main.py

from __future__ import division
from __future__ import absolute_import

import os
import sys
import shutil
import time
import random
import argparse
import torch
import torch.backends.cudnn as cudnn
import torchvision.datasets as dset
import torchvision.transforms as transforms
from utils import AverageMeter, RecorderMeter, time_string, convert_secs2time
from utils_.reorganize_param import reorganize_param
# from tensorboardX import SummaryWriter
import models

# import yellowFin tuner
sys.path.append("./tuner_utils")
from tuner_utils.yellowfin import YFOptimizer

model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))

################# Options ##################################################
############################################################################
parser = argparse.ArgumentParser(description='Training network for image classification',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)

parser.add_argument('--data_path', default='/home/elliot/data/pytorch/svhn/',
                    type=str, help='Path to dataset')
parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'imagenet', 'svhn', 'stl10', 'mnist'],
                    help='Choose between Cifar10/100 and ImageNet.')
parser.add_argument('--arch', metavar='ARCH', default='lbcnn', choices=model_names,
                    help='model architecture: ' + ' | '.join(model_names) + ' (default: resnext29_8_64)')
# Optimization options
parser.add_argument('--epochs', type=int, default=200,
                    help='Number of epochs to train.')
parser.add_argument('--optimizer', type=str, default='SGD',
                    choices=['SGD', 'Adam', 'YF'])
parser.add_argument('--batch_size', type=int, default=128, help='Batch size.')
parser.add_argument('--learning_rate', type=float,
                    default=0.001, help='The Learning Rate.')
parser.add_argument('--momentum', type=float, default=0.9, help='Momentum.')
parser.add_argument('--decay', type=float, default=1e-4,
                    help='Weight decay (L2 penalty).')
parser.add_argument('--schedule', type=int, nargs='+', default=[80, 120],
                    help='Decrease learning rate at these epochs.')
parser.add_argument('--gammas', type=float, nargs='+', default=[0.1, 0.1],
                    help='LR is multiplied by gamma on schedule, number of gammas should be equal to schedule')
# Checkpoints
parser.add_argument('--print_freq', default=100, type=int,
                    metavar='N', help='print frequency (default: 200)')
parser.add_argument('--save_path', type=str, default='./save/',
                    help='Folder to save checkpoints and log.')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('--start_epoch', default=0, type=int,
                    metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('--evaluate', dest='evaluate',
                    action='store_true', help='evaluate model on validation set')
parser.add_argument('--fine_tune', dest='fine_tune', action='store_true',
                    help='fine tuning from the pre-trained model, force the start epoch be zero')
parser.add_argument('--model_only', dest='model_only', action='store_true',
                    help='only save the model without external utils_')
# Acceleration
parser.add_argument('--ngpu', type=int, default=1, help='0 = CPU.')
parser.add_argument('--gpu_id', type=int, default=0,
                    help='device range [0,ngpu-1]')
parser.add_argument('--workers', type=int, default=4,
                    help='number of data loading workers (default: 2)')
# random seed
parser.add_argument('--manualSeed', type=int, default=5000, help='manual seed')

##########################################################################

args = parser.parse_args()

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if args.ngpu == 1:
    # make only device #gpu_id visible, then
    os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id)

args.use_cuda = args.ngpu > 0 and torch.cuda.is_available()  # check GPU

# Give a random seed if no manual configuration
if args.manualSeed is None:
    args.manualSeed = random.randint(1, 10000)
random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)

if args.use_cuda:
    torch.cuda.manual_seed_all(args.manualSeed)

cudnn.benchmark = True


###############################################################################
###############################################################################

def main():
    # Init logger6
    if not os.path.isdir(args.save_path):
        os.makedirs(args.save_path)
    log = open(os.path.join(args.save_path,
                            'log_seed_{}.txt'.format(args.manualSeed)), 'w')
    print_log('save path : {}'.format(args.save_path), log)
    state = {k: v for k, v in args._get_kwargs()}
    print_log(state, log)
    print_log("Random Seed: {}".format(args.manualSeed), log)
    print_log("python version : {}".format(
        sys.version.replace('\n', ' ')), log)
    print_log("torch  version : {}".format(torch.__version__), log)
    print_log("cudnn  version : {}".format(
        torch.backends.cudnn.version()), log)

    # Init the tensorboard path and writer
    tb_path = os.path.join(args.save_path, 'tb_log')
    # logger = Logger(tb_path)
    # writer = SummaryWriter(tb_path)

    # Init dataset
    if not os.path.isdir(args.data_path):
        os.makedirs(args.data_path)

    if args.dataset == 'cifar10':
        mean = [x / 255 for x in [125.3, 123.0, 113.9]]
        std = [x / 255 for x in [63.0, 62.1, 66.7]]
    elif args.dataset == 'cifar100':
        mean = [x / 255 for x in [129.3, 124.1, 112.4]]
        std = [x / 255 for x in [68.2, 65.4, 70.4]]
    elif args.dataset == 'svhn':
        mean = [0.5, 0.5, 0.5]
        std = [0.5, 0.5, 0.5]
    elif args.dataset == 'mnist':
        mean = [0.5, 0.5, 0.5]
        std = [0.5, 0.5, 0.5]
    elif args.dataset == 'imagenet':
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
    else:
        assert False, "Unknow dataset : {}".format(args.dataset)

    if args.dataset == 'imagenet':
        train_transform = transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize(mean, std)
        ])
        test_transform = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean, std)
        ])  # here is actually the validation dataset
    else:
        train_transform = transforms.Compose([
            transforms.RandomHorizontalFlip(),
            transforms.RandomCrop(32, padding=4),
            transforms.ToTensor(),
            transforms.Normalize(mean, std)
        ])
        test_transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(mean, std)
        ])

    if args.dataset == 'mnist':
        train_data = dset.MNIST(
            args.data_path, train=True, transform=train_transform, download=True)
        test_data = dset.MNIST(args.data_path, train=False,
                               transform=test_transform, download=True)
        num_classes = 10
    elif args.dataset == 'cifar10':
        train_data = dset.CIFAR10(
            args.data_path, train=True, transform=train_transform, download=True)
        test_data = dset.CIFAR10(
            args.data_path, train=False, transform=test_transform, download=True)
        num_classes = 10
    elif args.dataset == 'cifar100':
        train_data = dset.CIFAR100(
            args.data_path, train=True, transform=train_transform, download=True)
        test_data = dset.CIFAR100(
            args.data_path, train=False, transform=test_transform, download=True)
        num_classes = 100
    elif args.dataset == 'svhn':
        train_data = dset.SVHN(args.data_path, split='train',
                               transform=train_transform, download=True)
        test_data = dset.SVHN(args.data_path, split='test',
                              transform=test_transform, download=True)
        num_classes = 10
    elif args.dataset == 'stl10':
        train_data = dset.STL10(
            args.data_path, split='train', transform=train_transform, download=True)
        test_data = dset.STL10(args.data_path, split='test',
                               transform=test_transform, download=True)
        num_classes = 10
    elif args.dataset == 'imagenet':
        train_dir = os.path.join(args.data_path, 'train')
        test_dir = os.path.join(args.data_path, 'val')
        train_data = dset.ImageFolder(train_dir, transform=train_transform)
        test_data = dset.ImageFolder(test_dir, transform=test_transform)
        num_classes = 1000
    else:
        assert False, 'Do not support dataset : {}'.format(args.dataset)

    train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True,
                                               num_workers=args.workers, pin_memory=True)
    test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False,
                                              num_workers=args.workers, pin_memory=True)

    print_log("=> creating model '{}'".format(args.arch), log)

    # Init model, criterion, and optimizer
    net = models.__dict__[args.arch](num_classes)
    print_log("=> network :\n {}".format(net), log)

    if args.use_cuda:
        if args.ngpu > 1:
            net = torch.nn.DataParallel(net, device_ids=list(range(args.ngpu)))

    # define loss function (criterion) and optimizer
    criterion = torch.nn.CrossEntropyLoss()

    if args.optimizer == "SGD":
        print("using SGD as optimizer")
        optimizer = torch.optim.SGD(filter(lambda param: param.requires_grad, net.parameters()),
                                    lr=state['learning_rate'],
                                    momentum=state['momentum'], weight_decay=state['decay'], nesterov=True)

    elif args.optimizer == "Adam":
        print("using Adam as optimizer")
        optimizer = torch.optim.Adam(filter(lambda param: param.requires_grad, net.parameters()),
                                     lr=state['learning_rate'],
                                     weight_decay=state['decay'])

    elif args.optimizer == "YF":
        print("using YellowFin as optimizer")
        optimizer = YFOptimizer(filter(lambda param: param.requires_grad, net.parameters()), lr=state['learning_rate'],
                                mu=state['momentum'], weight_decay=state['decay'])

    elif args.optimizer == "RMSprop":
        print("using RMSprop as optimizer")
        optimizer = torch.optim.RMSprop(filter(lambda param: param.requires_grad, net.parameters()),
                                        lr=state['learning_rate'], alpha=0.99, eps=1e-08, weight_decay=0, momentum=0)

    if args.use_cuda:
        net.cuda()
        criterion.cuda()

    recorder = RecorderMeter(args.epochs)  # count number of epoches

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print_log("=> loading checkpoint '{}'".format(args.resume), log)
            checkpoint = torch.load(args.resume)
            if not (args.fine_tune):
                args.start_epoch = checkpoint['epoch']
                recorder = checkpoint['recorder']
                optimizer.load_state_dict(checkpoint['optimizer'])

            state_tmp = net.state_dict()
            if 'state_dict' in checkpoint.keys():
                state_tmp.update(checkpoint['state_dict'])
            else:
                state_tmp.update(checkpoint)

            net.load_state_dict(state_tmp)
            # net.load_state_dict(checkpoint['state_dict'])

            print_log("=> loaded checkpoint '{}' (epoch {})".format(
                args.resume, args.start_epoch), log)
        else:
            print_log("=> no checkpoint found at '{}'".format(args.resume), log)
    else:
        print_log(
            "=> do not use any checkpoint for {} model".format(args.arch), log)

    if args.evaluate:
        validate(test_loader, net, criterion, log)
        return


    # Main loop
    start_time = time.time()
    epoch_time = AverageMeter()

    for epoch in range(args.start_epoch, args.epochs):
        current_learning_rate, current_momentum = adjust_learning_rate(
            optimizer, epoch, args.gammas, args.schedule)
        # Display simulation time
        need_hour, need_mins, need_secs = convert_secs2time(
            epoch_time.avg * (args.epochs - epoch))
        need_time = '[Need: {:02d}:{:02d}:{:02d}]'.format(
            need_hour, need_mins, need_secs)

        print_log(
            '\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} [LR={:6.4f}][M={:1.2f}]'.format(time_string(), epoch, args.epochs,
                                                                                   need_time, current_learning_rate,
                                                                                   current_momentum)
            + ' [Best : Accuracy={:.2f}, Error={:.2f}]'.format(recorder.max_accuracy(False),
                                                               100 - recorder.max_accuracy(False)), log)

        # # ============ TensorBoard logging ============#
        # # we show the model param initialization to give a intuition when we do the fine tuning

        # for name, param in net.named_parameters():
        #     name = name.replace('.', '/')
        #     if "delta_th" not in name:
        #         writer.add_histogram(name, param.clone().cpu().detach().numpy(), epoch)

        # # ============ TensorBoard logging ============#

        # train for one epoch
        train_acc, train_los = train(
            train_loader, net, criterion, optimizer, epoch, log)

        # evaluate on validation set
        val_acc, val_los = validate(test_loader, net, criterion, log)

        is_best = val_acc > recorder.max_accuracy(istrain=False)
        recorder.update(epoch, train_los, train_acc, val_los, val_acc)

        if args.model_only:
            checkpoint_state = {'state_dict': net.state_dict()}
        else:
            checkpoint_state = {
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': net.state_dict(),
                'recorder': recorder,
                'optimizer': optimizer.state_dict(),
            }

        save_checkpoint(checkpoint_state, is_best,
                        args.save_path, 'checkpoint.pth.tar', log)

        # measure elapsed time
        epoch_time.update(time.time() - start_time)
        start_time = time.time()
        recorder.plot_curve(os.path.join(args.save_path, 'curve.png'))

        # save addition accuracy log for plotting
        accuracy_logger(base_dir=args.save_path,
                        epoch=epoch,
                        train_accuracy=train_acc,
                        test_accuracy=val_acc)

        # ============ TensorBoard logging ============#

        # Log the graidents distribution
        # for name, param in net.named_parameters():
        #     name = name.replace('.', '/')
        #     writer.add_histogram(name + '/grad',
        #                          param.grad.clone().cpu().data.numpy(), epoch + 1, bins='tensorflow')

        # ## Log the weight and bias distribution
        # for name, module in net.named_modules():
        #     name = name.replace('.', '/')
        #     class_name = str(module.__class__).split('.')[-1].split("'")[0]

        #     if "Conv2d" in class_name or "Linear" in class_name:
        #         if module.weight is not None:
        #             writer.add_histogram(name + '/weight/',
        #                                  module.weight.clone().cpu().data.numpy(), epoch + 1, bins='tensorflow')

        # writer.add_scalar('loss/train_loss', train_los, epoch + 1)
        # writer.add_scalar('loss/test_loss', val_los, epoch + 1)
        # writer.add_scalar('accuracy/train_accuracy', train_acc, epoch + 1)
        # writer.add_scalar('accuracy/test_accuracy', val_acc, epoch + 1)
    # ============ TensorBoard logging ============#

    log.close()


# train function (forward, backward, update)
def train(train_loader, model, criterion, optimizer, epoch, log):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (input, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        if args.use_cuda:
            # the copy will be asynchronous with respect to the host.
            target = target.cuda(async=True)
            input = input.cuda()

        # compute output
        output = model(input)
        loss = criterion(output, target)

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

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

        if i % args.print_freq == 0:
            print_log('  Epoch: [{:03d}][{:03d}/{:03d}]   '
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})   '
                      'Data {data_time.val:.3f} ({data_time.avg:.3f})   '
                      'Loss {loss.val:.4f} ({loss.avg:.4f})   '
                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})   '
                      'Prec@5 {top5.val:.3f} ({top5.avg:.3f})   '.format(
                          epoch, i, len(train_loader), batch_time=batch_time,
                          data_time=data_time, loss=losses, top1=top1, top5=top5) + time_string(), log)
    print_log(
        '  **Train** Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Error@1 {error1:.3f}'.format(top1=top1, top5=top5,
                                                                                              error1=100 - top1.avg),
        log)
    return top1.avg, losses.avg


def validate(val_loader, model, criterion, log):
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        for i, (input, target) in enumerate(val_loader):
            if args.use_cuda:
                target = target.cuda(async=True)
                input = input.cuda()

            # compute output
            output = model(input)
            loss = criterion(output, target)

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

        print_log(
            '  **Test** Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Error@1 {error1:.3f}'.format(top1=top1, top5=top5,
                                                                                                 error1=100 - top1.avg),
            log)

    return top1.avg, losses.avg


def print_log(print_string, log):
    print("{}".format(print_string))
    log.write('{}\n'.format(print_string))
    log.flush()


def save_checkpoint(state, is_best, save_path, filename, log):
    filename = os.path.join(save_path, filename)
    torch.save(state, filename)
    if is_best:  # copy the checkpoint to the best model if it is the best_accuracy
        bestname = os.path.join(save_path, 'model_best.pth.tar')
        shutil.copyfile(filename, bestname)
        print_log("=> Obtain best accuracy, and update the best model", log)


def adjust_learning_rate(optimizer, epoch, gammas, schedule):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.learning_rate
    mu = args.momentum

    if args.optimizer != "YF":
        assert len(gammas) == len(
            schedule), "length of gammas and schedule should be equal"
        for (gamma, step) in zip(gammas, schedule):
            if (epoch >= step):
                lr = lr * gamma
            else:
                break
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr

    elif args.optimizer == "YF":
        lr = optimizer._lr
        mu = optimizer._mu

    return lr, mu


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


def accuracy_logger(base_dir, epoch, train_accuracy, test_accuracy):
    file_name = 'accuracy.txt'
    file_path = "%s/%s" % (base_dir, file_name)
    # create and format the log file if it does not exists
    if not os.path.exists(file_path):
        create_log = open(file_path, 'w')
        create_log.write('epochs train test\n')
        create_log.close()

    recorder = {}
    recorder['epoch'] = epoch
    recorder['train'] = train_accuracy
    recorder['test'] = test_accuracy
    # append the epoch index, train accuracy and test accuracy:
    with open(file_path, 'a') as accuracy_log:
        accuracy_log.write(
            '{epoch}       {train}    {test}\n'.format(**recorder))


if __name__ == '__main__':
    main()