mdd.py

"""
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""
import random
import time
import warnings
import argparse
import os.path as osp
import shutil

import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.optim import SGD
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
import torch.nn.functional as F

import utils
from tllib.alignment.mdd import ClassificationMarginDisparityDiscrepancy \
    as MarginDisparityDiscrepancy, ImageClassifier
from tllib.utils.data import ForeverDataIterator
from tllib.utils.metric import accuracy
from tllib.utils.meter import AverageMeter, ProgressMeter
from tllib.utils.logger import CompleteLogger
from tllib.utils.analysis import collect_feature, tsne, a_distance

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def main(args: argparse.Namespace):
    logger = CompleteLogger(args.log, args.phase)
    print(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.')

    cudnn.benchmark = True

    # Data loading code
    train_transform = utils.get_train_transform(args.train_resizing, scale=args.scale, ratio=args.ratio,
                                                random_horizontal_flip=not args.no_hflip,
                                                random_color_jitter=False, resize_size=args.resize_size,
                                                norm_mean=args.norm_mean, norm_std=args.norm_std)
    val_transform = utils.get_val_transform(args.val_resizing, resize_size=args.resize_size,
                                            norm_mean=args.norm_mean, norm_std=args.norm_std)
    print("train_transform: ", train_transform)
    print("val_transform: ", val_transform)

    train_source_dataset, train_target_dataset, val_dataset, test_dataset, num_classes, args.class_names = \
        utils.get_dataset(args.data, args.root, args.source, args.target, train_transform, val_transform)
    train_source_loader = DataLoader(train_source_dataset, batch_size=args.batch_size,
                                     shuffle=True, num_workers=args.workers, drop_last=True)
    train_target_loader = DataLoader(train_target_dataset, batch_size=args.batch_size,
                                     shuffle=True, num_workers=args.workers, drop_last=True)
    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
    test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)

    train_source_iter = ForeverDataIterator(train_source_loader)
    train_target_iter = ForeverDataIterator(train_target_loader)

    # create model
    print("=> using model '{}'".format(args.arch))
    backbone = utils.get_model(args.arch, pretrain=not args.scratch)
    pool_layer = nn.Identity() if args.no_pool else None
    classifier = ImageClassifier(backbone, num_classes, bottleneck_dim=args.bottleneck_dim,
                                 width=args.bottleneck_dim, pool_layer=pool_layer).to(device)
    mdd = MarginDisparityDiscrepancy(args.margin).to(device)

    # define optimizer and lr_scheduler
    # The learning rate of the classifiers are set 10 times to that of the feature extractor by default.
    optimizer = SGD(classifier.get_parameters(), args.lr, momentum=args.momentum, weight_decay=args.wd, nesterov=True)
    lr_scheduler = LambdaLR(optimizer, lambda x: args.lr * (1. + args.lr_gamma * float(x)) ** (-args.lr_decay))

    # resume from the best checkpoint
    if args.phase != 'train':
        checkpoint = torch.load(logger.get_checkpoint_path('best'), map_location='cpu')
        classifier.load_state_dict(checkpoint)

    # analysis the model
    if args.phase == 'analysis':
        # extract features from both domains
        feature_extractor = nn.Sequential(classifier.backbone, classifier.bottleneck).to(device)
        source_feature = collect_feature(train_source_loader, feature_extractor, device)
        target_feature = collect_feature(train_target_loader, feature_extractor, device)
        # plot t-SNE
        tSNE_filename = osp.join(logger.visualize_directory, 'TSNE.pdf')
        tsne.visualize(source_feature, target_feature, tSNE_filename)
        print("Saving t-SNE to", tSNE_filename)
        # calculate A-distance, which is a measure for distribution discrepancy
        A_distance = a_distance.calculate(source_feature, target_feature, device)
        print("A-distance =", A_distance)
        return

    if args.phase == 'test':
        acc1 = utils.validate(test_loader, classifier, args, device)
        print(acc1)
        return

    # start training
    best_acc1 = 0.
    for epoch in range(args.epochs):
        print(lr_scheduler.get_lr())
        # train for one epoch
        train(train_source_iter, train_target_iter, classifier, mdd, optimizer,
              lr_scheduler, epoch, args)

        # evaluate on validation set
        acc1 = utils.validate(val_loader, classifier, args, device)

        # remember best acc@1 and save checkpoint
        torch.save(classifier.state_dict(), logger.get_checkpoint_path('latest'))
        if acc1 > best_acc1:
            shutil.copy(logger.get_checkpoint_path('latest'), logger.get_checkpoint_path('best'))
        best_acc1 = max(acc1, best_acc1)

    print("best_acc1 = {:3.1f}".format(best_acc1))

    # evaluate on test set
    classifier.load_state_dict(torch.load(logger.get_checkpoint_path('best')))
    acc1 = utils.validate(test_loader, classifier, args, device)
    print("test_acc1 = {:3.1f}".format(acc1))

    logger.close()


def train(train_source_iter: ForeverDataIterator, train_target_iter: ForeverDataIterator,
          classifier: ImageClassifier, mdd: MarginDisparityDiscrepancy, optimizer: SGD,
          lr_scheduler: LambdaLR, epoch: int, args: argparse.Namespace):
    batch_time = AverageMeter('Time', ':3.1f')
    data_time = AverageMeter('Data', ':3.1f')
    losses = AverageMeter('Loss', ':3.2f')
    trans_losses = AverageMeter('Trans Loss', ':3.2f')
    cls_accs = AverageMeter('Cls Acc', ':3.1f')

    progress = ProgressMeter(
        args.iters_per_epoch,
        [batch_time, data_time, losses, trans_losses, cls_accs],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    classifier.train()
    mdd.train()

    end = time.time()
    for i in range(args.iters_per_epoch):
        optimizer.zero_grad()

        x_s, labels_s = next(train_source_iter)[:2]
        x_t, = next(train_target_iter)[:1]

        x_s = x_s.to(device)
        x_t = x_t.to(device)
        labels_s = labels_s.to(device)

        # measure data loading time
        data_time.update(time.time() - end)

        # compute output
        x = torch.cat((x_s, x_t), dim=0)
        outputs, outputs_adv = classifier(x)
        y_s, y_t = outputs.chunk(2, dim=0)
        y_s_adv, y_t_adv = outputs_adv.chunk(2, dim=0)

        # compute cross entropy loss on source domain
        cls_loss = F.cross_entropy(y_s, labels_s)
        # compute margin disparity discrepancy between domains
        # for adversarial classifier, minimize negative mdd is equal to maximize mdd
        transfer_loss = -mdd(y_s, y_s_adv, y_t, y_t_adv)
        loss = cls_loss + transfer_loss * args.trade_off
        classifier.step()

        cls_acc = accuracy(y_s, labels_s)[0]

        losses.update(loss.item(), x_s.size(0))
        cls_accs.update(cls_acc.item(), x_s.size(0))
        trans_losses.update(transfer_loss.item(), x_s.size(0))

        # compute gradient and do SGD step
        loss.backward()
        optimizer.step()
        lr_scheduler.step()

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

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


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='MDD for Unsupervised Domain Adaptation')
    # dataset parameters
    parser.add_argument('root', metavar='DIR',
                        help='root path of dataset')
    parser.add_argument('-d', '--data', metavar='DATA', default='Office31', choices=utils.get_dataset_names(),
                        help='dataset: ' + ' | '.join(utils.get_dataset_names()) +
                             ' (default: Office31)')
    parser.add_argument('-s', '--source', help='source domain(s)', nargs='+')
    parser.add_argument('-t', '--target', help='target domain(s)', nargs='+')
    parser.add_argument('--train-resizing', type=str, default='default')
    parser.add_argument('--val-resizing', type=str, default='default')
    parser.add_argument('--resize-size', type=int, default=224,
                        help='the image size after resizing')
    parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
                        help='Random resize scale (default: 0.08 1.0)')
    parser.add_argument('--ratio', type=float, nargs='+', default=[3. / 4., 4. / 3.], metavar='RATIO',
                        help='Random resize aspect ratio (default: 0.75 1.33)')
    parser.add_argument('--no-hflip', action='store_true', help='no random horizontal flipping during training')
    parser.add_argument('--norm-mean', type=float, nargs='+', default=(0.485, 0.456, 0.406), help='normalization mean')
    parser.add_argument('--norm-std', type=float, nargs='+', default=(0.229, 0.224, 0.225), help='normalization std')
    # model parameters
    parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                        choices=utils.get_model_names(),
                        help='backbone architecture: ' +
                             ' | '.join(utils.get_model_names()) +
                             ' (default: resnet18)')
    parser.add_argument('--bottleneck-dim', default=1024, type=int)
    parser.add_argument('--no-pool', action='store_true',
                        help='no pool layer after the feature extractor.')
    parser.add_argument('--scratch', action='store_true', help='whether train from scratch.')
    parser.add_argument('--margin', type=float, default=4., help="margin gamma")
    parser.add_argument('--trade-off', default=1., type=float,
                        help='the trade-off hyper-parameter for transfer loss')
    # training parameters
    parser.add_argument('-b', '--batch-size', default=32, type=int,
                        metavar='N',
                        help='mini-batch size (default: 32)')
    parser.add_argument('--lr', '--learning-rate', default=0.004, type=float,
                        metavar='LR', help='initial learning rate', dest='lr')
    parser.add_argument('--lr-gamma', default=0.0002, type=float)
    parser.add_argument('--lr-decay', default=0.75, type=float, help='parameter for lr scheduler')
    parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum')
    parser.add_argument('--wd', '--weight-decay', default=0.0005, type=float,
                        metavar='W', help='weight decay (default: 5e-4)')
    parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
                        help='number of data loading workers (default: 4)')
    parser.add_argument('--epochs', default=20, type=int, metavar='N',
                        help='number of total epochs to run')
    parser.add_argument('-i', '--iters-per-epoch', default=1000, type=int,
                        help='Number of iterations per epoch')
    parser.add_argument('-p', '--print-freq', default=100, type=int,
                        metavar='N', help='print frequency (default: 100)')
    parser.add_argument('--seed', default=None, type=int,
                        help='seed for initializing training. ')
    parser.add_argument('--per-class-eval', action='store_true',
                        help='whether output per-class accuracy during evaluation')
    parser.add_argument("--log", type=str, default='mdd',
                        help="Where to save logs, checkpoints and debugging images.")
    parser.add_argument("--phase", type=str, default='train', choices=['train', 'test', 'analysis'],
                        help="When phase is 'test', only test the model."
                             "When phase is 'analysis', only analysis the model.")
    args = parser.parse_args()
    main(args)