def init_settings(exec_params):
settings = dict()
PROJECT_ROOT = Path(__file__).resolve().parent.parent
CONFIGS_PATH = PROJECT_ROOT / 'configs'
CONFIG_FILES_PATHS = {
'creds': CONFIGS_PATH / exec_params['creds_path'],
'filtration_rules': CONFIGS_PATH / exec_params['filtration_path'],
'db_connection': CONFIGS_PATH / 'db.yml',
'sheets_data': CONFIGS_PATH / 'sheets_data.yml',
}
settings['PROJECT_ROOT'] = PROJECT_ROOT
settings['CONFIGS_PATH'] = CONFIGS_PATH
settings['CONFIG_FILES_PATHS'] = CONFIG_FILES_PATHS
settings.update(
{section:load_yml(path) for (section, path) in CONFIG_FILES_PATHS.items()}
)
# stored in json and parsed by oauth2
settings['sheets_creds_path'] = CONFIGS_PATH / 'flatty_spreadsheets.json'
settings['filter_query'] = build_query_tree(settings['filtration_rules'])
# write to google sheet when number of voters exceed this threshold
settings['sheet_write_threshold'] = 1
settings['tel_regex'] = '\+\s\d+\s\d{2}\s\d{3}-\d{2}-\d{2}'
settings['url_regex'] = 'https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)'
module = sys.modules[__name__]
for (key, val) in settings.items():
setattr(module, key.upper(), val)
return settings
def test_factory():
config = load_yml('factory_pattern/config.yml')
factory_pattern = config['factory_pattern']
factory = get_instance_of_attr(factory_pattern['factory'])
chart = factory.get_chart(factory_pattern['chart_type'])
chart.display()
def test_facade():
config = load_yml('facade_pattern/config.yml')
facade_pattern = config['facade']
facade = get_instance_of_attr(facade_pattern)('test_file')
facade.encrypt()
import os
import glob
import socket
import tensorflow as tf
import time
import datetime
import neuralgym as ng
from model import HinpaintModel
from utils import load_yml
from trainer import HiTrainer, d_graph_deploy, get_batch, g_graph_deploy, get_input_queue
if __name__ == "__main__":
config = load_yml('config.yml')
if config.GPU_ID != -1:
gpu_ids = config.GPU_ID
else:
gpu_ids = [0]
print('building networks and losses...')
enq_ops = []
# load training data
with open(config.TRAIN_LIST) as f:
fnames = f.read().splitlines()
endnd = (len(fnames) // config.BATCH_SIZE) * config.BATCH_SIZE
fnames = fnames[:endnd]
#input_queue, enq_op = get_input_queue(fnames)
data = ng.data.DataFromFNames(fnames, config.IMG_SHAPE, random_crop=config.RANDOM_CROP, \
enqueue_size=32, queue_size=256, nthreads=config.N_THREADS)
#enq_ops.append(enq_op)
def test_abstract_factory():
config = load_yml('factory_pattern/config.yml')
factory_pattern = config['abstract_factory']
chart = get_instance_of_attr(factory_pattern['chart'])()
chart.draw_chart()
def test_bridge():
config = load_yml('bridge_pattern/config.yml')
os = get_instance_of_attr(config['os'])()
image = get_instance_of_attr(config['image'])()
image.setImageImp(os)
image.parseFile('leo.jpg')