main.py

import argparse
import builtins
import math
import os
import sys
import random
import shutil
import time
import warnings
import apex

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

from JigClu import resnet_jigclu as models
from JigClu.loader import GaussianBlur, JigCluTransform
from JigClu.jigclu import JigClu
from JigClu.util import format_time

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='resnet50',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet50)')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
                    help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=200, 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=0.03, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--min-lr', default=0.0, type=float,
                    metavar='LR', help='min learning rate for cos decay')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum of SGD solver')
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('--save-dir', default='outputs', type=str, metavar='PATH',
                    help='path to save checkpoint')
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('--proj-dim', default=128, type=int,
                    help='feature dimension (default: 128)')
parser.add_argument('--loss-t', default=0.07, type=float,
                    help='softmax temperature (default: 0.07)')

# Jigsaw Clustering 
parser.add_argument('--crop-size', default=112, type=int,
                    help='crop size for the patch')
parser.add_argument('--cross-ratio', default=0.3, type=float,
                    help='four patches crop cross ratio')

parser.add_argument('--loss_clu', default=1.0, type=float,
                    help='loss weight for clustering branch')
parser.add_argument('--loss_loc', default=1.0, type=float,
                    help='loss weight for location branch')



def main():
    args = parser.parse_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:
        args.world_size = ngpus_per_node * args.world_size
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
    progress = ProgressMeter(args)
    info = ''
    for arg in vars(args):
        info += arg+': '+str(getattr(args, arg))+'\n'
    progress.write(info)

    args.gpu = gpu

    # suppress printing if not master
    if args.multiprocessing_distributed and args.gpu != 0:
        def print_pass(*args):
            pass
        builtins.print = print_pass

    if args.gpu is not None:
        progress.write("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:
            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
    progress.write("=> creating model '{}'".format(args.arch))
    model = JigClu(
        models.__dict__[args.arch],
        args.proj_dim, args.loss_t)
    model = apex.parallel.convert_syncbn_model(model)
    progress.write(str(model))

    if args.distributed:
        if args.gpu is not None:
            torch.cuda.set_device(args.gpu)
            model.cuda(args.gpu)
            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()
            model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
        raise NotImplementedError("Only DistributedDataParallel is supported.")
    else:
        raise NotImplementedError("Only DistributedDataParallel is supported.")

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

    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            progress.write("=> loading checkpoint '{}'".format(args.resume))
            if args.gpu is None:
                checkpoint = torch.load(args.resume)
            else:
                # Map model to be loaded to specified single gpu.
                loc = 'cuda:{}'.format(args.gpu)
                checkpoint = torch.load(args.resume, map_location=loc)
            args.start_epoch = checkpoint['epoch']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            progress.write("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            progress.write("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    augmentation = [
        transforms.RandomResizedCrop(args.crop_size, scale=(0.2, 1.0)),
        transforms.RandomApply([
            transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)  # not strengthened
        ], p=0.8),
        transforms.RandomGrayscale(p=0.2),
        transforms.RandomApply([GaussianBlur([.1, 2.])], p=0.5),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        normalize
    ]

    train_dataset = datasets.ImageFolder(
        traindir,
        JigCluTransform(transforms.Compose(augmentation), args.cross_ratio))

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_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, drop_last=True)

    st_time = time.time()
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, epoch, args)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, args)

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                and args.rank % ngpus_per_node == 0):
            save_checkpoint(args, {
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'optimizer' : optimizer.state_dict(),
            }, is_best=True, filename='checkpoint_{:04d}.pth.tar'.format(epoch))
        info = 'Time: ' + format_time(time.time()-st_time) + '\n'\
               'Eta: ' + format_time((time.time()-st_time)/(epoch-args.start_epoch+1)*(args.epochs-epoch-1))
        progress.write(info)


def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    losses_clu = AverageMeter('Loss_clu', ':.4e')
    losses_loc = AverageMeter('Loss_loc', ':.4e')
    progress = ProgressMeter(
        args,
        len(train_loader),
        [batch_time, data_time, losses, losses_clu, losses_loc],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

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

        '''
        n,c,h,w = images[0].shape
        permute = torch.randperm(n*4)
        images = torch.cat(images, dim=0)
        images = images[permute,:,:,:]
        col1 = torch.cat([images[0:n], images[n:2*n]], dim=3)
        col2 = torch.cat([images[2*n:3*n], images[3*n:]], dim=3)
        images = torch.cat([col1, col2], dim=2)
        
        target = permute % n

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)
        '''
        for j in range(4):
            images[j] = images[j].cuda(args.gpu, non_blocking=True)

        # compute output
        loss_clu, loss_loc = model(images, progress)
        loss_clu = loss_clu * args.loss_clu
        loss_loc = loss_loc * args.loss_loc
        loss = loss_clu + loss_loc
        # record loss
        losses.update(loss.item(), images[0].size(0))
        losses_clu.update(loss_clu.item(), images[0].size(0))
        losses_loc.update(loss_loc.item(), images[0].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 save_checkpoint(args, state, is_best, filename='checkpoint.pth.tar'):
    filename = os.path.join(args.save_dir, filename)
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, os.path.join(args.save_dir, 'model_best.pth.tar'))


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, args, num_batches=0, meters=None, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix
        self.file = open(os.path.join(args.save_dir, 'log.txt'), 'a')

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        info = '\t'.join(entries)
        print(info)
        self.file.write(info+'\n')
        sys.stdout.flush()
        self.file.flush()

    def write(self, info):
        print(info)
        self.file.write(info+'\n')
        sys.stdout.flush()
        self.file.flush()

    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):
    """Decay the learning rate based on schedule"""
    lr = args.lr
    lr *= (0.5-args.min_lr) * (1. + math.cos(math.pi * epoch / args.epochs)) + args.min_lr
    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].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


if __name__ == '__main__':
    main()