def set_cfg():
c = config.parameter()
datacfg = mFlags.data
c.cfgfile = mFlags.config
c.weightfile = mFlags.weights
no_eval = mFlags.no_eval
data_options = read_data_cfg(datacfg)
net_options = parse_cfg(c.cfgfile)[0]
c.use_cuda = torch.cuda.is_available() and (True if c.use_cuda is None else
c.use_cuda)
c.trainlist = data_options['train']
c.testlist = data_options['valid']
c.backupdir = data_options['backup']
c.gpus = data_options['gpus']
c.ngpus = len(c.gpus.split(','))
c.num_workers = int(data_options['num_workers'])
c.batch_size = int(net_options['batch'])
c.max_batches = 10 * int(net_options['max_batches'])
c.learning_rate = float(net_options['learning_rate'])
c.momentum = float(net_options['momentum'])
c.decay = float(net_options['decay'])
c.steps = [float(step) for step in net_options['steps'].split(',')]
c.scales = [float(scale) for scale in net_options['scales'].split(',')]
try:
c.max_epochs = int(net_options['max_epochs'])
except KeyError:
nsamples = file_lines(c.trainlist)
c.max_epochs = (c.max_batches * c.batch_size) // nsamples + 1
seed = int(time.time())
torch.manual_seed(seed)
if c.use_cuda:
os.environ['CUDA_VISIBLE_DEVICES'] = c.gpus
torch.cuda.manual_seed(seed)
c.device = torch.device("cuda" if c.use_cuda else "cpu")
print('set_cfg')
return c
