train_cifar.py

import datetime
import os
import time
import torch
from torch.utils.data import DataLoader

import torch.nn as nn
import torch.nn.functional as F

from torch.utils.tensorboard import SummaryWriter
import sys
from torch.cuda import amp
from models import spiking_vgg
from modules import neuron, surrogate
import argparse
import math
from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torchtoolbox.transform import Cutout

_seed_ = 2022
import random
random.seed(_seed_)

torch.manual_seed(_seed_)  # use torch.manual_seed() to seed the RNG for all devices (both CPU and CUDA)
torch.cuda.manual_seed_all(_seed_)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

import numpy as np
np.random.seed(_seed_)

def main():

    parser = argparse.ArgumentParser(description='Classify CIFAR')
    parser.add_argument('-T', default=6, type=int, help='simulating time-steps')
    parser.add_argument('-tau', default=2., type=float)
    parser.add_argument('-b', default=128, type=int, help='batch size')
    parser.add_argument('-epochs', default=300, type=int, metavar='N',
                        help='number of total epochs to run')
    parser.add_argument('-j', default=4, type=int, metavar='N',
                        help='number of data loading workers (default: 4)')
    parser.add_argument('-data_dir', type=str, default=None)
    parser.add_argument('-dataset', default='cifar10', type=str)
    parser.add_argument('-out_dir', type=str, help='root dir for saving logs and checkpoint', default='./logs')

    parser.add_argument('-resume', type=str, help='resume from the checkpoint path')
    parser.add_argument('-amp', action='store_true', help='automatic mixed precision training')

    parser.add_argument('-opt', type=str, help='use which optimizer. SGD or Adam', default='SGD')
    parser.add_argument('-lr', default=0.1, type=float, help='learning rate')
    parser.add_argument('-momentum', default=0.9, type=float, help='momentum for SGD')
    parser.add_argument('-lr_scheduler', default='CosALR', type=str, help='use which schedule. StepLR or CosALR')
    parser.add_argument('-step_size', default=100, type=float, help='step_size for StepLR')
    parser.add_argument('-gamma', default=0.1, type=float, help='gamma for StepLR')
    parser.add_argument('-T_max', default=300, type=int, help='T_max for CosineAnnealingLR')
    parser.add_argument('-model', type=str, default='online_spiking_vgg11_ws')
    parser.add_argument('-drop_rate', type=float, default=0.0)
    parser.add_argument('-stochdepth_rate', type=float, default=0.0)
    parser.add_argument('-weight_decay', type=float, default=0.0)
    parser.add_argument('-cnf', type=str)
    parser.add_argument('-T_train', default=None, type=int)
    parser.add_argument('-dts_cache', type=str, default='./dts_cache')
    parser.add_argument('-loss_lambda', type=float, default=0.05)
    parser.add_argument('-online_update', action='store_true')

    parser.add_argument('-gpu-id', default='0', type=str, help='gpu id')

    args = parser.parse_args()
    #print(args)
    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id

    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        Cutout(),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])
    if args.dataset == 'cifar10':
        dataloader = datasets.CIFAR10
        num_classes = 10
    else:
        dataloader = datasets.CIFAR100
        num_classes = 100


    trainset = dataloader(root=args.data_dir, train=True, download=True, transform=transform_train)
    train_data_loader = data.DataLoader(trainset, batch_size=args.b, shuffle=True, num_workers=args.j)

    testset = dataloader(root=args.data_dir, train=False, download=False, transform=transform_test)
    test_data_loader = data.DataLoader(testset, batch_size=args.b, shuffle=False, num_workers=args.j)

    #net = spiking_vgg.__dict__[args.model](single_step_neuron=neuron.OnlineLIFNode, tau=args.tau, surrogate_function=surrogate.QuasiClamp(alpha=1.), track_rate=True, c_in=3, num_classes=num_classes, neuron_dropout=args.drop_rate, grad_with_rate=True, fc_hw=1, v_reset=None)
    net = spiking_vgg.__dict__[args.model](single_step_neuron=neuron.OnlineLIFNode, tau=args.tau, surrogate_function=surrogate.Sigmoid(alpha=4.), track_rate=True, c_in=3, num_classes=num_classes, neuron_dropout=args.drop_rate, grad_with_rate=True, fc_hw=1, v_reset=None)
    #print(net)
    print('Total Parameters: %.2fM' % (sum(p.numel() for p in net.parameters()) / 1000000.0))
    net.cuda()


    optimizer = None
    if args.opt == 'SGD':
        optimizer = torch.optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
    elif args.opt == 'Adam':
        optimizer = torch.optim.AdamW(net.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    else:
        raise NotImplementedError(args.opt)

    lr_scheduler = None
    if args.lr_scheduler == 'StepLR':
        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=args.gamma)
    elif args.lr_scheduler == 'CosALR':
        lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.T_max)
    else:
        raise NotImplementedError(args.lr_scheduler)

    scaler = None
    if args.amp:
        scaler = amp.GradScaler()

    start_epoch = 0
    max_test_acc = 0

    if args.resume:
        checkpoint = torch.load(args.resume, map_location='cpu')
        net.load_state_dict(checkpoint['net'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
        start_epoch = checkpoint['epoch'] + 1
        max_test_acc = checkpoint['max_test_acc']

    out_dir = os.path.join(args.out_dir, f'{args.model}_{args.cnf}_T_{args.T}_T_train_{args.T_train}_{args.opt}_lr_{args.lr}_')
    if args.lr_scheduler == 'CosALR':
        out_dir += f'CosALR_{args.T_max}'
    elif args.lr_scheduler == 'StepLR':
        out_dir += f'StepLR_{args.step_size}_{args.gamma}'
    else:
        raise NotImplementedError(args.lr_scheduler)

    if args.amp:
        out_dir += '_amp'


    if not os.path.exists(out_dir):
        os.makedirs(out_dir)
        print(f'Mkdir {out_dir}.')
    else:
        print(out_dir)
        #assert args.resume is not None

    pt_dir = out_dir + '_pt'
    if not os.path.exists(pt_dir):
        os.makedirs(pt_dir)
        print(f'Mkdir {pt_dir}.')


    with open(os.path.join(out_dir, 'args.txt'), 'w', encoding='utf-8') as args_txt:
        args_txt.write(str(args))

    writer = SummaryWriter(os.path.join(out_dir, 'logs'), purge_step=start_epoch)
    
    criterion_mse = nn.MSELoss(reduce=True)

    for epoch in range(start_epoch, args.epochs):
        start_time = time.time()
        net.train()

        batch_time = AverageMeter()
        data_time = AverageMeter()
        losses = AverageMeter()
        top1 = AverageMeter()
        top5 = AverageMeter()
        end = time.time()

        bar = Bar('Processing', max=len(train_data_loader))

        train_loss = 0
        train_acc = 0
        train_samples = 0
        batch_idx = 0
        for frame, label in train_data_loader:
            batch_idx += 1
            frame = frame.float().cuda()
            t_step = args.T

            label = label.cuda()

            batch_loss = 0
            if not args.online_update:
                optimizer.zero_grad()
            for t in range(t_step):
                if args.online_update:
                    optimizer.zero_grad()
                input_frame = frame
                if args.amp:
                    with amp.autocast():
                        if t == 0:
                            out_fr = net(input_frame, init=True)
                            total_fr = out_fr.clone().detach()
                        else:
                            out_fr = net(input_frame)
                            total_fr += out_fr.clone().detach()
                            #total_fr = total_fr * (1 - 1. / args.tau) + out_fr
                        if args.loss_lambda > 0.0:
                            label_one_hot = F.one_hot(label, num_classes).float()
                            mse_loss = criterion_mse(out_fr, label_one_hot)
                            loss = ((1 - args.loss_lambda) * F.cross_entropy(out_fr, label) + args.loss_lambda * mse_loss) / t_step
                        else:
                            loss = F.cross_entropy(out_fr, label) / t_step
                    scaler.scale(loss).backward()

                    if args.online_update:
                        scaler.step(optimizer)
                        scaler.update()
                else:
                    if t == 0:
                        out_fr = net(input_frame, init=True)
                        total_fr = out_fr.clone().detach()
                    else:
                        out_fr = net(input_frame)
                        total_fr += out_fr.clone().detach()
                        #total_fr = total_fr * (1 - 1. / args.tau) + out_fr
                    if args.loss_lambda > 0.0:
                        label_one_hot = F.one_hot(label, num_classes).float()
                        mse_loss = criterion_mse(out_fr, label_one_hot)
                        loss = ((1 - args.loss_lambda) * F.cross_entropy(out_fr, label) + args.loss_lambda * mse_loss) / t_step
                    else:
                        loss = F.cross_entropy(out_fr, label) / t_step
                    loss.backward()

                    if args.online_update:
                        optimizer.step()

                batch_loss += loss.item()
                train_loss += loss.item() * label.numel()
            if not args.online_update:
                if args.amp:
                    scaler.step(optimizer)
                    scaler.update()
                else:
                    optimizer.step()

            # measure accuracy and record loss
            prec1, prec5 = accuracy(total_fr.data, label.data, topk=(1, 5))
            losses.update(batch_loss, input_frame.size(0))
            top1.update(prec1.item(), input_frame.size(0))
            top5.update(prec5.item(), input_frame.size(0))


            train_samples += label.numel()
            train_acc += (total_fr.argmax(1) == label).float().sum().item()

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            # plot progress
            bar.suffix  = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
                        batch=batch_idx,
                        size=len(train_data_loader),
                        data=data_time.avg,
                        bt=batch_time.avg,
                        total=bar.elapsed_td,
                        eta=bar.eta_td,
                        loss=losses.avg,
                        top1=top1.avg,
                        top5=top5.avg,
                        )
            bar.next()
        bar.finish()

        train_loss /= train_samples
        train_acc /= train_samples

        writer.add_scalar('train_loss', train_loss, epoch)
        writer.add_scalar('train_acc', train_acc, epoch)
        lr_scheduler.step()

        net.eval()

        batch_time = AverageMeter()
        data_time = AverageMeter()
        losses = AverageMeter()
        top1 = AverageMeter()
        top5 = AverageMeter()
        end = time.time()
        bar = Bar('Processing', max=len(test_data_loader))

        test_loss = 0
        test_acc = 0
        test_samples = 0
        batch_idx = 0
        with torch.no_grad():
            for frame, label in test_data_loader:
                batch_idx += 1
                frame = frame.float().cuda()
                label = label.cuda()
                t_step = args.T
                total_loss = 0

                for t in range(t_step):
                    input_frame = frame
                    if t == 0:
                        out_fr = net(input_frame, init=True)
                        total_fr = out_fr.clone().detach()
                    else:
                        out_fr = net(input_frame)
                        total_fr += out_fr.clone().detach()
                        #total_fr = total_fr * (1 - 1. / args.tau) + out_fr
                    if args.loss_lambda > 0.0:
                        label_one_hot = F.one_hot(label, num_classes).float()
                        mse_loss = criterion_mse(out_fr, label_one_hot)
                        loss = ((1 - args.loss_lambda) * F.cross_entropy(out_fr, label) + args.loss_lambda * mse_loss) / t_step
                    else:
                        loss = F.cross_entropy(out_fr, label) / t_step
                    total_loss += loss

                test_samples += label.numel()
                test_loss += total_loss.item() * label.numel()
                test_acc += (total_fr.argmax(1) == label).float().sum().item()

                # measure accuracy and record loss
                prec1, prec5 = accuracy(total_fr.data, label.data, topk=(1, 5))
                losses.update(total_loss, input_frame.size(0))
                top1.update(prec1.item(), input_frame.size(0))
                top5.update(prec5.item(), input_frame.size(0))

                # measure elapsed time
                batch_time.update(time.time() - end)
                end = time.time()

                # plot progress
                bar.suffix  = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
                            batch=batch_idx,
                            size=len(test_data_loader),
                            data=data_time.avg,
                            bt=batch_time.avg,
                            total=bar.elapsed_td,
                            eta=bar.eta_td,
                            loss=losses.avg,
                            top1=top1.avg,
                            top5=top5.avg,
                            )
                bar.next()
        bar.finish()

        test_loss /= test_samples
        test_acc /= test_samples
        writer.add_scalar('test_loss', test_loss, epoch)
        writer.add_scalar('test_acc', test_acc, epoch)

        save_max = False
        if test_acc > max_test_acc:
            max_test_acc = test_acc
            save_max = True

        checkpoint = {
            'net': net.state_dict(),
            'optimizer': optimizer.state_dict(),
            'lr_scheduler': lr_scheduler.state_dict(),
            'epoch': epoch,
            'max_test_acc': max_test_acc
        }

        if save_max:
            torch.save(checkpoint, os.path.join(pt_dir, 'checkpoint_max.pth'))

        torch.save(checkpoint, os.path.join(pt_dir, 'checkpoint_latest.pth'))
        #for item in sys.argv:
        #    print(item, end=' ')
        #print('')
        #print(args)
        #print(out_dir)
        total_time = time.time() - start_time
        print(f'epoch={epoch}, train_loss={train_loss}, train_acc={train_acc}, test_loss={test_loss}, test_acc={test_acc}, max_test_acc={max_test_acc}, total_time={total_time}, escape_time={(datetime.datetime.now()+datetime.timedelta(seconds=total_time * (args.epochs - epoch))).strftime("%Y-%m-%d %H:%M:%S")}')

if __name__ == '__main__':
    main()