train_vgg.py

import argparse
import os
import shutil
import time

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.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
from torch.optim.lr_scheduler import CosineAnnealingLR
# from torch.utils.tensorboard import SummaryWriter
import numpy as np

from resnet import resnet50
# from cusvgg import get_RepVGG_func_by_name
from mcunet import get_RepVGG_func_by_name
from prunesearch import append_loss

# admm tools
# import newadmm as admm
# from testers import *
# import yaml

try:
    from apex.parallel import DistributedDataParallel as DDP
    from apex.fp16_utils import *
    from apex import amp, optimizers
    from apex.multi_tensor_apply import multi_tensor_applier
except ImportError:
    raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run.")

IMAGENET_TRAINSET_SIZE = 1281167


def fast_collate(batch, memory_format):
    imgs = [img[0] for img in batch]
    targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
    w = imgs[0].size[0]
    h = imgs[0].size[1]
    tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8).contiguous(memory_format=memory_format)
    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        if (nump_array.ndim < 3):
            nump_array = np.expand_dims(nump_array, axis=-1)
        nump_array = np.rollaxis(nump_array, 2)
        tensor[i] += torch.from_numpy(nump_array.copy())
    return tensor, targets


def sgd_optimizer(model, lr, momentum, weight_decay):
    params = []
    for key, value in model.named_parameters():
        if not value.requires_grad:
            continue
        apply_weight_decay = weight_decay
        apply_lr = lr
        if 'bn' in key:
            apply_weight_decay = 0
            print('set weight decay=0 for {}'.format(key))
        if 'bias' in key:
            apply_lr = 2 * lr  # Just a Caffe-style common practice. Made no difference.
        params += [{'params': [value], 'lr': apply_lr, 'weight_decay': apply_weight_decay}]
    optimizer = torch.optim.SGD(params, lr, momentum=momentum)
    return optimizer


def parse():
    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('--arch', '-a', metavar='ARCH', default='mobilenet_v2',
                        # 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=120, 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 per process (default: 256)')
    parser.add_argument('--lr', '--learning-rate', default=0.256, type=float,
                        metavar='LR',
                        help='Initial learning rate.  Will be scaled by <global batch size>/256: '
                             'args.lr = args.lr*float(args.batch_size*args.world_size)/256.  '
                             'A warmup schedule will also be applied over the first 5 epochs.')
    parser.add_argument('--momentum', default=0.875, type=float, metavar='M',
                        help='momentum')
    parser.add_argument('--weight-decay', '--wd', default=3.052e-5, type=float,
                        metavar='W', help='weight decay (default: 1e-4)')
    parser.add_argument('--print-freq', '-p', 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('--prune', default=0.75, type=float, metavar='P',
                        help='how many percent of MACs to prune')
    parser.add_argument('--regularization', action='store_true', default=False, help='whether to regularize')

    # parser.add_argument('--prof', default=-1, type=int,
    #                     help='Only run 10 iterations for profiling.')
    parser.add_argument('--deterministic', action='store_true')

    parser.add_argument("--local_rank", default=0, type=int)
    parser.add_argument('--sync_bn', action='store_true',
                        help='enabling apex sync BN.')

    parser.add_argument('--opt-level', type=str)
    parser.add_argument('--keep-batchnorm-fp32', type=str, default=None)
    parser.add_argument('--loss-scale', type=str, default=None)
    parser.add_argument('--channels-last', type=bool, default=False)

    parser.add_argument('--freeze', action='store_true', default=False, help='pure fine tune')
    # # admm
    # parser.add_argument('--admm', action='store_true', default=False, help='admm pruning')
    # parser.add_argument('--masked_retrain', action='store_true', default=False, help='mask zeros and fine tune')
    # parser.add_argument('--verify', action='store_true', default=False,
    #                     help='verify model sparsity and accuracy')
    # parser.add_argument('--verbose', action='store_true', default=False,
    #                     help='whether to report admm convergence condition')
    # parser.add_argument('--admm-epoch', type=int, default=1,
    #                     help="how often we do admm update")
    # parser.add_argument('--rho', type=float, default=0.001,
    #                     help="define rho for ADMM")
    # parser.add_argument('--rho-num', type=int, default=4,
    #                     help="define how many rohs for ADMM training")
    # parser.add_argument('--sparsity-type', type=str, default='bn',
    #                     help="define sparsity_type: [irregular,filter,pattern]")
    # parser.add_argument('--prune_setting', type=str, default='./config_mobilenetv2_65',
    #                     help="select config for pruning")
    # parser.add_argument('--combine-progressive', action='store_true', default=False,
    #                     help="for filter pruning after column pruning")
    # parser.add_argument('--cross-x', default=4, type=int,
    #                     help='the cross_x of poplarization and block prune')
    # parser.add_argument('--cross-f', default=1, type=int,
    #                     help='the cross_f of poplarization and block prune')

    args = parser.parse_args()
    return args


def main():
    global best_prec1, args

    args = parse()
    if args.local_rank == 0:
        print("opt_level = {}".format(args.opt_level))
        print("keep_batchnorm_fp32 = {}".format(args.keep_batchnorm_fp32), type(args.keep_batchnorm_fp32))
        print("loss_scale = {}".format(args.loss_scale), type(args.loss_scale))
        print("\nCUDNN VERSION: {}\n".format(torch.backends.cudnn.version()))

    cudnn.benchmark = True
    best_prec1 = 0
    if args.deterministic:
        cudnn.benchmark = False
        cudnn.deterministic = True
        torch.manual_seed(args.local_rank)
        torch.set_printoptions(precision=10)

    args.distributed = False
    if 'WORLD_SIZE' in os.environ:
        args.distributed = int(os.environ['WORLD_SIZE']) > 1

    args.gpu = 0
    args.world_size = 1

    if args.distributed:
        args.gpu = args.local_rank
        torch.cuda.set_device(args.gpu)
        torch.distributed.init_process_group(backend='nccl',
                                             init_method='env://')
        args.world_size = torch.distributed.get_world_size()

    assert torch.backends.cudnn.enabled, "Amp requires cudnn backend to be enabled."

    if args.channels_last:
        memory_format = torch.channels_last
    else:
        memory_format = torch.contiguous_format

    # # create model
    # if args.pretrained:
    #     print("=> using pre-trained model '{}'".format(args.arch))
    #     model = models.__dict__[args.arch](pretrained=True)
    # else:
    #     print("=> creating model '{}'".format(args.arch))
    #     model = models.__dict__[args.arch]()
    #     # model = resnet18()
    # model = resnet50(pretrained=True)
    repvgg_build_func = get_RepVGG_func_by_name(args.arch)
    model = repvgg_build_func(deploy=False)
    print(model)

    if args.sync_bn:
        import apex
        print("using apex synced BN")
        model = apex.parallel.convert_syncbn_model(model)

    model = model.cuda().to(memory_format=memory_format)

    # Scale learning rate based on global batch size
    args.lr = args.lr * float(args.batch_size * args.world_size) / 256.
    # optimizer = torch.optim.SGD(model.parameters(), args.lr,
    #                             momentum=args.momentum,
    #                             weight_decay=args.weight_decay)
    optimizer = sgd_optimizer(model, args.lr, args.momentum, args.weight_decay)
    lr_scheduler = CosineAnnealingLR(optimizer=optimizer,
                                     T_max=args.epochs * IMAGENET_TRAINSET_SIZE // args.batch_size // 8)

    # Initialize Amp.  Amp accepts either values or strings for the optional override arguments,
    # for convenient interoperation with argparse.
    model, optimizer = amp.initialize(model, optimizer,
                                      opt_level=args.opt_level,
                                      keep_batchnorm_fp32=args.keep_batchnorm_fp32,
                                      loss_scale=args.loss_scale
                                      )

    # For distributed training, wrap the model with apex.parallel.DistributedDataParallel.
    # This must be done AFTER the call to amp.initialize.  If model = DDP(model) is called
    # before model, ... = amp.initialize(model, ...), the call to amp.initialize may alter
    # the types of model's parameters in a way that disrupts or destroys DDP's allreduce hooks.
    if args.distributed:
        # By default, apex.parallel.DistributedDataParallel overlaps communication with
        # computation in the backward pass.
        # model = DDP(model)
        # delay_allreduce delays all communication to the end of the backward pass.
        model = DDP(model, delay_allreduce=True)

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

    # Optionally resume from a checkpoint
    if args.resume:
        # Use a local scope to avoid dangling references
        def resume():
            if os.path.isfile(args.resume):
                print("=> loading checkpoint '{}'".format(args.resume))
                checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage.cuda(args.gpu))
                # args.start_epoch = checkpoint['epoch']
                global best_prec1
                best_prec1 = checkpoint['best_prec1']
                model.load_state_dict(checkpoint['state_dict'])
                # optimizer.load_state_dict(checkpoint['optimizer'])
                # lr_scheduler.load_state_dict(checkpoint['scheduler'])
                print("=> loaded checkpoint '{}' (epoch {})"
                      .format(args.resume, checkpoint['epoch']))
            else:
                print("=> no checkpoint found at '{}'".format(args.resume))

        resume()
    # args.start_epoch = 50

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')

    if (args.arch == "inception_v3"):
        raise RuntimeError("Currently, inception_v3 is not supported by this example.")
        # crop_size = 299
        # val_size = 320 # I chose this value arbitrarily, we can adjust.
    else:
        crop_size = 224
        val_size = 256

    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(crop_size),
            transforms.RandomHorizontalFlip(),
            # transforms.ToTensor(), Too slow
            # normalize,
        ]))
    val_dataset = datasets.ImageFolder(valdir, transforms.Compose([
        transforms.Resize(val_size),
        transforms.CenterCrop(crop_size),
    ]))

    train_sampler = None
    val_sampler = None
    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)

    collate_fn = lambda b: fast_collate(b, memory_format)

    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, collate_fn=collate_fn)

    val_loader = torch.utils.data.DataLoader(
        val_dataset,
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True,
        sampler=val_sampler,
        collate_fn=collate_fn)

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

    # print model parameters
    # if args.local_rank == 0:
    #     for name, params in model.named_parameters():
    #         if 'bias' in name:
    #             print(name)
    # exit()
    # admm setup
    # if args.admm or args.masked_retrain:
    #     ADMM = admm.ADMM(model, file_name=args.prune_setting + ".yaml", rho=args.rho)
    #     if args.admm:
    #         admm.admm_initialization(args, ADMM=ADMM, model=model)  # intialize Z variable
    #     elif args.masked_retrain:
    #         admm.hard_prune(args, ADMM, model)
    #         if args.local_rank == 0:
    #             test_bn_sparsity(model)

    # if args.masked_retrain and not args.combine_progressive:
    #     print("full acc re-train masking")
    #     masks = {}
    #     for name, W in (model.named_parameters()):
    #         masks[name] = (W.detach() != 0).float()
    # for name, W in model.named_parameters():
    #     with torch.no_grad():
    #         W *= masks[name]
    # test_bn_sparsity(model)
    # elif args.combine_progressive:
    #     print("progressive admm-train/re-train masking")
    #     masks = {}
    #     for name, W in (model.named_parameters()):
    #         masks[name] = (W.detach() != 0).float()
    #     test_irregular_sparsity(model)
    #     print('start new admm prune from this sparsity')

    # training start
    for epoch in range(args.start_epoch, args.epochs):
        if args.freeze:
            for name, params in model.named_parameters():
                if 'scale' in name:
                    params.requires_grad = False
                    print('epoch and freezed params: ', epoch, name)
        if args.distributed:
            train_sampler.set_epoch(epoch)

        # train for one epoch
        # if args.admm:
        #     train(train_loader, model, criterion, optimizer, epoch, ADMM)
        # elif args.masked_retrain or args.combine_progressive:
        #     train(train_loader, model, criterion, optimizer, epoch, ADMM=ADMM, masks=masks)
        # else:
        train(train_loader, model, criterion, optimizer, epoch, args, lr_scheduler)

        # evaluate on validation set
        prec1 = validate(val_loader, model, criterion)

        # remember best prec@1 and save checkpoint
        if args.local_rank == 0:
            is_best = prec1 > best_prec1
            best_prec1 = max(prec1, best_prec1)
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_prec1': best_prec1,
                'optimizer': optimizer.state_dict(),
                'scheduler': lr_scheduler.state_dict(),
            }, is_best)
    # if args.local_rank == 0 and args.admm:
    #     torch.save(torch.tensor(ADMM.ratio_history), 'prune_process.pt')


class data_prefetcher():
    def __init__(self, loader):
        self.loader = iter(loader)
        self.stream = torch.cuda.Stream()
        self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1, 3, 1, 1)
        self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1, 3, 1, 1)
        # With Amp, it isn't necessary to manually convert data to half.
        # if args.fp16:
        #     self.mean = self.mean.half()
        #     self.std = self.std.half()
        self.preload()

    def preload(self):
        try:
            self.next_input, self.next_target = next(self.loader)
        except StopIteration:
            self.next_input = None
            self.next_target = None
            return
        # if record_stream() doesn't work, another option is to make sure device inputs are created
        # on the main stream.
        # self.next_input_gpu = torch.empty_like(self.next_input, device='cuda')
        # self.next_target_gpu = torch.empty_like(self.next_target, device='cuda')
        # Need to make sure the memory allocated for next_* is not still in use by the main stream
        # at the time we start copying to next_*:
        # self.stream.wait_stream(torch.cuda.current_stream())
        with torch.cuda.stream(self.stream):
            self.next_input = self.next_input.cuda(non_blocking=True)
            self.next_target = self.next_target.cuda(non_blocking=True)
            # more code for the alternative if record_stream() doesn't work:
            # copy_ will record the use of the pinned source tensor in this side stream.
            # self.next_input_gpu.copy_(self.next_input, non_blocking=True)
            # self.next_target_gpu.copy_(self.next_target, non_blocking=True)
            # self.next_input = self.next_input_gpu
            # self.next_target = self.next_target_gpu

            # With Amp, it isn't necessary to manually convert data to half.
            # if args.fp16:
            #     self.next_input = self.next_input.half()
            # else:
            self.next_input = self.next_input.float()
            self.next_input = self.next_input.sub_(self.mean).div_(self.std)

    def next(self):
        torch.cuda.current_stream().wait_stream(self.stream)
        input = self.next_input
        target = self.next_target
        if input is not None:
            input.record_stream(torch.cuda.current_stream())
        if target is not None:
            target.record_stream(torch.cuda.current_stream())
        self.preload()
        return input, target


def train(train_loader, model, criterion, optimizer, epoch, args, lr_scheduler):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()
    # writer = SummaryWriter()

    # adjust rho and Z U update
    # if args.admm and epoch % 2 == 0:
    #     ADMM.adjust_rho(2.0)
    # if args.admm:
    #     admm.z_u_update(args, ADMM, model, epoch, (epoch % 1))  # update Z and U variables

    # switch to train mode
    model.train()
    if args.local_rank == 0:
        print('current learning rate: ', lr_scheduler.get_lr()[0])
    end = time.time()

    prefetcher = data_prefetcher(train_loader)
    input, target = prefetcher.next()
    i = 0
    while input is not None:
        i += 1
        # if args.prof >= 0 and i == args.prof:
        #     print("Profiling begun at iteration {}".format(i))
        #     torch.cuda.cudart().cudaProfilerStart()

        # if args.prof >= 0: torch.cuda.nvtx.range_push("Body of iteration {}".format(i))

        # adjust_learning_rate(optimizer, epoch, i, len(train_loader))

        # compute output
        # if args.prof >= 0: torch.cuda.nvtx.range_push("forward")
        output = model(input)
        # if args.prof >= 0: torch.cuda.nvtx.range_pop()
        origin_loss = criterion(output, target)
        # append branch loss????????????
        if args.regularization:
            reg_loss = append_loss(model, epoch, args.prune)
        else:
            reg_loss = torch.tensor(0.)
        loss = origin_loss + reg_loss
        # compute gradient and do SGD step
        optimizer.zero_grad()
        # if args.local_rank == 0:
        #     test_bn_sparsity(model)

        # admm update
        # if args.admm:
        #     loss, admm_loss, mixed_loss = admm.append_admm_loss(args, ADMM,
        #                                                         model, loss)  # append admm losss
        #
        #     with amp.scale_loss(mixed_loss, optimizer) as scaled_loss:
        #         scaled_loss.backward()
        with amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward()

        # if args.masked_retrain or args.combine_progressive:
        #     with torch.no_grad():
        #         for name, W in (model.named_parameters()):
        #             if 'weight' in name and 'bn' in name:
        #                 W.grad *= masks[name]

        # if args.prof >= 0: torch.cuda.nvtx.range_push("backward")
        # with amp.scale_loss(loss, optimizer) as scaled_loss:
        #     scaled_loss.backward()
        # if args.prof >= 0: torch.cuda.nvtx.range_pop()

        # if args.prof >= 0: torch.cuda.nvtx.range_push("optimizer.step()")
        # if args.local_rank == 0:
        #     test_bn_sparsity(model)
        optimizer.step()

        # if args.masked_retrain or args.combine_progressive:
        #     admm.hard_prune(args, ADMM, model)
        # test_bn_sparsity(model)
        # if args.prof >= 0: torch.cuda.nvtx.range_pop()

        if i % args.print_freq == 0:
            # Every print_freq iterations, check the loss, accuracy, and speed.
            # For best performance, it doesn't make sense to print these metrics every
            # iteration, since they incur an allreduce and some host<->device syncs.

            # Measure accuracy
            prec1, prec5 = accuracy(output.data, target, topk=(1, 5))

            # Average loss and accuracy across processes for logging
            if args.distributed:
                reduced_loss = reduce_tensor(loss.data)
                prec1 = reduce_tensor(prec1)
                prec5 = reduce_tensor(prec5)
            else:
                reduced_loss = loss.data

            # to_python_float incurs a host<->device sync
            losses.update(to_python_float(reduced_loss), input.size(0))
            top1.update(to_python_float(prec1), input.size(0))
            top5.update(to_python_float(prec5), input.size(0))

            torch.cuda.synchronize()
            batch_time.update((time.time() - end) / args.print_freq)
            end = time.time()

            if args.local_rank == 0:
                print('Epoch: [{0}][{1}/{2}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Speed {3:.3f} ({4:.3f})\t'
                      'Loss {loss.val:.10f} ({loss.avg:.4f})\t'
                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                    epoch, i, len(train_loader),
                    args.world_size * args.batch_size / batch_time.val,
                    args.world_size * args.batch_size / batch_time.avg,
                    batch_time=batch_time,
                    loss=losses, top1=top1, top5=top5))
                if args.regularization:
                    print('current binary loss: ', reg_loss.item())

            # if args.admm and args.local_rank == 0:
            #     print('current admm loss: ', mixed_loss.item() - loss.item())
            # if i % (args.print_freq * 40) == 0 and args.admm:
            #     ADMM.adjust_ratio(0.01, writer, i // (args.print_freq * 40) + (epoch-60) * 3)
            # if args.masked_retrain and args.local_rank == 0:
            #     test_bn_sparsity(model)
        # if args.prof >= 0: torch.cuda.nvtx.range_push("prefetcher.next()")
        input, target = prefetcher.next()
        lr_scheduler.step()
        # if args.prof >= 0: torch.cuda.nvtx.range_pop()

        # Pop range "Body of iteration {}".format(i)
        # if args.prof >= 0: torch.cuda.nvtx.range_pop()
        #
        # if args.prof >= 0 and i == args.prof + 10:
        #     print("Profiling ended at iteration {}".format(i))
        #     torch.cuda.cudart().cudaProfilerStop()
        #     quit()


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

    # switch to evaluate mode
    model.eval()

    end = time.time()

    prefetcher = data_prefetcher(val_loader)
    input, target = prefetcher.next()
    i = 0
    while input is not None:
        i += 1

        # compute output
        with torch.no_grad():
            output = model(input)
            loss = criterion(output, target)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))

        if args.distributed:
            reduced_loss = reduce_tensor(loss.data)
            prec1 = reduce_tensor(prec1)
            prec5 = reduce_tensor(prec5)
        else:
            reduced_loss = loss.data

        losses.update(to_python_float(reduced_loss), input.size(0))
        top1.update(to_python_float(prec1), input.size(0))
        top5.update(to_python_float(prec5), input.size(0))

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

        # TODO:  Change timings to mirror train().
        if args.local_rank == 0 and i % args.print_freq == 0:
            print('Test: [{0}/{1}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Speed {2:.3f} ({3:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                i, len(val_loader),
                args.world_size * args.batch_size / batch_time.val,
                args.world_size * args.batch_size / batch_time.avg,
                batch_time=batch_time, loss=losses,
                top1=top1, top5=top5))

        input, target = prefetcher.next()

    print(' * Prec@1 {top1.avg:.3f} Prec@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, 'model_best.pth.tar')


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        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 adjust_learning_rate(optimizer, epoch, step, len_epoch):
    """LR schedule that should yield 76% converged accuracy with batch size 256"""
    factor = epoch // 30

    if epoch >= 80:
        factor = factor + 1

    lr = args.lr * (0.1 ** factor)

    """Warmup"""
    if epoch < 5:
        lr = lr * float(1 + step + epoch * len_epoch) / (5. * len_epoch)

    # if(args.local_rank == 0):
    #     print("epoch = {}, step = {}, lr = {}".format(epoch, step, lr))

    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    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


def reduce_tensor(tensor):
    rt = tensor.clone()
    dist.all_reduce(rt, op=dist.reduce_op.SUM)
    rt /= args.world_size
    return rt


if __name__ == '__main__':
    main()