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args.py
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108 lines (89 loc) · 4.16 KB
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import argparse
import torch
class Args(object):
def __init__(self):
super(Args, self).__init__()
self.args = argparse.ArgumentParser()
def parse_args(self):
"""Parse arguments."""
self.configure_device()
self.configure_data()
self.configure_model()
self.configure_optimizer()
self.configure_training()
args = self.args.parse_args()
if args.cpu:
args.device = torch.device('cpu')
elif args.gpu:
args.device = torch.device('cuda:' + args.use_cuda)
args = self.set_checkpoint_path(args)
return args
def configure_device(self):
"""Device-related configurations."""
grp = self.args.add_argument_group('Device', 'Config devices.')
mode = grp.add_mutually_exclusive_group(required=True)
mode.add_argument('--gpu', action='store_true', help='Use single GPU.')
mode.add_argument('--cpu', action='store_true', help='Use CPUs.')
grp.add_argument('--use-cuda', type=str, default='0',
help='Which GPU to use?')
return grp
def configure_data(self):
"""Data-related configurations."""
grp = self.args.add_argument_group('Data',
'Data-related configurations.')
grp.add_argument('--config', type=str, required=True,
help='Path to data config file.')
grp.add_argument('--ob-ratio', type=float, default=0.8,
help='Ratio of sequence as observation side.')
return grp
def configure_model(self):
"""Model-related configurations."""
grp = self.args.add_argument_group('Model',
'Model-related configurations.')
grp.add_argument('--t-emb-size', type=int, default=32,
help='Size of the embedded timestamp.')
grp.add_argument('--e-emb-size', type=int, default=32,
help='Size of the embedded event type.')
grp.add_argument('--hidden-size', type=int, default=16,
help='Size of the hidden state of RNNs.')
grp.add_argument('--dropout', type=float, default=0.0,
help='Dropout rate.')
grp.add_argument('--n-heads', type=int, default=4,
help='d_h')
grp.add_argument('--m1', type=int, default=4, help='m_1')
grp.add_argument('--m2', type=int, default=2, help='m_2')
grp.add_argument('--dq', type=int, default=4,
help='Dimension of head-wise query.')
grp.add_argument('--dk', type=int, default=4,
help='Dimension of head-wise key.')
grp.add_argument('--dv', type=int, default=4,
help='Dimension of head-wise value.')
return grp
def configure_optimizer(self):
"""Optimizer-related configurations."""
grp = self.args.add_argument_group('optimizer',
'Optimizer-related configurations.')
grp.add_argument('--lr', type=float, default=0.0001,
help='Learning rate.')
grp.add_argument('--weight-decay', type=float, default=0.0,
help='Weight decay for optimizer.')
return grp
def configure_training(self):
"""Training process related parameters."""
grp = self.args.add_argument_group('Training',
'Training process related '
'parameters.')
grp.add_argument('--epochs', type=int, default=10,
help='# of epochs for training.')
grp.add_argument('--batch-size', type=int, default=128,
help='Size of minibatch.')
return grp
def set_checkpoint_path(self, args):
"""Set up checkpoint path."""
device = str(args.device)
args.checkpoint = ('.').join(['checkpoint', device, 'pth'])
signature = '[dma-nets]'
args.checkpoint = args.checkpoint[:-3] + signature + '.pth'
return args
args = Args().parse_args()
print(args)