## PyTorch模型导出为ONNX模型

目前pytorch2paddle主要支持pytorch ScriptModule。 用户可通过如下示例代码，将torchvison或者自己开发写的模型转换成ScriptModule model:
```
#coding: utf-8
import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
# 定义模型
class AlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(0.0),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(0.0),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        for i in range(1):
            x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x
# 初始化模型
model = AlexNet()
# 加载参数
state_dict = load_state_dict_from_url('https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
                                      progress=True)
model.load_state_dict(state_dict)
# 设置模式
model.eval()
# 生成ScriptModule并保存
script = torch.jit.script(model)
torch.jit.save(script, "alexnet.pt")
```