def test_load_with_param_filename(self):
self.save_dirname = "static_mnist.load_state_dict.param_filename"
self.model_filename = None
self.params_filename = "static_mnist.params"
orig_param_dict = self.train_and_save_model()
configs = paddle.SaveLoadConfig()
configs.params_filename = self.params_filename
load_param_dict, _ = paddle.load(self.save_dirname, configs)
self.check_load_state_dict(orig_param_dict, load_param_dict)
def test_load_default(self):
self.save_dirname = "static_mnist.load_state_dict.default"
self.model_filename = None
self.params_filename = None
orig_param_dict = self.train_and_save_model()
configs = paddle.SaveLoadConfig()
configs.separate_params = True
load_param_dict, _ = paddle.load(self.save_dirname, configs)
self.check_load_state_dict(orig_param_dict, load_param_dict)
shuffle=True,
drop_last=True,
num_workers=2)
# train model
for data in loader():
exe.run(fluid.default_main_program(), feed=data, fetch_list=[avg_loss])
# save with params_filename
model_path = "fc.example.model.with_params_filename"
fluid.io.save_inference_model(model_path, ["image"], [pred],
exe,
params_filename="__params__")
# enable dygraph mode
paddle.disable_static(place)
# load
config = paddle.SaveLoadConfig()
config.params_filename = "__params__"
fc = paddle.jit.load(model_path, config=config)
# inference
fc.eval()
x = paddle.randn([1, IMAGE_SIZE], 'float32')
pred = fc(x)
config = paddle.SaveLoadConfig()
config.params_filename = "__params__"
load_param_dict, _ = paddle.load(model_path, config)
import paddle
paddle.disable_static()
linear = paddle.nn.Linear(5, 1)
state_dict = linear.state_dict()
paddle.save(state_dict, "paddle_dy")
configs = paddle.SaveLoadConfig()
configs.keep_name_table = True
para_state_dict, _ = paddle.load("paddle_dy", configs)
print(para_state_dict)
# the name_table is 'StructuredToParameterName@@'
# {'bias': array([0.], dtype=float32),
# 'StructuredToParameterName@@':
# {'bias': u'linear_0.b_0', 'weight': u'linear_0.w_0'},
# 'weight': array([[ 0.04230034],
# [-0.1222527 ],
# [ 0.7392676 ],
# [-0.8136974 ],
# [ 0.01211023]], dtype=float32)}