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def get_base_params(dataset_name, checkpoint_dir):
    params = {}

    # change name and gpuid later
    basic_params = {'dataset': dataset_name,
                    'dataroot': './data/' + dataset_name,
                    'name': '',
                    'gpu_ids': [0],  
                    'checkpoints_dir': checkpoint_dir,
                    'verbose': False,
                    'print_freq': 100
                    }
    params.update(basic_params)
    
    dataset_params = {'dataset_mode': 'aligned',
                      'direction': 'BtoA',
                      'num_threads': 4,
                      'max_dataset_size': float('inf'),
                      'preprocess': 'resize_and_crop',
                      'display_winsize': 256,
                      'input_nc': 3,
                      'output_nc': 3}
    params.update(dataset_params)  

    model_params = {'model': 'pix2pix',
                    # 'ngf': 64,
                    # 'ndf': 64,
                    # 'netD': 'basic',
                    # 'netG': 'unet_256',
                    'n_layers_D': 3, 
                    # 'norm': 'batch',
                    # 'gan_mode': 'lsgan',
                    # 'init_type': 'normal',
                    'init_gain': 0.02,
                    'no_dropout': False}
    params.update(model_params)
    
    train_params = {'phase': 'train',
                    'isTrain': True,
                    'serial_batches': False,
                    'load_size': 286,
                    'crop_size': 256,
                    'no_flip': False,
                    # 'batch_size': 1,
                    # 'beta1': 0.5,
                    'pool_size': 0,
                    # 'lr_policy': 'linear',
                    'lr_decay_iters': 50,
                    #'lr': 0.0002,
                    # 'lambda_L1': 100,
                    'epoch_count': 1,
                    # 'n_epochs': 10,           # 100
                    # 'n_epochs_decay': 0,      # 100
                    'continue_train': False}
    train_params.update(params)
    
    test_params = {'phase': 'test',
                   'isTrain': False,
                   'load_iter': -1,
                   'epoch': 'latest',
                   'load_size': 256,
                   'crop_size': 256,
                   # 'batch_size': 1,
                   'serial_batches': True,
                   'no_flip': True,
                   'eval': True}
    test_params.update(params)
    
    return train_params, test_params