def build(is_train):
print("Point 1.1")
print(args.TrainArgs().parse() if is_train else args.TestArgs().parse())
print("Point 1.2")
opt, log = args.TrainArgs().parse() if is_train else args.TestArgs().parse(
)
print("Point 1.3")
if not is_train:
print("Point 1.4")
print('Options:')
opt_dict = vars(opt)
for key in sorted(opt_dict):
print('{}: {}'.format(key, opt_dict[key]))
if is_train:
print('lr_init:', opt.lr_init)
print('wd:', opt.wd)
print('ckpt:', opt.ckpt_path)
print()
os.makedirs(opt.ckpt_path, exist_ok=True)
# Set seed
torch.manual_seed(2019)
torch.cuda.manual_seed_all(2019)
np.random.seed(2019)
random.seed(2019)
logger = Logger(opt.ckpt_path, opt.split)
return opt, logger
def build(is_train, tb_dir=None):
'''
Parse arguments, setup logger and tensorboardX directory.
'''
opt, log = args.TrainArgs().parse() if is_train else args.TestArgs().parse()
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus
os.makedirs(opt.ckpt_path, exist_ok=True)
# Set seed
torch.manual_seed(666)
torch.cuda.manual_seed_all(666)
np.random.seed(666)
random.seed(666)
logger = Logger(opt.ckpt_path, opt.split)
if tb_dir is not None:
tb_path = os.path.join(opt.ckpt_path, tb_dir)
vis = Visualizer(tb_path)
else:
vis = None
logger.print(log)
return opt, logger, vis
def build(is_train, tb_dir=None, logging=True):
opt, log = args.TrainArgs().parse() if is_train else args.TestArgs().parse(
)
#os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus
os.makedirs(opt.ckpt_path, exist_ok=True)
# Set seed
torch.manual_seed(666)
torch.cuda.manual_seed_all(666)
np.random.seed(666)
random.seed(666)
# Add geometry to path name
if tb_dir is not None:
tb_path = os.path.join(opt.ckpt_path,
'{}_{}'.format(tb_dir, opt.geometry))
if opt.poisson:
tb_path = tb_path + '_poisson'
vis = Visualizer(tb_path)
else:
vis = None
if logging:
logger_name = '{}_{}'.format(opt.split, opt.geometry)
if opt.poisson:
logger_name = logger_name + '_poisson'
logger = Logger(opt.ckpt_path, logger_name)
logger.print(log)
else:
logger = None
stats = Statistics(opt.ckpt_path)
return opt, logger, stats, vis
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from time import time
import random
import pdb
SILENT = False
VERBOSE = False
DTYPE = torch.DoubleTensor
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# device='cpu'
TIME = 0
args = args.TrainArgs().parse()
os.makedirs('images', exist_ok=True)
out_dir = '{}_L{}_bt{}{}'.format(args.rotation_type, args.n_layer, args.bt,
args.save_suffix)
args.wb_name = out_dir
out_dir = os.path.join('outputs/', 'RBIG', args.dataset, out_dir)
if os.path.exists(out_dir):
print('Dir exist:', out_dir)
proceed = input("Do you want to proceed? (y/N)")
if 'y' not in proceed:
print('Exiting. Bye!')
exit(0)
print("out_dir:", out_dir)
os.makedirs(out_dir, exist_ok=True)
os.makedirs(os.path.join(out_dir, 'images'), exist_ok=True)