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1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
219 lines
9.0 KiB
Python
219 lines
9.0 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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import time
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import numpy as np
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import pandas as pd
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import logger
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import presets
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import utils
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if __name__ == '__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('-itf', '--ignore_tensorflow',
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help="(flag) Don't test TensorFlow presets.",
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action='store_true')
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parser.add_argument('-in', '--ignore_neon',
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help="(flag) Don't test neon presets.",
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action='store_true')
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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('-l', '--list_presets',
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help="(flag) list all the presets that are tested",
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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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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 = utils.list_all_classes_in_module(presets)
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win_size = 10
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fail_count = 0
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test_count = 0
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read_csv_tries = 70
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# create a clean experiment directory
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test_name = '__test'
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test_path = os.path.join('./experiments', test_name)
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if os.path.exists(test_path):
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shutil.rmtree(test_path)
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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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if args.list_presets:
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for idx, preset_name in enumerate(presets_lists):
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preset = eval('presets.{}()'.format(preset_name))
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if preset.test and preset_name not in presets_to_ignore:
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print(preset_name)
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exit(0)
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for idx, preset_name in enumerate(presets_lists):
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preset = eval('presets.{}()'.format(preset_name))
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if preset.test and preset_name not in presets_to_ignore:
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frameworks = []
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if preset.agent.tensorflow_support and not args.ignore_tensorflow:
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frameworks.append('tensorflow')
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if preset.agent.neon_support and not args.ignore_neon:
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frameworks.append('neon')
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for framework in frameworks:
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if args.stop_after_first_failure and fail_count > 0:
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break
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test_count += 1
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# run the experiment in a separate thread
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logger.screen.log_title("Running test {} - {}".format(preset_name, framework))
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log_file_name = 'test_log_{preset_name}_{framework}.txt'.format(
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preset_name=preset_name,
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framework=framework,
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)
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cmd = (
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'CUDA_VISIBLE_DEVICES='' python3 coach.py '
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'-p {preset_name} '
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'-f {framework} '
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'-e {test_name} '
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'-n {num_workers} '
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'-cp "seed=0" '
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'&> {log_file_name} '
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).format(
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preset_name=preset_name,
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framework=framework,
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test_name=test_name,
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num_workers=preset.test_num_workers,
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log_file_name=log_file_name,
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)
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p = subprocess.Popen(cmd, shell=True, executable="/bin/bash", preexec_fn=os.setsid)
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# get the csv with the results
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csv_path = None
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csv_paths = []
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if preset.test_num_workers > 1:
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# we have an evaluator
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reward_str = 'Evaluation Reward'
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filename_pattern = 'evaluator*.csv'
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else:
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reward_str = 'Training Reward'
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filename_pattern = 'worker*.csv'
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initialization_error = False
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test_passed = False
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tries_counter = 0
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while not csv_paths:
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csv_paths = glob.glob(os.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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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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while csv is None or csv['Episode #'].values[-1] < preset.test_max_step_threshold:
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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) >= win_size:
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averaged_rewards = np.convolve(rewards, np.ones(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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# print progress
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percentage = int((100*last_num_episodes)/preset.test_max_step_threshold)
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sys.stdout.write("\rReward: ({}/{})".format(round(averaged_rewards[-1], 1), preset.test_min_return_threshold))
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sys.stdout.write(' Episode: ({}/{})'.format(last_num_episodes, preset.test_max_step_threshold))
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sys.stdout.write(' {}%|{}{}| '.format(percentage, '#'*int(percentage/10), ' '*(10-int(percentage/10))))
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sys.stdout.flush()
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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.test_min_return_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.SIGTERM)
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if test_passed:
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logger.screen.success("Passed successfully")
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else:
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if csv_paths:
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logger.screen.error("Failed due to insufficient reward", crash=False)
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logger.screen.error("preset.test_max_step_threshold: {}".format(preset.test_max_step_threshold), crash=False)
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logger.screen.error("preset.test_min_return_threshold: {}".format(preset.test_min_return_threshold), crash=False)
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logger.screen.error("averaged_rewards: {}".format(averaged_rewards), crash=False)
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logger.screen.error("episode number: {}".format(csv['Episode #'].values[-1]), crash=False)
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else:
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logger.screen.error("csv file never found", crash=False)
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if args.verbose:
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logger.screen.error("command exitcode: {}".format(p.returncode), crash=False)
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logger.screen.error(open(log_file_name).read(), crash=False)
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fail_count += 1
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shutil.rmtree(test_path)
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logger.screen.separator()
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if fail_count == 0:
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logger.screen.success(" Summary: " + str(test_count) + "/" + str(test_count) + " tests passed successfully")
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else:
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logger.screen.error(" Summary: " + str(test_count - fail_count) + "/" + str(test_count) + " tests passed successfully")
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