def main():
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)
global_config.train = False
num_gpus = args.num_gpus
if num_gpus > 0:
cuda.get_device(0).use()
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
num_gpus=num_gpus)
val_iterator, val_dataset_len = get_val_data_iterator(
dataset_name=args.dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
assert (args.use_pretrained or args.resume.strip())
test(
net=net,
val_iterator=val_iterator,
val_dataset_len=val_dataset_len,
num_gpus=num_gpus,
calc_weight_count=True,
extended_log=True)
def main():
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)
num_gpus = args.num_gpus
if num_gpus > 0:
cuda.get_device(0).use()
batch_size = args.batch_size
net = prepare_model(model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
num_gpus=num_gpus)
train_iter, val_iter = get_data_iterators(batch_size=batch_size,
num_workers=args.num_workers)
trainer = prepare_trainer(net=net,
optimizer_name=args.optimizer_name,
lr=args.lr,
momentum=args.momentum,
num_epochs=args.num_epochs,
train_iter=train_iter,
val_iter=val_iter,
logging_dir_path=args.save_dir,
num_gpus=num_gpus)
# if args.save_dir and args.save_interval:
# lp_saver = TrainLogParamSaver(
# checkpoint_file_name_prefix='imagenet_{}'.format(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=['.npz', '.states'],
# save_interval=args.save_interval,
# num_epochs=args.num_epochs,
# param_names=['Val.Top1', 'Train.Top1', 'Val.Top5', 'Train.Loss', 'LR'],
# acc_ind=2,
# # 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
trainer.run()
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.batch_size == 1)
assert (ds_metainfo.ml_type != "imgseg") or args.disable_cudnn_autotune
global_config.train = False
use_gpus = prepare_ch_context(args.num_gpus)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
use_gpus=use_gpus,
net_extra_kwargs=ds_metainfo.test_net_extra_kwargs,
num_classes=(args.num_classes if ds_metainfo.ml_type != "hpe" else None),
in_channels=args.in_channels)
assert (hasattr(net, "classes") or (ds_metainfo.ml_type == "hpe"))
assert (hasattr(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)
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)
assert (args.use_pretrained or args.resume.strip())
acc_values = calc_model_accuracy(
net=net,
test_data=test_data,
metric=test_metric,
calc_weight_count=True,
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()
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
global_config.train = False
use_gpus = prepare_ch_context(args.num_gpus)
net = prepare_model(model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
use_gpus=use_gpus,
net_extra_kwargs=ds_metainfo.net_extra_kwargs,
num_classes=args.num_classes,
in_channels=args.in_channels)
assert (hasattr(net, "classes"))
assert (hasattr(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)
assert (args.use_pretrained or args.resume.strip())
test(net=net,
test_data=test_data,
metric=test_metric,
calc_weight_count=True,
extended_log=True)
def main():
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)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
use_gpus = prepare_ch_context(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(),
use_gpus=use_gpus,
num_classes=args.num_classes,
in_channels=args.in_channels)
assert (hasattr(net, "classes"))
assert (hasattr(net, "in_size"))
train_data = get_train_data_source(
ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
val_data = get_val_data_source(
ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
trainer = prepare_trainer(
net=net,
optimizer_name=args.optimizer_name,
lr=args.lr,
momentum=args.momentum,
num_epochs=args.num_epochs,
train_data=train_data,
val_data=val_data,
logging_dir_path=args.save_dir,
use_gpus=use_gpus)
trainer.run()
def main():
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)
global_config.train = False
num_gpus = args.num_gpus
if num_gpus > 0:
cuda.get_device(0).use()
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
num_gpus=num_gpus)
num_classes = net.classes if hasattr(net, 'classes') else 1000
input_image_size = net.in_size[0] if hasattr(net, 'in_size') else args.input_size
val_iterator, val_dataset_len = get_val_data_iterator(
data_dir=args.data_dir,
batch_size=args.batch_size,
num_workers=args.num_workers,
num_classes=num_classes)
assert (args.use_pretrained or args.resume.strip())
test(
net=net,
val_iterator=val_iterator,
val_dataset_len=val_dataset_len,
num_gpus=num_gpus,
input_image_size=input_image_size,
resize_inv_factor=args.resize_inv_factor,
calc_weight_count=True,
extended_log=True)
def main():
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)
global_config.train = False
num_gpus = args.num_gpus
if num_gpus > 0:
cuda.get_device(0).use()
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
net_extra_kwargs={"aux": False, "fixed_size": False},
use_gpus=(num_gpus > 0))
test_dataset = get_test_dataset(
dataset_name=args.dataset,
dataset_dir=args.data_dir)
assert (args.use_pretrained or args.resume.strip())
test(
net=net,
test_dataset=test_dataset,
num_gpus=num_gpus,
num_classes=args.num_classes,
calc_weight_count=True,
extended_log=True,
dataset_metainfo=get_metainfo(args.dataset))
