log_path = os.path.join(args.work_dir, 'ckpt', DATETIME) if not os.path.exists(log_path): os.makedirs(log_path) log = Logger(os.path.join(log_path, DATETIME + '.log')) log.logger.info(args) # prepare val data DAVIS_ROOT = args.davis palette = Image.open(DAVIS_ROOT + '/Annotations/606332/00000.png').getpalette() val_dataset = DAVIS(DAVIS_ROOT, phase='val', imset='total_val.txt', resolution='480p', separate_instance=False, only_single=False, target_size=(832, 448)) val_loader = data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=2, pin_memory=True) model = nn.DataParallel(STM()) if torch.cuda.is_available(): model.cuda() # load weights.pth
#prepare data clip_size = 8 iou_ignore_bg = True BATCH_SIZE = 1 base_lr = 1e-4 # 1e-4 DAVIS_ROOT = '/cfs/mazhongke/databases/DAVIS2017/' YOUTUBE_ROOT = '/cfs/dataset/youtube_complete/' palette = Image.open(DAVIS_ROOT + '/Annotations/480p/blackswan/00000.png').getpalette() val_dataset = DAVIS(DAVIS_ROOT, phase='val', imset='2016/val.txt', resolution='480p', separate_instance=False, only_single=False, target_size=(864, 480)) val_loader = data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=2, pin_memory=True) train_dataset = DAVIS(YOUTUBE_ROOT, phase='train', imset='train.txt', resolution='480p', separate_instance=True, only_single=False,
if not os.path.exists(args.work_dir): os.makedirs(args.work_dir) GPU = args.gpu YEAR = args.year #prepare val data DAVIS_ROOT = args.davis palette = Image.open(DAVIS_ROOT + '/Annotations/480p/blackswan/00000.png').getpalette() if args.year == 2016: val_dataset_2016 = DAVIS(DAVIS_ROOT, phase='val', imset='2016/val.txt', resolution='480p', separate_instance=False, only_single=False, target_size=(864, 480)) val_loader_2016 = data.DataLoader(val_dataset_2016, batch_size=1, shuffle=False, num_workers=2, pin_memory=True) elif args.year == 2017: val_dataset_2017 = DAVIS(DAVIS_ROOT, phase='val', imset='2017/val.txt', resolution='480p', separate_instance=False, only_single=False,
if not os.path.exists(args.work_dir): os.makedirs(args.work_dir) GPU = args.gpu YEAR = args.year #prepare val data DAVIS_ROOT = args.davis palette = Image.open(DAVIS_ROOT + '/Annotations/480p/blackswan/00000.png').getpalette() val_dataset = DAVIS(DAVIS_ROOT, phase='val', imset=str(args.year) + '/val.txt', resolution='480p', separate_instance=False, only_single=False, target_size=(864, 480)) val_loader = data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=2, pin_memory=True) # build model model = nn.DataParallel(STM()) if torch.cuda.is_available(): model.cuda() # load weights.pth