# 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.transforms as transforms import torch.utils.data as data from utils.dataset_utils import split_data, save_file from os import path from scipy.io import loadmat from PIL import Image from torch.utils.data import DataLoader # https://github.com/FengHZ/KD3A/blob/master/datasets/DigitFive.py def load_mnist(base_path): print("load mnist") mnist_data = loadmat(path.join(base_path, "mnist_data.mat")) mnist_train = np.reshape(mnist_data['train_32'], (55000, 32, 32, 1)) mnist_test = np.reshape(mnist_data['test_32'], (10000, 32, 32, 1)) # turn to the 3 channel image with C*H*W mnist_train = np.concatenate([mnist_train, mnist_train, mnist_train], 3) mnist_test = np.concatenate([mnist_test, mnist_test, mnist_test], 3) mnist_train = mnist_train.transpose(0, 3, 1, 2).astype(np.float32) mnist_test = mnist_test.transpose(0, 3, 1, 2).astype(np.float32) # get labels mnist_labels_train = mnist_data['label_train'] mnist_labels_test = mnist_data['label_test'] # random sample 25000 from train dataset and random sample 9000 from test dataset train_label = np.argmax(mnist_labels_train, axis=1) inds = np.random.permutation(mnist_train.shape[0]) mnist_train = mnist_train[inds] train_label = train_label[inds] test_label = np.argmax(mnist_labels_test, axis=1) mnist_train = mnist_train[:25000] train_label = train_label[:25000] mnist_test = mnist_test[:9000] test_label = test_label[:9000] return mnist_train, train_label, mnist_test, test_label def load_mnist_m(base_path): print("load mnist_m") mnistm_data = loadmat(path.join(base_path, "mnistm_with_label.mat")) mnistm_train = mnistm_data['train'] mnistm_test = mnistm_data['test'] mnistm_train = mnistm_train.transpose(0, 3, 1, 2).astype(np.float32) mnistm_test = mnistm_test.transpose(0, 3, 1, 2).astype(np.float32) # get labels mnistm_labels_train = mnistm_data['label_train'] mnistm_labels_test = mnistm_data['label_test'] # random sample 25000 from train dataset and random sample 9000 from test dataset train_label = np.argmax(mnistm_labels_train, axis=1) inds = np.random.permutation(mnistm_train.shape[0]) mnistm_train = mnistm_train[inds] train_label = train_label[inds] test_label = np.argmax(mnistm_labels_test, axis=1) mnistm_train = mnistm_train[:25000] train_label = train_label[:25000] mnistm_test = mnistm_test[:9000] test_label = test_label[:9000] return mnistm_train, train_label, mnistm_test, test_label def load_svhn(base_path): print("load svhn") svhn_train_data = loadmat(path.join(base_path, "svhn_train_32x32.mat")) svhn_test_data = loadmat(path.join(base_path, "svhn_test_32x32.mat")) svhn_train = svhn_train_data['X'] svhn_train = svhn_train.transpose(3, 2, 0, 1).astype(np.float32) svhn_test = svhn_test_data['X'] svhn_test = svhn_test.transpose(3, 2, 0, 1).astype(np.float32) train_label = svhn_train_data["y"].reshape(-1) test_label = svhn_test_data["y"].reshape(-1) inds = np.random.permutation(svhn_train.shape[0]) svhn_train = svhn_train[inds] train_label = train_label[inds] svhn_train = svhn_train[:25000] train_label = train_label[:25000] svhn_test = svhn_test[:9000] test_label = test_label[:9000] train_label[train_label == 10] = 0 test_label[test_label == 10] = 0 return svhn_train, train_label, svhn_test, test_label def load_syn(base_path): print("load syn") syn_train_data = loadmat(path.join(base_path, "synth_train_32x32.mat")) syn_test_data = loadmat(path.join(base_path, "synth_test_32x32.mat")) syn_train = syn_train_data["X"] syn_test = syn_test_data["X"] syn_train = syn_train.transpose(3, 2, 0, 1).astype(np.float32) syn_test = syn_test.transpose(3, 2, 0, 1).astype(np.float32) train_label = syn_train_data["y"].reshape(-1) test_label = syn_test_data["y"].reshape(-1) syn_train = syn_train[:25000] syn_test = syn_test[:9000] train_label = train_label[:25000] test_label = test_label[:9000] train_label[train_label == 10] = 0 test_label[test_label == 10] = 0 return syn_train, train_label, syn_test, test_label def load_usps(base_path): print("load usps") usps_dataset = loadmat(path.join(base_path, "usps_28x28.mat")) usps_dataset = usps_dataset["dataset"] usps_train = usps_dataset[0][0] train_label = usps_dataset[0][1] train_label = train_label.reshape(-1) train_label[train_label == 10] = 0 usps_test = usps_dataset[1][0] test_label = usps_dataset[1][1] test_label = test_label.reshape(-1) test_label[test_label == 10] = 0 usps_train = usps_train * 255 usps_test = usps_test * 255 usps_train = np.concatenate([usps_train, usps_train, usps_train], 1) usps_train = np.tile(usps_train, (4, 1, 1, 1)) train_label = np.tile(train_label,4) usps_train = usps_train[:25000] train_label = train_label[:25000] usps_test = np.concatenate([usps_test, usps_test, usps_test], 1) return usps_train, train_label, usps_test, test_label class Digit5Dataset(data.Dataset): def __init__(self, data, labels, transform=None, target_transform=None): super(Digit5Dataset, self).__init__() self.data = data self.labels = labels self.transform = transform self.target_transform = target_transform def __getitem__(self, index): img, label = self.data[index], self.labels[index] if img.shape[0] != 1: # transpose to Image type,so that the transform function can be used img = Image.fromarray(np.uint8(np.asarray(img.transpose((1, 2, 0))))) elif img.shape[0] == 1: im = np.uint8(np.asarray(img)) # turn the raw image into 3 channels im = np.vstack([im, im, im]).transpose((1, 2, 0)) img = Image.fromarray(im) # do transform with PIL if self.transform is not None: img = self.transform(img) if self.target_transform is not None: label = self.target_transform(label) return img, label def __len__(self): return self.data.shape[0] def digit5_dataset_read(base_path, domain): if domain == "mnist": train_image, train_label, test_image, test_label = load_mnist(base_path) elif domain == "mnistm": train_image, train_label, test_image, test_label = load_mnist_m(base_path) elif domain == "svhn": train_image, train_label, test_image, test_label = load_svhn(base_path) elif domain == "syn": train_image, train_label, test_image, test_label = load_syn(base_path) elif domain == "usps": train_image, train_label, test_image, test_label = load_usps(base_path) else: raise NotImplementedError("Domain {} Not Implemented".format(domain)) # define the transform function transform = transforms.Compose([ transforms.Resize(32), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # raise train and test data loader train_dataset = Digit5Dataset(data=train_image, labels=train_label, transform=transform) train_loader = DataLoader(dataset=train_dataset, batch_size=len(train_dataset), shuffle=False) test_dataset = Digit5Dataset(data=test_image, labels=test_label, transform=transform) test_loader = DataLoader(dataset=test_dataset, batch_size=len(test_dataset), shuffle=False) return train_loader, test_loader random.seed(1) np.random.seed(1) data_path = "Digit5/" dir_path = "Digit5/" # Allocate data to usersz`` 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 = data_path+"rawdata" # Get Digit5 data if not os.path.exists(root): os.makedirs(root) os.system(f'wget https://drive.google.com/u/0/uc?id=1PT6K-_wmsUEUCxoYzDy0mxF-15tvb2Eu&export=download -P {root}') os.system(f'unzip {root}/Digit5.zip -d {root}') X, y = [], [] domains = ['mnistm', 'mnist', 'syn', 'usps', 'svhn'] for d in domains: train_loader, test_loader = digit5_dataset_read(root, d) for _, tt in enumerate(train_loader): train_data, train_label = tt for _, tt in enumerate(test_loader): test_data, test_label = tt dataset_image = [] dataset_label = [] dataset_image.extend(train_data.cpu().detach().numpy()) dataset_image.extend(test_data.cpu().detach().numpy()) dataset_label.extend(train_label.cpu().detach().numpy()) dataset_label.extend(test_label.cpu().detach().numpy()) X.append(np.array(dataset_image)) y.append(np.array(dataset_label)) labelss = [] for yy in y: labelss.append(len(set(yy))) num_clients = len(y) print(f'Number of labels: {labelss}') print(f'Number of clients: {num_clients}') statistic = [[] for _ in range(num_clients)] for client in range(num_clients): for i in np.unique(y[client]): statistic[client].append((int(i), int(sum(y[client]==i)))) train_data, test_data = split_data(X, y) save_file(config_path, train_path, test_path, train_data, test_data, num_clients, max(labelss), statistic, None, None, None) if __name__ == "__main__": generate_dataset(dir_path)