pytorch训练图像分类模型,并部署到MNN

1. Pytorch分类器网络

# 定义一个简单的分类网络
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        # 三个卷积层用于提取特征
        # 1 input channel image 90x90, 8 output channel image 44x44
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=8, kernel_size=3, stride=1, padding=0),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        # 8 input channel image 44x44, 16 output channel image 22x22
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=8, out_channels=8, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        # 16 input channel image 22x22, 32 output channel image 10x10
        self.conv3 = nn.Sequential(
            nn.Conv2d(in_channels=8, out_channels=8, kernel_size=3, stride=1, padding=0),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.conv4 = nn.Sequential(
            nn.Conv2d(in_channels=8, out_channels=8, kernel_size=3, stride=1, padding=0),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.conv5 = nn.Sequential(
            nn.Conv2d(in_channels=8, out_channels=8, kernel_size=3, stride=1, padding=0),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        # 分类
        self.classifier = nn.Sequential(
            nn.Linear(400, 3)
        )

    def forward(self, x):
        #print(x.size())
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        #print(x.size())
        x = x.view(-1, 400)
        x = self.classifier(x)
        return x

2. pth转ONNX

# 转换pytorch训练的pth模型到ONNX模型
def convert_model_to_ONNX(input_pth_model, output_ONNX):
    dummy_input = torch.randn(1, 1, 192, 108)
    model = SimpleNet()

    state_dict = torch.load(input_pth_model, map_location='cpu')

    new_state_dict = collections.OrderedDict()
    for k, v in state_dict.items():
        name = k[7:] # remove `module.`
        new_state_dict[name] = v

    model.load_state_dict(new_state_dict)

    input_names = ["input_image"]
    output_names = ["output_classification"]

    torch.onnx.export(model, dummy_input, output_ONNX, verbose=True, input_names=input_names,
                      output_names=output_names)

3. ONNX转MNN

MNN提供了转换ONNX到MNN模型的工具，执行如下脚本即可，关于MNN转换工具编译可以参考Model Conversion。下面是转换脚本：

./MNNConvert -f ONNX --modelFile classify.onnx --MNNModel classify.mnn --bizCode MNN

4. 在线部署

在线部署流程在这里，为使用MNN加载解析好的mnn模型参数进行inference等一系列业务操作。

5. 最后