def load_ernie_model(static_model_path):
model = ErnieModel.from_pretrained('ernie-1.0')
program_state = static.load_program_state(static_model_path)
ret_dict = static_params_to_dygraph(model, program_state)
print('转换前的参数：')
print(model.embeddings.word_embeddings.weight)
model.load_dict(ret_dict)
print('转换后的参数：')
print(model.embeddings.word_embeddings.weight)
model.save_pretrained("./ernie_checkpoint")
def load_dygraph_pretrain(model, path=None, load_static_weights=False):
if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
raise ValueError("Model pretrain path {} does not "
"exists.".format(path))
if load_static_weights:
pre_state_dict = load_program_state(path)
param_state_dict = {}
model_dict = model.state_dict()
for key in model_dict.keys():
weight_name = model_dict[key].name
if weight_name in pre_state_dict.keys():
logger.info('Load weight: {}, shape: {}'.format(
weight_name, pre_state_dict[weight_name].shape))
param_state_dict[key] = pre_state_dict[weight_name]
else:
param_state_dict[key] = model_dict[key]
model.set_dict(param_state_dict)
return
param_state_dict = paddle.load(path + ".pdparams")
model.set_dict(param_state_dict)
return
def load_dygraph_pretrain(model, path=None, load_static_weights=False):
if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
raise ValueError("Model pretrain path {} does not "
"exists.".format(path))
if load_static_weights:
pre_state_dict = load_program_state(path)
param_state_dict = {}
model_dict = model.state_dict()
for key in model_dict.keys():
weight_name = model_dict[key].name
if weight_name in pre_state_dict.keys():
logger.info('Load weight: {}, shape: {}'.format(
weight_name, pre_state_dict[weight_name].shape))
param_state_dict[key] = pre_state_dict[weight_name]
else:
param_state_dict[key] = model_dict[key]
model.set_dict(param_state_dict)
return
param_state_dict = paddle.load(path + ".pdparams")
state = fluid.io.load_program_state(path + ".pdparams")
model.set_dict(param_state_dict)
print("parameters of paddle", type(state), len(state)) # dict 1628
filename = open('./output/hrnet_parameter.txt', 'w')
i = 0
for key, value in state.items():
filename.write("\n")
if type(value) is np.ndarray:
filename.write("key{}-".format(i) + key +
" :({})".format(value.shape))
else:
filename.write("key{}-".format(i) + key + " :" + str(value))
filename.write("\n")
i += 1
filename.close()
# print(state.keys())
return state
import paddle
import paddle.static as static
paddle.enable_static()
x = static.data(name="x", shape=[10, 10], dtype='float32')
y = static.nn.fc(x, 10)
z = static.nn.fc(y, 10)
place = paddle.CPUPlace()
exe = static.Executor(place)
exe.run(static.default_startup_program())
prog = static.default_main_program()
static.save(prog, "./temp")
program_state = static.load_program_state("./temp")
static.set_program_state(prog, program_state)