 6.)197      onp.testing.assert_array_equal(y, 6.)198    else:199      stats_0_stats = state_0['get_bounds']['stats']200      if use_cams:201        expected_y = onp.abs(onp.mean(202            self.x)) + self.hyperparam.stddev_coeff * onp.std(self.x)203      else:204        expected_y = (205            self.hyperparam.stddev_coeff * self.hyperparam.mix_coeff *206            jnp.sqrt(stats_0_stats.mean_sq) + self.hyperparam.absdev_coeff *207            (1 - self.hyperparam.mix_coeff) * stats_0_stats.mean_abs)208      onp.testing.assert_array_equal(state_0['get_bounds']['bounds'], y)209      onp.testing.assert_allclose(expected_y, y)210    y2, state = model.apply(211        state_0, self.x2, bounds_params=params, mutable='get_bounds')212    onp.testing.assert_array_equal(state['get_bounds']['bounds'], y2)213  @parameterized.named_parameters(214      dict(testcase_name='no_ema', ema_coeff=None),215      dict(testcase_name='ema_.8', ema_coeff=0.8),216      dict(testcase_name='ema_.1', ema_coeff=0.1))217  def test_ema_coeff(self, ema_coeff):218    x1 = jnp.array(1.0)219    x2 = jnp.array(-2.0)220    model, state, params = self.init_model(False, ema_coeff=ema_coeff)221    _, state1 = model.apply(222        state, x1, bounds_params=params, mutable='get_bounds')223    _, state2 = model.apply(224        state1, x2, bounds_params=params, mutable='get_bounds')225    stats = state2['get_bounds']['stats']226    def compute_ema_two_steps(x1, x2, alpha):227      initial_value = 0.0228      ema_step_1 = initial_value + alpha * (x1 - initial_value)229      ema_step_2 = ema_step_1 + alpha * (x2 - ema_step_1)230      return ema_step_2231    if ema_coeff is None:232      exp_mean = (x1 + x2) / 2233      exp_mean_sq = (x1**2 + x2**2) / 2234      exp_mean_abs = (jnp.abs(x1) + jnp.abs(x2)) / 2235    else:236      exp_mean = compute_ema_two_steps(x1, x2, ema_coeff)237      exp_mean_sq = compute_ema_two_steps(x1**2, x2**2, ema_coeff)238      exp_mean_abs = compute_ema_two_steps(jnp.abs(x1), jnp.abs(x2), ema_coeff)239    onp.testing.assert_allclose(stats.mean, exp_mean)240    onp.testing.assert_allclose(stats.mean_sq, exp_mean_sq)241    onp.testing.assert_allclose(stats.mean_abs, exp_mean_abs)242    print(stats)243    return244if __name__ == '__main__':...

game_stats.py

Source: game_stats.py

...9#10#     def __init__(self, ai_settings):11#         """åå§åç»è®¡ä¿¡æ¯"""12#         self.ai_settings = ai_settings13#         self.reset_stats()14#15#         # æ¸¸æåå¯å¨æ¶å¤äºæ´»å¨ç¶æ16#         self.game_active = True17#18#     def reset_stats(self):19#         """åå§åå¨æ¸¸æè¿è¡æé´å¯è½ååçç»è®¡ä¿¡æ¯"""20#         self.ships_left = self.ai_settings.ship_limit21# ------------------ example01 ------------------22# print("*" * 20)23# ------------------ example02 ------------------24# class GameStats():25#     """è·è¸ªæ¸¸æçç»è®¡ä¿¡æ¯"""26#27#     def __init__(self, ai_settings):28#         """åå§åç»è®¡ä¿¡æ¯"""29#         self.ai_settings = ai_settings30#         self.reset_stats()31#32#         # è®©æ¸¸æä¸å¼å§å¤äºéæ´»å¨ç¶æ33#         self.game_active = False34#35#     def reset_stats(self):36#         """åå§åå¨æ¸¸æè¿è¡æé´å¯è½ååçç»è®¡ä¿¡æ¯"""37#         self.ships_left = self.ai_settings.ship_limit38# ------------------ example02 ------------------39# print("*" * 20)40# ------------------ example03 ------------------41# class GameStats():42#     """è·è¸ªæ¸¸æçç»è®¡ä¿¡æ¯"""43#44#     def __init__(self, ai_settings):45#         """åå§åç»è®¡ä¿¡æ¯"""46#         self.ai_settings = ai_settings47#         self.reset_stats()48#49#         # è®©æ¸¸æä¸å¼å§å¤äºéæ´»å¨ç¶æ50#         self.game_active = False51#52#     def reset_stats(self):53#         """åå§åå¨æ¸¸æè¿è¡æé´å¯è½ååçç»è®¡ä¿¡æ¯"""54#         self.ships_left = self.ai_settings.ship_limit55#         self.score = 056# ------------------ example03 ------------------57# print("*" * 20)58# ------------------ example04 ------------------59# class GameStats():60#     """è·è¸ªæ¸¸æçç»è®¡ä¿¡æ¯"""61#62#     def __init__(self, ai_settings):63#         """åå§åç»è®¡ä¿¡æ¯"""64#         self.ai_settings = ai_settings65#         self.reset_stats()66#67#         # è®©æ¸¸æä¸å¼å§å¤äºéæ´»å¨ç¶æ68#         self.game_active = False69#70#         # å¨ä»»ä½æåµä¸é½ä¸åºéç½®æé«å¾å71#         self.high_score = 072#73#     def reset_stats(self):74#         """åå§åå¨æ¸¸æè¿è¡æé´å¯è½ååçç»è®¡ä¿¡æ¯"""75#         self.ships_left = self.ai_settings.ship_limit76#         self.score = 077# ------------------ example04 ------------------78# print("*" * 20)79# ------------------ example05 ------------------80class GameStats():81    """è·è¸ªæ¸¸æçç»è®¡ä¿¡æ¯"""82    def __init__(self, ai_settings):83        """åå§åç»è®¡ä¿¡æ¯"""84        self.ai_settings = ai_settings85        self.reset_stats()86        # è®©æ¸¸æä¸å¼å§å¤äºéæ´»å¨ç¶æ87        self.game_active = False88        # å¨ä»»ä½æåµä¸é½ä¸åºéç½®æé«å¾å89        self.high_score = 090    def reset_stats(self):91        """åå§åå¨æ¸¸æè¿è¡æé´å¯è½ååçç»è®¡ä¿¡æ¯"""92        self.ships_left = self.ai_settings.ship_limit93        self.score = 094        self.level = 1...

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