def test_load_cifar100_dataset(self):
# if not os.path.exists('./datasets/cifar100'):
# return
dataset = data_utils.load_cifar100_dataset(root=self.dataset_dir)
img = dataset[0][0]
assert img.shape == (3, 32, 32)
def get_cifar100_images(num_samples, root="./datasets", **kwargs):
"""
Loads randomly sampled CIFAR-100 training images.
Args:
num_samples (int): The number of images to randomly sample.
root (str): The root directory where all datasets are stored.
Returns:
Tensor: Batch of num_samples images in np array form.
"""
dataset = data_utils.load_cifar100_dataset(root=root,
split='train',
download=True,
transform_data=False,
**kwargs)
images = get_random_images(dataset, num_samples)
return images
def get_cifar100_images_with_index(index, root="./dataset", **kwargs):
"""
Loads sampled CIFAR-100 training images with index.
Args:
index (ndarray): The index of images.
root (str): The root directory where all datasets are stored.
Returns:
ndarray: Batch of len(index) images in np array form.
"""
dataset = data_utils.load_cifar100_dataset(root=root,
split='train',
download=True,
transform_data=False,
**kwargs)
images = get_index_images(dataset, index)
return images
def test_load_cifar100_dataset(self):
dataset = data_utils.load_cifar100_dataset(root=self.dataset_dir)
img = dataset[0][0]
assert img.shape == (3, 32, 32)