def test_batch_xyz(ADNI_names, path_dicts, signal_params):
# Check for multiple overlaps if data == xyz_to_batch(batch_to_xyz(data))
for overlap_coeff in range(1, 4):
signal_params['overlap_coeff'] = overlap_coeff
dataset = SHDataset(path_dicts,
patch_size=signal_params['patch_size'],
signal_parameters=signal_params,
transformations=None,
cache_dir=None)
patient = np.random.choice(ADNI_names)
data_patient = dataset.get_data_by_name(patient)
data_batch = torch.FloatTensor(data_patient['sh'])
data_xyz = batch_to_xyz(data_batch,
data_patient['real_size'],
overlap_coeff=signal_params['overlap_coeff'],
empty=data_patient['empty'])
assert data_xyz.shape[:3] == data_patient['real_size']
data_batch_2, number_of_patches = xyz_to_batch(
data_xyz,
signal_params['patch_size'],
overlap_coeff=signal_params['overlap_coeff'])
data_batch_2 = data_batch_2[~data_patient['empty']]
assert torch.isclose(data_batch, data_batch_2, rtol=0.05,
atol=1e-6).all()
def test_normalization(ADNI_names, path_dicts, signal_params):
dataset = SHDataset(path_dicts,
patch_size=signal_params['patch_size'],
signal_parameters=signal_params,
transformations=None,
normalize_data=False,
cache_dir=None)
patient = np.random.choice(ADNI_names)
data_raw = dataset.get_data_by_name(patient)['sh']
dataset.normalize_data()
mean, std = dataset.mean, dataset.std
data_normalized = dataset.get_data_by_name(patient)['sh']
assert np.isclose(data_normalized * std + mean,
data_raw,
rtol=0.05,
atol=1e-6).all()
def test_dataset(ADNI_names, path_dicts, signal_params):
dataset = SHDataset(path_dicts,
patch_size=signal_params['patch_size'],
signal_parameters=signal_params,
transformations=None,
n_jobs=-1,
cache_dir=None)
sh_order = signal_params['sh_order']
ncoef = (sh_order + 2) * (sh_order + 1) / 2
signal, mask = dataset[300]
assert list(signal.shape) == signal_params['patch_size'] + [ncoef]
assert len(dataset) == 608
patient = np.random.choice(ADNI_names)
dataset.get_data_by_name(patient)
normalize_data=True,
mean=mean,
std=std,
b0_mean=b0_mean,
b0_std=b0_std,
n_jobs=8)
# Load the network
net, _ = ENet.load(save_folder + net_file)
net = net.to("cuda")
net.eval()
# Get the dmri name
dwi_name = dataset.names[0]
data = dataset.get_data_by_name(dwi_name)
sh_true = batch_to_xyz(
data['sh'],
data['real_size'],
empty=data['empty'],
overlap_coeff=SIGNAL_PARAMETERS['overlap_coeff'])
sh_true = sh_true * dataset.std + dataset.mean
net_pred = net.predict_dataset(dataset, batch_size=16, numpy=False)[dwi_name]
sh_pred = net_pred['sh_pred']
mean_b0_pred = net_pred['mean_b0_pred']
alpha = net_pred['alpha']
beta = net_pred['beta']
sh_pred = batch_to_xyz(