train_VVTQ.py

import argparse
import os
import random
import shutil
import time
import math
import warnings
import logging
import numpy as np

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
from utils_FKD import RandomResizedCrop_FKD, RandomHorizontalFlip_FKD, ImageFolder_FKD, Compose_FKD, Soft_CrossEntropy, Recover_soft_label
from torchvision.transforms import InterpolationMode
from timm.scheduler import create_scheduler
from timm.optim import create_optimizer
from timm.models import create_model
#from models.mobilenet_imagenet_pact import *
#from models.mobilenet_imagenet_prelu import *
from quantization import DeiT_quant, SReT_quant, Swin_quant
from engine import initialize_quantization
from util_loss import BinReg, CosineTempDecay

# timm is used to build the optimizer and learning rate scheduler (https://github.com/rwightman/pytorch-image-models)


parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--data', metavar='DIR', default='../dataset/imagenet',
                    help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50')
parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
                    help='number of data loading workers (default: 24)')
parser.add_argument('--epochs', default=150, 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=0.002, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--schedule', default=[120, 160], nargs='*', type=int,
                    help='learning rate schedule (when to drop lr by 10x)')
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('--num_crops', default=4, type=int,
                    help='number of crops in each image, 1 is the standard training')
parser.add_argument('--softlabel_path', default='./FKD_soft_label_500_crops_marginal_smoothing_k_5', type=str, metavar='PATH',
                    help='path to soft label files (default: none)')
parser.add_argument('--save_checkpoint_path', default='./FKD_checkpoints_output', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('--soft_label_type', default='marginal_smoothing_k5', type=str, metavar='TYPE',
                    help='(1) ori; (2) hard; (3) smoothing; (4) marginal_smoothing_k5; (5) marginal_smoothing_k10; (6) marginal_renorm_k5')
parser.add_argument('--num_classes', default=1000, type=int,
                    help='number of classes.')
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                    help='Dropout rate (default: 0.)')
parser.add_argument('--drop-path', type=float, default=0.1, metavar='PCT',
                    help='Drop path rate (default: 0.1)')
parser.add_argument('--log', default='log_examp.log', type=str,
                    help='names of logging file.')

# Optimizer parameters
parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
                    help='Optimizer (default: "adamw"')
parser.add_argument('--opt-eps', default=1e-8, type=float, metavar='EPSILON',
                    help='Optimizer Epsilon (default: 1e-8)')
parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
                    help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
                    help='Clip gradient norm (default: None, no clipping)')

# Learning rate schedule parameters
parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                    help='LR scheduler (default: "cosine"')
parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                    help='learning rate noise on/off epoch percentages')
parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                    help='learning rate noise limit percent (default: 0.67)')
parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                    help='learning rate noise std-dev (default: 1.0)')
parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR',
                    help='warmup learning rate (default: 1e-6)')
parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                    help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--cos', action='store_true',
                    help='use cosine lr schedule')
parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
                    help='epoch interval to decay LR')
parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
                    help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
                    help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
                    help='patience epochs for Plateau LR scheduler (default: 10')
parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                    help='LR decay rate (default: 0.1)')

# Vit parameters
parser.add_argument('--model', default='deit_base_patch16_224_quant', type=str, metavar='MODEL',
                        help='Name of model to train')
parser.add_argument('--wbits', default=-1, type=int)
parser.add_argument('--abits', default=-1, type=int)
parser.add_argument('--act-unsigned', action='store_true', default=False, help='using unsigned activations')
parser.add_argument('--use-offset', action='store_true', default=False, help='using offset')
parser.add_argument('--act-layer', default='gelu', type=str)
parser.add_argument('--fixed-scale', action='store_true', default=False, help='use fixed scale and offset')
parser.add_argument('--mixpre', action='store_true', default=False, help='whether to learn the bit-width')
parser.add_argument('--bitops-scaler', type=float, default=0.)
parser.add_argument('--show-bit-state', action='store_true', default=False)
parser.add_argument('--head-wise', action='store_true', default=False)
parser.add_argument('--reg', action='store_true', default=False)
parser.add_argument('--finetune', default='', help='finetune from checkpoint')

best_acc1 = 0


def main():
    args = parser.parse_args()
    
    if not os.path.exists(args.save_checkpoint_path):
        os.makedirs(args.save_checkpoint_path)

    # convert to TRUE number of loading-images and #epochs since we use multiple crops from the same image within a minbatch
    args.batch_size = math.ceil(args.batch_size / args.num_crops)

    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)


def main_worker(gpu, ngpus_per_node, args):
    global best_acc1
    args.gpu = gpu

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))
    
    if not args.multiprocessing_distributed or (args.multiprocessing_distributed and args.rank % ngpus_per_node == 0):
        logging.basicConfig(filename=args.log,level=logging.INFO)

    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
    print(f"Creating model: {args.model}")
    model = create_model(
        args.model,
        pretrained=False,
        num_classes=1000,
        drop_rate=0.0,
        drop_path_rate=0.1,
        drop_block_rate=None,
        wbits=args.wbits,
        abits=args.abits,
        act_layer=nn.GELU,
        offset=args.use_offset,
        learned=not args.fixed_scale,
        mixpre=args.mixpre,
        headwise=args.head_wise
    )

    if args.finetune:
        if args.finetune.startswith('https'):
            checkpoint = torch.hub.load_state_dict_from_url(
                args.finetune, map_location='cpu', check_hash=True)
        else:
            checkpoint = torch.load(args.finetune, map_location='cpu')

        checkpoint_model = checkpoint['model']
        model.load_state_dict(checkpoint_model, strict=False)

    if not torch.cuda.is_available():
        print('using CPU, this will be slow')
    elif 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
        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
            model.features = torch.nn.DataParallel(model.features)
            model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()

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

    optimizer = create_optimizer(args, model)

    lr_scheduler, _ = create_scheduler(args, optimizer)

    # optionally resume from a checkpointresume
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> 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']
            best_acc1 = checkpoint['best_acc1']
            if args.gpu is not None:
                # best_acc1 may be from a checkpoint from a different GPU
                best_acc1 = best_acc1.to(args.gpu)
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    
    train_dataset = ImageFolder_FKD(
        num_crops=args.num_crops,
        softlabel_path=args.softlabel_path,
        root=traindir,
        transform=Compose_FKD(transforms=[
            RandomResizedCrop_FKD(size=224,
                                  interpolation='bilinear'), 
            RandomHorizontalFlip_FKD(),
            transforms.ToTensor(),
            normalize,
        ]))
    train_dataset_single_crop = ImageFolder_FKD(
        num_crops=1,
        softlabel_path=args.softlabel_path,
        root=traindir,
        transform=Compose_FKD(transforms=[
            RandomResizedCrop_FKD(size=224,
                                  interpolation='bilinear'), 
            RandomHorizontalFlip_FKD(),
            transforms.ToTensor(),
            normalize,
        ]))

    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)

    train_loader_single_crop = torch.utils.data.DataLoader(
        train_dataset_single_crop, batch_size=args.batch_size*args.num_crops, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    dataset_val = datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ]))

    val_loader = torch.utils.data.DataLoader(
        dataset_val,
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    data_loader_sampler = torch.utils.data.DataLoader(
        dataset_val, sampler=torch.utils.data.SequentialSampler(dataset_val),
        batch_size=64,
        num_workers=args.workers,
        pin_memory=True,
        drop_last=False
    )

    from pathlib import Path
    output_dir = Path(args.save_checkpoint_path)
    device = torch.device('cuda')

    if args.resume == '' and (args.abits > 0 or args.wbits > 0):
        print("Starting quantization scale initialization")
        initialize_quantization(data_loader_sampler, model, device, output_dir, sample_iters=1)

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

    # warmup with single crop, "=" is used to let start_epoch to be 0 for the corner case.
    if args.start_epoch <= args.warmup_epochs:
        for epoch in range(args.start_epoch, args.warmup_epochs):
            if args.distributed:
                train_sampler.set_epoch(epoch)
            # train for one epoch
            train(train_loader_single_crop, model, criterion_sce, optimizer, epoch, args)
            lr_scheduler.step(epoch + 1)

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

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

            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, filename=args.save_checkpoint_path+'/checkpoint.pth.tar')
        args.start_epoch = 0 # for resume
    else:
        args.warmup_epochs = args.num_crops - 1  # for resume

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

        # for fine-grained evaluation at last a few epochs
        if epoch >= (args.epochs-args.num_crops):
            start_epoch = epoch
            for epoch in range(start_epoch, args.epochs):
                if args.distributed:
                    train_sampler.set_epoch(epoch)
                lr_scheduler.step(epoch+1)
                # train for one epoch
                train(train_loader_single_crop, model, criterion_sce, optimizer, epoch, args)

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

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

                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, filename=args.save_checkpoint_path+'/checkpoint.pth.tar')
            return 
        else:
            # train for one epoch
            train(train_loader, model, criterion_sce, optimizer, epoch, args)
        lr_scheduler.step(epoch+args.num_crops)

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

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

        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, filename=args.save_checkpoint_path+'/checkpoint.pth.tar')


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')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    if args.reg:
        oscreg = AverageMeter('Loss', ':.4e')
        progress = ProgressMeter(
            len(train_loader),
            [batch_time, data_time, oscreg, losses, top1, top5, 'LR {lr:.5f}'.format(lr=_get_learning_rate(optimizer))],
            prefix="Epoch: [{}]".format(epoch))
    else:
        progress = ProgressMeter(
            len(train_loader),
            [batch_time, data_time, losses, top1, top5, 'LR {lr:.5f}'.format(lr=_get_learning_rate(optimizer))],
            prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

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

        images = torch.cat(images, dim=0)
        soft_label = torch.cat(soft_label, dim=0)
        target = torch.cat(target, dim=0)

        if args.soft_label_type != 'ori':
            soft_label = Recover_soft_label(soft_label, args.soft_label_type, args.num_classes)

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)
        if torch.cuda.is_available():
            target = target.cuda(args.gpu, non_blocking=True)
            soft_label = soft_label.cuda(args.gpu, non_blocking=True)

        # compute output
        output = model(images)
        loss = criterion(output, soft_label)
        
        # 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))

        # add regularization
        if args.reg:
            annealing_schedule = CosineTempDecay(t_max=args.epochs, temp_range=(0, 0.01), rel_decay_start=0.25)
            annealing_schedule_reg = annealing_schedule(epoch)
            #print("current annealing regularization weighting coefficient:{}".format(annealing_schedule_reg))
            bin_regularizer = BinReg(annealing_schedule_reg)
            loss_reg = bin_regularizer(model)
            loss += loss_reg
            oscreg.update(loss_reg.item(), images.size(0))

        # compute gradient and update
        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:
            t = time.localtime()
            current_time = time.strftime("%H:%M:%S", t)
            #print(current_time)
            if not args.multiprocessing_distributed or (args.multiprocessing_distributed and args.rank == 0):
                progress.display(i)


def validate(val_loader, model, criterion, args):
    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],
        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)
            if torch.cuda.is_available():
                target = target.cuda(args.gpu, non_blocking=True)

            # compute output
            output = model(images)
            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)

        # TODO: this should also be done with the ProgressMeter
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))
        if not args.multiprocessing_distributed or (args.multiprocessing_distributed and args.rank == 0):
            logging.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, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, filename[:-19]+'/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, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))
        logging.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):
    """Decay the learning rate based on schedule"""
    lr = args.lr
    if args.cos:  # cosine lr schedule
        lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))
    else:  # stepwise lr schedule
        for milestone in args.schedule:
            lr *= 0.1 if epoch >= milestone else 1.
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

def _get_learning_rate(optimizer):
    return max(param_group['lr'] for param_group in optimizer.param_groups)

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()