def test_patient_ids(self):
if not LoDoPaBDataset.check_for_lodopab():
return
d = LoDoPaBDataset(impl='skimage')
if d.rel_patient_ids is not None:
for part in ['train', 'validation', 'test']:
self.assertEqual(len(d.rel_patient_ids[part]), d.get_len(part))
self.assertTrue(
np.all(
np.unique(d.rel_patient_ids[part]) == range(
d.get_num_patients(part))))
self.assertTrue(
np.all(
np.diff(d.rel_patient_ids[part][
LoDoPaBDataset.get_idx_sorted_by_patient()[part]])
>= 0))
d2 = LoDoPaBDataset(sorted_by_patient=True, impl='skimage')
REL_PATIENT_ID = 42
ifp = d.get_indices_for_patient(REL_PATIENT_ID, part)
ifp2 = d2.get_indices_for_patient(REL_PATIENT_ID, part)
self.assertGreater(len(ifp), 0)
self.assertEqual(len(ifp), len(ifp2))
for i, i2 in zip(ifp[:3], ifp2[:3]):
self.assertEqual(d.get_sample(i, part),
d2.get_sample(i2, part))
def test_get_samples(self):
# TODO: use dataset that is always available instead of lodopab
if not LoDoPaBDataset.check_for_lodopab():
return
d = LoDoPaBDataset(impl='skimage')
angle_indices = range(0, d.shape[0][0], 2)
asd = AngleSubsetDataset(d, angle_indices)
obs_arr_asd, gt_arr_asd = asd.get_samples(range(3))
obs_arr, gt_arr = d.get_samples(range(3))
obs_arr_subset = np.asarray(obs_arr)[:, np.asarray(angle_indices), :]
self.assertEqual(obs_arr_asd.shape, obs_arr_subset.shape)
self.assertEqual(gt_arr_asd.shape, gt_arr.shape)
self.assertTrue(np.all(np.asarray(obs_arr_asd) == obs_arr_subset))
self.assertTrue(np.all(np.asarray(gt_arr_asd) == np.asarray(gt_arr)))
def test_generator(self):
if not LoDoPaBDataset.check_for_lodopab():
return
NUM_SAMPLES = 3
d = LoDoPaBDataset(impl='skimage')
for part in ['train', 'validation', 'test']:
samples = [d.get_sample(i, part) for i in range(NUM_SAMPLES)]
samples2 = [s for s in islice(d.generator(part), NUM_SAMPLES)]
for (s_obs, s_gt), (s2_obs, s2_gt) in zip(samples, samples2):
self.assertTrue(np.all(np.asarray(s_obs) == s2_obs))
self.assertTrue(np.all(np.asarray(s_gt) == s2_gt))
if d.rel_patient_ids is not None:
d2 = LoDoPaBDataset(sorted_by_patient=True, impl='skimage')
for part in ['train', 'validation', 'test']:
samples = [d2.get_sample(i, part) for i in range(NUM_SAMPLES)]
samples2 = [s for s in islice(d2.generator(part), NUM_SAMPLES)]
for (s_obs, s_gt), (s2_obs, s2_gt) in zip(samples, samples2):
self.assertTrue(np.all(np.asarray(s_obs) == s2_obs))
self.assertTrue(np.all(np.asarray(s_gt) == s2_gt))
def test_get_samples(self):
if not LoDoPaBDataset.check_for_lodopab():
return
KEY = range(420, 423)
d = LoDoPaBDataset(impl='skimage')
for part in ['train', 'validation', 'test']:
samples = [d.get_sample(i, part) for i in KEY]
samples2 = d.get_samples(KEY, part)
for (s_obs, s_gt), s2_obs, s2_gt in zip(samples, samples2[0],
samples2[1]):
self.assertTrue(np.all(np.asarray(s_obs) == s2_obs))
self.assertTrue(np.all(np.asarray(s_gt) == s2_gt))
if d.rel_patient_ids is not None:
d2 = LoDoPaBDataset(sorted_by_patient=True, impl='skimage')
for part in ['train', 'validation', 'test']:
samples = [d2.get_sample(i, part) for i in KEY]
samples2 = d2.get_samples(KEY, part)
for (s_obs, s_gt), s2_obs, s2_gt in zip(samples, samples2[0],
samples2[1]):
self.assertTrue(np.all(np.asarray(s_obs) == s2_obs))
self.assertTrue(np.all(np.asarray(s_gt) == s2_gt))