baseline.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 SGD
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.utils.data import DataLoader
import torch.nn.functional as F

sys.path.append('../../..')
from dglib.modules.classifier import ImageClassifier as Classifier
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 tsne, a_distance

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

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

class TransformerDecorator(torch.nn.Module):
    def __init__(self, backbone=None, add_bt=3, dim=2048, eval_global=0):
        super(TransformerDecorator, self).__init__()
        self.encoder_layers = torch.nn.TransformerEncoderLayer(dim, 4, dim, 0.5)
        self.eval_global = eval_global
        self.add_bt = add_bt
        self.backbone = backbone

    def forward_feats(self, feature):
        feature = self.backbone(feature)
        if self.training and self.add_bt > 0:
            pre_feature = feature
            feature = feature.unsqueeze(1)
            feature = self.encoder_layers(feature)
            # feature = self.encoder_layers(feature, feature, feature)
            feature = feature.squeeze(1)
            if self.add_bt:
                return torch.cat([pre_feature, feature], dim=0)
        return feature

    def forward(self, feature):
        feature = self.forward_feats(feature)
        return feature


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, random_horizontal_flip=True,
                                                random_color_jitter=True, random_gray_scale=True)
    val_transform = utils.get_val_transform(args.val_resizing)
    print("train_transform: ", train_transform)
    print("val_transform: ", val_transform)

    train_dataset, num_classes = utils.get_dataset(dataset_name=args.data, root=args.root, task_list=args.sources,
                                                   split='train', download=True, transform=train_transform,
                                                   seed=args.seed)
    train_loader = DataLoader(train_dataset, batch_size=args.batch_size,
                              shuffle=True, num_workers=args.workers, drop_last=True)
    val_dataset, _ = utils.get_dataset(dataset_name=args.data, root=args.root, task_list=args.sources, split='val',
                                       download=True, transform=val_transform, seed=args.seed)
    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
    test_dataset, _ = utils.get_dataset(dataset_name=args.data, root=args.root, task_list=args.targets, split='test',
                                        download=True, transform=val_transform, seed=args.seed)
    test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)

    print("train_dataset_size: ", len(train_dataset))
    print('val_dataset_size: ', len(val_dataset))
    print("test_dataset_size: ", len(test_dataset))
    train_iter = ForeverDataIterator(train_loader)

    # create model
    print("=> using pre-trained model '{}'".format(args.arch))
    backbone = utils.get_model(args.arch)
    pool_layer = nn.Identity() if args.no_pool else None
    pool_layer = nn.Sequential(
        nn.AdaptiveAvgPool2d(output_size=(1, 1)),
        nn.Flatten(),
    )
    pool_layer = TransformerDecorator(pool_layer, add_bt=args.add_bt, dim=backbone.out_features)
    classifier = Classifier(backbone, num_classes, freeze_bn=args.freeze_bn, dropout_p=args.dropout_p,
                            finetune=args.finetune, pool_layer=pool_layer).to(device)

    # define optimizer and lr scheduler
    optimizer = SGD(classifier.get_parameters(base_lr=args.lr), args.lr, momentum=args.momentum, weight_decay=args.wd,
                    nesterov=True)
    lr_scheduler = CosineAnnealingLR(optimizer, args.epochs * args.iters_per_epoch)

    # 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 = utils.collect_feature(val_loader, feature_extractor, device, max_num_features=100)
        target_feature = utils.collect_feature(test_loader, feature_extractor, device, max_num_features=100)
        print(len(source_feature), len(target_feature))
        # plot t-SNE
        tSNE_filename = osp.join(logger.visualize_directory, 'TSNE.png')
        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_val_acc1 = 0.
    best_test_acc1 = 0.
    for epoch in range(args.epochs):
        print(lr_scheduler.get_lr())
        # train for one epoch
        train(train_iter, classifier, optimizer, lr_scheduler, epoch, args)

        # evaluate on validation set
        print("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_val_acc1:
            shutil.copy(logger.get_checkpoint_path('latest'), logger.get_checkpoint_path('best'))
        best_val_acc1 = max(acc1, best_val_acc1)

        # evaluate on test set
        print("Evaluate on test set...")
        best_test_acc1 = max(best_test_acc1, utils.validate(test_loader, classifier, args, device))

    # 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 acc on test set = {}".format(acc1))
    print("oracle acc on test set = {}".format(best_test_acc1))
    logger.close()


def train(train_iter: ForeverDataIterator, model: Classifier, optimizer,
          lr_scheduler: CosineAnnealingLR, epoch: int, args: argparse.Namespace):
    batch_time = AverageMeter('Time', ':4.2f')
    data_time = AverageMeter('Data', ':3.1f')
    losses = AverageMeter('Loss', ':3.2f')
    cls_accs = AverageMeter('Cls Acc', ':3.1f')

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

    # switch to train mode
    model.train()

    end = time.time()
    for i in range(args.iters_per_epoch):
        x, labels, _ = next(train_iter)
        x = x.to(device)
        labels = labels.to(device)

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

        # compute output
        y, _ = model(x)
        if len(y) > len(labels):
            labels = torch.cat([labels, labels], dim=0)

        loss = F.cross_entropy(y, labels)

        cls_acc = accuracy(y, labels)[0]
        losses.update(loss.item(), x.size(0))
        cls_accs.update(cls_acc.item(), x.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='Baseline for Domain Generalization')
    # dataset parameters
    parser.add_argument('root', metavar='DIR',
                        help='root path of dataset')
    parser.add_argument('-d', '--data', metavar='DATA', default='PACS',
                        help='dataset: ' + ' | '.join(utils.get_dataset_names()) +
                             ' (default: PACS)')
    parser.add_argument('-s', '--sources', nargs='+', default=None,
                        help='source domain(s)')
    parser.add_argument('-t', '--targets', nargs='+', default=None,
                        help='target domain(s)')
    parser.add_argument('--train-resizing', type=str, default='default')
    parser.add_argument('--val-resizing', type=str, default='default')
    # model parameters
    parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
                        choices=utils.get_model_names(),
                        help='backbone architecture: ' +
                             ' | '.join(utils.get_model_names()) +
                             ' (default: resnet50)')
    parser.add_argument('--no-pool', action='store_true', help='no pool layer after the feature extractor.')
    parser.add_argument('--finetune', action='store_true', help='whether use 10x smaller lr for backbone')
    parser.add_argument('--freeze-bn', action='store_true', help='whether freeze all bn layers')
    parser.add_argument('--dropout-p', type=float, default=0.1, help='only activated when freeze-bn is True')
    # 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-3, type=float,
                        metavar='LR', help='initial learning rate', dest='lr')
    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=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=500, 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=0, type=int,
                        help='seed for initializing training. ')
    parser.add_argument("--log", type=str, default='baseline',
                        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("--add_bt", type=int, default=0,
                        help="add_bt")
    args = parser.parse_args()
    main(args)