args = parser.parse_args() # GPU Setting os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num # gpus 0, 1 device0 = torch.device('cuda:0') device1 = torch.device('cuda:1') if not torch.cuda.is_available(): raise Exception("No GPU found") else: print("===> GPU on") # Normalization method mean = get_mean(args.norm_value) std = get_std(args.norm_value) norm_method = Normalize(mean, std) # path setting train_path = join(args.root_path, args.train_data) pretrain_path = join(args.root_path, args.pretrain_path) log_path = join(args.root_path, args.log_path) print("===> Loading datasets") train_data = DatasetFromFolder(train_path, args.resize_w, args.resize_h, args.crop_size, args.fps, args.train_frames, args.horizontal_flip, args.norm_value, norm_method, args.num_of_vid, args.train_frames)
help='save exp name') args = parser.parse_args() # GPU Setting os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num device = torch.device('cuda:0') if not torch.cuda.is_available(): raise Exception("No GPU found") else: print("===> GPU on") cuda = args.gpu_mode # Normalization method mean = get_mean(args.norm_value, dataset='kinetics') std = get_std(args.norm_value) norm_method = Normalize(mean, std) # path setting train_path = join(args.root_path, args.train_data) pretrain_path = join(args.root_path, args.pretrain_path) log_path = join(args.root_path, args.log_path) print("===> Loading datasets") train_data = DatasetFromFolder(train_path, args.resize_w, args.resize_h, args.crop_size, args.fps,