bsp.py

"""
@author: Baixu Chen
@contact: cbx_99_hasta@outlook.com
"""
import random
import time
import warnings
import argparse
import shutil
import os.path as osp

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.dann import DomainAdversarialLoss
from tllib.alignment.bsp import BatchSpectralPenalizationLoss, ImageClassifier
from tllib.modules.domain_discriminator import DomainDiscriminator
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,
                                 pool_layer=pool_layer, finetune=not args.scratch).to(device)
    domain_discri = DomainDiscriminator(in_feature=classifier.features_dim, hidden_size=1024).to(device)

    # define optimizer and lr scheduler
    optimizer = SGD(classifier.get_parameters() + domain_discri.get_parameters(),
                    args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
    lr_scheduler = LambdaLR(optimizer, lambda x: args.lr * (1. + args.lr_gamma * float(x)) ** (-args.lr_decay))

    # define loss function
    domain_adv = DomainAdversarialLoss(domain_discri).to(device)
    bsp_penalty = BatchSpectralPenalizationLoss().to(device)

    # 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.pool_layer, 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

    if args.pretrain is None:
        # first pretrain the classifier wish source data
        print("Pretraining the model on source domain.")
        args.pretrain = logger.get_checkpoint_path('pretrain')
        pretrain_model = ImageClassifier(backbone, num_classes, bottleneck_dim=args.bottleneck_dim,
                                         pool_layer=pool_layer, finetune=not args.scratch).to(device)
        pretrain_optimizer = SGD(pretrain_model.get_parameters(), args.pretrain_lr, momentum=args.momentum,
                                 weight_decay=args.weight_decay, nesterov=True)
        pretrain_lr_scheduler = LambdaLR(pretrain_optimizer,
                                         lambda x: args.pretrain_lr * (1. + args.lr_gamma * float(x)) ** (
                                             -args.lr_decay))
        # start pretraining
        for epoch in range(args.pretrain_epochs):
            print("lr:", pretrain_lr_scheduler.get_lr())
            # pretrain for one epoch
            utils.empirical_risk_minimization(train_source_iter, pretrain_model, pretrain_optimizer,
                                              pretrain_lr_scheduler, epoch, args,
                                              device)
            # validate to show pretrain process
            utils.validate(val_loader, pretrain_model, args, device)

        torch.save(pretrain_model.state_dict(), args.pretrain)
        print("Pretraining process is done.")

    checkpoint = torch.load(args.pretrain, map_location='cpu')
    classifier.load_state_dict(checkpoint)

    # start training
    best_acc1 = 0.
    for epoch in range(args.epochs):
        print("lr:", lr_scheduler.get_last_lr()[0])
        # train for one epoch
        train(train_source_iter, train_target_iter, classifier, domain_adv, bsp_penalty, 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,
          model: ImageClassifier, domain_adv: DomainAdversarialLoss, bsp_penalty: BatchSpectralPenalizationLoss,
          optimizer: SGD, lr_scheduler: LambdaLR, epoch: int, args: argparse.Namespace):
    batch_time = AverageMeter('Time', ':5.2f')
    data_time = AverageMeter('Data', ':5.2f')
    losses = AverageMeter('Loss', ':6.2f')
    cls_accs = AverageMeter('Cls Acc', ':3.1f')
    domain_accs = AverageMeter('Domain Acc', ':3.1f')
    progress = ProgressMeter(
        args.iters_per_epoch,
        [batch_time, data_time, losses, cls_accs, domain_accs],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()
    domain_adv.train()

    end = time.time()
    for i in range(args.iters_per_epoch):
        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)
        y, f = model(x)
        y_s, y_t = y.chunk(2, dim=0)
        f_s, f_t = f.chunk(2, dim=0)

        cls_loss = F.cross_entropy(y_s, labels_s)
        transfer_loss = domain_adv(f_s, f_t)
        bsp_loss = bsp_penalty(f_s, f_t)
        domain_acc = domain_adv.domain_discriminator_accuracy
        loss = cls_loss + transfer_loss * args.trade_off + bsp_loss * args.trade_off_bsp

        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))
        domain_accs.update(domain_acc.item(), x_s.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        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='BSP 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('--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('--resize-size', type=int, default=224,
                        help='the image size after resizing')
    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('--pretrain', type=str, default=None,
                        help='pretrain checkpoint for classification model')
    parser.add_argument('--bottleneck-dim', default=256, type=int,
                        help='Dimension of bottleneck')
    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('--trade-off', default=1., type=float,
                        help='the trade-off hyper-parameter for transfer loss')
    parser.add_argument('--trade-off-bsp', default=2e-4, type=float,
                        help='the trade-off hyper-parameter for bsp 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=0.003, type=float,
                        metavar='LR', help='initial learning rate', dest='lr')
    parser.add_argument('--pretrain-lr', default=0.001, type=float, help='initial pretrain learning rate')
    parser.add_argument('--lr-gamma', default=0.001, type=float, help='parameter for lr scheduler')
    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=1e-3, type=float,
                        metavar='W', help='weight decay (default: 1e-3)',
                        dest='weight_decay')
    parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
                        help='number of data loading workers (default: 2)')
    parser.add_argument('--epochs', default=20, type=int, metavar='N',
                        help='number of total epochs to run')
    parser.add_argument('--pretrain-epochs', default=3, type=int, metavar='N',
                        help='number of total epochs(pretrain) to run (default: 3)')
    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='bsp',
                        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)