load

paddle. load ( path, **configs ) [源代码]

从指定路径载入可以在paddle中使用的对象实例。

注解

目前支持载入:Layer 或者 Optimizer 的 state_dict,Tensor以及包含Tensor的嵌套list、tuple、dict,Program。对于Tensor对象,只保存了它的名字和数值,没有保存stop_gradient等属性,如果您需要这些没有保存的属性,请调用set_value接口将数值设置到带有这些属性的Tensor中。

遇到使用问题,请参考:

参数

  • path (str) – 载入目标对象实例的路径对象。通常该路径是目标文件的路径,当从用于存储预测模型API的存储结果中载入state_dict时,该路径可能是一个文件前缀或者目录。

  • **config (dict, 可选) - 其他用于兼容的载入配置选项。这些选项将来可能被移除,如果不是必须使用,不推荐使用这些配置选项。默认为 None。目前支持以下配置选项:(1) model_filename (str) - paddle 1.x版本 save_inference_model 接口存储格式的预测模型文件名,原默认文件名为 __model__ ; (2) params_filename (str) - paddle 1.x版本 save_inference_model 接口存储格式的参数文件名,没有默认文件名,默认将各个参数分散存储为单独的文件; (3) return_numpy(bool) - 如果被指定为 Trueload 的结果中的Tensor会被转化为 numpy.ndarray ,默认为 False

返回

Object,一个可以在paddle中使用的对象实例

代码示例

# example 1: dynamic graph
import paddle
emb = paddle.nn.Embedding(10, 10)
layer_state_dict = emb.state_dict()

# save state_dict of emb
paddle.save(layer_state_dict, "emb.pdparams")

scheduler = paddle.optimizer.lr.NoamDecay(
    d_model=0.01, warmup_steps=100, verbose=True)
adam = paddle.optimizer.Adam(
    learning_rate=scheduler,
    parameters=emb.parameters())
opt_state_dict = adam.state_dict()

# save state_dict of optimizer
paddle.save(opt_state_dict, "adam.pdopt")
# save weight of emb
paddle.save(emb.weight, "emb.weight.pdtensor")

# load state_dict of emb
load_layer_state_dict = paddle.load("emb.pdparams")
# load state_dict of optimizer
load_opt_state_dict = paddle.load("adam.pdopt")
# load weight of emb
load_weight = paddle.load("emb.weight.pdtensor")
emb.weight.set_value(load_weight)
# example 2: Load multiple state_dict at the same time
import paddle
from paddle import nn
from paddle.optimizer import Adam

layer = paddle.nn.Linear(3, 4)
adam = Adam(learning_rate=0.001, parameters=layer.parameters())
obj = {'model': layer.state_dict(), 'opt': adam.state_dict(), 'epoch': 100}
path = 'example/model.pdparams'
paddle.save(obj, path)
obj_load = paddle.load(path)
# example 3: static graph
import paddle
import paddle.static as static

paddle.enable_static()

# create network
x = paddle.static.data(name="x", shape=[None, 224], dtype='float32')
z = paddle.static.nn.fc(x, 10)

place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
prog = paddle.static.default_main_program()
for var in prog.list_vars():
    if list(var.shape) == [224, 10]:
        tensor = var.get_value()
        break

# save/load tensor
path_tensor = 'temp/tensor.pdtensor'
paddle.save(tensor, path_tensor)
load_tensor = paddle.load(path_tensor)

# save/load state_dict
path_state_dict = 'temp/model.pdparams'
paddle.save(prog.state_dict("param"), path_tensor)
load_state_dict = paddle.load(path_tensor)
# example 4: load program
import paddle

paddle.enable_static()

data = paddle.static.data(
    name='x_static_save', shape=(None, 224), dtype='float32')
y_static = z = paddle.static.nn.fc(data, 10)
main_program = paddle.static.default_main_program()
path = "example/main_program.pdmodel"
paddle.save(main_program, path)
load_main = paddle.load(path)
print(load_main)