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1、Paddle模型字典形式存储

paddle保存模型参数是parambase格式，paddle.save对要保存的字典对象的值会进行解码，对于parambase格式会进行转换。如果我们保存的格式是{‘model’: model.state_dict()}，字典的值是字典（相当于2级字典），该2级字典的值是模型参数（parambase格式），但是paddle.save只对字典的值进行解码，对于该2级字典的值不会进行解译，因此需要手动修改。

def model_rebuild(model_state_dict):new_dict = {}for k, v in model_state_dict.items():new_dict[k] = v.numpy()return new_dictckpt = {'acc': val_dict['acc_now'],'loss': val_dict['loss_now'],'epoch': val_dict['epoch_now'],'model_state_dict': model_rebuild(model.state_dict()),'optimizer_state_dict': optimizer.state_dict(),
}paddle.save(ckpt, save_path)

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2、动态图存储载入体系

为提升框架使用体验，飞桨框架2.0将主推动态图模式，动态图模式下的存储载入接口包括：

paddle.save
paddle.load
paddle.jit.save
paddle.jit.load

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save

参数存储时，先获取目标对象（Layer或者Optimzier）的state_dict，然后将state_dict存储至磁盘，示例如下（接前述示例）:

paddle.save(layer.state_dict(), "linear_net.pdparams")
paddle.save(adam.state_dict(), "adam.pdopt")

load

参数载入时，先从磁盘载入保存的state_dict，然后通过set_state_dict方法配置到目标对象中，示例如下（接前述示例）：

layer_state_dict = paddle.load("linear_net.pdparams")
opt_state_dict = paddle.load("adam.pdopt")layer.set_state_dict(layer_state_dict)
adam.set_state_dict(opt_state_dict)

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import numpy as np
import paddle
import paddle.nn as nn
import paddle.optimizer as optBATCH_SIZE = 16
BATCH_NUM = 4
EPOCH_NUM = 4IMAGE_SIZE = 784
CLASS_NUM = 10# define a random dataset
class RandomDataset(paddle.io.Dataset):def __init__(self, num_samples):self.num_samples = num_samplesdef __getitem__(self, idx):image = np.random.random([IMAGE_SIZE]).astype('float32')label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')return image, labeldef __len__(self):return self.num_samplesclass LinearNet(nn.Layer):def __init__(self):super(LinearNet, self).__init__()self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)def forward(self, x):return self._linear(x)def train(layer, loader, loss_fn, opt):for epoch_id in range(EPOCH_NUM):for batch_id, (image, label) in enumerate(loader()):out = layer(image)loss = loss_fn(out, label)loss.backward()opt.step()opt.clear_grad()print("Epoch {} batch {}: loss = {}".format(epoch_id, batch_id, np.mean(loss.numpy())))# create network
layer = LinearNet()
loss_fn = nn.CrossEntropyLoss()
adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters())# create data loader
dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
loader = paddle.io.DataLoader(dataset,batch_size=BATCH_SIZE,shuffle=True,drop_last=True,num_workers=2)# train
train(layer, loader, loss_fn, adam)# save
paddle.save(layer.state_dict(), "linear_net.pdparams")
paddle.save(adam.state_dict(), "adam.pdopt")# load
layer_state_dict = paddle.load("linear_net.pdparams")
opt_state_dict = paddle.load("adam.pdopt")layer.set_state_dict(layer_state_dict)
adam.set_state_dict(opt_state_dict)

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动态图训练 + 模型&参数存储

class LinearNet(nn.Layer):def __init__(self):super(LinearNet, self).__init__()self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)def forward(self, x):return self._linear(x)layer = LinearNet()
# save
path = "example.dy_model/linear"
paddle.jit.save(layer=layer,path=path,input_spec=[InputSpec(shape=[None, 784], dtype='float32')])

动转静训练 + 模型&参数存储

class LinearNet(nn.Layer):def __init__(self):super(LinearNet, self).__init__()self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)@paddle.jit.to_staticdef forward(self, x):return self._linear(x)layer = LinearNet()
# save
path = "example.model/linear"
paddle.jit.save(layer, path)

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3、模型的载入

import paddle
import paddle.nn as nnIMAGE_SIZE = 784
CLASS_NUM = 10class LinearNet(nn.Layer):def __init__(self):super(LinearNet, self).__init__()self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)@paddle.jit.to_staticdef forward(self, x):return self._linear(x)# create network
layer = LinearNet()# load
path = "example.model/linear"
state_dict = paddle.load(path)# inference
layer.set_state_dict(state_dict, use_structured_name=False)
layer.eval()
x = paddle.randn([1, IMAGE_SIZE], 'float32')
pred = layer(x)

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4、ＯＮＮＸ的转化

动态图导出ONNX协议

安装pip install paddle2onnx onnx onnxruntime

import paddle
from paddle import nn
from paddle.static import InputSpecclass LinearNet(nn.Layer):def __init__(self):super(LinearNet, self).__init__()self._linear = nn.Linear(784, 10)def forward(self, x):return self._linear(x)# export to ONNX
layer = LinearNet()
save_path = 'onnx.save/linear_net'
x_spec = InputSpec([None, 784], 'float32', 'x')
paddle.onnx.export(layer, save_path, input_spec=[x_spec])

ONNX模型的验证

# check by ONNX
import onnxonnx_file = save_path +  '.onnx'
onnx_model = onnx.load(onnx_file)
onnx.checker.check_model(onnx_model)
print('The model is checked!')

ONNXRuntime推理

import numpy as np
import onnxruntimex = np.random.random((2, 784)).astype('float32')# predict by ONNX Runtime
ort_sess = onnxruntime.InferenceSession(onnx_file)
ort_inputs = {ort_sess.get_inputs()[0].name: x}
ort_outs = ort_sess.run(None, ort_inputs)print("Exported model has been predicted by ONNXRuntime!")# predict by Paddle
layer.eval()
paddle_outs = layer(x)# compare ONNX Runtime and Paddle results
np.testing.assert_allclose(ort_outs[0], paddle_outs.numpy(), rtol=1.0, atol=1e-05)print("The difference of results between ONNXRuntime and Paddle looks good!")

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6、动态图与静态图

动态图有诸多优点，包括易用的接口，Python风格的编程体验，友好的debug交互机制等。 在动态图模式下，代码是按照我们编写的顺序依次执行。这种机制更符合Python程序员的习 惯，可以很方便地将大脑中的想法快速地转化为实际代码，也更容易调试。但在性能方面， Python执行开销较大，与C++有一定差距。因此在工业界的许多部署场景中（如大型推荐系统、移动端）都倾向于直接使用C++来提速。

相比动态图，静态图在部署方面更具有性能的优势。静态图程序在编译执行时，先搭建模型 的神经网络结构，然后再对神经网络执行计算操作。预先搭建好的神经网络可以脱离Python依赖，在C++端被重新解析执行，而且拥有整体网络结构也能进行一些网络结构的优化。

动态图代码更易编写和debug，但在部署性能上，静态图更具优势。