def test_different_modes(self):
"""Test ClassificationDataset object for different modes"""
test_dataset_config = {
'name': 'CIFAR10',
'version': 'default',
'mode': 'test'
}
train_dataset_config = {
'name': 'CIFAR10',
'version': 'default',
'mode': 'train'
}
test_dataset = get_classification_dataset(BaseImageDataset)(
DATA_ROOT, [test_dataset_config])
train_dataset = get_classification_dataset(BaseImageDataset)(
DATA_ROOT, [train_dataset_config])
self.assertTrue(len(test_dataset.items) != len(train_dataset.items))
def test_dataset_with_signal_transform(self):
"""Checks dataset with signal transform"""
dataset = get_classification_dataset(BaseImageDataset)(
DATA_ROOT,
self.dataset_config,
signal_transform=self.signal_transform,
target_transform=self.target_transform)
instance = dataset[0]
self.assertEqual(instance['signal'].shape, (3, 30, 30))
def test_dataset_no_transform(self):
"""Checks dataset using no transform"""
dataset = get_classification_dataset(BaseImageDataset)(
DATA_ROOT, self.dataset_config)
instance = dataset[0]
self.assertEqual(instance['item'].path,
'/data/CIFAR10/processed/images/50000.png')
self.assertTrue(isinstance(instance['signal'], torch.Tensor))
self.assertEqual(instance['label'], [3])
def test_fraction(self):
"""Test creating ClassificationDataset object using fraction < 1"""
dataset_config = {
'name': 'CIFAR10',
'version': 'default',
'mode': 'test'
}
fraction = 0.5
dataset = get_classification_dataset(BaseImageDataset)(
DATA_ROOT, [dataset_config], fraction=fraction)
self.assertEqual(5000, len(dataset.items))