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# PFLlib: Personalized Federated Learning Algorithm Library
# Copyright (C) 2021 Jianqing Zhang
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
import numpy as np
import os
import random
import torchvision
from utils.dataset_utils import split_data, save_file
from PIL import Image
random.seed(1)
np.random.seed(1)
dir_path = "Omniglot/"
# Allocate data to users
def generate_dataset(dir_path):
if not os.path.exists(dir_path):
os.makedirs(dir_path)
# Setup directory for train/test data
config_path = dir_path + "config.json"
train_path = dir_path + "train/"
test_path = dir_path + "test/"
if not os.path.exists(train_path):
os.makedirs(train_path)
if not os.path.exists(test_path):
os.makedirs(test_path)
root = dir_path+"rawdata"
# Get Omniglot data
torchvision.datasets.Omniglot(root=root, background=True, download=True)
torchvision.datasets.Omniglot(root=root, background=False, download=True)
X = [[] for _ in range(20)]
y = [[] for _ in range(20)]
dir = os.path.join(root, "omniglot-py/")
dirs = os.listdir(dir)
label = 0
for ddir in dirs:
if '.' not in ddir:
ddir = os.path.join(dir, ddir)
ddirs = os.listdir(ddir)
for dddir in ddirs:
if '.' not in dddir:
dddir = os.path.join(ddir, dddir)
dddirs = os.listdir(dddir)
for ddddir in dddirs:
ddddir = os.path.join(dddir, ddddir)
file_names = os.listdir(ddddir)
for i, fn in enumerate(file_names):
fn = os.path.join(ddddir, fn)
img = Image.open(fn)
X[i].append(np.expand_dims(np.asarray(img), axis=0))
y[i].append(label)
label += 1
print(f'Number of labels: {label}')
train_data, test_data = split_data(X, y)
save_file(config_path, train_path, test_path, train_data, test_data, 20, label,
None, None, None, None)
if __name__ == "__main__":
generate_dataset(dir_path) |