logger.addHandler(file_handler)
# Experiment parameters
logger.info('batch_size : {}'.format(batch_size))
logger.info('max_epochs : {}'.format(max_epochs))
logger.info('cuda available : {}'.format(torch.cuda.is_available()))
# Data
if hexa:
camera_layout = 'Hex'
logger.info('Hexagonal CIFAR')
img, _ = datasets.CIFAR10(data_directory,
train=True,
download=True,
transform=transforms.ToTensor())[0]
index_matrix = utils.square_to_hexagonal_index_matrix(img)
if not os.path.exists(data_directory + '/cifar10.hdf5'):
train_set = datasets.CIFAR10(data_directory,
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
utils.SquareToHexa()
]))
with h5py.File(data_directory + '/cifar10.hdf5', 'w') as f:
images = []
labels = []
for i in range(len(train_set)):
image, label = train_set[i]
images.append(image.numpy())
def test_square_to_hexagonal_index_matrix(self):
torch.testing.assert_allclose(utils.square_to_hexagonal_index_matrix(self.square_image),
self.square_to_hexagonal_index_matrix)
