def train(cfg, cfg_hr):
# prepare dataset
train_loader, val_loader, num_query, num_classes = make_data_loader(cfg)
# prepare model
model = build_model(cfg, cfg_hr, num_classes)
model = nn.DataParallel(model)
if cfg.MODEL.IF_WITH_CENTER == 'no':
print('Train without center loss, the loss type is', cfg.MODEL.METRIC_LOSS_TYPE)
optimizer = make_optimizer(cfg, model)
print(cfg.SOLVER.MARGIN)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
loss_func = make_loss(cfg, num_classes) # modified by gu
arguments = {}
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
scheduler,
loss_func,
num_query
)
elif cfg.MODEL.IF_WITH_CENTER == 'yes':
print('Train with center loss, the loss type is', cfg.MODEL.METRIC_LOSS_TYPE)
loss_func, center_criterion = make_loss_with_center(cfg, num_classes) # modified by gu
optimizer, optimizer_center = make_optimizer_with_center(cfg, model, center_criterion)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
arguments = {}
do_train_with_center(
cfg,
model,
center_criterion,
train_loader,
val_loader,
optimizer,
optimizer_center,
scheduler,
loss_func,
num_query
)
else:
print("Unsupported value for cfg.MODEL.IF_WITH_CENTER {}, only support yes or no!\n".format(cfg.MODEL.IF_WITH_CENTER))
def train(cfg):
# prepare dataset
train_loader, val_loader, num_query, num_classes = make_data_loader(cfg)
# prepare model
model = build_model(cfg, num_classes)
if cfg.MODEL.IF_WITH_CENTER == 'no':
print('Train without center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
optimizer = make_optimizer(cfg, model)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
loss_func = make_loss(cfg, num_classes) # modified by gu
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'self':
start_epoch = eval(
cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')
[-1])
print('Start epoch:', start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer')
print('Path to the checkpoint of optimizer:', path_to_optimizer)
model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
optimizer.load_state_dict(torch.load(path_to_optimizer))
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD,
start_epoch)
elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
else:
print(
'Only support pretrain_choice for imagenet and self, but got {}'
.format(cfg.MODEL.PRETRAIN_CHOICE))
arguments = {}
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch # add for using self trained model
)
elif cfg.MODEL.IF_WITH_CENTER == 'yes':
print('Train with center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
loss_func, center_criterion = make_loss_with_center(
cfg, num_classes) # modified by gu
optimizer, optimizer_center = make_optimizer_with_center(
cfg, model, center_criterion)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
arguments = {}
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'self':
start_epoch = eval(
cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')
[-1])
print('Start epoch:', start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer')
print('Path to the checkpoint of optimizer:', path_to_optimizer)
path_to_center_param = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'center_param')
print('Path to the checkpoint of center_param:',
path_to_center_param)
path_to_optimizer_center = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer_center')
print('Path to the checkpoint of optimizer_center:',
path_to_optimizer_center)
model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
optimizer.load_state_dict(torch.load(path_to_optimizer))
center_criterion.load_state_dict(torch.load(path_to_center_param))
optimizer_center.load_state_dict(
torch.load(path_to_optimizer_center))
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD,
start_epoch)
elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
else:
print(
'Only support pretrain_choice for imagenet and self, but got {}'
.format(cfg.MODEL.PRETRAIN_CHOICE))
do_train_with_center(
cfg,
model,
center_criterion,
train_loader,
val_loader,
optimizer,
optimizer_center,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch # add for using self trained model
)
else:
print(
"Unsupported value for cfg.MODEL.IF_WITH_CENTER {}, only support yes or no!\n"
.format(cfg.MODEL.IF_WITH_CENTER))
def train(cfg):
# prepare dataset
train_loader, val_loader, num_query, num_classes, clustering_loader = make_data_loader(
cfg)
# prepare model
model = build_model(cfg, num_classes)
if cfg.MODEL.IF_WITH_CENTER == 'on':
loss_func, center_criterion_part, center_criterion_global, center_criterion_fore = make_loss_with_center(
cfg, num_classes)
optimizer, optimizer_center = make_optimizer_with_center(
cfg, model, center_criterion_part, center_criterion_global,
center_criterion_fore)
else:
loss_func = make_loss(cfg, num_classes)
optimizer = make_optimizer(cfg, model)
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
else:
print('Only support pretrain_choice for imagenet, but got {}'.format(
cfg.MODEL.PRETRAIN_CHOICE))
if cfg.MODEL.IF_WITH_CENTER == 'on':
do_train_with_center(
cfg,
model,
center_criterion_part,
center_criterion_global,
center_criterion_fore,
train_loader,
val_loader,
optimizer,
optimizer_center,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch, # add for using self trained model
clustering_loader)
else:
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch, # add for using self trained model
clustering_loader)
def train(cfg):
# prepare dataset
# train_loader, val_loader, num_query, num_classes = make_data_loader(cfg)
train_loader, val_loader, num_query, num_classes = make_data_loader_train(
cfg)
# prepare model
if 'prw' in cfg.DATASETS.NAMES:
num_classes = 483
elif "market1501" in cfg.DATASETS.NAMES:
num_classes = 751
elif "duke" in cfg.DATASETS.NAMES:
num_classes = 702
elif "cuhk" in cfg.DATASETS.NAMES:
num_classes = 5532
model = build_model(cfg, num_classes)
if cfg.MODEL.IF_WITH_CENTER == 'no':
print('Train without center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
optimizer = make_optimizer(cfg, model)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
loss_func = make_loss(cfg, num_classes) # modified by gu
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'self':
# start_epoch = eval(cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')[-1])
start_epoch = 0
print('Start epoch:', start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer')
print('Path to the checkpoint of optimizer:', path_to_optimizer)
pretrained_dic = torch.load(cfg.MODEL.PRETRAIN_PATH).state_dict()
model_dict = model.state_dict()
model_dict.update(pretrained_dic)
model.load_state_dict(model_dict)
if cfg.MODEL.WHOLE_MODEL_TRAIN == "no":
for name, value in model.named_parameters():
if "Query_Guided_Attention" not in name and "non_local" not in name and "classifier_attention" not in name:
value.requires_grad = False
optimizer = make_optimizer(cfg, model)
# else:
# cfg.SOLVER.BASE_LR = 0.0000035
# optimizer.load_state_dict(torch.load(path_to_optimizer))
# #####
# for state in optimizer.state.values():
# for k, v in state.items():
# if isinstance(v, torch.Tensor):
# state[k] = v.cuda()
# #####
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
else:
print(
'Only support pretrain_choice for imagenet and self, but got {}'
.format(cfg.MODEL.PRETRAIN_CHOICE))
arguments = {}
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch # add for using self trained model
)
elif cfg.MODEL.IF_WITH_CENTER == 'yes':
print('Train with center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
loss_func, center_criterion = make_loss_with_center(
cfg, num_classes) # modified by gu
optimizer, optimizer_center = make_optimizer_with_center(
cfg, model, center_criterion)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
arguments = {}
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'self':
start_epoch = eval(
cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')
[-1])
print('Start epoch:', start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer')
print('Path to the checkpoint of optimizer:', path_to_optimizer)
path_to_center_param = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'center_param')
print('Path to the checkpoint of center_param:',
path_to_center_param)
path_to_optimizer_center = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer_center')
print('Path to the checkpoint of optimizer_center:',
path_to_optimizer_center)
model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
optimizer.load_state_dict(torch.load(path_to_optimizer))
#####
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
#####
center_criterion.load_state_dict(torch.load(path_to_center_param))
optimizer_center.load_state_dict(
torch.load(path_to_optimizer_center))
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD,
start_epoch)
elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
else:
print(
'Only support pretrain_choice for imagenet and self, but got {}'
.format(cfg.MODEL.PRETRAIN_CHOICE))
do_train_with_center(
cfg,
model,
center_criterion,
train_loader,
val_loader,
optimizer,
optimizer_center,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch # add for using self trained model
)
else:
print(
"Unsupported value for cfg.MODEL.IF_WITH_CENTER {}, only support yes or no!\n"
.format(cfg.MODEL.IF_WITH_CENTER))
def train(cfg):
# prepare dataset
train_loader, val_loader, num_query, num_classes = make_data_loader(cfg)
# prepare model
model = build_model(cfg, num_classes)
if cfg.MODEL.IF_WITH_CENTER == "no":
print("Train without center loss, the loss type is", cfg.MODEL.METRIC_LOSS_TYPE)
optimizer = make_optimizer(cfg, model)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
loss_func = make_loss(cfg, num_classes) # modified by gu
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == "self":
start_epoch = eval(
cfg.MODEL.PRETRAIN_PATH.split("/")[-1].split(".")[0].split("_")[-1]
)
print("Start epoch:", start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace("model", "optimizer")
print("Path to the checkpoint of optimizer:", path_to_optimizer)
model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
optimizer.load_state_dict(torch.load(path_to_optimizer))
scheduler = WarmupMultiStepLR(
optimizer,
cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD,
cfg.SOLVER.MODE,
cfg.SOLVER.MAX_EPOCHS,
start_epoch
)
else:
start_epoch = 0
scheduler = WarmupMultiStepLR(
optimizer,
cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD,
cfg.SOLVER.MODE,
cfg.SOLVER.MAX_EPOCHS
)
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch, # add for using self trained model
)
elif cfg.MODEL.IF_WITH_CENTER == "yes":
print("Train with center loss, the loss type is", cfg.MODEL.METRIC_LOSS_TYPE)
loss_func, center_criterion = make_loss_with_center(
cfg, num_classes
) # modified by gu
optimizer, optimizer_center = make_optimizer_with_center(
cfg, model, center_criterion
)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == "self":
start_epoch = eval(
cfg.MODEL.PRETRAIN_PATH.split("/")[-1].split(".")[0].split("_")[-1]
)
print("Start epoch:", start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace("model", "optimizer")
print("Path to the checkpoint of optimizer:", path_to_optimizer)
path_to_center_param = cfg.MODEL.PRETRAIN_PATH.replace(
"model", "center_param"
)
print("Path to the checkpoint of center_param:", path_to_center_param)
path_to_optimizer_center = cfg.MODEL.PRETRAIN_PATH.replace(
"model", "optimizer_center"
)
print(
"Path to the checkpoint of optimizer_center:", path_to_optimizer_center
)
model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
optimizer.load_state_dict(torch.load(path_to_optimizer))
center_criterion.load_state_dict(torch.load(path_to_center_param))
optimizer_center.load_state_dict(torch.load(path_to_optimizer_center))
scheduler = WarmupMultiStepLR(
optimizer,
cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD,
cfg.SOLVER.MODE,
cfg.SOLVER.MAX_EPOCHS,
start_epoch,
)
else:
start_epoch = 0
scheduler = WarmupMultiStepLR(
optimizer,
cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD,
cfg.SOLVER.MODE,
cfg.SOLVER.MAX_EPOCHS,
)
do_train_with_center(
cfg,
model,
center_criterion,
train_loader,
val_loader,
optimizer,
optimizer_center,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch, # add for using self trained model
)
else:
print(
"Unsupported value for cfg.MODEL.IF_WITH_CENTER {}, only support yes or no!\n".format(
cfg.MODEL.IF_WITH_CENTER
)
)
def train(cfg):
# prepare dataset
train_loader, val_loader, num_query, num_classes = make_data_loader(cfg)
target_train_loader, target_val_loader = make_data_loader2(cfg)
# prepare model
model = build_model(cfg, num_classes)
print('Train with center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
loss_func, center_criterion = make_loss_with_center(
cfg, num_classes) # modified by gu
cluster_num_classes = cfg.INPUT.CLUSTER_NUMBER
loss_cluster_func, cluster_criterion = make_loss_with_cluster(
cfg, cluster_num_classes)
optimizer, optimizer_center, optimizer_cluster = make_optimizer_with_center2(
cfg, model, center_criterion, cluster_criterion)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
arguments = {}
"""center_criterion = torch.load('D:\download\chromedownload\\resnet50_center_param_30 (1).pth')
center_criterion.centers = nn.Parameter(center_criterion.centers)
for param in center_criterion.parameters():
param.grad.data *= (1. / center_loss_weight)"""
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'self':
start_epoch = eval(
cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')
[-1])
print('Start epoch:', start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer')
print('Path to the checkpoint of optimizer:', path_to_optimizer)
path_to_center_param = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'center_param')
print('Path to the checkpoint of center_param:', path_to_center_param)
path_to_optimizer_center = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer_center')
print('Path to the checkpoint of optimizer_center:',
path_to_optimizer_center)
model = torch.load(cfg.MODEL.PRETRAIN_PATH)
optimizer = torch.load(path_to_optimizer)
center_criterion = torch.load(path_to_center_param)
optimizer_center = torch.load(path_to_optimizer_center)
###
if start_epoch >= cfg.SOLVER.MY_START_EPOCH:
path_to_cluster_param = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'cluster_param')
print('Path to the checkpoint of cluster_param:',
path_to_cluster_param)
path_to_optimizer_cluster = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer_cluster')
print('Path to the checkpoint of optimizer_cluster:',
path_to_optimizer_cluster)
cluster_criterion = torch.load(path_to_cluster_param)
optimizer_cluster = torch.load(path_to_optimizer_cluster)
###
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD, start_epoch)
elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
else:
print('Only support pretrain_choice for imagenet and self, but got {}'.
format(cfg.MODEL.PRETRAIN_CHOICE))
#trainr2.py
do_train_with_center2(
cfg,
model,
center_criterion,
cluster_criterion, #
train_loader,
val_loader,
target_train_loader, #
target_val_loader, #
optimizer,
optimizer_center,
optimizer_cluster, #
scheduler, # modify for using self trained model
loss_func,
loss_cluster_func, #
num_query,
start_epoch, # add for using self trained model
cfg.SOLVER.MY_START_EPOCH #开始聚类损失的EPOCH
)
