self_ensemble.py

"""
@author: Baixu Chen
@contact: cbx_99_hasta@outlook.com
"""
import random
import time
import warnings
import sys
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 Adam
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
import torchvision.transforms as T
import torch.nn.functional as F

sys.path.append('../../..')
from dalib.adaptation.self_ensemble import EmaTeacher, L2ConsistencyLoss, ClassBalanceLoss, ImageClassifier
from common.vision.transforms import ResizeImage, MultipleApply
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

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

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
    normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    train_transform = T.Compose([
        ResizeImage(256),
        T.RandomCrop(224),
        T.RandomHorizontalFlip(),
        T.ColorJitter(brightness=0.7, contrast=0.7, saturation=0.7, hue=0.5),
        T.RandomGrayscale(),
        T.ToTensor(),
        normalize
    ])
    val_transform = T.Compose([
        ResizeImage(256),
        T.CenterCrop(224),
        T.ToTensor(),
        normalize
    ])

    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, MultipleApply([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)

    # define optimizer and lr scheduler
    optimizer = Adam(classifier.get_parameters(), args.lr)
    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.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 = Adam(pretrain_model.get_parameters(), args.pretrain_lr)
        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):
            # pretrain for one epoch
            utils.pretrain(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)
    teacher = EmaTeacher(classifier, alpha=args.alpha)
    consistent_loss = L2ConsistencyLoss().to(device)
    class_balance_loss = ClassBalanceLoss(num_classes).to(device)

    # 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, teacher, consistent_loss, class_balance_loss, 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,
          teacher: EmaTeacher, consistent_loss, class_balance_loss,
          optimizer: Adam, lr_scheduler: LambdaLR, epoch: int, args: argparse.Namespace):
    batch_time = AverageMeter('Time', ':3.1f')
    data_time = AverageMeter('Data', ':3.1f')
    cls_losses = AverageMeter('Cls Loss', ':3.2f')
    cons_losses = AverageMeter('Cons Loss', ':3.2f')
    cls_accs = AverageMeter('Cls Acc', ':3.1f')
    tgt_accs = AverageMeter('Tgt Acc', ':3.1f')

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

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

    end = time.time()
    for i in range(args.iters_per_epoch):
        x_s, labels_s = next(train_source_iter)
        (x_t1, x_t2), labels_t = next(train_target_iter)

        x_s = x_s.to(device)
        x_t1 = x_t1.to(device)
        x_t2 = x_t2.to(device)
        labels_s = labels_s.to(device)
        labels_t = labels_t.to(device)

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

        # compute output
        y_s, _ = model(x_s)
        y_t, _ = model(x_t1)
        y_t_teacher, _ = teacher(x_t2)

        # classification loss
        cls_loss = F.cross_entropy(y_s, labels_s)
        # compute output and mask
        y_t = F.softmax(y_t, dim=1)
        y_t_teacher = F.softmax(y_t_teacher, dim=1)
        max_prob, _ = y_t_teacher.max(dim=1)
        mask = (max_prob > args.threshold).float()

        # consistent loss
        cons_loss = consistent_loss(y_t, y_t_teacher, mask)
        # balance loss
        balance_loss = class_balance_loss(y_t) * mask.mean()

        loss = cls_loss + args.trade_off_cons * cons_loss + args.trade_off_balance * balance_loss

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

        # update teacher
        teacher.update()

        # update statistics
        cls_acc = accuracy(y_s, labels_s)[0]
        tgt_acc = accuracy(y_t, labels_t)[0]
        cls_losses.update(cls_loss.item(), x_s.size(0))
        cons_losses.update(cons_loss.item(), x_s.size(0))
        cls_accs.update(cls_acc.item(), x_s.size(0))
        tgt_accs.update(tgt_acc.item(), x_s.size(0))

        # 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='Self Ensemble 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('--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.')
    # training parameters
    parser.add_argument('-b', '--batch-size', default=36, type=int,
                        metavar='N',
                        help='mini-batch size (default: 36)')
    parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float,
                        metavar='LR', help='initial learning rate', dest='lr')
    parser.add_argument('--pretrain-lr', '--pretrain-learning-rate', default=3e-5, type=float,
                        help='initial pretrain learning rate', dest='pretrain_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('--alpha', default=0.99, type=float, help='ema decay rate (default: 0.99)')
    parser.add_argument('--threshold', default=0.8, type=float, help='confidence threshold')
    parser.add_argument('--trade-off-cons', default=3, type=float, help='trade off parameter for consistent loss')
    parser.add_argument('--trade-off-balance', default=0.01, type=float,
                        help='trade off parameter for class balance loss')
    parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
                        help='number of data loading workers (default: 2)')
    parser.add_argument('--pretrain-epochs', default=0, type=int, metavar='N',
                        help='number of total epochs(pretrain) to run')
    parser.add_argument('--epochs', default=10, 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='self_ensemble',
                        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)