def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
dy_model_class = load_dy_model_class(args.abs_dir)
config["config_abs_dir"] = args.abs_dir
# tools.vars
use_gpu = config.get("runner.use_gpu", True)
train_data_dir = config.get("runner.train_data_dir", None)
epochs = config.get("runner.epochs", None)
print_interval = config.get("runner.print_interval", None)
model_save_path = config.get("runner.model_save_path", "model_output")
model_init_path = config.get("runner.model_init_path", None)
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, train_data_dir: {}, epochs: {}, print_interval: {}, model_save_path: {}".
format(use_gpu, train_data_dir, epochs, print_interval,
model_save_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
dy_model = dy_model_class.create_model(config)
load_model(model_init_path, dy_model)
# example dnn model forward
dy_model = paddle.jit.to_static(
dy_model,
input_spec=[[
paddle.static.InputSpec(
shape=[None, 1], dtype='int64') for jj in range(26)
], paddle.static.InputSpec(
shape=[None, 13], dtype='float32')])
save_jit_model(dy_model, model_save_path, prefix='tostatic')
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
dy_model_class = load_dy_model_class(args.abs_dir)
config["config_abs_dir"] = args.abs_dir
# modify config from command
if args.opt:
for parameter in args.opt:
parameter = parameter.strip()
key, value = parameter.split("=")
if type(config.get(key)) is int:
value = int(value)
if type(config.get(key)) is bool:
value = (True if value.lower() == "true" else False)
config[key] = value
# tools.vars
use_gpu = config.get("runner.use_gpu", True)
train_data_dir = config.get("runner.train_data_dir", None)
epochs = config.get("runner.epochs", None)
print_interval = config.get("runner.print_interval", None)
model_save_path = config.get("runner.model_save_path", "model_output")
model_init_path = config.get("runner.model_init_path", None)
end_epoch = config.get("runner.infer_end_epoch", 0)
CE = config.get("runner.CE", False)
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, train_data_dir: {}, epochs: {}, print_interval: {}, model_save_path: {}"
.format(use_gpu, train_data_dir, epochs, print_interval,
model_save_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
dy_model = dy_model_class.create_model(config)
if not CE:
model_save_path = os.path.join(model_save_path, str(end_epoch - 1))
load_model(model_init_path, dy_model)
dy_model = paddle.jit.to_static(
dy_model,
input_spec=[[
paddle.static.InputSpec(shape=[None, 15], dtype='int'),
paddle.static.InputSpec(shape=[
None,
], dtype='float32'),
paddle.static.InputSpec(shape=[None, 10, 10], dtype='int'),
paddle.static.InputSpec(shape=[
None,
], dtype='int')
]])
save_jit_model(dy_model, model_save_path, prefix='tostatic')