def SaveEvaluation(args, known_acc, auc):
filefolder = osp.join('results', 'Test', 'accuracy',
args.datasetname + '-' + args.split)
mkdir(filefolder)
filepath = osp.join(
filefolder, 'adv-' + str(args.adv) + '-defense-' + str(args.defense) +
'-' + args.denoisemean + '-' + str(args.defensesnapshot) + '.txt')
output_file = open(filepath, 'w')
output_file.write('Close-set Accuracy:\n' + str(np.array(known_acc.cpu())))
output_file.write('\nOpen-set AUROC:\n' + str(auc))
output_file.close()
def Test(args, FeatExtor, DepthEsmator, FeatEmbder, data_loader_target,
savefilename):
print("***The type of norm is: {}".format(normtype))
savepath = os.path.join(args.results_path, savefilename)
mkdir(savepath)
####################
# 1. setup network #
####################
# set train state for Dropout and BN layers
FeatExtor.eval()
DepthEsmator.eval()
FeatEmbder.eval()
FeatExtor = DataParallel(FeatExtor)
DepthEsmator = DataParallel(DepthEsmator)
FeatEmbder = DataParallel(FeatEmbder)
score_list = []
label_list = []
idx = 0
for (catimages, labels) in data_loader_target:
images = catimages.cuda()
# labels = labels.long().squeeze().cuda()
feat_ext_all, feat_ext = FeatExtor(images)
_, label_pred = FeatEmbder(feat_ext)
score = F.sigmoid(label_pred).cpu().detach().numpy()
labels = labels.numpy()
score_list.append(score.squeeze())
label_list.append(labels)
print('SampleNum:{} in total:{}, score:{}'.format(
idx, len(data_loader_target), score.squeeze()))
idx += 1
with h5py.File(os.path.join(savepath, 'Test_data.h5'), 'w') as hf:
hf.create_dataset('score', data=score_list)
hf.create_dataset('label', data=label_list)
def Train(args, FeatExtor, DepthEstor, FeatEmbder, data_loader1_real,
data_loader1_fake, data_loader2_real, data_loader2_fake,
data_loader3_real, data_loader3_fake, data_loader_target,
summary_writer, Saver, savefilename):
####################
# 1. setup network #
####################
# set train state for Dropout and BN layers
FeatExtor.train()
DepthEstor.train()
FeatEmbder.train()
FeatExtor = DataParallel(FeatExtor)
DepthEstor = DataParallel(DepthEstor)
# setup criterion and optimizer
criterionCls = nn.BCEWithLogitsLoss()
criterionDepth = torch.nn.MSELoss()
if args.optimizer_meta is 'adam':
optimizer_all = optim.Adam(itertools.chain(FeatExtor.parameters(),
DepthEstor.parameters(),
FeatEmbder.parameters()),
lr=args.lr_meta,
betas=(args.beta1, args.beta2))
else:
raise NotImplementedError('Not a suitable optimizer')
iternum = max(len(data_loader1_real), len(data_loader1_fake),
len(data_loader2_real), len(data_loader2_fake),
len(data_loader3_real), len(data_loader3_fake))
print('iternum={}'.format(iternum))
####################
# 2. train network #
####################
global_step = 0
for epoch in range(args.epochs):
data1_real = get_inf_iterator(data_loader1_real)
data1_fake = get_inf_iterator(data_loader1_fake)
data2_real = get_inf_iterator(data_loader2_real)
data2_fake = get_inf_iterator(data_loader2_fake)
data3_real = get_inf_iterator(data_loader3_real)
data3_fake = get_inf_iterator(data_loader3_fake)
for step in range(iternum):
#============ one batch extraction ============#
cat_img1_real, depth_img1_real, lab1_real = next(data1_real)
cat_img1_fake, depth_img1_fake, lab1_fake = next(data1_fake)
cat_img2_real, depth_img2_real, lab2_real = next(data2_real)
cat_img2_fake, depth_img2_fake, lab2_fake = next(data2_fake)
cat_img3_real, depth_img3_real, lab3_real = next(data3_real)
cat_img3_fake, depth_img3_fake, lab3_fake = next(data3_fake)
#============ one batch collection ============#
catimg1 = torch.cat([cat_img1_real, cat_img1_fake], 0).cuda()
depth_img1 = torch.cat([depth_img1_real, depth_img1_fake],
0).cuda()
lab1 = torch.cat([lab1_real, lab1_fake], 0).float().cuda()
catimg2 = torch.cat([cat_img2_real, cat_img2_fake], 0).cuda()
depth_img2 = torch.cat([depth_img2_real, depth_img2_fake],
0).cuda()
lab2 = torch.cat([lab2_real, lab2_fake], 0).float().cuda()
catimg3 = torch.cat([cat_img3_real, cat_img3_fake], 0).cuda()
depth_img3 = torch.cat([depth_img3_real, depth_img3_fake],
0).cuda()
lab3 = torch.cat([lab3_real, lab3_fake], 0).float().cuda()
catimg = torch.cat([catimg1, catimg2, catimg3], 0)
depth_GT = torch.cat([depth_img1, depth_img2, depth_img3], 0)
label = torch.cat([lab1, lab2, lab3], 0)
#============ doamin list augmentation ============#
catimglist = [catimg1, catimg2, catimg3]
lablist = [lab1, lab2, lab3]
deplist = [depth_img1, depth_img2, depth_img3]
domain_list = list(range(len(catimglist)))
random.shuffle(domain_list)
meta_train_list = domain_list[:args.metatrainsize]
meta_test_list = domain_list[args.metatrainsize:]
print('metatrn={}, metatst={}'.format(meta_train_list,
meta_test_list[0]))
#============ meta training ============#
Loss_dep_train = 0.0
Loss_cls_train = 0.0
adapted_state_dicts = []
for index in meta_train_list:
catimg_meta = catimglist[index]
lab_meta = lablist[index]
depGT_meta = deplist[index]
batchidx = list(range(len(catimg_meta)))
random.shuffle(batchidx)
img_rand = catimg_meta[batchidx, :]
lab_rand = lab_meta[batchidx]
depGT_rand = depGT_meta[batchidx, :]
feat_ext_all, feat = FeatExtor(img_rand)
pred = FeatEmbder(feat)
depth_Pre = DepthEstor(feat_ext_all)
Loss_cls = criterionCls(pred.squeeze(), lab_rand)
Loss_dep = criterionDepth(depth_Pre, depGT_rand)
Loss_dep_train += Loss_dep
Loss_cls_train += Loss_cls
zero_param_grad(FeatEmbder.parameters())
grads_FeatEmbder = torch.autograd.grad(Loss_cls,
FeatEmbder.parameters(),
create_graph=True)
fast_weights_FeatEmbder = FeatEmbder.cloned_state_dict()
adapted_params = OrderedDict()
for (key, val), grad in zip(FeatEmbder.named_parameters(),
grads_FeatEmbder):
adapted_params[key] = val - args.meta_step_size * grad
fast_weights_FeatEmbder[key] = adapted_params[key]
adapted_state_dicts.append(fast_weights_FeatEmbder)
#============ meta testing ============#
Loss_dep_test = 0.0
Loss_cls_test = 0.0
index = meta_test_list[0]
catimg_meta = catimglist[index]
lab_meta = lablist[index]
depGT_meta = deplist[index]
batchidx = list(range(len(catimg_meta)))
random.shuffle(batchidx)
img_rand = catimg_meta[batchidx, :]
lab_rand = lab_meta[batchidx]
depGT_rand = depGT_meta[batchidx, :]
feat_ext_all, feat = FeatExtor(img_rand)
depth_Pre = DepthEstor(feat_ext_all)
Loss_dep = criterionDepth(depth_Pre, depGT_rand)
for n_scr in range(len(meta_train_list)):
a_dict = adapted_state_dicts[n_scr]
pred = FeatEmbder(feat, a_dict)
Loss_cls = criterionCls(pred.squeeze(), lab_rand)
Loss_cls_test += Loss_cls
Loss_dep_test = Loss_dep
Loss_dep_train_ave = Loss_dep_train / len(meta_train_list)
Loss_dep_test = Loss_dep_test
Loss_meta_train = Loss_cls_train + args.W_depth * Loss_dep_train
Loss_meta_test = Loss_cls_test + args.W_depth * Loss_dep_test
Loss_all = Loss_meta_train + args.W_metatest * Loss_meta_test
optimizer_all.zero_grad()
Loss_all.backward()
optimizer_all.step()
if (step + 1) % args.log_step == 0:
errors = OrderedDict([
('Loss_meta_train', Loss_meta_train.item()),
('Loss_meta_test', Loss_meta_test.item()),
('Loss_cls_train', Loss_cls_train.item()),
('Loss_cls_test', Loss_cls_test.item()),
('Loss_dep_train_ave', Loss_dep_train_ave.item()),
('Loss_dep_test', Loss_dep_test.item()),
])
Saver.print_current_errors((epoch + 1), (step + 1), errors)
#============ tensorboard the log info ============#
info = {
'Loss_meta_train': Loss_meta_train.item(),
'Loss_meta_test': Loss_meta_test.item(),
'Loss_cls_train': Loss_cls_train.item(),
'Loss_cls_test': Loss_cls_test.item(),
'Loss_dep_train_ave': Loss_dep_train_ave.item(),
'Loss_dep_test': Loss_dep_test.item(),
}
for tag, value in info.items():
summary_writer.add_scalar(tag, value, global_step)
global_step += 1
#############################
# 2.4 save model parameters #
#############################
if ((step + 1) % args.model_save_step == 0):
model_save_path = os.path.join(args.results_path, 'snapshots',
savefilename)
mkdir(model_save_path)
torch.save(
FeatExtor.state_dict(),
os.path.join(
model_save_path,
"FeatExtor-{}-{}.pt".format(epoch + 1, step + 1)))
torch.save(
FeatEmbder.state_dict(),
os.path.join(
model_save_path,
"FeatEmbder-{}-{}.pt".format(epoch + 1, step + 1)))
torch.save(
DepthEstor.state_dict(),
os.path.join(
model_save_path,
"DepthEstor-{}-{}.pt".format(epoch + 1, step + 1)))
if ((epoch + 1) % args.model_save_epoch == 0):
model_save_path = os.path.join(args.results_path, 'snapshots',
savefilename)
mkdir(model_save_path)
torch.save(
FeatExtor.state_dict(),
os.path.join(model_save_path,
"FeatExtor-{}.pt".format(epoch + 1)))
torch.save(
FeatEmbder.state_dict(),
os.path.join(model_save_path,
"FeatEmbder-{}.pt".format(epoch + 1)))
torch.save(
DepthEstor.state_dict(),
os.path.join(model_save_path,
"DepthEstor-{}.pt".format(epoch + 1)))
torch.save(FeatExtor.state_dict(),
os.path.join(model_save_path, "FeatExtor-final.pt"))
torch.save(FeatEmbder.state_dict(),
os.path.join(model_save_path, "FeatEmbder-final.pt"))
torch.save(DepthEstor.state_dict(),
os.path.join(model_save_path, "DepthEstor-final.pt"))
def Pre_train(args, FeatExtor, DepthEsmator, data_loader_real,
data_loader_fake, summary_writer, saver, savefilename):
# savepath = os.path.join(args.results_path, savefilename)
# mkdir(savepath)
####################
# 1. setup network #
####################
# set train state for Dropout and BN layers
FeatExtor.train()
DepthEsmator.train()
FeatExtor = DataParallel(FeatExtor)
DepthEsmator = DataParallel(DepthEsmator)
criterionDepth = torch.nn.MSELoss()
optimizer_DG_depth = optim.Adam(list(FeatExtor.parameters()) +
list(DepthEsmator.parameters()),
lr=args.lr_DG_depth,
betas=(args.beta1, args.beta2))
iternum = max(len(data_loader_real), len(data_loader_fake))
print('iternum={}'.format(iternum))
####################
# 2. train network #
####################
global_step = 0
for epoch in range(args.pre_epochs):
# epoch=epochNum+5
data_real = get_inf_iterator(data_loader_real)
data_fake = get_inf_iterator(data_loader_fake)
for step in range(iternum):
cat_img_real, depth_img_real, lab_real = next(data_real)
cat_img_fake, depth_img_fake, lab_fake = next(data_fake)
ori_img = torch.cat([cat_img_real, cat_img_fake], 0)
ori_img = ori_img.cuda()
depth_img = torch.cat([depth_img_real, depth_img_fake], 0)
depth_img = depth_img.cuda()
feat_ext, _ = FeatExtor(ori_img)
depth_Pre = DepthEsmator(feat_ext)
Loss_depth = criterionDepth(depth_Pre, depth_img)
optimizer_DG_depth.zero_grad()
Loss_depth.backward()
optimizer_DG_depth.step()
info = {
'Loss_depth': Loss_depth.item(),
}
for tag, value in info.items():
summary_writer.add_scalar(tag, value, global_step)
#============ print the log info ============#
if (step + 1) % args.log_step == 0:
errors = OrderedDict([('Loss_depth', Loss_depth.item())])
saver.print_current_errors((epoch + 1), (step + 1), errors)
global_step += 1
if ((epoch + 1) % args.model_save_epoch == 0):
model_save_path = os.path.join(args.results_path, 'snapshots',
savefilename)
mkdir(model_save_path)
torch.save(
FeatExtor.state_dict(),
os.path.join(model_save_path,
"DGFA-Ext-{}.pt".format(epoch + 1)))
torch.save(
DepthEsmator.state_dict(),
os.path.join(model_save_path,
"DGFA-Depth-{}.pt".format(epoch + 1)))
torch.save(FeatExtor.state_dict(),
os.path.join(model_save_path, "DGFA-Ext-final.pt"))
torch.save(DepthEsmator.state_dict(),
os.path.join(model_save_path, "DGFA-Depth-final.pt"))
def train_Ours(args, train_loader, val_loader, knownclass, Encoder, Decoder,
NorClsfier, SSDClsfier, summary_writer, saver):
seed = init_random_seed(args.manual_seed)
criterionCls = nn.CrossEntropyLoss()
criterionRec = nn.MSELoss()
if args.parallel_train:
Encoder = DataParallel(Encoder)
Decoder = DataParallel(Decoder)
NorClsfier = DataParallel(NorClsfier)
SSDClsfier = DataParallel(SSDClsfier)
optimizer = optim.Adam(
list(Encoder.parameters()) + list(NorClsfier.parameters()) +
list(SSDClsfier.parameters()) + list(Decoder.parameters()),
lr=args.lr)
if args.adv is 'PGDattack':
print("**********Defense PGD Attack**********")
elif args.adv is 'FGSMattack':
print("**********Defense FGSM Attack**********")
if args.adv is 'PGDattack':
from advertorch.attacks import PGDAttack
nor_adversary = PGDAttack(predict1=Encoder,
predict2=NorClsfier,
nb_iter=args.adv_iter)
rot_adversary = PGDAttack(predict1=Encoder,
predict2=SSDClsfier,
nb_iter=args.adv_iter)
elif args.adv is 'FGSMattack':
from advertorch.attacks import GradientSignAttack
nor_adversary = GradientSignAttack(predict1=Encoder,
predict2=NorClsfier)
rot_adversary = GradientSignAttack(predict1=Encoder,
predict2=SSDClsfier)
global_step = 0
# ----------
# Training
# ----------
for epoch in range(args.n_epoch):
Encoder.train()
Decoder.train()
NorClsfier.train()
SSDClsfier.train()
for steps, (orig, label, rot_orig,
rot_label) in enumerate(train_loader):
label = lab_conv(knownclass, label)
orig, label = orig.cuda(), label.long().cuda()
rot_orig, rot_label = rot_orig.cuda(), rot_label.long().cuda()
with ctx_noparamgrad_and_eval(Encoder):
with ctx_noparamgrad_and_eval(NorClsfier):
with ctx_noparamgrad_and_eval(SSDClsfier):
adv = nor_adversary.perturb(orig, label)
rot_adv = rot_adversary.perturb(rot_orig, rot_label)
latent_feat = Encoder(adv)
norpred = NorClsfier(latent_feat)
norlossCls = criterionCls(norpred, label)
recon = Decoder(latent_feat)
lossRec = criterionRec(recon, orig)
ssdpred = SSDClsfier(Encoder(rot_adv))
rotlossCls = criterionCls(ssdpred, rot_label)
loss = args.norClsWgt * norlossCls + args.rotClsWgt * rotlossCls + args.RecWgt * lossRec
optimizer.zero_grad()
loss.backward()
optimizer.step()
#============ tensorboard the log info ============#
lossinfo = {
'loss': loss.item(),
'norlossCls': norlossCls.item(),
'lossRec': lossRec.item(),
'rotlossCls': rotlossCls.item(),
}
global_step += 1
#============ print the log info ============#
if (steps + 1) % args.log_step == 0:
errors = OrderedDict([
('loss', loss.item()),
('norlossCls', norlossCls.item()),
('lossRec', lossRec.item()),
('rotlossCls', rotlossCls.item()),
])
saver.print_current_errors((epoch + 1), (steps + 1), errors)
# evaluate performance on validation set periodically
if ((epoch + 1) % args.val_epoch == 0):
# switch model to evaluation mode
Encoder.eval()
NorClsfier.eval()
running_corrects = 0.0
epoch_size = 0.0
val_loss_list = []
# calculate accuracy on validation set
for steps, (images, label) in enumerate(val_loader):
label = lab_conv(knownclass, label)
images, label = images.cuda(), label.long().cuda()
adv = nor_adversary.perturb(images, label)
with torch.no_grad():
logits = NorClsfier(Encoder(adv))
_, preds = torch.max(logits, 1)
running_corrects += torch.sum(preds == label.data)
epoch_size += images.size(0)
val_loss = criterionCls(logits, label)
val_loss_list.append(val_loss.item())
val_loss_mean = sum(val_loss_list) / len(val_loss_list)
val_acc = running_corrects.double() / epoch_size
print('Val Acc: {:.4f}, Val Loss: {:.4f}'.format(
val_acc, val_loss_mean))
valinfo = {
'Val Acc': val_acc.item(),
'Val Loss': val_loss.item(),
}
for tag, value in valinfo.items():
summary_writer.add_scalar(tag, value, (epoch + 1))
orig_show = vutils.make_grid(orig, normalize=True, scale_each=True)
recon_show = vutils.make_grid(recon,
normalize=True,
scale_each=True)
summary_writer.add_image('Ori_Image', orig_show, (epoch + 1))
summary_writer.add_image('Rec_Image', recon_show, (epoch + 1))
if ((epoch + 1) % args.model_save_epoch == 0):
model_save_path = os.path.join(args.results_path,
args.training_type, 'snapshots',
args.datasetname + '-' + args.split,
args.denoisemean,
args.adv + str(args.adv_iter))
mkdir(model_save_path)
torch.save(
Encoder.state_dict(),
os.path.join(model_save_path,
"Encoder-{}.pt".format(epoch + 1)))
torch.save(
NorClsfier.state_dict(),
os.path.join(model_save_path,
"NorClsfier-{}.pt".format(epoch + 1)))
torch.save(
Decoder.state_dict(),
os.path.join(model_save_path,
"Decoder-{}.pt".format(epoch + 1)))
torch.save(Encoder.state_dict(),
os.path.join(model_save_path, "Encoder-final.pt"))
torch.save(NorClsfier.state_dict(),
os.path.join(model_save_path, "NorClsfier-final.pt"))
torch.save(Decoder.state_dict(),
os.path.join(model_save_path, "Decoder-final.pt"))
def Train(args, FeatExtor, DepthEsmator, FeatEmbder, Discriminator1,
Discriminator2, Discriminator3, PreFeatExtorS1, PreFeatExtorS2,
PreFeatExtorS3, data_loader1_real, data_loader1_fake,
data_loader2_real, data_loader2_fake, data_loader3_real,
data_loader3_fake, data_loader_target, summary_writer, Saver,
savefilename):
####################
# 1. setup network #
####################
# set train state for Dropout and BN layers
FeatExtor.train()
FeatEmbder.train()
DepthEsmator.train()
Discriminator1.train()
Discriminator2.train()
Discriminator3.train()
PreFeatExtorS1.eval()
PreFeatExtorS2.eval()
PreFeatExtorS3.eval()
FeatExtor = DataParallel(FeatExtor)
FeatEmbder = DataParallel(FeatEmbder)
DepthEsmator = DataParallel(DepthEsmator)
Discriminator1 = DataParallel(Discriminator1)
Discriminator2 = DataParallel(Discriminator2)
Discriminator3 = DataParallel(Discriminator3)
PreFeatExtorS1 = DataParallel(PreFeatExtorS1)
PreFeatExtorS2 = DataParallel(PreFeatExtorS2)
PreFeatExtorS3 = DataParallel(PreFeatExtorS3)
# setup criterion and optimizer
criterionDepth = torch.nn.MSELoss()
criterionAdv = loss.GANLoss()
criterionCls = torch.nn.BCEWithLogitsLoss()
optimizer_DG_depth = optim.Adam(itertools.chain(FeatExtor.parameters(),
DepthEsmator.parameters()),
lr=args.lr_DG_depth,
betas=(args.beta1, args.beta2))
optimizer_DG_conf = optim.Adam(itertools.chain(FeatExtor.parameters(),
FeatEmbder.parameters()),
lr=args.lr_DG_conf,
betas=(args.beta1, args.beta2))
optimizer_critic1 = optim.Adam(Discriminator1.parameters(),
lr=args.lr_critic,
betas=(args.beta1, args.beta2))
optimizer_critic2 = optim.Adam(Discriminator2.parameters(),
lr=args.lr_critic,
betas=(args.beta1, args.beta2))
optimizer_critic3 = optim.Adam(Discriminator3.parameters(),
lr=args.lr_critic,
betas=(args.beta1, args.beta2))
iternum = max(len(data_loader1_real), len(data_loader1_fake),
len(data_loader2_real), len(data_loader2_fake),
len(data_loader3_real), len(data_loader3_fake))
print('iternum={}'.format(iternum))
####################
# 2. train network #
####################
global_step = 0
for epoch in range(args.epochs):
data1_real = get_inf_iterator(data_loader1_real)
data1_fake = get_inf_iterator(data_loader1_fake)
data2_real = get_inf_iterator(data_loader2_real)
data2_fake = get_inf_iterator(data_loader2_fake)
data3_real = get_inf_iterator(data_loader3_real)
data3_fake = get_inf_iterator(data_loader3_fake)
for step in range(iternum):
FeatExtor.train()
FeatEmbder.train()
DepthEsmator.train()
Discriminator1.train()
Discriminator2.train()
Discriminator3.train()
#============ one batch extraction ============#
cat_img1_real, depth_img1_real, lab1_real = next(data1_real)
cat_img1_fake, depth_img1_fake, lab1_fake = next(data1_fake)
cat_img2_real, depth_img2_real, lab2_real = next(data2_real)
cat_img2_fake, depth_img2_fake, lab2_fake = next(data2_fake)
cat_img3_real, depth_img3_real, lab3_real = next(data3_real)
cat_img3_fake, depth_img3_fake, lab3_fake = next(data3_fake)
#============ one batch collection ============#
ori_img1 = torch.cat([cat_img1_real, cat_img1_fake], 0).cuda()
depth_img1 = torch.cat([depth_img1_real, depth_img1_fake], 0)
lab1 = torch.cat([lab1_real, lab1_fake], 0)
ori_img2 = torch.cat([cat_img2_real, cat_img2_fake], 0).cuda()
depth_img2 = torch.cat([depth_img2_real, depth_img2_fake], 0)
lab2 = torch.cat([lab2_real, lab2_fake], 0)
ori_img3 = torch.cat([cat_img3_real, cat_img3_fake], 0).cuda()
depth_img3 = torch.cat([depth_img3_real, depth_img3_fake], 0)
lab3 = torch.cat([lab3_real, lab3_fake], 0)
ori_img = torch.cat([ori_img1, ori_img2, ori_img3], 0)
# ori_img = ori_img.cuda()
depth_GT = torch.cat([depth_img1, depth_img2, depth_img3], 0)
depth_GT = depth_GT.cuda()
label = torch.cat([lab1, lab2, lab3], 0)
label = label.long().squeeze().cuda()
with torch.no_grad():
pre_feat_ext1 = PreFeatExtorS1(ori_img1)[1]
pre_feat_ext2 = PreFeatExtorS2(ori_img2)[1]
pre_feat_ext3 = PreFeatExtorS3(ori_img3)[1]
#============ Depth supervision ============#
######### 1. depth loss #########
optimizer_DG_depth.zero_grad()
feat_ext_all, feat_ext = FeatExtor(ori_img)
depth_Pre = DepthEsmator(feat_ext_all)
Loss_depth = args.W_depth * criterionDepth(depth_Pre, depth_GT)
Loss_depth.backward()
optimizer_DG_depth.step()
#============ domain generalization supervision ============#
optimizer_DG_conf.zero_grad()
_, feat_ext = FeatExtor(ori_img)
feat_tgt = feat_ext
#************************* confusion all **********************************#
# predict on generator
loss_generator1 = criterionAdv(Discriminator1(feat_tgt), True)
loss_generator2 = criterionAdv(Discriminator2(feat_tgt), True)
loss_generator3 = criterionAdv(Discriminator3(feat_tgt), True)
feat_embd, label_pred = FeatEmbder(feat_ext)
########## cross-domain triplet loss #########
Loss_triplet = TripletLossCal(args, feat_embd, lab1, lab2, lab3)
Loss_cls = criterionCls(label_pred.squeeze(), label.float())
Loss_gen = args.W_genave * (loss_generator1 + loss_generator2 +
loss_generator3)
Loss_G = args.W_trip * Loss_triplet + args.W_cls * Loss_cls + args.W_gen * Loss_gen
Loss_G.backward()
optimizer_DG_conf.step()
#************************* confusion domain 1 with 2,3 **********************************#
feat_src = torch.cat([pre_feat_ext1, pre_feat_ext1, pre_feat_ext1],
0)
# predict on discriminator
optimizer_critic1.zero_grad()
real_loss = criterionAdv(Discriminator1(feat_src), True)
fake_loss = criterionAdv(Discriminator1(feat_tgt.detach()), False)
loss_critic1 = 0.5 * (real_loss + fake_loss)
loss_critic1.backward()
optimizer_critic1.step()
#************************* confusion domain 2 with 1,3 **********************************#
feat_src = torch.cat([pre_feat_ext2, pre_feat_ext2, pre_feat_ext2],
0)
# predict on discriminator
optimizer_critic2.zero_grad()
real_loss = criterionAdv(Discriminator2(feat_src), True)
fake_loss = criterionAdv(Discriminator2(feat_tgt.detach()), False)
loss_critic2 = 0.5 * (real_loss + fake_loss)
loss_critic2.backward()
optimizer_critic2.step()
#************************* confusion domain 3 with 1,2 **********************************#
feat_src = torch.cat([pre_feat_ext3, pre_feat_ext3, pre_feat_ext3],
0)
# predict on discriminator
optimizer_critic3.zero_grad()
real_loss = criterionAdv(Discriminator3(feat_src), True)
fake_loss = criterionAdv(Discriminator3(feat_tgt.detach()), False)
loss_critic3 = 0.5 * (real_loss + fake_loss)
loss_critic3.backward()
optimizer_critic3.step()
#============ tensorboard the log info ============#
info = {
'Loss_depth': Loss_depth.item(),
'Loss_triplet': Loss_triplet.item(),
'Loss_cls': Loss_cls.item(),
'Loss_G': Loss_G.item(),
'loss_critic1': loss_critic1.item(),
'loss_generator1': loss_generator1.item(),
'loss_critic2': loss_critic2.item(),
'loss_generator2': loss_generator2.item(),
'loss_critic3': loss_critic3.item(),
'loss_generator3': loss_generator3.item(),
}
for tag, value in info.items():
summary_writer.add_scalar(tag, value, global_step)
if (step + 1) % args.tst_step == 0:
depth_Pre_real = torch.cat([
depth_Pre[0:args.batchsize],
depth_Pre[2 * args.batchsize:3 * args.batchsize],
depth_Pre[4 * args.batchsize:5 * args.batchsize]
], 0)
depth_Pre_fake = torch.cat([
depth_Pre[args.batchsize:2 * args.batchsize],
depth_Pre[3 * args.batchsize:4 * args.batchsize],
depth_Pre[5 * args.batchsize:6 * args.batchsize]
], 0)
depth_Pre_all = vutils.make_grid(depth_Pre,
normalize=True,
scale_each=True)
depth_Pre_real = vutils.make_grid(depth_Pre_real,
normalize=True,
scale_each=True)
depth_Pre_fake = vutils.make_grid(depth_Pre_fake,
normalize=True,
scale_each=True)
summary_writer.add_image('Depth_Image_all', depth_Pre_all,
global_step)
summary_writer.add_image('Depth_Image_real', depth_Pre_real,
global_step)
summary_writer.add_image('Depth_Image_fake', depth_Pre_fake,
global_step)
#============ print the log info ============#
if (step + 1) % args.log_step == 0:
errors = OrderedDict([
('Loss_depth', Loss_depth.item()),
('Loss_triplet', Loss_triplet.item()),
('Loss_cls', Loss_cls.item()), ('Loss_G', Loss_G.item()),
('loss_critic1', loss_critic1.item()),
('loss_generator1', loss_generator1.item()),
('loss_critic2', loss_critic2.item()),
('loss_generator2', loss_generator2.item()),
('loss_critic3', loss_critic3.item()),
('loss_generator3', loss_generator3.item())
])
Saver.print_current_errors((epoch + 1), (step + 1), errors)
if (step + 1) % args.tst_step == 0:
evaluate.evaluate_img(FeatExtor, DepthEsmator,
data_loader_target, (epoch + 1),
(step + 1), Saver)
global_step += 1
#############################
# 2.4 save model parameters #
#############################
if ((step + 1) % args.model_save_step == 0):
model_save_path = os.path.join(args.results_path, 'snapshots',
savefilename)
mkdir(model_save_path)
torch.save(
FeatExtor.state_dict(),
os.path.join(
model_save_path,
"DGFA-Ext-{}-{}.pt".format(epoch + 1, step + 1)))
torch.save(
FeatEmbder.state_dict(),
os.path.join(
model_save_path,
"DGFA-Embd-{}-{}.pt".format(epoch + 1, step + 1)))
torch.save(
DepthEsmator.state_dict(),
os.path.join(
model_save_path,
"DGFA-Depth-{}-{}.pt".format(epoch + 1, step + 1)))
torch.save(
Discriminator1.state_dict(),
os.path.join(
model_save_path,
"DGFA-D1-{}-{}.pt".format(epoch + 1, step + 1)))
torch.save(
Discriminator2.state_dict(),
os.path.join(
model_save_path,
"DGFA-D2-{}-{}.pt".format(epoch + 1, step + 1)))
torch.save(
Discriminator3.state_dict(),
os.path.join(
model_save_path,
"DGFA-D3-{}-{}.pt".format(epoch + 1, step + 1)))
if ((epoch + 1) % args.model_save_epoch == 0):
model_save_path = os.path.join(args.results_path, 'snapshots',
savefilename)
mkdir(model_save_path)
torch.save(
FeatExtor.state_dict(),
os.path.join(model_save_path,
"DGFA-Ext-{}.pt".format(epoch + 1)))
torch.save(
FeatEmbder.state_dict(),
os.path.join(model_save_path,
"DGFA-Embd-{}.pt".format(epoch + 1)))
torch.save(
DepthEsmator.state_dict(),
os.path.join(model_save_path,
"DGFA-Depth-{}.pt".format(epoch + 1)))
torch.save(
Discriminator1.state_dict(),
os.path.join(model_save_path,
"DGFA-D1-{}.pt".format(epoch + 1)))
torch.save(
Discriminator2.state_dict(),
os.path.join(model_save_path,
"DGFA-D2-{}.pt".format(epoch + 1)))
torch.save(
Discriminator3.state_dict(),
os.path.join(model_save_path,
"DGFA-D3-{}.pt".format(epoch + 1)))
torch.save(FeatExtor.state_dict(),
os.path.join(model_save_path, "DGFA-Ext-final.pt"))
torch.save(FeatEmbder.state_dict(),
os.path.join(model_save_path, "DGFA-Embd-final.pt"))
torch.save(DepthEsmator.state_dict(),
os.path.join(model_save_path, "DGFA-Depth-final.pt"))
torch.save(Discriminator1.state_dict(),
os.path.join(model_save_path, "DGFA-D1-final.pt"))
torch.save(Discriminator2.state_dict(),
os.path.join(model_save_path, "DGFA-D2-final.pt"))
torch.save(Discriminator3.state_dict(),
os.path.join(model_save_path, "DGFA-D3-final.pt"))