def test(cfg, model, distributed):
if distributed:
model = model.module
torch.cuda.empty_cache() # TODO check if it helps
iou_types = ("bbox",)
if cfg.MODEL.MASK_ON:
iou_types = iou_types + ("segm",)
output_folders = [None] * len(cfg.DATASETS.TEST)
dataset_names = cfg.DATASETS.TEST
if cfg.OUTPUT_DIR:
for idx, dataset_name in enumerate(dataset_names):
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
mkdir(output_folder)
output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg, is_train=True, is_distributed=distributed)
meters = TensorboardLogger(
log_dir=cfg.TENSORBOARD_EXPERIMENT,
delimiter=" ")
for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
inference(
model,
cfg,
data_loader_val,
dataset_name=dataset_name,
meters=meters,
iou_types=iou_types,
box_only=cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
)
synchronize()
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
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,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if cfg.USE_TENSORBOARD_LOGS:
meters = TensorboardLogger(
log_dir=os.path.join(output_dir, 'tensorboard_logs'),
start_iter=arguments['iteration'],
delimiter=" ",
)
else:
meters = MetricLogger(delimiter=" ")
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
meters,
)
return model
def do_train(model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
tb_log_dir,
use_tensorboard=False):
logger = logging.getLogger("maskrcnn_benchmark.trainer")
logger.info("Start training")
meters = TensorboardLogger(log_dir=tb_log_dir,
delimiter=" ") \
if use_tensorboard else MetricLogger(delimiter=" ")
max_iter = len(data_loader)
start_iter = arguments["iteration"]
model.train()
start_training_time = time.time()
end = time.time()
for iteration, (images, targets, _) in enumerate(data_loader, start_iter):
data_time = time.time() - end
arguments["iteration"] = iteration
scheduler.step()
images = images.to(device)
targets = [target.to(device) for target in targets]
loss_dict, preds = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
meters.update(iteration, loss=losses_reduced, **loss_dict_reduced)
optimizer.zero_grad()
losses.backward()
accum_grad = 0
for p in list(filter(lambda p: p.grad is not None,
model.parameters())):
accum_grad += p.grad.data.norm(2).item()
if iteration > 500 and accum_grad > 200:
torch.nn.utils.clip_grad_norm_(model.parameters(), 200)
optimizer.step()
batch_time = time.time() - end
end = time.time()
meters.update(iteration, time=batch_time, data=data_time)
eta_seconds = meters.time.global_avg * (max_iter - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % 20 == 0 or iteration == (max_iter - 1):
logger.info(
meters.delimiter.join([
"eta: {eta}",
"iter: {iter}",
"{meters}",
"lr: {lr:.6f}",
"max mem: {memory:.0f}",
]).format(
eta=eta_string,
iter=iteration,
meters=str(meters),
lr=optimizer.param_groups[0]["lr"],
memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
))
if iteration % checkpoint_period == 0:
meters.update_image(iteration, images.tensors[0], preds[0],
targets[0])
checkpointer.save("model_{:07d}".format(iteration), **arguments)
checkpointer.save("model_{:07d}".format(iteration), **arguments)
total_training_time = time.time() - start_training_time
total_time_str = str(datetime.timedelta(seconds=total_training_time))
logger.info("Total training time: {} ({:.4f} s / it)".format(
total_time_str, total_training_time / (max_iter)))
def train(cfg,
local_rank,
distributed,
model_config=None,
use_tensorboard=True):
model = build_detection_model(cfg, model_config)
if get_rank() == 0:
if 'search' in cfg.MODEL.BACKBONE.CONV_BODY:
print('backbone search space:', blocks_key)
else:
print('backbone:', cfg.MODEL.BACKBONE)
if 'search' in cfg.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR or 'search' in cfg.MODEL.SEG_BRANCH.SEGMENT_BRANCH:
print('head search space:', head_ss_keys)
else:
print('head:', cfg.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR,
cfg.MODEL.SEG_BRANCH.SEGMENT_BRANCH)
if 'search' in cfg.MODEL.INTER_MODULE.NAME:
print('inter search space:', inter_ss_keys)
else:
print('inter:', cfg.MODEL.INTER_MODULE.NAME)
print(model)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer, lr_dict = make_optimizer(cfg, model)
if get_rank() == 0:
for item in lr_dict:
print(item)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
if not ('search' in cfg.MODEL.BACKBONE.CONV_BODY
or 'search' in cfg.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR
or 'search' in cfg.MODEL.SEG_BRANCH.SEGMENT_BRANCH):
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
find_unused_parameters=True)
# if 'search' in cfg.MODEL.BACKBONE.CONV_BODY:
# def forward_hook(module: Module, inp: (Tensor,)):
# if module.weight is not None:
# module.weight.requires_grad = True
# if module.bias is not None:
# module.bias.requires_grad = True
# all_modules = (nn.Conv2d, nn.Linear, nn.BatchNorm2d, nn.GroupNorm, ) # group norm更新！！
# for m in model.modules():
# if isinstance(m, all_modules):
# m.register_forward_pre_hook(forward_hook)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(cfg,
is_train=False,
is_distributed=distributed,
is_for_period=True)
else:
data_loader_val = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if use_tensorboard:
meters = TensorboardLogger(cfg=cfg,
log_dir=cfg.TENSORBOARD_EXPERIMENT,
start_iter=arguments['iteration'],
delimiter=" ")
else:
meters = MetricLogger(delimiter=" ")
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
meters,
)
return model
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default=
"/homes/maskrcnn/configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
distributed = num_gpus > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = ""
logger = setup_logger("maskrcnn_benchmark", save_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info(cfg)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
output_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
iou_types = ("bbox", )
if cfg.MODEL.MASK_ON:
iou_types = iou_types + ("segm", )
output_folders = [None] * len(cfg.DATASETS.TEST)
dataset_names = cfg.DATASETS.TEST
if cfg.OUTPUT_DIR:
for idx, dataset_name in enumerate(dataset_names):
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference",
dataset_name)
mkdir(output_folder)
output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg,
is_train=False,
is_distributed=distributed)
meters = TensorboardLogger(log_dir=cfg.TENSORBOARD_EXPERIMENT,
delimiter=" ")
_ = checkpointer.load(cfg.MODEL.WEIGHT)
for output_folder, dataset_name, data_loader_val in zip(
output_folders, dataset_names, data_loaders_val):
inference(
model,
cfg,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
meters=meters,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
)
synchronize()
def train(cfg, local_rank, distributed, use_tensorboard=False):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model, optimizer, opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(cfg, is_train=False, is_distributed=distributed, is_for_period=True)
else:
data_loader_val = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if use_tensorboard:
meters = TensorboardLogger(
log_dir=cfg.TENSORBOARD_EXPERIMENT,
stage = 'train',
start_iter=arguments['iteration'],
delimiter=" ")
meters_val = TensorboardLogger(
log_dir=cfg.TENSORBOARD_EXPERIMENT,
stage = 'val',
start_iter=arguments['iteration'],
delimiter=" ")
else:
meters = MetricLogger(delimiter=" ")
meters_val = MetricLogger(delimiter=" ")
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
meters,
meters_val,
)
return model
def do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
):
logger = logging.getLogger("maskrcnn_benchmark.trainer")
logger.info("Start training")
# meters = MetricLogger(delimiter=" ")
if "tb_log_dir" in arguments and "tb_exp_name" in arguments:
meters = TensorboardLogger(log_dir=arguments["tb_log_dir"],
exp_name=arguments["tb_exp_name"],
start_iter=arguments['iteration'],
delimiter=" ")
else:
meters = MetricLogger(delimiter=" ")
max_iter = len(data_loader)
start_iter = arguments["iteration"]
model.train()
start_training_time = time.time()
end = time.time()
for iteration, (images, targets, _) in enumerate(data_loader, start_iter):
data_time = time.time() - end
iteration = iteration + 1
arguments["iteration"] = iteration
scheduler.step()
images = images.to(device)
targets = [target.to(device) for target in targets]
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
meters.update(loss=losses_reduced, **loss_dict_reduced)
optimizer.zero_grad()
losses.backward()
optimizer.step()
batch_time = time.time() - end
end = time.time()
meters.update(time=batch_time, data=data_time)
eta_seconds = meters.time.global_avg * (max_iter - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % 20 == 0 or iteration == max_iter:
logger.info(
meters.delimiter.join([
"eta: {eta}",
"iter: {iter}",
"{meters}",
"lr: {lr:.6f}",
"max mem: {memory:.0f}",
]).format(
eta=eta_string,
iter=iteration,
meters=str(meters),
lr=optimizer.param_groups[0]["lr"],
memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
))
if iteration % checkpoint_period == 0:
checkpointer.save("model_{:07d}".format(iteration), **arguments)
if iteration == max_iter:
checkpointer.save("model_final", **arguments)
total_training_time = time.time() - start_training_time
total_time_str = str(datetime.timedelta(seconds=total_training_time))
logger.info("Total training time: {} ({:.4f} s / it)".format(
total_time_str, total_training_time / (max_iter)))
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Training")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--skip-test",
dest="skip_test",
help="Do not test the final model",
action="store_true",
)
parser.add_argument(
"--use-tensorboard",
dest="use_tensorboard",
help="Use tensorboardX logger (Requires tensorboardX installed)",
action="store_true",
default=False)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
args.distributed = num_gpus > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
synchronize()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
# print("num_classes", cfg)
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info(args)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
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))
if args.use_tensorboard:
extra_name = cfg.OUTPUT_DIR.split('/')[-1]
meters = TensorboardLogger(log_dir=cfg.TENSORBOARD_EXPERIMENT,
start_iter=0,
delimiter=" ",
extra_name=extra_name)
else:
meters = MetricLogger(delimiter=" ")
model = train(cfg, args.local_rank, args.distributed, meters)
if not args.skip_test:
run_test(cfg, model, args.distributed, meters)
def do_train(model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
snapshot,
tb_log_dir,
tb_exp_name,
use_tensorboard=False):
logger = logging.getLogger("maskrcnn_benchmark.trainer")
logger.info("Start training")
meters = TensorboardLogger(log_dir=tb_log_dir,
exp_name=tb_exp_name,
start_iter=arguments['iteration'],
delimiter=" ") \
if use_tensorboard else MetricLogger(delimiter=" ")
max_iter = len(data_loader)
start_iter = arguments["iteration"]
model.train()
start_training_time = time.time()
end = time.time()
for iteration, (images, targets,
img_idx) in enumerate(data_loader, start_iter):
data_time = time.time() - end
iteration = iteration + 1
arguments["iteration"] = iteration
images = images.to(device)
targets = [target.to(device) for target in targets]
optimizer.zero_grad()
loss_dict = model(images, targets)
# we only sum up the loss but no other metrics
losses = sum([
v for (k, v) in loss_dict.items() if k.split('/')[-1][:4] == 'loss'
])
losses.backward()
optimizer.step()
scheduler.step()
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(loss_dict)
losses_reduced = sum([
v for (k, v) in loss_dict_reduced.items()
if k.split('/')[-1][:4] == 'loss'
])
# Prepend the key name so as to allow better visualisation in tensorboard
loss_dict_reduced['detector_losses/total_loss'] = losses_reduced
meters.update(**loss_dict_reduced)
meters.update(lr=optimizer.param_groups[-1]['lr'])
batch_time = time.time() - end
end = time.time()
process_time = {
'time/batch_time': batch_time,
'time/data_time': data_time
}
meters.update(**process_time)
eta_seconds = meters.meters['time/batch_time'].global_avg * (max_iter -
iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % snapshot == 0 or iteration == max_iter:
logger.info(
meters.delimiter.join([
"\n \t eta: {eta}",
"iter: {iter}",
"lr: {lr:.6f}",
"max mem: {memory:.0f}",
"\n \t {meters}",
]).format(
eta=eta_string,
iter=iteration,
lr=optimizer.param_groups[0]["lr"],
memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
meters=str(meters),
))
if iteration % checkpoint_period == 0:
checkpointer.save("model_{:07d}".format(iteration), **arguments)
if iteration == max_iter:
checkpointer.save("model_final", **arguments)
total_training_time = time.time() - start_training_time
total_time_str = str(datetime.timedelta(seconds=total_training_time))
logger.info("Total training time: {} ({:.4f} s / it)".format(
total_time_str, total_training_time / (max_iter)))
def train(cfg, local_rank, distributed, use_tensorboard=False):
model = build_detection_model(cfg)
# print(model)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
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,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
# return
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if use_tensorboard:
meters = TensorboardLogger(log_dir=cfg.TENSORBOARD_EXPERIMENT,
start_iter=arguments['iteration'],
delimiter=" ")
else:
meters = MetricLogger(delimiter=" ")
if cfg.MODEL.DOMAIN_ADAPTATION_ON:
source_data_loader = make_data_loader(
cfg,
is_train=True,
is_source=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
target_data_loader = make_data_loader(
cfg,
is_train=True,
is_source=False,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
do_da_train(model, source_data_loader, target_data_loader, optimizer,
scheduler, checkpointer, device, checkpoint_period,
arguments, cfg, meters)
else:
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
do_train(model, data_loader, optimizer, scheduler, checkpointer,
device, checkpoint_period, arguments, meters)
return model
def train(cfg, local_rank, distributed, use_tensorboard=False):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
# load_scheduler_only_epoch will prefer the scheduler specified in the
# config rather than the one in the checkpoint, and will load only the
# last_epoch from the checkpoint.
extra_checkpoint_data = checkpointer.load(
cfg.MODEL.WEIGHT,
load_model_only=cfg.MODEL.LOAD_ONLY_WEIGHTS,
load_scheduler_only_epoch=True)
if not cfg.MODEL.LOAD_ONLY_WEIGHTS:
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if use_tensorboard:
meters = TensorboardLogger(log_dir=output_dir,
exp_name=cfg.TENSORBOARD_EXP_NAME,
start_iter=arguments['iteration'],
delimiter=" ")
else:
meters = MetricLogger(delimiter=" ")
do_train(model, data_loader, optimizer, scheduler, checkpointer, device,
checkpoint_period, arguments, meters)
return model
