mirror of
https://github.com/gryf/coach.git
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257 lines
9.5 KiB
Python
257 lines
9.5 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import argparse
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import glob
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import os
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import shutil
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import signal
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import subprocess
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import sys
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from importlib import import_module
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from os import path
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sys.path.append('.')
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import numpy as np
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import pandas as pd
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import time
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# -*- coding: utf-8 -*-
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from rl_coach.logger import screen
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def read_csv_paths(test_path, filename_pattern, read_csv_tries=100):
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csv_paths = []
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tries_counter = 0
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while not csv_paths:
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csv_paths = glob.glob(path.join(test_path, '*', filename_pattern))
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if tries_counter > read_csv_tries:
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break
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tries_counter += 1
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time.sleep(1)
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return csv_paths
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def print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, args):
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percentage = int((100 * last_num_episodes) / preset_validation_params.max_episodes_to_achieve_reward)
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sys.stdout.write("\rReward: ({}/{})".format(round(averaged_rewards[-1], 1),
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preset_validation_params.min_reward_threshold))
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sys.stdout.write(' Time (sec): ({}/{})'.format(round(time.time() - start_time, 2), args.time_limit))
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sys.stdout.write(' Episode: ({}/{})'.format(last_num_episodes,
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preset_validation_params.max_episodes_to_achieve_reward))
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sys.stdout.write(
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' {}%|{}{}| '.format(percentage, '#' * int(percentage / 10), ' ' * (10 - int(percentage / 10))))
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sys.stdout.flush()
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def perform_reward_based_tests(args, preset_validation_params, preset_name):
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win_size = 10
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test_name = '__test_reward'
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test_path = os.path.join('./experiments', test_name)
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if path.exists(test_path):
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shutil.rmtree(test_path)
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# run the experiment in a separate thread
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screen.log_title("Running test {}".format(preset_name))
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log_file_name = 'test_log_{preset_name}.txt'.format(preset_name=preset_name)
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cmd = [
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'python3',
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'rl_coach/coach.py',
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'-p', '{preset_name}'.format(preset_name=preset_name),
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'-e', '{test_name}'.format(test_name=test_name),
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'-n', '{num_workers}'.format(num_workers=preset_validation_params.num_workers),
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'--seed', '0',
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'-c'
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]
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if preset_validation_params.reward_test_level:
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cmd += ['-lvl', '{level}'.format(level=preset_validation_params.reward_test_level)]
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stdout = open(log_file_name, 'w')
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p = subprocess.Popen(cmd, stdout=stdout, stderr=stdout)
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start_time = time.time()
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reward_str = 'Evaluation Reward'
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if preset_validation_params.num_workers > 1:
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filename_pattern = 'worker_0*.csv'
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else:
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filename_pattern = '*.csv'
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test_passed = False
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# get the csv with the results
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csv_paths = read_csv_paths(test_path, filename_pattern)
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if csv_paths:
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csv_path = csv_paths[0]
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# verify results
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csv = None
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time.sleep(1)
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averaged_rewards = [0]
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last_num_episodes = 0
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if not args.no_progress_bar:
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print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, args)
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while csv is None or (csv['Episode #'].values[
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-1] < preset_validation_params.max_episodes_to_achieve_reward and time.time() - start_time < args.time_limit):
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try:
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csv = pd.read_csv(csv_path)
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except:
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# sometimes the csv is being written at the same time we are
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# trying to read it. no problem -> try again
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continue
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if reward_str not in csv.keys():
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continue
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rewards = csv[reward_str].values
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rewards = rewards[~np.isnan(rewards)]
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if len(rewards) >= 1:
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averaged_rewards = np.convolve(rewards, np.ones(min(len(rewards), win_size)) / win_size, mode='valid')
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else:
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time.sleep(1)
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continue
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if not args.no_progress_bar:
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print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, args)
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if csv['Episode #'].shape[0] - last_num_episodes <= 0:
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continue
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last_num_episodes = csv['Episode #'].values[-1]
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# check if reward is enough
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if np.any(averaged_rewards >= preset_validation_params.min_reward_threshold):
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test_passed = True
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break
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time.sleep(1)
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# kill test and print result
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# os.killpg(os.getpgid(p.pid), signal.SIGKILL)
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p.kill()
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screen.log('')
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if test_passed:
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screen.success("Passed successfully")
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else:
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if time.time() - start_time > args.time_limit:
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screen.error("Failed due to exceeding time limit", crash=False)
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if args.verbose:
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screen.error("command exitcode: {}".format(p.returncode), crash=False)
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screen.error(open(log_file_name).read(), crash=False)
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elif csv_paths:
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screen.error("Failed due to insufficient reward", crash=False)
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if args.verbose:
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screen.error("command exitcode: {}".format(p.returncode), crash=False)
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screen.error(open(log_file_name).read(), crash=False)
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screen.error("preset_validation_params.max_episodes_to_achieve_reward: {}".format(
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preset_validation_params.max_episodes_to_achieve_reward), crash=False)
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screen.error("preset_validation_params.min_reward_threshold: {}".format(
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preset_validation_params.min_reward_threshold), crash=False)
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screen.error("averaged_rewards: {}".format(averaged_rewards), crash=False)
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screen.error("episode number: {}".format(csv['Episode #'].values[-1]), crash=False)
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else:
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screen.error("csv file never found", crash=False)
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if args.verbose:
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screen.error("command exitcode: {}".format(p.returncode), crash=False)
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screen.error(open(log_file_name).read(), crash=False)
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shutil.rmtree(test_path)
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os.remove(log_file_name)
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return test_passed
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def all_presets():
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return [
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f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets'))
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if f[-3:] == '.py' and not f == '__init__.py'
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]
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('-p', '--preset',
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help="(string) Name of a preset to run (as configured in presets.py)",
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default=None,
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type=str)
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parser.add_argument('-ip', '--ignore_presets',
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help="(string) Name of a preset(s) to ignore (comma separated, and as configured in presets.py)",
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default=None,
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type=str)
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parser.add_argument('-v', '--verbose',
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help="(flag) display verbose logs in the event of an error",
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action='store_true')
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parser.add_argument('--stop_after_first_failure',
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help="(flag) stop executing tests after the first error",
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action='store_true')
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parser.add_argument('-tl', '--time_limit',
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help="time limit for each test in minutes",
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default=60, # setting time limit to be so high due to DDPG being very slow - its tests are long
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type=int)
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parser.add_argument('-np', '--no_progress_bar',
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help="(flag) Don't print the progress bar (makes jenkins logs more readable)",
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action='store_true')
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args = parser.parse_args()
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if args.preset is not None:
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presets_lists = [args.preset]
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else:
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presets_lists = all_presets()
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fail_count = 0
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test_count = 0
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args.time_limit = 60 * args.time_limit
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if args.ignore_presets is not None:
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presets_to_ignore = args.ignore_presets.split(',')
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else:
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presets_to_ignore = []
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for idx, preset_name in enumerate(sorted(presets_lists)):
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if args.stop_after_first_failure and fail_count > 0:
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break
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if preset_name not in presets_to_ignore:
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try:
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preset = import_module('rl_coach.presets.{}'.format(preset_name))
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except:
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screen.error("Failed to load preset <{}>".format(preset_name), crash=False)
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fail_count += 1
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test_count += 1
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continue
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preset_validation_params = preset.graph_manager.preset_validation_params
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if not preset_validation_params.test:
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continue
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test_count += 1
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test_passed = perform_reward_based_tests(args, preset_validation_params, preset_name)
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if not test_passed:
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fail_count += 1
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screen.separator()
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if fail_count == 0:
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screen.success(" Summary: " + str(test_count) + "/" + str(test_count) + " tests passed successfully")
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else:
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screen.error(" Summary: " + str(test_count - fail_count) + "/" + str(test_count) + " tests passed successfully")
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if __name__ == '__main__':
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main()
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