set_seed(1)
if __name__ == '__main__':
args_opt = train_parse_args()
args_opt.dataset_path = os.path.abspath(args_opt.dataset_path)
config = set_config(args_opt)
start = time.time()
print(f"train args: {args_opt}\ncfg: {config}")
#set context and device init
context_device_init(config)
# define network
backbone_net, head_net, net = define_net(config, args_opt.is_training)
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, config=config)
step_size = dataset.get_dataset_size()
if args_opt.pretrain_ckpt:
if args_opt.freeze_layer == "backbone":
load_ckpt(backbone_net, args_opt.pretrain_ckpt, trainable=False)
step_size = extract_features(backbone_net, args_opt.dataset_path, config)
else:
load_ckpt(net, args_opt.pretrain_ckpt)
if step_size == 0:
raise ValueError("The step_size of dataset is zero. Check if the images' count of train dataset is more \
than batch_size in config.py")
# Currently, only Ascend support switch precision.
switch_precision(net, mstype.float16, config)
type=str,
choices=["AIR", "ONNX", "MINDIR"],
default="AIR",
help="file format")
parser.add_argument('--platform',
type=str,
default="Ascend",
choices=("Ascend", "GPU", "CPU"),
help='run platform, only support GPU, CPU and Ascend')
args = parser.parse_args()
args.is_training = False
args.run_distribute = False
context.set_context(mode=context.GRAPH_MODE, device_target=args.platform)
if args.platform == "Ascend":
context.set_context(device_id=args.device_id)
if __name__ == '__main__':
cfg = set_config(args)
set_context(cfg)
_, _, net = define_net(cfg, args.is_training)
load_ckpt(net, args.ckpt_file)
input_shp = [args.batch_size, 3, cfg.image_height, cfg.image_width]
input_array = Tensor(
np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
export(net,
input_array,
file_name=args.file_name,
file_format=args.file_format)
from src.models import CrossEntropyWithLabelSmooth, define_net
set_seed(1)
if __name__ == '__main__':
args_opt = train_parse_args()
config = set_config(args_opt)
start = time.time()
print(f"train args: {args_opt}\ncfg: {config}")
#set context and device init
context_device_init(config)
# define network
backbone_net, head_net, net = define_net(args_opt, config)
# CPU only support "incremental_learn"
if args_opt.train_method == "incremental_learn":
step_size = extract_features(backbone_net, args_opt.dataset_path,
config)
net = head_net
elif args_opt.train_method in ("train", "fine_tune"):
if args_opt.platform == "CPU":
raise ValueError(
"Currently, CPU only support \"incremental_learn\", not \"fine_tune\" or \"train\"."
)
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
config=config)
eval.
"""
from mindspore import nn
from mindspore.train.model import Model
from mindspore.common import dtype as mstype
from src.dataset import create_dataset
from src.config import set_config
from src.args import eval_parse_args
from src.models import define_net, load_ckpt
from src.utils import switch_precision, set_context
if __name__ == '__main__':
args_opt = eval_parse_args()
config = set_config(args_opt)
backbone_net, head_net, net = define_net(config)
#load the trained checkpoint file to the net for evaluation
if args_opt.head_ckpt:
load_ckpt(backbone_net, args_opt.pretrain_ckpt)
load_ckpt(head_net, args_opt.head_ckpt)
else:
load_ckpt(net, args_opt.pretrain_ckpt)
set_context(config)
switch_precision(net, mstype.float16, config)
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, config=config)
step_size = dataset.get_dataset_size()
if step_size == 0:
raise ValueError("The step_size of dataset is zero. Check if the images count of train dataset is more \
