def parse_args():
"""
Parse python script parameters (common part).
Returns
-------
ArgumentParser
Resulted args.
"""
parser = argparse.ArgumentParser(
description="Evaluate a model for image classification (Gluon/FDV)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--dataset",
type=str,
default="FDV1",
help="dataset name. option are FDV1, FDV2")
parser.add_argument(
"--work-dir",
type=str,
default=os.path.join("..", "facedetver_data"),
help="path to working directory only for dataset root path preset")
args, _ = parser.parse_known_args()
dataset_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
dataset_metainfo.add_dataset_parser_arguments(
parser=parser,
work_dir_path=args.work_dir)
add_eval_parser_arguments(parser)
args = parser.parse_args()
return args
def parse_args():
"""
Parse python script parameters (common part).
Returns:
-------
ArgumentParser
Resulted args.
"""
parser = argparse.ArgumentParser(
description="Train a model for image classification (Gluon)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--dataset",
type=str,
default="ImageNet1K_rec",
help=
"dataset name. options are ImageNet1K, ImageNet1K_rec, CUB200_2011, CIFAR10, CIFAR100, SVHN"
)
parser.add_argument(
"--work-dir",
type=str,
default=os.path.join("..", "imgclsmob_data"),
help="path to working directory only for dataset root path preset")
args, _ = parser.parse_known_args()
dataset_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
dataset_metainfo.add_dataset_parser_arguments(parser=parser,
work_dir_path=args.work_dir)
add_train_cls_parser_arguments(parser)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(
description="Evaluate a model for image matching (Gluon/HPatches)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--dataset",
type=str,
default="HPatches",
help="dataset name")
parser.add_argument(
"--work-dir",
type=str,
default=os.path.join("..", "imgclsmob_data"),
help="path to working directory only for dataset root path preset")
args, _ = parser.parse_known_args()
dataset_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
dataset_metainfo.add_dataset_parser_arguments(
parser=parser,
work_dir_path=args.work_dir)
add_eval_parser_arguments(parser)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(
description="Evaluate a model for image classification/segmentation (Gluon)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--dataset",
type=str,
default="ImageNet1K_rec",
help="dataset name. options are ImageNet1K, ImageNet1K_rec, CUB200_2011, CIFAR10, CIFAR100, SVHN, VOC2012, "
"ADE20K, Cityscapes, COCO")
parser.add_argument(
"--work-dir",
type=str,
default=os.path.join("..", "imgclsmob_data"),
help="path to working directory only for dataset root path preset")
args, _ = parser.parse_known_args()
dataset_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
dataset_metainfo.add_dataset_parser_arguments(
parser=parser,
work_dir_path=args.work_dir)
add_eval_parser_arguments(parser)
args = parser.parse_args()
return args
def main():
args = parse_args()
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
assert (args.batch_size == 1)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.net_extra_kwargs,
load_ignore_extra=False,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=False,
ctx=ctx)
test_data = get_val_data_source(ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
calc_detector_repeatability(test_data=test_data, net=net, ctx=ctx)
def main():
args = parse_args()
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
assert (args.batch_size == 1)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=None,
load_ignore_extra=False,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=False,
ctx=ctx)
test_data = get_val_data_source(
ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
tic = time.time()
for batch in test_data:
data_src_list, data_dst_list, labels_list = batch_fn(batch, ctx)
outputs_src_list = [net(X) for X in data_src_list]
assert (outputs_src_list is not None)
pass
logging.info("Time cost: {:.4f} sec".format(
time.time() - tic))
def main():
"""
Main body of script.
"""
args = parse_args()
args.seed = init_rand(seed=args.seed)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
use_teacher = (args.teacher_models
is not None) and (args.teacher_models.strip() != "")
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.train_net_extra_kwargs,
tune_layers=args.tune_layers,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(not args.not_hybridize),
initializer=get_initializer(initializer_name=args.initializer),
ctx=ctx)
assert (hasattr(net, "classes"))
num_classes = net.classes
teacher_net = None
discrim_net = None
discrim_loss_func = None
if use_teacher:
teacher_nets = []
for teacher_model in args.teacher_models.split(","):
teacher_net = prepare_model(
model_name=teacher_model.strip(),
use_pretrained=True,
pretrained_model_file_path="",
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.train_net_extra_kwargs,
do_hybridize=(not args.not_hybridize),
ctx=ctx)
assert (teacher_net.classes == net.classes)
assert (teacher_net.in_size == net.in_size)
teacher_nets.append(teacher_net)
if len(teacher_nets) > 0:
teacher_net = Concurrent(stack=True,
prefix="",
branches=teacher_nets)
for k, v in teacher_net.collect_params().items():
v.grad_req = "null"
if not args.not_discriminator:
discrim_net = MealDiscriminator()
discrim_net.cast(args.dtype)
if not args.not_hybridize:
discrim_net.hybridize(static_alloc=True, static_shape=True)
discrim_net.initialize(mx.init.MSRAPrelu(), ctx=ctx)
for k, v in discrim_net.collect_params().items():
v.lr_mult = args.dlr_factor
discrim_loss_func = MealAdvLoss()
train_data = get_train_data_source(ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
val_data = get_val_data_source(ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
batch_fn = get_batch_fn(ds_metainfo=ds_metainfo)
num_training_samples = len(
train_data._dataset
) if not ds_metainfo.use_imgrec else ds_metainfo.num_training_samples
trainer, lr_scheduler = prepare_trainer(
net=net,
optimizer_name=args.optimizer_name,
wd=args.wd,
momentum=args.momentum,
lr_mode=args.lr_mode,
lr=args.lr,
lr_decay_period=args.lr_decay_period,
lr_decay_epoch=args.lr_decay_epoch,
lr_decay=args.lr_decay,
target_lr=args.target_lr,
poly_power=args.poly_power,
warmup_epochs=args.warmup_epochs,
warmup_lr=args.warmup_lr,
warmup_mode=args.warmup_mode,
batch_size=batch_size,
num_epochs=args.num_epochs,
num_training_samples=num_training_samples,
dtype=args.dtype,
gamma_wd_mult=args.gamma_wd_mult,
beta_wd_mult=args.beta_wd_mult,
bias_wd_mult=args.bias_wd_mult,
state_file_path=args.resume_state)
if args.save_dir and args.save_interval:
param_names = ds_metainfo.val_metric_capts + ds_metainfo.train_metric_capts + [
"Train.Loss", "LR"
]
lp_saver = TrainLogParamSaver(
checkpoint_file_name_prefix="{}_{}".format(ds_metainfo.short_label,
args.model),
last_checkpoint_file_name_suffix="last",
best_checkpoint_file_name_suffix=None,
last_checkpoint_dir_path=args.save_dir,
best_checkpoint_dir_path=None,
last_checkpoint_file_count=2,
best_checkpoint_file_count=2,
checkpoint_file_save_callback=save_params,
checkpoint_file_exts=(".params", ".states"),
save_interval=args.save_interval,
num_epochs=args.num_epochs,
param_names=param_names,
acc_ind=ds_metainfo.saver_acc_ind,
# bigger=[True],
# mask=None,
score_log_file_path=os.path.join(args.save_dir, "score.log"),
score_log_attempt_value=args.attempt,
best_map_log_file_path=os.path.join(args.save_dir, "best_map.log"))
else:
lp_saver = None
val_metric = get_composite_metric(ds_metainfo.val_metric_names,
ds_metainfo.val_metric_extra_kwargs)
train_metric = get_composite_metric(ds_metainfo.train_metric_names,
ds_metainfo.train_metric_extra_kwargs)
loss_metrics = [LossValue(name="loss"), LossValue(name="dloss")]
loss_kwargs = {
"sparse_label": (not (args.mixup or args.label_smoothing)
and not (use_teacher and (teacher_net is not None)))
}
if ds_metainfo.loss_extra_kwargs is not None:
loss_kwargs.update(ds_metainfo.loss_extra_kwargs)
loss_func = get_loss(ds_metainfo.loss_name, loss_kwargs)
train_net(batch_size=batch_size,
num_epochs=args.num_epochs,
start_epoch1=args.start_epoch,
train_data=train_data,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
dtype=args.dtype,
net=net,
teacher_net=teacher_net,
discrim_net=discrim_net,
trainer=trainer,
lr_scheduler=lr_scheduler,
lp_saver=lp_saver,
log_interval=args.log_interval,
mixup=args.mixup,
mixup_epoch_tail=args.mixup_epoch_tail,
label_smoothing=args.label_smoothing,
num_classes=num_classes,
grad_clip_value=args.grad_clip,
batch_size_scale=args.batch_size_scale,
val_metric=val_metric,
train_metric=train_metric,
loss_metrics=loss_metrics,
loss_func=loss_func,
discrim_loss_func=discrim_loss_func,
ctx=ctx)
def test_model(args):
"""
Main test routine.
Parameters:
----------
args : ArgumentParser
Main script arguments.
Returns:
-------
float
Main accuracy value.
"""
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
assert (ds_metainfo.ml_type !=
"imgseg") or (args.data_subset != "test") or (args.batch_size == 1)
assert (ds_metainfo.ml_type != "imgseg") or args.disable_cudnn_autotune
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.test_net_extra_kwargs,
load_ignore_extra=ds_metainfo.load_ignore_extra,
classes=(args.num_classes if ds_metainfo.ml_type != "hpe" else None),
in_channels=args.in_channels,
do_hybridize=(ds_metainfo.allow_hybridize and (not args.calc_flops)),
ctx=ctx)
assert (hasattr(net, "in_size"))
input_image_size = net.in_size
get_test_data_source_class = get_val_data_source if args.data_subset == "val" else get_test_data_source
test_data = get_test_data_source_class(ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
batch_fn = get_batch_fn(ds_metainfo=ds_metainfo)
if args.data_subset == "val":
test_metric = get_composite_metric(
metric_names=ds_metainfo.val_metric_names,
metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs)
else:
test_metric = get_composite_metric(
metric_names=ds_metainfo.test_metric_names,
metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs)
if not args.not_show_progress:
from tqdm import tqdm
test_data = tqdm(test_data)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
acc_values = calc_model_accuracy(
net=net,
test_data=test_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
metric=test_metric,
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
# calc_weight_count=(not log_file_exist),
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True)
return acc_values[
ds_metainfo.saver_acc_ind] if len(acc_values) > 0 else None
def main():
"""
Main body of script.
"""
args = parse_args()
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.net_extra_kwargs,
load_ignore_extra=ds_metainfo.load_ignore_extra,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(ds_metainfo.allow_hybridize and (not args.calc_flops)),
ctx=ctx)
assert (hasattr(net, "in_size"))
input_image_size = net.in_size
if args.data_subset == "val":
get_test_data_source_class = get_val_data_source
test_metric = get_composite_metric(
metric_names=ds_metainfo.val_metric_names,
metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs)
else:
get_test_data_source_class = get_test_data_source
test_metric = get_composite_metric(
metric_names=ds_metainfo.test_metric_names,
metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs)
test_data = get_test_data_source_class(
ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
batch_fn = get_batch_fn(use_imgrec=ds_metainfo.use_imgrec)
if not args.not_show_progress:
test_data = tqdm(test_data)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
test(
net=net,
test_data=test_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
metric=test_metric,
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True,
show_bad_samples=args.show_bad_samples)
def main():
"""
Main body of script.
"""
args = parse_args()
args.seed = init_rand(seed=args.seed)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
tune_layers=args.tune_layers,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(not args.not_hybridize),
initializer=get_initializer(initializer_name=args.initializer),
ctx=ctx)
assert (hasattr(net, "classes"))
num_classes = net.classes
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
train_data = get_train_data_source(ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
val_data = get_val_data_source(ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
batch_fn = get_batch_fn(use_imgrec=ds_metainfo.use_imgrec)
num_training_samples = len(
train_data._dataset
) if not ds_metainfo.use_imgrec else ds_metainfo.num_training_samples
trainer, lr_scheduler = prepare_trainer(
net=net,
optimizer_name=args.optimizer_name,
wd=args.wd,
momentum=args.momentum,
lr_mode=args.lr_mode,
lr=args.lr,
lr_decay_period=args.lr_decay_period,
lr_decay_epoch=args.lr_decay_epoch,
lr_decay=args.lr_decay,
target_lr=args.target_lr,
poly_power=args.poly_power,
warmup_epochs=args.warmup_epochs,
warmup_lr=args.warmup_lr,
warmup_mode=args.warmup_mode,
batch_size=batch_size,
num_epochs=args.num_epochs,
num_training_samples=num_training_samples,
dtype=args.dtype,
gamma_wd_mult=args.gamma_wd_mult,
beta_wd_mult=args.beta_wd_mult,
bias_wd_mult=args.bias_wd_mult,
state_file_path=args.resume_state)
if args.save_dir and args.save_interval:
param_names = ds_metainfo.val_metric_capts + ds_metainfo.train_metric_capts + [
"Train.Loss", "LR"
]
lp_saver = TrainLogParamSaver(
checkpoint_file_name_prefix="{}_{}".format(ds_metainfo.short_label,
args.model),
last_checkpoint_file_name_suffix="last",
best_checkpoint_file_name_suffix=None,
last_checkpoint_dir_path=args.save_dir,
best_checkpoint_dir_path=None,
last_checkpoint_file_count=2,
best_checkpoint_file_count=2,
checkpoint_file_save_callback=save_params,
checkpoint_file_exts=(".params", ".states"),
save_interval=args.save_interval,
num_epochs=args.num_epochs,
param_names=param_names,
acc_ind=ds_metainfo.saver_acc_ind,
# bigger=[True],
# mask=None,
score_log_file_path=os.path.join(args.save_dir, "score.log"),
score_log_attempt_value=args.attempt,
best_map_log_file_path=os.path.join(args.save_dir, "best_map.log"))
else:
lp_saver = None
train_net(batch_size=batch_size,
num_epochs=args.num_epochs,
start_epoch1=args.start_epoch,
train_data=train_data,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
dtype=args.dtype,
net=net,
trainer=trainer,
lr_scheduler=lr_scheduler,
lp_saver=lp_saver,
log_interval=args.log_interval,
mixup=args.mixup,
mixup_epoch_tail=args.mixup_epoch_tail,
label_smoothing=args.label_smoothing,
num_classes=num_classes,
grad_clip_value=args.grad_clip,
batch_size_scale=args.batch_size_scale,
val_metric=get_composite_metric(
ds_metainfo.val_metric_names,
ds_metainfo.val_metric_extra_kwargs),
train_metric=get_composite_metric(
ds_metainfo.train_metric_names,
ds_metainfo.train_metric_extra_kwargs),
ctx=ctx)
def main():
args = parse_args()
if args.disable_cudnn_autotune:
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
assert (ds_metainfo.ml_type != "imgseg") or (args.batch_size == 1)
assert (ds_metainfo.ml_type != "imgseg") or args.disable_cudnn_autotune
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.net_extra_kwargs,
load_ignore_extra=ds_metainfo.load_ignore_extra,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(ds_metainfo.allow_hybridize and (not args.calc_flops)),
ctx=ctx)
assert (hasattr(net, "in_size"))
input_image_size = net.in_size
if args.data_subset == "val":
test_data = get_val_data_source(
ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
test_metric = get_composite_metric(
metric_names=ds_metainfo.val_metric_names,
metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs)
else:
test_data = get_test_data_source(
ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
test_metric = get_composite_metric(
metric_names=ds_metainfo.test_metric_names,
metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs)
batch_fn = get_batch_fn(use_imgrec=ds_metainfo.use_imgrec)
if args.show_progress:
from tqdm import tqdm
test_data = tqdm(test_data)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
test(
net=net,
test_data=test_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
metric=test_metric,
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
# calc_weight_count=(not log_file_exist),
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True)
def parse_args():
parser = argparse.ArgumentParser(
description='Train a model for image classification (Gluon/CIFAR)',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--dataset',
type=str,
default="CIFAR10",
help='dataset name. options are CIFAR10 and CIFAR100')
parser.add_argument(
"--work-dir",
type=str,
default=os.path.join("..", "imgclsmob_data"),
help="path to working directory only for dataset root path preset")
args, _ = parser.parse_known_args()
dataset_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
dataset_metainfo.add_dataset_parser_arguments(parser=parser,
work_dir_path=args.work_dir)
parser.add_argument(
'--model',
type=str,
required=True,
help='type of model to use. see model_provider for options.')
parser.add_argument('--use-pretrained',
action='store_true',
help='enable using pretrained model from gluon.')
parser.add_argument('--dtype',
type=str,
default='float32',
help='data type for training')
parser.add_argument('--not-hybridize',
action='store_true',
help='do not hybridize model')
parser.add_argument(
'--resume',
type=str,
default='',
help='resume from previously saved parameters if not None')
parser.add_argument(
'--resume-state',
type=str,
default='',
help='resume from previously saved optimizer state if not None')
parser.add_argument('--num-gpus',
type=int,
default=0,
help='number of gpus to use.')
parser.add_argument('-j',
'--num-data-workers',
dest='num_workers',
default=4,
type=int,
help='number of preprocessing workers')
parser.add_argument('--batch-size',
type=int,
default=128,
help='training batch size per device (CPU/GPU).')
parser.add_argument('--batch-size-scale',
type=int,
default=1,
help='manual batch-size increasing factor.')
parser.add_argument('--num-epochs',
type=int,
default=200,
help='number of training epochs.')
parser.add_argument(
'--start-epoch',
type=int,
default=1,
help='starting epoch for resuming, default is 1 for new training')
parser.add_argument('--attempt',
type=int,
default=1,
help='current number of training')
parser.add_argument('--optimizer-name',
type=str,
default='nag',
help='optimizer name')
parser.add_argument('--lr', type=float, default=0.1, help='learning rate')
parser.add_argument(
'--lr-mode',
type=str,
default='cosine',
help='learning rate scheduler mode. options are step, poly and cosine')
parser.add_argument('--lr-decay',
type=float,
default=0.1,
help='decay rate of learning rate')
parser.add_argument(
'--lr-decay-period',
type=int,
default=0,
help=
'interval for periodic learning rate decays. default is 0 to disable.')
parser.add_argument('--lr-decay-epoch',
type=str,
default='40,60',
help='epoches at which learning rate decays')
parser.add_argument('--target-lr',
type=float,
default=1e-8,
help='ending learning rate')
parser.add_argument('--poly-power',
type=float,
default=2,
help='power value for poly LR scheduler')
parser.add_argument('--warmup-epochs',
type=int,
default=0,
help='number of warmup epochs.')
parser.add_argument('--warmup-lr',
type=float,
default=1e-8,
help='starting warmup learning rate')
parser.add_argument(
'--warmup-mode',
type=str,
default='linear',
help=
'learning rate scheduler warmup mode. options are linear, poly and constant'
)
parser.add_argument('--momentum',
type=float,
default=0.9,
help='momentum value for optimizer')
parser.add_argument('--wd',
type=float,
default=0.0001,
help='weight decay rate')
parser.add_argument('--gamma-wd-mult',
type=float,
default=1.0,
help='weight decay multiplier for batchnorm gamma')
parser.add_argument('--beta-wd-mult',
type=float,
default=1.0,
help='weight decay multiplier for batchnorm beta')
parser.add_argument('--bias-wd-mult',
type=float,
default=1.0,
help='weight decay multiplier for bias')
parser.add_argument('--grad-clip',
type=float,
default=None,
help='max_norm for gradient clipping')
parser.add_argument('--label-smoothing',
action='store_true',
help='use label smoothing')
parser.add_argument('--mixup',
action='store_true',
help='use mixup strategy')
parser.add_argument(
'--mixup-epoch-tail',
type=int,
default=20,
help='number of epochs without mixup at the end of training')
parser.add_argument('--log-interval',
type=int,
default=200,
help='number of batches to wait before logging.')
parser.add_argument(
'--save-interval',
type=int,
default=4,
help='saving parameters epoch interval, best model will always be saved'
)
parser.add_argument('--save-dir',
type=str,
default='',
help='directory of saved models and log-files')
parser.add_argument('--logging-file-name',
type=str,
default='train.log',
help='filename of training log')
parser.add_argument('--seed',
type=int,
default=-1,
help='Random seed to be fixed')
parser.add_argument('--log-packages',
type=str,
default='mxnet',
help='list of python packages for logging')
parser.add_argument('--log-pip-packages',
type=str,
default='mxnet-cu100',
help='list of pip packages for logging')
parser.add_argument('--tune-layers',
type=str,
default='',
help='Regexp for selecting layers for fine tuning')
args = parser.parse_args()
return args
