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,
