def main(args):
path = os.path.join(os.getcwd(), 'soft_label', 'soft_label_resnet50.txt')
if not os.path.isfile(path):
print('soft label file is not exist')
train_loader = getTrainLoader(args, path)
_, val_loader, num_query, num_classes, train_size = make_data_loader(args)
#train_loader, val_loader, num_query, num_classes, train_size = make_data_loader(args)
model = build_model(args, num_classes)
optimizer = make_optimizer(args, model)
scheduler = WarmupMultiStepLR(optimizer, [30, 55], 0.1, 0.01, 5, "linear")
loss_func = make_loss(args)
model.to(device)
for epoch in range(args.Epochs):
model.train()
running_loss = 0.0
running_klloss = 0.0
running_softloss = 0.0
running_corrects = 0.0
for index, data in enumerate(tqdm(train_loader)):
img, target, soft_target = data
img = img.cuda()
target = target.cuda()
soft_target = soft_target.cuda()
score, _ = model(img)
preds = torch.max(score.data, 1)[1]
loss, klloss, softloss = loss_func(score, target, soft_target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
running_klloss += klloss.item()
running_softloss += softloss.item()
running_corrects += float(torch.sum(preds == target.data))
scheduler.step()
epoch_loss = running_loss / train_size
epoch_klloss = running_klloss / train_size
epoch_softloss = running_softloss / train_size
epoch_acc = running_corrects / train_size
print(
"Epoch {} Loss : {:.4f} KLLoss:{:.8f} SoftLoss:{:.4f} Acc:{:.4f}"
.format(epoch, epoch_loss, epoch_klloss, epoch_softloss,
epoch_acc))
if (epoch + 1) % args.n_save == 0:
evaluator = Evaluator(model, val_loader, num_query)
cmc, mAP = evaluator.run()
print('---------------------------')
print("CMC Curve:")
for r in [1, 5, 10]:
print("Rank-{} : {:.1%}".format(r, cmc[r - 1]))
print("mAP : {:.1%}".format(mAP))
print('---------------------------')
save_model(args, model, optimizer, epoch)
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):
model = build_model(cfg)
# model.fix_bn()
model = nn.DataParallel(model)
torch.backends.cudnn.benchmark = True
optimizer = make_optimizer(cfg, model)
criterion = make_criterion(cfg)
scheduler = make_lr_scheduler(cfg, optimizer)
metrics = make_metrics(cfg)
train_loader = make_dataloader(cfg, is_train=True)
val_loader = make_dataloader(cfg, is_train=False)
cfg.TOOLS.image_n = 3
#image_3_dataloader = make_inference_dataloader(cfg=cfg)
image_3_dataloader = None
cfg.TOOLS.image_n = 4
#image_4_dataloader = make_inference_dataloader(cfg=cfg)
image_4_dataloader = None
do_train(cfg,
model=model,
train_loader=train_loader,
val_loader=val_loader,
optimizer=optimizer,
scheduler=scheduler,
loss_fn=criterion,
metrics=metrics,
image_3_dataloader=image_3_dataloader,
image_4_dataloader=image_4_dataloader)
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 __init__(self, cfg, num_classes):
self.device = cfg.MODEL.DEVICE
self.model = build_model(cfg, num_classes)
self.loss = Loss(cfg, num_classes, self.model.in_planes)
self.optimizer = make_optimizer(cfg, self.model)
self.scheduler = WarmupMultiStepLR(self.optimizer,
cfg.WARMUP.STEPS,
cfg.WARMUP.GAMMA,
cfg.WARMUP.FACTOR,
cfg.WARMUP.MAX_EPOCHS,
cfg.WARMUP.METHOD)
if cfg.APEX.IF_ON:
logger.info("Using apex")
try:
import apex
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.")
assert torch.backends.cudnn.enabled, "Amp requires cudnn backend to be enabled."
#
# if cfg.APEX.IF_SYNC_BN:
# logger.info("Using apex synced BN")
# self.module = apex.parallel.convert_syncbn_model(self.module)
if self.device is 'cuda':
self.model = self.model.cuda()
if cfg.APEX.IF_ON:
from apex import amp
self.model, self.optimizer = amp.initialize(self.model,
self.optimizer,
opt_level=cfg.APEX.OPT_LEVEL,
keep_batchnorm_fp32=None if cfg.APEX.OPT_LEVEL == 'O1' else True,
loss_scale=cfg.APEX.LOSS_SCALE[0])
def main():
output_dir = cfg.MODEL.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = setup_logger("tracker", output_dir, 0)
logger.info("Running with config:\n{}".format(cfg))
torch.backends.cudnn.benchmark = True
train_loader, val_loader = make_data_loader(cfg)
model = build_model(cfg)
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)
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
scheduler,
loss_func
)
def train(train_loader, num_classes):
parser = argparse.ArgumentParser(description="ReID Baseline Training")
parser.add_argument(
"--config_file", default="", help="path to config file", type=str
)
parser.add_argument("opts", help="Modify config options using the command-line", default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.config_file != "":
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
set_seed(cfg.SOLVER.SEED)
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = setup_logger("reid_baseline", output_dir, if_train=True)
logger.info("Saving model in the path :{}".format(cfg.OUTPUT_DIR))
logger.info(args)
if args.config_file != "":
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, 'r') as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
if cfg.MODEL.PRETRAIN_CHOICE == 'finetune':
model = make_model(cfg, num_class=num_classes)
model.load_param_finetune(cfg.MODEL.PRETRAIN_PATH)
print('Loading pretrained model for finetuning......')
else:
model = make_model(cfg, num_class=num_classes)
loss_func = make_loss(cfg, num_classes=num_classes)
optimizer = make_optimizer(cfg, model)
scheduler = WarmupCosineAnnealingLR(optimizer, cfg.SOLVER.MAX_EPOCHS, cfg.SOLVER.DELAY_ITERS, cfg.SOLVER.ETA_MIN_LR,
cfg.SOLVER.WARMUP_FACTOR, cfg.SOLVER.WARMUP_EPOCHS, cfg.SOLVER.WARMUP_METHOD)
logger.info("use WarmupCosineAnnealingLR, delay_step:{}".format(cfg.SOLVER.DELAY_ITERS))
do_train(
cfg,
model,
train_loader,
optimizer,
scheduler, # modify for using self trained model
loss_func
)
def train(cfg):
logger = setup_logger("reid_baseline", cfg.OUTPUT_DIR)
logger.info("Running with config:\n{}".format(cfg))
# prepare camstyle dataset
train_loader, train_camstyle_loader, val_loader, num_query, num_classes = make_camstyle_data_loader(
cfg)
num_classes.append(-1)
# prepare model
model = build_model(cfg, num_classes)
optimizer, _ = make_optimizer(cfg, model)
loss_fn = make_loss(cfg, num_classes)
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'resume':
start_epoch = eval(
cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')
[-1])
logger.info('Start epoch:%d' % start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer')
logger.info('Path to the checkpoint of optimizer:%s' %
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 == 'self' or cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
model.load_param(cfg.MODEL.PRETRAIN_PATH, cfg.MODEL.PRETRAIN_CHOICE)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
else:
logger.info(
'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_fn,
num_query,
start_epoch, # add for using self trained model
0,
train_camstyle_loader)
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 get_autoencoder_component(self, cfg):
self.device = cfg.MODEL.DEVICE
self.model = AutoEncoder(cfg.EBLL.IN_PLANES, cfg.EBLL.CODE_SIZE)
self.loss = AutoEncoderLoss(cfg)
copy_cfg = copy.deepcopy(cfg)
copy_cfg['OPTIMIZER']['BASE_LR'] = copy_cfg.EBLL.OPTIMIZER_BASE_LR
copy_cfg['OPTIMIZER']['NAME'] = 'SGD'
self.optimizer = make_optimizer(copy_cfg, self.model)
self.scheduler = ExponentialLR(self.optimizer, gamma=0.9)
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 get_component(self, cfg, num_classes):
self.device = cfg.MODEL.DEVICE
self.model = build_model(cfg, num_classes)
self.loss = Loss(cfg, num_classes, self.model.in_planes)
self.optimizer = make_optimizer(cfg, self.model)
self.scheduler = WarmupMultiStepLR(self.optimizer,
cfg.WARMUP.STEPS,
cfg.WARMUP.GAMMA,
cfg.WARMUP.FACTOR,
cfg.WARMUP.MAX_EPOCHS,
cfg.WARMUP.METHOD)
def train(cfg):
model = build_model(cfg)
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, F.cross_entropy)
def main(args):
sys.stdout = Logger(
os.path.join(args.log_path, args.log_description,
'log' + time.strftime(".%m_%d_%H:%M:%S") + '.txt'))
train_loader, val_loader, num_query, num_classes, train_size = make_data_loader(
args)
model = build_model(args, num_classes)
print(model)
optimizer = make_optimizer(args, model)
scheduler = WarmupMultiStepLR(optimizer, [30, 55], 0.1, 0.01, 5, "linear")
loss_func = make_loss(args)
model.to(device)
for epoch in range(args.Epochs):
model.train()
running_loss = 0.0
running_corrects = 0.0
for index, data in enumerate(tqdm(train_loader)):
img, target = data
img = img.cuda()
target = target.cuda()
score, _ = model(img)
preds = torch.max(score.data, 1)[1]
loss = loss_func(score, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
running_corrects += float(torch.sum(preds == target.data))
scheduler.step()
epoch_loss = running_loss / train_size
epoch_acc = running_corrects / train_size
print("Epoch {} Loss : {:.6f} Acc:{:.4f}".format(
epoch, epoch_loss, epoch_acc))
if (epoch + 1) % args.n_save == 0:
evaluator = Evaluator(model, val_loader, num_query)
cmc, mAP = evaluator.run()
print('---------------------------')
print("CMC Curve:")
for r in [1, 5, 10]:
print("Rank-{} : {:.1%}".format(r, cmc[r - 1]))
print("mAP : {:.1%}".format(mAP))
print('---------------------------')
save_model(args, model, optimizer, epoch)
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):
logger = setup_logger("reid_baseline", cfg.OUTPUT_DIR)
logger.info("Running with config:\n{}".format(cfg))
# prepare dataset
val_data_loader, num_query = make_val_data_loader(cfg)
num_classes = np.zeros(len(cfg.DATALOADER.SAMPLER_PROB)).astype(int) - 1
source_dataset = init_dataset(cfg.SRC_DATA.NAMES, root_train=cfg.SRC_DATA.TRAIN_DIR, transfered=cfg.SRC_DATA.TRANSFERED)
num_classes[0] = source_dataset.num_train_pids
num_classes[1] = cfg.TGT_UNSUPDATA.CLUSTER_TOPK
if cfg.MODEL.FINETUNE:
num_classes[1] += 200
# prepare model
model = build_model(cfg, num_classes)
optimizer,fixed_lr_idxs = make_optimizer(cfg, model)
loss_fn = make_loss(cfg, num_classes)
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'resume':
start_epoch = eval(cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')[-1])
logger.info('Start epoch:%d' %start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace('model', 'optimizer')
logger.info('Path to the checkpoint of optimizer:%s' %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, fixed_lr_idxs)
elif cfg.MODEL.PRETRAIN_CHOICE == 'self' or cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
model.load_param(cfg.MODEL.PRETRAIN_PATH, cfg.MODEL.PRETRAIN_CHOICE)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR, cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD, -1, fixed_lr_idxs)
camera_model = build_camera_model(cfg, num_classes=5)
camera_model.load_param(cfg.TEST.CAMERA_WEIGHT, cfg.MODEL.PRETRAIN_CHOICE)
else:
logger.info('Only support pretrain_choice for imagenet and self, but got {}'.format(cfg.MODEL.PRETRAIN_CHOICE))
do_train(cfg,
model,
camera_model,
val_data_loader,
optimizer,
scheduler, # modify for using self trained model
loss_fn,
num_query,
start_epoch, # add for using self trained model
0
)
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)
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 main():
init_env('1')
loaders = make_data_loaders(cfg)
model = build_model(cfg)
model = model.cuda()
task_name = 'base_unet'
log_dir = os.path.join(cfg.LOG_DIR, task_name)
cfg.TASK_NAME = task_name
mkdir(log_dir)
logger = setup_logger('train', log_dir, filename='train.log')
logger.info(cfg)
logger = setup_logger('eval', log_dir, filename='eval.log')
optimizer, scheduler = make_optimizer(cfg, model)
metrics = get_metrics(cfg)
losses = get_losses(cfg)
train_val(model, loaders, optimizer, scheduler, losses, metrics)
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):
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 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, local_rank, distributed):
pretrained_base = os.path.join(cfg.TRAIN.model_root,
cfg.TRAIN.backbone + '.pth')
model = get_model(cfg.MODEL.model, pretrained_base=pretrained_base)
device = torch.device(cfg.MODEL.device)
model.to(device)
if distributed:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.CONFIG.output_dir
save_to_disk = ptutil.get_rank() == 0
checkpointer = ptutil.CheckPointer(model, optimizer, scheduler, output_dir,
save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.TRAIN.weight)
arguments.update(extra_checkpoint_data)
data_loader = build_dataloader(
cfg,
train=True,
distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.TRAIN.checkpoint_period
training(model, data_loader, optimizer, scheduler, checkpointer, device,
checkpoint_period, arguments)
return model
def train_net(cfg):
print(cfg.NAME)
print(cfg.DESCRIPTION)
if not os.path.exists(cfg.OUTPUT_DIR):
os.mkdir(cfg.OUTPUT_DIR)
model = Network(cfg)
device = torch.device(cfg.DEVICE)
if cfg.PRETRAINED:
model_path = cfg.PRETRAIN_MODEL
model.load_state_dict(torch.load(model_path))
print('loding weight from ', model_path)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# output_dir = cfg.OUTPUT_DIR
data_loader = make_data_loader(cfg, is_train=True)
args = {}
args['iteration'] = 0
do_train(model, data_loader, optimizer, scheduler, device, args, cfg)
print(cfg.NAME)
return model
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
train_loader, val_loader, num_query, num_classes = make_dataloader(cfg)
if cfg.MODEL.PRETRAIN_CHOICE == 'finetune':
model = make_model(cfg, num_class=num_classes)
model.load_param_finetune(cfg.MODEL.PRETRAIN_PATH)
print('Loading pretrained model for finetuning......')
else:
model = make_model(cfg, num_class=num_classes)
loss_func, center_criterion = make_loss(cfg, num_classes=num_classes)
optimizer, optimizer_center = make_optimizer(cfg, model, center_criterion)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_EPOCHS,
cfg.SOLVER.WARMUP_METHOD)
do_train(
cfg,
model,
center_criterion,
train_loader,
val_loader,
optimizer,
optimizer_center,
scheduler, # modify for using self trained model
loss_func,
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))
if __name__ == '__main__':
Cfg.freeze()
log_dir = Cfg.DATALOADER.LOG_DIR
logger = setup_logger('pose-transfer-gan.train', log_dir)
logger.info("Running with config:\n{}".format(Cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = Cfg.MODEL.DEVICE_ID
cudnn.benchmark = True
# This flag allows you to enable the inbuilt cudnn auto-tuner to find the best algorithm to use for your hardware.
train_loader, val_loader = make_dataloader(Cfg)
model_G, model_Dip, model_Dii, model_D_reid = make_model(Cfg)
optimizerG = make_optimizer(Cfg, model_G)
optimizerDip = make_optimizer(Cfg, model_Dip)
optimizerDii = make_optimizer(Cfg, model_Dii)
schedulerG = WarmupMultiStepLR(optimizerG, Cfg.SOLVER.STEPS,
Cfg.SOLVER.GAMMA, Cfg.SOLVER.WARMUP_FACTOR,
Cfg.SOLVER.WARMUP_EPOCHS,
Cfg.SOLVER.WARMUP_METHOD)
schedulerDip = WarmupMultiStepLR(optimizerDip, Cfg.SOLVER.STEPS,
Cfg.SOLVER.GAMMA,
Cfg.SOLVER.WARMUP_FACTOR,
Cfg.SOLVER.WARMUP_EPOCHS,
Cfg.SOLVER.WARMUP_METHOD)
schedulerDii = WarmupMultiStepLR(optimizerDii, Cfg.SOLVER.STEPS,
Cfg.SOLVER.GAMMA,
Cfg.SOLVER.WARMUP_FACTOR,
cfg.merge_from_file('../configs/config.yml')
cfg.freeze()
train_loader, dataset = make_data_loader(cfg, is_train=True)
val_loader, dataset_val = make_data_loader_view(cfg, is_train=True)
model = build_model(cfg).cuda()
maxs = torch.max(dataset.bbox[0], dim=0).values.cuda()+0.5
mins = torch.min(dataset.bbox[0], dim=0).values.cuda()-0.5
model.set_max_min(maxs,mins)
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_fn = make_loss(cfg)
model, optimizer = amp.initialize(model, optimizer, opt_level="O1")
beg = time.time()
for batch in train_loader:
beg = time.time()
time_str = datetime.datetime.strftime(curr_time,'%Y%m%d_')
save_dir = os.path.join(cfg['save_dir'],time_str+cfg['tag'])
log_dir = os.path.join(cfg['log_dir'],"log_"+time_str+cfg['tag'])
cfg['save_dir'] = save_dir
cfg['log_dir'] = log_dir
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
if not os.path.isdir(log_dir):
os.makedirs(log_dir)
print("Save_dir :",save_dir)
print("Log_dir :", log_dir)
# import pdb; pdb.set_trace()
model = build_model(cfg,pretrain_path=arg['load_path'])
optimizer = make_optimizer(cfg['optimizer'],model)
lr_scheduler = wrapper_lr_scheduler(cfg['lr_scheduler'],optimizer)
if arg['device']: # 传入命令指定 device id
free_device_ids = arg['device']
else:
free_device_ids = get_free_device_ids()
max_num_devices = cfg['max_num_devices']
if len(free_device_ids)>=max_num_devices:
free_device_ids = free_device_ids[:max_num_devices]
master_device = free_device_ids[0]
model.cuda(master_device)
model = nn.DataParallel(model,device_ids=free_device_ids).cuda(master_device)