def prepare_mnist(config):
dataset = build_dataset('mnist', val_size=config['val_size'])
x_set, y_set = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
shape = (-1, 1, 28, 28)
x_set = ((x_set - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
train_tfms = []
return x_set, y_set, x_val, y_val, train_tfms
def prepare_cifar(config):
dataset = build_dataset('cifar_10', val_size=config['val_size'])
x_set, y_set = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
shape = (-1, 3, 32, 32)
x_set = ((x_set - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
train_tfms = [*rand_pad(4, 32), flip_lr(p=0.5)] # Transformation to augment images
return x_set, y_set, x_val, y_val, train_tfms
def load_data():
dataset = build_dataset('cifar_10', val_size=val_size)
x_train, y_train = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
shape = (-1, 3, 32, 32)
x_train = ((x_train - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
train_tfms = [*rand_pad(4, 32), flip_lr(p=0.5)]
train_ds = ImageArrayDS(x_train, y_train, train_tfms)
val_ds = ImageArrayDS(x_val, y_val)
data = ImageDataBunch.create(train_ds, val_ds, bs=256)
return data, x_train, y_train, x_val, y_val
def prepare_svhn(config):
dataset = build_dataset('svhn', val_size=config['val_size'])
x_set, y_set = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
y_set[y_set == 10] = 0
y_val[y_val == 10] = 0
shape = (-1, 32, 32, 3)
x_set = ((x_set - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
x_set = np.rollaxis(x_set, 3, 1)
x_val = np.rollaxis(x_val, 3, 1)
train_tfms = [*rand_pad(4, 32),
flip_lr(p=0.5)] # Transformation to augment images
return x_set, y_set, x_val, y_val, train_tfms
# == Place to develop, experiment and debug new methods of active learning == #
# torch.cuda.set_device(1)
total_size = 60_000
val_size = 10_000
start_size = 1_000
step_size = 10
steps = 8
reload = True
nn_runs = 100
pool_size = 200
# Load data
dataset = build_dataset('cifar_10', val_size=val_size)
x_set, y_set = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
shape = (-1, 3, 32, 32)
x_set = ((x_set - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
# x_pool, x_train, y_pool, y_train = train_test_split(x_set, y_set, test_size=start_size, stratify=y_set)
x_train, y_train = x_set, y_set
train_tfms = [*rand_pad(4, 32), flip_lr(p=0.5)]
train_ds = ImageArrayDS(x_train, y_train, train_tfms)
val_ds = ImageArrayDS(x_val, y_val)
data = ImageDataBunch.create(train_ds, val_ds, bs=256)