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jan.py
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jan.py
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"""
@author: Junguang Jiang
@contact: JiangJunguang1123@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 LambdaLR
from torch.utils.data import DataLoader
import torch.nn.functional as F
sys.path.append('../../..')
from dalib.adaptation.jan import JointMultipleKernelMaximumMeanDiscrepancy, ImageClassifier, Theta
from dalib.modules.kernels import GaussianKernel
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
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)
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 loss function
if args.adversarial:
thetas = [Theta(dim).to(device) for dim in (classifier.features_dim, num_classes)]
else:
thetas = None
jmmd_loss = JointMultipleKernelMaximumMeanDiscrepancy(
kernels=(
[GaussianKernel(alpha=2 ** k) for k in range(-3, 2)],
(GaussianKernel(sigma=0.92, track_running_stats=False),)
),
linear=args.linear, thetas=thetas
).to(device)
parameters = classifier.get_parameters()
if thetas is not None:
parameters += [{"params": theta.parameters(), 'lr': 0.1} for theta in thetas]
# define optimizer
optimizer = SGD(parameters, args.lr, momentum=args.momentum, weight_decay=args.wd, 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):
# train for one epoch
train(train_source_iter, train_target_iter, classifier, jmmd_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,
jmmd_loss: JointMultipleKernelMaximumMeanDiscrepancy, optimizer: SGD,
lr_scheduler: LambdaLR, epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':4.2f')
data_time = AverageMeter('Data', ':3.1f')
losses = AverageMeter('Loss', ':3.2f')
trans_losses = AverageMeter('Trans Loss', ':5.4f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
tgt_accs = AverageMeter('Tgt Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, trans_losses, cls_accs, tgt_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
jmmd_loss.train()
end = time.time()
for i in range(args.iters_per_epoch):
x_s, labels_s = next(train_source_iter)
x_t, labels_t = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.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
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)
transfer_loss = jmmd_loss(
(f_s, F.softmax(y_s, dim=1)),
(f_t, F.softmax(y_t, dim=1))
)
loss = cls_loss + transfer_loss * args.trade_off
cls_acc = accuracy(y_s, labels_s)[0]
tgt_acc = accuracy(y_t, labels_t)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
tgt_accs.update(tgt_acc.item(), x_t.size(0))
trans_losses.update(transfer_loss.item(), x_s.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='JAN 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('--linear', default=False, action='store_true',
help='whether use the linear version')
parser.add_argument('--adversarial', default=False, action='store_true',
help='whether use adversarial theta')
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=0.003, type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--lr-gamma', default=0.0003, 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=0.0005, type=float,
metavar='W', help='weight decay (default: 5e-4)')
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=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('--per-class-eval', action='store_true',
help='whether output per-class accuracy during evaluation')
parser.add_argument("--log", type=str, default='jan',
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)