# TODO: Implement unittests for setup.py
from setup import read_config
from setup import setup_model
config = read_config("config.json")
model, index2word, word2index = setup_model(datasets, config)
from setup import setup
from utils import freeze_weights
# Read in command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("config_file",
type=str,
help="The path to the config file describing the model.")
parser.add_argument("--freeze",
action="store_true",
default=False,
help="Specifies whether the CNN layers are frozen.")
args = parser.parse_args()
# Setup the model
config = read_config(args.config_file)
_, model = setup(config)
if args.freeze:
print("Freezing weights of CNN layers.")
model = freeze_weights(model)
# Calculate total number of parameters and number of learnable parmaeters
num_learnable_params = 0
num_params = 0
for name, weight in model.named_parameters():
params_in_weight = functools.reduce(operator.mul, list(weight.size()), 1)
if weight.requires_grad:
num_learnable_params += params_in_weight
num_params += params_in_weight
if weight.requires_grad:
# Parse command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--config_path", type=str, help="path to the config file")
parser.add_argument("--checkpoint_path", type=str, help="path to the checkpoint file")
parser.add_argument("--examples_path", type=str, help="path to examples file")
parser.add_argument("--output_dir", type=str, help="output path for generated plots")
args = parser.parse_args()
# Sanity check inputs
assert(os.path.isfile(args.config_path))
assert(os.path.isfile(args.checkpoint_path))
assert(os.path.isfile(args.examples_path))
# Read in the configuration file
print("Reading config file from: {}".format(args.config_path))
config = read_config(args.config_path)
# Create the model and overwrite its weights
print("Creating model with weights from: {}".format(args.checkpoint_path))
model = setup_model(config)
state = load_checkpoint(args.checkpoint_path)
model_dict, _, _, _ = state
model.load_state_dict(model_dict)
# Create the individual blocks of the model
use_all_layers = config["model"]["use_all_layers"]
max_length = config["model"]["max_length"]
embeddings_size = config["embeddings"]["size"]
block_configs = config["model"]["blocks"]
blocks = []
output_sizes = [embeddings_size]
parser.add_argument("--eval",
action="store_true",
default=False,
help="evaluate a model")
parser.add_argument("--path", default=None, help="path to model checkpoint")
parser.add_argument("--freeze",
action="store_true",
default=False,
help="freeze the weights in the conv-pool layers.")
args = parser.parse_args()
# Sanity check command line arguments
assert (args.train or args.eval)
# Basic setup
config = read_config(args.config)
datasets, model = setup_v2(config)
loss_fn = nn.CrossEntropyLoss()
optimizer = \
optim.Adagrad(
filter(lambda p: p.requires_grad, model.parameters()),
lr=config["optim"]["lr"],
weight_decay=config["optim"]["weight_decay"]
)
history = defaultdict(list)
# Load trained model if specified
if args.path is not None:
print("Loading model from checkpoint...")
state = load_checkpoint(args.path)
model_dict, optim_dict, history, epoch = state
def setUp(self):
cfg = setup.read_config()
self.nbr_address = cfg.get('nbr','address')
print "Setup - using NBR Address: ", self.nbr_address
print('Starting PowerDnThread')
pwrdnthread = PowerDnThread()
pwrdnthread.start()
@socketio.on('connect', namespace='/test')
def test_connect():
global thread
if thread is None:
thread = socketio.start_background_task(target=background_thread)
#emit('my_response', {'data': 'Connected', 'count': 0})
@socketio.on('disconnect', namespace='/test')
def test_disconnect():
print('Client disconnected', request.sid)
def signal_handler(signal, frame1):
GPIO.output(2, GPIO.HIGH)
print('Exiting after GPIO.cleanup.')
GPIO.cleanup()
sys.exit(0)
if __name__ == '__main__':
signal.signal(signal.SIGINT, signal_handler)
setup.read_config()
setup.init_GPIO()
socketio.run(app, host='0.0.0.0', port=5000, debug=True)
def __init__(self):
Attack.__init__(self)
JunkRemover.__init__(self)
self.config = read_config()