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main.py
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from attr_utils import AttributeDict
import json
import argparse
def parse():
parser = argparse.ArgumentParser(description="darkflow_configs")
parser.add_argument('--mode', type=str, default='training', choices=['training', 'testing'], help='Choose if are you training or testing the model')
parser.add_argument('--configs_file', type=str, default='/global/u2/a/agarabag/DarkFlow/configs.json')
parser.add_argument('--network', type=str, default='convnet', choices=['convnet', 'gcnnet'])
parser.add_argument('--flow', type=str, default='convflow', choices=['noflow', 'planar', 'orthosnf', 'householdersnf', 'triangularsnf', 'iaf', 'convflow'])
args = parser.parse_args()
return args
def run(args):
if args.mode == 'training':
print('Preparing to Train Model')
args.train_net = 1
elif args.mode == 'testing':
print('Preparing to Test Model')
args.test_net = 1
else:
raise argparse.ArgumentTypeError('Run mode not chosen correctly. Choose if you want to train (--mode training) or test (--mode testing).')
if args.network == 'convnet':
from convnet_runner import ConvNetRunner
network = ConvNetRunner(args=args)
elif args.network == 'gcnnet':
from gcnnet_runner import GCNNetRunner
network = GCNNetRunner(args=args)
else:
raise argparse.ArgumentTypeError('Network not chosen correctly. Choose if you want ConvVAE (--network convnet) or GCNVAE (--network gcnnet).')
if args.train_net:
network.trainer()
if args.test_net:
network.tester()
if __name__ == '__main__':
args = parse().__dict__
configs = json.load(open(args['configs_file']))
configs = {**configs, **args}
configs = AttributeDict(configs)
run(configs)
"""
Current Configs (Chan3):
"num_classes": 1,
"training_fraction": 0.98,
"batch_size": 16,
"test_batch_size": 1,
"learning_rate": 0.001,
"latent_dim": 15,
"beta": 1,
"num_epochs": 10,
"q_z_output_dim" : 20,
"num_flows" : 6, # 4 gave problematic ROC for PF; Keep at 4 for ConvF(0.92AUC) and the rest
"num_ortho_vecs" : 8,
"num_householder" : 12, # 8 gave problematic ROC
"made_h_size" : 330,
"convFlow_kernel_size" : 7 # 5 gave problematic ROC
Current Configs (Chan1):
"num_classes": 1,
"training_fraction": 0.98,
"batch_size": 16,
"test_batch_size": 1,
"learning_rate": 0.0001,
"latent_dim": 15,
"beta": 1,
"num_epochs": 10,
"q_z_output_dim" : 20,
"num_flows" : 3, # 4 for iaf, tri, house; 6 for convF
"num_ortho_vecs" : 5,
"num_householder" : 8, # 6
"made_h_size" : 330,
"convFlow_kernel_size" : 7
Current Configs (Chan2b):
"num_classes": 1,
"training_fraction": 0.98,
"batch_size": 16,
"test_batch_size": 1,
"learning_rate": 0.0001,
"latent_dim": 15,
"beta": 1,
"num_epochs": 10,
"q_z_output_dim" : 20,
"num_flows" : 3, # 4 for iaf, tri, house; 6 for convF
"num_ortho_vecs" : 5,
"num_householder" : 8,
"made_h_size" : 330,
"convFlow_kernel_size" : 7
"""