class DataLoadingTests(unittest.TestCase):
def setUp(self):
with open('training_set_list.pickle', 'rb') as handle:
self.training_dict = pickle.load(handle)
with open('validation_set_list.pickle', 'rb') as handle:
self.validation_dict = pickle.load(handle)
with open('test_set_list.pickle', 'rb') as handle:
self.test_dict = pickle.load(handle)
with open('genres.json') as json_data:
self.genres = json.load(json_data)
with open('labels.json') as json_data:
self.dataset = json.load(json_data)
self.dataset_manager = DatasetManager(self.training_dict,
self.validation_dict,
self.test_dict, self.genres,
self.dataset)
self.batch_size = 50
def test_normal_training_image_load(self):
images = self.dataset_manager.next_batch(50, "train")
self.assertEqual(images[0].shape, (50, 227, 227, 3))
def test_normal_training_labels_load(self):
images = self.dataset_manager.next_batch(50, "train")
self.assertEqual(images[1].shape, (50, 26))
def test_last_traninig_image_load(self):
self.dataset_manager.cur_train = \
len(self.dataset_manager.training_list) - \
(self.batch_size / 2)
images = self.dataset_manager.next_batch(50, "train")
self.assertEqual(images[0].shape, (50, 227, 227, 3))
def test_last_traninig_labels_load(self):
self.dataset_manager.cur_train = \
len(self.dataset_manager.training_list) - \
(self.batch_size / 2)
images = self.dataset_manager.next_batch(50, "train")
self.assertEqual(images[1].shape, (50, 26))
def test_normal_validation_image_load(self):
images = self.dataset_manager.next_batch(50, "val")
self.assertEqual(images[0].shape, (50, 227, 227, 3))
def test_normal_validation_labels_load(self):
images = self.dataset_manager.next_batch(50, "val")
self.assertEqual(images[1].shape, (50, 26))
def test_last_validation_image_load(self):
self.dataset_manager.cur_val = \
len(self.dataset_manager.validation_list) - \
(self.batch_size / 2)
images = self.dataset_manager.next_batch(50, "val")
self.assertEqual(images[0].shape, (50, 227, 227, 3))
def test_last_validation_labels_load(self):
self.dataset_manager.cur_val = \
len(self.dataset_manager.validation_list) - \
(self.batch_size / 2)
images = self.dataset_manager.next_batch(50, "val")
self.assertEqual(images[1].shape, (50, 26))
def test_normal_test_image_load(self):
images = self.dataset_manager.next_batch(50, "test")
self.assertEqual(images[0].shape, (50, 227, 227, 3))
def test_normal_test_labels_load(self):
images = self.dataset_manager.next_batch(50, "test")
self.assertEqual(images[1].shape, (50, 26))
def test_last_test_image_load(self):
self.dataset_manager.cur_test = \
len(self.dataset_manager.test_list) - \
(self.batch_size / 2)
images = self.dataset_manager.next_batch(50, "test")
self.assertEqual(images[0].shape, (50, 227, 227, 3))
def test_last_test_labels_load(self):
self.dataset_manager.cur_test = \
len(self.dataset_manager.test_list) - \
(self.batch_size / 2)
images = self.dataset_manager.next_batch(50, "test")
self.assertEqual(images[1].shape, (50, 26))
def test_create_label_vector(self):
label_vector = self.dataset_manager.create_label_vector(
[" Action", " Documentary", " Drama", " Horror", " News", " War"])
self.assertEqual(label_vector, [
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 1, 0
])
def test_create_label_vector_end(self):
label_vector = self.dataset_manager.create_label_vector([
" Action", " Documentary", " Drama", " Horror", " News", " War",
" Western"
])
self.assertEqual(label_vector, [
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 1, 1
])
def test_no_duplicate_between_test_and_train(self):
self.assertEqual(self.training_dict.intersection(self.test_dict),
set())
