def get_model_and_optimizer(model_class, model_path, cfg):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
ngpu = 1 if torch.cuda.is_available() else 0
network = model_class(ngpu, cfg).to(device)
if cfg["RMSprop"]:
optimizer = optim.RMSprop(network.parameters(), lr=cfg["lr"])
else:
optimizer = optim.Adam(network.parameters(),
lr=cfg["lr"],
betas=(cfg["beta1"], 0.999))
initialise(network, model_path)
print(network)
return network, optimizer
def index():
"""index view showing the user their board(s)"""
models.User.update(UserType=99).where(
models.User.UserName == 'admin').execute()
if current_user.get_usertype() == 99:
boards = models.Board.select()
return render_template('index.html',
boards=boards,
admin=True,
models=models)
boards = models.User.get_boards(g.user.id)
return render_template('index.html', boards=boards)
if __name__ == 'app':
models.initialise()
try:
# creates a user for testing
models.User.create_user(username="******",
email="*****@*****.**",
password=os.environ.get('ADMIN_PASS'),
usertype=99)
print('created admin user')
except ValueError:
pass
# different host for web server
if os.uname().nodename == 'Georges-MacBook-Pro-2.local':
app.run(debug=DEBUG,
port=int(os.environ.get('PORT', 5000)),
use_reloader=True,
mqtt_client.disconnect()
logger.info('Dereticulating splines... Done!')
sys.exit(0)
if __name__ == '__main__':
arguments = docopt(__doc__, version='Listener v1.0')
logger = log.init_log('listener')
signal.signal(signal.SIGTERM, shutdown)
cfg = get_config(None)
initialise(cfg.get('KEYSPACE', 'drastic'),
hosts=cfg.get('CASSANDRA_HOSTS', ('127.0.0.1', )))
if arguments['--verbose']:
logger.setLevel(logging.DEBUG)
elif arguments['--quiet']:
logger.setLevel(logging.WARNING)
else:
logger.setLevel(logging.INFO)
script_directory_topic = '+/resource/{0}/#'.format(
arguments['<script_collection>'])
script_directory_topic = '/'.join(
filter(None, script_directory_topic.split('/')))
script_directory = arguments['<script_directory>']
scan_script_collection(arguments['<script_collection>'])
logger.info('Dereticulating splines... Done!')
sys.exit(0)
if __name__ == '__main__':
arguments = docopt(__doc__, version='Listener v1.0')
logger = log.init_log('listener')
signal.signal(signal.SIGTERM, shutdown)
cfg = get_config(None)
initialise(keyspace=cfg.get('KEYSPACE', 'indigo'),
hosts=cfg.get('CASSANDRA_HOSTS', ('127.0.0.1', )),
repl_factor=cfg.get('REPLICATION_FACTOR', 1))
if arguments['--verbose']:
logger.setLevel(logging.DEBUG)
elif arguments['--quiet']:
logger.setLevel(logging.WARNING)
else:
logger.setLevel(logging.INFO)
script_directory_topic = '+/resource/{0}/#'.format(
arguments['<script_collection>'])
script_directory_topic = '/'.join(
filter(None, script_directory_topic.split('/')))
script_directory = arguments['<script_directory>']
scan_script_collection(arguments['<script_collection>'])
_test_dir = CONFIG['testing directory']
_pre_img_size = CONFIG['pre image size']
_post_img_size = CONFIG['post image size']
_lr = CONFIG['learning rate']
_output = CONFIG['output']
_network_name = CONFIG['network name']
cam = camera.Feed()
draw = camera.Draw()
model = models.initialise(model_name=MODEL_NAME,
network_name=_network_name,
image_size=_post_img_size,
learning_rate=_lr,
output=_output)
def predict_and_parse(image_a, image_b):
image_a = cam.resize(image_a, (200, 200))
image_b = cam.resize(image_b, (200, 200))
images = np.concatenate((image_a, image_b), axis=1)
cam.out_one('ims1', images)
test_img = cam.resize(images, _post_img_size)
cam.out_one('ims2', test_img)