def test_whenIndexToBig_shouldRaise(self):
datasets = [
create_random_dataset(NUM_ITEMS, NUM_CLASSES, SHAPE),
create_random_dataset(NUM_ITEMS, NUM_CLASSES, SHAPE),
]
dataset = ComposedDataset(datasets)
self.assertRaises(ValueError, lambda: dataset[200])
self.assertRaises(ValueError, lambda: dataset[201])
self.assertRaises(ValueError, lambda: dataset[-201])
def test_shouldNotHaveDupplicatedSamples(self):
dataset = create_random_dataset(NUM_ITEMS, NUM_CLASSES, SHAPE)
train_dataset, test_dataset = create_few_shot_datasets(dataset, 5)
samples = unique_samples([train_dataset, test_dataset])
self.assertEqual(len(samples), len(train_dataset) + len(test_dataset))
def test_givenOneNumSample_trainDatasetShouldHaveOneSamplePerClass(self):
dataset = create_random_dataset(NUM_ITEMS, NUM_CLASSES, SHAPE)
train_dataset, test_dataset = create_few_shot_datasets(dataset, 1)
classes_train = unique_classes(train_dataset)
self.assertEqual(len(train_dataset), NUM_CLASSES)
self.assertEqual(len(classes_train), NUM_CLASSES)
def setUp(self):
samples = create_random_dataset(NUM_ITEMS, NUM_CLASSES,
SHAPE).samples # type: ignore
self.dataset_1 = ListDataset(samples[0:20])
self.dataset_2 = ListDataset(samples[20:40])
self.dataset_3 = ListDataset(samples[70:100])
self.composed_dataset = ComposedDataset(
[self.dataset_1, self.dataset_2, self.dataset_3])
def test_given5NumSamples_trainDatasetShouldHave5SamplesPerClass(self):
num_samples = 5
# We add more items to make sure all classes are included
dataset = create_random_dataset(NUM_ITEMS * num_samples, NUM_CLASSES,
SHAPE)
train_dataset, test_dataset = create_few_shot_datasets(
dataset, num_samples)
classes_train = unique_classes(train_dataset)
self.assertEqual(len(train_dataset), num_samples * NUM_CLASSES)
self.assertEqual(len(classes_train), NUM_CLASSES)
def setUp(self):
samples = create_random_dataset(NUM_ITEMS, NUM_CLASSES,
SHAPE).samples # type: ignore
self.dataset = create_few_shot_datasets(ListDataset(samples), N_WAY)
self.dataloader_factory = DataLoaderFactory(BATCH_SIZE, SHUFFLE,
PIN_MEMORY)
def setUp(self):
self.dataset = create_random_dataset(100, 3, (3, 12, 12))
self.transforms = KorniaTransforms(MEAN, STD, (10, 10), (10, 10), 2)
def test_shouldKeepAllSamples(self):
dataset = create_random_dataset(NUM_ITEMS, NUM_CLASSES, SHAPE)
train_dataset, test_dataset = create_few_shot_datasets(dataset, 5)
self.assertEqual(NUM_ITEMS, len(train_dataset) + len(test_dataset))