def __init__(self,
data_path: str = "",
split: str = "train",
download: bool = True,
seed: int = 1):
if split not in ("train", "val", "test"):
raise ValueError(
f"Split must be train, val, or test; not {split}.")
train = split in ("train", "val")
datasets = [
CIFAR10(data_path=data_path, train=train, download=download),
MNIST(data_path=data_path, train=train, download=download),
DTD(data_path=data_path, train=train, download=download),
FashionMNIST(data_path=data_path, train=train, download=download),
SVHN(data_path=data_path, train=train, download=download),
CIFAR10(data_path=data_path, train=train, download=download)
]
if split == "train":
proportions = [4000, 400, 400, 400, 400, 400]
elif split == "val":
proportions = [2000, 200, 200, 200, 200, 200]
else:
proportions = None
super().__init__(datasets=datasets,
proportions=proportions,
class_counter=[0, 10, 20, 67, 77, 87],
seed=seed,
split=split)
def get_permuted_CIFAR10(path, batch_size,train):
im_width = im_height = 32
rand_perm = RandomPermutation(0, 0, im_width, im_height)
normalization = transforms.Normalize((0.1307,), (0.3081,))
#Todo: rethink RandomPermutation usage slows down dataloading by a factor > 6, Should try directly on batches.
transfrom = transforms.Compose([
transforms.ToTensor(),
rand_perm,
normalization]
)
if(train):
set = ClassIncremental(
CIFAR10(data_path="./src/data/CIFAR10", download=True, train=True),
increment=2
)
else:
set = ClassIncremental(
CIFAR10(data_path="./src/data/CIFAR10", download=True, train=False),
increment=2
)
return set
def test_background_tranformation():
"""
Example code using TransformationIncremental to create a setting with 3 tasks.
"""
cifar = CIFAR10(DATA_PATH, train=True)
mnist = MNIST(DATA_PATH, download=False, train=True)
nb_task = 3
list_trsf = []
for i in range(nb_task):
list_trsf.append([
torchvision.transforms.ToTensor(),
BackgroundSwap(cifar, bg_label=i, input_dim=(28, 28)),
torchvision.transforms.ToPILImage()
])
scenario = TransformationIncremental(
mnist,
base_transformations=[torchvision.transforms.ToTensor()],
incremental_transformations=list_trsf)
folder = "tests/samples/background_trsf/"
if not os.path.exists(folder):
os.makedirs(folder)
for task_id, task_data in enumerate(scenario):
task_data.plot(path=folder,
title=f"background_{task_id}.jpg",
nb_samples=100,
shape=[28, 28, 3])
loader = DataLoader(task_data)
_, _, _ = next(iter(loader))
def test_background_swap_numpy():
"""
Test background swap on a single ndarray input.
"""
mnist = MNIST(DATA_PATH, download=True, train=True)
cifar = CIFAR10(DATA_PATH, download=True, train=True)
bg_swap = BackgroundSwap(cifar, input_dim=(28, 28))
im = mnist.get_data()[0][0]
im = bg_swap(im)
def test_background_swap_torch():
"""
Test background swap on a single tensor input.
"""
cifar = CIFAR10(DATA_PATH, download=True, train=True)
mnist = torchvision.datasets.MNIST(
DATA_PATH,
train=True,
download=True,
transform=torchvision.transforms.Compose(
[torchvision.transforms.ToTensor()]))
bg_swap = BackgroundSwap(cifar, input_dim=(28, 28))
im = mnist[0][0]
im = bg_swap(im)
def __init__(self,
data_path: str = "",
split: str = "train",
download: bool = True,
seed: int = 1):
if split not in ("train", "val", "test"):
raise ValueError(
f"Split must be train, val, or test; not {split}.")
train = split in ("train", "val")
color1, color2 = np.random.RandomState(seed=seed).choice(
["red", "blue", "green"], 2)
datasets = [
RainbowMNIST(data_path=data_path,
train=train,
download=download,
color=color1),
CIFAR10(data_path=data_path, train=train, download=download),
DTD(data_path=data_path, train=train, download=download),
FashionMNIST(data_path=data_path, train=train, download=download),
SVHN(data_path=data_path, train=train, download=download),
RainbowMNIST(data_path=data_path,
train=train,
download=download,
color=color2)
]
if split == "train":
proportions = [4000, 400, 400, 400, 400, 50]
elif split == "val":
proportions = [2000, 200, 200, 200, 200, 30]
else:
proportions = None
super().__init__(datasets=datasets,
proportions=proportions,
class_counter=[0, 10, 20, 67, 77, 0],
seed=seed,
split=split)