aDPtorch/src/federated_main.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6


import os
import copy
import time
import pickle
import numpy as np
from tqdm import tqdm

import torch
from tensorboardX import SummaryWriter

from options import args_parser
from update import LocalUpdate, test_inference
from models import MLP, CNNMnist, CNNFashion_Mnist, CNNCifar
from utils import get_dataset, average_weights, exp_details
# os.environ['CUDA_VISIBLE_DEVICES'] ='0'

if __name__ == '__main__':
    start_time = time.time()

    # define paths
    path_project = os.path.abspath('..')
    logger = SummaryWriter('../logs')

    args = args_parser()
    exp_details(args)

    if args.gpu:
        torch.cuda.set_device(args.gpu)
        # torch.cuda.set_device(0)
    device = 'cuda' if args.gpu else 'cpu'

    # load dataset and user groups
    train_dataset, test_dataset, user_groups = get_dataset(args)

    # BUILD MODEL
    if args.model == 'cnn':
        # Convolutional neural netork
        if args.dataset == 'mnist':
            global_model = CNNMnist(args=args)
        elif args.dataset == 'fmnist':
            global_model = CNNFashion_Mnist(args=args)
        elif args.dataset == 'cifar':
            global_model = CNNCifar(args=args)

    elif args.model == 'mlp':
        # Multi-layer preceptron
        img_size = train_dataset[0][0].shape
        len_in = 1
        for x in img_size:
            len_in *= x
            global_model = MLP(dim_in=len_in, dim_hidden=args.mlpdim,
                               dim_out=args.num_classes)
    else:
        exit('Error: unrecognized model')

    # Set the model to train and send it to device.
    global_model.to(device)
    global_model.train()
    print(global_model)
    
    # MODEL PARAM SUMMARY
    pytorch_total_params = sum(p.numel() for p in global_model.parameters())
    print("Model total number of parameters: ", pytorch_total_params)
    # print(global_model.parameters())

    # copy weights
    global_weights = global_model.state_dict()

    # Training
    train_loss, train_accuracy = [], []
    val_acc_list, net_list = [], []
    cv_loss, cv_acc = [], []
    print_every = 1
    val_loss_pre, counter = 0, 0
    testacc_check, epoch = 0, 0 

    # for epoch in tqdm(range(args.epochs)):  # global training epochs
    for epoch in range(args.epochs):
    # while testacc_check < args.test_acc or epoch < args.epochs:
    # while testacc_check < args.test_acc:
        local_weights, local_losses = [], [] # init empty local weights and local losses
        print(f'\n | Global Training Round : {epoch+1} |\n') # starting with | Global Training Round : 1 |

        """
        model.train() tells your model that you are training the model. So effectively layers like dropout, batchnorm etc. which behave different on the train and test procedures know what is going on and hence can behave accordingly.

        More details: It sets the mode to train (see source code). You can call either model.eval() or model.train(mode=False) to tell that you are testing. It is somewhat intuitive to expect train function to train model but it does not do that. It just sets the mode.
        """
        # ============== TRAIN ============== 
        global_model.train()
        m = max(int(args.frac * args.num_users), 1) # C = args.frac. Setting number of clients m for training
        idxs_users = np.random.choice(range(args.num_users), m, replace=False) # args.num_users=100 total clients. Choosing a random array of indices. Subset of clients.

        for idx in idxs_users: # For each client in the subset.
            local_model = LocalUpdate(args=args, dataset=train_dataset,
                                      idxs=user_groups[idx], logger=logger)
            w, loss = local_model.update_weights( # update_weights() contain multiple prints
                model=copy.deepcopy(global_model), global_round=epoch) # w = local model weights
            local_weights.append(copy.deepcopy(w))
            local_losses.append(copy.deepcopy(loss))

        # Averaging m local client weights
        global_weights = average_weights(local_weights)

        # update global weights
        global_model.load_state_dict(global_weights)

        loss_avg = sum(local_losses) / len(local_losses)
        train_loss.append(loss_avg) # Performance measure

        # ============== EVAL ============== 
        # Calculate avg training accuracy over all users at every epoch
        list_acc, list_loss = [], []
        global_model.eval() # must set your model into evaluation mode when computing model output values if dropout or bach norm used for training.

        for c in range(args.num_users): # 0 to 99
            # local_model = LocalUpdate(args=args, dataset=train_dataset,
            #                           idxs=user_groups[idx], logger=logger)
            # Fix error idxs=user_groups[idx] to idxs=user_groups[c]                                      
            local_model = LocalUpdate(args=args, dataset=train_dataset,
                                      idxs=user_groups[c], logger=logger)
            acc, loss = local_model.inference(model=global_model)
            list_acc.append(acc)
            list_loss.append(loss)
        train_accuracy.append(sum(list_acc)/len(list_acc)) # Performance measure

        # Add
        testacc_check = 100*train_accuracy[-1]
        epoch = epoch + 1

        # print global training loss after every 'i' rounds
        if (epoch+1) % print_every == 0: # If print_every=2, => print every 2 rounds
            print(f' \nAvg Training Stats after {epoch+1} global rounds:')
            print(f'Training Loss : {np.mean(np.array(train_loss))}')
            print('Train Accuracy: {:.2f}% \n'.format(100*train_accuracy[-1]))

    # Test inference after completion of training
    test_acc, test_loss = test_inference(args, global_model, test_dataset)

    print(f' \n Results after {epoch} global rounds of training:')
    print("|---- Avg Train Accuracy: {:.2f}%".format(100*train_accuracy[-1]))
    print("|---- Test Accuracy: {:.2f}%".format(100*test_acc))

    # Saving the objects train_loss and train_accuracy:
    file_name = '../save/objects/FL_{}_{}_{}_lr[{}]_C[{}]_iid[{}]_E[{}]_B[{}].pkl'.\
        format(args.dataset, args.model, epoch, args.lr, args.frac, args.iid,
               args.local_ep, args.local_bs)

    with open(file_name, 'wb') as f:
        pickle.dump([train_loss, train_accuracy], f)

    print('\n Total Run Time: {0:0.4f}'.format(time.time()-start_time))

    # PLOTTING (optional)
    # import matplotlib
    # import matplotlib.pyplot as plt
    # matplotlib.use('Agg')

    # Plot Loss curve
    # plt.figure()
    # plt.title('Training Loss vs Communication rounds')
    # plt.plot(range(len(train_loss)), train_loss, color='r')
    # plt.ylabel('Training loss')
    # plt.xlabel('Communication Rounds')
    # plt.savefig('../save/fed_{}_{}_{}_C[{}]_iid[{}]_E[{}]_B[{}]_loss.png'.
    #             format(args.dataset, args.model, args.epochs, args.frac,
    #                    args.iid, args.local_ep, args.local_bs))
    #
    # # Plot Average Accuracy vs Communication rounds
    # plt.figure()
    # plt.title('Average Accuracy vs Communication rounds')
    # plt.plot(range(len(train_accuracy)), train_accuracy, color='k')
    # plt.ylabel('Average Accuracy')
    # plt.xlabel('Communication Rounds')
    # plt.savefig('../save/fed_{}_{}_{}_C[{}]_iid[{}]_E[{}]_B[{}]_acc.png'.
    #             format(args.dataset, args.model, args.epochs, args.frac,
    #                    args.iid, args.local_ep, args.local_bs))