def make_unmnist(config): """Creates the MNIST dataset.""" def to_float(x): return tf.to_float(x) / 255. transform = [to_float] if config.canvas_size != 28: transform.append( functools.partial(preprocess.pad_and_shift, output_size=config.canvas_size, shift=None)) batch_size = config.batch_size trainset = image.create('mnist', subset='train', batch_size=batch_size, transforms=transform) del trainset["label"] validset = image.create('mnist', subset='test', batch_size=batch_size, transforms=transform) del validset["label"] res = AttrDict(trainset=trainset, validset=validset) return res
def make_svhn(config): """Creates the svhn dataset.""" def to_float(x): return tf.to_float(x) / 255. transform = [to_float] if config.canvas_size != 32: transform.append( functools.partial(preprocess.pad_and_shift, output_size=config.canvas_size, shift=None)) #transform.append( # functools.partial(preprocess.normalized_sobel_edges)) batch_size = config.batch_size res = AttrDict(trainset=image.create('svhn', subset='train', batch_size=batch_size, transforms=transform), validset=image.create('svhn', subset='test', batch_size=batch_size, transforms=transform)) return res
def make_dataset256(config): """Creates the dataset_256 dataset.""" # data is created online, so there is no point in having # a separate dataset for validation def to_float(x): return tf.to_float(x) / 255. transform = [to_float] transform.append( functools.partial(preprocess.pad_and_shift, output_size=config.canvas_size, shift=None)) batch_size = config.batch_size res = AttrDict(trainset=image.create('dataset256', subset='train', batch_size=batch_size, transforms=transform), validset=image.create('dataset256', subset='test', batch_size=batch_size, transforms=transform)) return res
def make_clevr_veggies(config): """Creates the CLEVR Veggies dataset.""" def to_float(x): return tf.to_float(x) / 255. transform = [to_float] batch_size = config.batch_size res = AttrDict(trainset=image.create('clevr_veggies', subset='train', batch_size=batch_size, transforms=transform), validset=image.create('clevr_veggies', subset='val', batch_size=batch_size, transforms=transform)) return res