eval_lincls_imagenet.py

import warnings
import builtins
import os
import random
import shutil
import time
import math
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 hcsc.loader import GaussianBlur
from utils.utils import mkdir, accuracy, concat_all_gather
from utils.options import parse_args_lincls_imagenet
warnings.filterwarnings('ignore')

model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))
best_acc1 = 0

def main():
    args = parse_args_lincls_imagenet()
    choose = args.choose
    if choose is not None:
        os.environ['CUDA_VISIBLE_DEVICES'] = choose
    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
    data_path = args.data  # the path stored
    args.data = data_path
    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
    # 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:
        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
    print("=> creating model '{}'".format(args.arch))
    num_classes = 1000
    
    model = models.__dict__[args.arch](num_classes=num_classes)

    # freeze all layers but the last fc
    for name, param in model.named_parameters():
        if name not in ['fc.weight', 'fc.bias']:
            param.requires_grad = False

    # init the fc layer
    model.fc.weight.data.normal_(mean=0.0, std=0.01)
    model.fc.bias.data.zero_()

    # load from pre-trained, before DistributedDataParallel constructor
    if args.pretrained:
        if os.path.isfile(args.pretrained):
            print("=> loading checkpoint '{}'".format(args.pretrained))

            checkpoint = torch.load(args.pretrained, map_location="cpu")
            state_dict = checkpoint['state_dict']
            for k in list(state_dict.keys()):
                # retain only encoder_q up to before the embedding layer
                if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
                    # remove prefix
                    state_dict[k[len("module.encoder_q."):]] = state_dict[k]
                    # delete renamed or unused k
                    del state_dict[k]

            args.start_epoch = 0
            msg = model.load_state_dict(state_dict, strict=False)
            assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}

            print("=> loaded pre-trained model '{}'".format(args.pretrained))
        else:
            print("=> no checkpoint found at '{}'".format(args.pretrained))

    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
        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 = nn.CrossEntropyLoss().cuda(args.gpu)

    # optimize only the linear classifier
    parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
    assert len(parameters) == 2  # fc.weight, fc.bias
    optimizer = torch.optim.SGD(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):
            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 = torch.tensor(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

    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                        std=[0.229, 0.224, 0.225])
    data_path = args.data
    traindir = os.path.join(data_path, 'train')
    valdir = os.path.join(data_path, 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                        std=[0.229, 0.224, 0.225])

    transform_train = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        normalize, ])
    transform_test = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        normalize,
    ])
    train_dataset = datasets.ImageFolder(traindir, transform_train)
    val_dataset = datasets.ImageFolder(valdir, transform_test)

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset,
                                                                        shuffle=True)  # different gpu forward individual based on its own statistics
        # val_sampler=None
    else:
        train_sampler = None
        val_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)

    val_loader = torch.utils.data.DataLoader(
        val_dataset, sampler=val_sampler,
        batch_size=args.batch_size, shuffle=(val_sampler is None),
        # different gpu forward is different, thus it's necessary
        num_workers=args.workers, pin_memory=True)

    import datetime
    today = datetime.date.today()
    formatted_today = today.strftime('%y%m%d')
    now = time.strftime("%H:%M:%S")
    save_path = os.path.join(args.save_path, args.log_path)
    log_path = os.path.join(save_path, 'Finetune_log')
    log_path = os.path.join(log_path, formatted_today + now)
    if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                and args.rank % ngpus_per_node == 0):
        mkdir(log_path)


    # model_path=os.path.join(log_path,'checkpoint.pth.tar')
    
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)

        adjust_learning_rate(optimizer, epoch, args)
        train(train_loader, model, criterion, optimizer, epoch, args)
        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, 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):
            # add timestamp
            tmp_save_path = os.path.join(log_path, 'checkpoint.pth.tar')
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer': optimizer.state_dict(),
            }, is_best, filename=tmp_save_path)
                
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')
    mAP = AverageMeter("mAP", ":6.2f")
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5, mAP],
        prefix="Epoch: [{}]".format(epoch))

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

        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)

        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.item(), images.size(0))
        top5.update(acc5.item(), 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):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    mAP = AverageMeter("mAP", ":6.2f")
    progress = ProgressMeter(
        len(val_loader),

        [batch_time, losses, top1, top5, mAP],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()
    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            target = target.cuda(args.gpu, non_blocking=True)
            output = model(images)
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            acc1 = torch.mean(concat_all_gather(acc1.unsqueeze(0)), dim=0, keepdim=True)
            acc5 = torch.mean(concat_all_gather(acc5.unsqueeze(0)), dim=0, keepdim=True)
            top1.update(acc1.item(), images.size(0))
            top5.update(acc5.item(), images.size(0))
            loss = criterion(output, target)
            losses.update(loss.item(), images.size(0))
            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

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

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

    return top1.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        root_path = os.path.split(filename)[0]
        best_path = os.path.join(root_path, "model_best.pth.tar")
        shutil.copyfile(filename, best_path)

def sanity_check(state_dict, pretrained_weights):
    """
    Linear classifier should not change any weights other than the linear layer.
    This sanity check asserts nothing wrong happens (e.g., BN stats updated).
    """
    print("=> loading '{}' for sanity check".format(pretrained_weights))
    checkpoint = torch.load(pretrained_weights, map_location="cpu")
    state_dict_pre = checkpoint['state_dict']

    for k in list(state_dict.keys()):
        # only ignore fc layer
        if 'fc.weight' in k or 'fc.bias' in k:
            continue

        # name in pretrained model
        k_pre = 'module.encoder_q.' + k[len('module.'):] \
            if k.startswith('module.') else 'module.encoder_q.' + k

        assert ((state_dict[k].cpu() == state_dict_pre[k_pre]).all()), \
            '{} is changed in linear classifier training.'.format(k)

    print("=> sanity check passed.")


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))

    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
    end_lr = args.final_lr
    # update on cos scheduler
    # this scheduler is not proper enough
    if args.cos:
        lr = 0.5 * (1. + math.cos(math.pi * epoch / args.epochs)) * (lr - end_lr) + end_lr
    else:
        for milestone in args.schedule:
            lr *= 0.1 if epoch >= milestone else 1.
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


if __name__ == '__main__':
    main()