def train(cfg, logger):
model = build_model(cfg)
device = cfg.MODEL.DEVICE
optimizer, lr_schedule = make_optimizer(cfg, model)
metric_fc = None
loss_fn = get_loss_fn(cfg, logger)
logger.info("----------------------------------------------")
train_loader = make_data_loader(cfg, is_train=True)
val_loader = make_data_loader(cfg, is_train=False)
loss_fn2 = torch.nn.MSELoss()
do_train(
cfg,
model,
metric_fc,
train_loader,
val_loader,
optimizer,
lr_schedule,
loss_fn,
loss_fn2,
logger,
)
def main():
# 读取配置文件
with open('config/default.yml') as fin:
config = yaml.load(fin, Loader=yaml.SafeLoader)
# 生成 train 和 valid 数据集
train_config = config['dataset']['train']
train_df = pd.read_csv(train_config['data_path'], sep='\t')
train_df.sample(frac=1)
train, valid = train_test_split(train_df,
test_size=config['train_valid_split'])
train_dataset = build_dataloader(train, train_config, device=device)
valid_dataset = build_dataloader(valid, train_config, device=device)
# 建立模型
model_config = config['model']
model = BertClassifier(model_config)
model.to(device)
optimizer = build_optimizer(model, config['optimizer'])
# 计算训练步数
num_train_steps = int(
len(train_dataset) / train_dataset.batch_size * config['num_epochs'])
num_warmup_steps = int(num_train_steps *
config['optimizer']['warmup_proportion'])
scheduler = build_scheduler(optimizer, num_train_steps, num_warmup_steps)
# 训练
trainer.do_train(model,
train_loader=train_dataset,
valid_loader=valid_dataset,
optimizer=optimizer,
scheduler=scheduler,
cfg=config)
def train(cfg):
logger = setup_logger(name='Train', level=cfg.LOGGER.LEVEL)
logger.info(cfg)
model = build_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
#model.to(cuda_device = 'cuda:9')
criterion = build_loss(cfg)
optimizer = build_optimizer(cfg, model)
scheduler = build_lr_scheduler(cfg, optimizer)
train_loader = build_data(cfg, is_train=True)
val_loader = build_data(cfg, is_train=False)
logger.info(train_loader.dataset)
logger.info(val_loader.dataset)
arguments = dict()
arguments["iteration"] = 0
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
checkpointer = Checkpointer(model, optimizer, scheduler, cfg.SAVE_DIR)
do_train(cfg, model, train_loader, val_loader, optimizer, scheduler,
criterion, checkpointer, device, checkpoint_period, arguments,
logger)
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.SOLVER.FINETUNE:
model.load_state_dict(torch.load(cfg.TEST.WEIGHT).module.state_dict())
model = nn.DataParallel(model)
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)
# scheduler = WarmupStepLR(optimizer,3, 9, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
loss_func = make_loss(cfg)
arguments = {}
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
scheduler,
loss_func,
num_query
)
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,
local_rank,
distributed,
logger=None,
tblogger=None,
transfer_weight=False,
change_lr=False):
device = torch.device('cuda')
# create model
logger.info('Creating model "{}"'.format(cfg.MODEL.ARCHITECTURE))
model = build_model(cfg).to(device)
criterion = torch.nn.CrossEntropyLoss(ignore_index=255).to(device)
optimizer = make_optimizer(cfg, model)
# model, optimizer = apex.amp.initialize(model, optimizer, opt_level='O2')
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
# model = apex.parallel.DistributedDataParallel(model)
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
broadcast_buffers=True,
)
save_to_disk = get_rank() == 0
# checkpoint
arguments = {}
arguments['iteration'] = 0
arguments['best_iou'] = 0
checkpointer = Checkpointer(model, optimizer, scheduler, cfg.LOGS.DIR,
save_to_disk, logger)
extra_checkpoint_data = checkpointer.load(
f=cfg.MODEL.WEIGHT,
model_weight_only=transfer_weight,
change_scheduler=change_lr)
arguments.update(extra_checkpoint_data)
# data_loader
logger.info('Loading dataset "{}"'.format(cfg.DATASETS.TRAIN))
data_loader = make_data_loader(cfg, 'train', distributed)
data_loader_val = make_data_loader(cfg, 'val', distributed)
do_train(cfg,
model=model,
data_loader=data_loader,
optimizer=optimizer,
scheduler=scheduler,
criterion=criterion,
checkpointer=checkpointer,
device=device,
arguments=arguments,
tblogger=tblogger,
data_loader_val=data_loader_val,
distributed=distributed)
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)
total = sum([param.nelement() for param in model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))
if cfg.MODEL.METRIC_LOSS_TYPE == 'triplet':
print('Train without center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
optimizer = make_optimizer(cfg, model)
loss_func = make_loss(cfg, num_classes)
# 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))
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
)
def train(cfg, local_rank):
# prepare dataset
tng_loader, val_loader, num_classes, num_query = get_dataloader(cfg)
do_train(
cfg,
local_rank,
tng_loader,
val_loader,
num_classes,
num_query,
)
def train(cfg):
model = build_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = build_optimizer(cfg, model)
scheduler = build_lr_scheduler(cfg, optimizer)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = cfg.SAVE_TO_DISK
checkpointer = Checkpointer(model, optimizer, scheduler, output_dir,
save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = build_data_loader(
cfg,
is_train=True,
start_iter=arguments["iteration"],
)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = build_data_loader(cfg,
is_train=False,
is_for_period=True)
else:
data_loader_val = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
)
return model
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)
print('Train without center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
optimizer = make_optimizer(cfg, model)
loss_func = make_loss(cfg, num_classes)
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))
if cfg.MODEL.DEVICE == "cuda":
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, 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:
start_epoch = 0
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,
loss_func, num_query, start_epoch)
def train(cfg):
model = build_model(cfg)
data_rows_num = get_data_rows_num(cfg)
k_fold = KFold(n_splits=10, shuffle=True, random_state=1)
n_fold = 1
for train_idx, val_idx in k_fold.split(
[i for i in range(1, data_rows_num)]):
optimizer = make_optimizer(cfg, model)
train_loader = make_data_loader(cfg, train_idx, is_train=True)
val_loader = make_data_loader(cfg, val_idx, is_train=True)
loss_functions = [bce_with_logits_loss, bce_with_logits_loss]
do_train(cfg, model, train_loader, val_loader, optimizer,
loss_functions, n_fold)
n_fold += 1
pass
def train(cfg):
model = build_model(cfg)
device = cfg.MODEL.DEVICE
optimizer = make_optimizer(cfg, model)
train_loader = make_data_loader(cfg, csv = cfg.DATASETS.TRAIN, is_train=True)
do_train(
cfg,
model,
train_loader,
optimizer,
losses = [F.mse_loss, F.nll_loss],
)
def train(cfg, saver):
dataset_name = [cfg.DATASET.NAME]
if cfg.JOINT.IF_ON:
for name in cfg.JOINT.DATASET_NAME:
dataset_name.append(name)
train_loader, num_classes = make_train_data_loader_with_expand(
cfg, dataset_name)
else:
train_loader, num_classes = make_train_data_loader(
cfg, dataset_name[0])
valid = make_multi_valid_data_loader(cfg, dataset_name)
train_component = TrainComponent(cfg, num_classes)
do_train(cfg, train_loader, valid, train_component, saver)
def train(cfg):
# prepare dataset
train_loader, val_loader, num_query, num_classes, dataset = make_data_loader(cfg)
# prepare model
model = build_model(cfg, num_classes)
print('Train without center loss, the loss type is', cfg['MODEL.METRIC_LOSS_TYPE'])
optimizer = make_optimizer(cfg, model)
loss_func = make_loss(cfg, num_classes, model.in_planes) # modified by gu
# Add for using self trained model
if cfg['MODEL.PRETRAIN_CHOICE'] == 'continue':
#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)
model.load_param(cfg['MODEL.PRETRAIN_PATH'])
#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'])
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'])
elif cfg['MODEL.PRETRAIN_CHOICE'] == 'self' or cfg['MODEL.PRETRAIN_CHOICE'] == 'self-no-head':
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(
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
dataset
)
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)
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)
arguments = {}
do_train(cfg, model, train_loader, val_loader, optimizer, scheduler,
loss_func, num_query)
def train(cfg, saver):
dataset_name = [cfg.DATASET.NAME]
if cfg.JOINT.IF_ON:
for name in cfg.JOINT.DATASET_NAME:
dataset_name.append(name)
train_loader, num_classes = make_train_data_loader_with_expand(
cfg, dataset_name)
else:
train_loader, num_classes = make_train_data_loader(
cfg, dataset_name[0])
valid_dict = make_multi_valid_data_loader(cfg, dataset_name)
train_component = TrainComponent(cfg, num_classes)
saver.checkpoint_params['model'] = train_component.model
do_train(cfg, train_loader, valid_dict, train_component, saver)
def train(cfg):
model = build_fcn_model(cfg)
optimizer = make_optimizer(cfg, model)
arguments = {}
data_loader = make_data_loader(cfg, is_train=True)
val_loader = make_data_loader(cfg, is_train=False)
do_train(
cfg,
model,
data_loader,
val_loader,
optimizer,
cross_entropy2d,
)
def cluster_train(self, train_loader, num_classes):
tr_comp = TrainComponent(self.cfg, num_classes)
load_weight(self.model, tr_comp.model)
del self.model
if self.device == 'cuda':
torch.cuda.empty_cache()
do_train(self.cfg,
tr_comp.model,
train_loader,
self.val_loader,
tr_comp.optimizer,
tr_comp.scheduler,
tr_comp.loss_function,
self.num_query,
self.saver,
center_criterion=tr_comp.loss_center,
optimizer_center=tr_comp.optimizer_center)
self.model = tr_comp.model
def train(cfg):
# prepare dataset 训练集，验证集，验证集大小，行人类别数量
train_loader, val_loader, num_query, num_classes = get_data(cfg)
# prepare model
model = Baseline(num_classes, cfg.MODEL.LAST_STRIDE,
cfg.MODEL.PRETRAIN_PATH, cfg.MODEL.NAME,
cfg.MODEL.PRETRAIN_CHOICE, cfg.MODEL.BREACH)
print('Train with the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE) # 损失函数为ranked_loss
optimizer = make_optimizer(cfg, model)
loss_func = make_loss(cfg, num_classes)
# 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))
do_train(cfg, model, train_loader, val_loader, optimizer, scheduler,
loss_func, num_query, start_epoch)
def train(cfg):
model = build_model(cfg).to(cfg.MODEL.DEVICE)
device = cfg.MODEL.DEVICE
optimizer = make_optimizer(cfg, model)
scheduler = None
arguments = {}
train_loader = make_data_loader(cfg, is_train=True)
val_loader = make_data_loader(cfg, is_train=False)
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
None,
F.mse_loss,
)
def train(cfg):
# prepare dataset
data_bunch, test_labels, num_query = get_data_bunch(cfg)
# prepare model
model = build_model(cfg, data_bunch.c)
if cfg.SOLVER.OPT == 'adam': opt_fns = partial(torch.optim.Adam)
elif cfg.SOLVER.OPT == 'sgd':
opt_fns = partial(torch.optim.SGD, momentum=0.9)
else:
raise NameError(f'optimizer {cfg.SOLVER.OPT} not support')
def lr_multistep(start: float, end: float, pct: float):
warmup_factor = 1
gamma = cfg.SOLVER.GAMMA
milestones = [
1.0 * s / cfg.SOLVER.MAX_EPOCHS for s in cfg.SOLVER.STEPS
]
warmup_iter = 1.0 * cfg.SOLVER.WARMUP_ITERS / cfg.SOLVER.MAX_EPOCHS
if pct < warmup_iter:
alpha = pct / warmup_iter
warmup_factor = cfg.SOLVER.WARMUP_FACTOR * (1 - alpha) + alpha
return start * warmup_factor * gamma**bisect_right(milestones, pct)
lr_sched = Scheduler(cfg.SOLVER.BASE_LR, cfg.SOLVER.MAX_EPOCHS,
lr_multistep)
loss_func = reidLoss(cfg.SOLVER.LOSSTYPE, cfg.SOLVER.MARGIN, data_bunch.c)
do_train(
cfg,
model,
data_bunch,
test_labels,
opt_fns,
lr_sched,
loss_func,
num_query,
)
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, num_classes2, image_map_label2 = make_data_loader(
cfg)
#print('\n\n*** image_map_label2:')
# prepare model
model = build_model(cfg, num_classes, num_classes2)
#print(list(model.children()))
#print(model.state_dict().keys())
#exit(0)
#print('model.named_children(): \n\n', model.named_children())
'''
kk = 1
for name, child in model.base.named_children():
print(kk, name)
kk += 1
print(len(list(model.base.children())))
exit(0)
for i in range(len(list(model.base.children()))):
print(' +++', i+1)
print(list(model.base.children())[i])
exit(0)
'''
if len(cfg.MODEL.PRETRAIN_PATH2) > 5:
print('--- resume from ', cfg.MODEL.PRETRAIN_PATH2)
#model.load_param(cfg.MODEL.PRETRAIN_PATH)
#model.loiad_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH2, map_location=lambda storage, loc: storage))
if cfg.MODEL.ONCE_LOAD == 'yes':
print('\n---ONCE_LOAD...\n')
model.load_state_dict(
torch.load(cfg.MODEL.PRETRAIN_PATH2,
map_location=lambda storage, loc: storage))
#if cfg.MODEL.FREEZE_BASE == 'yes':
# functions.freeze_layer(model, 'base', False)
#functions.freeze_global_model(model, False)
else:
functions.load_state_dict_distill(model, cfg.MODEL.PRETRAIN_PATH2,
cfg.MODEL.ONLY_BASE,
cfg.MODEL.WITHOUT_FC)
print('**** Successfully load ', cfg.MODEL.PRETRAIN_PATH2)
if cfg.MODEL.FREEZE_BASE:
#functions.freeze_layer(model, 'base', False)
functions.freeze_global_model(model, False)
if cfg.MODEL.IF_WITH_CENTER == 'no':
print('Train without center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
if cfg.SOLVER.MY_OPTIMIZER == "yes":
print('---* my optimizer:', cfg.SOLVER.MY_OPTIMIZER_NAME)
other_params = [
p for n, p in model.named_parameters()
if not n.startswith('base')
]
optimizer = optim.SGD([{
'params': model.base.parameters(),
'lr': cfg.SOLVER.LR / 10
}, {
'params': other_params,
'lr': cfg.SOLVER.LR
}],
momentum=0.9,
weight_decay=5e-4,
nesterov=True)
else:
print('---* not my optimizer')
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)
#_C.SOLVER.MY_SCHEDULER = "no"
#_C.SOLVER.MY_WARMUP = "no"
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
if cfg.SOLVER.MY_SCHEDULER == "yes":
print('cfg.SOLVER.MY_SCHEDULER_STEP:',
cfg.SOLVER.MY_SCHEDULER_STEP)
print('---* my scheduler: ', cfg.SOLVER.MY_SCHEDULER_NAME)
if cfg.SOLVER.MY_SCHEDULER_NAME == 'SL':
scheduler = lr_scheduler.StepLR(
optimizer,
step_size=cfg.SOLVER.MY_SCHEDULER_STEP[0],
gamma=0.1)
elif cfg.SOLVER.MY_SCHEDULER_NAME == 'MSL':
scheduler = lr_scheduler.MultiStepLR(
optimizer, cfg.SOLVER.MY_SCHEDULER_STEP, gamma=0.1)
else:
print(cfg.SOLVER.MY_SCHEDULER_NAME, ' not found!')
eixt(0)
else:
print('---* not my scheduler')
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 = {}
print('************ do_train')
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
image_map_label2,
num_classes2)
# 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_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))
# 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)
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))
if not os.path.exists(args.tensorboard_logs_path):
os.mkdir(args.tensorboard_logs_path)
#else:
#clean_dir(args.tensorboard_logs_path)
#dataloader
train_loader, test_loader = make_data_loader(
args.dataset, args.datapath, preprocess_all[cfg['preprocess']],
args.num_workers, args.batch_size, cfg['ratio'])
logger = setup_logger('face_recognition', args.output_dir)
logger.info('pid {}, {}'.format(threading.currentThread().ident, args))
logger.info('pid {} running with config {}'.format(
threading.currentThread().ident, cfg))
#model
creterion = make_creterion(cfg, args)
model = make_model(cfg['model']['name'], cfg['model']['layers'],
cfg['num_embeddings'], cfg['num_class'])
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume is not None:
model, optimizer, cfg['start_iter'] = load_model(
model, optimizer, args)
# else:
# model.backbone.apply(weights_init)
do_train(model, creterion, optimizer, train_loader, test_loader, args, cfg)
def train(cfg, local_rank, distributed):
logger = logging.getLogger(cfg.NAME)
# build model
model = build_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
# build solver
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {"iteration": 0}
save_dir = os.path.join(cfg.CHECKPOINTER.DIR, cfg.CHECKPOINTER.NAME)
save_to_disk = get_rank() == 0
checkpointer = Checkpointer(
model=model, optimizer=optimizer, scheduler=scheduler,
save_dir=save_dir, save_to_disk=save_to_disk, logger=logger
)
extra_checkpoint_data = checkpointer.load(cfg.CHECKPOINTER.LOAD_NAME)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
evaluate = cfg.SOLVER.EVALUATE
if evaluate:
synchronize()
data_loader_val = make_data_loader(cfg, is_train=False, is_distributed=distributed, is_for_period=True)
synchronize()
else:
data_loader_val = None
save_to_disk = get_rank() == 0
if cfg.SUMMARY_WRITER and save_to_disk:
save_dir = os.path.join(cfg.CHECKPOINTER.DIR, cfg.CHECKPOINTER.NAME)
summary_writer = make_summary_writer(cfg.SUMMARY_WRITER, save_dir, model_name=cfg.MODEL.NAME)
else:
summary_writer = None
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
arguments,
summary_writer
)
return model
def main():
parser = argparse.ArgumentParser(description="Baidu URFC")
parser.add_argument("-p", dest="root_path", help="path to data")
parser.add_argument(
"-o",
dest="output_dir",
help="output dir",
default="ouputs",
)
parser.add_argument("-bs", dest="batch_size", default=64, type=int)
parser.add_argument("-n", dest="nepochs", default=30, type=int)
parser.add_argument("-cp", dest="checkpoint_period", default=1, type=int)
parser.add_argument("-lr", dest="lr", default=0.01, type=float)
parser.add_argument("-ckt", dest="ckt")
parser.add_argument("-name", dest="name", default="URFC")
parser.add_argument("-s", type=int, dest="split", default=0)
args = parser.parse_args()
output_dir = os.path.join(args.output_dir, args.name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
logger = setupLogger(args.name,
output_dir,
filename=args.name + "_log.txt")
logger.info(args)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MultiModalNet("se_resnext101_32x4d",
"dpn26",
0.5,
num_classes=9,
pretrained=True)
# model = MultiModalNet_MS("se_resnext101_32x4d", "dpn26", 0.5, num_classes=9, pretrained=True)
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=1e-4)
criterion = torch.nn.CrossEntropyLoss().to(device)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
# if torch.cuda.device_count() > 1:
# model = torch.nn.DataParallel(model)
model.to(device)
train_files = pd.read_csv("train.csv")
test_files = pd.read_csv("test.csv")
kf = KFold(n_splits=10, random_state=2050)
splits = []
for train_list, test_list in kf.split(train_files):
splits.append((train_list, test_list))
val_files = train_files.drop(splits[args.split][0])
train_files = train_files.drop(splits[args.split][1])
# train_datalist, val_datalist = train_test_split(all_files, test_size=0.1, random_state=2050)
train_img = os.path.join(args.root_path, "train")
test_img = os.path.join(args.root_path, "test")
train_visit = os.path.join(args.root_path, "npy", "train_visit")
test_visit = os.path.join(args.root_path, "npy", "test_visit")
trndatasets = MMDataset(train_files, train_img, train_visit, mode="train")
trndata_loader = DataLoader(trndatasets,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=1)
valdatasets = MMDataset(val_files,
train_img,
train_visit,
augment=False,
mode="val")
valdata_loader = DataLoader(valdatasets,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=1)
testdatasets = MMDataset(test_files,
test_img,
test_visit,
augment=False,
mode="test")
test_loader = DataLoader(testdatasets,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=1)
checkpointer = Checkpointer(
model,
optimizer,
criterion,
scheduler,
output_dir,
)
do_train(args.name, model, device, trndata_loader, valdata_loader,
optimizer, criterion, scheduler, args.nepochs,
args.checkpoint_period, checkpointer)
checkpoint = torch.load(os.path.join(output_dir, "best_model.pth"),
map_location=torch.device("cpu"))
model.load_state_dict(checkpoint["model"])
logger.info("load model from " +
os.path.join(output_dir, "best_model.pth"))
test_submit(model, test_loader, device, output_dir)
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)
print('Train with 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)
start_epoch = 120
print('Start epoch:', start_epoch)
model.load_state_dict(
torch.load('work_space_tri/se_resnet101_ibn_a_model_120.pth'))
optimizer.load_state_dict(
torch.load('work_space_tri/se_resnet101_ibn_a_optimizer_120.pth'))
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
)
writer = SummaryWriter(log_dir=os.path.join(output_dir, 'tensorboard'))
logger = setup_logger("rendering_model", output_dir, 0)
logger.info("Running with config:\n{}".format(cfg))
shutil.copy('../configs/config.yml', os.path.join(cfg.OUTPUT_DIR,
'configs.yml'))
# In[ ]:
train_loader, dataset = make_data_loader(cfg,
is_train=True,
is_center=cfg.DATASETS.CENTER)
model = build_model(cfg)
# In[ ]:
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)
#scheduler = build_scheduler(optimizer, cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.START_ITERS, cfg.SOLVER.END_ITERS, cfg.SOLVER.LR_SCALE)
loss_fn = make_loss(cfg)
model, optimizer = amp.initialize(model, optimizer, opt_level="O1")
do_train(cfg, model, train_loader, None, optimizer, scheduler, loss_fn, writer)
