from mindspore.common import set_seed
from src.dataset import create_dataset, extract_features
from src.lr_generator import get_lr
from src.config import set_config
from src.args import train_parse_args
from src.utils import context_device_init, switch_precision, config_ckpoint
from src.models import CrossEntropyWithLabelSmooth, define_net, load_ckpt
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)
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)
