def save_ds_samples():
"""Load datasets from pickle files. Get samples from random indices and save them as JPEG images"""
pickles = [
"../datasets/stl10/original_train.pkl",
"../datasets/stl10/mirror_train.pkl",
"../datasets/stl10/rot_90_1_train.pkl",
"../datasets/stl10/rot_90_3_train.pkl",
"../datasets/stl10/rand_distorted_train_0.pkl",
"../datasets/stl10/rand_distorted_train_1.pkl",
"../datasets/stl10/rand_distorted_train_2.pkl",
]
dataset = ImageDataset.load_from_pickles(pickles)
items_per_pickle = 11000
for i in range(20):
images = []
idx = randint(0, items_per_pickle - 1)
for j in range(len(pickles)):
images.append(
LabeledImage.load_from_dataset(dataset,
index=j * items_per_pickle +
idx,
max_value=1), )
LabeledImage(np.concatenate([x.image for x in images], axis=1), images[0].name) \
.save(location="../samples/", name="{}_{}".format(idx, images[0].name))
def load_eval_dataset():
"""Load Evaluation dataset from list of pickle files"""
dataset = ImageDataset.load_from_pickles([
"../datasets/stl10/original_test.pkl",
])
return dataset.x, dataset.y
def load_train_dataset():
"""Load Training dataset from list of pickle files"""
dataset = ImageDataset.load_from_pickles([
"../datasets/stl10/original_train.pkl",
"../datasets/stl10/mirror_train.pkl",
# "../datasets/stl10/rot_90_1_train.pkl",
# "../datasets/stl10/rot_90_3_train.pkl",
"../datasets/stl10/rand_distorted_train_0.pkl",
# "../datasets/stl10/rand_distorted_train_1.pkl",
# "../datasets/stl10/rand_distorted_train_2.pkl",
])
return dataset.x, dataset.y
def load_cifar10():
train_ds = ImageDataset.load_from_pickles([
"/datasets/cifar10/original_train.pkl",
"/datasets/cifar10/mirror_train.pkl",
"/datasets/cifar10/rand_distorted_train.pkl",
])
test_ds = ImageDataset.load_from_pickles([
"/datasets/cifar10/original_test.pkl",
])
offset = 1024
img0 = LabeledImage.load_from_dataset_tuple((train_ds.x, train_ds.y),
0 + offset)
img1 = LabeledImage.load_from_dataset_tuple((train_ds.x, train_ds.y),
50000 + offset)
img2 = LabeledImage.load_from_dataset_tuple((train_ds.x, train_ds.y),
100000 + offset)
mixed_img = np.concatenate([img0.image, img1.image, img2.image], axis=1)
LabeledImage(mixed_img, "mixed_" + img0.name) \
.save_image()
return (train_ds.x, train_ds.y), (test_ds.x, test_ds.y)