def csv2npy():
# Process each data split separately
for n, f, fs in zip(names_lists, feats_paths, folders_save):
print "Preparing features %s" % f
feats_dict = dict()
# Get file names
names = []
with open(base_path + '/' + n, 'r') as file:
for line in file:
line = line.rstrip('\n')
line = line.split('.')[0]
names.append(line)
# Get features
with open(base_path + '/' + f, 'r') as file:
for i, line in enumerate(file):
feats = np.fromstring(line.rstrip('\n'), sep=',')
if (apply_L2):
feats = feats / np.linalg.norm(feats, ord=2)
# Insert in dictionary
feats_dict[names[i]] = feats[:n_feats]
# Store dict
print "Saving features in %s" % (base_path_save + '/' + fs + '/' +
file_save + '.npy')
create_dir_if_not_exists(base_path_save + '/' + fs)
np.save(base_path_save + '/' + fs + '/' + file_save + '.npy',
feats_dict)
print
def main(src_dir, dst_dir, start, end):
create_dir_if_not_exists(dst_dir)
for subdir in ["%02d" % num for num in range(start, end + 1)]:
from_dir = os.path.join(src_dir, subdir)
to_dir = os.path.join(dst_dir, subdir)
logging.info("copy %s to %s", from_dir, to_dir)
copy_dir_by_sys(from_dir, to_dir)
def csv2npy():
# Process each data split separately
for n, f, fs in zip(names_lists, feats_paths, folders_save):
print "Preparing features %s" % f
feats_dict = dict()
# Get file names
names = []
with open(base_path + '/' + n, 'r') as file:
for line in file:
line = line.rstrip('\n')
#line = line.split('.')[0]
names.append(line)
# Get features
with open(base_path + '/' + f, 'r') as file:
for i, line in enumerate(file):
feats = np.fromstring(line.rstrip('\n'), sep=',')
if(apply_L2):
feats = feats/np.linalg.norm(feats, ord=2)
# Insert in dictionary
feats_dict[names[i]] = feats[:n_feats]
# Store dict
print "Saving features in %s" % (base_path_save +'/'+ fs +'/'+ file_save)
create_dir_if_not_exists(base_path_save +'/'+ fs)
np.save(base_path_save + '/' + fs + '/' + file_save + '.npy', feats_dict)
print
def copy_dir(src_dir, dst_dir):
create_dir_if_not_exists(dst_dir)
csv_files = glob.glob(os.path.join(src_dir, '*.csv'))
for idx, csv_file in enumerate(csv_files):
if idx % 50 == 0 and idx != 0:
logging.info('Already %d files from %s => %s',
idx, src_dir, dst_dir)
shutil.copy(csv_file, dst_dir)
def main(src_dir, dst_dir, speed_thresh):
files = glob.glob(os.path.join(src_dir, '*', '*.csv'))
total_file_cnt = len(files)
for cnt, csvfile in enumerate(files):
logging.info('[%d/%d] processing: %s',
cnt + 1, total_file_cnt, csvfile)
uid = os.path.splitext(os.path.basename(csvfile))[0]
dstdir = os.path.join(dst_dir, uid[-2:])
create_dir_if_not_exists(dstdir)
dstfile = os.path.join(dstdir, uid + '.csv')
if os.path.isfile(dstfile):
continue
per_file(csvfile, dstfile, dstdir, uid, speed_thresh)
def configure_environment(self):
log('Configure environment')
delete(self.yaml_config_dir)
create_dir_if_not_exists(self.yaml_config_dir)
env_name = self.dea.get_env_name()
env_net_segment_type = self.dea.get_env_net_segment_type()
log('Creating environment %s release %s net-segment-type %s'
% (env_name, self.release_id, env_net_segment_type))
exec_cmd('fuel env create --name "%s" --release %s --net-segment-type %s'
% (env_name, self.release_id, env_net_segment_type))
if not self.env_exists(env_name):
err('Failed to create environment %s' % env_name)
self.config_settings()
self.config_network()
self.config_nodes()
def configure_environment(self):
log('Configure environment')
delete(self.yaml_config_dir)
create_dir_if_not_exists(self.yaml_config_dir)
env_name = self.dea.get_env_name()
env_net_segment_type = self.dea.get_env_net_segment_type()
log('Creating environment %s release %s net-segment-type %s' %
(env_name, self.release_id, env_net_segment_type))
exec_cmd(
'fuel env create --name "%s" --release %s --net-segment-type %s' %
(env_name, self.release_id, env_net_segment_type))
if not self.env_exists(env_name):
err('Failed to create environment %s' % env_name)
self.config_settings()
self.config_network()
self.config_nodes()
def main(src_dir, dst_dir, valid_user_file):
create_dir_if_not_exists(dst_dir)
csv_files = glob.glob(os.path.join(src_dir, '*', '*.csv'))
total_file_cnt = len(csv_files)
valid_users = set(map(lambda x: x.strip(),
open(valid_user_file).readlines()))
for cnt, csv_file in enumerate(csv_files):
logging.info('[%d/%d] processing: %s costs: %s',
cnt + 1, total_file_cnt, csv_file, costs())
filename = os.path.basename(csv_file)
dirname = os.path.splitext(filename)[0][-2:]
uid = os.path.splitext(filename)[0]
if uid not in valid_users:
continue
create_dir_if_not_exists(os.path.join(dst_dir, dirname))
dst_csv_file = os.path.join(dst_dir, dirname, filename)
clean_data(csv_file, dst_csv_file, raw_log_header)
def main(src_dir, dst_dir, valid_user_file, speed_thresh):
files = glob.glob(os.path.join(src_dir, '*', '*.csv'))
total_file_cnt = len(files)
valid_users = set(map(lambda x: x.strip(),
open(valid_user_file).readlines()))
for cnt, csvfile in enumerate(files):
logging.info('[%d/%d] processing: %s costs: %s',
cnt + 1, total_file_cnt, csvfile, costs())
uid = os.path.splitext(os.path.basename(csvfile))[0]
if uid not in valid_users:
continue
dstdir = os.path.join(dst_dir, uid[-2:])
create_dir_if_not_exists(dstdir)
dstfile = os.path.join(dstdir, uid + '.csv')
if os.path.isfile(dstfile):
continue
per_file(csvfile, dstfile, dstdir, uid, speed_thresh)
def train(run_name: str, epochs: int, validation_split: float):
from cc_model import create_model
from keras.callbacks import ModelCheckpoint, TensorBoard
db = get_database()
color_records = db.child('colors').get()
colors = []
labels = []
for c in color_records.each():
c_val = c.val()
colors.append(color_record_to_array(c_val))
labels.append(color_record_to_label_index(c_val))
colors_np = np.array(colors)
labels_np = one_hot_encode_labels(labels)
CHECKPOINT_PATH = os.path.join(RESULT_PATH, run_name)
create_dir_if_not_exists(CHECKPOINT_PATH)
# Callbacks
tensorboard = TensorBoard(log_dir=os.path.join(LOGS_PATH, run_name))
checkpoint = ModelCheckpoint(os.path.join(CHECKPOINT_PATH,
"weights{epoch:03d}.hdf5"),
monitor='val_loss',
save_weights_only=True,
mode='auto',
period=1,
verbose=1,
save_best_only=True)
model = create_model()
# Compiling and running training
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x=colors_np,
y=labels_np,
epochs=epochs,
validation_split=validation_split,
callbacks=[tensorboard, checkpoint])
def parse_arguments():
parser = ArgParser(prog='python %s' % __file__)
parser.add_argument('-nf',
dest='no_fuel',
action='store_true',
default=False,
help='Do not install Fuel Master (and Node VMs when '
'using libvirt)')
parser.add_argument('-nh',
dest='no_health_check',
action='store_true',
default=False,
help='Don\'t run health check after deployment')
parser.add_argument('-fo',
dest='fuel_only',
action='store_true',
default=False,
help='Install Fuel Master only (and Node VMs when '
'using libvirt)')
parser.add_argument('-co',
dest='cleanup_only',
action='store_true',
default=False,
help='Cleanup VMs and Virtual Networks according to '
'what is defined in DHA')
parser.add_argument('-c',
dest='cleanup',
action='store_true',
default=False,
help='Cleanup after deploy')
if {'-iso', '-dea', '-dha', '-h'}.intersection(sys.argv):
parser.add_argument('-iso',
dest='iso_file',
action='store',
nargs='?',
default='%s/OPNFV.iso' % CWD,
help='ISO File [default: OPNFV.iso]')
parser.add_argument('-dea',
dest='dea_file',
action='store',
nargs='?',
default='%s/dea.yaml' % CWD,
help='Deployment Environment Adapter: dea.yaml')
parser.add_argument('-dha',
dest='dha_file',
action='store',
nargs='?',
default='%s/dha.yaml' % CWD,
help='Deployment Hardware Adapter: dha.yaml')
else:
parser.add_argument('iso_file',
action='store',
nargs='?',
default='%s/OPNFV.iso' % CWD,
help='ISO File [default: OPNFV.iso]')
parser.add_argument('dea_file',
action='store',
nargs='?',
default='%s/dea.yaml' % CWD,
help='Deployment Environment Adapter: dea.yaml')
parser.add_argument('dha_file',
action='store',
nargs='?',
default='%s/dha.yaml' % CWD,
help='Deployment Hardware Adapter: dha.yaml')
parser.add_argument('-s',
dest='storage_dir',
action='store',
default='%s/images' % CWD,
help='Storage Directory [default: images]')
parser.add_argument('-b',
dest='pxe_bridge',
action='append',
default=[],
help='Linux Bridge for booting up the Fuel Master VM '
'[default: pxebr]')
parser.add_argument('-p',
dest='fuel_plugins_dir',
action='store',
help='Fuel Plugins directory')
parser.add_argument('-pc',
dest='fuel_plugins_conf_dir',
action='store',
help='Fuel Plugins Configuration directory')
parser.add_argument('-np',
dest='no_plugins',
action='store_true',
default=False,
help='Do not install Fuel Plugins')
parser.add_argument('-dt',
dest='deploy_timeout',
action='store',
default=240,
help='Deployment timeout (in minutes) '
'[default: 240]')
parser.add_argument('-nde',
dest='no_deploy_environment',
action='store_true',
default=False,
help=('Do not launch environment deployment'))
parser.add_argument('-log',
dest='deploy_log',
action='store',
default='../ci/.',
help=('Path and name of the deployment log archive'))
args = parser.parse_args()
log(args)
if not args.pxe_bridge:
args.pxe_bridge = ['pxebr']
check_file_exists(args.dha_file)
check_dir_exists(os.path.dirname(args.deploy_log))
if not args.cleanup_only:
check_file_exists(args.dea_file)
check_fuel_plugins_dir(args.fuel_plugins_dir)
iso_abs_path = os.path.abspath(args.iso_file)
if not args.no_fuel and not args.cleanup_only:
log('Using OPNFV ISO file: %s' % iso_abs_path)
check_file_exists(iso_abs_path)
log('Using image directory: %s' % args.storage_dir)
create_dir_if_not_exists(args.storage_dir)
for bridge in args.pxe_bridge:
check_bridge(bridge, args.dha_file)
kwargs = {
'no_fuel': args.no_fuel,
'fuel_only': args.fuel_only,
'no_health_check': args.no_health_check,
'cleanup_only': args.cleanup_only,
'cleanup': args.cleanup,
'storage_dir': args.storage_dir,
'pxe_bridge': args.pxe_bridge,
'iso_file': iso_abs_path,
'dea_file': args.dea_file,
'dha_file': args.dha_file,
'fuel_plugins_dir': args.fuel_plugins_dir,
'fuel_plugins_conf_dir': args.fuel_plugins_conf_dir,
'no_plugins': args.no_plugins,
'deploy_timeout': args.deploy_timeout,
'no_deploy_environment': args.no_deploy_environment,
'deploy_log': args.deploy_log
}
return kwargs
def main(src_dir, dst_dir, valid_user_file, speed_thresh):
files = glob.glob(os.path.join(src_dir, '*', '*.csv'))
total_file_cnt = len(files)
valid_users = set(map(lambda x: x.strip(),
open(valid_user_file).readlines()))
for cnt, csvfile in enumerate(files):
logging.info('[%d/%d] processing: %s costs: %s',
cnt + 1, total_file_cnt, csvfile, costs())
uid = os.path.splitext(os.path.basename(csvfile))[0]
if uid not in valid_users:
continue
dstdir = os.path.join(dst_dir, uid[-2:])
create_dir_if_not_exists(dstdir)
dstfile = os.path.join(dstdir, uid + '.csv')
if os.path.isfile(dstfile):
continue
per_file(csvfile, dstfile, dstdir, uid, speed_thresh)
if __name__ == '__main__':
import sys
src_dir = sys.argv[1]
dst_dir = "%s-no-invalidpoint" % src_dir
speed_thresh = int(sys.argv[2]) if len(sys.argv) >= 3 else 300
create_dir_if_not_exists(dst_dir)
main(src_dir, dst_dir, 'valid_users.csv', speed_thresh)
end_at = datetime.now()
delta = end_at - start_at
#!/usr/bin/python3
import sys, os
import numpy as np
import common
# Load data
prefix = "t10k"
images = common.read_images(prefix)
labels = common.read_labels(prefix)
# Load result
try:
result_filepath = sys.argv[1]
result = np.loadtxt(result_filepath).astype("float32")
result = result.reshape([result.shape[0], 1])
except IndexError:
print("Usage: " + os.path.basename(__file__) + " <result_filepath>")
sys.exit(1)
# Get false indices
false_indices = np.argwhere(labels != result)[:, 0]
# Debugging
debug_dir = "debugging"
common.create_dir_if_not_exists(debug_dir)
for i in false_indices:
common.debug(debug_dir, i, images[i], labels[i][0], result[i][0])
import common
# Load data
prefix = "t10k"
images = common.read_images(prefix)
ori_labels = common.read_labels(prefix)
new_labels = common.category2binary(ori_labels)
# Create model
model = common.create_model()
# Load weights
try:
checkpoint_filepath = sys.argv[1]
model.load_weights(checkpoint_filepath)
except IndexError:
print("Usage: " + os.path.basename(__file__) + " <checkpoint_filepath>")
sys.exit(1)
# Testing
score = model.evaluate(images, new_labels)
common.print_score(score)
# Get and save result
result = common.binary2category(model.predict(images))
testing_dir = "testing"
filepath = "result.txt"
common.create_dir_if_not_exists(testing_dir)
np.savetxt(testing_dir + "/" + filepath, result, fmt="%d")
def create_tmp_dir(self):
self.tmp_dir = '%s/fueltmp' % CWD
delete(self.tmp_dir)
create_dir_if_not_exists(self.tmp_dir)
#!/usr/bin/python3
import common
# Metadata
batch_size = 128
epochs = 150
training_dir = "training"
checkpoint_format = "weights.{epoch:04d}-{val_loss:.2f}.h5"
period = 5
# Create checkpoint directory if not exists
common.create_dir_if_not_exists(training_dir)
# Load data
prefix = "train"
images = common.read_images(prefix)
ori_labels = common.read_labels(prefix)
new_labels = common.category2binary(ori_labels)
# Create model
model = common.create_model()
# Summary model
print("=" * 80)
model.summary()
input("Press Enter to continue...")
print("=" * 80)
# Train model
history = model.fit(images,
def parse_arguments():
parser = ArgParser(prog='python %s' % __file__)
parser.add_argument('-nf', dest='no_fuel', action='store_true',
default=False,
help='Do not install Fuel Master (and Node VMs when '
'using libvirt)')
parser.add_argument('-nh', dest='no_health_check', action='store_true',
default=False,
help='Don\'t run health check after deployment')
parser.add_argument('-fo', dest='fuel_only', action='store_true',
default=False,
help='Install Fuel Master only (and Node VMs when '
'using libvirt)')
parser.add_argument('-co', dest='cleanup_only', action='store_true',
default=False,
help='Cleanup VMs and Virtual Networks according to '
'what is defined in DHA')
parser.add_argument('-c', dest='cleanup', action='store_true',
default=False,
help='Cleanup after deploy')
if {'-iso', '-dea', '-dha', '-h'}.intersection(sys.argv):
parser.add_argument('-iso', dest='iso_file', action='store', nargs='?',
default='%s/OPNFV.iso' % CWD,
help='ISO File [default: OPNFV.iso]')
parser.add_argument('-dea', dest='dea_file', action='store', nargs='?',
default='%s/dea.yaml' % CWD,
help='Deployment Environment Adapter: dea.yaml')
parser.add_argument('-dha', dest='dha_file', action='store', nargs='?',
default='%s/dha.yaml' % CWD,
help='Deployment Hardware Adapter: dha.yaml')
else:
parser.add_argument('iso_file', action='store', nargs='?',
default='%s/OPNFV.iso' % CWD,
help='ISO File [default: OPNFV.iso]')
parser.add_argument('dea_file', action='store', nargs='?',
default='%s/dea.yaml' % CWD,
help='Deployment Environment Adapter: dea.yaml')
parser.add_argument('dha_file', action='store', nargs='?',
default='%s/dha.yaml' % CWD,
help='Deployment Hardware Adapter: dha.yaml')
parser.add_argument('-s', dest='storage_dir', action='store',
default='%s/images' % CWD,
help='Storage Directory [default: images]')
parser.add_argument('-b', dest='pxe_bridge', action='store',
default='pxebr',
help='Linux Bridge for booting up the Fuel Master VM '
'[default: pxebr]')
parser.add_argument('-p', dest='fuel_plugins_dir', action='store',
help='Fuel Plugins directory')
parser.add_argument('-pc', dest='fuel_plugins_conf_dir', action='store',
help='Fuel Plugins Configuration directory')
parser.add_argument('-np', dest='no_plugins', action='store_true',
default=False, help='Do not install Fuel Plugins')
args = parser.parse_args()
log(args)
check_file_exists(args.dha_file)
if not args.cleanup_only:
check_file_exists(args.dea_file)
check_fuel_plugins_dir(args.fuel_plugins_dir)
if not args.no_fuel and not args.cleanup_only:
log('Using OPNFV ISO file: %s' % args.iso_file)
check_file_exists(args.iso_file)
log('Using image directory: %s' % args.storage_dir)
create_dir_if_not_exists(args.storage_dir)
check_bridge(args.pxe_bridge, args.dha_file)
kwargs = {'no_fuel': args.no_fuel, 'fuel_only': args.fuel_only,
'no_health_check': args.no_health_check,
'cleanup_only': args.cleanup_only, 'cleanup': args.cleanup,
'storage_dir': args.storage_dir, 'pxe_bridge': args.pxe_bridge,
'iso_file': args.iso_file, 'dea_file': args.dea_file,
'dha_file': args.dha_file,
'fuel_plugins_dir': args.fuel_plugins_dir,
'fuel_plugins_conf_dir': args.fuel_plugins_conf_dir,
'no_plugins': args.no_plugins}
return kwargs
def create_tmp_dir(self):
self.tmp_dir = '%s/fueltmp' % CWD
delete(self.tmp_dir)
create_dir_if_not_exists(self.tmp_dir)
def parse_arguments():
parser = ArgParser(prog='python %s' % __file__)
parser.add_argument('-nf', dest='no_fuel', action='store_true',
default=False,
help='Do not install Fuel Master (and Node VMs when '
'using libvirt)')
parser.add_argument('-nh', dest='no_health_check', action='store_true',
default=False,
help='Don\'t run health check after deployment')
parser.add_argument('-fo', dest='fuel_only', action='store_true',
default=False,
help='Install Fuel Master only (and Node VMs when '
'using libvirt)')
parser.add_argument('-co', dest='cleanup_only', action='store_true',
default=False,
help='Cleanup VMs and Virtual Networks according to '
'what is defined in DHA')
parser.add_argument('-c', dest='cleanup', action='store_true',
default=False,
help='Cleanup after deploy')
if {'-iso', '-dea', '-dha', '-h'}.intersection(sys.argv):
parser.add_argument('-iso', dest='iso_file', action='store', nargs='?',
default='%s/OPNFV.iso' % CWD,
help='ISO File [default: OPNFV.iso]')
parser.add_argument('-dea', dest='dea_file', action='store', nargs='?',
default='%s/dea.yaml' % CWD,
help='Deployment Environment Adapter: dea.yaml')
parser.add_argument('-dha', dest='dha_file', action='store', nargs='?',
default='%s/dha.yaml' % CWD,
help='Deployment Hardware Adapter: dha.yaml')
else:
parser.add_argument('iso_file', action='store', nargs='?',
default='%s/OPNFV.iso' % CWD,
help='ISO File [default: OPNFV.iso]')
parser.add_argument('dea_file', action='store', nargs='?',
default='%s/dea.yaml' % CWD,
help='Deployment Environment Adapter: dea.yaml')
parser.add_argument('dha_file', action='store', nargs='?',
default='%s/dha.yaml' % CWD,
help='Deployment Hardware Adapter: dha.yaml')
parser.add_argument('-s', dest='storage_dir', action='store',
default='%s/images' % CWD,
help='Storage Directory [default: images]')
parser.add_argument('-b', dest='pxe_bridge', action='store',
default='pxebr',
help='Linux Bridge for booting up the Fuel Master VM '
'[default: pxebr]')
parser.add_argument('-p', dest='fuel_plugins_dir', action='store',
help='Fuel Plugins directory')
parser.add_argument('-pc', dest='fuel_plugins_conf_dir', action='store',
help='Fuel Plugins Configuration directory')
parser.add_argument('-np', dest='no_plugins', action='store_true',
default=False, help='Do not install Fuel Plugins')
parser.add_argument('-dt', dest='deploy_timeout', action='store',
default=240, help='Deployment timeout (in minutes) '
'[default: 240]')
parser.add_argument('-nde', dest='no_deploy_environment',
action='store_true', default=False,
help=('Do not launch environment deployment'))
args = parser.parse_args()
log(args)
check_file_exists(args.dha_file)
if not args.cleanup_only:
check_file_exists(args.dea_file)
check_fuel_plugins_dir(args.fuel_plugins_dir)
iso_abs_path = os.path.abspath(args.iso_file)
if not args.no_fuel and not args.cleanup_only:
log('Using OPNFV ISO file: %s' % iso_abs_path)
check_file_exists(iso_abs_path)
log('Using image directory: %s' % args.storage_dir)
create_dir_if_not_exists(args.storage_dir)
check_bridge(args.pxe_bridge, args.dha_file)
kwargs = {'no_fuel': args.no_fuel, 'fuel_only': args.fuel_only,
'no_health_check': args.no_health_check,
'cleanup_only': args.cleanup_only, 'cleanup': args.cleanup,
'storage_dir': args.storage_dir, 'pxe_bridge': args.pxe_bridge,
'iso_file': iso_abs_path, 'dea_file': args.dea_file,
'dha_file': args.dha_file,
'fuel_plugins_dir': args.fuel_plugins_dir,
'fuel_plugins_conf_dir': args.fuel_plugins_conf_dir,
'no_plugins': args.no_plugins,
'deploy_timeout': args.deploy_timeout,
'no_deploy_environment': args.no_deploy_environment}
return kwargs
# Retrieves the images of a given split and sorts them according to that split
import shutil
from common import create_dir_if_not_exists
image_dir = '/data/DATASETS/Flickr8k/Images'
annotatios_dir = '/data/DATASETS/Flickr8k/Annotations'
split_name = 'val'
dest_dir = image_dir + '/' + split_name + '_images'
ext = '.jpg'
with open(annotatios_dir + '/' + split_name + '_list_ids.txt') as f:
lines = f.readlines()
create_dir_if_not_exists(dest_dir)
n_items = len(str(len(lines))) + 1
i = 0
for filename in lines:
i += 1
shutil.copyfile(image_dir + '/' + filename[:-1] + ext, dest_dir + '/' + str(i).zfill(n_items) + ext)
import datetime
import os
import pyrebase
import sys
import numpy as np
from common import create_dir_if_not_exists, color_record_to_array, color_record_to_label_index, one_hot_encode_labels
ROOT_PATH = os.path.dirname(os.path.realpath(__file__))
OUTPUT_PATH = os.path.realpath(os.path.join(ROOT_PATH, 'data'))
RESULT_PATH = os.path.realpath(os.path.join(OUTPUT_PATH, 'results'))
LOGS_PATH = os.path.realpath(os.path.join(OUTPUT_PATH, 'logs'))
create_dir_if_not_exists(RESULT_PATH)
create_dir_if_not_exists(LOGS_PATH)
def get_database():
firebase_config = {
'apiKey': 'AIzaSyDPekCKX4ee6h9NVR2lEITGAM0XIHn-c7c',
'authDomain': 'color-classification.firebaseapp.com',
'databaseURL': 'https://color-classification.firebaseio.com',
'projectId': 'color-classification',
'storageBucket': '',
'messagingSenderId': '590040209608'
}
firebase = pyrebase.initialize_app(firebase_config)
return firebase.database()
