def train_an_epoch(config, base, loaders):
base.set_train()
meter = MultiItemAverageMeter()
### we assume 200 iterations as an epoch
for _ in range(200):
### load a batch data
imgs, pids, _ = loaders.train_iter.next_one()
imgs, pids = imgs.to(base.device), pids.to(base.device)
### forward
logits_list, embeddings_list = base.model(imgs)
### loss
ide_loss, avg_logits = base.compute_ide_loss(logits_list, pids)
source_acc = accuracy(avg_logits, pids, [1])[0]
### optimize
base.optimizer.zero_grad()
ide_loss.backward()
base.optimizer.step()
### recored
meter.update({'ide_loss': ide_loss, 'acc': source_acc})
return meter.get_val(), meter.get_str()
def train_stage1(config, base, loaders):
base.set_train()
source_loader = loaders.source_loader
meter = MultiItemAverageMeter()
for i in range(1032):
source_imgs, source_pids, source_cids = source_loader.next_one()
source_imgs, source_pids, source_cids = source_imgs.to(base.device), source_pids.to(base.device), \
source_cids.to(base.device)
source_features = base.feature_extractor(source_imgs)
pid_cls_score = base.identity_classifier(source_features)
pid_domain_cls_score = base.identitydomain_classifier(source_features)
pid_loss = base.source_pid_creiteron(pid_cls_score, source_pids)
pid_domain_loss = base.source_identity_domain_creiteron(
pid_domain_cls_score, source_pids)
loss = pid_loss + pid_domain_loss
base.feature_extractor_optimizer.zero_grad()
base.identity_classifier_optimizer.zero_grad()
base.identitydomain_classifier_optimizer.zero_grad()
loss.backward()
base.feature_extractor_optimizer.step()
base.identity_classifier_optimizer.step()
base.identitydomain_classifier_optimizer.step()
meter.update({
'pid_loss': pid_loss.data,
'pid_domain_loss': pid_domain_loss.data
})
return meter.get_val(), meter.get_str()
def train_an_epoch(config, base, loaders):
base.set_train()
meter = MultiItemAverageMeter()
### we assume 200 iterations as an epoch
for _ in range(200):
### load a batch data
imgs, pids, _ = loaders.train_iter.next_one()
imgs, pids = imgs.to(base.device), pids.to(base.device)
### forward
features, cls_score = base.model(imgs)
### loss
ide_loss = base.ide_creiteron(cls_score, pids)
triplet_loss = base.triplet_creiteron(features, features, features,
pids, pids, pids)
loss = ide_loss + triplet_loss
acc = accuracy(cls_score, pids, [1])[0]
### optimize
base.optimizer.zero_grad()
loss.backward()
base.optimizer.step()
### recored
meter.update({
'ide_loss': ide_loss.data,
'triplet_loss': triplet_loss.data,
'acc': acc
})
return meter.get_val(), meter.get_str()
def train_multi_view(config, base, loaders):
base.set_train()
target_loader = loaders.target_loader
meter = MultiItemAverageMeter()
for i in range(100):
target_imgs, target_pids, target_cids = IterLoader(
target_loader).next_one()
target_imgs, target_pids, target_cids = target_imgs.to(base.device), target_pids.to(base.device), \
target_cids.to(base.device)
target_features, target_bn_features, target_local_features, target_bn_local_features = \
base.feature_extractor(target_imgs)
target_graph_global_features = base.graph(target_local_features,
target_features)
target_cls_score = base.classifier2(target_bn_features)
target_local_cls_score = base.local_classifier(
target_bn_local_features)
target_graph_cls_score = base.classifier3(target_graph_global_features)
target_ide_loss = base.target_ide_creiteron(target_cls_score,
target_pids)
target_local_ide_loss = base.compute_local_pid_loss(
target_local_cls_score, target_pids)
target_graph_ide_loss = base.target_ide_creiteron(
target_graph_cls_score, target_pids)
total_loss = target_ide_loss + target_local_ide_loss + config.lambda1 * target_graph_ide_loss
base.feature_extractor_optimizer.zero_grad()
base.graph_optimizer.zero_grad()
base.classifier2_optimizer.zero_grad()
base.local_classifier_optimizer.zero_grad()
base.classifier3_optimizer.zero_grad()
total_loss.backward()
base.feature_extractor_optimizer.step()
base.graph_optimizer.step()
base.classifier2_optimizer.step()
base.local_classifier_optimizer.step()
base.classifier3_optimizer.step()
meter.update({
'target_ide_loss': target_ide_loss.data,
'target_local_ide_loss': target_local_ide_loss.data,
'target_graph_ide_loss': target_graph_ide_loss.data
})
return meter.get_val(), meter.get_str()
def train_an_epoch(config, base, loaders, epoch=None):
base.set_train()
meter = MultiItemAverageMeter()
### we assume 200 iterations as an epoch
base.lr_scheduler.step(epoch)
for _ in range(config.steps):
### load a batch data
imgs, pids, _ = loaders.train_iter.next_one()
imgs, pids = imgs.to(base.device), pids.to(base.device)
if 'res' in config.cnnbackbone:
### forward
features, cls_score = base.model(imgs)
### loss
ide_loss = base.ide_creiteron(cls_score, pids)
triplet_loss = base.triplet_creiteron(features, features, features, pids, pids, pids)
loss = ide_loss + triplet_loss
acc = accuracy(cls_score, pids, [1])[0]
### optimize
base.optimizer.zero_grad()
loss.backward()
base.optimizer.step()
### recored
meter.update({'ide_loss': ide_loss.data, 'triplet_loss': triplet_loss.data, 'acc': acc})
elif config.cnnbackbone == 'osnetain':
### forward
if epoch < 10:
cls_score = base.model(imgs, fixed_cnn=True)
else:
cls_score = base.model(imgs, fixed_cnn=False)
### loss
ide_loss = base.ide_creiteron(cls_score, pids)
acc = accuracy(cls_score, pids, [1])[0]
### optimize
base.optimizer.zero_grad()
ide_loss.backward()
base.optimizer.step()
### recored
meter.update({'ide_loss': ide_loss.data, 'acc': acc})
return meter.get_val(), meter.get_str()
def train_an_epoch(config,
iter_n,
loaders,
base,
current_epoch,
train_gan,
train_reid,
self_training,
train_adaptation,
optimize_sl_enc,
trg_labeled_loader=None):
# set train mode
base.set_train()
base.lr_scheduler_step(current_epoch)
meter = MultiItemAverageMeter()
# train loop
for _ in range(5):
# zero grad
base.sl_enc_optimizer.zero_grad()
base.gen_optimizer.zero_grad()
base.dis_optimizer.zero_grad()
base.il_enc_optimizer.zero_grad()
#
results = {}
# gan
if train_gan:
gen_loss_without_feature, gen_loss_gan_feature, dis_loss, image_list = train_gan_an_iter(
config, loaders, base)
gen_loss_gan_feature.backward(retain_graph=True)
base.sl_enc_optimizer.zero_grad()
gen_loss_without_feature.backward()
base.dis_optimizer.zero_grad()
dis_loss.backward()
results['gen_loss_gan_feature'] = gen_loss_gan_feature.item()
results[
'gen_loss_without_feature'] = gen_loss_without_feature.item()
results['dis_loss'] = dis_loss.item()
if train_reid:
cls_loss, triplet_loss, acc = train_reid_an_iter(
config, loaders, base, True)
reid_loss = cls_loss + triplet_loss
reid_loss.backward()
results['cls_loss'] = cls_loss.item()
results['triplet_loss'] = triplet_loss.item()
results['acc'] = acc
if self_training:
assert trg_labeled_loader is not None, "self training requires labeled target loader"
cls_loss, triplet_loss, acc = train_reid_an_iter(
config, loaders, base, False, trg_labeled_loader)
reid_loss = cls_loss + triplet_loss
reid_loss.backward()
results['cls_loss'] = cls_loss.item()
results['triplet_loss'] = triplet_loss.item()
results['acc'] = acc
if train_adaptation:
gen_loss_real, gen_loss_fake, gen_loss_cycle, dis_loss_real, dis_loss_fake, dis_loss_cycle = \
train_adaptation_an_iter(config,loaders,base)
gen_loss = gen_loss_real + gen_loss_fake + gen_loss_cycle
dis_loss = dis_loss_real + dis_loss_fake + dis_loss_cycle
base.dom_gen_optimizer.zero_grad()
gen_loss.backward()
base.dom_dis_optimizer.zero_grad()
dis_loss.backward()
results['dom_gen_loss'] = gen_loss.item()
results['dom_dis_loss'] = dis_loss.item()
# optimize
if optimize_sl_enc:
base.sl_enc_optimizer.step()
if train_gan:
base.gen_optimizer.step()
base.dis_optimizer.step()
if train_reid or self_training:
base.il_enc_optimizer.step()
if train_adaptation:
base.dom_gen_optimizer.step()
base.dom_dis_optimizer.step()
# record
print("a new iter")
meter.update(results)
return meter.get_str()
def train_stage2(config, base, loaders):
base.set_train()
source_loader = loaders.source_loader
target_loader = loaders.target_loader
meter = MultiItemAverageMeter()
for i in range(808):
source_imgs, source_pids, source_cids = source_loader.next_one()
source_imgs, source_pids, source_cids = source_imgs.to(base.device), source_pids.to(base.device), \
source_cids.to(base.device)
target_imgs, target_pids, target_cids = target_loader.next_one()
target_imgs, target_pids, target_cids = target_imgs.to(base.device), target_pids.to(base.device), \
target_cids.to(base.device)
source_features = base.feature_extractor(source_imgs)
target_features = base.feature_extractor(target_imgs)
source_pid_cls_score = base.identity_classifier(source_features)
source_cid_cls_score = base.camera_classifier(source_features)
multi_view_features = MultiViewReasoning().__call__(
source_features, source_features, source_pids, source_pids,
source_pid_cls_score)
multi_view_cls_score = base.identity_classifier(multi_view_features)
source_pid_domain_cls_score = base.identitydomain_classifier(
source_features)
target_cid_cls_score = base.camera_classifier(target_features)
target_pid_domain_cls_score = base.identitydomain_classifier(
target_features)
source_pid_loss = base.source_pid_creiteron(source_pid_cls_score,
source_pids)
multi_view_pid_loss = base.source_pid_creiteron(
multi_view_cls_score, source_pids)
source_pid_domain_loss = base.source_identity_domain_creiteron(
source_pid_domain_cls_score, source_pids)
target_pid_domain_loss = base.target_identity_domain_creiteron(
target_pid_domain_cls_score)
source_dispency_loss = base.dispency_creiteron(
source_pid_cls_score, source_pid_domain_cls_score)
cid_classifier_loss = base.camera_classifier_creiteron(
source_cid_cls_score, target_cid_cls_score, source_cids,
target_cids)
loss = source_pid_loss + multi_view_pid_loss + source_pid_domain_loss + config.lambda1 * target_pid_domain_loss\
- config.lambda2 * source_dispency_loss + config.lambda3 * cid_classifier_loss
base.feature_extractor_optimizer.zero_grad()
base.identity_classifier_optimizer.zero_grad()
base.identitydomain_classifier_optimizer.zero_grad()
base.camera_classifier_optimizer.zero_grad()
loss.backward()
base.identity_classifier_optimizer.step()
base.identitydomain_classifier_optimizer.step()
base.camera_classifier_optimizer.step()
source_features = base.feature_extractor(source_imgs)
target_features = base.feature_extractor(target_imgs)
source_pid_cls_score = base.identity_classifier(source_features)
source_cid_cls_score = base.camera_classifier(source_features)
multi_view_features = MultiViewReasoning().__call__(
source_features, source_features, source_pids, source_pids,
source_pid_cls_score)
multi_view_cls_score = base.identity_classifier(multi_view_features)
source_pid_domain_cls_score = base.identitydomain_classifier(
source_features)
target_cid_cls_score = base.camera_classifier(target_features)
target_pid_domain_cls_score = base.identitydomain_classifier(
target_features)
source_pid_loss = base.source_pid_creiteron(source_pid_cls_score,
source_pids)
multi_view_pid_loss = base.source_pid_creiteron(
multi_view_cls_score, source_pids)
single_multi_view_feature_loss = base.single_multi_creiteron(
source_features, multi_view_features)
source_pid_domain_loss = base.source_identity_domain_creiteron(
source_pid_domain_cls_score, source_pids)
target_pid_domain_loss = base.target_identity_domain_creiteron(
target_pid_domain_cls_score)
source_dispency_loss = base.dispency_creiteron(
source_pid_cls_score, source_pid_domain_cls_score)
cid_feature_extractor_loss = base.camera_feature_extractor_creiteron(
source_cid_cls_score, target_cid_cls_score)
loss = source_pid_loss + multi_view_pid_loss + source_pid_domain_loss + \
config.beta * single_multi_view_feature_loss + config.lambda1 * target_pid_domain_loss \
+ config.lambda2 * source_dispency_loss + config.lambda3 * cid_feature_extractor_loss
base.feature_extractor_optimizer.zero_grad()
base.identity_classifier_optimizer.zero_grad()
base.identitydomain_classifier_optimizer.zero_grad()
base.camera_classifier_optimizer.zero_grad()
loss.backward()
base.feature_extractor_optimizer.step()
meter.update({
'source_pid_loss':
source_pid_loss.data,
'multi_view_pid_loss':
multi_view_pid_loss.data,
'single_multi_view_feature_loss':
single_multi_view_feature_loss.data,
'source_pid_domain_loss':
source_pid_domain_loss.data,
'target_pid_domain_loss':
target_pid_domain_loss.data,
'source_dispency_loss':
source_dispency_loss.data,
'cid_classifier_loss':
cid_classifier_loss.data,
'cid_feature_extractor_loss':
cid_feature_extractor_loss
})
return meter.get_val(), meter.get_str()
def train_meta_learning(base, loaders):
base.set_train()
source_loader = loaders.source_loader
target_loader = loaders.target_loader
meter = MultiItemAverageMeter()
for i in range(400):
source_imgs, source_pids, source_cids = IterLoader(
source_loader).next_one()
source_imgs, source_pids, source_cids = source_imgs.to(base.device), source_pids.to(base.device), \
source_cids.to(base.device)
source_features, source_bn_features, source_local_features, source_bn_local_features = base.feature_extractor(
source_imgs)
source_cls_score = base.classifier1(source_bn_features)
source_ide_loss = base.source_ide_creiteron(source_cls_score,
source_pids)
grad = torch.autograd.grad(
source_ide_loss,
nn.ModuleList([base.feature_extractor,
base.classifier1]).parameters(),
retain_graph=True)
fast_weights = list(
map(
lambda p: p[1] - 0.0005 * p[0],
zip(
grad,
nn.ModuleList([base.feature_extractor,
base.classifier1]).parameters())))
feature_extractor_param_keys = []
for k, v in base.feature_extractor.state_dict().items():
if k.split('.')[-1] == 'weight' or k.split('.')[-1] == 'bias':
feature_extractor_param_keys.append(k)
feature_extractor_param_dict = OrderedDict()
for i in range(len(fast_weights) - 1):
feature_extractor_param_dict[
feature_extractor_param_keys[i]] = fast_weights[i]
base.feature_extractor.state_dict().update(
feature_extractor_param_dict)
target_imgs, target_pids, target_cids = IterLoader(
target_loader).next_one()
target_imgs, target_pids, target_cids = target_imgs.to(base.device), target_pids.to(base.device), \
target_cids.to(base.device)
target_features, target_bn_features, _, _ = base.feature_extractor(
target_imgs)
target_cls_score = base.classifier2(target_bn_features)
target_ide_loss = base.target_ide_creiteron(target_cls_score,
target_pids)
total_loss = source_ide_loss + target_ide_loss
base.feature_extractor_optimizer.zero_grad()
base.classifier1_optimizer.zero_grad()
base.classifier2_optimizer.zero_grad()
total_loss.backward()
base.feature_extractor_optimizer.step()
base.classifier1_optimizer.step()
base.classifier2_optimizer.step()
meter.update({
'source_ide_loss': source_ide_loss.data,
'target_ide_loss': target_ide_loss.data
})
return meter.get_val(), meter.get_str()
def train_an_epoch(config, loaders, base, current_epoch, train_gan, train_reid,
train_pixel, optimize_sl_enc):
# set train mode
base.set_train()
base.lr_scheduler_step(current_epoch)
meter = MultiItemAverageMeter()
# train loop
for _ in range(200):
# zero grad
base.sl_enc_optimizer.zero_grad()
base.gen_optimizer.zero_grad()
base.dis_optimizer.zero_grad()
base.il_enc_optimizer.zero_grad()
#
results = {}
# gan
if train_gan:
gen_loss_without_feature, gen_loss_gan_feature, dis_loss, image_list = train_gan_an_iter(
config, loaders, base)
gen_loss_gan_feature.backward(retain_graph=True)
base.sl_enc_optimizer.zero_grad()
gen_loss_without_feature.backward()
base.dis_optimizer.zero_grad()
dis_loss.backward()
results['gen_loss_gan_feature'] = gen_loss_gan_feature.data
results['gen_loss_without_feature'] = gen_loss_without_feature.data
results['dis_loss'] = dis_loss.data
# pixel
if train_pixel:
assert train_gan
pixel_loss = train_pixel_an_iter(config, loaders, base, image_list)
(config.weight_pixel_loss * pixel_loss).backward()
results['pixel_loss'] = pixel_loss.data
# reid
if train_reid:
cls_loss, triplet_loss, acc = train_reid_an_iter(
config, loaders, base)
reid_loss = cls_loss + triplet_loss
reid_loss.backward()
results['cls_loss'] = cls_loss.data
results['triplet_loss'] = triplet_loss.data
results['acc'] = acc
# optimize
if optimize_sl_enc:
base.sl_enc_optimizer.step()
if train_gan:
base.gen_optimizer.step()
base.dis_optimizer.step()
if train_reid:
base.il_enc_optimizer.step()
# record
meter.update(results)
return meter.get_str()