irm.py

"""
Adapted from https://github.com/facebookresearch/DomainBed
@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 CosineAnnealingLR
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.autograd as autograd

import utils
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 tsne, a_distance

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


class InvariancePenaltyLoss(nn.Module):
    r"""Invariance Penalty Loss from `Invariant Risk Minimization <https://arxiv.org/pdf/1907.02893.pdf>`_.
    We adopt implementation from `DomainBed <https://github.com/facebookresearch/DomainBed>`_. Given classifier
    output :math:`y` and ground truth :math:`labels`, we split :math:`y` into two parts :math:`y_1, y_2`, corresponding
    labels are :math:`labels_1, labels_2`. Next we calculate cross entropy loss with respect to a dummy classifier
    :math:`w`, resulting in :math:`grad_1, grad_2` . Invariance penalty is then :math:`grad_1*grad_2`.

    Inputs:
        - y: predictions from model
        - labels: ground truth

    Shape:
        - y: :math:`(N, C)` where C means the number of classes.
        - labels: :math:`(N, )` where N mean mini-batch size
    """

    def __init__(self):
        super(InvariancePenaltyLoss, self).__init__()
        self.scale = torch.tensor(1.).requires_grad_()

    def forward(self, y: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
        loss_1 = F.cross_entropy(y[::2] * self.scale, labels[::2])
        loss_2 = F.cross_entropy(y[1::2] * self.scale, labels[1::2])
        grad_1 = autograd.grad(loss_1, [self.scale], create_graph=True)[0]
        grad_2 = autograd.grad(loss_2, [self.scale], create_graph=True)[0]
        penalty = torch.sum(grad_1 * grad_2)
        return penalty


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)
    sampler = utils.RandomDomainSampler(train_dataset, args.batch_size, n_domains_per_batch=args.n_domains_per_batch)
    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.workers,
                              sampler=sampler, 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
    classifier = utils.ImageClassifier(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)

    # define loss function
    invariance_penalty_loss = InvariancePenaltyLoss().to(device)

    # for simplicity
    assert args.anneal_iters % args.iters_per_epoch == 0

    # 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):
        if epoch * args.iters_per_epoch == args.anneal_iters:
            # reset optimizer to avoid sharp jump in gradient magnitudes
            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 - args.anneal_iters)

        print(lr_scheduler.get_lr())
        # train for one epoch
        train(train_iter, classifier, optimizer, lr_scheduler, invariance_penalty_loss, args.n_domains_per_batch, 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, optimizer, lr_scheduler: CosineAnnealingLR,
          invariance_penalty_loss: InvariancePenaltyLoss, n_domains_per_batch: int, epoch: int,
          args: argparse.Namespace):
    batch_time = AverageMeter('Time', ':4.2f')
    data_time = AverageMeter('Data', ':3.1f')
    losses = AverageMeter('Loss', ':3.2f')
    losses_ce = AverageMeter('CELoss', ':3.2f')
    losses_penalty = AverageMeter('Penalty Loss', ':3.2f')
    cls_accs = AverageMeter('Cls Acc', ':3.1f')

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

    # switch to train mode
    model.train()

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

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

        # compute output
        y_all, _ = model(x_all)

        # cls loss
        loss_ce = F.cross_entropy(y_all, labels_all)
        # penalty loss
        loss_penalty = 0
        for y_per_domain, labels_per_domain in zip(y_all.chunk(n_domains_per_batch, dim=0),
                                                   labels_all.chunk(n_domains_per_batch, dim=0)):
            # normalize loss by domain num
            loss_penalty += invariance_penalty_loss(y_per_domain, labels_per_domain) / n_domains_per_batch

        global_iter = epoch * args.iters_per_epoch + i
        if global_iter >= args.anneal_iters:
            trade_off = args.trade_off
        else:
            trade_off = 1
        loss = loss_ce + loss_penalty * trade_off
        cls_acc = accuracy(y_all, labels_all)[0]

        losses.update(loss.item(), x_all.size(0))
        losses_ce.update(loss_ce.item(), x_all.size(0))
        losses_penalty.update(loss_penalty.item(), x_all.size(0))
        cls_accs.update(cls_acc.item(), x_all.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='IRM 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('--trade-off', default=1, type=float,
                        help='the trade off hyper parameter for irm penalty')
    parser.add_argument('--anneal-iters', default=500, type=int,
                        help='anneal iterations (trade off is set to 1 during these iterations)')
    parser.add_argument('-b', '--batch-size', default=36, type=int,
                        metavar='N',
                        help='mini-batch size (default: 36)')
    parser.add_argument('--n-domains-per-batch', default=3, type=int,
                        help='number of domains in each mini-batch')
    parser.add_argument('--lr', '--learning-rate', default=5e-4, 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=None, type=int,
                        help='seed for initializing training. ')
    parser.add_argument("--log", type=str, default='irm',
                        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)