cifar_cifarnetf.py

from __future__ import print_function

import argparse
import os
import shutil
import time
import random

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.nn.functional as F
import numpy as np
from models.snn_spide_cifar import SNNSPIDEConvMultiLayerNet

from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig


parser = argparse.ArgumentParser(description='PyTorch CIFAR10/100 SNN Training')
# Datasets
parser.add_argument('-d', '--dataset', default='cifar10', type=str)
parser.add_argument('--path', default='./data', type=str)
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
# SNN settings
parser.add_argument('-t', '--time_step', default=30, type=int)
parser.add_argument('-tb', '--time_step_back', default=250, type=int)
parser.add_argument('--vth', default=1., type=float)
parser.add_argument('--vth_back', default=0.5, type=float)
parser.add_argument('--u_rest', default=None, type=float)
parser.add_argument('--u_rest_back', default=None, type=float)
# for IF neuron, leaky=1., for LIF neuron, leaky<1
parser.add_argument('--leaky', default=1., type=float)
# Optimization options
parser.add_argument('--epochs', default=100, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('--train-batch', default=128, type=int, metavar='N',
                    help='train batchsize')
parser.add_argument('--test-batch', default=200, type=int, metavar='N',
                    help='test batchsize')
parser.add_argument('--lr', '--learning-rate', default=0.05, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--warmup', default=400, type=int)
parser.add_argument('--drop', '--dropout', default=0.25, type=float,
                    metavar='Dropout', help='Dropout ratio')
parser.add_argument('--schedule', type=int, nargs='+', default=[50, 75],
                        help='Decrease learning rate at these epochs.')
parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--optimizer', default='SGD', type=str, help='which optimizer')
parser.add_argument('--scale_factor', default=400., type=float)
# Checkpoints
parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',
                    help='path to save checkpoint (default: checkpoint)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
# Miscs
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
#Device options
parser.add_argument('--gpu-id', default='0', type=str,
                    help='id(s) for CUDA_VISIBLE_DEVICES')

args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}

# Validate dataset
assert args.dataset == 'cifar10' or args.dataset == 'cifar100', 'Dataset can only be cifar10 or cifar100.'

# Use CUDA
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
use_cuda = torch.cuda.is_available()

# Random seed
if args.manualSeed is None:
    args.manualSeed = random.randint(1, 10000)
random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
if use_cuda:
    torch.cuda.manual_seed_all(args.manualSeed)

best_acc = 0  # best test accuracy
current_iter = 0

def main():
    global best_acc
    start_epoch = args.start_epoch  # start from epoch 0 or last checkpoint epoch

    if not os.path.isdir(args.checkpoint):
        mkdir_p(args.checkpoint)

    # Data
    print('==> Preparing dataset %s' % args.dataset)
    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        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.path, train=True, download=True, transform=transform_train)
    trainloader = data.DataLoader(trainset, batch_size=args.train_batch, shuffle=True, num_workers=args.workers)

    testset = dataloader(root=args.path, train=False, download=False, transform=transform_test)
    testloader = data.DataLoader(testset, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)

    # Model
    print("==> creating model SNN_Conv")
    in_planes = 3
    out_planes = num_classes
    in_pixels = 32
    vth = torch.tensor(args.vth).cuda()

    config = {}
    config['MODEL'] = {}
    config['MODEL']['c_in'] = in_planes
    config['MODEL']['c_hidden'] = 128
    config['MODEL']['num_classes'] = num_classes
    config['MODEL']['kernel_size_x'] = 3
    config['MODEL']['stride_x'] = 2
    config['MODEL']['padding_x'] = 1
    config['MODEL']['pooling_x'] = False
    config['MODEL']['c_s1'] = 256
    config['MODEL']['c_s2'] = 512
    config['MODEL']['c_s3'] = 1024
    config['MODEL']['c_s4'] = 512
    config['MODEL']['kernel_size_s'] = 3
    config['MODEL']['h_hidden'] = 8
    config['MODEL']['w_hidden'] = 8
    config['MODEL']['dropout'] = args.drop
    config['MODEL']['time_step'] = args.time_step
    config['MODEL']['time_step_back'] = args.time_step_back
    config['MODEL']['vth'] = args.vth
    if args.vth_back != 0:
        config['MODEL']['vth_back'] = args.vth_back
    if args.u_rest != None:
        config['MODEL']['u_rest'] = args.u_rest
    if args.u_rest_back != None:
        config['MODEL']['u_rest_back'] = args.u_rest_back
    if args.leaky < 1.:
        config['MODEL']['leaky'] = args.leaky
    else:
        config['MODEL']['leaky'] = None
    model = SNNSPIDEConvMultiLayerNet(config)

    model = torch.nn.DataParallel(model).cuda()
    cudnn.benchmark = True
    print('    Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
    criterion = nn.CrossEntropyLoss()
    criterion_mse = nn.MSELoss(reduce=True)
    if args.optimizer == 'SGD':
        optimizer = optim.SGD(model.parameters(), lr=args.lr/args.scale_factor, momentum=args.momentum, weight_decay=args.weight_decay)
    else:
        optimizer = optim.Adam(model.parameters(), lr=args.lr/args.scale_factor, weight_decay=args.weight_decay, betas=(0.9, 0.999))

    # Resume
    title = 'cifar-' + 'SNN_Conv'
    if args.resume:
        # Load checkpoint.
        print('==> Resuming from checkpoint..')
        assert os.path.isfile(args.resume), 'Error: no checkpoint directory found!'
        args.checkpoint = os.path.dirname(args.resume)
        checkpoint = torch.load(args.resume)
        best_acc = checkpoint['best_acc']
        start_epoch = checkpoint['epoch']
        model.load_state_dict(checkpoint['state_dict'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
    else:
        logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
        logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.', 'Train Forward Firing Rate', 'Train Backward Firing Rate', 'Test Forward Firing Rate'])


    if args.evaluate:
        print('\nEvaluation only')
        test_loss, test_acc, test_forward_firing_rate = test(testloader, model, criterion, start_epoch, use_cuda)
        print(' Test Loss:  %.8f, Test Acc:  %.2f' % (test_loss, test_acc))
        return

    # Train and val
    for epoch in range(start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch)

        print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, state['lr']))

        train_loss, train_acc, train_forward_firing_rate, train_backward_firing_rate = train(trainloader, model, criterion, optimizer, epoch, use_cuda, args.time_step, num_classes, warmup=args.warmup)
        test_loss, test_acc, test_forward_firing_rate = test(testloader, model, criterion, epoch, use_cuda, args.time_step, num_classes)

        # append logger file
        logger.append([state['lr'], train_loss, test_loss, train_acc, test_acc, train_forward_firing_rate, train_backward_firing_rate, test_forward_firing_rate])

        ## save model
        is_best = test_acc > best_acc
        best_acc = max(test_acc, best_acc)
        save_checkpoint({
                'epoch': epoch + 1,
                'state_dict': model.state_dict(),
                'acc': test_acc,
                'best_acc': best_acc,
                'optimizer' : optimizer.state_dict(),
            }, is_best, checkpoint=args.checkpoint)

    logger.close()

    print('Best acc:')
    print(best_acc)

    logger.plot()
    savefig(os.path.join(args.checkpoint, 'log.eps'))


def train(trainloader, model, criterion, optimizer, epoch, use_cuda, time_step, num_classes, warmup=0):
    # switch to train mode
    model.train()

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

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

    global current_iter

    average_forward_firing_rate = 0.
    average_backward_firing_rate = 0.
    batch_num = 0

    for batch_idx, (inputs, targets) in enumerate(trainloader):
        if warmup != 0 and current_iter < warmup:
            adjust_warmup_lr(optimizer, current_iter, warmup)
            current_iter += 1
        # measure data loading time
        data_time.update(time.time() - end)

        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()
        inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)

        # compute output
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
        losses.update(loss.data.item(), inputs.size(0))
        top1.update(prec1.item(), inputs.size(0))
        top5.update(prec5.item(), inputs.size(0))

        # compute gradient and do SGD step
        scaled_loss = loss * args.scale_factor
        optimizer.zero_grad()
        #loss.backward()
        scaled_loss.backward()
        optimizer.step()

        # get firing rate statistics
        forward_firing_rate = model.module.get_forward_firing_rate()
        backward_firing_rate = model.module.get_backward_firing_rate()
        average_forward_firing_rate += forward_firing_rate
        average_backward_firing_rate += backward_firing_rate
        batch_num += inputs.shape[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 + 1,
                    size=len(trainloader),
                    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()
    return (losses.avg, top1.avg, average_forward_firing_rate/batch_num, average_backward_firing_rate/batch_num)


def test(testloader, model, criterion, epoch, use_cuda, time_step, num_classes):
    global best_acc

    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    bar = Bar('Processing', max=len(testloader))

    average_forward_firing_rate = 0.
    batch_num = 0

    for batch_idx, (inputs, targets) in enumerate(testloader):
        # measure data loading time
        data_time.update(time.time() - end)

        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()
        inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)

        # compute output
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
        losses.update(loss.data.item(), inputs.size(0))
        top1.update(prec1.item(), inputs.size(0))
        top5.update(prec5.item(), inputs.size(0))

        # get firing rate statistics
        forward_firing_rate = model.module.get_forward_firing_rate()
        average_forward_firing_rate += forward_firing_rate
        batch_num += inputs.shape[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 + 1,
                    size=len(testloader),
                    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()
    return (losses.avg, top1.avg, average_forward_firing_rate/batch_num)

def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar'):
    filepath = os.path.join(checkpoint, filename)
    torch.save(state, filepath)
    if is_best:
        shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar'))

def adjust_learning_rate(optimizer, epoch):
    global state
    if epoch in args.schedule:
        state['lr'] *= args.gamma
        for param_group in optimizer.param_groups:
            param_group['lr'] = state['lr'] / state['scale_factor']

def adjust_warmup_lr(optimizer, citer, warmup):
    for param_group in optimizer.param_groups:
        param_group['lr'] = state['lr'] / state['scale_factor'] * (citer + 1.) / warmup

if __name__ == '__main__':
    main()