options.py
3.92 KB
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import argparse
import os
import torch
class Options():
"""Options class
Returns:
[argparse]: argparse containing train and test options
"""
def __init__(self):
##
#
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
##
# Base
self.parser.add_argument('--dataset', default='ecg', help='ecg dataset')
self.parser.add_argument('--dataroot', default='', help='path to dataset')
self.parser.add_argument('--batchsize', type=int, default=64, help='input batch size')
self.parser.add_argument('--workers', type=int, help='number of data loading workers', default=1)
self.parser.add_argument('--isize', type=int, default=320, help='input sequence size.')
self.parser.add_argument('--nc', type=int, default=1, help='input sequence channels')
self.parser.add_argument('--nz', type=int, default=50, help='size of the latent z vector')
self.parser.add_argument('--ngf', type=int, default=32)
self.parser.add_argument('--ndf', type=int, default=32)
self.parser.add_argument('--device', type=str, default='gpu', help='Device: gpu | cpu')
self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
self.parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')
self.parser.add_argument('--model', type=str, default='beatgan', help='choose model')
self.parser.add_argument('--outf', default='./output', help='output folder')
##
# Train
self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
self.parser.add_argument('--niter', type=int, default=100, help='number of epochs to train for')
self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
self.parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam')
self.parser.add_argument('--w_adv', type=float, default=1, help='parameter')
self.parser.add_argument('--folder', type=int, default=0, help='folder index 0-4')
self.parser.add_argument('--n_aug', type=int, default=0, help='aug data times')
## Test
self.parser.add_argument('--istest',action='store_true',help='train model or test model')
self.parser.add_argument('--threshold', type=float, default=0.05, help='threshold score for anomaly')
self.opt = None
def parse(self):
""" Parse Arguments.
"""
self.opt = self.parser.parse_args()
str_ids = self.opt.gpu_ids.split(',')
self.opt.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
self.opt.gpu_ids.append(id)
# set gpu ids
if self.opt.device == 'gpu':
torch.cuda.set_device(self.opt.gpu_ids[0])
args = vars(self.opt)
# print('------------ Options -------------')
# for k, v in sorted(args.items()):
# print('%s: %s' % (str(k), str(v)))
# print('-------------- End ----------------')
# save to the disk
self.opt.name = "%s/%s" % (self.opt.model, self.opt.dataset)
expr_dir = os.path.join(self.opt.outf, self.opt.name, 'train')
test_dir = os.path.join(self.opt.outf, self.opt.name, 'test')
if not os.path.isdir(expr_dir):
os.makedirs(expr_dir)
if not os.path.isdir(test_dir):
os.makedirs(test_dir)
file_name = os.path.join(expr_dir, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write('------------ Options -------------\n')
for k, v in sorted(args.items()):
opt_file.write('%s: %s\n' % (str(k), str(v)))
opt_file.write('-------------- End ----------------\n')
return self.opt