def test_transpose_axes(self):
n_iter = 1000
tmp = 0
for i in range(n_iter):
data_out, seg_out = augment_transpose_axes(self.data_3D, self.seg_3D, axes=(1, 0))
if np.array_equal(data_out, np.swapaxes(self.data_3D, 1, 2)):
tmp += 1
self.assertAlmostEqual(tmp, n_iter/2., delta=10)
def __call__(self, **data_dict):
data = data_dict.get(self.data_key)
seg = data_dict.get(self.label_key)
ret_val = augment_transpose_axes(data, seg, self.transpose_any_of_these)
data_dict[self.data_key] = ret_val[0]
if seg is not None:
data_dict[self.label_key] = ret_val[1]
return data_dict
def __call__(self, **data_dict):
data = data_dict.get(self.data_key)
seg = data_dict.get(self.label_key)
ret_val = augment_transpose_axes(data, seg, self.transpose_any_of_these)
data_dict[self.data_key] = ret_val[0]
if seg is not None:
data_dict[self.label_key] = ret_val[1]
return data_dict
def __call__(self, **data_dict):
data = data_dict.get(self.data_key)
seg = data_dict.get(self.label_key)
for b in range(len(data)):
if np.random.uniform() < self.p_per_sample:
if seg is not None:
s = seg[b]
else:
s = None
ret_val = augment_transpose_axes(data[b], s, self.transpose_any_of_these)
data[b] = ret_val[0]
if seg is not None:
seg[b] = ret_val[1]
data_dict[self.data_key] = data
if seg is not None:
data_dict[self.label_key] = seg
return data_dict
