cdan_mcc_sdat_masking.py

# Credits: https://github.com/thuml/Transfer-Learning-Library
import random
import time
import warnings
import sys
import argparse
import shutil
import os.path as osp
import os
import wandb

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

sys.path.append('../')
from dalib.modules.domain_discriminator import DomainDiscriminator
from dalib.adaptation.cdan import ConditionalDomainAdversarialLoss, ImageClassifier
from dalib.adaptation.mcc import MinimumClassConfusionLoss
from dalib.modules.masking import Masking
from dalib.modules.teacher import EMATeacher
from common.utils.data import ForeverDataIterator
from common.utils.metric import accuracy
from common.utils.meter import AverageMeter, ProgressMeter
from common.utils.logger import CompleteLogger
from common.utils.analysis import collect_feature, tsne, a_distance
from common.utils.sam import SAM

sys.path.append('.')
import utils


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

    if args.log_results:
        wandb.init(
            project="MIC",
            name=args.log_name)
        wandb.config.update(args)
    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
    device = args.device

    # Data loading code
    train_transform = utils.get_train_transform(args.train_resizing, 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)
    print(backbone)
    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)
    classifier_feature_dim = classifier.features_dim

    if args.randomized:
        domain_discri = DomainDiscriminator(
            args.randomized_dim, hidden_size=1024).to(device)
    else:
        domain_discri = DomainDiscriminator(
            classifier_feature_dim * num_classes, hidden_size=1024).to(device)

    # define optimizer and lr scheduler
    base_optimizer = torch.optim.SGD
    ad_optimizer = SGD(domain_discri.get_parameters(
    ), args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
    optimizer = SAM(classifier.get_parameters(), base_optimizer, rho=args.rho, adaptive=False,
                    lr=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))
    lr_scheduler_ad = LambdaLR(
        ad_optimizer, lambda x: args.lr * (1. + args.lr_gamma * float(x)) ** (-args.lr_decay))

    # define loss function
    domain_adv = ConditionalDomainAdversarialLoss(
        domain_discri, entropy_conditioning=args.entropy,
        num_classes=num_classes, features_dim=classifier_feature_dim, randomized=args.randomized,
        randomized_dim=args.randomized_dim
    ).to(device)

    mcc_loss = MinimumClassConfusionLoss(temperature=args.temperature)

    teacher = EMATeacher(classifier, alpha=args.alpha, pseudo_label_weight=args.pseudo_label_weight).to(device)
    masking = Masking(
        block_size=args.mask_block_size,
        ratio=args.mask_ratio,
        color_jitter_s=args.mask_color_jitter_s,
        color_jitter_p=args.mask_color_jitter_p,
        blur=args.mask_blur,
        mean=args.norm_mean,
        std=args.norm_std)

    # 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

    # start training
    best_acc1 = 0.
    for epoch in range(args.epochs):
        print("lr_bbone:", lr_scheduler.get_last_lr()[0])
        print("lr_btlnck:", lr_scheduler.get_last_lr()[1])
        if args.log_results:
            wandb.log({"lr_bbone": lr_scheduler.get_last_lr()[0],
                       "lr_btlnck": lr_scheduler.get_last_lr()[1]})
        # train for one epoch

        train(train_source_iter, train_target_iter, classifier, teacher,
              domain_adv, mcc_loss, masking, optimizer, ad_optimizer,
              lr_scheduler, lr_scheduler_ad, epoch, args)
        # evaluate on validation set
        acc1 = utils.validate(val_loader, classifier, args, device)
        if args.log_results:
            wandb.log({'epoch': epoch, 'val_acc': acc1})

        # 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))
    if args.log_results:
        wandb.log({'epoch': epoch, 'test_acc': acc1})

    logger.close()


def train(train_source_iter: ForeverDataIterator, train_target_iter: ForeverDataIterator,
          model: ImageClassifier, teacher: EMATeacher,
          domain_adv: ConditionalDomainAdversarialLoss, mcc, masking, optimizer, ad_optimizer,
          lr_scheduler: LambdaLR, lr_scheduler_ad, 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')
    domain_accs = AverageMeter('Domain Acc', ':3.1f')
    progress = ProgressMeter(
        args.iters_per_epoch,
        [batch_time, data_time, losses, trans_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)
        x_t, _ = next(train_target_iter)

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

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

        # generate pseudo-label
        teacher.update_weights(model, epoch * args.iters_per_epoch + i)
        pseudo_label_t, pseudo_prob_t = teacher(x_t)

        # 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)
        mcc_loss_value = mcc(y_t)
        y_t_masked, _ = model(x_t_masked)
        if teacher.pseudo_label_weight is not None:
            ce = F.cross_entropy(y_t_masked, pseudo_label_t, reduction='none')
            masking_loss_value = torch.mean(pseudo_prob_t * ce)
        else:
            masking_loss_value = F.cross_entropy(y_t_masked, pseudo_label_t)
        loss = cls_loss + mcc_loss_value + masking_loss_value

        loss.backward()

        # Calculate ϵ̂ (w) and add it to the weights
        optimizer.first_step(zero_grad=True)

        # Calculate task loss and domain loss
        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)
        y_t_masked, _ = model(x_t_masked)
        transfer_loss = domain_adv(y_s, f_s, y_t, f_t) + mcc(y_t) + \
                        F.cross_entropy(y_t_masked, pseudo_label_t)
        domain_acc = domain_adv.domain_discriminator_accuracy
        loss = cls_loss + transfer_loss * args.trade_off

        cls_acc = accuracy(y_s, labels_s)[0]
        if args.log_results:
            # masked_img = wandb.Image(x_t_masked, caption="Masked Image")
            wandb.log({
                'iteration': epoch*args.iters_per_epoch + i, 'loss': loss, 'cls_loss': cls_loss,
                'transfer_loss': transfer_loss, 'domain_acc': domain_acc, 'pseudo_weight_avg': torch.mean(pseudo_prob_t),
                # 'masked_img': masked_img,
           })

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

        loss.backward()
        # Update parameters of domain classifier
        ad_optimizer.step()
        # Update parameters (Sharpness-Aware update)
        optimizer.second_step(zero_grad=True)
        lr_scheduler.step()
        lr_scheduler_ad.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='CDAN+MCC with SDAT 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('--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=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('-r', '--randomized', action='store_true',
                        help='using randomized multi-linear-map (default: False)')
    parser.add_argument('-rd', '--randomized-dim', default=1024, type=int,
                        help='randomized dimension when using randomized multi-linear-map (default: 1024)')
    parser.add_argument('--entropy', default=False,
                        action='store_true', help='use entropy conditioning')
    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=0.01, type=float,
                        metavar='LR', help='initial learning rate', dest='lr')
    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('-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='cdan',
                        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.")
    parser.add_argument('--log_results', action='store_true',
                        help="To log results in wandb")
    parser.add_argument('--gpu', type=str, default="0", help="GPU ID")
    parser.add_argument('--log_name', type=str,
                        default="log", help="log name for wandb")
    parser.add_argument('--rho', type=float, default=0.05, help="GPU ID")
    parser.add_argument('--temperature', default=2.0,
                        type=float, help='parameter temperature scaling')
    # masked image consistency
    parser.add_argument('--alpha', default=0.999, type=float)
    parser.add_argument('--pseudo_label_weight', default=None)
    parser.add_argument('--mask_block_size', default=32, type=int)
    parser.add_argument('--mask_ratio', default=0.5, type=float)
    parser.add_argument('--mask_color_jitter_s', default=0, type=float)
    parser.add_argument('--mask_color_jitter_p', default=0, type=float)
    parser.add_argument('--mask_blur', default=False, type=bool)

    args = parser.parse_args()
    os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    args.device = device
    main(args)