train_trm.py

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

import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.utils as vutils
import wandb
from timm.models import create_model

import dataset
import utils.train_utils as utils
from vit_models import deit_ensemble

best_acc1 = 0
ALL_MODELS = vars(deit_ensemble)


def get_args():
    parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
    parser.add_argument('--data', default="", help='path to dataset')
    parser.add_argument('--dataset', default="cifar10", help='dataset name')
    parser.add_argument('--model', default='tiny_patch16_224', type=str, help='Name of model to train')
    parser.add_argument('--num_classes', default=1000, help='number of classes')
    parser.add_argument('--seed', default=None, type=int, help='seed for initializing training. ')
    parser.add_argument('--project', default='deit', type=str, help='project name')
    parser.add_argument('--exp', default='exp01', type=str, help='experiment name')
    parser.add_argument('--output_dir', default='ckpts/exp01', type=str, help='experiment name')

    # train settings
    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')
    # learning rate
    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')
    # regularize
    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)')

    # cpu / gpu settings
    parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                        help='number of data loading workers (default: 4)')
    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('--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')

    # adv settings
    parser.add_argument('--index', default=0, type=int, help='transformer head: 0-10')
    parser.add_argument('--ssl_type', default="cls", type=str, help='rot / jigsaw')
    parser.add_argument('--jigsaw_count', default=4, type=int, help='number of jigsaw permutations to use')
    parser.add_argument('--save_img', action='store_true', default=False, help='save initial image example')

    parser.add_argument('--pretrained', default='', help='start training from checkpoint')
    parser.add_argument('-p', '--print-freq', default=10, type=int, metavar='N', help='print frequency (default: 10)')
    parser.add_argument('--save_all', action='store_true', default=False, help='save each ckpt')
    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')

    return parser.parse_args()


def main():
    args = get_args()

    if not args.evaluate and utils.is_main_process():
        with open(f"{args.output_dir}/config.json", "w") as fo:
            fo.write(json.dumps(vars(args), indent=4))
        wandb.init(project=args.project)
        wandb.run.name = args.exp
        wandb.config.update(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)


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 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)

    model = create_model(
        args.model,
        pretrained=False,
        num_classes=args.num_classes,
        drop_rate=args.drop,
        drop_path_rate=args.drop_path,
        drop_block_rate=None,
    )

    if args.pretrained:
        if args.pretrained.startswith('https'):
            checkpoint = torch.hub.load_state_dict_from_url(
                args.pretrained, map_location='cpu', check_hash=True)
        else:
            checkpoint = torch.load(args.pretrained, map_location='cpu')
        if "model" in checkpoint:
            checkpoint_model = checkpoint['model']
        else:
            from collections import OrderedDict
            new_state_dict = OrderedDict()
            for k, v in checkpoint["state_dict"].items():
                name = k[7:]  # remove `module.' from state dict
                new_state_dict[name] = v
            checkpoint_model = new_state_dict
        msg = model.load_state_dict(checkpoint_model, strict=False)
        print(msg)
        print("NOTE: transformer head weights will be reported as missing - they are trained from scratch")

    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.model.startswith('alexnet') or args.model.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)

    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):
            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_transform = transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ])
    test_transform = transforms.Compose([
                    transforms.Resize(256),
                    transforms.CenterCrop(224),
                    transforms.ToTensor(),
                    normalize,
                ])

    if args.dataset == "cifar10":
        train_dataset = datasets.CIFAR10(
            root=args.data,
            download=True,
            train=True,
            transform=train_transform)
    elif args.dataset == "flowers102":
        train_dataset = dataset.Flowers102Dataset(
            image_root_path=args.data,
            split="trainval",
            transform=train_transform)
    else:
        train_dataset = datasets.ImageFolder(traindir,train_transform)

    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)

    if args.dataset == "cifar10":
        val_loader = torch.utils.data.DataLoader(
            datasets.CIFAR10(
                root=args.data,
                train=False,
                transform=test_transform),
            batch_size=args.batch_size, shuffle=False,
            num_workers=args.workers, pin_memory=True)
    elif args.dataset == "flowers102":
        val_loader = torch.utils.data.DataLoader(
            dataset.Flowers102Dataset(
                image_root_path=args.data,
                split="test",
                transform=test_transform),
            batch_size=args.batch_size, shuffle=False,
            num_workers=args.workers, pin_memory=True)
    else:
        val_loader = torch.utils.data.DataLoader(
            datasets.ImageFolder(valdir, test_transform),
            batch_size=args.batch_size, shuffle=False,
            num_workers=args.workers, pin_memory=True)

    if args.evaluate:
        eval_dict = {}
        for index_idx in range(11):
            acc1, acc5, _ = validate(val_loader, model, criterion, args, epoch=index_idx)
            eval_dict[f"head_{index_idx:-2d}"] = {"acc1": acc1, "acc5": acc5}
        acc1, acc5, _ = validate(val_loader, model, criterion, args, epoch=-1)
        eval_dict[f"head_last"] = {"acc1": acc1, "acc5": acc5}

        print(eval_dict)
        return

    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
        for param in model.parameters():  # freeze all
            param.requires_grad = False
            param.grad = None
        for param in model.module.transformerheads[epoch].parameters():  # unfreeze nth head
            param.requires_grad = True

        tr_acc1, tr_acc5, tr_loss = train(train_loader, model, criterion, optimizer, epoch, args)

        # evaluate on validation set
        acc1, acc5, test_loss = validate(val_loader, model, criterion, args, epoch=epoch)

        # log results
        if utils.is_main_process():
            log_stats = {
                "train/acc1": tr_acc1,
                "train/acc5": tr_acc5,
                "train/loss": tr_loss,
                "test/acc1": acc1,
                "test/acc5": acc5,
                "test/loss": test_loss
            }

            wandb.log(log_stats)
            wandb.run.summary["n_parameters"] = sum(p.numel() for p in model.parameters() if p.requires_grad)
            wandb.run.summary["best/test_acc@1"] = best_acc1

            log_stats['epoch'] = epoch
            with open(f"{args.output_dir}/logs.txt", "a") as fo:
                fo.write(json.dumps(log_stats) + "\n")

        # 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.model,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer': optimizer.state_dict(),
            }, is_best, every_epoch=args.save_all, save_dir=args.output_dir)


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')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

    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)
        if torch.cuda.is_available():
            target = target.cuda(args.gpu, non_blocking=True)

        if i == 0 and args.save_img:
            vutils.save_image(vutils.make_grid(images[:16], normalize=False, scale_each=True),
                              f"example_before.jpg")

        output = model(images)
        output = output[epoch]
        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)

    return top1.avg, top5.avg, losses.avg


def validate(val_loader, model, criterion, args, epoch=0):
    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)

            output = model(images)
            output = output[epoch]

            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))

            # 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))

    return top1.avg, top5.avg, losses.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', every_epoch=False, save_dir="ckpts"):
    os.makedirs(save_dir, exist_ok=True)
    file_path = f"{save_dir}/{filename}"
    torch.save(state, file_path)
    if is_best:
        shutil.copyfile(file_path, f'{save_dir}/model_best.pth.tar')
    if every_epoch:
        shutil.copyfile(file_path, f"{save_dir}/model_{state['epoch']:02d}.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))

    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 // 90))
    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()