飞桨框架模型导出¶
本节以LeNet网络为例，介绍从训练LeNet网络存储动态图模型，到存储部署模型流程。包含PaddleSlim输出压缩模型部分，分为以下三部分：
1.Paddle训练模型¶
该节参考 LeNet 的 MNIST 数据集图像分类 ，使用 Paddle 训练LeNet模型，并存储成训练模型（即动态图模型，模型参数文件名为*.pdparams和*.pdopt）。
依赖包导入
import paddle
import paddle.nn.functional as F
from paddle.nn import Layer
from paddle.vision.datasets import MNIST
from paddle.metric import Accuracy
from paddle.nn import Conv2D, MaxPool2D, Linear
from paddle.static import InputSpec
from paddle.jit import to_static
from paddle.vision.transforms import ToTensor
查看 Paddle 版本
print(paddle.__version__)
数据集准备
train_dataset = MNIST(mode='train', transform=ToTensor())
test_dataset = MNIST(mode='test', transform=ToTensor())
构建 LeNet 网络
class LeNet(paddle.nn.Layer):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
self.max_pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1)
self.max_pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)
self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)
self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.max_pool1(x)
x = F.relu(x)
x = self.conv2(x)
x = self.max_pool2(x)
x = paddle.flatten(x, start_axis=1,stop_axis=-1)
x = self.linear1(x)
x = F.relu(x)
x = self.linear2(x)
x = F.relu(x)
x = self.linear3(x)
return x
模型训练
train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
model = LeNet()
optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
def train(model, optim):
model.train()
epochs = 2
for epoch in range(epochs):
for batch_id, data in enumerate(train_loader()):
x_data = data[0]
y_data = data[1]
predicts = model(x_data)
loss = F.cross_entropy(predicts, y_data)
acc = paddle.metric.accuracy(predicts, y_data)
loss.backward()
if batch_id % 300 == 0:
print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy()))
optim.step()
optim.clear_grad()
train(model, optim)
存储训练模型（训练格式）：该操作会保存动态图模型（模型参数文件名为*.pdparams和*.pdopt），您可以参考 参数保存 ，了解如何在动态图下存储训练格式的模型。只需调用
paddle.save接口即可。
paddle.save(model.state_dict(), 'lenet.pdparams')
paddle.save(optim.state_dict(), "lenet.pdopt")
2.训练模型转换为预测部署模型¶
加载预训练模型：您可以参考参数载入了解如何在动态图下加载训练格式的模型，此方法可帮助您完成恢复训练，即模型状态回到训练中断的时刻，恢复训练之后的梯度更新走向是和恢复训练前的梯度走向完全相同的。只需调用
paddle.load接口加载训练格式的模型，再调用set_state_dict接口恢复模型训练中断时刻的状态。
model_state_dict = paddle.load('lenet.pdparams')
opt_state_dict = paddle.load('lenet.pdopt')
model.set_state_dict(model_state_dict)
optim.set_state_dict(opt_state_dict)
存储为预测部署模型（即动态图转静态图操作）：部署时需要使用预测格式的模型。预测格式模型相对训练格式模型而言，在拓扑上裁剪掉了预测不需要的算子，并且会做特定部署优化。您可以参考InputSpec来完成动转静功能。只需InputSpec标记模型的输入，调用
paddle.jit.to_static和paddle.jit.save即可得到预测格式的模型（即保存成静态图模型，模型参数文件名为*.pdmodel和*.pdiparams）。
注：InputSpec中shape第一个维度设置成None，表示推理时接受任意batch的输入。更详细的InputSpec使用可参考InputSpec
net = to_static(model, input_spec=[InputSpec(shape=[None, 1, 28, 28], name='x')])
paddle.jit.save(net, 'inference_model/lenet')
参考代码¶
import paddle
import paddle.nn.functional as F
from paddle.nn import Layer
from paddle.vision.datasets import MNIST
from paddle.metric import Accuracy
from paddle.nn import Conv2D, MaxPool2D, Linear
from paddle.static import InputSpec
from paddle.jit import to_static
from paddle.vision.transforms import ToTensor
class LeNet(paddle.nn.Layer):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = paddle.nn.Conv2D(in_channels=1,
out_channels=6,
kernel_size=5,
stride=1,
padding=2)
self.max_pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2 = paddle.nn.Conv2D(in_channels=6,
out_channels=16,
kernel_size=5,
stride=1)
self.max_pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.linear1 = paddle.nn.Linear(in_features=16 * 5 * 5,
out_features=120)
self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)
self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)
def forward(self, x):
# x = x.reshape((-1, 1, 28, 28))
x = self.conv1(x)
x = F.relu(x)
x = self.max_pool1(x)
x = F.relu(x)
x = self.conv2(x)
x = self.max_pool2(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self.linear1(x)
x = F.relu(x)
x = self.linear2(x)
x = F.relu(x)
x = self.linear3(x)
return x
def train(model, optim):
model.train()
epochs = 2
for epoch in range(epochs):
for batch_id, data in enumerate(train_loader()):
x_data = data[0]
y_data = data[1]
predicts = model(x_data)
loss = F.cross_entropy(predicts, y_data)
# calc loss
acc = paddle.metric.accuracy(predicts, y_data)
loss.backward()
if batch_id % 300 == 0:
print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(
epoch, batch_id, loss.numpy(), acc.numpy()))
optim.step()
optim.clear_grad()
if __name__ == '__main__':
# paddle version
print(paddle.__version__)
# prepare datasets
train_dataset = MNIST(mode='train', transform=ToTensor())
test_dataset = MNIST(mode='test', transform=ToTensor())
# load dataset
train_loader = paddle.io.DataLoader(train_dataset,
batch_size=64,
shuffle=True)
# build network
model = LeNet()
# prepare optimizer
optim = paddle.optimizer.Adam(learning_rate=0.001,
parameters=model.parameters())
# train network
train(model, optim)
# save training format model
paddle.save(model.state_dict(), 'lenet.pdparams')
paddle.save(optim.state_dict(), "lenet.pdopt")
# load training format model
model_state_dict = paddle.load('lenet.pdparams')
opt_state_dict = paddle.load('lenet.pdopt')
model.set_state_dict(model_state_dict)
optim.set_state_dict(opt_state_dict)
# save inferencing format model
net = to_static(model,
input_spec=[InputSpec(shape=[None, 1, 28, 28], name='x')])
paddle.jit.save(net, 'inference_model/lenet')