class testUtils(unittest.TestCase):
"test class for the DataProvider module"
def setUp(self):
self.data_provider = DataProvider()
self.number = 2
def test_datasets(self):
attrs = dict(size=1, files=2)
datasets = self.data_provider.datasets(self.number, **attrs)
print "\ndatasets:\n", datasets
self.assertEqual(self.number, len(datasets))
def test_blocks(self):
attrs = {'is-open':'y'}
blocks = self.data_provider.blocks(self.number, **attrs)
print "\nblocks:\n", blocks
self.assertEqual(self.number, len(blocks))
def test_files(self):
attrs = dict(adler32='abcd')
files = self.data_provider.files(self.number, **attrs)
print "\nfiles:\n", files
self.assertEqual(self.number, len(files))
def test_lumis(self):
attrs = {}
lumis = self.data_provider.lumis(self.number, **attrs)
print "\nlumis:\n", lumis
self.assertEqual(self.number, len(lumis))
valid_set = fold1_list.copy()
test_set = np.load(data_path +
'/whole_data/validation_file.npy').tolist().copy()
org_suffix = '_orig.nii.gz'
lab_suffix = '_multiclass_pve.nii.gz'
pre = {
org_suffix: [('channelcheck', 1)],
lab_suffix: [('one-hot', 5), ('channelcheck', 5)]
}
processor = SimpleImageProcessor(pre=pre)
train_provider = DataProvider(train_set, [org_suffix, lab_suffix],
is_pre_load=False,
processor=processor)
validation_provider = DataProvider(valid_set, [org_suffix, lab_suffix],
is_pre_load=False,
processor=processor)
u_net = UNet3D(n_class=5, n_layer=4, root_filters=16, use_bn=True)
model = SimpleTFModel(u_net,
org_suffix,
lab_suffix,
dropout=0,
loss_function={'cross-entropy': 1.},
weight_function={'balance'})
optimizer = tf.keras.optimizers.Adam(args.learning_rate)
def setUp(self):
self.data_provider = DataProvider()
self.number = 2
train_list = file_mat['train_set']
valid_list = file_mat['validation_set']
test_list = file_mat['test_set']
# add path to filename
org_suffix = '.nii.gz'
age_suffix = '_lab.txt'
pre = {org_suffix: [('zero-mean'), ('min-max'), ('channelcheck', 1)]}
# processor = Processor()
processor = SimpleImageProcessor(pre=pre)
train_provider = DataProvider(
train_list,
[org_suffix, age_suffix],
is_pre_load=False,
is_shuffle=True,
# temp_dir=output_path,
processor=processor)
valid_provider = DataProvider(
valid_list,
[org_suffix, age_suffix],
is_pre_load=False,
# temp_dir=output_path,
processor=processor)
# build model
gen = Generator(output_channels=1, use_bn=use_bn)
disc = Discriminator(use_bn=use_bn)
# gen = Generator(n_class=1, n_layer=6, root_filters=16, use_bn=use_bn, use_res=use_res)
def get_phonebook_name(data_provider: DataProvider) -> Union[str, None]:
if Validator.check_arguments(data_provider):
return data_provider.get_argument()
return None
def get_data(self):
""" Get data to show in the view; format depends on the type """
dp = DataProvider(
) # could use the Todoist API directly, this is a bit for the future; to support weird data formats
return {} # wx2 NotImplementedError on get data for view
def __init__(self, type):
if type not in self.SUPPORTED_TYPES:
pass # wx3: type validation: without valid type we shouldn't init
self.type = type
self.dp = DataProvider()