def Main(*argv):
parse_args(*argv)
stats_before = ParseStats()
stats_before.read_file()
stats_before.parse()
stats_before.get_max_size()
sys.stdout.write(term.CLEAR_SCREEN)
for i in range(term.LINES):
sys.stdout.write(term.DOWN)
sys.stdout.write(term.HIDE_CURSOR)
sys.stdout.flush()
getkey_thread = threading.Thread(
group=None,
target=getkey,
name="keythread"
)
getkey_thread.start()
while True:
queue = Queue.Queue()
thread_ = threading.Thread(
group=None,
target=process,
name="Thread1",
args=(stats_before.columns, queue)
)
thread_.start()
time.sleep(Config.timewait)
stats_before.columns = queue.get()
def update_run_log(fx_app, new_data=None):
meta = {'run_id': IrisCore.get_run_id(),
'fx_version': fx_app.version,
'fx_build_id': fx_app.build_id,
'platform': Settings.get_os(),
'config': '%s, %s-bit, %s' % (Platform.OS_VERSION, Platform.OS_BITS, Platform.PROCESSOR),
'channel': fx_app.channel,
'locale': fx_app.locale,
'args': ' '.join(sys.argv),
'params': vars(parse_args()),
'log': os.path.join(IrisCore.get_current_run_dir(), 'iris_log.log')}
repo = git.Repo(IrisCore.get_module_dir())
meta['iris_version'] = 1.0
meta['iris_repo'] = repo.working_tree_dir
if parse_args().headless_run:
pass
else:
meta['iris_branch'] = repo.active_branch.name
meta['iris_branch_head'] = repo.head.object.hexsha
if new_data is None:
logger.debug('Updating run.json with initial run data.')
meta['total'] = 0
meta['passed'] = 0
meta['failed'] = 0
meta['skipped'] = 0
meta['errors'] = 0
meta['start_time'] = 0
meta['end_time'] = 0
meta['total_time'] = 0
tests = []
else:
logger.debug('Updating runs.json with completed run data.')
meta['total'] = new_data['total']
meta['passed'] = new_data['passed']
meta['failed'] = new_data['failed']
meta['failed_tests'] = new_data['failed_tests']
meta['skipped'] = new_data['skipped']
meta['errors'] = new_data['errors']
meta['start_time'] = new_data['start_time']
meta['end_time'] = new_data['end_time']
meta['total_time'] = new_data['total_time']
tests = new_data['tests']
run_file = os.path.join(IrisCore.get_current_run_dir(), 'run.json')
run_file_data = {'meta': meta, 'tests': tests}
with open(run_file, 'w') as f:
json.dump(run_file_data, f, sort_keys=True, indent=True)
def main():
import configspark
sc = configspark.SPARK_CONTEXT
args = parse_args(HELP_PROMPT, sys.argv)
filename = args.get("filename")
user_id = args.get("user_id")
# Load the Users/Songs ID maps
full_text = sc.textFile(config.MSD_DATA)
full_raw = full_text.map(msd_parse.parse_line)
users, songs, songs_reverse_map = msd_parse.get_user_song_maps(full_raw)
# Load the new ratings (if any) and replace raw IDs with int IDs
user_id, raw_plays = get_training_data(sc, filename, user_id, users, songs)
converted_user_id = users.get(user_id)
ratings_train = raw_plays.map(msd_parse.rating_convert)
user_songs_unheard = unheard_songs(converted_user_id, ratings_train,
songs, songs_reverse_map)
model = prepare_model(sc, filename, converted_user_id, ratings_train)
make_recommendations(user_id, model, sc.parallelize(user_songs_unheard),
songs_reverse_map)
sc.stop()
def run() -> None:
values: Tuple[int, int, int, int, int, bool] = parse_args()
N: int = values[0]
M: int = values[1]
E: int = values[3]
fill: bool = values[-1]
nrepeat: int = 1000
print(f"Total size S = {N * M} N = {N} M = {M} E = {E}")
w = Workload(N, M, E, fill)
p = pk.TeamPolicy(E, "auto", 32, pk.get_default_space())
timer = pk.Timer()
for i in range(nrepeat):
result = pk.parallel_reduce(p, w.yAx)
timer_result = timer.seconds()
print(f"Computed result for {N} x {M} x {E} is {result}")
solution: float = N * M * E
if result != solution:
pk.printf("Error: result (%lf) != solution (%lf)\n", result, solution)
print(
f"N({N}) M({M}) E({E}) nrepeat({nrepeat}) problem(MB) time({timer_result}) bandwidth(GB/s)"
)
def run() -> None:
values: Tuple[int, int, int, int, int, bool] = parse_args()
N: int = values[0]
M: int = values[1]
nrepeat: int = 1
print(f"Total size S = {N * M} N = {N} M = {M}")
y = pk.View([N], pk.double)
x = pk.View([M], pk.double)
A = pk.View([N * M], pk.double)
p = pk.RangePolicy(pk.get_default_space(), 0, N)
pk.parallel_for(p, y_init, y=y)
pk.parallel_for(pk.RangePolicy(pk.get_default_space(), 0, M), y_init, y=x)
pk.parallel_for(p, matrix_init, M=M, A=A)
timer = pk.Timer()
for i in range(nrepeat):
result = pk.parallel_reduce(p, yAx, M=M, y=y, x=x, A=A)
timer_result = timer.seconds()
print(f"Computed result for {N} x {M} is {result}")
solution = N * M
if result != solution:
pk.printf("Error: result (%lf) != solution (%lf)\n", result, solution)
print(f"N({N}) M({M}) nrepeat({nrepeat}) problem(MB) time({timer_result}) bandwidth(GB/s)")
def run() -> None:
values: Tuple[int, int, int, int, int, bool] = parse_args()
N: int = values[0]
M: int = values[1]
nrepeat: int = 100
print(f"Total size S = {N * M} N = {N} M = {M}")
p = pk.RangePolicy(pk.get_default_space(), 0, N)
w = Workload(N, M)
pk.parallel_for(p, w.y_init)
pk.parallel_for(pk.RangePolicy(pk.get_default_space(), 0, M), w.x_init)
pk.parallel_for(p, w.matrix_init)
timer = pk.Timer()
for i in range(nrepeat):
result = pk.parallel_reduce(p, w.yAx)
timer_result = timer.seconds()
print(f"Computed result for {N} x {M} is {result}")
solution = N * M
if result != solution:
pk.printf("Error: result (%lf) != solution (%lf)\n", result, solution)
print(f"N({N}) M({M}) nrepeat({nrepeat}) problem(MB) time({timer_result}) bandwidth(GB/s)")
def get_image_debug_path():
"""Returns the root directory where a test's debug images are located."""
parent, test = IrisCore.parse_module_path()
path = os.path.join(parse_args().workdir, 'runs',
IrisCore.get_run_id(), parent, test,
'debug_images')
return path
def main():
temp = parse_args()
if temp == (0, 0):
return
else:
board_filename = "boards" + os.sep + "%s" % temp[0] + "-" + "%s" % temp[1] + ".txt"
board = read_board(board_filename)
print_board(board)
pieces = parse_board(board)
# Get starting and ending pieces
# The starting piece is denoted by a capital letter
starting_piece = next((x for x in pieces if x.kind.isupper()), None)
if starting_piece is None:
print("Could not find starting piece!")
return
else:
print("Starting piece:")
starting_piece.display_piece()
# The ending piece is the king k
ending_piece = next((x for x in pieces if x.kind == "k"), None)
if ending_piece is None:
print("Could not find ending piece!")
return
else:
print("Ending piece:")
ending_piece.display_piece()
solve_board(starting_piece, pieces)
def main():
temp = parse_args()
if temp == (0, 0):
return
else:
board_filename = 'boards' + os.sep + "%s" % temp[
0] + '-' + "%s" % temp[1] + '.txt'
board = read_board(board_filename)
print_board(board)
pieces = parse_board(board)
# Get starting and ending pieces
# The starting piece is denoted by a capital letter
starting_piece = next((x for x in pieces if x.kind.isupper()), None)
if starting_piece is None:
print("Could not find starting piece!")
return
else:
print("Starting piece:")
starting_piece.display_piece()
# The ending piece is the king k
ending_piece = next((x for x in pieces if x.kind == 'k'), None)
if ending_piece is None:
print("Could not find ending piece!")
return
else:
print("Ending piece:")
ending_piece.display_piece()
solve_board(starting_piece, pieces)
def test_directory_returns_value(self):
args = parse_args.parse_args([
"--directory", "/some", "--database", "file.db", "--config-file",
"file.yml"
])
result = args.directory
expect = "/some"
self.assertEqual(expect, result)
def create_run_directory():
IrisCore.create_working_directory()
master_run_directory = os.path.join(parse_args().workdir, 'runs')
if not os.path.exists(master_run_directory):
os.mkdir(master_run_directory)
run_directory = os.path.join(master_run_directory, IrisCore.get_run_id())
if not os.path.exists(run_directory):
os.mkdir(run_directory)
def main():
args = parse_args()
csv_filename = args.data
result_csv = args.outfile
data_dict = load_data(csv_filename)
calculate(data_dict)
write_csv(data_dict, result_csv)
write_sql(result_csv, args=args)
def __init__(self, *w, **kw):
if not sys.stdin.isatty():
self.input = sys.stdin.read()
else:
self.input = ''
self.__dict__.update(self._default_fields)
self.__dict__.update(parse_args())
self.banzai(*w, **kw)
pass
def main():
args = parse_args(infer=True)
if args.gpu:
dev = '/gpu:0'
else:
dev = '/cpu:0'
with tf.device(dev):
infer(args)
def main():
args = parse_args()
tic = time.time()
remote_thr(args.url, args.count, args.size, args.poll, args.copy)
toc = time.time()
if (toc - tic) < 3:
print ("For best results, tests should take at least a few seconds.")
def main():
"""The big tent."""
args = parse_args(sys.argv[1:])
repo = Repo(args.repo)
cpw = commits_per_week(repo)
if args.cumulative:
cpw = cums(cpw)
for commits in cpw:
print(commits)
def main():
args = parse_args()
print ("Running program...")
tic = time.time()
local_thr(args.url, args.count, args.size, args.poll, args.copy)
toc = time.time()
if (toc - tic) < 3:
print ("For best results, tests should take at least a few seconds.")
def create_working_directory():
if not os.path.exists(parse_args().workdir):
logger.debug('Creating working directory %s' % parse_args().workdir)
os.makedirs(parse_args().workdir)
if not os.path.exists(os.path.join(parse_args().workdir, 'data')):
os.makedirs(os.path.join(parse_args().workdir, 'data'))
if parse_args().clear:
master_run_directory = os.path.join(parse_args().workdir, 'runs')
if os.path.exists(master_run_directory):
shutil.rmtree(master_run_directory, ignore_errors=True)
run_file = os.path.join(parse_args().workdir, 'data', 'all_runs.json')
if os.path.exists(run_file):
os.remove(run_file)
cache_builds_directory = os.path.join(parse_args().workdir, 'cache')
if os.path.exists(cache_builds_directory):
shutil.rmtree(cache_builds_directory, ignore_errors=True)
def main():
"""
Export video files
"""
event_ids, series_ids = parse_args()
digest_login = DigestLogin(user=config.digest_user,
password=config.digest_pw)
# get events from all series
if series_ids:
print("Getting events for series.")
events = []
for series_id in series_ids:
try:
events_of_series = get_events_of_series(
config.url, digest_login, series_id)
events += events_of_series
except Exception as e:
print("Events of series {} could not be requested: {}".format(
series_id, str(e)))
if not events:
__abort_script("No events found.")
event_ids = [get_id(event) for event in events]
print("Starting export process.")
for event_id in event_ids:
try:
print("Exporting videos of media package {}".format(event_id))
mp_xml = get_media_package(config.url, digest_login, event_id)
mp = parse_manifest_from_endpoint(mp_xml, event_id, False, True)
if config.create_series_dirs and mp.series_id:
mp_dir = os.path.join(config.target_directory, mp.series_id,
mp.id)
else:
mp_dir = os.path.join(config.target_directory, mp.id)
export_videos(mp, mp_dir, config.url, digest_login,
config.export_archived, config.export_publications,
config.export_mimetypes, config.export_flavors,
config.stream_security)
except Exception as e:
print(
"Tracks of media package {} could not be exported: {}".format(
event_id, str(e)))
print("Done.")
def update_run_index(fx_app, new_data=None):
current_run = {
'id': IrisCore.get_run_id(),
'version': fx_app.version,
'build': fx_app.build_id,
'channel': fx_app.channel,
'locale': fx_app.locale
}
if new_data is None:
logger.debug('Updating runs.json with initial run data.')
current_run['total'] = '*'
current_run['failed'] = '*'
else:
logger.debug('Updating runs.json with completed run data.')
current_run['total'] = new_data['total']
current_run['failed'] = new_data['failed']
old_js_folder = os.path.join(parse_args().workdir, 'js')
if os.path.exists(old_js_folder):
shutil.rmtree(old_js_folder, ignore_errors=True)
run_file = os.path.join(parse_args().workdir, 'data', 'all_runs.json')
if os.path.exists(run_file):
logger.debug('Updating run file: %s' % run_file)
with open(run_file, 'r') as f:
run_file_data = json.load(f)
for run in run_file_data['runs']:
if run['id'] == IrisCore.get_run_id():
run_file_data['runs'].remove(run)
run_file_data['runs'].append(current_run)
else:
logger.debug('Creating run file: %s' % run_file)
run_file_data = {'runs': []}
run_file_data['runs'].append(current_run)
with open(run_file, 'w') as f:
json.dump(run_file_data, f, sort_keys=True, indent=True)
def run() -> None:
values: Tuple[int, int, int, int, int, bool] = parse_args()
N: int = values[0]
M: int = values[1]
fill: bool = values[-1]
nrepeat: int = 100
print(f"Total size S = {N * M} N = {N} M = {M}")
pk.set_default_space(pk.ExecutionSpace.Cuda)
y: pk.View1D = pk.View([N], pk.double)
x: pk.View1D = pk.View([M], pk.double)
A: pk.View2D = pk.View([N, M], pk.double)
p = pk.RangePolicy(pk.get_default_space(), 0, N)
pk.parallel_for(p, y_init, y=y)
pk.parallel_for(pk.RangePolicy(pk.get_default_space(), 0, M), y_init, y=x)
pk.parallel_for(p, matrix_init, M=M, A=A)
# if fill:
# y.fill(1)
# x.fill(1)
# A.fill(1)
# else:
# for i in range(N):
# y[i] = 1
# for i in range(M):
# x[i] = 1
# for j in range(N):
# for i in range(M):
# A[j][i] = 1
timer = pk.Timer()
for i in range(nrepeat):
result = pk.parallel_reduce(p, yAx, M=M, y=y, x=x, A=A)
timer_result = timer.seconds()
print(f"Computed result for {N} x {M} is {result}")
solution: float = N * M
if result != solution:
pk.printf("Error: result (%lf) != solution (%lf)\n", result, solution)
print(
f"N({N}) M({M}) nrepeat({nrepeat}) problem(MB) time({timer_result}) bandwidth(GB/s)"
)
def run():
"""Runs dilated residual network model in either train or predict mode"""
config = parse_args()
if not os.path.isdir(config.logs):
os.makedirs(config.logs)
if config.mode == 'train':
train(config)
elif config.mode == 'predict':
predict(config)
else:
ValueError("Mode must be either train or predict")
def verify_test_compat(test, browser):
if browser.channel is None or browser.version is None:
return False
not_excluded = True
exclude = [test.exclude] if isinstance(test.exclude, str) else [i for i in test.exclude]
for item in exclude:
if item in browser.channel or item in get_os() or item in browser.locale:
not_excluded = False
correct_version = True if test.fx_version == '' else check_version(browser.version, test.fx_version)
correct_channel = browser.channel in test.channel
correct_locale = parse_args().locale in test.locale
correct_platform = get_os() in test.platform
result = True == correct_platform == correct_version == correct_channel == correct_locale == not_excluded
return result
def run() -> None:
values: Tuple[int, int, int, int, int, bool] = parse_args()
N: int = values[0]
M: int = values[1]
E: int = values[3]
fill: bool = values[-1]
nrepeat: int = 1000
print(f"Total size S = {N * M} N = {N} M = {M} E = {E}")
y: pk.View2D = pk.View([E, N], pk.double, layout=pk.Layout.LayoutRight)
x: pk.View2D = pk.View([E, M], pk.double, layout=pk.Layout.LayoutRight)
A: pk.View3D = pk.View([E, N, M], pk.double, layout=pk.Layout.LayoutRight)
if fill:
y.fill(1)
x.fill(1)
A.fill(1)
else:
for e in range(E):
for i in range(N):
y[e][i] = 1
for i in range(M):
x[e][i] = 1
for j in range(N):
for i in range(M):
A[e][j][i] = 1
p = pk.TeamPolicy(E, "auto", 32, pk.get_default_space())
timer = pk.Timer()
for i in range(nrepeat):
result = pk.parallel_reduce(p, yAx, N=N, M=M, y=y, x=x, A=A)
timer_result = timer.seconds()
print(
f"Computed result for {N} x {M} x {E} is {result}")
solution: float = N * M * E
if result != solution:
pk.printf("Error: result (%lf) != solution (%lf)\n",
result, solution)
print(f"N({N}) M({M}) E({E}) nrepeat({nrepeat}) problem(MB) time({timer_result}) bandwidth(GB/s)")
def main():
debug('started!')
args = parse_args()
status_callback = "http://{}:{}/status".format(args.host, args.port)
debug(status_callback)
callmgr = Callmgr(status_callback)
for i in range(0, 15):
t = CallTask(callmgr, queue)
t.start()
app.run(host='0.0.0.0', port=args.port, debug=True)
# app.run(host='127.0.0.1', debug=True)
queue.join()
t.stop()
def create_arg_json():
arg_data = {'email': {'type': 'bool', 'value': ['true', 'false'], 'default': 'false', 'label': 'Email results'},
'firefox': {'type': 'str', 'value': ['local', 'latest', 'latest-esr', 'latest-beta', 'nightly'],
'default': 'latest-beta', 'label': 'Firefox'},
'highlight': {'type': 'bool', 'value': ['true', 'false'], 'default': 'false',
'label': 'Debug using highlighting'},
'locale': {'type': 'str', 'value': Settings.LOCALES, 'default': 'en-US', 'label': 'Locale'},
'mouse': {'type': 'float', 'value': ['0.0', '0.5', '1.0', '2.0'], 'default': '0.5',
'label': 'Mouse speed'},
'override': {'type': 'bool', 'value': ['true', 'false'], 'default': 'false',
'label': 'Run disabled tests'},
'port': {'type': 'int', 'value': ['2000'], 'default': '2000', 'label': 'Local web server port'},
'report': {'type': 'bool', 'value': ['true', 'false'], 'default': 'false',
'label': 'Create TestRail report'},
'save': {'type': 'bool', 'value': ['true', 'false'], 'default': 'false',
'label': 'Save profiles to disk'}}
arg_log_file = os.path.join(parse_args().workdir, 'data', 'all_args.json')
with open(arg_log_file, 'w') as f:
json.dump(arg_data, f, sort_keys=True, indent=True)
def init_config():
args = parse_args()
# below automatically set
args.in_H, args.in_W = [int(x) for x in args.inSize.split("x")]
args.depth_H, args.depth_W = [int(x) for x in args.depthSize.split("x")]
args.datalist_path = os.path.join(args.dataset_path, args.datalist_path)
args.pcgt_path = os.path.join(args.dataset_path, args.pcgt_path)
args.rendering_path = os.path.join(args.dataset_path, args.rendering_path)
if args.cat == 1:
args.cat_list = CAT_LIST_1
args.test_iter = 2000
elif args.cat == 13:
args.cat_list = CAT_LIST_13
args.test_iter = 5000
else:
raise NotImplementedError
args.gpu_id = str(args.gpu)
# below constant
args.K = CAMERA_INTRINSIC
return args
def main():
args = parse_args()
glue_process(args)
def delete_run_directory():
master_run_directory = os.path.join(parse_args().workdir, 'runs')
run_directory = os.path.join(master_run_directory,
IrisCore.get_run_id())
if os.path.exists(run_directory):
shutil.rmtree(run_directory, ignore_errors=True)
import cv2
from libs.config import load_config
from libs.timer import Timer
from parse_args import parse_args
import libs.utils as utils
import libs.font_utils as font_utils
from textrenderer.corpus.corpus_utils import corpus_factory
from textrenderer.renderer import Renderer
from tenacity import retry
lock = mp.Lock()
counter = mp.Value('i', 0)
STOP_TOKEN = 'kill'
flags = parse_args()
cfg = load_config(flags.config_file)
fonts = font_utils.get_font_paths_from_list(flags.fonts_list)
bgs = utils.load_bgs(flags.bg_dir)
corpus = corpus_factory(flags.corpus_mode, flags.chars_file, flags.corpus_dir,
flags.length)
renderer = Renderer(corpus,
fonts,
bgs,
cfg,
height=flags.img_height,
width=flags.img_width,
clip_max_chars=flags.clip_max_chars,
def main():
(args, kw) = parse_args('error_thresh pareto_layers rank_top subsample filespec'.split(), usage)
error_thresh = float(kw.get('error_thresh', '0.5'))
pareto_layers = int(kw.get('pareto_layers', '4'))
rank_top = kw.get('rank_top', '')
subsample = int(kw.get('subsample', '1'))
filespec = kw.get('filespec', '*')
if len(args) < 1:
usage()
outdir = args[0]
pareto_full = ''
if len(args) > 1:
pareto_full = args[1]
T0 = time.time()
L = []
avg_errorL = []
suffix = 'pareto_full.txt'
filenameL = glob.glob(os.path.join(outdir, filespec + suffix))
filename_successL = []
for filename in filenameL:
try:
obj_current = json.loads(open(filename, 'rt').read())
trace_filename = filename[:len(filename)-len(suffix)] + 'trace.json'
# print trace_filename
trace_current = json.loads(open(trace_filename, 'rt').read())
for obj in obj_current:
obj['filename'] = filename
L.append(obj_current)
avg_errorL.append(trace_current[-1]['avg_time20'])
filename_successL.append(filename)
except:
print 'Warning: could not load %s' % filename
print 'Loaded %d Pareto frontiers in %f secs' % (len(L), time.time()-T0)
object_L = []
error_L = []
time_L = []
index_L = []
for i in range(len(L)):
for obj in L[i]:
error_L.append(obj['error'])
time_L.append(obj['time'])
filename = os.path.split(filenameL[i])[1]
prefix = filename.split('_')[0]
obj['filename'] = os.path.abspath(filenameL[i])
obj['name'] = prefix + '_' + obj['name']
object_L.append(obj)
index_L.append(len(object_L)-1)
error_L = numpy.array(error_L)
time_L = numpy.array(time_L)
index_L = numpy.array(index_L)
print 'Number of scattered points: %d' % len(error_L)
ans = []
for it in range(pareto_layers):
paretoL = get_pareto(error_L, time_L)
for idx in paretoL:
ans.append(object_L[index_L[idx]])
error_L = numpy.delete(error_L, paretoL)
time_L = numpy.delete(time_L, paretoL)
index_L = numpy.delete(index_L, paretoL)
ans = [obj for obj in ans if obj['error'] <= error_thresh]
if subsample > 1:
ans = ans[::subsample]
if len(rank_top):
collect_count = collections.defaultdict(lambda: 0)
for obj in ans:
collect_count[obj['filename']] += 1
count_filename = sorted([(count, filename) for (filename, count) in collect_count.items()], reverse=True)
with open(rank_top, 'wt') as f:
for (count, filename) in count_filename:
print >>f, filename
print 'Number of Pareto points written: %d' % len(ans)
if len(pareto_full):
with open(pareto_full, 'wt') as f:
f.write(json.dumps(ans)) #, sort_keys=True, indent=4)) #, separators=(',', ': ')))
def get_base_local_web_url():
return 'http://127.0.0.1:%s' % parse_args().port
def get_current_run_dir():
"""Returns the directory inside the working directory of the active run."""
IrisCore.create_run_directory()
return os.path.join(parse_args().workdir, 'runs',
IrisCore.get_run_id())
def get_working_dir():
"""Returns the path to the root of the directory where local data is stored."""
IrisCore.create_working_directory()
return parse_args().workdir
def main():
(args, kw) = parse_args('population_size generations frac_elitism frac_mutate prob_modify_consts tournament_size finalize_only finalize_filter loop_perf_only run_remotely_on_tablet training_images_directory cross_validation_directory resume_previous_search sample_importance_only sample_grid_only run_on_fir init_pop run_final_generation use_adaptive reconstruct_hints reduce_space'.split(), usage)
if len(args) < 2:
usage()
program_name = args[0]
result_dir = args[1]
makedirs(result_dir)
start_gen_index = 0
d = {}
exec "init_pop = "+kw.get('init_pop', '[]'.encode('hex')).decode('hex') in globals(), d
init_pop = d['init_pop']
pop_size0 = int(kw.get('population_size', '30'))
if len(init_pop) > pop_size0:
random.shuffle(init_pop)
init_pop = init_pop[:pop_size0]
p = AttrDict(population_size=pop_size0,
generations=int(kw.get('generations', '25')),
frac_elitism=float(kw.get('frac_elitism', '0.3')),
frac_mutate=float(kw.get('frac_mutate', '0.3')),
tournament_size=int(kw.get('tournament_size', '8')),
loops=get_loops(program_name),
program_name=program_name,
result_dir=os.path.abspath(result_dir),
prob_modify_consts=0.3,
seen=set(),
all_rank0=init_pop,
finalize_only=int(kw.get('finalize_only', '0')),
finalize_filter=kw.get('finalize_filter', ''),
loop_perf_only=int(kw.get('loop_perf_only', '0')),
run_remotely_on_tablet=int(kw.get('run_remotely_on_tablet', '0')),
training_images_directory=kw.get('training_images_directory', os.path.abspath('../images/train/')),
cross_validation_directory=kw.get('cross_validation_directory', os.path.abspath('../images/train/')),
resume_previous_search=int(kw.get('resume_previous_search', '0')),
sample_importance_only=int(kw.get('sample_importance_only', '0')),
sample_grid_only=int(kw.get('sample_grid_only', '0')),
run_on_fir=int(kw.get('run_on_fir', '0')),
run_final_generation=int(kw.get('run_final_generation', '0')),
use_adaptive=int(kw.get('use_adaptive', '1')),
reconstruct_hints=int(kw.get('reconstruct_hints', '0')),
reduce_space=int(kw.get('reduce_space', '1')))
set_sample_lines(p)
print '\n\n'
print 'Parameters:'
print ' population_size:', p['population_size']
print ' generations:', p['generations']
print ' frac_elitism:', p['frac_elitism']
print ' frac_mutate:', p['frac_mutate']
print ' tournament_size:', p['tournament_size']
# print ' loops:', p['loops']
print ' program_name:', p['program_name']
print ' result_dir:', p['result_dir']
print ' prob_modify_consts:', p['prob_modify_consts']
print ' seen:', p['seen']
print ' finalize_only:', p['finalize_only']
print ' finalize_filter:', p['finalize_filter']
print ' loop_perf_only:', p['loop_perf_only']
print ' run_remotely_on_tablet:', p['run_remotely_on_tablet']
print ' training_images_directory:', p['training_images_directory']
print ' cross_validation_directory:', p['cross_validation_directory']
print ' resume_previous_search:', p['resume_previous_search']
print ' sample_importance_only:', p['sample_importance_only']
print ' sample_grid_only:', p['sample_grid_only']
print ' run_on_fir:', p['run_on_fir']
print ' run_final_generation:', p['run_final_generation']
print ' use_adaptive:', p['use_adaptive']
print ' reconstruct_hints:', p['reconstruct_hints']
print ' reduce_space:', p['reduce_space']
print '\n\n'
if p['sample_importance_only'] and p['sample_grid_only']:
print >> sys.stderr, 'Cannot have both sample_importance_only and sample_grid_only be true.'
sys.exit(1)
global foreach_lines
if p['sample_importance_only']:
new_foreach_lines = []
for e in foreach_lines:
if 'importance' in e[0]:
new_foreach_lines.append(e)
foreach_lines = new_foreach_lines
if p['sample_grid_only']:
new_foreach_lines = []
for e in foreach_lines:
if 'sample_grid(' in e[0]:
new_foreach_lines.append(e)
foreach_lines = new_foreach_lines
if p['resume_previous_search']:
gen_name_list = [ int(e[3:]) for e in os.listdir(result_dir) if 'gen' in e ]
if 100000 in gen_name_list:
print >> sys.stderr, 'Cannot resume previous search since previous search completed. Utilize the -finalize_only option instead.'
sys.exit(1)
start_gen_index = max(gen_name_list) if len(gen_name_list)>0 else 0
if p['run_remotely_on_tablet']:
print "Running timings remotely on the tablet."
os.system("ssh phablet@"+TABLET_IP+" '(rm -rf "+TABLET_INPUT_IMAGES_DIRECTORY+")'")
os.system("ssh phablet@"+TABLET_IP+" '(mkdir "+TABLET_INPUT_IMAGES_DIRECTORY+")'")
os.system('scp '+p['training_images_directory']+'/*.png phablet@'+TABLET_IP+':'+TABLET_INPUT_IMAGES_DIRECTORY)
curve_area_list = []
if not p.finalize_only:
if os.path.exists(result_dir) and not os.path.samefile(result_dir, '.') and not p['resume_previous_search']:
shutil.rmtree(result_dir, ignore_errors=True)
for generation in xrange(start_gen_index, p.generations):
T0 = time.time()
if generation == 0:
pop = initial_population(p)
elif generation == start_gen_index:
# Update current generation
gen_dirname = os.path.join(p.result_dir, 'gen%06d' % generation)
d = {}
s = open(os.path.join(gen_dirname, 'current_gen.py'), 'rt').read()
exec s in globals(), d
pop = d['current_gen']
# if not p['run_on_fir']:
if True:
# Make sure curve_area_list is updated
list_of_dirs_with_convergence_values = sorted([ f for f in list_dir_abs(p.result_dir) if 'gen' in path_leaf(f)], key=lambda x: int(path_leaf(x)[3:]))[:-1]
for i,e in enumerate(list_of_dirs_with_convergence_values):
curve_area_list.append( (i,float(open(os.path.join(e, 'pareto_over_all_approximations_so_far/area_under_pareto_frontier.txt'),'r').read())) )
# Recalculate area_under_pareto_frontier_for_all_generations_so_far.csv for each generation in case we decided to change curve_area_alpha
current_generation_dirname = os.path.join(p.result_dir, 'gen%06d' % i)
current_generation_pareto_dirname = os.path.join(current_generation_dirname, 'pareto_over_all_approximations_so_far')
area_under_pareto_frontier_for_all_generations_so_far_csv_location = os.path.join(current_generation_pareto_dirname, 'area_under_pareto_frontier_for_all_generations_so_far.csv')
with open(area_under_pareto_frontier_for_all_generations_so_far_csv_location, 'wt') as f:
smoothed_curve_area_list = [ curve_area_list[0][1] ]
if len(curve_area_list)>1:
for i_0 in xrange(len(curve_area_list[1:])):
i = i_0+1
smoothed_curve_area_list.append( curve_area_alpha*curve_area_list[i][1] + (1-curve_area_alpha)*smoothed_curve_area_list[i-1] )
f.write('Generation, Area Under Curve, Smoothed Area Under Curve (alpha='+str(curve_area_alpha)+') \n')
for i,e in enumerate(curve_area_list):
f.write(str(e[0])+', '+str(e[1])+', '+str(smoothed_curve_area_list[i])+'\n')
# Graph area_under_pareto_frontier_for_all_generations_so_far.csv data for Area Under Curve and Smoothed Area Under Curve
commands = '''
set datafile separator ","
set title "Area Under Pareto Curve as Generations Pass"
set ylabel "Area Under Curve"
set xlabel "Generation"
#set ytic 0.1
#set xtic 1
set xr [0:*]
set yr [0:*]
set terminal png size 1200,900
'''
plot_command_area_under_curve = '"'+area_under_pareto_frontier_for_all_generations_so_far_csv_location+'" using 1:2 with linespoints title "Area Under Curve" '
plot_command_smoothed_area_under_curve = '"'+area_under_pareto_frontier_for_all_generations_so_far_csv_location+'" using 1:3 with linespoints title "Smoothed Area Under Curve" '
gnuplot_script_location = os.path.join(current_generation_pareto_dirname, 'script.gnuplot')
with open(gnuplot_script_location, 'wt') as f:
f.write( commands + '\nset output "area_under_curve.png" \n' + '\nplot '+ plot_command_area_under_curve + '\n')
os.system("(cd "+current_generation_pareto_dirname+" && cat "+gnuplot_script_location+" | gnuplot)")
with open(gnuplot_script_location, 'wt') as f:
f.write( commands + '\nset output "area_under_curve_smoothed.png" \n' + '\nplot '+ plot_command_smoothed_area_under_curve + '\n')
os.system("(cd "+current_generation_pareto_dirname+" && cat "+gnuplot_script_location+" | gnuplot)")
with open(gnuplot_script_location, 'wt') as f:
f.write( commands + '\nset output "area_under_curve_both.png" \n' + '\nplot '+ plot_command_area_under_curve + ', ' +plot_command_smoothed_area_under_curve + '\n')
os.system("(cd "+current_generation_pareto_dirname+" && cat "+gnuplot_script_location+" | gnuplot)")
assert len(curve_area_list) == start_gen_index, "Did not recompute curve_area_list correctly from .txt files"
# Update p.all_rank0
load_past_run(p)
else:
pop = next_generation(p, pop)
print_generation(generation)
print 'Generated new population'
sys.stdout.flush()
get_time_error(p, pop, generation)
print_population(pop)
track_rank0(p, pop)
# Save Pareto frontier over all the approximations we've explored so far
dirname = os.path.join(p.result_dir, 'gen%06d' % generation)
makedirs(dirname)
pareto_dirname = os.path.join(dirname, 'pareto_over_all_approximations_so_far')
makedirs(pareto_dirname)
pareto_frontier = [ p.all_rank0[i] for (i,rank) in enumerate(get_pareto_rank(p.all_rank0)) if rank==0 ]
pareto_values = []
#if not p['run_on_fir']:
f_pareto_csv = open(os.path.join(pareto_dirname,'pareto.csv'), 'wt')
f_pareto_csv.write('Approx File, Time, Mean Lab \n')
for (i,indiv) in enumerate(pareto_frontier):
filename = os.path.join(pareto_dirname, individual_filename(i))
# if not p['run_on_fir']:
f_pareto_csv.write(individual_filename(i)+', '+str(indiv.time)+', '+str(indiv.error)+'\n')
pareto_values.append((indiv.time,indiv.error))
with open(filename, 'wt') as f_approx_file:
f_approx_file.write(str(indiv))
with open(os.path.join(pareto_dirname,'pareto_so_far.py'), 'wt') as f:
f.write('pareto_so_far = ' + repr(pareto_frontier))
# if not p['run_on_fir']:
f_pareto_csv.close()
# if not p['run_on_fir']:
if True:
# Record current convergence
CONVERGENCE_MEASURE_TIME_LOWER_BOUND = 0.6
CONVERGENCE_MEASURE_TIME_UPPER_BOUND = 1.0
TIME_INDEX = 0
ERROR_INDEX = 1
pareto_values_for_area = sorted(pareto_values, key=lambda x:x[TIME_INDEX])
area_under_pareto_frontier = 0
if CONVERGENCE_MEASURE_TIME_LOWER_BOUND < pareto_values_for_area[0][TIME_INDEX]:
area_under_pareto_frontier = float('inf')
else:
# Clip the right hand side
while CONVERGENCE_MEASURE_TIME_UPPER_BOUND <= pareto_values_for_area[-1][TIME_INDEX]:
pareto_values_for_area.pop(-1)
pareto_values_for_area.append( (1.0,0.0) )
# Clip the left hand side
while CONVERGENCE_MEASURE_TIME_LOWER_BOUND >= pareto_values_for_area[1][TIME_INDEX]:
pareto_values_for_area.pop(0)
first_point = pareto_values_for_area[0]
second_point = pareto_values_for_area[1]
frac = (CONVERGENCE_MEASURE_TIME_LOWER_BOUND-first_point[TIME_INDEX])/(second_point[TIME_INDEX]-first_point[TIME_INDEX])
new_first_point = (first_point[TIME_INDEX]+frac*(second_point[TIME_INDEX]-first_point[TIME_INDEX]),first_point[ERROR_INDEX]+frac*(second_point[ERROR_INDEX]-first_point[ERROR_INDEX]))
pareto_values_for_area[0] = new_first_point
# Calculate area under curve
for i in xrange(len(pareto_values_for_area)-1):
point_a = pareto_values_for_area[i]
point_b = pareto_values_for_area[i+1]
area = 0.5*(point_a[ERROR_INDEX]+point_b[ERROR_INDEX])*(point_b[TIME_INDEX]-point_a[TIME_INDEX])
area_under_pareto_frontier += area
print 'Area Under Pareto Frontier:', area_under_pareto_frontier, '\n'
# Write pareto area into .txt file
with open(os.path.join(pareto_dirname, 'area_under_pareto_frontier.txt'), 'wt') as f:
f.write(str(area_under_pareto_frontier))
curve_area_list.append( (generation,area_under_pareto_frontier) )
# Add pareto area into area_under_pareto_frontier_for_all_generations_so_far.csv
area_under_pareto_frontier_for_all_generations_so_far_csv_location = os.path.join(pareto_dirname, 'area_under_pareto_frontier_for_all_generations_so_far.csv')
with open(area_under_pareto_frontier_for_all_generations_so_far_csv_location, 'wt') as f:
smoothed_curve_area_list = [ curve_area_list[0][1] ]
if len(curve_area_list)>1:
for i_0 in xrange(len(curve_area_list[1:])):
i = i_0+1
smoothed_curve_area_list.append( curve_area_alpha*curve_area_list[i][1] + (1-curve_area_alpha)*smoothed_curve_area_list[i-1] )
f.write('Generation, Area Under Curve, Smoothed Area Under Curve (alpha='+str(curve_area_alpha)+') \n')
for i,e in enumerate(curve_area_list):
f.write(str(e[0])+', '+str(e[1])+', '+str(smoothed_curve_area_list[i])+'\n')
# if not p['run_on_fir']:
if True:
# Graph area_under_pareto_frontier_for_all_generations_so_far.csv data for Area Under Curve and Smoothed Area Under Curve
commands = '''
set datafile separator ","
set title "Area Under Pareto Curve as Generations Pass"
set ylabel "Area Under Curve"
set xlabel "Generation"
#set ytic 0.1
#set xtic 1
set xr [0:*]
set yr [0:*]
set terminal png size 1200,900
'''
plot_command_area_under_curve = '"'+area_under_pareto_frontier_for_all_generations_so_far_csv_location+'" using 1:2 with linespoints title "Area Under Curve" '
plot_command_smoothed_area_under_curve = '"'+area_under_pareto_frontier_for_all_generations_so_far_csv_location+'" using 1:3 with linespoints title "Smoothed Area Under Curve" '
gnuplot_script_location = os.path.join(pareto_dirname, 'script.gnuplot')
with open(gnuplot_script_location, 'wt') as f:
f.write( commands + '\nset output "area_under_curve.png" \n' + '\nplot '+ plot_command_area_under_curve + '\n')
os.system("(cd "+pareto_dirname+" && cat "+gnuplot_script_location+" | gnuplot)")
with open(gnuplot_script_location, 'wt') as f:
f.write( commands + '\nset output "area_under_curve_smoothed.png" \n' + '\nplot '+ plot_command_smoothed_area_under_curve + '\n')
os.system("(cd "+pareto_dirname+" && cat "+gnuplot_script_location+" | gnuplot)")
with open(gnuplot_script_location, 'wt') as f:
f.write( commands + '\nset output "area_under_curve_both.png" \n' + '\nplot '+ plot_command_area_under_curve + ', ' +plot_command_smoothed_area_under_curve + '\n')
os.system("(cd "+pareto_dirname+" && cat "+gnuplot_script_location+" | gnuplot)")
# Copy rank0.py for each generation
shutil.copyfile( os.path.join(p.result_dir, 'rank0.py') , os.path.join(dirname, 'rank0_gen%06d.py' % generation) )
if p['reduce_space']:
reduce_space_generation(p, pop, generation)
print 'Tuning time for generation %d: %f secs' % (generation, time.time()-T0)
print
sys.stdout.flush()
else:
load_past_run(p)
if p['run_final_generation']:
generation = FINAL_GENERATION_INDEX
print '='*80
print 'Computing Time and Error on Cross Validation Images'
print '='*80
if (p['run_remotely_on_tablet']):
os.system("ssh phablet@"+TABLET_IP+" '(rm -rf "+TABLET_INPUT_IMAGES_DIRECTORY+")'")
os.system("ssh phablet@"+TABLET_IP+" '(mkdir "+TABLET_INPUT_IMAGES_DIRECTORY+")'")
os.system('scp '+p['cross_validation_directory']+'/*.png phablet@'+TABLET_IP+':'+TABLET_INPUT_IMAGES_DIRECTORY)
# get_time_error(p, p.all_rank0, FINAL_GENERATION_INDEX, False) # uncomment this line if we want our GA to get the pareto frontier of all approximations explored by our search using our testing/cross validation images
rank = get_pareto_rank(p.all_rank0)
p.all_rank0 = [x for (i, x) in enumerate(p.all_rank0) if rank[i] == 0]
p.all_rank0 = sorted(p.all_rank0, key=lambda x: x.error)
print_generation(generation, ' (Pareto frontier over all generations, size is %d)' % len(p.all_rank0))
get_time_error(p, p.all_rank0, FINAL_GENERATION_INDEX, False)
print "\n\nGA complete."
print e return p.pid # start the attack by spawning instances for each client on the network that's not in the whitelist def start_attack(output, iface, bssid): while True: clients = get_clients(output) diff = list(set(clients) - set(attack_clients)) for item in diff: if item != bssid and item not in args.whitelist: pid = spawn_attack(iface, item, bssid) attack_pids.append(pid) time.sleep(5) if __name__ == "__main__": args = parse_args() print "Bringing up interface \""+args.interface+"\"..." initialise_interface() print "> OK" print "Finding BSSID for \""+args.essid+"\"..." info = get_ap_bssid() if not info: print "Failed to find BSSID for \""+args.essid+"\"! Check that the ESSID is correct and you're in range." sys.exit() print "> Found \""+args.essid+"\": BSSID="+info.bssid+", Channel="+str(info.channel)+", Power="+str(info.power) print "Creating monitoring interface on channel "+str(info.channel)+"..." mon = create_monitor_interface(info.channel) if not mon: print "Failed to create monitoring interface!"
def create_test_json(master_test_list):
test_log_file = os.path.join(parse_args().workdir, 'data',
'all_tests.json')
with open(test_log_file, 'w') as f:
json.dump(master_test_list, f, sort_keys=True, indent=True)
