def test(config, **kwargs):
evaluator = Evaluator(config, **kwargs)
evaluator.run()
dir_prediction = 'predictions/2d/'
create_dir(dir_checkpoint)
create_dir(dir_prediction)
dataset_props = load_pickle('dataset_props.pkl')
pool_layer_kernel_sizes = dataset_props['plan_2d'][
'pool_layer_kernel_sizes']
args = get_args()
model = ResUNet(in_ch=1,
base_num_features=30,
num_classes=3,
norm_type='batch',
nonlin_type='relu',
pool_type='max',
pool_layer_kernel_sizes=pool_layer_kernel_sizes,
deep_supervision=True,
mode='2D')
trainer = Trainer(model, dir_h5_train, dir_checkpoint, args)
trainer.run()
model = ResUNet(in_ch=1,
base_num_features=30,
num_classes=3,
norm_type='batch',
nonlin_type='relu',
pool_type='max',
pool_layer_kernel_sizes=pool_layer_kernel_sizes,
deep_supervision=False,
mode='2D')
evaluator = Evaluator(model, dir_h5_train, dir_checkpoint, dir_prediction,
args)
evaluator.run()
