E_model.cuda() loss_f_ARE = loss_func.loss_f_ARE() loss_f_ARE.cuda() loss_f_E = loss_func.loss_f_E() loss_f_E.cuda() #img_train_list, img_test_list = dataset.load_all_h5(H5_address) txt_list = os.listdir(H5_address) txt_list.sort(key=lambda x: int(x[1:3])) set_dict = dict(zip(person_set, txt_list)) train_list = [] test_list = [] for key in set_dict: h5_list = dataset.load_h5_list(os.path.join(H5_address, set_dict[key])) if key != person_num: train_list.extend(h5_list) else: test_list.extend(h5_list) train_Dataset = dataset.gaze_dataset(train_list) test_Dataset = dataset.gaze_dataset(test_list) train_loader = torch.utils.data.DataLoader(train_Dataset, shuffle=True, batch_size=BatchSize, num_workers=4) test_loader = torch.utils.data.DataLoader(test_Dataset, shuffle=True, batch_size=BatchSize, num_workers=4)
#AR_down_model = nn.DataParallel(AR_down_model) #AR_down_model.cuda() #AR_up_model = model_ns.AR_Net_up() #AR_up_model = nn.DataParallel(AR_down_model) #AR_up_model.cuda() loss_f_ARE = loss_func.loss_f_ARE() loss_f_ARE.cuda() loss_f_E = loss_func.loss_f_E() loss_f_E.cuda() #img_train_list, img_test_list = dataset.load_all_h5(H5_address) train_list = dataset.load_all_h5(H5_train_address) test_list = dataset.load_h5_list(H5_test_address) train_Dataset = dataset.gaze_train_dataset(train_list) test_Dataset = dataset.gaze_test_dataset(test_list) train_loader = torch.utils.data.DataLoader(train_Dataset, shuffle=True, batch_size=BatchSize, num_workers=4) test_loader = torch.utils.data.DataLoader(test_Dataset, shuffle=True, batch_size=BatchSize, num_workers=4) #L1_loss = nn.SmoothL1Loss().cuda() #l1_loss = nn.MSELoss().cuda() #optimizer = torch.optim.Adam(gaze_model.parameters(),lr=0.01)