How to use reset_stats method in locust
Best Python code snippet using locust
get_bounds_test.py
Source: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# # 游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# # 让游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# # 让游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# # 让游æä¸å¼å§å¤äºéæ´»å¨ç¶æ68# self.game_active = False69#70# # å¨ä»»ä½æ
åµä¸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 # 让游æä¸å¼å§å¤äºéæ´»å¨ç¶æ87 self.game_active = False88 # å¨ä»»ä½æ
åµä¸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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