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)
batch_size = prepare_ke_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())
num_classes = net.classes if hasattr(net, "classes") else 1000
input_image_size = net.in_size if hasattr(
net, "in_size") else (args.input_size, args.input_size)
train_data, val_data = get_data_rec(
rec_train=args.rec_train,
rec_train_idx=args.rec_train_idx,
rec_val=args.rec_val,
rec_val_idx=args.rec_val_idx,
batch_size=batch_size,
num_workers=args.num_workers,
input_image_size=input_image_size,
resize_inv_factor=args.resize_inv_factor)
train_gen = get_data_generator(data_iterator=train_data,
num_classes=num_classes)
val_gen = get_data_generator(data_iterator=val_data,
num_classes=num_classes)
net = prepare_trainer(net=net,
optimizer_name=args.optimizer_name,
momentum=args.momentum,
lr=args.lr,
num_gpus=args.num_gpus,
state_file_path=args.resume_state)
train_net(net=net,
train_gen=train_gen,
val_gen=val_gen,
train_num_examples=1281167,
val_num_examples=50048,
num_epochs=args.num_epochs,
checkpoint_filepath=os.path.join(
args.save_dir, "imagenet_{}.h5".format(args.model)),
start_epoch1=args.start_epoch)
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)
batch_size = prepare_ke_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())
num_classes = net.classes if hasattr(net, "classes") else 1000
input_image_size = net.in_size if hasattr(net, "in_size") else (args.input_size, args.input_size)
train_data, val_data = get_data_rec(
rec_train=args.rec_train,
rec_train_idx=args.rec_train_idx,
rec_val=args.rec_val,
rec_val_idx=args.rec_val_idx,
batch_size=batch_size,
num_workers=args.num_workers,
input_image_size=input_image_size,
resize_inv_factor=args.resize_inv_factor,
only_val=True)
val_gen = get_data_generator(
data_iterator=val_data,
num_classes=num_classes)
val_size = 50000
assert (args.use_pretrained or args.resume.strip())
test(
net=net,
val_gen=val_gen,
val_size=val_size,
batch_size=batch_size,
num_gpus=args.num_gpus,
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)
batch_size = prepare_ke_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
num_classes = 1000
net = prepare_model(model_name=args.model,
classes=num_classes,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip())
train_data, val_data = get_data_rec(rec_train=args.rec_train,
rec_train_idx=args.rec_train_idx,
rec_val=args.rec_val,
rec_val_idx=args.rec_val_idx,
batch_size=batch_size,
num_workers=args.num_workers)
val_gen = get_data_generator(data_iterator=val_data,
num_classes=num_classes)
val_size = 50000
assert (args.use_pretrained or args.resume.strip())
test(net=net,
val_gen=val_gen,
val_size=val_size,
batch_size=batch_size,
num_gpus=args.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)
batch_size = prepare_ke_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())
num_classes = net.classes if hasattr(net, 'classes') else 1000
input_image_size = net.in_size if hasattr(
net, 'in_size') else (args.input_size, args.input_size)
train_data, val_data = get_data_rec(
rec_train=args.rec_train,
rec_train_idx=args.rec_train_idx,
rec_val=args.rec_val,
rec_val_idx=args.rec_val_idx,
batch_size=batch_size,
num_workers=args.num_workers,
input_image_size=input_image_size,
resize_inv_factor=args.resize_inv_factor)
train_gen = get_data_generator(data_iterator=train_data,
num_classes=num_classes)
val_gen = get_data_generator(data_iterator=val_data,
num_classes=num_classes)
net = prepare_trainer(net=net,
optimizer_name=args.optimizer_name,
momentum=args.momentum,
lr=args.lr,
num_gpus=args.num_gpus,
state_file_path=args.resume_state)
# 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=('.h5', '.h5states'),
# 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
train_net(net=net,
train_gen=train_gen,
val_gen=val_gen,
train_num_examples=1281167,
val_num_examples=50048,
num_epochs=args.num_epochs,
checkpoint_filepath=os.path.join(
args.save_dir, 'imagenet_{}.h5'.format(args.model)),
start_epoch1=args.start_epoch)
