PFLlib/dataset/generate_Country211.py

import numpy as np
import os
import sys
import random
import torch
import torchvision
import torchvision.transforms as transforms
from utils.dataset_utils import check, separate_data, split_data, save_file


random.seed(1)
np.random.seed(1)
num_clients = 20
dir_path = "Country211/"


# Allocate data to users
def generate_dataset(dir_path, num_clients, niid, balance, partition):
    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 check(config_path, train_path, test_path, num_clients, niid, balance, partition):
        return

    dataset_image = []
    dataset_label = []
        
    # Get Country211 data
    transform = transforms.Compose(
        [transforms.Resize((64, 64)), 
        transforms.ToTensor(), 
        transforms.Normalize((0.5), (0.5))]
    )

    def load_data(split="train"):
        trainset = torchvision.datasets.Country211(
            root=dir_path+"rawdata", split=split, download=True, transform=transform)
        trainloader = torch.utils.data.DataLoader(
            trainset, batch_size=len(trainset), shuffle=False)
        for _, train_data in enumerate(trainloader, 0):
            trainset.data, trainset.targets = train_data
        dataset_image.extend(trainset.data.cpu().detach().numpy())
        dataset_label.extend(trainset.targets.cpu().detach().numpy())

    load_data("train")
    load_data("valid")
    load_data("test")

    dataset_image = np.array(dataset_image)
    dataset_label = np.array(dataset_label)

    num_classes = len(set(dataset_label))
    print(f'Number of classes: {num_classes}')

    X, y, statistic = separate_data((dataset_image, dataset_label), num_clients, num_classes, 
                                    niid, balance, partition, class_per_client=20)
    train_data, test_data = split_data(X, y)
    save_file(config_path, train_path, test_path, train_data, test_data, num_clients, num_classes, 
        statistic, niid, balance, partition)


if __name__ == "__main__":
    niid = True if sys.argv[1] == "noniid" else False
    balance = True if sys.argv[2] == "balance" else False
    partition = sys.argv[3] if sys.argv[3] != "-" else None

    generate_dataset(dir_path, num_clients, niid, balance, partition)