# 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 time import numpy as np import os import random import torchvision.transforms as transforms from utils.dataset_utils import split_data, save_file from os import path from PIL import Image from torch.utils.data import DataLoader, Dataset # https://github.com/FengHZ/KD3A/blob/master/datasets/DomainNet.py def read_domainnet_data(dataset_path, domain_name, split="train"): data_paths = [] data_labels = [] split_file = path.join(dataset_path, "splits", "{}_{}.txt".format(domain_name, split)) with open(split_file, "r") as f: lines = f.readlines() for line in lines: line = line.strip() data_path, label = line.split(' ') data_path = path.join(dataset_path, data_path) label = int(label) data_paths.append(data_path) data_labels.append(label) return data_paths, data_labels class DomainNet(Dataset): def __init__(self, data_paths, data_labels, transforms, domain_name): super(DomainNet, self).__init__() self.data_paths = data_paths self.data_labels = data_labels self.transforms = transforms self.domain_name = domain_name def __getitem__(self, index): img = Image.open(self.data_paths[index]) if not img.mode == "RGB": img = img.convert("RGB") label = self.data_labels[index] img = self.transforms(img) return img, label def __len__(self): return len(self.data_paths) def get_domainnet_dloader(dataset_path, domain_name): train_data_paths, train_data_labels = read_domainnet_data(dataset_path, domain_name, split="train") test_data_paths, test_data_labels = read_domainnet_data(dataset_path, domain_name, split="test") transforms_train = transforms.Compose([ transforms.RandomResizedCrop(64, scale=(0.75, 1)), transforms.RandomHorizontalFlip(), transforms.ToTensor() ]) transforms_test = transforms.Compose([ transforms.Resize((64, 64)), transforms.ToTensor() ]) train_dataset = DomainNet(train_data_paths, train_data_labels, transforms_train, domain_name) train_loader = DataLoader(dataset=train_dataset, batch_size=len(train_dataset), shuffle=False) test_dataset = DomainNet(test_data_paths, test_data_labels, transforms_test, domain_name) 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 = "DomainNet/" dir_path = "DomainNet/" # 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 = data_path+"rawdata" domains = ['clipart', 'infograph', 'painting', 'quickdraw', 'real', 'sketch'] urls = [ 'http://csr.bu.edu/ftp/visda/2019/multi-source/groundtruth/clipart.zip', 'http://csr.bu.edu/ftp/visda/2019/multi-source/infograph.zip', 'http://csr.bu.edu/ftp/visda/2019/multi-source/groundtruth/painting.zip', 'http://csr.bu.edu/ftp/visda/2019/multi-source/quickdraw.zip', 'http://csr.bu.edu/ftp/visda/2019/multi-source/real.zip', 'http://csr.bu.edu/ftp/visda/2019/multi-source/sketch.zip', ] http_head = 'http://csr.bu.edu/ftp/visda/2019/multi-source/' # Get DomainNet data if not os.path.exists(root): os.makedirs(root) for d, u in zip(domains, urls): os.system(f'wget {u} -P {root}') os.system(f'unzip {root}/{d}.zip -d {root}') os.system(f'wget {http_head}domainnet/txt/{d}_train.txt -P {root}/splits') os.system(f'wget {http_head}domainnet/txt/{d}_test.txt -P {root}/splits') X, y = [], [] for d in domains: train_loader, test_loader = get_domainnet_dloader(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) # modify the code in YOUR_ENV/lib/python3.8/site-packages/numpy/lib Line #678 from protocol=3 to protocol=4 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)