def start(self):
"""
Starts the server. Call this after the handlers have been set.
"""
import utils
utils.makedirs(SOCKET_PATH)
serversock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
sockfile = os.path.join(SOCKET_PATH, DISPLAY)
try:
serversock.bind(sockfile)
except socket.error:
try:
os.remove(sockfile)
except OSError:
log("Couldn't remove dead socket file \"%s\"." % sockfile)
sys.exit(1)
try:
serversock.bind(sockfile)
except socket.error:
log("Couldn't bind to socket. Aborting.")
sys.exit(1)
serversock.listen(3)
serversock.setblocking(False)
atexit.register(self.__shutdown, serversock, sockfile)
gobject.timeout_add(100, self.__server_handler, serversock, sockfile)
def generate(self, in_base_path, out_base_path):
self.in_base_path = in_base_path;
self.out_base_path = out_base_path;
utils.makedirs(out_base_path);
imgutils.init(in_base_path);
utils.init(in_base_path);
self.blog = Struct(json.load(utils.open_file(self.in_base_path + "/blog.json")));
# copy static content
cmd = "cp -rf " + in_base_path + "/static/* " + out_base_path;
print("copy static content: " + cmd)
proc = utils.execute_shell(cmd);
# 'dynamic' content
for c in ["sticky", "posts"]:
setattr(self, c, []);
self.generate_content(c);
# home page
self.generate_home();
# feed
self.generate_feed();
def save_lyric(self, data, sid, name, artist):
save_path = os.path.expanduser(config.get("lyrics", "save_lrc_path"))
if not os.path.exists(save_path):
utils.makedirs(save_path)
try:
lrc = data['lrc']['lyric']
except:
lrc = "[00:00.00] "+name+' - '+artist+"\n[99:59:99] No lyric found\n"
# deepin music 好像不支持tlyric, tlyric应该是英文歌词的翻译
# 最好能把英文和翻译合并起来
#try:
#tlyric = data['tlyric']['lyric']
#except:
#tlyric = None
#try:
#klyric = data['klyric']['lyric']
#except:
#klyric = None
#lrc_content = klyric or lrc or tlyric
lrc_content = lrc
lrc_path = os.path.join(save_path, str(sid)+'.lrc')
if not os.path.exists(lrc_path) and lrc_content:
with open(lrc_path, 'w') as f:
f.write(str(lrc_content))
return lrc_path
def write_tiff( hf, filename):
#Make the directory path if it doesn't exist
dirname = os.path.dirname(filename)
if not os.path.exists(dirname):
makedirs(dirname)
import struct
#Get the GTiff driver
driver = gdal.GetDriverByName("GTiff")
#Create the dataset
ds = driver.Create(filename, hf.width, hf.height, 1, gdal.GDT_Float32)
band = ds.GetRasterBand( 1 )
for r in range(0, hf.height):
scanline = []
for c in range(0, hf.width):
height = hf.get_height(c,r)
scanline.append( height )
packed_data = struct.pack('f'*len(scanline), *scanline)
line = hf.height-r-1
band.WriteRaster(0, line, band.XSize, 1, packed_data, buf_type=gdal.GDT_Float32)
ds.FlushCache()
ds = None
def setup_spy():
# Nothing to do if already in LD_PRELOAD
if "police-hook.so" in os.environ.get("LD_PRELOAD", ""):
return
# Setup environment
os.environ["LD_PRELOAD"] = "police-hook.so"
ldlibpath = [
os.environ.get("LD_LIBRARY_PATH", ""),
os.path.join(dragon.POLICE_HOME, "hook", "lib32"),
os.path.join(dragon.POLICE_HOME, "hook", "lib64"),
]
os.environ["LD_LIBRARY_PATH"] = ":".join(ldlibpath)
os.environ["POLICE_HOOK_LOG"] = dragon.POLICE_SPY_LOG
os.environ["POLICE_HOOK_RM_SCRIPT"] = os.path.join(
dragon.POLICE_HOME, "police-rm.sh")
os.environ["POLICE_HOOK_NO_ENV"] = "1"
# Setup directory
utils.makedirs(os.path.dirname(dragon.POLICE_SPY_LOG))
# Keep previous logs. Spy will append to existing if any
# If a fresh spy is required, user shall clean it before
if not os.path.exists(dragon.POLICE_SPY_LOG):
fd = open(dragon.POLICE_SPY_LOG, "w")
fd.close()
def install_addon(self, path):
"""Installs the given addon or directory of addons in the profile."""
# if the addon is a directory, install all addons in it
addons = [path]
if not path.endswith('.xpi') and not os.path.exists(os.path.join(path, 'install.rdf')):
addons = [os.path.join(path, x) for x in os.listdir(path)]
for addon in addons:
if addon.endswith('.xpi'):
tmpdir = tempfile.mkdtemp(suffix = "." + os.path.split(addon)[-1])
compressed_file = zipfile.ZipFile(addon, "r")
for name in compressed_file.namelist():
if name.endswith('/'):
makedirs(os.path.join(tmpdir, name))
else:
if not os.path.isdir(os.path.dirname(os.path.join(tmpdir, name))):
makedirs(os.path.dirname(os.path.join(tmpdir, name)))
data = compressed_file.read(name)
f = open(os.path.join(tmpdir, name), 'wb')
f.write(data) ; f.close()
addon = tmpdir
# determine the addon id
addon_id = Profile.addon_id(addon)
assert addon_id is not None, "The addon id could not be found: %s" % addon
# copy the addon to the profile
addon_path = os.path.join(self.profile, 'extensions', addon_id)
copytree(addon, addon_path, preserve_symlinks=1)
self.addons_installed.append(addon_path)
def build_xsl_reports(
locate_root_dir
, tag
, expected_results_file
, failures_markup_file
, comment_file
, results_dir
, result_file_prefix
, dont_collect_logs = 0
, reports = report_types
, warnings = []
, user = None
, upload = False
):
( run_date ) = time.strftime( '%Y-%m-%dT%H:%M:%SZ', time.gmtime() )
root_paths.append( locate_root_dir )
root_paths.append( results_dir )
bin_boost_dir = os.path.join( locate_root_dir, 'bin', 'boost' )
output_dir = os.path.join( results_dir, result_file_prefix )
utils.makedirs( output_dir )
if expected_results_file != '':
expected_results_file = os.path.abspath( expected_results_file )
else:
expected_results_file = os.path.abspath( map_path( 'empty_expected_results.xml' ) )
extended_test_results = os.path.join( output_dir, 'extended_test_results.xml' )
execute_tasks(
tag
, user
, run_date
, comment_file
, results_dir
, output_dir
, reports
, warnings
, extended_test_results
, dont_collect_logs
, expected_results_file
, failures_markup_file
)
if upload:
upload_dir = 'regression-logs/'
utils.log( 'Uploading results into "%s" [connecting as %s]...' % ( upload_dir, user ) )
archive_name = '%s.tar.gz' % result_file_prefix
utils.tar(
os.path.join( results_dir, result_file_prefix )
, archive_name
)
utils.sourceforge.upload( os.path.join( results_dir, archive_name ), upload_dir, user )
utils.sourceforge.untar( os.path.join( upload_dir, archive_name ), user, background = True )
def _import_path(self, src_path):
src_path_sha = sha1_for_path(src_path)
dst_path = self._path_for_file_with_sha(src_path, src_path_sha)
dst_path_gz = dst_path+'.gz'
# TODO: this is lame
if os.path.exists(dst_path):
return (dst_path, os.path.getsize(dst_path))
elif os.path.exists(dst_path_gz):
return (dst_path_gz, os.path.getsize(dst_path_gz))
# gzip the file first, and see if it passes the compression
# threshhold
makedirs(os.path.dirname(dst_path))
mygzip(src_path, dst_path_gz)
src_size = os.path.getsize(src_path)
dst_gz_size = os.path.getsize(dst_path_gz)
if float(dst_gz_size) / src_size <= self.config.gzip_threshhold:
final_dst_path = dst_path_gz
final_dst_size = dst_gz_size
else:
final_dst_path = dst_path
final_dst_size = src_size
copyfile(src_path, dst_path)
os.remove(dst_path_gz)
logging.info("Imported %s --> %s" % (src_path, final_dst_path))
return (final_dst_path, final_dst_size)
def _create_store(prefix):
r1 = random.randrange(0xffffffL, 0x7fffffffL)
r2 = random.randrange(0xffffffL, 0x7fffffffL)
fname1 = ".!pwstore" + str(r1)
fname2 = ".!" + str(r2)
utils.makedirs(os.path.join(prefix, fname1))
path = os.path.join(prefix, fname1, fname2)
# is this equivalent to the commented code below? i think so
# chars = [chr(a) for a in range(256)*16 if a!=26]
# better :D
chars = [chr(a) for a in range(256) if a!=26] * 16
#chars = []
#for i in xrange(4096):
# a = i % 256
# if (a == 26): continue
# chars.append(chr(a))
#end for
data = ""
while chars:
index = random.randrange(len(chars))
c = chars.pop(index)
data += c
#end while
fd = open(path, "w")
fd.write(data)
fd.close()
os.chmod(path, 0400)
def init_create(cls, source_path, deck_path):
if exists(deck_path) and \
(not isdir(deck_path) or len(os.listdir(deck_path)) != 0):
raise Error("`%s' exists and is not an empty directory" % deck_path)
storage = DeckStorage(deck_path)
makedirs(deck_path)
storage.create(source_path)
if os.geteuid() == 0:
mounts = Mounts(source_path)
if len(mounts):
id = deckcache.new_id()
deckcache.blob(id, "w").write(str(mounts))
storage.mounts_id = id
elif storage.mounts_id:
# make an identical copy of the blob
newid = deckcache.new_id()
print >> deckcache.blob(newid, "w"), deckcache.blob(storage.mounts_id).read()
storage.mounts_id = newid
deck = cls(deck_path)
deck.mount()
return deck
def create(self, source_path):
if self.exists():
raise Error("deck `%s' already exists" % self.name)
if not isdir(source_path):
raise Error("source `%s' is not a directory" % source_path)
if is_deck(source_path):
source = Deck(source_path)
if source.storage.paths.path != self.paths.path:
raise Error("cannot branch a new deck from a deck in another directory")
levels = source.storage.get_levels()
makedirs(self.stack_path)
os.symlink(levels[0], join(self.stack_path, "0"))
# we only need to clone last level if the deck is dirty
if source.is_dirty():
levels = levels[1:]
source.add_level()
else:
levels = levels[1:-1]
for level in levels:
level_id = basename(level)
self.add_level(level_id)
self.mounts_id = source.storage.mounts_id
else:
makedirs(self.stack_path)
os.symlink(realpath(source_path), join(self.stack_path, "0"))
self.add_level()
def create_gdk_pixbuf_loaders_setup(self):
modulespath = self.project.get_bundle_path("Contents/Resources/lib/gtk-2.0/" +
"${pkg:gtk+-2.0:gtk_binary_version}/"+
"loaders")
modulespath = utils.evaluate_pkgconfig_variables (modulespath)
cmd = "GDK_PIXBUF_MODULEDIR=" + modulespath + " gdk-pixbuf-query-loaders"
f = os.popen(cmd)
path = self.project.get_bundle_path("Contents/Resources/etc/gtk-2.0")
utils.makedirs(path)
fout = open(os.path.join(path, "gdk-pixbuf.loaders"), "w")
prefix = "\"" + self.project.get_bundle_path("Contents/Resources")
for line in f:
line = line.strip()
if line.startswith("#"):
continue
# Replace the hardcoded bundle path with @executable_path...
if line.startswith(prefix):
line = line[len(prefix):]
line = "\"@executable_path/../Resources" + line
fout.write(line)
fout.write("\n")
fout.close()
def convert(self, acquisition, participantName):
cmdTpl = self.mrconvertTpl[acquisition.getDataType()]
dicomsDir = acquisition.getDicomsDir()
outputDir = acquisition.getOutputDir()
utils.makedirs(outputDir)
outputWithoutExt = acquisition.getOutputWithoutExt()
call(cmdTpl.format(dicomsDir, outputWithoutExt), shell=True)
def __init__(self, path, architecture, cpp_opts=[]):
self.base = path
makedirs(self.base)
cpp_opts.append("-I" + self.base)
cpp_opts.append("-D%s=y" % architecture.upper())
self.cpp_opts = cpp_opts
def __init__(self, basedir, serial, name):
self.path = os.path.join(basedir, str(serial))
self.serial = serial
self.name = name
# check profile version, if not a new device
if os.path.isdir(self.path):
if self.version < self.PROFILE_VERSION:
raise Device.ProfileVersionMismatch("Version on disk is too old")
elif self.version > self.PROFILE_VERSION:
raise Device.ProfileVersionMismatch("Version on disk is too new")
# create directories
utils.makedirs(self.path)
for directory in DIRECTORIES:
directory_path = os.path.join(self.path, directory)
utils.makedirs(directory_path)
# write profile version (if none)
path = os.path.join(self.path, self.PROFILE_VERSION_FILE)
if not os.path.exists(path):
with open(path, 'wb') as f:
f.write(str(self.PROFILE_VERSION))
# write device name
path = os.path.join(self.path, self.NAME_FILE)
if not os.path.exists(path):
with open(path, 'w') as f:
f.write(self.name)
def save(self, filename='index', metadata=True, resources=True):
''' save metadata and content
filename - defines just filename for three files:
- <filename>.html
- <filename>.metadata
- <filename>.resources
Only the first one is HTML file, the rest of files are JSON files
metadata - if True, HTTP response information will be stored into .metadata file
resources - If True, resources metadata will be stores into .resources file
'''
if not self.path:
raise RuntimeError('Error! The path for storing content is not defined')
if not os.path.exists(self.path):
utils.makedirs(self.path)
# save content metadata
if metadata:
utils.save(os.path.join(self.path, '%s.metadata' % filename),
content=unicode(json.dumps(self.metadata['headers'], indent=4, sort_keys=True)))
# save resources metadata
if resources and self.metadata['resources']:
utils.save(os.path.join(self.path, '%s.resources' % filename),
content=unicode(json.dumps(self.metadata['resources'], indent=4, sort_keys=True)))
# save content
offline_content = copy.deepcopy(self.content)
offline_links = dict([(url, self.metadata['resources'][url]['filename']) for url in self.metadata['resources']])
offline_content.make_links_offline(offline_links=offline_links)
offline_content.save(os.path.join(self.path, '%s.html' % filename))
def __init__(self, in_base_path, in_path, out_path):
self.directory = os.path.basename(in_path);
self.in_path = in_path;
self.out_path = out_path + "/" + self.directory;
self.small_dirname = "small";
self.date_str = "";
utils.makedirs(self.out_path + "/" + self.small_dirname);
try:
self.date = time.strptime(self.directory[:10], "%Y-%m-%d");
except ValueError as e:
self.date = None;
pass;
self.attachments = [];
for f in os.listdir(in_path):
f_full = in_path + "/" + f;
if (os.path.isfile(f_full)):
if (f[-4:] == ".mml"):
if (hasattr(self, "page_path")):
utils.fatal("multipe content found in " + in_path);
self.page_path = f_full;
self.processHeader();
else:
self.attachments.append(f);
self.processAttachments();
def get_resources(self, pattern=None):
''' fetch resources (images, css, javascript, video, ...)
'''
if not pattern:
raise RuntimeError('Error! The pattern is not defined')
pattern = re.compile(pattern)
if self.cached and self.path:
cache_dir = os.path.join(self.path, 'files/')
fetcher = self._get_fetcher(headers=self.headers, cached=True, cache_dir=cache_dir)
else:
fetcher = self._get_fetcher(headers=self.headers)
for link in self.content.links():
if pattern and pattern.search(link):
offline_filename = os.path.join(self.path, utils.offline_link(link))
utils.makedirs(offline_filename)
response = fetcher.fetch(link, to_file=offline_filename)
response.pop(u'content')
url = response.pop(u'url')
if url is not self.metadata['resources']:
self.metadata['resources'][url] = response
response['filename'] = response['filename'].replace(self.path, '')
self.metadata['resources'][url]['filename'] = response['filename']
def copy_path(self, Path):
_doRecurse = False
source = self.project.evaluate_path(Path.source)
if Path.dest:
dest = self.project.evaluate_path(Path.dest)
else:
# Source must begin with a prefix if we don't have a
# dest. Skip past the source prefix and replace it with
# the right bundle path instead.
p = re.compile("^\${prefix(:.*?)?}/")
m = p.match(Path.source)
if m:
relative_dest = self.project.evaluate_path(Path.source[m.end() :])
dest = self.project.get_bundle_path("Contents/Resources", relative_dest)
else:
print "Invalid bundle file, missing or invalid 'dest' property: " + Path.dest
sys.exit(1)
(dest_parent, dest_tail) = os.path.split(dest)
utils.makedirs(dest_parent)
# Check that the source only has wildcards in the last component.
p = re.compile("[\*\?]")
(source_parent, source_tail) = os.path.split(source)
if p.search(source_parent):
print "Can't have wildcards except in the last path component: " + source
sys.exit(1)
if p.search(source_tail):
source_check = source_parent
if Path.recurse:
_doRecurse = True
else:
source_check = source
if not os.path.exists(source_check):
print "Cannot find source to copy: " + source
sys.exit(1)
# If the destination has a wildcard as last component (copied
# from the source in dest-less paths), ignore the tail.
if p.search(dest_tail):
dest = dest_parent
if _doRecurse:
for root, dirs, files in os.walk(source_parent):
destdir = os.path.join(dest, os.path.relpath(root, source_parent))
utils.makedirs(destdir)
for globbed_source in glob.glob(os.path.join(root, source_tail)):
try:
# print "Copying %s to %s" % (globbed_source, destdir)
shutil.copy(globbed_source, destdir)
except EnvironmentError, e:
if e.errno == errno.ENOENT:
print "Warning, source file missing: " + globbed_source
elif e.errno == errno.EEXIST:
print "Warning, path already exits: " + dest
else:
print "Error %s when copying file: %s" % (str(e), globbed_source)
sys.exit(1)
def run_g2s(a_output_dir, a_filename, a_f107, a_f107a, a_ap):
"""
$g2smodel_dir/$g2smodel -v -d $output_dir -i $F107 $F107a $Ap $ECMWF_ops/$ENfilename >& $logdir/$g2s_logfile
"""
conf = Conf.get_instance()
#get info from conf
log_dir = conf.get('G2S', 'g2s_log_dir')
#the_dir = conf.get('G2S', 'dir')
exe = conf.get('G2S', 'exe')
log_file = '%s/g2s_%s.log' % (log_dir, os.path.basename(a_filename))
print("log_dir = %s\n" % (log_dir))
print("output_dir = %s\n" % (a_output_dir))
# makedirs
makedirs(a_output_dir)
makedirs(log_dir)
#check if the bin file already exists. If yes don't do anything
the_date = get_datetime_from_ecmwf_file_name(a_filename)
bin_file = 'G2SGCSx%s_HWM07.bin' % (the_date.strftime('%Y%m%d%H'))
# look for bin_file
if not os.path.exists('%s/%s' % (a_output_dir, bin_file)):
# substitute command line
exe = re.sub(r'\${log_file}', log_file, exe)
exe = re.sub(r'\${output_dir}', a_output_dir, exe)
# substitue params
exe = re.sub(r'\${f107}', a_f107, exe)
exe = re.sub(r'\${f107a}', a_f107a, exe)
exe = re.sub(r'\${ap}', a_ap, exe)
# substitute ecmwf file
exe = re.sub(r'\${ecmwf_file}', a_filename, exe)
command = '%s' % (exe)
print('will run [%s]\n' % (command))
# call the command
retcode = call(command, shell=True)
print("retcode %s\n" % (retcode))
if retcode != 0:
raise Exception("Cannot run g2s for %s. Check error in %s" %
(a_filename, log_file))
print("G2S bin file %s/%s generated \n" % (a_output_dir, bin_file))
else:
print("G2S bin file %s/%s previously generated \n" % (a_output_dir,
bin_file))
return '%s/%s' % (a_output_dir, bin_file)
def extract_symlink(self, arcname):
link_name = self.get_dst(arcname)
source = self.z.read(arcname)
dirn, filename = os.path.split(link_name)
makedirs(dirn)
if os.path.exists(link_name):
os.unlink(link_name)
os.symlink(source, link_name)
return link_name
def build():
makedirs(DIR)
locations = []
for canonical, region in REGIONS.items():
location = build_region(canonical, region)
locations.append(location)
build_index(sorted(
locations,
key = lambda location: location['name']
))
def __set__(self, obj, val):
if not exists(obj.paths.mounts):
makedirs(obj.paths.mounts)
path = join(obj.paths.mounts, obj.name)
if val is None:
if exists(path):
os.remove(path)
else:
file(path, "w").write(val + "\n")
def create_catalog_at_path(path, id):
path = canonical_path(path)
try:
makedirs(path)
except OSError, e:
if e.errno == 17:
pass # directory already exists
else:
raise e
def new_id(self, seed=None):
def digest(s):
return hashlib.md5(s).hexdigest()
id = digest(`seed` + `time.time()` + `random.SystemRandom().getrandbits(128)`)
while self.exists(id):
id = digest(id)
makedirs(dirname(self._get_blob_path(id)))
return id
def create_gdk_pixbuf_loaders_setup(self):
modulespath = ""
cachepath = ""
if os.path.exists(os.path.join(self.project.get_prefix(), "lib",
"gdk-pixbuf-2.0")):
modulespath = self.project.get_bundle_path("Contents/Resources/lib/",
"gdk-pixbuf-2.0",
"${pkg:gdk-pixbuf-2.0:gdk_pixbuf_binary_version}",
"loaders")
cachepath = self.project.get_bundle_path("Contents/Resources/lib/",
"gdk-pixbuf-2.0",
"${pkg:gdk-pixbuf-2.0:gdk_pixbuf_binary_version}",
"loaders.cache")
elif os.path.exists(os.path.join(self.project.get_prefix(), "lib",
"gdk-pixbuf-3.0")):
modulespath = self.project.get_bundle_path("Contents/Resources/lib/",
"gdk-pixbuf-3.0",
"${pkg:gdk-pixbuf-3.0:gdk_pixbuf_binary_version}",
"loaders")
cachepath = self.project.get_bundle_path("Contents/Resources/lib/",
"gdk-pixbuf-3.0",
"${pkg:gdk-pixbuf-3.0:gdk_pixbuf_binary_version}",
"loaders.cache")
else:
modulespath = self.project.get_bundle_path("Contents/Resources/lib/",
self.project.get_gtk_dir(),
"${pkg:" + self.meta.gtk + ":gtk_binary_version}",
"loaders")
cachepath = self.project.get_bundle_path("Contents/Resources/etc/",
self.project.get_gtk_dir(),
"gdk-pixbuf.loaders")
modulespath = utils.evaluate_pkgconfig_variables (modulespath)
cachepath = utils.evaluate_pkgconfig_variables (cachepath)
cmd = "GDK_PIXBUF_MODULEDIR=" + modulespath + " gdk-pixbuf-query-loaders"
f = os.popen(cmd)
utils.makedirs(os.path.dirname(cachepath))
fout = open(cachepath, "w")
prefix = "\"" + self.project.get_bundle_path("Contents/Resources")
for line in f:
line = line.strip()
if line.startswith("#"):
continue
# Replace the hardcoded bundle path with @executable_path...
if line.startswith(prefix):
line = line[len(prefix):]
line = "\"@executable_path/../Resources" + line
fout.write(line)
fout.write("\n")
fout.close()
def __init__(self, vars, mode):
utils.makedirs(self.mntpnt)
try:
utils.sysexec('mount -t auto -U %s %s' % (vars['DISCONNECTED_OPERATION'], self.mntpnt))
# make sure it's not read-only
f = file('%s/api.cache' % self.mntpnt, 'a')
f.close()
file.__init__(self, '%s/api.cache' % self.mntpnt, mode)
except:
utils.sysexec_noerr('umount %s' % self.mntpnt)
raise BootManagerException, "Couldn't find API-cache for disconnected operation"
def install_addon(self, path):
"""Installs the given addon or directory of addons in the profile."""
addons = [path]
if not path.endswith('.xpi') and not os.path.exists(os.path.join(path, 'install.rdf')):
addons = [os.path.join(path, x) for x in os.listdir(path)]
for addon in addons:
tmpdir = None
if addon.endswith('.xpi'):
tmpdir = tempfile.mkdtemp(suffix = "." + os.path.split(addon)[-1])
compressed_file = zipfile.ZipFile(addon, "r")
for name in compressed_file.namelist():
if name.endswith('/'):
makedirs(os.path.join(tmpdir, name))
else:
if not os.path.isdir(os.path.dirname(os.path.join(tmpdir, name))):
makedirs(os.path.dirname(os.path.join(tmpdir, name)))
data = compressed_file.read(name)
f = open(os.path.join(tmpdir, name), 'wb')
f.write(data) ; f.close()
addon = tmpdir
tree = ElementTree.ElementTree(file=os.path.join(addon, 'install.rdf'))
# description_element =
# tree.find('.//{http://www.w3.org/1999/02/22-rdf-syntax-ns#}Description/')
desc = tree.find('.//{http://www.w3.org/1999/02/22-rdf-syntax-ns#}Description')
apps = desc.findall('.//{http://www.mozilla.org/2004/em-rdf#}targetApplication')
for app in apps:
desc.remove(app)
if len(desc) and desc.attrib.has_key('{http://www.mozilla.org/2004/em-rdf#}id'):
addon_id = desc.attrib['{http://www.mozilla.org/2004/em-rdf#}id']
elif len(desc) and desc.find('.//{http://www.mozilla.org/2004/em-rdf#}id') is not None:
addon_id = desc.find('.//{http://www.mozilla.org/2004/em-rdf#}id').text
else:
about = [e for e in tree.findall(
'.//{http://www.w3.org/1999/02/22-rdf-syntax-ns#}Description') if
e.get('{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about') ==
'urn:mozilla:install-manifest'
]
x = e.find('.//{http://www.w3.org/1999/02/22-rdf-syntax-ns#}Description')
if len(about) == 0:
addon_element = tree.find('.//{http://www.mozilla.org/2004/em-rdf#}id')
addon_id = addon_element.text
else:
addon_id = about[0].get('{http://www.mozilla.org/2004/em-rdf#}id')
addon_path = os.path.join(self.profile, 'extensions', addon_id)
copytree(addon, addon_path, preserve_symlinks=1)
self.addons_installed.append(addon_path)
def run(self):
'''
:return:
'''
#command example
#rtg-tools-3.8.4-bdba5ea_install/rtg vcfeval --baseline truth.vcf.gz \
#--calls compare1.vcf.gz -o vcfeval_split_snp -t ref.sdf --output-mode=annotate --sample xx --squash-ploidy --regions ?? \
cmd = ['java', utils.JAVA_XMX, '-jar', utils.RTGJAR, 'vcfeval',
'-o', self.prefix, '--baseline',
self.true_vcf,
'-t', self.reference,
]
if not self.exclude_filtered:
cmd.append('--all-records')
if not self.match_geno:
cmd.append('--squash-ploidy')
if self.sample:
cmd.append('--sample')
cmd.append(self.sample)
if self.regions:
cmd.append('--bed-regions')
cmd.append(self.regions)
if self.opts:
cmd.append(self.opts)
if len(self.vcfs) != 1:
raise ValueError('vcfeval only takes 1 prediction VCF and 1 truth VCF: {0}'.format(self.vcfs))
cmd.append('--calls')
cmd.append(self.vcfs[0])
tp = os.path.join(self.prefix, 'tp-baseline.vcf.gz')
tp_predict = os.path.join(self.prefix, 'tp.vcf.gz')
fn = os.path.join(self.prefix, 'fn.vcf.gz')
fp = os.path.join(self.prefix, 'fp.vcf.gz')
if utils.count_variants(self.true_vcf) == 0 and utils.count_variants(self.vcfs[0]) == 0:
#both truth and prediction are empty, do nothing
utils.makedirs([self.prefix])
shutil.copyfile(self.true_vcf, tp)
shutil.copyfile(self.true_vcf, fn)
shutil.copyfile(self.vcfs[0], tp_predict)
shutil.copyfile(self.vcfs[0], fp)
else:
if self.log_to_file:
with utils.versatile_open(self.log_to_file, 'a') as logout:
utils.run_shell_command(cmd, sys.stderr, logout)
else:
utils.run_shell_command(cmd, sys.stderr, sys.stderr)
for i in (tp, tp_predict, fn, fp):
if not os.path.exists(i):
raise Exception('{0} was not generated by vcfeval. Please check and rerun.'.format(i))
self.tp, self.tp_predict, self.fn, self.fp = tp, tp_predict, fn, fp
def __init__(self):
ant.fs.manager.Application.__init__(self)
GObject.GObject.__init__(self)
self.path = os.path.join(GLib.get_user_data_dir(), self.PRODUCT_NAME)
utils.makedirs(self.path)
self.status = Garmin.Status.NONE
self.device = None
self.funcs = []
self.loop = None
self.timeout_source = None
import torch
import torchvision as tv
import numpy as np
from torch.utils.data import DataLoader
from torchvision import models
import torch.nn as nn
from utils import makedirs, tensor2cuda, load_model
from argument import parser
from visualization import VanillaBackprop
import patch_dataset as patd
from model.resnetdsbn import *
args = parser()
img_folder = 'grad_img'
img_folder = os.path.join(img_folder, args.dataset, args.affix)
makedirs(img_folder)
out_num = 1
transform_test = tv.transforms.Compose(
[tv.transforms.Resize(256),
tv.transforms.ToTensor()])
te_dataset = patd.PatchDataset(path_to_images=args.data_root,
fold='test',
transform=tv.transforms.ToTensor())
te_loader = DataLoader(te_dataset, batch_size=1, shuffle=False, num_workers=1)
counter = 0
input_list = []
grad_list = []
label_list = []
for data, label in te_loader:
def populate_relation(result,
name,
alphas=[0.05],
split_detect=True,
multiple_test_method='fdr_by'):
print("Populate relation", name)
# create save folder
save_dir = utils.makedirs([config.analysis_dir, name])
relation_dir = utils.makedirs([save_dir, 'relations'])
metric_dir = utils.makedirs([save_dir, 'four_metrics'])
# get other attributes
attr_mean_acc = Compare(
result, mean_acc,
test_acc).compare_result # attr: dirty_acc, clean_acc
attr_diff_acc = Compare(result, diff_acc,
test_acc).compare_result # attr: diff_acc
attr_mean_f1 = Compare(result, mean_f1,
test_f1).compare_result # attr: dirty_f1, clean_f1
attr_diff_f1 = Compare(result, diff_f1,
test_f1).compare_result # attr: diff_f1
attr_count = Compare(
result, direct_count,
mixed_f1_acc).compare_result # attr: pos count, neg count, same count
# run t-test
t_test_comp = Compare(result, t_test, mixed_f1_acc)
t_test_comp.save_four_metrics(metric_dir)
# hypothesis test
for alpha in alphas:
# print(alpha)
# get attribute flag by multiple hypothesis test
attr_flag = hypothesis_test(t_test_comp.compare_result, alpha,
multiple_test_method)
# populate relation with all of attributes
relation = {
**attr_flag,
**attr_mean_acc,
**attr_mean_f1,
**attr_diff_acc,
**attr_diff_f1,
**attr_count
}
# split detect
if split_detect and name != "R3":
relation = split_clean_method(relation)
# eliminate redundant attribute for R2 and R3
if name == "R2":
redundant_dims = [4] if split_detect else [3]
relation = elim_redundant_dim(relation, redundant_dims)
if name == "R3":
redundant_dims = [2, 3]
relation = elim_redundant_dim(relation, redundant_dims)
# convert dict to df
n_key = len(list(relation.keys())[0])
relation_df = utils.dict_to_df(relation, list(range(n_key - 1)),
[n_key - 1])
# save relation to csv and pkl
relation_csv_dir = utils.makedirs([relation_dir, 'csv'])
save_path = os.path.join(
relation_csv_dir,
'{}_{}.csv'.format(name, "{:.6f}".format(alpha).rstrip('0')))
relation_df.to_csv(save_path)
relation_pkl_dir = utils.makedirs([relation_dir, 'pkl'])
save_path = os.path.join(
relation_pkl_dir,
'{}_{}.pkl'.format(name, "{:.6f}".format(alpha).rstrip('0')))
utils.df_to_pickle(relation_df, save_path)
wav_end_idx = None
wav = wav[wav_start_idx:wav_end_idx]
if end_of_sentence:
wav = np.lib.pad(wav, ((0, 20), (0, 0)), 'constant', constant_values=0)
else:
wav = np.lib.pad(wav, ((0, 10), (0, 0)), 'constant', constant_values=0)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--load_path', required=True)
parser.add_argument('--sample_path', default="samples")
parser.add_argument('--text', required=True)
parser.add_argument('--num_speakers', default=1, type=int)
parser.add_argument('--speaker_id', default=0, type=int)
parser.add_argument('--checkpoint_step', default=None, type=int)
config = parser.parse_args()
makedirs(config.sample_path)
synthesizer = Synthesizer()
synthesizer.load(config.load_path, config.num_speakers,
config.checkpoint_step)
audio = synthesizer.synthesize(texts=[config.text],
base_path=config.sample_path,
speaker_ids=[config.speaker_id],
attention_trim=False)[0]
# test2017.zip, for those who want to attend the competition.
# ('http://images.cocodataset.org/zips/test2017.zip',
# '4e443f8a2eca6b1dac8a6c57641b67dd40621a49'),
]
makedirs(path)
for url, checksum in _DOWNLOAD_URLS:
filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
# extract
with zipfile.ZipFile(filename) as zf:
zf.extractall(path=path)
if __name__ == '__main__':
args = parse_args()
path = os.path.expanduser(args.download_dir)
if not os.path.isdir(path) or not os.path.isdir(os.path.join(path, 'train2017')) \
or not os.path.isdir(os.path.join(path, 'val2017')) \
or not os.path.isdir(os.path.join(path, 'annotations')):
if args.no_download:
raise ValueError(('{} is not a valid directory, make sure it is present.'
' Or you should not disable "--no-download" to grab it'.format(path)))
else:
download_coco(path, overwrite=args.overwrite)
# make symlink
makedirs(os.path.expanduser('~/.torch/datasets'))
if os.path.isdir(_TARGET_DIR):
os.remove(_TARGET_DIR)
os.symlink(path, _TARGET_DIR)
try_import_pycocotools()
def output_visuals(batch_data, outputs, args):
mag_mix = batch_data['mag_mix']
phase_mix = batch_data['phase_mix']
features = batch_data['feats']
coords = batch_data['coords']
infos = batch_data['infos']
pred_masks_ = outputs['pred_masks']
gt_masks_ = outputs['gt_masks']
mag_mix_ = outputs['mag_mix']
weight_ = outputs['weight']
# unwarp log scale
N = args.num_mix
FN = args.num_frames
B = mag_mix.size(0)
pred_masks_linear = [None for n in range(N)]
gt_masks_linear = [None for n in range(N)]
for n in range(N):
if args.log_freq:
grid_unwarp = torch.from_numpy(
warpgrid(B,
args.stft_frame // 2 + 1,
gt_masks_[0].size(3),
warp=False)).to(args.device)
pred_masks_linear[n] = F.grid_sample(pred_masks_[n], grid_unwarp)
gt_masks_linear[n] = F.grid_sample(gt_masks_[n], grid_unwarp)
else:
pred_masks_linear[n] = pred_masks_[n]
gt_masks_linear[n] = gt_masks_[n]
# convert into numpy
mag_mix = mag_mix.numpy()
mag_mix_ = mag_mix_.detach().cpu().numpy()
phase_mix = phase_mix.numpy()
weight_ = weight_.detach().cpu().numpy()
for n in range(N):
pred_masks_[n] = pred_masks_[n].detach().cpu().numpy()
pred_masks_linear[n] = pred_masks_linear[n].detach().cpu().numpy()
gt_masks_[n] = gt_masks_[n].detach().cpu().numpy()
gt_masks_linear[n] = gt_masks_linear[n].detach().cpu().numpy()
# threshold if binary mask
if args.binary_mask:
pred_masks_[n] = (pred_masks_[n] > args.mask_thres).astype(
np.float32)
pred_masks_linear[n] = (pred_masks_linear[n] >
args.mask_thres).astype(np.float32)
# loop over each sample
for j in range(B):
# video names
prefix = []
for n in range(N):
prefix.append('-'.join(
infos[n][0][j].split('/')[-2:]).split('.')[0])
prefix = '+'.join(prefix)
makedirs(os.path.join(args.vis, prefix))
# save mixture
mix_wav = istft_reconstruction(mag_mix[j, 0],
phase_mix[j, 0],
hop_length=args.stft_hop)
mix_amp = magnitude2heatmap(mag_mix_[j, 0])
weight = magnitude2heatmap(weight_[j, 0], log=False, scale=100.)
filename_mixwav = os.path.join(prefix, 'mix.wav')
filename_mixmag = os.path.join(prefix, 'mix.jpg')
filename_weight = os.path.join(prefix, 'weight.jpg')
imageio.imwrite(os.path.join(args.vis, filename_mixmag),
mix_amp[::-1, :, :])
imageio.imwrite(os.path.join(args.vis, filename_weight),
weight[::-1, :])
wavfile.write(os.path.join(args.vis, filename_mixwav), args.audRate,
mix_wav)
# save each component
preds_wav = [None for n in range(N)]
for n in range(N):
# GT and predicted audio recovery
gt_mag = mag_mix[j, 0] * gt_masks_linear[n][j, 0]
gt_wav = istft_reconstruction(gt_mag,
phase_mix[j, 0],
hop_length=args.stft_hop)
pred_mag = mag_mix[j, 0] * pred_masks_linear[n][j, 0]
preds_wav[n] = istft_reconstruction(pred_mag,
phase_mix[j, 0],
hop_length=args.stft_hop)
# output masks
filename_gtmask = os.path.join(prefix,
'gtmask{}.jpg'.format(n + 1))
filename_predmask = os.path.join(prefix,
'predmask{}.jpg'.format(n + 1))
gt_mask = (np.clip(gt_masks_[n][j, 0], 0, 1) * 255).astype(
np.uint8)
pred_mask = (np.clip(pred_masks_[n][j, 0], 0, 1) * 255).astype(
np.uint8)
imageio.imwrite(os.path.join(args.vis, filename_gtmask),
gt_mask[::-1, :])
imageio.imwrite(os.path.join(args.vis, filename_predmask),
pred_mask[::-1, :])
# ouput spectrogram (log of magnitude, show colormap)
filename_gtmag = os.path.join(prefix, 'gtamp{}.jpg'.format(n + 1))
filename_predmag = os.path.join(prefix,
'predamp{}.jpg'.format(n + 1))
gt_mag = magnitude2heatmap(gt_mag)
pred_mag = magnitude2heatmap(pred_mag)
imageio.imwrite(os.path.join(args.vis, filename_gtmag),
gt_mag[::-1, :, :])
imageio.imwrite(os.path.join(args.vis, filename_predmag),
pred_mag[::-1, :, :])
# output audio
filename_gtwav = os.path.join(prefix, 'gt{}.wav'.format(n + 1))
filename_predwav = os.path.join(prefix, 'pred{}.wav'.format(n + 1))
wavfile.write(os.path.join(args.vis, filename_gtwav), args.audRate,
gt_wav)
wavfile.write(os.path.join(args.vis, filename_predwav),
args.audRate, preds_wav[n])
#output pointclouds
for f in range(FN):
idx = torch.where(coords[n][f][:, 0] == j)
path_point = os.path.join(
args.vis, prefix,
'point{}_frame{}.ply'.format(n + 1, f + 1))
if args.rgbs_feature:
colors = np.asarray(features[n][f][idx])
xyz = np.asarray(coords[n][f][idx][:, 1:4])
xyz = xyz * args.voxel_size
save_points(path_point, xyz, colors)
else:
xyz = np.asarray(features[n][f][idx])
save_points(path_point, xyz)
def run(args):
# setting the GPU #
os.environ['CUDA_DEVICE_ORDER'] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
logger.info('Read data:')
train_A, train_B, test_A, test_B = get_data(args.task, args.image_size)
logger.info('Build graph:')
model = BicycleGAN(args)
variables_to_save = tf.global_variables()
init_op = tf.variables_initializer(variables_to_save)
init_all_op = tf.global_variables_initializer()
saver = FastSaver(variables_to_save)
logger.info('Trainable vars:')
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
for v in var_list:
logger.info(' %s %s', v.name, v.get_shape())
if args.load_model != '':
model_name = args.load_model
else:
model_name = '{}_{}'.format(
args.task,
datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
logdir = './logs'
makedirs(logdir)
logdir = os.path.join(logdir, model_name)
logger.info('Events directory: %s', logdir)
summary_writer = tf.summary.FileWriter(logdir)
makedirs('./results')
def init_fn(sess):
logger.info('Initializing all parameters.')
sess.run(init_all_op)
sv = tf.train.Supervisor(
is_chief=True,
logdir=logdir,
saver=saver,
summary_op=None,
init_op=init_op,
init_fn=init_fn,
summary_writer=summary_writer,
ready_op=tf.report_uninitialized_variables(variables_to_save),
global_step=model.global_step,
save_model_secs=300,
save_summaries_secs=30)
if args.train:
logger.info("Starting training session.")
with sv.managed_session() as sess:
model.train(sess, summary_writer, train_A, train_B)
logger.info("Starting testing session.")
with sv.managed_session() as sess:
base_dir = os.path.join('results', model_name)
makedirs(base_dir)
model.test(sess, test_A, test_B, base_dir)
def run_experiment(self):
"""
Runs a full training experiment with evaluations of the model on the val set at every epoch.
"""
if self.args.mode == 'test':
print('Start testing')
num_test_tasks = self.data.dataset.data_length['test']
with tqdm(total=int(num_test_tasks /
self.args.test_batch_size)) as pbar_test:
for _, test_sample in enumerate(
self.data.get_test_batches(
total_batches=int(num_test_tasks /
self.args.test_batch_size))):
outputs = self.test_iteration(test_sample=test_sample,
pbar_test=pbar_test,
phase='test')
batch_size = test_sample[0][0].shape[0]
for k in range(batch_size):
imgpath1 = test_sample[1]['imgpaths'][1][k]
imgpath2 = test_sample[1]['imgpaths'][2][k]
filename1 = imgpath1.split('/')[-1]
filename2 = imgpath2.split('/')[-1]
float_ind1 = float(filename1.split('_')[-1][:-4])
float_ind2 = float(filename2.split('_')[-1][:-4])
if float_ind2 == 0:
float_ind2 = 1.0
im_path = os.path.join(
self.args.data_root, '%s_%.06f.%s' %
(filename1.split('_')[0],
(float_ind1 + float_ind2) / 2, self.args.img_fmt))
utils.save_image(outputs[k], im_path)
print('Test finished.')
return
elif self.args.mode == 'val':
print('Validation only')
total_losses = dict()
val_losses = dict()
metrics_accumulator = {
'psnr': utils.AverageMeter(),
'ssim': utils.AverageMeter()
}
num_evaluation_tasks = self.data.dataset.data_length['val']
with tqdm(total=int(num_evaluation_tasks /
self.args.val_batch_size)) as pbar_val:
for _, val_sample in enumerate(
self.data.get_val_batches(
total_batches=int(num_evaluation_tasks /
self.args.val_batch_size))):
val_losses, outputs, metrics = self.evaluation_iteration(
val_sample=val_sample,
total_losses=total_losses,
pbar_val=pbar_val,
phase='val')
batch_size = val_sample[0][0].shape[0]
for k, v in metrics.items():
metrics_accumulator[k].update(v.avg, n=v.count)
for k in range(batch_size):
paths = val_sample[1]['imgpaths'][3][k].split('/')
save_dir = os.path.join('checkpoint',
self.args.exp_name,
self.args.dataset, paths[-3],
paths[-2])
if not os.path.exists(save_dir):
utils.makedirs(save_dir)
im_path = os.path.join(
save_dir,
paths[-1]) # 'im4.png' for VimeoSeptuplet
utils.save_image(outputs[0][k], im_path)
del val_losses, outputs, metrics
print("%d examples processed" % metrics_accumulator['psnr'].count)
print("PSNR: %.2f, SSIM: %.4f\n" %
(metrics_accumulator['psnr'].avg,
metrics_accumulator['ssim'].avg))
return
with tqdm(initial=self.state['current_iter'],
total=int(self.args.total_iter_per_epoch *
self.args.max_epoch)) as pbar_train:
# training main loop
while (self.state['current_iter'] <
(self.args.max_epoch * self.args.total_iter_per_epoch)):
for train_sample_idx, train_sample in enumerate(
self.data.get_train_batches(
total_batches=int(self.args.total_iter_per_epoch *
self.args.max_epoch) -
self.state['current_iter'])):
train_losses, outputs, metrics, self.state[
'current_iter'] = self.train_iteration(
train_sample=train_sample,
total_losses=self.total_losses,
epoch_idx=(self.state['current_iter'] /
self.args.total_iter_per_epoch),
pbar_train=pbar_train,
current_iter=self.state['current_iter'],
sample_idx=self.state['current_iter'],
do_evaluation=(self.state['current_iter'] %
self.args.eval_iter == 0))
# Log to Tensorboard
if self.state[
'current_iter'] % self.args.log_iter == 1 and self.use_tensorboard:
utils.log_tensorboard(
self.writer,
train_losses,
metrics['psnr'].avg,
metrics['ssim'].avg,
None,
self.model.optimizer.param_groups[0]['lr'],
self.state['current_iter'],
mode='train')
# validation
if self.state[
'current_iter'] % self.args.total_iter_per_epoch == 0:
total_losses = dict()
val_losses = {}
metrics_accumulator = {
'psnr': utils.AverageMeter(),
'ssim': utils.AverageMeter()
}
num_evaluation_tasks = self.data.dataset.data_length[
'val']
with tqdm(total=int(num_evaluation_tasks /
self.args.val_batch_size +
0.99)) as pbar_val:
for _, val_sample in enumerate(
self.data.get_val_batches(
total_batches=int(
num_evaluation_tasks /
self.args.val_batch_size + 0.99))):
val_loss, outputs, metrics = self.evaluation_iteration(
val_sample=val_sample,
total_losses=total_losses,
pbar_val=pbar_val,
phase='val')
for k, v in metrics.items():
metrics_accumulator[k].update(v.avg,
n=v.count)
for k, v in val_loss.items():
if k not in val_losses.keys():
val_losses[k] = utils.AverageMeter()
val_losses[k].update(v)
del val_loss, outputs, metrics
for k, v in val_losses.items():
val_losses[k] = v.avg
if val_losses["total"] < self.state[
'best_val_loss']:
print("Best validation loss",
val_losses["total"])
self.state['best_val_loss'] = val_losses[
"total"]
self.state['best_val_psnr'] = self.state[
'current_iter']
self.state['best_epoch'] = int(
self.state['best_val_iter'] /
self.args.total_iter_per_epoch)
print("validation PSNR: %.2f, SSIM: %.4f\n" %
(metrics_accumulator['psnr'].avg,
metrics_accumulator['ssim'].avg))
# log to TensorBoard
if self.use_tensorboard:
utils.log_tensorboard(
self.writer,
val_losses,
metrics_accumulator['psnr'].avg,
metrics_accumulator['ssim'].avg,
None,
self.model.optimizer.param_groups[0]['lr'],
self.state['current_iter'],
mode='val')
self.epoch += 1
PSNR = metrics_accumulator['psnr'].avg
is_best = PSNR > self.best_PSNR
self.best_PSNR = max(PSNR, self.best_PSNR)
utils.save_checkpoint(
{
'epoch': self.epoch,
'arch': self.args,
'state_dict': self.model.state_dict(),
# 'optimizer': optimizer.state_dict(),
'best_PSNR': self.best_PSNR
},
is_best,
self.args.exp_name)
self.model.scheduler.step(val_losses['total'])
self.total_losses = dict()
self.epochs_done_in_this_run += 1
metavar="DGV_FILE",
help="DGV file containing structural variants",
required=False)
rand_dgv_group.add_argument(
"--sv_prop_het",
metavar="FLOAT",
help="Proportion of heterozygous structural variants",
default=0.6,
type=float)
args = main_parser.parse_args()
args.java = utils.get_java(args.java)
check_java(args.java)
utils.JAVA_XMX = utils.JAVA_XMX + args.java_max_mem
makedirs([args.out_dir])
# Setup logging
FORMAT = '%(levelname)s %(asctime)-15s %(name)-20s %(message)s'
loglevel = get_loglevel(args.loglevel)
if not args.log_to_stderr:
logging.basicConfig(filename=os.path.join(args.out_dir, "varsim.log"),
filemode="w",
level=loglevel,
format=FORMAT)
else:
logging.basicConfig(level=loglevel, format=FORMAT)
simulator = None if args.disable_sim else args.simulator
simulator_opts = args.simulator_options
check_simulator_opts(simulator, simulator_opts)
if args.binary_mask:
assert args.loss == 'bce', 'Binary Mask should go with BCE loss'
args.id += '-binary'
else:
args.id += '-ratio'
if args.weighted_loss:
args.id += '-weightedLoss'
args.id += '-channels{}'.format(args.num_channels)
args.id += '-epoch{}'.format(args.num_epoch)
args.id += '-step' + '_'.join([str(x) for x in args.lr_steps])
print('Model ID: {}'.format(args.id))
# paths to save/load output
args.ckpt = os.path.join(args.ckpt, args.id)
args.vis = os.path.join(args.ckpt, 'visualization/')
if args.mode == 'train':
makedirs(args.ckpt, remove=True)
elif args.mode == 'eval':
args.weights_sound = os.path.join(args.ckpt, 'sound_best.pth')
args.weights_frame = os.path.join(args.ckpt, 'frame_best.pth')
args.weights_synthesizer = os.path.join(args.ckpt,
'synthesizer_best.pth')
# initialize best error with a big number
args.best_err = float("inf")
random.seed(args.seed)
torch.manual_seed(args.seed)
main(args)
def generate_audio_response(textList, speaker_id, alarm_id):
#global global_config
#model_name = os.path.basename(global_config.load_path)
#iskorean=global_config.is_korean
audio_clear()
global member_id, method_id
if member_id != speaker_id:
if speaker_id == 0:
if not (member_id==0):
if member_id != -1:
synthesizer.close()
synthesizer.load('logs/backup_log/son+yuinna', 2)
elif speaker_id == 3:
if not (member_id==3):
if member_id != -1:
synthesizer.close()
synthesizer.load('logs/backup_log/new_inna+kss+leejh+nandong2',4)
else:
if not (member_id==1 or member_id==2 or member_id==4):
if member_id != -1:
synthesizer.close()
synthesizer.load('logs/backup_log/new_inna+kss+leejh', 3)
member_id = speaker_id
if speaker_id==0:
model_name='손석희'
#speaker_id=0
elif speaker_id==1:
model_name='유인나'
speaker_id=0
elif speaker_id==2:
model_name='코퍼스' #한국어 코퍼스
speaker_id=1
elif speaker_id==3:
model_name='김난희'
#speaker_id=3
else:
model_name='이주형'
speaker_id=2
###########################################################################################
# 이 부분 반목문 돌림
textcnt = 0 # 몇번째 텍스트인지 확인 용도
audio_list = [] #체크 용도
print(textList)
for text in textList:
# hashed_text = hashlib.md5(text.encode('utf-8')).hexdigest() # 텍스트
hashed_text = "{}".format(str(textcnt))
# 이 부분을 반복문
# 이 부분이 경로 생성 하는 부분
relative_dir_path = os.path.join(AUDIO_DIR, model_name)
relative_audio_path = os.path.join(
relative_dir_path, "{}.{}.wav".format(hashed_text, speaker_id))
real_path = os.path.join(ROOT_PATH, relative_audio_path)
makedirs(os.path.dirname(real_path))
if not os.path.exists(add_postfix(real_path, 0)):
try:
#audio는 파일명임
audio = synthesizer.synthesize(
[text], paths=[real_path], speaker_ids=[speaker_id],
attention_trim=True)[0]
audio_list.append(audio)
except:
return jsonify(success=False), 400
textcnt +=1
###########################################################################################
# 음성 합치기
# 합친 음성 이름은 'output.wav'
CUR_PATH = os.getcwd()
#print(CUR_PATH) # audio 이름 체크용
FILE_PATH = os.path.join(AUDIO_PATH, model_name)
#print(FILE_PATH) # audio 이름 체크용
print("method {} 실행중".format(method_id))
alarm_type = 0
alarm_id -= 1
if (method_id == 1) or (method_id == 2): # basic
combine_audio(os.path.join(CUR_PATH, FILE_PATH))
elif method_id == 3: # morning_call
combine_audio(os.path.join(CUR_PATH, FILE_PATH)) # web\audio\model_name\output.wav
if alarm_id == 0 or alarm_id == 1 or alarm_id == 2 or alarm_id == 3:
alarm_type = 0
else:
alarm_id = (alarm_id - 4)
alarm_type = 1
create_alarm(alarm_id, model_name, alarm_type) # bgm_select, model_name, type
elif method_id == 4: # briefing
combine_audio(os.path.join(CUR_PATH, FILE_PATH)) # web\audio\model_name\output.wav
create_briefing(alarm_id, model_name) # bgm_select, model_name, #0 1 2 3
elif method_id == 5: # birthday
combine_audio(os.path.join(CUR_PATH, FILE_PATH)) # web\audio\model_name\output.wav
create_birthday(0, model_name) # bgm_select, model_name, #0 1 2 3
#print(os.path.join(CUR_PATH, FILE_PATH))
#print(TEST_PATH)
###########################################################################################
return send_file(
os.path.join('audio', model_name, 'output.wav'),
mimetype="audio/wav",
as_attachment=True,
attachment_filename=hashed_text + ".wav")
###########################################################################################
# 합친 파일 불러와서 audio에 넣기
response = make_response(os.path.join('web', 'audio', model_name, 'output.wav'))
response.headers['Content-Type'] = 'audio/wav'
response.headers['Content-Disposition'] = 'attachment; filename=sound.wav'
return response
parser.add_argument('--load_path', required=True)
parser.add_argument('--sample_dir_name', default="samples")
parser.add_argument('--text', required=True)
parser.add_argument('--num_speakers', default=1, type=int)
parser.add_argument('--speaker_id', default=0, type=int)
parser.add_argument('--checkpoint_step', default=None, type=int)
parser.add_argument('--is_korean', default=True, type=str2bool)
# Added parts
# add for manipulating attention module result
parser.add_argument('--manual_attention_mode', default=0, type=int, choices=[0, 1, 2, 3])
# parser.add_argument('--get_base_alignment_path', default=None, type=int, choices=[1])
config = parser.parse_args()
makedirs(config.sample_dir_name)
synthesizer = Synthesizer()
synthesizer.load(config.load_path, config.num_speakers, config.checkpoint_step)
# if config.get_base_alignment_path is not None:
# base_alignment_path = os.path.abspath('')
# else:
# base_alignment_path = config.get_base_alignment_path
if config.manual_attention_mode > 0:
base_alignment_path = os.path.abspath('')
else:
base_alignment_path = None
import sys
sys.path.append('..')
import os
import report
import merger
import utils
tag = "1_30_0"
utils.makedirs("results")
all_xml_file = "results/all.xml"
all_xml_writer = open(all_xml_file, "w")
merger.merge_test_runs(".", tag, all_xml_writer, 1)
all_xml_writer.close()
report.make_result_pages(
test_results_file=os.path.abspath(all_xml_file),
expected_results_file="",
failures_markup_file=os.path.abspath("explicit-failures-markup.xml"),
tag=tag,
run_date="Today date",
comment_file=os.path.abspath("comment.html"),
results_dir="results",
result_prefix="",
reports=["x", "ds", "dd"],
v2=1)
def output_visuals(vis_rows, batch_data, outputs, args):
# fetch data and predictions
mag_mix = batch_data['mag_mix']
phase_mix = batch_data['phase_mix']
frames = batch_data['frames']
infos = batch_data['infos']
pred_masks_ = outputs['pred_masks']
gt_masks_ = outputs['gt_masks']
mag_mix_ = outputs['mag_mix']
weight_ = outputs['weight']
# unwarp log scale
N = args.num_mix
B = mag_mix.size(0)
pred_masks_linear = [None for n in range(N)]
gt_masks_linear = [None for n in range(N)]
for n in range(N):
if args.log_freq:
grid_unwarp = torch.from_numpy(
warpgrid(B,
args.stft_frame // 2 + 1,
gt_masks_[0].size(3),
warp=False)).to(args.device)
pred_masks_linear[n] = F.grid_sample(pred_masks_[n], grid_unwarp)
gt_masks_linear[n] = F.grid_sample(gt_masks_[n], grid_unwarp)
else:
pred_masks_linear[n] = pred_masks_[n]
gt_masks_linear[n] = gt_masks_[n]
# convert into numpy
mag_mix = mag_mix.numpy()
mag_mix_ = mag_mix_.detach().cpu().numpy()
phase_mix = phase_mix.numpy()
weight_ = weight_.detach().cpu().numpy()
for n in range(N):
pred_masks_[n] = pred_masks_[n].detach().cpu().numpy()
pred_masks_linear[n] = pred_masks_linear[n].detach().cpu().numpy()
gt_masks_[n] = gt_masks_[n].detach().cpu().numpy()
gt_masks_linear[n] = gt_masks_linear[n].detach().cpu().numpy()
# threshold if binary mask
if args.binary_mask:
pred_masks_[n] = (pred_masks_[n] > args.mask_thres).astype(
np.float32)
pred_masks_linear[n] = (pred_masks_linear[n] >
args.mask_thres).astype(np.float32)
# loop over each sample
for j in range(B):
row_elements = []
# video names
prefix = []
for n in range(N):
prefix.append('-'.join(
infos[n][0][j].split('/')[-2:]).split('.')[0])
prefix = '+'.join(prefix)
makedirs(os.path.join(args.vis, prefix))
# save mixture
mix_wav = istft_reconstruction(mag_mix[j, 0],
phase_mix[j, 0],
hop_length=args.stft_hop)
mix_amp = magnitude2heatmap(mag_mix_[j, 0])
weight = magnitude2heatmap(weight_[j, 0], log=False, scale=100.)
filename_mixwav = os.path.join(prefix, 'mix.wav')
filename_mixmag = os.path.join(prefix, 'mix.jpg')
filename_weight = os.path.join(prefix, 'weight.jpg')
imageio.imwrite(os.path.join(args.vis, filename_mixmag),
mix_amp[::-1, :, :])
imageio.imwrite(os.path.join(args.vis, filename_weight),
weight[::-1, :])
wavfile.write(os.path.join(args.vis, filename_mixwav), args.audRate,
mix_wav)
row_elements += [{
'text': prefix
}, {
'image': filename_mixmag,
'audio': filename_mixwav
}]
# save each component
preds_wav = [None for n in range(N)]
for n in range(N):
# GT and predicted audio recovery
gt_mag = mag_mix[j, 0] * gt_masks_linear[n][j, 0]
gt_wav = istft_reconstruction(gt_mag,
phase_mix[j, 0],
hop_length=args.stft_hop)
pred_mag = mag_mix[j, 0] * pred_masks_linear[n][j, 0]
preds_wav[n] = istft_reconstruction(pred_mag,
phase_mix[j, 0],
hop_length=args.stft_hop)
# output masks
filename_gtmask = os.path.join(prefix,
'gtmask{}.jpg'.format(n + 1))
filename_predmask = os.path.join(prefix,
'predmask{}.jpg'.format(n + 1))
gt_mask = (np.clip(gt_masks_[n][j, 0], 0, 1) * 255).astype(
np.uint8)
pred_mask = (np.clip(pred_masks_[n][j, 0], 0, 1) * 255).astype(
np.uint8)
imageio.imwrite(os.path.join(args.vis, filename_gtmask),
gt_mask[::-1, :])
imageio.imwrite(os.path.join(args.vis, filename_predmask),
pred_mask[::-1, :])
# ouput spectrogram (log of magnitude, show colormap)
filename_gtmag = os.path.join(prefix, 'gtamp{}.jpg'.format(n + 1))
filename_predmag = os.path.join(prefix,
'predamp{}.jpg'.format(n + 1))
gt_mag = magnitude2heatmap(gt_mag)
pred_mag = magnitude2heatmap(pred_mag)
imageio.imwrite(os.path.join(args.vis, filename_gtmag),
gt_mag[::-1, :, :])
imageio.imwrite(os.path.join(args.vis, filename_predmag),
pred_mag[::-1, :, :])
# output audio
filename_gtwav = os.path.join(prefix, 'gt{}.wav'.format(n + 1))
filename_predwav = os.path.join(prefix, 'pred{}.wav'.format(n + 1))
wavfile.write(os.path.join(args.vis, filename_gtwav), args.audRate,
gt_wav)
wavfile.write(os.path.join(args.vis, filename_predwav),
args.audRate, preds_wav[n])
# output video
frames_tensor = [
recover_rgb(frames[n][j, :, t]) for t in range(args.num_frames)
]
frames_tensor = np.asarray(frames_tensor)
path_video = os.path.join(args.vis, prefix,
'video{}.mp4'.format(n + 1))
save_video(path_video,
frames_tensor,
fps=args.frameRate / args.stride_frames)
# combine gt video and audio
filename_av = os.path.join(prefix, 'av{}.mp4'.format(n + 1))
combine_video_audio(path_video,
os.path.join(args.vis, filename_gtwav),
os.path.join(args.vis, filename_av))
row_elements += [{
'video': filename_av
}, {
'image': filename_predmag,
'audio': filename_predwav
}, {
'image': filename_gtmag,
'audio': filename_gtwav
}, {
'image': filename_predmask
}, {
'image': filename_gtmask
}]
row_elements += [{'image': filename_weight}]
vis_rows.append(row_elements)
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_virtual_device_configuration(
gpus[0],
[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=256)])
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_size', type=int, default=10)
parser.add_argument('--lr', type=float, default=3e-2)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--batch_time', type=int, default=16)
args = parser.parse_args()
PLOT_DIR = 'plots/mass_spring_damper/lstm/'
TIME_OF_RUN = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
makedirs(PLOT_DIR)
if not os.path.isfile('experiments/datasets/mass_spring_damper_x_train.npy'):
x_train, _, x_val, _ = create_dataset()
x_train, _, x_val, _ = load_dataset()
# Offset input/output by one timestep
x_val_ref = x_val
y_train = x_train[:, 1:]
x_train = x_train[:, :-1]
y_val = x_val[:, 1:]
x_val = x_val[:, :-1]
class TrainDatagen(tf.keras.utils.Sequence):
def __len__(self):
return x_train.shape[0] * x_train.shape[1] // args.batch_size
except OSError, e:
pass
# NOTE: added /sys and /dev b/c some nodes fail due to this when disk is
# nearly full.
utils.sysexec_noerr("umount {}/proc".format(SYSIMG_PATH), log)
utils.sysexec_noerr("umount {}/mnt/cdrom".format(SYSIMG_PATH), log)
utils.sysexec_noerr("umount {}/vservers".format(SYSIMG_PATH), log)
utils.sysexec_noerr("umount {}/sys".format(SYSIMG_PATH), log)
utils.sysexec_noerr("umount {}/dev".format(SYSIMG_PATH), log)
utils.sysexec_noerr("umount {}".format(SYSIMG_PATH), log)
vars['ROOT_MOUNTED'] = 0
# log.write( "Removing any old files, directories\n" )
# utils.removedir(TEMP_PATH)
log.write("Cleaning up any existing PlanetLab config files\n")
try:
flist = os.listdir(PLCONF_DIR)
for file in flist:
utils.removedir(file)
except OSError:
pass
# create the temp path and sysimg path. since sysimg
# path is in temp path, both are created here
log.write("Creating system image path\n")
utils.makedirs(SYSIMG_PATH)
return 1
import torch.nn.functional as F import torch.optim as optim from sklearn.metrics import precision_recall_fscore_support from sklearn.metrics import accuracy_score from sklearn.metrics import classification_report from utils import load_data, accuracy, dense_tensor_to_sparse, resample, makedirs from utils_inductive import transform_dataset_by_idx from models import HGAT import os, gc, sys from print_log import Logger logdir = "log/" savedir = 'model/' embdir = 'embeddings/' makedirs([logdir, savedir, embdir]) os.environ["CUDA_VISIBLE_DEVICES"] = "2" write_embeddings = True HOP = 2 dataset = 'example' LR = 0.01 if dataset == 'snippets' else 0.005 DP = 0.95 if dataset in ['agnews', 'tagmynews'] else 0.8 WD = 0 if dataset == 'snippets' else 5e-6 LR = 0.05 if 'multi' in dataset else LR DP = 0.5 if 'multi' in dataset else DP WD = 0 if 'multi' in dataset else WD
def Run(vars, upgrade, log):
"""
Download core + extensions bootstrapfs tarballs and install on the hard drive
the upgrade boolean is True when we are upgrading a node root install while
preserving its slice contents; in that case we just perform extra cleanup
before unwrapping the bootstrapfs
this is because the running system may have extraneous files
that is to say, files that are *not* present in the bootstrapfs
and that can impact/clobber the resulting upgrade
Expect the following variables from the store:
SYSIMG_PATH the path where the system image will be mounted
PARTITIONS dictionary of generic part. types (root/swap)
and their associated devices.
NODE_ID the id of this machine
Sets the following variables:
TEMP_BOOTCD_PATH where the boot cd is remounted in the temp
path
ROOT_MOUNTED set to 1 when the the base logical volumes
are mounted.
"""
log.write("\n\nStep: Install: bootstrapfs tarball (upgrade={}).\n".format(
upgrade))
# make sure we have the variables we need
try:
SYSIMG_PATH = vars["SYSIMG_PATH"]
if SYSIMG_PATH == "":
raise ValueError("SYSIMG_PATH")
PARTITIONS = vars["PARTITIONS"]
if PARTITIONS == None:
raise ValueError("PARTITIONS")
NODE_ID = vars["NODE_ID"]
if NODE_ID == "":
raise ValueError("NODE_ID")
VERSION = vars['VERSION'] or 'unknown'
except KeyError as var:
raise BootManagerException(
"Missing variable in vars: {}\n".format(var))
except ValueError as var:
raise BootManagerException(
"Variable in vars, shouldn't be: {}\n".format(var))
try:
# make sure the required partitions exist
val = PARTITIONS["root"]
val = PARTITIONS["swap"]
val = PARTITIONS["vservers"]
except KeyError as part:
log.write("Missing partition in PARTITIONS: {}\n".format(part))
return 0
bs_request = BootServerRequest.BootServerRequest(vars)
# in upgrade mode, since we skip InstallPartitionDisks
# we need to run this
if upgrade:
log.write("Upgrade mode init : Scanning for devices\n")
systeminfo.get_block_devices_dict(vars, log)
utils.sysexec_noerr("vgscan --mknodes", log)
utils.sysexec_noerr("vgchange -ay", log)
# debugging info - show in either mode
utils.display_disks_status(PARTITIONS, "In InstallBootstrapFS", log)
utils.breakpoint("we need to make /dev/mapper/* appear")
log.write("turning on swap space\n")
utils.sysexec("swapon {}".format(PARTITIONS["swap"]), log)
# make sure the sysimg dir is present
utils.makedirs(SYSIMG_PATH)
log.write("mounting root file system\n")
utils.sysexec(
"mount -t ext3 {} {}".format(PARTITIONS["root"], SYSIMG_PATH), log)
fstype = 'ext3' if vars['virt'] == 'vs' else 'btrfs'
one_partition = vars['ONE_PARTITION'] == '1'
if (not one_partition):
log.write("mounting vserver partition in root file system (type {})\n".
format(fstype))
utils.makedirs(SYSIMG_PATH + "/vservers")
utils.sysexec("mount -t {} {} {}/vservers"\
.format(fstype, PARTITIONS["vservers"], SYSIMG_PATH), log)
#if vars['virt']=='lxc':
# NOTE: btrfs quota is supported from version: >= btrfs-progs-0.20 (f18+)
# older versions will not recongize the 'quota' command.
#log.write("Enabling btrfs quota on {}/vservers\n".format(SYSIMG_PATH))
#utils.sysexec_noerr("btrfs quota enable {}/vservers".format(SYSIMG_PATH))
vars['ROOT_MOUNTED'] = 1
# this is now retrieved in GetAndUpdateNodeDetails
nodefamily = vars['nodefamily']
extensions = vars['extensions']
# in upgrade mode: we need to cleanup the disk to make
# it safe to just untar the new bootstrapfs tarball again
# on top of the hard drive
if upgrade:
CleanupSysimgBeforeUpgrade(SYSIMG_PATH, nodefamily, log)
# the 'plain' option is for tests mostly
plain = vars['plain']
if plain:
download_suffix = ".tar"
uncompress_option = ""
log.write("Using plain bootstrapfs images\n")
else:
download_suffix = ".tar.bz2"
uncompress_option = "-j"
log.write("Using compressed bootstrapfs images\n")
log.write("Using nodefamily={}\n".format(nodefamily))
if not extensions:
log.write("Installing only core software\n")
else:
log.write("Requested extensions {}\n".format(extensions))
bootstrapfs_names = [nodefamily] + extensions
for name in bootstrapfs_names:
tarball = "bootstrapfs-{}{}".format(name, download_suffix)
source_file = "/boot/{}".format(tarball)
dest_file = "{}/{}".format(SYSIMG_PATH, tarball)
source_hash_file = "/boot/{}.sha1sum".format(tarball)
dest_hash_file = "{}/{}.sha1sum".format(SYSIMG_PATH, tarball)
time_beg = time.time()
log.write("downloading {}\n".format(source_file))
# 120 is the connect timeout, 86400 is the max transfer time in
# seconds (1 day)
result = bs_request.DownloadFile(source_file, None, None, 1, 1,
dest_file, 120, 86400)
time_end = time.time()
duration = int(time_end - time_beg)
log.write("Done downloading ({} seconds)\n".format(duration))
if result:
# Download SHA1 checksum file
log.write("downloading sha1sum for {}\n".format(source_file))
result = bs_request.DownloadFile(source_hash_file, None, None, 1,
1, dest_hash_file, 120, 86400)
log.write("verifying sha1sum for {}\n".format(source_file))
if not utils.check_file_hash(dest_file, dest_hash_file):
raise BootManagerException(
"FATAL: SHA1 checksum does not match between {} and {}"\
.format(source_file, source_hash_file))
time_beg = time.time()
log.write("extracting {} in {}\n".format(dest_file, SYSIMG_PATH))
result = utils.sysexec(
"tar -C {} -xpf {} {}".format(SYSIMG_PATH, dest_file,
uncompress_option), log)
time_end = time.time()
duration = int(time_end - time_beg)
log.write("Done extracting ({} seconds)\n".format(duration))
utils.removefile(dest_file)
else:
# the main tarball is required
if name == nodefamily:
raise BootManagerException(
"FATAL: Unable to download main tarball {} from server."\
.format(source_file))
# for extensions, just issue a warning
else:
log.write(
"WARNING: tarball for extension {} not found\n".format(
name))
# copy resolv.conf from the base system into our temp dir
# so DNS lookups work correctly while we are chrooted
log.write("Copying resolv.conf to temp dir\n")
utils.sysexec("cp /etc/resolv.conf {}/etc/".format(SYSIMG_PATH), log)
# Copy the boot server certificate(s) and GPG public key to
# /usr/boot in the temp dir.
log.write("Copying boot server certificates and public key\n")
if os.path.exists("/usr/boot"):
# do nothing in case of upgrade
if not os.path.exists(SYSIMG_PATH + "/usr/boot"):
utils.makedirs(SYSIMG_PATH + "/usr")
shutil.copytree("/usr/boot", SYSIMG_PATH + "/usr/boot")
elif os.path.exists("/usr/bootme"):
# do nothing in case of upgrade
if not os.path.exists(SYSIMG_PATH + "/usr/bootme"):
utils.makedirs(SYSIMG_PATH + "/usr/boot")
boot_server = file("/usr/bootme/BOOTSERVER").readline().strip()
shutil.copy("/usr/bootme/cacert/" + boot_server + "/cacert.pem",
SYSIMG_PATH + "/usr/boot/cacert.pem")
file(SYSIMG_PATH + "/usr/boot/boot_server", "w").write(boot_server)
shutil.copy("/usr/bootme/pubring.gpg",
SYSIMG_PATH + "/usr/boot/pubring.gpg")
# For backward compatibility
if os.path.exists("/usr/bootme"):
# do nothing in case of upgrade
if not os.path.exists(SYSIMG_PATH + "/mnt/cdrom/bootme"):
utils.makedirs(SYSIMG_PATH + "/mnt/cdrom")
shutil.copytree("/usr/bootme", SYSIMG_PATH + "/mnt/cdrom/bootme")
# ONE_PARTITION => new distribution type
if (vars['ONE_PARTITION'] != '1'):
# Import the GPG key into the RPM database so that RPMS can be verified
utils.makedirs(SYSIMG_PATH + "/etc/pki/rpm-gpg")
# see also myplc/plc.d/gpg
utils.sysexec("type -p gpg1 >& /dev/null && GPG=gpg1 || GPG=gpg; "
"$GPG --homedir=/root --export --armor"
" --no-default-keyring --keyring {}/usr/boot/pubring.gpg"
" > {}/etc/pki/rpm-gpg/RPM-GPG-KEY-planetlab".format(
SYSIMG_PATH, SYSIMG_PATH),
log,
shell=True)
utils.sysexec_chroot(
SYSIMG_PATH, "rpm --import /etc/pki/rpm-gpg/RPM-GPG-KEY-planetlab",
log)
# keep a log on the installed hdd
stamp = file(SYSIMG_PATH + "/bm-install.txt", 'a')
now = time.strftime("%Y-%b-%d @ %H:%M %Z", time.gmtime())
stamp.write("Hard drive installed by BootManager {}\n".format(VERSION))
stamp.write("Finished extraction of bootstrapfs on {}\n".format(now))
# do not modify this, the upgrade code uses this line for checking compatibility
stamp.write("Using nodefamily {}\n".format(nodefamily))
stamp.close()
return 1
def main(args):
save_folder = '%s_%s' % (args.dataset, args.affix)
log_folder = os.path.join(args.log_root, save_folder)
model_folder = os.path.join(args.model_root, save_folder)
makedirs(log_folder)
makedirs(model_folder)
setattr(args, 'log_folder', log_folder)
setattr(args, 'model_folder', model_folder)
logger = create_logger(log_folder, args.todo, 'info')
print_args(args, logger)
if args.nexpa_train:
from model.l2_nonexpansive_model import Model
else:
from model.model import Model
model = Model(i_c=1, n_c=10)
attack = FastGradientSignUntargeted(model,
args.epsilon,
args.alpha,
min_val=0,
max_val=1,
max_iters=args.k,
_type=args.perturbation_type)
if torch.cuda.is_available():
model.cuda()
trainer = Trainer(args, logger, attack)
if args.todo == 'train':
tr_dataset = tv.datasets.MNIST(args.data_root,
train=True,
transform=tv.transforms.ToTensor(),
download=True)
tr_loader = DataLoader(tr_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# evaluation during training
te_dataset = tv.datasets.MNIST(args.data_root,
train=False,
transform=tv.transforms.ToTensor(),
download=True)
te_loader = DataLoader(te_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
trainer.train(model, tr_loader, te_loader, args.adv_train)
elif args.todo == 'test':
pass
else:
raise NotImplementedError
def inner(path):
root, dirname, basename = utils.tripple(from_directory, path)
destination = os.path.join(to_directory, dirname, basename)
utils.makedirs(os.path.join(to_directory, dirname))
shutil.copy(path, destination)
return {'files': [destination]}
def Run(vars, log):
"""
See if a node installation is valid. More checks should certainly be
done in the future, but for now, make sure that the sym links kernel-boot
exist in /boot
Expect the following variables to be set:
SYSIMG_PATH the path where the system image will be mounted
(always starts with TEMP_PATH)
ROOT_MOUNTED the node root file system is mounted
NODE_ID The db node_id for this machine
PLCONF_DIR The directory to store the configuration file in
Set the following variables upon successfully running:
ROOT_MOUNTED the node root file system is mounted
"""
log.write("\n\nStep: Validating node installation.\n")
# make sure we have the variables we need
try:
SYSIMG_PATH = vars["SYSIMG_PATH"]
if SYSIMG_PATH == "":
raise ValueError("SYSIMG_PATH")
NODE_ID = vars["NODE_ID"]
if NODE_ID == "":
raise ValueError("NODE_ID")
PLCONF_DIR = vars["PLCONF_DIR"]
if PLCONF_DIR == "":
raise ValueError("PLCONF_DIR")
NODE_MODEL_OPTIONS = vars["NODE_MODEL_OPTIONS"]
PARTITIONS = vars["PARTITIONS"]
if PARTITIONS == None:
raise ValueError("PARTITIONS")
except KeyError as var:
raise BootManagerException("Missing variable in vars: {}\n".format(var))
except ValueError as var:
raise BootManagerException("Variable in vars, shouldn't be: {}\n".format(var))
ROOT_MOUNTED = 0
if vars.has_key('ROOT_MOUNTED'):
ROOT_MOUNTED = vars['ROOT_MOUNTED']
# mount the root system image if we haven't already.
# capture BootManagerExceptions during the vgscan/change and mount
# calls, so we can return 0 instead
if ROOT_MOUNTED == 0:
# simply creating an instance of this class and listing the system
# block devices will make them show up so vgscan can find the planetlab
# volume group
systeminfo.get_block_devices_dict(vars, log)
try:
utils.sysexec("vgscan", log)
utils.sysexec("vgchange -ay planetlab", log)
except BootManagerException as e:
log.write("BootManagerException during vgscan/vgchange: {}\n".format(e))
return 0
utils.makedirs(SYSIMG_PATH)
# xxx - TODO - need to fsck the btrfs partition
if vars['virt'] == 'vs':
filesystems_tocheck = ['root', 'vservers']
else:
filesystems_tocheck = ['root']
for filesystem in filesystems_tocheck:
try:
# first run fsck to prevent fs corruption from hanging mount...
log.write("fsck {} file system\n".format(filesystem))
utils.sysexec("e2fsck -v -p {}".format(PARTITIONS[filesystem]), log, fsck=True)
except BootManagerException as e:
log.write("BootManagerException during fsck of {} ({}) filesystem : {}\n"\
.format(filesystem, PARTITIONS[filesystem], str(e)))
try:
log.write("Trying to recover filesystem errors on {}\n".format(filesystem))
utils.sysexec("e2fsck -v -y {}".format(PARTITIONS[filesystem]), log, fsck=True)
except BootManagerException as e:
log.write("BootManagerException while trying to recover"
"filesystem errors on {} ({}) filesystem : {}\n"
.format(filesystem, PARTITIONS[filesystem], str(e)))
return -1
else:
# disable time/count based filesystems checks
utils.sysexec_noerr("tune2fs -c -1 -i 0 {}".format(PARTITIONS[filesystem]), log)
try:
# then attempt to mount them
log.write("mounting root file system\n")
utils.sysexec("mount -t ext3 {} {}".format(PARTITIONS["root"], SYSIMG_PATH),log)
except BootManagerException as e:
log.write("BootManagerException during mount of /root: {}\n".format(str(e)))
return -2
try:
PROC_PATH = "{}/proc".format(SYSIMG_PATH)
utils.makedirs(PROC_PATH)
log.write("mounting /proc\n")
utils.sysexec("mount -t proc none {}".format(PROC_PATH), log)
except BootManagerException as e:
log.write("BootManagerException during mount of /proc: {}\n".format(str(e)))
return -2
one_partition = vars['ONE_PARTITION']=='1'
if (not one_partition):
try:
VSERVERS_PATH = "{}/vservers".format(SYSIMG_PATH)
utils.makedirs(VSERVERS_PATH)
log.write("mounting vservers partition in root file system\n")
if vars['virt'] == 'vs':
utils.sysexec("mount -t ext3 {} {}".format(PARTITIONS["vservers"], VSERVERS_PATH), log)
else:
utils.sysexec("mount -t btrfs {} {}".format(PARTITIONS["vservers"], VSERVERS_PATH), log)
except BootManagerException as e:
log.write("BootManagerException while mounting /vservers: {}\n".format(str(e)))
return -2
ROOT_MOUNTED = 1
vars['ROOT_MOUNTED'] = 1
# check if the base kernel is installed
# these 2 links are created by our kernel's post-install scriplet
log.write("Checking for a custom kernel\n")
try:
if vars['virt'] == 'vs':
os.stat("{}/boot/kernel-boot".format(SYSIMG_PATH))
else:
try:
kversion = os.popen("chroot {} rpm -qa kernel | tail -1 | cut -c 8-"\
.format(SYSIMG_PATH)).read().rstrip()
os.stat("{}/boot/vmlinuz-{}".format(SYSIMG_PATH, kversion))
major_version = int(kversion[0]) # Check if the string looks like a kernel version
except:
kversion = os.popen("ls -lrt {}/lib/modules | tail -1 | awk '{{print $9;}}'"\
.format(SYSIMG_PATH)).read().rstrip()
except OSError as e:
log.write("Couldn't locate base kernel (you might be using the stock kernel).\n")
return -3
# check if the model specified kernel is installed
option = ''
if NODE_MODEL_OPTIONS & ModelOptions.SMP:
option = 'smp'
try:
os.stat("{}/boot/kernel-boot{}".format(SYSIMG_PATH, option))
except OSError as e:
# smp kernel is not there; remove option from modeloptions
# such that the rest of the code base thinks we are just
# using the base kernel.
NODE_MODEL_OPTIONS = NODE_MODEL_OPTIONS & ~ModelOptions.SMP
vars["NODE_MODEL_OPTIONS"] = NODE_MODEL_OPTIONS
log.write("WARNING: Couldn't locate smp kernel.\n")
# write out the node id to /etc/planetlab/node_id. if this fails, return
# 0, indicating the node isn't a valid install.
try:
node_id_file_path = "{}/{}/node_id".format(SYSIMG_PATH, PLCONF_DIR)
node_id_file = file(node_id_file_path, "w")
node_id_file.write(str(NODE_ID))
node_id_file.close()
node_id_file = None
log.write("Updated /etc/planetlab/node_id\n")
except IOError as e:
log.write("Unable to write out /etc/planetlab/node_id\n")
return 0
log.write("Node installation appears to be ok\n")
return 1
def varsim_multi(reference,
simulator,
simulator_exe,
total_coverage,
variant_vcfs=[],
sampling_vcf=None,
dgv_file=None,
regions=None,
randvcf_options=None,
randdgv_options=None,
nlanes=1,
simulator_options="",
samples=[],
out_dir="out",
sv_insert_seq=None,
seed=0,
sex="MALE",
remove_filtered=False,
keep_temp=False,
force_five_base_encoding=False,
lift_ref=False,
disable_vcf2diploid=False,
samples_random=0):
logger = logging.getLogger(varsim_multi.__name__)
makedirs([out_dir])
restricted_dir = os.path.join(out_dir, "restricted")
restricted_reference, restricted_vcfs = gen_restricted_ref_and_vcfs(
reference,
variant_vcfs,
regions,
samples,
restricted_dir,
flank=0,
short_contig_names=False)
dgv_vcf = None
if dgv_file:
assert sv_insert_seq, "SV insertion sequence file is required."
dgv_vcf_dir = os.path.join(out_dir, "tmp")
makedirs([dgv_vcf_dir])
dgv_vcf = os.path.join(dgv_vcf_dir, "dgv.vcf")
makedirs([os.path.join(out_dir, "log")])
dgv_err_file = os.path.join(out_dir, "log", "dgv2vcf.err")
randdgv_options2vcf = copy.copy(randdgv_options)
randdgv_options2vcf.output_all = "-all"
with open(dgv_vcf, "w") as dgv2vcf_out, open(dgv_err_file,
"w") as dgv2vcf_log:
run_randdgv(dgv_file, dgv2vcf_out, dgv2vcf_log, seed, sex,
randdgv_options2vcf, reference, sv_insert_seq)
if regions:
merged_bed = os.path.join(out_dir, "merged.bed")
pybedtools.BedTool(regions).merge().saveas(merged_bed)
restricted_dir = os.path.join(out_dir, "region_restricted")
if sampling_vcf:
_, [restricted_sampling_vcf
] = gen_restricted_ref_and_vcfs(reference, [sampling_vcf],
merged_bed, [],
restricted_dir,
flank=0)
# Now lift over the restricted_sampling_vcf to get the region-limited VCF
sampling_vcf = lift_vcfs([restricted_sampling_vcf],
os.path.join(
restricted_dir,
"region-restricted-sampling.vcf"),
reference)
if dgv_vcf:
convertCN([dgv_vcf], "two2one")
dgv_vcf = sort_and_compress(dgv_vcf)
_, [restricted_dgv_vcf
] = gen_restricted_ref_and_vcfs(reference, [dgv_vcf],
merged_bed, [],
restricted_dir,
flank=0)
# Now lift over the restricted_dgv_vcf to get the region-limited VCF
dgv_vcf = lift_vcfs([restricted_dgv_vcf],
os.path.join(restricted_dir,
"region-restricted-dgv.vcf"),
reference)
all_samples = samples + ["VarSim%d" % i for i in xrange(samples_random)]
for index, (sample, coverage) in enumerate(zip(all_samples,
total_coverage)):
sample_dir = os.path.join(out_dir, sample)
sample_seed = seed + 1000 * index
makedirs([sample_dir])
logger.info("Simulating sample {} in {}".format(sample, sample_dir))
sample_variant_vcfs = list(
restricted_vcfs if index < len(samples) else [])
# Run RandVCF first to get the sampled variants for the sample
if randvcf_options and sampling_vcf:
sampled_vcf = os.path.join(sample_dir, "randvcf.vcf")
with open(sampled_vcf, "w") as randvcf_out, open(
os.path.join(sample_dir, "randvcf.err"),
"w") as randvcf_log:
run_randvcf(sampling_vcf, randvcf_out, randvcf_log,
sample_seed, sex, randvcf_options, reference)
sampled_vcf = sort_and_compress(sampled_vcf)
# Now generate the restricted sampled VCF for the sample
_, [restricted_sampled_vcf] = gen_restricted_ref_and_vcfs(
reference, [sampled_vcf],
regions, [],
os.path.join(sample_dir, "restricted_randvcf"),
flank=0)
sample_variant_vcfs = sample_variant_vcfs + [
restricted_sampled_vcf
]
if randdgv_options and dgv_vcf:
sampled_dgv_vcf = os.path.join(sample_dir, "randdgvvcf.vcf")
randdgvvcf_options = randdgv_options2randvcf_options(
randdgv_options)
with open(sampled_dgv_vcf, "w") as randdgvvcf_out, open(
os.path.join(sample_dir, "randdgvvcf.err"),
"w") as randdgvvcf_log:
run_randvcf(dgv_vcf, randdgvvcf_out, randdgvvcf_log,
sample_seed, sex, randdgvvcf_options, reference)
sampled_dgv_vcf = sort_and_compress(sampled_dgv_vcf)
# Now generate the restricted sampled dgv VCF for the sample
_, [restricted_sampled_dgv_vcf] = gen_restricted_ref_and_vcfs(
reference, [sampled_dgv_vcf],
regions, [],
os.path.join(sample_dir, "restricted_randdgvvcf"),
flank=0)
convertCN([restricted_sampled_dgv_vcf], "one2two")
sample_variant_vcfs = sample_variant_vcfs + [
restricted_sampled_dgv_vcf
]
varsim_main(restricted_reference, simulator, simulator_exe, coverage,
sample_variant_vcfs, None, dgv_file, None, randdgv_options,
nlanes, simulator_options, sample,
os.path.join(sample_dir, "log"),
os.path.join(sample_dir, "out"), sv_insert_seq,
sample_seed, sex, remove_filtered, keep_temp,
force_five_base_encoding, lift_ref, disable_vcf2diploid)
with open(os.path.join(out_dir, "samples.txt"), "w") as samples_fd:
samples_fd.write("\n".join(all_samples))
def predict(
ckpt_path,
should_predict_captions: bool = False,
# metrics: Mapping[str, Callable] = {
# 'rc': rc,
# },
num_workers: int = 20,
use_gpu: bool = True,
model_name: str = "frames",
dset_name: str = "memento_frames",
batch_size: int = 1,
preds_savepath: Optional[str] = None,
use_val: bool = False,
debug_n: Optional[int] = None,
n_mem_repetitions=5):
print("ckpt path: {}".format(ckpt_path))
if preds_savepath is None:
fname = "_" + ("captions"
if should_predict_captions else "mems") + ".json"
preds_savepath = os.path.splitext(
ckpt_path.replace(cfg.CKPT_DIR, cfg.PREDS_DIR))[0] + fname
utils.makedirs([os.path.dirname(preds_savepath)])
print("preds savepath: {}".format(preds_savepath))
device = utils.set_device()
print('DEVICE', device)
# load the ckpt
print("Loading model from path: {}".format(ckpt_path))
ckpt = torch.load(ckpt_path)
# model
model = get_model(model_name, device)
model = nn.DataParallel(model)
model.load_state_dict(ckpt['model_state_dict'])
model.to(device)
model.eval()
print("USING MODEL TYPE {} ON DSET {}".format(model_name, dset_name))
# data loader
use_augmentations = (not should_predict_captions) and (n_mem_repetitions >
1)
print("Use augmentations?", use_augmentations)
test_transforms = VIDEO_TRAIN_TRANSFORMS if use_augmentations else VIDEO_TEST_TRANSFORMS
train, val, test = get_dataset(dset_name, test_transforms=test_transforms)
ds = val if use_val else test
if ds is None:
raise ValueError("No {} set available for this dataset.".format(
"val" if use_val else "test"))
ordered_fnames = ds.get_fnames()
if debug_n is not None:
ds = Subset(ds, range(debug_n))
dl = DataLoader(ds,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
# either do mem scores or captions
if should_predict_captions:
# load the vocab embedding
vocab_embedding = cap_utils.get_vocab_embedding()
# load the vocab itself
word2idx, idx2word = cap_utils.index_vocab()
calc_captions(model, dl, ds, batch_size, device, vocab_embedding,
idx2word, preds_savepath, ordered_fnames)
else:
_calc_mem_scores(model,
ckpt_path,
dl,
ds,
batch_size,
device,
preds_savepath,
n_times=n_mem_repetitions)
parser.add_argument('--burn_in', type=int, default=2000)
parser.add_argument('--arch', default='mlp', choices=["mlp", "mlp-large"])
args = parser.parse_args()
if args.data == "mnist" or args.data == "fashionmnist":
args.data_dim = 784
args.data_shape = (1, 28, 28)
sqrt = lambda x: int(torch.sqrt(torch.Tensor([x])))
plot = lambda p, x: tv.utils.save_image(
x.clamp(0, 1), p, normalize=False, nrow=sqrt(x.size(0)))
dload_train, dload_test = get_data(args)
# logger
utils.makedirs(args.save)
logger = utils.get_logger(logpath=os.path.join(args.save, 'logs'),
filepath=os.path.abspath(__file__))
logger.info(args)
if args.arch == "mlp":
if args.quadratic:
net = networks.QuadraticMLP(args.data_dim, n_hid=args.hidden_dim)
else:
net = networks.SmallMLP(args.data_dim,
n_hid=args.hidden_dim,
dropout=args.dropout)
critic = networks.SmallMLP(args.data_dim,
n_out=args.data_dim,
def make_result_pages(extended_test_results, expected_results_file,
failures_markup_file, tag, run_date, comment_file,
output_dir, reports, warnings):
utils.log('Producing the reports...')
__log__ = 1
warnings_text = '+'.join(warnings)
if comment_file != '':
comment_file = os.path.abspath(comment_file)
links = os.path.join(output_dir, 'links.html')
utils.makedirs(os.path.join(output_dir, 'output'))
for mode in ('developer', 'user'):
utils.makedirs(os.path.join(output_dir, mode, 'output'))
issues = os.path.join(output_dir, 'developer', 'issues.html')
if 'i' in reports:
utils.log(' Making issues list...')
utils.libxslt(
utils.log, extended_test_results, xsl_path('issues_page.xsl'),
issues, {
'source': tag,
'run_date': run_date,
'warnings': warnings_text,
'comment_file': comment_file,
'expected_results_file': expected_results_file,
'explicit_markup_file': failures_markup_file,
'release': "yes"
})
for mode in ('developer', 'user'):
if mode[0] + 'd' in reports:
utils.log(' Making detailed %s report...' % mode)
utils.libxslt(
utils.log, extended_test_results, xsl_path('result_page.xsl'),
os.path.join(output_dir, mode, 'index.html'), {
'links_file': 'links.html',
'mode': mode,
'source': tag,
'run_date': run_date,
'warnings': warnings_text,
'comment_file': comment_file,
'expected_results_file': expected_results_file,
'explicit_markup_file': failures_markup_file
})
for mode in ('developer', 'user'):
if mode[0] + 's' in reports:
utils.log(' Making summary %s report...' % mode)
utils.libxslt(
utils.log, extended_test_results, xsl_path('summary_page.xsl'),
os.path.join(output_dir, mode, 'summary.html'), {
'mode': mode,
'source': tag,
'run_date': run_date,
'warnings': warnings_text,
'comment_file': comment_file,
'explicit_markup_file': failures_markup_file
})
for mode in ('developer', 'user'):
if mode[0] + 'dr' in reports:
utils.log(' Making detailed %s release report...' % mode)
utils.libxslt(
utils.log, extended_test_results, xsl_path('result_page.xsl'),
os.path.join(output_dir, mode, 'index_release.html'), {
'links_file': 'links.html',
'mode': mode,
'source': tag,
'run_date': run_date,
'warnings': warnings_text,
'comment_file': comment_file,
'expected_results_file': expected_results_file,
'explicit_markup_file': failures_markup_file,
'release': "yes"
})
for mode in ('developer', 'user'):
if mode[0] + 'sr' in reports:
utils.log(' Making summary %s release report...' % mode)
utils.libxslt(
utils.log, extended_test_results, xsl_path('summary_page.xsl'),
os.path.join(output_dir, mode, 'summary_release.html'), {
'mode': mode,
'source': tag,
'run_date': run_date,
'warnings': warnings_text,
'comment_file': comment_file,
'explicit_markup_file': failures_markup_file,
'release': 'yes'
})
if 'e' in reports:
utils.log(' Generating expected_results ...')
utils.libxslt(utils.log, extended_test_results,
xsl_path('produce_expected_results.xsl'),
os.path.join(output_dir, 'expected_results.xml'))
if 'n' in reports:
utils.log(' Making runner comment files...')
utils.libxslt(utils.log, extended_test_results,
xsl_path('runners.xsl'),
os.path.join(output_dir, 'runners.html'))
shutil.copyfile(xsl_path('html/master.css'),
os.path.join(output_dir, 'master.css'))
def build_from_path(config):
warning("Sampling rate: {}".format(hparams.sample_rate))
executor = ProcessPoolExecutor(max_workers=config.num_workers)
futures = []
index = 1
base_dir = os.path.dirname(config.metadata_path)
data_dir = os.path.join(base_dir, config.data_dirname)
makedirs(data_dir)
loss_coeff = defaultdict(one)
if config.metadata_path.endswith("json"):
with open(config.metadata_path, encoding="utf-8") as f:
content = f.read()
info = json.loads(content)
elif config.metadata_path.endswith("csv"):
with open(config.metadata_path) as f:
info = {}
for line in f:
path, text = line.strip().split('|')
info[path] = text
else:
raise Exception(" [!] Unkown metadata format: {}".format(
config.metadata_path))
new_info = {}
for path in info.keys():
if not os.path.exists(path):
new_path = os.path.join(base_dir, path)
if not os.path.exists(new_path):
print(" [!] Audio not found: {}".format([path, new_path]))
continue
else:
new_path = path
new_info[new_path] = info[path]
info = new_info
for path in info.keys():
if type(info[path]) == list:
if hparams.ignore_recognition_level == 1 and len(info[path]) == 1 or \
hparams.ignore_recognition_level == 2:
loss_coeff[path] = hparams.recognition_loss_coeff
info[path] = info[path][0]
ignore_description = {
0: "use all",
1: "ignore only unmatched_alignment",
2: "fully ignore recognitio",
}
print(" [!] Skip recognition level: {} ({})". \
format(hparams.ignore_recognition_level,
ignore_description[hparams.ignore_recognition_level]))
for audio_path, text in info.items():
if hparams.ignore_recognition_level > 0 and loss_coeff[audio_path] != 1:
continue
if base_dir not in audio_path:
audio_path = os.path.join(base_dir, audio_path)
try:
tokens = text_to_sequence(text)
except:
continue
fn = partial(_process_utterance, audio_path, data_dir, tokens,
loss_coeff[audio_path])
futures.append(executor.submit(fn))
n_frames = [future.result() for future in tqdm(futures)]
n_frames = [n_frame for n_frame in n_frames if n_frame is not None]
hours = frames_to_hours(n_frames)
print(' [*] Loaded metadata for {} examples ({:.2f} hours)'.format(
len(n_frames), hours))
print(' [*] Max length: {}'.format(max(n_frames)))
print(' [*] Min length: {}'.format(min(n_frames)))
plot_n_frames(n_frames, os.path.join(base_dir,
"n_frames_before_filter.png"))
min_n_frame = hparams.reduction_factor * hparams.min_iters
max_n_frame = hparams.reduction_factor * hparams.max_iters - hparams.reduction_factor
n_frames = [n for n in n_frames if min_n_frame <= n <= max_n_frame]
hours = frames_to_hours(n_frames)
print(' [*] After filtered: {} examples ({:.2f} hours)'.format(
len(n_frames), hours))
print(' [*] Max length: {}'.format(max(n_frames)))
print(' [*] Min length: {}'.format(min(n_frames)))
plot_n_frames(n_frames, os.path.join(base_dir,
"n_frames_after_filter.png"))
experiment_name += f"_ot{args.tpp_otreg_strength}"
if args.tpp_cond:
experiment_name += "_cond"
if args.share_hidden and args.model in ["jumpcnf", "attncnf"
] and args.tpp == "neural":
experiment_name += "_sharehidden"
if args.solve_reverse and args.model == "jumpcnf" and args.tpp == "neural":
experiment_name += "_rev"
experiment_name += f"_lr{args.lr}"
experiment_name += f"_gc{args.gradclip}"
experiment_name += f"_bsz{args.max_events}x{args.ngpus}_wd{args.weight_decay}_s{args.seed}"
experiment_name += f"_{args.experiment_id}"
savepath = os.path.join(args.experiment_dir, experiment_name)
# Top-level logger for logging exceptions into the log file.
utils.makedirs(savepath)
logger = utils.get_logger(os.path.join(savepath, "logs"))
if args.gradclip == 0:
args.gradclip = 1e10
try:
mp.set_start_method("forkserver")
mp.spawn(main,
args=(args.ngpus, args, savepath),
nprocs=args.ngpus,
join=True)
except Exception:
import traceback
logger.error(traceback.format_exc())
sys.exit(1)
def download_news_video_and_content(news_id,
base_dir,
chunk_size=32 * 1024,
video_dir="video",
asset_dir="assets",
audio_dir="wavs"):
video_dir = os.path.join(base_dir, video_dir)
asset_dir = os.path.join(base_dir, asset_dir)
audio_dir = os.path.join(base_dir, audio_dir)
makedirs(video_dir)
makedirs(asset_dir)
makedirs(audio_dir)
text_path = os.path.join(asset_dir, "{}.txt".format(news_id))
original_text_path = os.path.join(asset_dir,
"original-{}.txt".format(news_id))
video_path = os.path.join(video_dir, "{}.ts".format(news_id))
audio_path = os.path.join(audio_dir, "{}.wav".format(news_id))
params = {
'NJC': 'NJC400',
'NID': news_id, # NB11515152
'CD': 'A0100',
}
response = requests.request(
method='GET',
url=BASE_URL,
params=params,
)
soup = soupify(response.text)
article_contents = soup.find_all('article_contents')
assert len(article_contents) == 1, \
"# of <article_contents> of {} should be 1: {}".format(news_id, response.text)
text = soupify(article_contents[0].text).get_text() # remove <div>
with open(original_text_path, "w", encoding='utf-8') as f:
f.write(text)
with open(text_path, "w", encoding='utf-8') as f:
from nltk import sent_tokenize
text = re.sub(r'\[.{0,80} :\s.+]', '', text) # remove quote
text = re.sub(r'☞.+http.+\)', '', text) # remove quote
text = re.sub(r'\(https?:\/\/.*[\r\n]*\)', '', text) # remove url
sentences = sent_tokenize(text)
sentences = [
sent for sentence in sentences for sent in sentence.split('\n')
if sent
]
new_texts = []
for sent in sentences:
sent = sent.strip()
sent = re.sub(r'\([^)]*\)', '', sent)
#sent = re.sub(r'\<.{0,80}\>', '', sent)
sent = sent.replace('…', '.')
new_texts.append(sent)
f.write("\n".join([sent for sent in new_texts if sent]))
vod_paths = soup.find_all('vod_path')
assert len(vod_paths) == 1, \
"# of <vod_path> of {} should be 1: {}".format(news_id, response.text)
if not os.path.exists(video_path):
redirect_url = soup.find_all('vod_path')[0].text
list_url = m3u8.load(redirect_url).playlists[0].absolute_uri
video_urls = [
segment.absolute_uri for segment in m3u8.load(list_url).segments
]
with open(video_path, "wb") as f:
for url in video_urls:
response = requests.get(url, stream=True)
total_size = int(response.headers.get('content-length', 0))
for chunk in response.iter_content(chunk_size):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
if not os.path.exists(audio_path):
encoder = get_encoder_name()
command = "{} -y -loglevel panic -i {} -ab 160k -ac 2 -ar 44100 -vn {}".\
format(encoder, video_path, audio_path)
subprocess.call(command, shell=True)
return True
# constants
ssh_source_files = "%s/debug_files/" % BM_SOURCE_DIR
ssh_dir = "/etc/ssh/"
key_gen_prog = "ssh-keygen"
ssh_home = "/root/.ssh"
cancel_boot_flag = "/tmp/CANCEL_BOOT"
sshd_started_flag = "/tmp/SSHD_RUNNING"
# pre-sshd
pre_sshd_script = os.path.join(ssh_source_files, "pre-sshd")
if os.path.exists(pre_sshd_script):
utils.sysexec_noerr(pre_sshd_script, log)
# create host keys if needed
if not os.path.isdir (ssh_dir):
utils.makedirs (ssh_dir)
# original code used to specify -b 1024 for the rsa1 key
# fedora23 seems to come with a release of openssh that lacks suppport
# for ssh1, and thus rsa1 keys; so we consider that failing to produce
# the rsa1 key is not a showstopper
key_specs = [
("/etc/ssh/ssh_host_key", 'rsa1', "SSH1 RSA", False),
("/etc/ssh/ssh_host_rsa_key", 'rsa', "SSH2 RSA", True),
("/etc/ssh/ssh_host_dsa_key", 'dsa', "SSH2 DSA", True),
]
for key_file, key_type, label, mandatory in key_specs:
if not os.path.exists(key_file):
log.write("Creating {} host key {}\n".format(label, key_file))
if mandatory:
'.txt', '.original.mp3')
out_path = os.path.join(base_dir, 'audio',
os.path.basename(d.text_path)).replace(
'.txt', '.wav')
options = {
'format':
'bestaudio/best',
'outtmpl':
original_path,
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'mp3',
'preferredquality': '320',
}],
}
with youtube_dl.YoutubeDL(options) as ydl:
ydl.download([d.video_url])
audio = AudioSegment.from_file(original_path)
audio[d.start:d.end].export(out_path, out_ext)
remove_file(original_path)
if __name__ == '__main__':
makedirs(os.path.join(base_dir, "audio"))
data = read_csv(os.path.join(base_dir, "metadata.csv"))
download_audio_with_urls(data)
def evaluate(netWrapper, loader, history, epoch, args):
print('Evaluating at {} epochs...'.format(epoch))
torch.set_grad_enabled(False)
# remove previous viz results
makedirs(args.vis, remove=True)
# switch to eval mode
netWrapper.eval()
# initialize meters
loss_meter = AverageMeter()
sdr_mix_meter = AverageMeter()
sdr_meter = AverageMeter()
sir_meter = AverageMeter()
sar_meter = AverageMeter()
# initialize HTML header
visualizer = HTMLVisualizer(os.path.join(args.vis, 'index.html'))
header = ['Filename', 'Input Mixed Audio']
for n in range(1, args.num_mix + 1):
header += [
'Video {:d}'.format(n), 'Predicted Audio {:d}'.format(n),
'GroundTruth Audio {}'.format(n), 'Predicted Mask {}'.format(n),
'GroundTruth Mask {}'.format(n)
]
header += ['Loss weighting']
visualizer.add_header(header)
vis_rows = []
for i, batch_data in enumerate(loader):
# forward pass
err, outputs = netWrapper.forward(batch_data, args)
err = err.mean()
loss_meter.update(err.item())
print('[Eval] iter {}, loss: {:.4f}'.format(i, err.item()))
# calculate metrics
sdr_mix, sdr, sir, sar = calc_metrics(batch_data, outputs, args)
sdr_mix_meter.update(sdr_mix)
sdr_meter.update(sdr)
sir_meter.update(sir)
sar_meter.update(sar)
# output visualization
if len(vis_rows) < args.num_vis:
output_visuals(vis_rows, batch_data, outputs, args)
print('[Eval Summary] Epoch: {}, Loss: {:.4f}, '
'SDR_mixture: {:.4f}, SDR: {:.4f}, SIR: {:.4f}, SAR: {:.4f}'.format(
epoch, loss_meter.average(), sdr_mix_meter.average(),
sdr_meter.average(), sir_meter.average(), sar_meter.average()))
history['val']['epoch'].append(epoch)
history['val']['err'].append(loss_meter.average())
history['val']['sdr'].append(sdr_meter.average())
history['val']['sir'].append(sir_meter.average())
history['val']['sar'].append(sar_meter.average())
print('Plotting html for visualization...')
visualizer.add_rows(vis_rows)
visualizer.write_html()
# Plot figure
if epoch > 0:
print('Plotting figures...')
plot_loss_metrics(args.ckpt, history)