def test_sample_dataset_10(self):
_, _, sample_train_dataset, sample_valid_dataset = make_train_and_valid_loaders(
1, max_images=100)
items = sample_dataset(sample_train_dataset, 10)
self.assertEqual(items.shape[0], 10)
def test_unequal_split(self):
_, _, sample_train_dataset, sample_valid_dataset = make_train_and_valid_loaders(
2, max_images=100, proportion_faces=0.6)
labels = [i[1][1] for i in list(enumerate(sample_train_dataset))]
face_labels = [i for i in labels if i == 1]
nonface_labels = [i for i in labels if i == 0]
self.assertGreater(len(face_labels), len(nonface_labels))
def test_5050_split(self):
_, _, sample_train_dataset, sample_valid_dataset = make_train_and_valid_loaders(
2, max_images=100)
labels = [i[1][1] for i in list(enumerate(sample_train_dataset))]
face_labels = [i for i in labels if i == 1]
nonface_labels = [i for i in labels if i == 0]
self.assertEqual(len(face_labels), len(nonface_labels))
def test_draw_10_samples_without_max_image(self):
_, _, sample_train_dataset, sample_valid_dataset = make_train_and_valid_loaders(
1)
idxs_train = np.random.choice(len(sample_train_dataset), 10)
idxs_valid = np.random.choice(len(sample_valid_dataset), 10)
for idx in idxs_train:
sample_train_dataset[idx]
for idx in idxs_valid:
sample_valid_dataset[idx]
sample_train_dataset[15600]
sample_valid_dataset[5600]
def test_variety_in_training(self):
_, _, sample_train_dataset, sample_valid_dataset = make_train_and_valid_loaders(
1, max_images=100)
counter = Counter()
for idx, _ in enumerate(sample_train_dataset):
item, label, idx = sample_train_dataset[idx]
if label == 0:
counter['nonface'] += 1
else:
counter['face'] += 1
self.assertGreaterEqual(counter['face'], 30)
self.assertGreaterEqual(counter['nonface'], 30)
def test_draws_max_100_total_samples(self):
_, _, sample_train_dataset, sample_valid_dataset = make_train_and_valid_loaders(
2, max_images=100)
self.assertEqual(
len(sample_train_dataset) + len(sample_valid_dataset), 100)
def sample_idxs_from_valid_dataset(self):
sample_train_loader, sample_valid_loader, sample_train_dataset, sample_valid_dataset = make_train_and_valid_loaders(
1, max_images=100)
subsample = sample_idxs_from_sub_dataset([100, 200, 700],
sample_valid_loader, 1)
return subsample