def _init_models(self):
self.net_G = CustomPoseGenerator(self.opt.pose_feature_size, 2048, self.opt.noise_feature_size,
dropout=self.opt.drop, norm_layer=self.norm_layer, fuse_mode=self.opt.fuse_mode, connect_layers=self.opt.connect_layers)
e_base_model = create(self.opt.arch, cut_at_pooling=True)
e_embed_model = EltwiseSubEmbed(use_batch_norm=True, use_classifier=True, num_features=2048, num_classes=2)
self.net_E = SiameseNet(e_base_model, e_embed_model)
di_base_model = create(self.opt.arch, cut_at_pooling=True)
di_embed_model = EltwiseSubEmbed(use_batch_norm=True, use_classifier=True, num_features=2048, num_classes=1)
self.net_Di = SiameseNet(di_base_model, di_embed_model)
self.net_Dp = NLayerDiscriminator(3+18, norm_layer=self.norm_layer)
if self.opt.stage==1:
init_weights(self.net_G)
init_weights(self.net_Dp)
state_dict = remove_module_key(torch.load(self.opt.netE_pretrain))
self.net_E.load_state_dict(state_dict)
state_dict['embed_model.classifier.weight'] = state_dict['embed_model.classifier.weight'][1]
state_dict['embed_model.classifier.bias'] = torch.FloatTensor([state_dict['embed_model.classifier.bias'][1]])
self.net_Di.load_state_dict(state_dict)
elif self.opt.stage==2:
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
else:
assert('unknown training stage')
self.net_E = torch.nn.DataParallel(self.net_E).cuda()
self.net_G = torch.nn.DataParallel(self.net_G).cuda()
self.net_Di = torch.nn.DataParallel(self.net_Di).cuda()
self.net_Dp = torch.nn.DataParallel(self.net_Dp).cuda()
def load_model(self, model_arch='resnet50'):
# Create model
# # 利用resnet50建立base_model
base_model = models.create(model_arch, cut_at_pooling=True)
# # 建立嵌入模型
embed_model = EltwiseSubEmbed(use_batch_norm=True,
use_classifier=True,
num_features=2048,
num_classes=2)
model = SiameseNet(base_model, embed_model)
model = nn.DataParallel(model).cuda()
checkpoint = load_checkpoint(self.model_path)
if 'state_dict' in checkpoint.keys():
checkpoint = checkpoint['state_dict']
model.load_state_dict(checkpoint)
return torch.nn.DataParallel(base_model).cuda()
def _init_models(self):
#self.net_G = CustomPoseGenerator(self.opt.pose_feature_size, 2048, self.opt.noise_feature_size,
self.net_G = CustomPoseGenerator(self.opt.pose_feature_size, 2048, 0, pose_nc=self.pose_size, dropout=self.opt.drop,
norm_layer=self.norm_layer, fuse_mode=self.opt.fuse_mode,
connect_layers=self.opt.connect_layers)
if (self.opt.emb_type == 'Single'):
self.net_E = SingleNet(self.opt.arch, self.emb_size, pretraind=True, use_bn=True, test_bn=False, last_stride=self.opt.last_stride)
elif (self.opt.emb_type == 'Siamese'):
self.net_E = SiameseNet(self.opt.arch, self.emb_size, pretraind=True, use_bn=True, test_bn=False, last_stride=self.opt.last_stride)
else:
raise ValueError('unrecognized model')
self.net_Di = SingleNet('resnet18', 1, pretraind=True, use_bn=True, test_bn=False, last_stride=2)
self.net_Dp = NLayerDiscriminator(3+self.pose_size, norm_layer=self.norm_layer)
if self.opt.stage==0: # This is for training end-to-end
init_weights(self.net_G)
init_weights(self.net_Dp)
elif self.opt.stage==1: # This is for training fixing a baseline model
init_weights(self.net_G)
init_weights(self.net_Dp)
checkpoint = load_checkpoint(self.opt.netE_pretrain)
if 'state_dict' in checkpoint.keys():
checkpoint = checkpoint['state_dict']
state_dict = remove_module_key(checkpoint)
self.net_E.load_state_dict(state_dict)
#state_dict['classifier.weight'] = state_dict['classifier.weight'][1:2]
#state_dict['classifier.bias'] = torch.FloatTensor([state_dict['classifier.bias'][1]])
#self.net_Di.load_state_dict(state_dict)
elif self.opt.stage==2: # This is for training with a provided model
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
else:
raise ValueError('unrecognized mode')
self.net_E = torch.nn.DataParallel(self.net_E).cuda()
self.net_G = torch.nn.DataParallel(self.net_G).cuda()
self.net_Di = torch.nn.DataParallel(self.net_Di).cuda()
self.net_Dp = torch.nn.DataParallel(self.net_Dp).cuda()
def main(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.benchmark = True
# Redirect print to both console and log file
if not args.evaluate:
sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt'))
else:
log_dir = osp.dirname(args.resume)
sys.stdout = Logger(osp.join(log_dir, 'log_test.txt'))
# print("==========\nArgs:{}\n==========".format(args))
# Create data loaders
if args.height is None or args.width is None:
args.height, args.width = (256, 128)
dataset, train_loader, val_loader, test_loader = \
get_data(args.dataset, args.split, args.data_dir, args.height,
args.width, args.batch_size, args.workers,
args.combine_trainval, args.np_ratio)
# Create model
base_model = models.create(args.arch, cut_at_pooling=True)
embed_model = EltwiseSubEmbed(use_batch_norm=True, use_classifier=True,
num_features=2048, num_classes=2)
model = SiameseNet(base_model, embed_model)
model = nn.DataParallel(model).cuda()
# Evaluator
evaluator = CascadeEvaluator(
torch.nn.DataParallel(base_model).cuda(),
embed_model,
embed_dist_fn=lambda x: F.softmax(Variable(x), dim=1).data[:, 0])
# Load from checkpoint
best_mAP = 0
if args.resume:
checkpoint = load_checkpoint(args.resume)
if 'state_dict' in checkpoint.keys():
checkpoint = checkpoint['state_dict']
model.load_state_dict(checkpoint)
print("Test the loaded model:")
top1, mAP = evaluator.evaluate(test_loader, dataset.query, dataset.gallery, rerank_topk=100, dataset=args.dataset)
best_mAP = mAP
if args.evaluate:
return
# Criterion
criterion = nn.CrossEntropyLoss().cuda()
# Optimizer
param_groups = [
{'params': model.module.base_model.parameters(), 'lr_mult': 1.0},
{'params': model.module.embed_model.parameters(), 'lr_mult': 1.0}]
optimizer = torch.optim.SGD(param_groups, args.lr, momentum=args.momentum,
weight_decay=args.weight_decay)
# Trainer
trainer = SiameseTrainer(model, criterion)
# Schedule learning rate
def adjust_lr(epoch):
lr = args.lr * (0.1 ** (epoch // args.step_size))
for g in optimizer.param_groups:
g['lr'] = lr * g.get('lr_mult', 1)
# Start training
for epoch in range(0, args.epochs):
adjust_lr(epoch)
trainer.train(epoch, train_loader, optimizer, base_lr=args.lr)
if epoch % args.eval_step==0:
mAP = evaluator.evaluate(val_loader, dataset.val, dataset.val, top1=False)
is_best = mAP > best_mAP
best_mAP = max(mAP, best_mAP)
save_checkpoint({
'state_dict': model.state_dict()
}, is_best, fpath=osp.join(args.logs_dir, 'checkpoint.pth.tar'))
print('\n * Finished epoch {:3d} mAP: {:5.1%} best: {:5.1%}{}\n'.
format(epoch, mAP, best_mAP, ' *' if is_best else ''))
# Final test
print('Test with best model:')
checkpoint = load_checkpoint(osp.join(args.logs_dir, 'model_best.pth.tar'))
model.load_state_dict(checkpoint['state_dict'])
evaluator.evaluate(test_loader, dataset.query, dataset.gallery, dataset=args.dataset)
class ST_ReIDNet(object):
def __init__(self, opt, emb_size):
self.opt = opt
self.save_dir = os.path.join(opt.checkpoints, opt.name)
self.norm_layer = get_norm_layer(norm_type=opt.norm)
self.emb_size = emb_size
if (self.opt.dataset == 'DanceReID'):
self.pose_size = 17
else:
self.pose_size = 18
self._init_models()
self._init_losses()
self._init_optimizers()
print('---------- Networks initialized -------------')
#print_network(self.net_E)
#print_network(self.net_G)
#print_network(self.net_Di)
#print_network(self.net_Dp)
#print('-----------------------------------------------')
def _init_models(self):
#self.net_G = CustomPoseGenerator(self.opt.pose_feature_size, 2048, self.opt.noise_feature_size,
self.net_G = CustomPoseGenerator(self.opt.pose_feature_size, 2048, 0, pose_nc=self.pose_size, dropout=self.opt.drop,
norm_layer=self.norm_layer, fuse_mode=self.opt.fuse_mode,
connect_layers=self.opt.connect_layers)
if (self.opt.emb_type == 'Single'):
self.net_E = SingleNet(self.opt.arch, self.emb_size, pretraind=True, use_bn=True, test_bn=False, last_stride=self.opt.last_stride)
elif (self.opt.emb_type == 'Siamese'):
self.net_E = SiameseNet(self.opt.arch, self.emb_size, pretraind=True, use_bn=True, test_bn=False, last_stride=self.opt.last_stride)
else:
raise ValueError('unrecognized model')
self.net_Di = SingleNet('resnet18', 1, pretraind=True, use_bn=True, test_bn=False, last_stride=2)
self.net_Dp = NLayerDiscriminator(3+self.pose_size, norm_layer=self.norm_layer)
if self.opt.stage==0: # This is for training end-to-end
init_weights(self.net_G)
init_weights(self.net_Dp)
elif self.opt.stage==1: # This is for training fixing a baseline model
init_weights(self.net_G)
init_weights(self.net_Dp)
checkpoint = load_checkpoint(self.opt.netE_pretrain)
if 'state_dict' in checkpoint.keys():
checkpoint = checkpoint['state_dict']
state_dict = remove_module_key(checkpoint)
self.net_E.load_state_dict(state_dict)
#state_dict['classifier.weight'] = state_dict['classifier.weight'][1:2]
#state_dict['classifier.bias'] = torch.FloatTensor([state_dict['classifier.bias'][1]])
#self.net_Di.load_state_dict(state_dict)
elif self.opt.stage==2: # This is for training with a provided model
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
else:
raise ValueError('unrecognized mode')
self.net_E = torch.nn.DataParallel(self.net_E).cuda()
self.net_G = torch.nn.DataParallel(self.net_G).cuda()
self.net_Di = torch.nn.DataParallel(self.net_Di).cuda()
self.net_Dp = torch.nn.DataParallel(self.net_Dp).cuda()
def reset_model_status(self):
if self.opt.stage==0:
self.net_E.train()
self.net_G.train()
self.net_Di.train()
self.net_Dp.train()
self.net_Di.apply(set_bn_fix)
elif self.opt.stage==1:
self.net_G.train()
self.net_Dp.train()
self.net_E.eval()
self.net_Di.train()
self.net_Di.apply(set_bn_fix)
elif self.opt.stage==2:
self.net_E.train()
self.net_G.train()
self.net_Di.train()
self.net_Dp.train()
self.net_E.apply(set_bn_fix)
self.net_Di.apply(set_bn_fix)
def _load_state_dict(self, net, path):
state_dict = remove_module_key(torch.load(path))
net.load_state_dict(state_dict)
def _init_losses(self):
if self.opt.smooth_label:
self.criterionGAN_D = GANLoss(smooth=True).cuda()
self.rand_list = [True] * 1 + [False] * 10000
else:
self.criterionGAN_D = GANLoss(smooth=False).cuda()
self.rand_list = [False]
self.criterionGAN_G = GANLoss(smooth=False).cuda()
if self.opt.soft_margin:
self.tri_criterion = TripletLoss(margin='soft', batch_hard=True, distractor=True).cuda()
else:
self.tri_criterion = TripletLoss(margin=self.opt.margin, batch_hard=True, distractor=True).cuda
if (self.opt.emb_type == 'Single'):
if (self.opt.emb_smooth):
self.class_criterion = CrossEntropyLabelSmooth(self.emb_size, epsilon=0.1).cuda()
else:
self.class_criterion = torch.nn.CrossEntropyLoss().cuda()
elif (self.opt.emb_type == 'Siamese'):
self.class_criterion = torch.nn.CrossEntropyLoss().cuda()
if self.opt.mask:
self.reco_criterion = MaskedL1loss(use_mask=True).cuda()
else:
self.reco_criterion = MaskedL1loss(use_mask=False).cuda()
def _init_optimizers(self):
if self.opt.stage==0:
param_groups = [{'params': self.net_E.module.base_model.parameters(), 'lr_mult': 0.1},
{'params': self.net_E.module.classifier.parameters(), 'lr_mult': 1.0}]
self.optimizer_E = torch.optim.SGD(param_groups, lr=self.opt.lr, momentum=0.9, weight_decay=5e-4)
#self.optimizer_E = torch.optim.Adam(param_groups, lr=self.opt.lr, betas=(0.9, 0.999), weight_decay=5e-4)
self.optimizer_G = torch.optim.Adam(self.net_G.parameters(),
lr=1e-5, betas=(0.5, 0.999))
self.optimizer_Di = torch.optim.SGD(self.net_Di.parameters(),
lr=4e-5, momentum=0.9, weight_decay=1e-4)
self.optimizer_Dp = torch.optim.SGD(self.net_Dp.parameters(),
lr=4e-5, momentum=0.9, weight_decay=1e-4)
elif self.opt.stage==1:
param_groups = [{'params': self.net_E.module.base_model.parameters(), 'lr_mult': 0.1},
{'params': self.net_E.module.classifier.parameters(), 'lr_mult': 0.1}]
self.optimizer_E = torch.optim.SGD(param_groups, lr=self.opt.lr, momentum=0.9, weight_decay=5e-4)
self.optimizer_G = torch.optim.Adam(self.net_G.parameters(),
lr=self.opt.lr*0.1, betas=(0.5, 0.999))
self.optimizer_Di = torch.optim.SGD(self.net_Di.parameters(),
lr=self.opt.lr, momentum=0.9, weight_decay=1e-4)
self.optimizer_Dp = torch.optim.SGD(self.net_Dp.parameters(),
lr=self.opt.lr, momentum=0.9, weight_decay=1e-4)
elif self.opt.stage==2:
param_groups = [{'params': self.net_E.module.base_model.parameters(), 'lr_mult': 0.01},
{'params': self.net_E.module.classifier.parameters(), 'lr_mult': 0.1}]
self.optimizer_E = torch.optim.SGD(param_groups, lr=self.opt.lr, momentum=0.9, weight_decay=5e-4)
self.optimizer_G = torch.optim.Adam(self.net_G.parameters(),
lr=1e-6, betas=(0.5, 0.999))
self.optimizer_Di = torch.optim.SGD(self.net_Di.parameters(),
lr=1e-5, momentum=0.9, weight_decay=1e-4)
self.optimizer_Dp = torch.optim.SGD(self.net_Dp.parameters(),
lr=1e-5, momentum=0.9, weight_decay=1e-4)
self.schedulers = []
self.optimizers = []
self.optimizers.append(self.optimizer_E)
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_Di)
self.optimizers.append(self.optimizer_Dp)
for optimizer in self.optimizers:
self.schedulers.append(get_scheduler(optimizer, self.opt))
def set_input(self, input):
input_ach, input_pos, input_neg = input
ids = torch.cat([input_ach['pid'], input_pos['pid'], input_neg['pid']]).long()
ones = torch.ones_like(input_ach['pid']).fill_(1.0)
zeros = torch.ones_like(input_ach['pid']).fill_(0.0)
labels = torch.cat([ones, zeros]).long()
noise = torch.randn(labels.size(0), self.opt.noise_feature_size)
noise = torch.cat((noise, noise, noise))
self.ach_gt = input_ach['origin'].cuda()
self.pos_gt = input_pos['origin'].cuda()
self.neg_gt = input_neg['origin'].cuda()
self.ach_img = input_ach['input'].cuda()
self.pos_img = input_pos['input'].cuda()
self.neg_img = input_neg['input'].cuda()
self.ach_pose = input_ach['posemap'].cuda()
self.pos_pose = input_pos['posemap'].cuda()
self.neg_pose = input_neg['posemap'].cuda()
self.ach_mask = input_ach['mask'].cuda()
self.pos_mask = input_pos['mask'].cuda()
self.neg_mask = input_neg['mask'].cuda()
self.gt = torch.cat( [self.ach_gt, self.pos_gt, self.neg_gt])
self.mask = torch.cat( [self.ach_mask, self.pos_mask, self.neg_mask])
self.posemap = torch.cat( [self.ach_pose, self.pos_pose, self.neg_pose])
self.two_gt = torch.cat([self.ach_gt, self.pos_gt])
self.two_mask = torch.cat( [self.ach_mask, self.pos_mask])
self.pos_posemap = torch.cat([self.ach_pose, self.pos_pose])
self.swap_posemap = torch.cat([self.pos_pose, self.ach_pose])
self.ids = ids.cuda()
self.labels = labels.cuda()
self.noise = noise.cuda()
def forward(self):
z = Variable(self.noise)
if (self.opt.emb_type == 'Single'):
if (self.opt.stage == 1):
self.A_ach = self.net_E(Variable(self.ach_img))
self.A_pos = self.net_E(Variable(self.pos_img))
self.A_neg = self.net_E(Variable(self.neg_img))
else:
self.A_ach, pred_ach = self.net_E(Variable(self.ach_img))
self.A_pos, pred_pos = self.net_E(Variable(self.pos_img))
self.A_neg, pred_neg = self.net_E(Variable(self.neg_img))
self.id_pred = torch.cat( [pred_ach, pred_pos, pred_neg])
elif (self.opt.emb_type == 'Siamese'):
self.A_ach, self.A_pos, pos_pred = self.net_E(torch.cat([Variable(self.ach_img), Variable(self.pos_img)]))
_, self.A_neg, neg_pred = self.net_E(torch.cat([Variable(self.ach_img), Variable(self.neg_img)]))
self.id_pred = torch.cat( [pos_pred, neg_pred])
self.fake_ach = self.net_G(Variable(self.ach_pose),
self.A_ach.view(self.A_ach.size(0), self.A_ach.size(1), 1, 1), None)
self.fake_pos = self.net_G(Variable(self.pos_pose),
self.A_pos.view(self.A_pos.size(0), self.A_pos.size(1), 1, 1), None)
self.fake_neg = self.net_G(Variable(self.neg_pose),
self.A_neg.view(self.A_neg.size(0), self.A_neg.size(1), 1, 1), None)
self.swap_ach = self.net_G(Variable(self.ach_pose),
self.A_pos.view(self.A_pos.size(0), self.A_pos.size(1), 1, 1), None)
self.swap_pos = self.net_G(Variable(self.pos_pose),
self.A_ach.view(self.A_ach.size(0), self.A_ach.size(1), 1, 1), None)
self.fake = torch.cat((self.fake_ach, self.fake_pos, self.fake_neg))
self.swap_fake = torch.cat((self.swap_ach, self.swap_pos))
def backward_Dp(self):
real_pose = torch.cat((Variable(self.posemap), Variable(self.gt)), dim=1)
fake_pose1 = torch.cat((Variable(self.posemap), self.fake.detach()), dim=1)
fake_pose2 = torch.cat((Variable(self.pos_posemap), self.swap_fake.detach()), dim=1)
fake_pose3 = torch.cat((Variable(self.swap_posemap), Variable(self.two_gt)), dim=1)
pred_real = self.net_Dp(real_pose)
pred_fake = self.net_Dp(torch.cat([fake_pose1, fake_pose2, fake_pose3]))
#print(pred_real.size())
#print(pred_fake.size())
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
#self.loss_Dp = loss_D.data[0]
self.loss_Dp = loss_D.item()
def backward_Di(self):
_, pred_real = self.net_Di(Variable(self.gt))
_, pred_fake1 = self.net_Di(self.fake.detach())
_, pred_fake2 = self.net_Di(self.swap_fake.detach())
pred_fake = torch.cat ([pred_fake1, pred_fake2])
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
#self.loss_Di = loss_D.data[0]
self.loss_Di = loss_D.item()
def backward_G(self):
loss_r1 = self.reco_criterion(self.fake, Variable(self.mask), Variable(self.gt))
loss_r2 = self.reco_criterion(self.swap_fake, Variable(self.two_mask), Variable(self.two_gt))
loss_r = 0.7 * loss_r2 + 0.3 * loss_r1
_, pred_fake_Di = self.net_Di(torch.cat([self.fake, self.swap_fake]))
fake_pose1 = torch.cat((Variable(self.posemap), self.fake), dim=1)
fake_pose2 = torch.cat((Variable(self.pos_posemap), self.swap_fake), dim=1)
fake_pose3 = torch.cat((Variable(self.swap_posemap), Variable(self.two_gt)), dim=1)
pred_fake_Dp = self.net_Dp(torch.cat([fake_pose1, fake_pose2, fake_pose3]))
loss_G_GAN_Di = self.criterionGAN_G(pred_fake_Di, True)
loss_G_GAN_Dp = self.criterionGAN_G(pred_fake_Dp, True)
loss_G = loss_G_GAN_Di * self.opt.lambda_d + \
loss_G_GAN_Dp * self.opt.lambda_dp + \
loss_r * self.opt.lambda_recon
loss_G.backward(retain_graph=True)
# Compute triplet loss
feat = torch.cat([self.A_ach, self.A_pos, self.A_neg])
loss_t, _ , _ = self.tri_criterion(feat, torch.squeeze(self.ids, 1))
# Classification loss
if (self.opt.stage==1):
loss_c = torch.tensor(0.0)
else:
if (self.opt.emb_type == 'Single'):
loss_c = self.class_criterion(self.id_pred, torch.squeeze(self.ids, 1))
elif (self.opt.emb_type == 'Siamese'):
loss_c = self.class_criterion(self.id_pred, torch.squeeze(self.labels, 1))
loss_E = loss_G + \
loss_t * self.opt.lambda_tri+ \
loss_c * self.opt.lambda_class
loss_E.backward()
self.loss_E = loss_E.item()
self.loss_t = loss_t.item()
self.loss_c = loss_c.item()
self.loss_G = loss_G.item()
self.loss_r = loss_r.item()
self.loss_G_GAN_Di = loss_G_GAN_Di.item()
self.loss_G_GAN_Dp = loss_G_GAN_Dp.item()
def optimize_parameters(self):
self.forward()
self.optimizer_Di.zero_grad()
self.backward_Di()
self.optimizer_Di.step()
self.optimizer_Dp.zero_grad()
self.backward_Dp()
self.optimizer_Dp.step()
self.optimizer_G.zero_grad()
self.optimizer_E.zero_grad()
self.backward_G()
self.optimizer_G.step()
if self.opt.stage!=1:
self.optimizer_E.step()
def get_current_errors(self):
return OrderedDict([('E_t', self.loss_t),
('E_c', self.loss_c),
('E', self.loss_E),
('G_r', self.loss_r),
('G_gan_Di', self.loss_G_GAN_Di),
('G_gan_Dp', self.loss_G_GAN_Dp),
('G', self.loss_G),
('D_i', self.loss_Di),
('D_p', self.loss_Dp)
])
def get_current_visuals(self):
ach = util.tensor2im(self.ach_img)
ach_map = self.ach_pose.sum(1)
ach_map[ach_map>1]=1
ach_pose = util.tensor2im(torch.unsqueeze(ach_map,1))
ach_mask = util.tensor2im(torch.unsqueeze(self.ach_mask,1))
ach_fake1 = util.tensor2im(self.fake_ach.data)
ach_fake2 = util.tensor2im(self.swap_ach.data)
###########
pos = util.tensor2im(self.pos_img)
pos_map = self.pos_pose.sum(1)
pos_map[pos_map>1]=1
pos_pose = util.tensor2im(torch.unsqueeze(pos_map,1))
pos_mask = util.tensor2im(torch.unsqueeze(self.pos_mask,1))
pos_fake1 = util.tensor2im(self.fake_pos.data)
pos_fake2 = util.tensor2im(self.swap_pos.data)
return OrderedDict([('ach', ach), ('ach_pose', ach_pose), ('ach_mask', ach_mask), ('ach_fake1', ach_fake1), ('ach_fake2', ach_fake2),
('pos', pos), ('pos_pose', pos_pose), ('pos_mask', pos_mask), ('pos_fake1', pos_fake1), ('pos_fake2', pos_fake2)])
def save(self, epoch):
self.save_network(self.net_E, 'E', epoch)
self.save_network(self.net_G, 'G', epoch)
self.save_network(self.net_Di, 'Di', epoch)
self.save_network(self.net_Dp, 'Dp', epoch)
def save_network(self, network, network_label, epoch_label):
save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
save_path = os.path.join(self.save_dir, save_filename)
torch.save(network.state_dict(), save_path)
def update_learning_rate(self):
for scheduler in self.schedulers:
scheduler.step()
lr = self.optimizers[0].param_groups[0]['lr']
class FDGANModel(object):
def __init__(self, opt):
self.opt = opt
self.save_dir = os.path.join(opt.checkpoints, opt.name)
self.norm_layer = get_norm_layer(norm_type=opt.norm)
self._init_models()
self._init_losses()
self._init_optimizers()
print('---------- Networks initialized -------------')
print_network(self.net_E)
print_network(self.net_G)
print_network(self.net_Di)
print_network(self.net_Dp)
print('-----------------------------------------------')
def _init_models(self):
self.net_G = CustomPoseGenerator(self.opt.pose_feature_size, 2048, self.opt.noise_feature_size,
dropout=self.opt.drop, norm_layer=self.norm_layer, fuse_mode=self.opt.fuse_mode, connect_layers=self.opt.connect_layers)
e_base_model = create(self.opt.arch, cut_at_pooling=True)
e_embed_model = EltwiseSubEmbed(use_batch_norm=True, use_classifier=True, num_features=2048, num_classes=2)
self.net_E = SiameseNet(e_base_model, e_embed_model)
di_base_model = create(self.opt.arch, cut_at_pooling=True)
di_embed_model = EltwiseSubEmbed(use_batch_norm=True, use_classifier=True, num_features=2048, num_classes=1)
self.net_Di = SiameseNet(di_base_model, di_embed_model)
self.net_Dp = NLayerDiscriminator(3+18, norm_layer=self.norm_layer)
if self.opt.stage==1:
init_weights(self.net_G)
init_weights(self.net_Dp)
state_dict = remove_module_key(torch.load(self.opt.netE_pretrain))
self.net_E.load_state_dict(state_dict)
state_dict['embed_model.classifier.weight'] = state_dict['embed_model.classifier.weight'][1]
state_dict['embed_model.classifier.bias'] = torch.FloatTensor([state_dict['embed_model.classifier.bias'][1]])
self.net_Di.load_state_dict(state_dict)
elif self.opt.stage==2:
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
else:
assert('unknown training stage')
self.net_E = torch.nn.DataParallel(self.net_E).cuda()
self.net_G = torch.nn.DataParallel(self.net_G).cuda()
self.net_Di = torch.nn.DataParallel(self.net_Di).cuda()
self.net_Dp = torch.nn.DataParallel(self.net_Dp).cuda()
def reset_model_status(self):
if self.opt.stage==1:
self.net_G.train()
self.net_Dp.train()
self.net_E.eval()
self.net_Di.train()
self.net_Di.apply(set_bn_fix)
elif self.opt.stage==2:
self.net_E.train()
self.net_G.train()
self.net_Di.train()
self.net_Dp.train()
self.net_E.apply(set_bn_fix)
self.net_Di.apply(set_bn_fix)
def _load_state_dict(self, net, path):
state_dict = remove_module_key(torch.load(path))
net.load_state_dict(state_dict)
def _init_losses(self):
if self.opt.smooth_label:
self.criterionGAN_D = GANLoss(smooth=True).cuda()
self.rand_list = [True] * 1 + [False] * 10000
else:
self.criterionGAN_D = GANLoss(smooth=False).cuda()
self.rand_list = [False]
self.criterionGAN_G = GANLoss(smooth=False).cuda()
def _init_optimizers(self):
if self.opt.stage==1:
self.optimizer_G = torch.optim.Adam(self.net_G.parameters(),
lr=self.opt.lr*0.1, betas=(0.5, 0.999))
self.optimizer_Di = torch.optim.SGD(self.net_Di.parameters(),
lr=self.opt.lr*0.01, momentum=0.9, weight_decay=1e-4)
self.optimizer_Dp = torch.optim.SGD(self.net_Dp.parameters(),
lr=self.opt.lr, momentum=0.9, weight_decay=1e-4)
elif self.opt.stage==2:
param_groups = [{'params': self.net_E.module.base_model.parameters(), 'lr_mult': 0.1},
{'params': self.net_E.module.embed_model.parameters(), 'lr_mult': 1.0},
{'params': self.net_G.parameters(), 'lr_mult': 0.1}]
self.optimizer_G = torch.optim.Adam(param_groups,
lr=self.opt.lr*0.1, betas=(0.5, 0.999))
self.optimizer_Di = torch.optim.SGD(self.net_Di.parameters(),
lr=self.opt.lr, momentum=0.9, weight_decay=1e-4)
self.optimizer_Dp = torch.optim.SGD(self.net_Dp.parameters(),
lr=self.opt.lr, momentum=0.9, weight_decay=1e-4)
self.schedulers = []
self.optimizers = []
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_Di)
self.optimizers.append(self.optimizer_Dp)
for optimizer in self.optimizers:
self.schedulers.append(get_scheduler(optimizer, self.opt))
def set_input(self, input):
input1, input2 = input
labels = (input1['pid']==input2['pid']).long()
noise = torch.randn(labels.size(0), self.opt.noise_feature_size)
# keep the same pose map for persons with the same identity
mask = labels.view(-1,1,1,1).expand_as(input1['posemap'])
input2['posemap'] = input1['posemap']*mask.float() + input2['posemap']*(1-mask.float())
mask = labels.view(-1,1,1,1).expand_as(input1['target'])
input2['target'] = input1['target']*mask.float() + input2['target']*(1-mask.float())
origin = torch.cat([input1['origin'], input2['origin']])
target = torch.cat([input1['target'], input2['target']])
posemap = torch.cat([input1['posemap'], input2['posemap']])
noise = torch.cat((noise, noise))
self.origin = origin.cuda()
self.target = target.cuda()
self.posemap = posemap.cuda()
self.labels = labels.cuda()
self.noise = noise.cuda()
def forward(self):
A = Variable(self.origin)
B_map = Variable(self.posemap)
z = Variable(self.noise)
bs = A.size(0)
A_id1, A_id2, self.id_score = self.net_E(A[:bs//2], A[bs//2:])
A_id = torch.cat((A_id1, A_id2))
self.fake = self.net_G(B_map, A_id.view(A_id.size(0), A_id.size(1), 1, 1), z.view(z.size(0), z.size(1), 1, 1))
def backward_Dp(self):
real_pose = torch.cat((Variable(self.posemap), Variable(self.target)),dim=1)
fake_pose = torch.cat((Variable(self.posemap), self.fake.detach()),dim=1)
pred_real = self.net_Dp(real_pose)
pred_fake = self.net_Dp(fake_pose)
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
self.loss_Dp = loss_D.data[0]
def backward_Di(self):
_, _, pred_real = self.net_Di(Variable(self.origin), Variable(self.target))
_, _, pred_fake = self.net_Di(Variable(self.origin), self.fake.detach())
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
self.loss_Di = loss_D.data[0]
def backward_G(self):
loss_v = F.cross_entropy(self.id_score, Variable(self.labels).view(-1))
loss_r = F.l1_loss(self.fake, Variable(self.target))
fake_1 = self.fake[:self.fake.size(0)//2]
fake_2 = self.fake[self.fake.size(0)//2:]
loss_sp = F.l1_loss(fake_1[self.labels.view(self.labels.size(0),1,1,1).expand_as(fake_1)==1],
fake_2[self.labels.view(self.labels.size(0),1,1,1).expand_as(fake_1)==1])
_, _, pred_fake_Di = self.net_Di(Variable(self.origin), self.fake)
pred_fake_Dp = self.net_Dp(torch.cat((Variable(self.posemap),self.fake),dim=1))
loss_G_GAN_Di = self.criterionGAN_G(pred_fake_Di, True)
loss_G_GAN_Dp = self.criterionGAN_G(pred_fake_Dp, True)
loss_G = loss_G_GAN_Di + loss_G_GAN_Dp + \
loss_r * self.opt.lambda_recon + \
loss_v * self.opt.lambda_veri + \
loss_sp * self.opt.lambda_sp
loss_G.backward()
del self.id_score
self.loss_G = loss_G.data[0]
self.loss_v = loss_v.data[0]
self.loss_sp = loss_sp.data[0]
self.loss_r = loss_r.data[0]
self.loss_G_GAN_Di = loss_G_GAN_Di.data[0]
self.loss_G_GAN_Dp = loss_G_GAN_Dp.data[0]
self.fake = self.fake.data
def optimize_parameters(self):
self.forward()
self.optimizer_Di.zero_grad()
self.backward_Di()
self.optimizer_Di.step()
self.optimizer_Dp.zero_grad()
self.backward_Dp()
self.optimizer_Dp.step()
self.optimizer_G.zero_grad()
self.backward_G()
self.optimizer_G.step()
def get_current_errors(self):
return OrderedDict([('G_v', self.loss_v),
('G_r', self.loss_r),
('G_sp', self.loss_sp),
('G_gan_Di', self.loss_G_GAN_Di),
('G_gan_Dp', self.loss_G_GAN_Dp),
('D_i', self.loss_Di),
('D_p', self.loss_Dp)
])
def get_current_visuals(self):
input = util.tensor2im(self.origin)
target = util.tensor2im(self.target)
fake = util.tensor2im(self.fake)
map = self.posemap.sum(1)
map[map>1]=1
map = util.tensor2im(torch.unsqueeze(map,1))
return OrderedDict([('input', input), ('posemap', map), ('fake', fake), ('target', target)])
def save(self, epoch):
self.save_network(self.net_E, 'E', epoch)
self.save_network(self.net_G, 'G', epoch)
self.save_network(self.net_Di, 'Di', epoch)
self.save_network(self.net_Dp, 'Dp', epoch)
def save_network(self, network, network_label, epoch_label):
save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
save_path = os.path.join(self.save_dir, save_filename)
torch.save(network.state_dict(), save_path)
def update_learning_rate(self):
for scheduler in self.schedulers:
scheduler.step()
lr = self.optimizers[0].param_groups[0]['lr']
def main(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.benchmark = True
# Redirect print to both console and log file
if not args.evaluate:
sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt'))
else:
log_dir = osp.dirname(args.resume)
sys.stdout = Logger(osp.join(log_dir, 'log_test.txt'))
# print("==========\nArgs:{}\n==========".format(args))
# Create data loaders
if args.height is None or args.width is None:
args.height, args.width = (256, 128)
dataset, train_loader, val_loader, test_loader = \
get_data(args.dataset, args.split, args.data_dir, args.height,
args.width, args.batch_size, args.workers,
args.combine_trainval, args.np_ratio,
args.emb_type, args.inst_mode, args.eraser)
if args.combine_trainval:
emb_size = dataset.num_trainval_ids
else:
emb_size = dataset.num_train_ids
# Create model
if (args.emb_type == 'Single'):
model = SingleNet(args.arch,
emb_size,
pretraind=True,
use_bn=args.use_bn,
test_bn=args.test_bn,
last_stride=args.last_stride)
elif (args.emb_type == 'Siamese'):
model = SiameseNet(args.arch,
emb_size,
pretraind=True,
use_bn=args.use_bn,
test_bn=args.test_bn,
last_stride=args.last_stride)
else:
raise ValueError('unrecognized model')
model = nn.DataParallel(model).cuda()
if args.resume:
checkpoint = load_checkpoint(args.resume)
if 'state_dict' in checkpoint.keys():
checkpoint = checkpoint['state_dict']
model.load_state_dict(checkpoint)
# Evaluator
evaluator = CascadeEvaluator(torch.nn.DataParallel(model).cuda(),
emb_size=emb_size)
# Load from checkpoint
best_mAP = 0
if args.resume:
print("Test the loaded model:")
top1, mAP = evaluator.evaluate(test_loader,
dataset.query,
dataset.gallery,
dataset=args.dataset)
best_mAP = mAP
if args.evaluate:
return
# Criterion
if args.soft_margin:
tri_criterion = TripletLoss(margin='soft').cuda()
else:
tri_criterion = TripletLoss(margin=args.margin).cuda()
if (args.emb_type == 'Single'):
if args.label_smoothing:
cla_criterion = CrossEntropyLabelSmooth(emb_size,
epsilon=0.1).cuda()
else:
cla_criterion = torch.nn.CrossEntropyLoss().cuda()
elif (args.emb_type == 'Siamese'):
cla_criterion = torch.nn.CrossEntropyLoss().cuda()
# Optimizer
param_groups = [{
'params': model.module.base_model.parameters(),
'lr_mult': 0.1
}, {
'params': model.module.classifier.parameters(),
'lr_mult': 1.0
}]
if (args.opt_name == 'SGD'):
optimizer = getattr(torch.optim,
args.opt_name)(param_groups,
lr=args.lr,
weight_decay=args.weight_decay,
momentum=args.momentum)
else:
optimizer = getattr(torch.optim,
args.opt_name)(param_groups,
lr=args.lr,
weight_decay=args.weight_decay)
# Trainer
if (args.emb_type == 'Single'):
trainer = TripletTrainer(model, tri_criterion, cla_criterion,
args.lambda_tri, args.lambda_cla)
elif (args.emb_type == 'Siamese'):
trainer = SiameseTrainer(model, tri_criterion, cla_criterion,
args.lambda_tri, args.lambda_cla)
#TODO:Warmup lr
# Schedule learning rate
def adjust_lr(epoch):
lr = args.lr * (0.1**(epoch // args.step_size))
for g in optimizer.param_groups:
g['lr'] = lr * g.get('lr_mult', 1)
# Start training
for epoch in range(0, args.epochs):
adjust_lr(epoch)
trainer.train(epoch, train_loader, optimizer, base_lr=args.lr)
if epoch % args.eval_step == 0:
#mAP = evaluator.evaluate(val_loader, dataset.val, dataset.val, top1=False, dataset=args.dataset)
mAP = evaluator.evaluate(test_loader,
dataset.query,
dataset.gallery,
top1=False,
dataset=args.dataset)
is_best = mAP > best_mAP
best_mAP = max(mAP, best_mAP)
save_checkpoint({'state_dict': model.state_dict()},
is_best,
fpath=osp.join(args.logs_dir,
'checkpoint.pth.tar'))
print('\n * Finished epoch {:3d} mAP: {:5.1%} best: {:5.1%}{}\n'.
format(epoch, mAP, best_mAP, ' *' if is_best else ''))
# Final test
print('Test with best model:')
checkpoint = load_checkpoint(osp.join(args.logs_dir, 'model_best.pth.tar'))
model.load_state_dict(checkpoint['state_dict'])
evaluator.evaluate(test_loader,
dataset.query,
dataset.gallery,
dataset=args.dataset)
def _init_models(self):
#G For source
self.net_G = CustomPoseGenerator(
self.opt.pose_feature_size,
2048,
self.opt.noise_feature_size,
dropout=self.opt.drop,
norm_layer=self.norm_layer,
fuse_mode=self.opt.fuse_mode,
connect_layers=self.opt.connect_layers)
#G For target
self.tar_net_G = CustomPoseGenerator(
self.opt.pose_feature_size,
2048,
self.opt.noise_feature_size,
dropout=self.opt.drop,
norm_layer=self.norm_layer,
fuse_mode=self.opt.fuse_mode,
connect_layers=self.opt.connect_layers)
#We share same E for cross-dataset
e_base_model = create(self.opt.arch, cut_at_pooling=True)
e_embed_model = EltwiseSubEmbed(use_batch_norm=True,
use_classifier=True,
num_features=2048,
num_classes=2)
self.net_E = SiameseNet(e_base_model, e_embed_model)
#Di For source
di_base_model = create(self.opt.arch, cut_at_pooling=True)
di_embed_model = EltwiseSubEmbed(use_batch_norm=True,
use_classifier=True,
num_features=2048,
num_classes=1)
self.net_Di = SiameseNet(di_base_model, di_embed_model)
#Di For target
di_base_model = create(self.opt.arch, cut_at_pooling=True)
di_embed_model = EltwiseSubEmbed(use_batch_norm=True,
use_classifier=True,
num_features=2048,
num_classes=1)
self.tar_net_Di = SiameseNet(di_base_model, di_embed_model)
#We share same Dp for cross-dataset
self.net_Dp = NLayerDiscriminator(3 + 18, norm_layer=self.norm_layer)
#Load model
if self.opt.stage == 1:
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
self._load_state_dict(self.tar_net_Di, self.opt.tar_netDi_pretrain)
self._load_state_dict(self.tar_net_G, self.opt.tar_netG_pretrain)
elif self.opt.stage == 2:
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
self._load_state_dict(self.tar_net_Di, self.opt.tar_netDi_pretrain)
self._load_state_dict(self.tar_net_G, self.opt.tar_netG_pretrain)
else:
assert ('unknown training stage')
self.net_E = torch.nn.DataParallel(self.net_E).cuda()
self.net_G = torch.nn.DataParallel(self.net_G).cuda()
self.net_Di = torch.nn.DataParallel(self.net_Di).cuda()
self.net_Dp = torch.nn.DataParallel(self.net_Dp).cuda()
self.tar_net_G = torch.nn.DataParallel(self.tar_net_G).cuda()
self.tar_net_Di = torch.nn.DataParallel(self.tar_net_Di).cuda()
class PDANetModel(object):
def __init__(self, opt):
self.opt = opt
self.save_dir = os.path.join(opt.checkpoints, opt.name)
self.norm_layer = get_norm_layer(norm_type=opt.norm)
self._init_models()
self._init_losses()
self._init_cross_optimizers()
def _init_models(self):
#G For source
self.net_G = CustomPoseGenerator(
self.opt.pose_feature_size,
2048,
self.opt.noise_feature_size,
dropout=self.opt.drop,
norm_layer=self.norm_layer,
fuse_mode=self.opt.fuse_mode,
connect_layers=self.opt.connect_layers)
#G For target
self.tar_net_G = CustomPoseGenerator(
self.opt.pose_feature_size,
2048,
self.opt.noise_feature_size,
dropout=self.opt.drop,
norm_layer=self.norm_layer,
fuse_mode=self.opt.fuse_mode,
connect_layers=self.opt.connect_layers)
#We share same E for cross-dataset
e_base_model = create(self.opt.arch, cut_at_pooling=True)
e_embed_model = EltwiseSubEmbed(use_batch_norm=True,
use_classifier=True,
num_features=2048,
num_classes=2)
self.net_E = SiameseNet(e_base_model, e_embed_model)
#Di For source
di_base_model = create(self.opt.arch, cut_at_pooling=True)
di_embed_model = EltwiseSubEmbed(use_batch_norm=True,
use_classifier=True,
num_features=2048,
num_classes=1)
self.net_Di = SiameseNet(di_base_model, di_embed_model)
#Di For target
di_base_model = create(self.opt.arch, cut_at_pooling=True)
di_embed_model = EltwiseSubEmbed(use_batch_norm=True,
use_classifier=True,
num_features=2048,
num_classes=1)
self.tar_net_Di = SiameseNet(di_base_model, di_embed_model)
#We share same Dp for cross-dataset
self.net_Dp = NLayerDiscriminator(3 + 18, norm_layer=self.norm_layer)
#Load model
if self.opt.stage == 1:
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
self._load_state_dict(self.tar_net_Di, self.opt.tar_netDi_pretrain)
self._load_state_dict(self.tar_net_G, self.opt.tar_netG_pretrain)
elif self.opt.stage == 2:
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
self._load_state_dict(self.tar_net_Di, self.opt.tar_netDi_pretrain)
self._load_state_dict(self.tar_net_G, self.opt.tar_netG_pretrain)
else:
assert ('unknown training stage')
self.net_E = torch.nn.DataParallel(self.net_E).cuda()
self.net_G = torch.nn.DataParallel(self.net_G).cuda()
self.net_Di = torch.nn.DataParallel(self.net_Di).cuda()
self.net_Dp = torch.nn.DataParallel(self.net_Dp).cuda()
self.tar_net_G = torch.nn.DataParallel(self.tar_net_G).cuda()
self.tar_net_Di = torch.nn.DataParallel(self.tar_net_Di).cuda()
def reset_model_status(self):
if self.opt.stage == 1:
self.net_G.train()
self.tar_net_G.train()
self.net_Dp.train()
self.net_E.eval()
self.net_Di.train()
self.net_Di.apply(set_bn_fix)
self.tar_net_Di.train()
self.tar_net_Di.apply(set_bn_fix)
elif self.opt.stage == 2:
self.net_E.train()
self.net_G.train()
self.tar_net_G.train()
self.net_Di.train()
self.tar_net_Di.train()
self.net_Dp.train()
self.net_E.apply(set_bn_fix)
self.net_Di.apply(set_bn_fix)
self.tar_net_Di.apply(set_bn_fix)
def _load_state_dict(self, net, path):
state_dict = remove_module_key(torch.load(path))
net.load_state_dict(state_dict)
def _init_losses(self):
self.criterion_Triplet = TripletLoss(margin=self.opt.tri_margin)
self.criterion_MMD = MMDLoss(sigma_list=[1, 2, 10])
#Smooth label is a mechanism
if self.opt.smooth_label:
self.criterionGAN_D = GANLoss(smooth=True).cuda()
self.rand_list = [True] * 1 + [False] * 10000
else:
self.criterionGAN_D = GANLoss(smooth=False).cuda()
self.rand_list = [False]
self.criterionGAN_G = GANLoss(smooth=False).cuda()
#For cross dataset optimizers
def _init_cross_optimizers(self):
if self.opt.stage == 1:
self.optimizer_G = torch.optim.Adam(self.net_G.parameters(),
lr=self.opt.lr * 0.1,
betas=(0.5, 0.999))
self.optimizer_tar_G = torch.optim.Adam(
self.tar_net_G.parameters(),
lr=self.opt.lr * 0.1,
betas=(0.5, 0.999))
self.optimizer_Di = torch.optim.SGD(self.net_Di.parameters(),
lr=self.opt.lr * 0.01,
momentum=0.9,
weight_decay=1e-4)
self.optimizer_tar_Di = torch.optim.SGD(
self.tar_net_Di.parameters(),
lr=self.opt.lr * 0.01,
momentum=0.9,
weight_decay=1e-4)
self.optimizer_Dp = torch.optim.SGD(self.net_Dp.parameters(),
lr=self.opt.lr,
momentum=0.9,
weight_decay=1e-4)
elif self.opt.stage == 2:
param_groups = [{
'params': self.net_E.module.base_model.parameters(),
'lr_mult': 0.1
}, {
'params':
self.net_E.module.embed_model.parameters(),
'lr_mult':
1.0
}, {
'params': self.net_G.parameters(),
'lr_mult': 0.1
}]
param_tar_groups = [{
'params':
self.net_E.module.base_model.parameters(),
'lr_mult':
0.1
}, {
'params':
self.net_E.module.embed_model.parameters(),
'lr_mult':
1.0
}, {
'params': self.tar_net_G.parameters(),
'lr_mult': 0.1
}]
self.optimizer_G = torch.optim.Adam(param_groups,
lr=self.opt.lr * 0.1,
betas=(0.5, 0.999))
self.optimizer_tar_G = torch.optim.Adam(param_tar_groups,
lr=self.opt.lr * 0.1,
betas=(0.5, 0.999))
self.optimizer_Di = torch.optim.SGD(self.net_Di.parameters(),
lr=self.opt.lr,
momentum=0.9,
weight_decay=1e-4)
self.optimizer_tar_Di = torch.optim.SGD(
self.tar_net_Di.parameters(),
lr=self.opt.lr,
momentum=0.9,
weight_decay=1e-4)
self.optimizer_Dp = torch.optim.SGD(self.net_Dp.parameters(),
lr=self.opt.lr,
momentum=0.9,
weight_decay=1e-4)
self.optimizer_E = torch.optim.Adam(
self.net_E.module.base_model.parameters(),
lr=self.opt.lr * 0.1,
betas=(0.5, 0.999))
self.schedulers = []
self.optimizers = []
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_Di)
self.optimizers.append(self.optimizer_Dp)
self.optimizers.append(self.optimizer_tar_G)
self.optimizers.append(self.optimizer_tar_Di)
for optimizer in self.optimizers:
self.schedulers.append(get_scheduler(optimizer, self.opt))
def set_input(self, input):
input1, input2 = input
labels = (input1['pid'] == input2['pid']).long()
noise = torch.randn(labels.size(0), self.opt.noise_feature_size)
# keep the same pose map for persons with the same identity
mask = labels.view(-1, 1, 1, 1).expand_as(input1['posemap'])
input2['posemap'] = input1['posemap'] * mask.float(
) + input2['posemap'] * (1 - mask.float())
mask = labels.view(-1, 1, 1, 1).expand_as(input1['target'])
input2['target'] = input1['target'] * mask.float(
) + input2['target'] * (1 - mask.float())
origin = torch.cat([input1['origin'], input2['origin']])
target = torch.cat([input1['target'], input2['target']])
posemap = torch.cat([input1['posemap'], input2['posemap']])
noise = torch.cat((noise, noise))
self.origin = origin.cuda()
self.target = target.cuda()
self.posemap = posemap.cuda()
self.labels = labels.cuda()
self.noise = noise.cuda()
#For cross-dataset
def set_inputs(self, target_data, source_data):
#=============source data===============================
input1, input2 = source_data
labels = (input1['pid'] == input2['pid']).long()
noise = torch.randn(labels.size(0), self.opt.noise_feature_size)
# keep the same pose map for persons with the same identity
mask = labels.view(-1, 1, 1, 1).expand_as(input1['posemap'])
input2['posemap'] = input1['posemap'] * mask.float(
) + input2['posemap'] * (1 - mask.float())
mask = labels.view(-1, 1, 1, 1).expand_as(input1['target'])
input2['target'] = input1['target'] * mask.float(
) + input2['target'] * (1 - mask.float())
origin = torch.cat([input1['origin'], input2['origin']])
target = torch.cat([input1['target'], input2['target']])
posemap = torch.cat([input1['posemap'], input2['posemap']])
noise = torch.cat((noise, noise))
plabels = torch.cat((input1['pid'].long(),
input2['pid'].long())) # Used for triplet loss
self.s_origin = origin.cuda()
self.s_target = target.cuda()
self.s_posemap = posemap.cuda()
self.s_labels = labels.cuda()
self.s_noise = noise.cuda()
self.s_plabels = plabels.cuda() # Used for triplet loss
#=============target data===============================
input1, input2 = target_data
noise = torch.randn(input1['origin'].size(0),
self.opt.noise_feature_size)
origin = torch.cat([input1['origin'], input2['origin']])
target = torch.cat([input1['target'], input2['target']])
posemap = torch.cat([input1['posemap'], input2['posemap']])
noise = torch.cat((noise, noise))
self.t_origin = origin.cuda()
self.t_target = target.cuda()
self.t_posemap = posemap.cuda()
self.t_noise = noise.cuda()
#forward cross
def forward_cross(self):
#source
A = Variable(self.s_origin)
B_map = Variable(self.s_posemap)
z = Variable(self.s_noise)
bs = A.size(0)
A_id1, A_id2, self.s_id_score = self.net_E(A[:bs // 2], A[bs // 2:])
A_id = torch.cat((A_id1, A_id2))
self.s_A_id = A_id
self.s_fake = self.net_G(B_map,
A_id.view(A_id.size(0), A_id.size(1), 1, 1),
z.view(z.size(0), z.size(1), 1, 1))
#source to target
A_id1_st = A_id1[:bs // 2]
z_st = z[:bs // 2]
self.st_fake = self.tar_net_G(
B_map[:bs // 2],
A_id1_st.view(A_id1_st.size(0), A_id1_st.size(1), 1, 1),
z_st.view(z_st.size(0), z_st.size(1), 1, 1))
# self.st_fake = self.tar_net_G(B_map, A_id.view(A_id.size(0), A_id.size(1), 1, 1), z.view(z.size(0), z.size(1), 1, 1))
#target
A = Variable(self.t_origin)
B_map = Variable(self.t_posemap)
z = Variable(self.t_noise)
bs = A.size(0)
A_id1, A_id2, self.t_id_score = self.net_E(A[:bs // 2], A[bs // 2:])
A_id = torch.cat((A_id1, A_id2))
self.t_A_id = A_id
self.t_fake = self.tar_net_G(
B_map, A_id.view(A_id.size(0), A_id.size(1), 1, 1),
z.view(z.size(0), z.size(1), 1, 1))
def backward_Dp(self):
real_pose = torch.cat((Variable(self.posemap), Variable(self.target)),
dim=1)
fake_pose = torch.cat((Variable(self.posemap), self.fake.detach()),
dim=1)
pred_real = self.net_Dp(real_pose)
pred_fake = self.net_Dp(fake_pose)
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
self.loss_Dp = loss_D.data[0]
def backward_Di(self):
_, _, pred_real = self.net_Di(Variable(self.origin),
Variable(self.target))
_, _, pred_fake = self.net_Di(Variable(self.origin),
self.fake.detach())
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
self.loss_Di = loss_D.data[0]
def backward_E_MMD(self):
loss_mmd = self.criterion_MMD(self.s_A_id, self.t_A_id)
self.loss_mmd = loss_mmd.item()
loss_mmd = loss_mmd * self.opt.lambda_mmd
loss_mmd.backward(retain_graph=True)
def backward_s_G(self):
# self.s_plabels
# self.criterion_Triplet
# self.s_A_id
# self.criterion_Triplet = TripletLoss(margin=opt.tri_margin)
# self.criterion_MMD = MMDLoss(sigma_list=[1, 2, 10])
loss_tri, prec = self.criterion_Triplet(self.s_A_id, self.s_plabels)
loss_v = F.cross_entropy(self.s_id_score,
Variable(self.s_labels).view(-1))
loss_r = F.l1_loss(self.s_fake, Variable(self.s_target))
fake_1 = self.s_fake[:self.s_fake.size(0) // 2]
fake_2 = self.s_fake[self.s_fake.size(0) // 2:]
_, _, pred_fake_Di = self.net_Di(Variable(self.s_origin), self.s_fake)
pred_fake_Dp = self.net_Dp(
torch.cat((Variable(self.s_posemap), self.s_fake), dim=1))
loss_G_GAN_Di = self.criterionGAN_G(pred_fake_Di, True)
loss_G_GAN_Dp = self.criterionGAN_G(pred_fake_Dp, True)
loss_G = loss_G_GAN_Di + loss_G_GAN_Dp + \
loss_r * self.opt.lambda_recon + \
loss_v * self.opt.lambda_veri + \
loss_tri * self.opt.lambda_tri
loss_G.backward()
del self.s_id_score
self.loss_s_G = loss_G.item()
self.loss_s_v = loss_v.item()
self.loss_s_r = loss_r.item()
self.loss_s_G_GAN_Di = loss_G_GAN_Di.item()
self.loss_s_G_GAN_Dp = loss_G_GAN_Dp.item()
self.loss_s_tri = loss_tri.item()
def backward_t_G(self):
loss_r = F.l1_loss(self.t_fake, Variable(self.t_target))
fake_1 = self.t_fake[:self.t_fake.size(0) // 2]
fake_2 = self.t_fake[self.t_fake.size(0) // 2:]
_, _, pred_fake_Di = self.tar_net_Di(Variable(self.t_origin),
self.t_fake)
pred_fake_Dp = self.net_Dp(
torch.cat((Variable(self.t_posemap), self.t_fake), dim=1))
loss_G_GAN_Di = self.criterionGAN_G(pred_fake_Di, True)
loss_G_GAN_Dp = self.criterionGAN_G(pred_fake_Dp, True)
loss_G = loss_G_GAN_Di + loss_G_GAN_Dp + \
loss_r * self.opt.lambda_recon
loss_G.backward()
del self.t_id_score
self.loss_t_G = loss_G.item()
self.loss_t_r = loss_r.item()
self.loss_t_G_GAN_Di = loss_G_GAN_Di.item()
self.loss_t_G_GAN_Dp = loss_G_GAN_Dp.item()
def backward_s_Di(self):
_, _, pred_real = self.net_Di(Variable(self.s_origin),
Variable(self.s_target))
_, _, pred_fake = self.net_Di(Variable(self.s_origin),
self.s_fake.detach())
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
# self.loss_s_Di = loss_D.data[0]
self.loss_s_Di = loss_D.item()
def backward_t_Di(self):
_, _, pred_real = self.tar_net_Di(Variable(self.t_origin),
Variable(self.t_target))
_, _, pred_fake = self.tar_net_Di(Variable(self.t_origin),
self.t_fake.detach())
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
# self.loss_t_Di = loss_D.data[0]
self.loss_t_Di = loss_D.item()
def backward_cross_Dp(self):
real_pose = torch.cat(
(Variable(self.s_posemap), Variable(self.s_target)), dim=1)
fake_pose = torch.cat((Variable(self.s_posemap), self.s_fake.detach()),
dim=1)
pred_real = self.net_Dp(real_pose)
pred_fake = self.net_Dp(fake_pose)
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
# self.loss_Dp = loss_D.data[0]
self.loss_Dp = loss_D.item()
real_pose = torch.cat(
(Variable(self.t_posemap), Variable(self.t_target)), dim=1)
fake_pose = torch.cat((Variable(self.t_posemap), self.t_fake.detach()),
dim=1)
pred_real = self.net_Dp(real_pose)
pred_fake = self.net_Dp(fake_pose)
if random.choice(self.rand_list):
loss_D_real = self.criterionGAN_D(pred_fake, True)
loss_D_fake = self.criterionGAN_D(pred_real, False)
else:
loss_D_real = self.criterionGAN_D(pred_real, True)
loss_D_fake = self.criterionGAN_D(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
# self.loss_Dp += loss_D.data[0]
self.loss_Dp += loss_D.item()
def optimize_cross_parameters(self):
self.forward_cross()
self.optimizer_E.zero_grad()
self.backward_E_MMD()
self.optimizer_E.step()
self.optimizer_Di.zero_grad()
self.backward_s_Di()
self.optimizer_Di.step()
self.optimizer_Dp.zero_grad()
self.backward_cross_Dp()
self.optimizer_Dp.step()
self.optimizer_G.zero_grad()
self.backward_s_G()
self.optimizer_G.step()
self.optimizer_tar_Di.zero_grad()
self.backward_t_Di()
self.optimizer_tar_Di.step()
self.optimizer_tar_G.zero_grad()
self.backward_t_G()
self.optimizer_tar_G.step()
def get_current_errors(self):
return OrderedDict([('G_v', self.loss_v), ('G_r', self.loss_r),
('G_sp', self.loss_sp),
('G_gan_Di', self.loss_G_GAN_Di),
('G_gan_Dp', self.loss_G_GAN_Dp),
('D_i', self.loss_Di), ('D_p', self.loss_Dp)])
def get_current_cross_errors(self):
return OrderedDict([('G_tar_rec', self.loss_t_r),
('G_tar_gan_Di', self.loss_t_G_GAN_Di),
('G_tar_gan_Dp', self.loss_t_G_GAN_Dp),
('tar_D_i', self.loss_t_Di), ('D_p', self.loss_Dp),
('G_src_v', self.loss_s_v),
('G_src_rec', self.loss_s_r),
('G_src_gan_Di', self.loss_s_G_GAN_Di),
('G_src_gan_Dp', self.loss_s_G_GAN_Dp),
('src_D_i', self.loss_s_Di),
('MMD', self.loss_mmd),
('src_tri', self.loss_s_tri)])
def get_current_visuals(self):
input = util.tensor2im(self.origin)
target = util.tensor2im(self.target)
fake = util.tensor2im(self.fake)
map = self.posemap.sum(1)
map[map > 1] = 1
map = util.tensor2im(torch.unsqueeze(map, 1))
return OrderedDict([('input', input), ('posemap', map), ('fake', fake),
('target', target)])
def get_tf_visuals(self):
input = util.tensor2ims(self.origin)
target = util.tensor2ims(self.target)
fake = util.tensor2ims(self.fake)
map = self.posemap.sum(1)
map[map > 1] = 1
map = util.tensor2ims(torch.unsqueeze(map, 1))
return OrderedDict([('input', input), ('posemap', map), ('fake', fake),
('target', target)])
# self.t_origin = origin.cuda()
# self.t_target = target.cuda()
# self.t_posemap = posemap.cuda()
# self.t_labels = labels.cuda()
# self.t_noise = noise.cuda()
def get_tf_cross_visuals(self):
src_input = util.tensor2ims(self.s_origin)
src_target = util.tensor2ims(self.s_target)
src_fake = util.tensor2ims(self.s_fake.data)
src_map = self.s_posemap.sum(1)
src_map[src_map > 1] = 1
src_map = util.tensor2ims(torch.unsqueeze(src_map, 1))
tar_input = util.tensor2ims(self.t_origin)
tar_target = util.tensor2ims(self.t_target)
tar_fake = util.tensor2ims(self.t_fake.data)
tar_map = self.t_posemap.sum(1)
tar_map[tar_map > 1] = 1
tar_map = util.tensor2ims(torch.unsqueeze(tar_map, 1))
src2tgt_fake = util.tensor2ims(self.st_fake.data)
return OrderedDict([('src_input', src_input), ('src_posemap', src_map),
('src_fake', src_fake), ('src_target', src_target),
('tar_input', tar_input), ('tar_posemap', tar_map),
('tar_fake', tar_fake), ('tar_target', tar_target),
('src2tgt_fake', src2tgt_fake)])
def save(self, epoch):
self.save_network(self.net_E, 'E', epoch)
self.save_network(self.net_G, 'G', epoch)
self.save_network(self.net_Di, 'Di', epoch)
self.save_network(self.net_Dp, 'Dp', epoch)
self.save_network(self.tar_net_G, 'tar_G', epoch)
self.save_network(self.tar_net_Di, 'tar_Di', epoch)
def save_network(self, network, network_label, epoch_label):
save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
save_path = os.path.join(self.save_dir, save_filename)
torch.save(network.state_dict(), save_path)
def update_learning_rate(self):
for scheduler in self.schedulers:
scheduler.step()
lr = self.optimizers[0].param_groups[0]['lr']
def forward_test_cross(self):
#source
A = Variable(self.s_origin)
B_map = Variable(self.s_posemap)
z = Variable(self.s_noise)
bs = A.size(0)
A_id1, A_id2, self.s_id_score = self.net_E(A[:bs // 2], A[bs // 2:])
A_id = torch.cat((A_id1, A_id2))
self.s_A_id = A_id
self.s_fake = self.net_G(B_map,
A_id.view(A_id.size(0), A_id.size(1), 1, 1),
z.view(z.size(0), z.size(1), 1, 1))
#source to target
# A_id1_st = A_id1[:bs//]
# z_st = z[:bs//]
# self.st_fake = self.tar_net_G(B_map[:bs//], A_id1_st.view(A_id1_st.size(0), A_id1_st.size(1), 1, 1), z_st.view(z_st.size(0), z_st.size(1), 1, 1))
self.st_fake = self.tar_net_G(
B_map, A_id.view(A_id.size(0), A_id.size(1), 1, 1),
z.view(z.size(0), z.size(1), 1, 1))
#target
A = Variable(self.t_origin)
B_map = Variable(self.s_posemap)
z = Variable(self.t_noise)
bs = A.size(0)
A_id1, A_id2, self.t_id_score = self.net_E(A[:bs // 2], A[bs // 2:])
A_id = torch.cat((A_id1, A_id2))
self.t_A_id = A_id
self.t_fake = self.tar_net_G(
B_map, A_id.view(A_id.size(0), A_id.size(1), 1, 1),
z.view(z.size(0), z.size(1), 1, 1))
self.ts_fake = self.net_G(B_map,
A_id.view(A_id.size(0), A_id.size(1), 1, 1),
z.view(z.size(0), z.size(1), 1, 1))
def get_test_cross_visuals(self):
src_input = util.tensor2ims(self.s_origin)
src_target = util.tensor2ims(self.s_target)
src_fake = util.tensor2ims(self.s_fake.data)
src_map = self.s_posemap.sum(1)
src_map[src_map > 1] = 1
src_map = util.tensor2ims(torch.unsqueeze(src_map, 1))
tar_input = util.tensor2ims(self.t_origin)
tar_target = util.tensor2ims(self.t_target)
tar_fake = util.tensor2ims(self.t_fake.data)
tar_map = self.t_posemap.sum(1)
tar_map[tar_map > 1] = 1
tar_map = util.tensor2ims(torch.unsqueeze(tar_map, 1))
src2tgt_fake = util.tensor2ims(self.st_fake.data)
tgt2src_fake = util.tensor2ims(self.ts_fake.data)
return OrderedDict([
('src_input', src_input),
('src_posemap', src_map),
('src_fake', src_fake),
('src_target', src_target),
('tar_input', tar_input),
('tar_fake', tar_fake),
('src2tgt_fake', src2tgt_fake),
('tgt2src_fake', tgt2src_fake),
])
def _init_models(self):
self.net_G = CustomPoseGenerator(
self.opt.pose_feature_size,
2048,
self.opt.noise_feature_size,
dropout=self.opt.drop,
norm_layer=self.norm_layer,
fuse_mode=self.opt.fuse_mode,
connect_layers=self.opt.connect_layers)
e_base_model = create(self.opt.arch, cut_at_pooling=True)
self.e_embed_model = Sub_model(num_features=256,
in_features=2048,
num_classes=751,
FCN=True,
dropout=0.5)
self.net_E = ENet(e_base_model, self.e_embed_model)
di_base_model = create(self.opt.arch, cut_at_pooling=True)
di_embed_model = EltwiseSubEmbed(use_batch_norm=True,
use_classifier=True,
num_features=2048,
num_classes=1)
self.net_Di = SiameseNet(di_base_model, di_embed_model)
self.net_Dp = NLayerDiscriminator(3 + 18, norm_layer=self.norm_layer)
if self.opt.stage == 1:
init_weights(self.net_G)
init_weights(self.net_Dp)
checkpoint = torch.load(self.opt.netE_pretrain)
model_dict = self.net_E.state_dict()
checkpoint_load = {
k: v
for k, v in (checkpoint['state_dict']).items()
if k in model_dict
}
model_dict.update(checkpoint_load)
self.net_E.load_state_dict(model_dict) #state_dict
model2_dict = self.net_Di.state_dict()
checkpoint_load = {
k: v
for k, v in (checkpoint['state_dict']).items()
if k in model2_dict
}
model2_dict.update(checkpoint_load)
self.net_Di.load_state_dict(model2_dict)
elif self.opt.stage == 2:
self._load_state_dict(self.net_E, self.opt.netE_pretrain)
self._load_state_dict(self.net_G, self.opt.netG_pretrain)
self._load_state_dict(self.net_Di, self.opt.netDi_pretrain)
self._load_state_dict(self.net_Dp, self.opt.netDp_pretrain)
else:
assert ('unknown training stage')
self.net_E = torch.nn.DataParallel(self.net_E).cuda()
self.net_G = torch.nn.DataParallel(self.net_G).cuda()
self.net_Di = torch.nn.DataParallel(self.net_Di).cuda()
self.net_Dp = torch.nn.DataParallel(self.net_Dp).cuda()
