train_imagenet_1pc_swav_cgc_unlabeled.py

## Code to train a ResNet model using 1pc of ImageNet as the labeled subset and the rest of the dataset as the unlabeled dataset with our CGC method
## This code is adapated from https://github.com/pytorch/examples/blob/master/imagenet/main.py

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

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import models.swav_resnet_cgc as resnet
from datasets.imagefolder_cgc_ssl import ImageFolder
import logging


def get_logger(logpath, filepath, package_files=[], displaying=True, saving=True, debug=False):
    logger = logging.getLogger()
    if debug:
        level = logging.DEBUG
    else:
        level = logging.INFO
    logger.setLevel(level)
    if saving:
        info_file_handler = logging.FileHandler(logpath, mode="a")
        info_file_handler.setLevel(level)
        logger.addHandler(info_file_handler)
    if displaying:
        console_handler = logging.StreamHandler()
        console_handler.setLevel(level)
        logger.addHandler(console_handler)
    logger.info(filepath)
    with open(filepath, "r") as f:
        logger.info(f.read())

    for f in package_files:
        logger.info(f)
        with open(f, "r") as package_f:
            logger.info(package_f.read())

    return logger

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

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, 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('-b', '--batch-size', default=256, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', 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('--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')
parser.add_argument('--save_dir', default='checkpoint', type=str, metavar='SV_PATH',
                    help='path to save checkpoints (default: none)')
parser.add_argument("--lr_last_layer", default=0.2, type=float, help="initial learning rate - head")
parser.add_argument("--decay_epochs", type=int, nargs="+", default=[12, 16],
                    help="Epochs at which to decay learning rate.")
parser.add_argument("--gamma", type=float, default=0.2, help="lr decay factor")
parser.add_argument('-t', type=float, default=1.0)
parser.add_argument('--lambda', default=1000, type=float,
                    metavar='LAM', help='lambda hyperparameter for GCAM loss', dest='lambda_val')
parser.add_argument('--log_dir', default='logs', type=str, metavar='LG_PATH',
                    help='path to write logs (default: logs)')

best_acc1 = 0


def main():
    args = parser.parse_args()
    os.makedirs(args.save_dir, exist_ok=True)
    os.makedirs(args.log_dir, exist_ok=True)
    logger = get_logger(logpath=os.path.join(args.log_dir, 'logs'), filepath=os.path.abspath(__file__))
    logger.info(args)

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('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 = 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, logger)


class NCESoftmaxLoss(nn.Module):
    """Softmax cross-entropy loss (a.k.a., info-NCE loss in CPC paper)"""
    def __init__(self, T=0.01):
        super(NCESoftmaxLoss, self).__init__()
        self.T = T
        self.criterion = nn.CrossEntropyLoss()

    def forward(self, x):
        # pdb.set_trace()
        bsz = x.shape[0]
        x = x.squeeze()
        x = torch.div(x, self.T)
        label = torch.zeros([bsz]).cuda().long()
        loss = self.criterion(x, label)
        return loss


def main_worker(gpu, ngpus_per_node, args, logger):
    global best_acc1
    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
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)
    # create model
    if args.arch.startswith('resnet'):
        print("=> creating model '{}'".format(args.arch))
        model = resnet.__dict__[args.arch](output_dim=1000)
    else:
        print('Only resnet is supported for swav')
        exit()

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            state_dict = torch.load(args.resume)
            if "state_dict" in state_dict:
                state_dict = state_dict["state_dict"]
            state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
            for k, v in model.state_dict().items():
                if k not in list(state_dict):
                    logger.info('key "{}" could not be found in provided state dict'.format(k))
                elif state_dict[k].shape != v.shape:
                    logger.info('key "{}" is of different shape in model and provided state dict'.format(k))
                    state_dict[k] = v
            msg = model.load_state_dict(state_dict, strict=False)
            logger.info("Load pretrained model with msg: {}".format(msg))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    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.batch_size = int(args.batch_size / ngpus_per_node)
            args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        else:
            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:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        # DataParallel will divide and allocate batch_size to all available GPUs
        model = torch.nn.DataParallel(model).cuda()

    # define loss function (criterion) and optimizer
    xent_criterion = nn.CrossEntropyLoss().cuda(args.gpu)
    contrastive_criterion = NCESoftmaxLoss(args.t).cuda(args.gpu)

    # set optimizer
    trunk_parameters = []
    head_parameters = []
    for name, param in model.named_parameters():
        if 'head' in name:
            head_parameters.append(param)
        else:
            trunk_parameters.append(param)
    optimizer = torch.optim.SGD(
        [{'params': trunk_parameters},
         {'params': head_parameters, 'lr': args.lr_last_layer}],
        lr=args.lr,
        momentum=0.9,
        weight_decay=0,
    )
    # set scheduler
    scheduler = torch.optim.lr_scheduler.MultiStepLR(
        optimizer, args.decay_epochs, gamma=args.gamma
    )

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    unlabeleddir = os.path.join('/datasets/imagenet', 'train')
    valdir = os.path.join('/datasets/imagenet', 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))

    unlabeled_dataset = ImageFolder(
        unlabeleddir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        unlabeled_sampler = torch.utils.data.distributed.DistributedSampler(unlabeled_dataset)
    else:
        train_sampler = None
        unlabeled_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    unlabeled_loader = torch.utils.data.DataLoader(
        unlabeled_dataset, batch_size=args.batch_size, shuffle=(unlabeled_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=unlabeled_sampler)

    val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    if args.evaluate:
        validate(val_loader, model, xent_criterion, args, logger)
        return

    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)

        # train for one epoch
        train(train_loader, unlabeled_loader, model, contrastive_criterion, xent_criterion, optimizer, epoch,
              args, logger)

        # evaluate on validation set
        acc1 = validate(val_loader, model, xent_criterion, args, logger)

        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)

        scheduler.step()

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                and args.rank % ngpus_per_node == 0):
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer' : optimizer.state_dict(),
            }, is_best, args.save_dir)


def train(train_loader, unlabeled_loader, model, contrastive_criterion, xent_criterion, optimizer, epoch,
          args, logger):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    xe_losses = AverageMeter('XE Loss', ':.4e')
    gc_losses = AverageMeter('GC Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, xe_losses, gc_losses, losses, top1, top5],
        logger,
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()
    train_len = len(train_loader)
    train_iter = iter(train_loader)
    unlabeled_iter = iter(unlabeled_loader)

    end = time.time()
    for i in range(train_len):
        try:
            _, unlbl_images, unlbl_aug_images, transforms_i, transforms_j, transforms_h, \
            transforms_w, hor_flip, _ = unlabeled_iter.__next__()
        except StopIteration:
            # reset the unlabeled iterator if exhausted
            unlabeled_iter = iter(unlabeled_loader)
            _, unlbl_images, unlbl_aug_images, transforms_i, transforms_j, transforms_h, \
            transforms_w, hor_flip, _ = unlabeled_iter.__next__()

        # no need for stop iteration since we loop over length of trainloader
        lbl_images, target = train_iter.__next__()

        # measure data loading time
        data_time.update(time.time() - end)

        lbl_images = lbl_images.cuda(args.gpu, non_blocking=True)
        target = target.cuda(args.gpu, non_blocking=True)
        unlbl_images = unlbl_images.cuda(args.gpu, non_blocking=True)
        unlbl_aug_images = unlbl_aug_images.cuda(args.gpu, non_blocking=True)
        aug_params_dict = {'transforms_i': transforms_i, 'transforms_j': transforms_j, 'transforms_h': transforms_h,
                            'transforms_w': transforms_w, 'hor_flip': hor_flip}

        lbl_output, xe_loss, contrastive_loss = model(lbl_images, contrastive_criterion=contrastive_criterion,
                                                      unlbl_images=unlbl_images, unlbl_aug_images=unlbl_aug_images,
                                                      aug_params_dict=aug_params_dict, targets=target, xent_criterion=xent_criterion)

        xe_loss = xe_loss.mean()
        contrastive_loss = contrastive_loss.mean()
        loss = xe_loss + args.lambda_val * contrastive_loss

        # measure accuracy and record loss
        acc1, acc5 = accuracy(lbl_output, target, topk=(1, 5))
        losses.update(loss.item(), lbl_images.size(0))
        xe_losses.update(xe_loss.item(), lbl_images.size(0))
        gc_losses.update(contrastive_loss.item(), lbl_images.size(0))

        top1.update(acc1[0], lbl_images.size(0))
        top5.update(acc5[0], lbl_images.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:
            progress.display(i)


def validate(val_loader, model, criterion, args, logger):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        logger,
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            if args.gpu is not None:
                images = images.cuda(args.gpu, non_blocking=True)
            target = target.cuda(args.gpu, non_blocking=True)

            # compute output
            output = model(images, vanilla=True)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

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

            if i % args.print_freq == 0:
                progress.display(i)

        logger.info(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
                    .format(top1=top1, top5=top5))

    return top1.avg


def save_checkpoint(state, is_best, save_dir):
    epoch = state['epoch']
    filename = 'checkpoint_' + str(epoch).zfill(3) + '.pth.tar'
    save_path = os.path.join(save_dir, filename)
    torch.save(state, save_path)
    if is_best:
        best_filename = 'model_best.pth.tar'
        best_save_path = os.path.join(save_dir, best_filename)
        shutil.copyfile(save_path, best_save_path)


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, logger, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix
        self.logger = logger

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        self.logger.info('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions 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].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


if __name__ == '__main__':
    main()