fixmatch.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.modules.classifier import Classifier
from tllib.self_training.pseudo_label import ConfidenceBasedSelfTrainingLoss
from tllib.vision.transforms import MultipleApply
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")


class ImageClassifier(Classifier):
    def __init__(self, backbone: nn.Module, num_classes: int, bottleneck_dim=1024, **kwargs):
        bottleneck = nn.Sequential(
            nn.Linear(backbone.out_features, bottleneck_dim),
            nn.BatchNorm1d(bottleneck_dim),
            nn.ReLU()
        )
        super(ImageClassifier, self).__init__(backbone, num_classes, bottleneck, bottleneck_dim, **kwargs)

    def forward(self, x: torch.Tensor):
        """"""
        f = self.pool_layer(self.backbone(x))
        f = self.bottleneck(f)
        predictions = self.head(f)
        return predictions


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_source_transform = utils.get_train_transform(args.train_resizing, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
                                                       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)
    weak_augment = 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)
    strong_augment = 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,
                                               auto_augment=args.auto_augment)
    train_target_transform = MultipleApply([weak_augment, strong_augment])
    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_source_transform: ", train_source_transform)
    print("train_target_transform: ", train_target_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_source_transform, val_transform,
                          train_target_transform=train_target_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.unlabeled_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)
    print(classifier)

    # define optimizer and lr scheduler
    optimizer = SGD(classifier.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))

    # 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:", lr_scheduler.get_last_lr())
        # train for one epoch
        train(train_source_iter, train_target_iter, classifier, 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, optimizer: SGD, lr_scheduler: LambdaLR, epoch: int, args: argparse.Namespace):
    batch_time = AverageMeter('Time', ':5.2f')
    data_time = AverageMeter('Data', ':5.2f')
    cls_losses = AverageMeter('Cls Loss', ':6.2f')
    self_training_losses = AverageMeter('Self Training Loss', ':6.2f')
    losses = AverageMeter('Loss', ':6.2f')
    cls_accs = AverageMeter('Cls Acc', ':3.1f')
    pseudo_label_ratios = AverageMeter('Pseudo Label Ratio', ':3.1f')
    pseudo_label_accs = AverageMeter('Pseudo Label Acc', ':3.1f')

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

    self_training_criterion = ConfidenceBasedSelfTrainingLoss(args.threshold).to(device)
    # switch to train mode
    model.train()

    end = time.time()
    for i in range(args.iters_per_epoch):
        x_s, labels_s = next(train_source_iter)[:2]
        (x_t, x_t_strong), labels_t = next(train_target_iter)[:2]

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

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

        # clear grad
        optimizer.zero_grad()

        # compute output
        with torch.no_grad():
            y_t = model(x_t)

        # cross entropy loss
        y_s = model(x_s)
        cls_loss = F.cross_entropy(y_s, labels_s)
        cls_loss.backward()

        # self-training loss
        y_t_strong = model(x_t_strong)
        self_training_loss, mask, pseudo_labels = self_training_criterion(y_t_strong, y_t)
        self_training_loss = args.trade_off * self_training_loss
        self_training_loss.backward()

        # measure accuracy and record loss
        loss = cls_loss + self_training_loss
        losses.update(loss.item(), x_s.size(0))
        cls_losses.update(cls_loss.item(), x_s.size(0))
        self_training_losses.update(self_training_loss.item(), x_s.size(0))

        cls_acc = accuracy(y_s, labels_s)[0]
        cls_accs.update(cls_acc.item(), x_s.size(0))

        # ratio of pseudo labels
        n_pseudo_labels = mask.sum()
        ratio = n_pseudo_labels / x_t.size(0)
        pseudo_label_ratios.update(ratio.item() * 100, x_t.size(0))

        # accuracy of pseudo labels
        if n_pseudo_labels > 0:
            pseudo_labels = pseudo_labels * mask - (1 - mask)
            n_correct = (pseudo_labels == labels_t).float().sum()
            pseudo_label_acc = n_correct / n_pseudo_labels * 100
            pseudo_label_accs.update(pseudo_label_acc.item(), n_pseudo_labels)

        # compute gradient and do SGD step
        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='FixMatch 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=[0.5, 1.0], metavar='PCT',
                        help='Random resize scale (default: 0.5 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')
    parser.add_argument('--auto-augment', default='rand-m10-n2-mstd2', type=str,
                        help='AutoAugment policy (default: rand-m10-n2-mstd2)')
    # 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,
                        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')
    # training parameters
    parser.add_argument('-b', '--batch-size', default=32, type=int,
                        metavar='N',
                        help='mini-batch size (default: 32)')
    parser.add_argument('-ub', '--unlabeled-batch-size', default=32, type=int,
                        help='mini-batch size of unlabeled data (target domain) (default: 32)')
    parser.add_argument('--threshold', default=0.9, type=float,
                        help='confidence threshold')
    parser.add_argument('--lr', '--learning-rate', default=0.003, type=float,
                        metavar='LR', help='initial learning rate', dest='lr')
    parser.add_argument('--lr-gamma', default=0.0004, 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=4, 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='fixmatch',
                        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)