main_squeezenet.py

from __future__ import print_function

import argparse
import os
import shutil
import time
import random
import warnings

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
from squeezenet import SqueezeNet_1x1LMP, SqueezeNet_1x1LAP

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

import torch.multiprocessing as mp


parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
# Datasets
parser.add_argument('-d', '--dataset', default='path to dataset', type=str)
parser.add_argument('-j', '--workers', default=64, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
# Optimization options
parser.add_argument('--epochs', default=30000, 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=256, type=int, metavar='N',
                    help='train batchsize')
parser.add_argument('--test-batch', default=256, type=int, metavar='N',
                    help='test batchsize')
parser.add_argument('--lr', '--learning-rate', default=0.04, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=0.0002, type=float,
                    metavar='W', help='weight decay (default: 1e-4)')
# 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)')
# Architecture
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet20', help='model architecture: ')
parser.add_argument('--layer', type=int)

# 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('--world-size', default=1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=0, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://127.0.0.1:22334', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--ngpus_per_node', default=8, type=int,
                    help='number of GPUs to use.')

parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')

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

best_acc = 0  # best test accuracy
total_iter = 170000 * 2


class ToTensor(object):
    def __call__(self, pic):
        # handle PIL Image
        if pic.mode == 'I':
            img = torch.from_numpy(np.array(pic, np.int32, copy=False))
        elif pic.mode == 'I;16':
            img = torch.from_numpy(np.array(pic, np.int16, copy=False))
        elif pic.mode == 'F':
            img = torch.from_numpy(np.array(pic, np.float32, copy=False))
        elif pic.mode == '1':
            img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
        else:
            img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
        # PIL image mode: L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK
        if pic.mode == 'YCbCr':
            nchannel = 3
        elif pic.mode == 'I;16':
            nchannel = 1
        else:
            nchannel = len(pic.mode)
        img = img.view(pic.size[1], pic.size[0], nchannel)
        # put it from HWC to CHW format
        # yikes, this transpose takes 80% of the loading time/CPU
        img = img.transpose(0, 1).transpose(0, 2).contiguous()
        return img.float().div(255)


class MeanSubtract(object):
    def __init__(self, mean):
        self.mean = torch.tensor(mean, dtype=torch.float32)

    def __call__(self, tensor):
        return tensor.sub_(self.mean[:, None, None])


def main():
    global args

    if args.manualSeed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)

    if args.gpu is not None:
        print('You have chosen a specific GPU. This will completely '
              'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed

    ngpus_per_node = args.ngpus_per_node  # torch.cuda.device_count()
    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
    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)

    args.gpu = gpu


    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * ngpus_per_node + gpu
        torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)

    num_classes = 1000

    # Model
    print("==> creating model '{}'".format(args.arch))
    if args.arch.endswith('squeezenet_1x1lmp'):
        model = SqueezeNet_1x1LMP(num_classes, args.layer)
    elif args.arch.endswith('squeezenet_1x1lap'):
        model = SqueezeNet_1x1LAP(num_classes, args.layer)
    else:
        raise Exception('arch can only be vgg16 or resnet50!')

    if args.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if args.gpu is not None:
            torch.cuda.set_device(args.gpu)
            model.cuda(args.gpu)
            # When using a single GPU per process and per
            # DistributedDataParallel, we need to divide the batch size
            # ourselves based on the total number of GPUs we have
            args.train_batch = int(args.train_batch / ngpus_per_node)
            args.test_batch = int(args.test_batch / ngpus_per_node)
            args.workers = int(args.workers / ngpus_per_node)
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        else:
            warnings.warn('NOT DISTRIBUTED!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
            model.cuda()
            # DistributedDataParallel will divide and allocate batch_size to all
            # available GPUs if device_ids are not set
            model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None:
        warnings.warn('NOT DISTRIBUTED!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        warnings.warn('NOT DISTRIBUTED!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
        # DataParallel will divide and allocate batch_size to all available GPUs
        model = torch.nn.DataParallel(model).cuda()

    # Allocate GPU memory
    mem = os.popen('"nvidia-smi" --query-gpu=memory.total,memory.used --format=csv,nounits,noheader').read().split('\n')
    total = mem[0].split(',')[0]
    total = int(total)
    max_mem = int(total * 0.7)
    x = torch.rand((256, 1024, max_mem)).cuda(args.gpu)
    del x

    criterion = nn.CrossEntropyLoss().cuda(args.gpu)
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
    # optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=args.weight_decay)

    cudnn.benchmark = True

    print('    Total params: %.2fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0))
    # for name, param in model.named_parameters():
    #     print(name)
    # for name in model.named_modules():
    #     print(name)
    # for param in model.parameters():
    #     print(param)

    # Data
    print('==> Preparing dataset %s' % args.dataset)
    transform_train = transforms.Compose([
        transforms.Resize(256),
        transforms.RandomCrop(227),
        ToTensor(),
        MeanSubtract((123 / 255, 104 / 255, 117 / 255)),
    ])
    transform_test = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(227),
        ToTensor(),
        MeanSubtract((123 / 255, 104 / 255, 117 / 255)),
    ])

    if args.dataset == 'xian':
        print('ImageNet from Xian is used!')
        traindir = '/BS/xian/work/data/imageNet1K/train/'
        valdir = '/BS/database11/ILSVRC2012/val/'
    else:
        traindir = os.path.join(args.dataset, 'train')
        valdir = os.path.join(args.dataset, 'val')

    trainset = datasets.ImageFolder(traindir, transform_train)
    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(trainset)
    else:
        train_sampler = None
    trainloader = data.DataLoader(trainset, batch_size=args.train_batch, shuffle=(train_sampler is None),
                                  num_workers=args.workers, pin_memory=True, sampler=train_sampler)
    print('--------------------------------------')
    print('the len of the trainloader should be 5005, which is ', len(trainloader))
    testset = datasets.ImageFolder(valdir, transform_test)
    testloader = data.DataLoader(testset, batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
                                 pin_memory=True)

    # Resume
    title = 'imagenet-' + args.arch
    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)
        num_step = checkpoint['num_step']
    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.'])
        num_step = 0

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

    # Train and val
    print(state['lr'])
    for epoch in range(start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)

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

        num_step, train_loss, train_acc = train(trainloader, model, criterion, optimizer, epoch, args, num_step)
        test_loss, test_acc = test(testloader, model, criterion, args)

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

        # save model
        is_best = test_acc > best_acc
        best_acc = max(test_acc, best_acc)
        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                    and args.rank % ngpus_per_node == 0):
            save_checkpoint({
                'epoch': epoch + 1,
                'acc': test_acc,
                'state_dict': model.state_dict(),
                'best_acc': best_acc,
                'optimizer': optimizer.state_dict(),
                'num_step': num_step
            }, is_best, checkpoint=args.checkpoint)
        if num_step == total_iter:
            break

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

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


def train(trainloader, model, criterion, optimizer, epoch, args, num_step):
    # 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))
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        adjust_learning_rate(optimizer, num_step)
        # measure data loading time
        data_time.update(time.time() - end)

        if args.gpu is not None:
            inputs = inputs.cuda(args.gpu, non_blocking=True)
        targets = targets.cuda(args.gpu, non_blocking=True)

        # 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.item(), inputs.size(0))
        top1.update(prec1.item(), inputs.size(0))
        top5.update(prec5.item(), inputs.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        num_step = num_step + 1.0

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

        # plot progress
        if batch_idx % 100 == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                epoch, batch_idx, len(trainloader), batch_time=batch_time,
                data_time=data_time, loss=losses, top1=top1, top5=top5))
        if num_step == total_iter:
            break
    #     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 num_step, losses.avg, top1.avg


def test(testloader, model, criterion, args):
    global best_acc

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

    # switch to evaluate mode
    model.eval()
    with torch.no_grad():
        end = time.time()
        # bar = Bar('Processing', max=len(testloader))
        for batch_idx, (inputs, targets) in enumerate(testloader):
            # measure data loading time
            data_time.update(time.time() - end)

            if args.gpu is not None:
                inputs = inputs.cuda(args.gpu, non_blocking=True)
            targets = targets.cuda(args.gpu, non_blocking=True)

            # 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.item(), inputs.size(0))
            top1.update(prec1.item(), inputs.size(0))
            top5.update(prec5.item(), inputs.size(0))

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

            # plot progress
            if batch_idx % 1000 == 0:
                print('Test: [{0}/{1}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                    batch_idx, len(testloader), batch_time=batch_time, loss=losses,
                    top1=top1, top5=top5))
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))
        #     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)


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, num_step):
    global state
    for param_group in optimizer.param_groups:
        param_group['lr'] = state['lr'] * (1 - num_step / total_iter)


if __name__ == '__main__':
    main()