def main():
print("-----", os.getenv("account_list_file"), os.getenv("scraping_id"),
os.getenv("scraper_type"))
config = Config()
scraper = AccountScrapper(config)
while True:
scraper.scrap_one_day_in_each_account()
def test_load_config(self):
config = Config()
self.assertEqual('foo'.upper(), 'FOO')
def __init__(self):
self.requester = Config()
def run_sensors():
config = Config()
print("running sensors")
camera = Camera(config)
camera.shot_periodically()
def test_load_image(self):
config = Config()
image = Image(config,"Config.py")
image.upload_image()
self.assertEqual('foo'.upper(), 'FOO')
torch.backends.cudnn.deterministic = True
dataset = args.datasetdir
outputdir = args.outputdir
embedding = ''
if args.embedding == 'random':
embedding = 'random'
else:
embedding = args.embedding
model_name = args.model
print(model_name)
x = import_module('models.' + model_name)
config = Config(dataset, outputdir, embedding)
# reset config
config.model_name = args.model
config.save_path = os.path.join(outputdir, args.model + '.ckpt')
config.log_path = os.path.join(outputdir, args.model + '.log')
config.dropout = float(args.dropout)
config.require_improvement = int(args.require_improvement)
config.num_epochs = int(args.num_epochs)
config.batch_size = int(args.batch_size)
config.max_length = int(args.max_length)
config.learning_rate = float(args.learning_rate)
config.embed = int(args.embed_dim)
config.bucket = int(args.bucket)
config.wordNgrams = int(args.wordNgrams)
config.lr_decay_rate = float(args.lr_decay_rate)
