mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 11:10:20 +01:00
297 lines
11 KiB
Python
297 lines
11 KiB
Python
#
|
|
# Copyright (c) 2017 Intel Corporation
|
|
#
|
|
# 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.
|
|
#
|
|
|
|
import argparse
|
|
import glob
|
|
import os
|
|
import shutil
|
|
import signal
|
|
import subprocess
|
|
import sys
|
|
from importlib import import_module
|
|
from os import path
|
|
sys.path.append('.')
|
|
import numpy as np
|
|
import pandas as pd
|
|
import time
|
|
import pytest
|
|
|
|
# -*- coding: utf-8 -*-
|
|
from rl_coach.logger import screen
|
|
|
|
|
|
def read_csv_paths(test_path, filename_pattern, read_csv_tries=200):
|
|
csv_paths = []
|
|
tries_counter = 0
|
|
while not csv_paths:
|
|
csv_paths = glob.glob(path.join(test_path, '*', filename_pattern))
|
|
if tries_counter > read_csv_tries:
|
|
break
|
|
tries_counter += 1
|
|
time.sleep(1)
|
|
return csv_paths
|
|
|
|
|
|
def print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, time_limit):
|
|
percentage = int((100 * last_num_episodes) / preset_validation_params.max_episodes_to_achieve_reward)
|
|
sys.stdout.write("\rReward: ({}/{})".format(round(averaged_rewards[-1], 1),
|
|
preset_validation_params.min_reward_threshold))
|
|
sys.stdout.write(' Time (sec): ({}/{})'.format(round(time.time() - start_time, 2), time_limit))
|
|
sys.stdout.write(' Episode: ({}/{})'.format(last_num_episodes,
|
|
preset_validation_params.max_episodes_to_achieve_reward))
|
|
sys.stdout.write(
|
|
' {}%|{}{}| '.format(percentage, '#' * int(percentage / 10), ' ' * (10 - int(percentage / 10))))
|
|
sys.stdout.flush()
|
|
|
|
|
|
def import_preset(preset_name):
|
|
return import_module('rl_coach.presets.{}'.format(preset_name))
|
|
|
|
|
|
def validation_params(preset_name):
|
|
return import_preset(preset_name).graph_manager.preset_validation_params
|
|
|
|
|
|
def all_presets():
|
|
return [
|
|
f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets'))
|
|
if f[-3:] == '.py' and not f == '__init__.py'
|
|
]
|
|
|
|
|
|
def importable(preset_name):
|
|
try:
|
|
import_preset(preset_name)
|
|
return True
|
|
except BaseException:
|
|
return False
|
|
|
|
|
|
def has_test_parameters(preset_name):
|
|
return bool(validation_params(preset_name).test)
|
|
|
|
|
|
def collect_presets():
|
|
for preset_name in all_presets():
|
|
# if it isn't importable, still include it so we can fail the test
|
|
if not importable(preset_name):
|
|
yield preset_name
|
|
# otherwise, make sure it has test parameters before including it
|
|
elif has_test_parameters(preset_name):
|
|
yield preset_name
|
|
|
|
|
|
@pytest.fixture(params=list(collect_presets()))
|
|
def preset_name(request):
|
|
return request.param
|
|
|
|
|
|
@pytest.mark.golden_test
|
|
def test_preset_reward(preset_name, no_progress_bar=True, time_limit=60 * 60, verbose=True):
|
|
preset_validation_params = validation_params(preset_name)
|
|
|
|
win_size = 10
|
|
|
|
test_name = '__test_reward_{}'.format(preset_name)
|
|
test_path = os.path.join('./experiments', test_name)
|
|
if path.exists(test_path):
|
|
shutil.rmtree(test_path)
|
|
|
|
# run the experiment in a separate thread
|
|
screen.log_title("Running test {}".format(preset_name))
|
|
log_file_name = 'test_log_{preset_name}.txt'.format(preset_name=preset_name)
|
|
cmd = [
|
|
'python3',
|
|
'rl_coach/coach.py',
|
|
'-p', '{preset_name}'.format(preset_name=preset_name),
|
|
'-e', '{test_name}'.format(test_name=test_name),
|
|
'-n', '{num_workers}'.format(num_workers=preset_validation_params.num_workers),
|
|
'--seed', '0',
|
|
'-c'
|
|
]
|
|
if preset_validation_params.reward_test_level:
|
|
cmd += ['-lvl', '{level}'.format(level=preset_validation_params.reward_test_level)]
|
|
|
|
stdout = open(log_file_name, 'w')
|
|
|
|
p = subprocess.Popen(cmd, stdout=stdout, stderr=stdout)
|
|
|
|
start_time = time.time()
|
|
|
|
reward_str = 'Evaluation Reward'
|
|
if preset_validation_params.num_workers > 1:
|
|
filename_pattern = 'worker_0*.csv'
|
|
else:
|
|
filename_pattern = '*.csv'
|
|
|
|
test_passed = False
|
|
|
|
# get the csv with the results
|
|
csv_paths = read_csv_paths(test_path, filename_pattern, read_csv_tries=preset_validation_params.read_csv_tries)
|
|
|
|
if csv_paths:
|
|
csv_path = csv_paths[0]
|
|
|
|
# verify results
|
|
csv = None
|
|
time.sleep(1)
|
|
averaged_rewards = [0]
|
|
|
|
last_num_episodes = 0
|
|
|
|
if not no_progress_bar:
|
|
print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, time_limit)
|
|
|
|
while csv is None or (csv[csv.columns[0]].values[
|
|
-1] < preset_validation_params.max_episodes_to_achieve_reward and time.time() - start_time < time_limit):
|
|
try:
|
|
csv = pd.read_csv(csv_path)
|
|
except:
|
|
# sometimes the csv is being written at the same time we are
|
|
# trying to read it. no problem -> try again
|
|
continue
|
|
|
|
if reward_str not in csv.keys():
|
|
continue
|
|
|
|
rewards = csv[reward_str].values
|
|
rewards = rewards[~np.isnan(rewards)]
|
|
|
|
if len(rewards) >= 1:
|
|
averaged_rewards = np.convolve(rewards, np.ones(min(len(rewards), win_size)) / win_size, mode='valid')
|
|
else:
|
|
time.sleep(1)
|
|
continue
|
|
|
|
if not no_progress_bar:
|
|
print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, time_limit)
|
|
|
|
if csv[csv.columns[0]].shape[0] - last_num_episodes <= 0:
|
|
continue
|
|
|
|
last_num_episodes = csv[csv.columns[0]].values[-1]
|
|
|
|
# check if reward is enough
|
|
if np.any(averaged_rewards >= preset_validation_params.min_reward_threshold):
|
|
test_passed = True
|
|
break
|
|
time.sleep(1)
|
|
|
|
# kill test and print result
|
|
# os.killpg(os.getpgid(p.pid), signal.SIGKILL)
|
|
p.kill()
|
|
screen.log('')
|
|
if test_passed:
|
|
screen.success("Passed successfully")
|
|
else:
|
|
if time.time() - start_time > time_limit:
|
|
screen.error("Failed due to exceeding time limit", crash=False)
|
|
if verbose:
|
|
screen.error("command exitcode: {}".format(p.returncode), crash=False)
|
|
screen.error(open(log_file_name).read(), crash=False)
|
|
elif csv_paths:
|
|
screen.error("Failed due to insufficient reward", crash=False)
|
|
if verbose:
|
|
screen.error("command exitcode: {}".format(p.returncode), crash=False)
|
|
screen.error(open(log_file_name).read(), crash=False)
|
|
screen.error("preset_validation_params.max_episodes_to_achieve_reward: {}".format(
|
|
preset_validation_params.max_episodes_to_achieve_reward), crash=False)
|
|
screen.error("preset_validation_params.min_reward_threshold: {}".format(
|
|
preset_validation_params.min_reward_threshold), crash=False)
|
|
screen.error("averaged_rewards: {}".format(averaged_rewards), crash=False)
|
|
screen.error("episode number: {}".format(csv['Episode #'].values[-1]), crash=False)
|
|
screen.error("training iteration: {}".format(csv['Training Iter'].values[-1]), crash=False)
|
|
else:
|
|
screen.error("csv file never found", crash=False)
|
|
if verbose:
|
|
screen.error("command exitcode: {}".format(p.returncode), crash=False)
|
|
screen.error(open(log_file_name).read(), crash=False)
|
|
|
|
shutil.rmtree(test_path)
|
|
os.remove(log_file_name)
|
|
if not test_passed:
|
|
raise ValueError('golden test failed')
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-p', '--preset', '--presets',
|
|
help="(string) Name of preset(s) to run (comma separated, and as configured in presets.py)",
|
|
default=None,
|
|
type=str)
|
|
parser.add_argument('-ip', '--ignore_presets',
|
|
help="(string) Name of preset(s) to ignore (comma separated, and as configured in presets.py)",
|
|
default=None,
|
|
type=str)
|
|
parser.add_argument('-v', '--verbose',
|
|
help="(flag) display verbose logs in the event of an error",
|
|
action='store_true')
|
|
parser.add_argument('--stop_after_first_failure',
|
|
help="(flag) stop executing tests after the first error",
|
|
action='store_true')
|
|
parser.add_argument('-tl', '--time_limit',
|
|
help="time limit for each test in minutes",
|
|
default=60, # setting time limit to be so high due to DDPG being very slow - its tests are long
|
|
type=int)
|
|
parser.add_argument('-np', '--no_progress_bar',
|
|
help="(flag) Don't print the progress bar (makes jenkins logs more readable)",
|
|
action='store_true')
|
|
|
|
args = parser.parse_args()
|
|
if args.preset is not None:
|
|
presets_lists = args.preset.split(',')
|
|
else:
|
|
presets_lists = all_presets()
|
|
|
|
fail_count = 0
|
|
test_count = 0
|
|
|
|
args.time_limit = 60 * args.time_limit
|
|
|
|
if args.ignore_presets is not None:
|
|
presets_to_ignore = args.ignore_presets.split(',')
|
|
else:
|
|
presets_to_ignore = []
|
|
for idx, preset_name in enumerate(sorted(presets_lists)):
|
|
if args.stop_after_first_failure and fail_count > 0:
|
|
break
|
|
if preset_name not in presets_to_ignore:
|
|
print("Attempting to run Preset: %s" % preset_name)
|
|
if not importable(preset_name):
|
|
screen.error("Failed to load preset <{}>".format(preset_name), crash=False)
|
|
fail_count += 1
|
|
test_count += 1
|
|
continue
|
|
|
|
if not has_test_parameters(preset_name):
|
|
continue
|
|
|
|
test_count += 1
|
|
try:
|
|
test_preset_reward(preset_name, args.no_progress_bar, args.time_limit, args.verbose)
|
|
except Exception as e:
|
|
fail_count += 1
|
|
|
|
screen.separator()
|
|
if fail_count == 0:
|
|
screen.success(" Summary: " + str(test_count) + "/" + str(test_count) + " tests passed successfully")
|
|
else:
|
|
screen.error(" Summary: " + str(test_count - fail_count) + "/" + str(test_count) + " tests passed successfully")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|