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)