首先需要安装protobuf(3.0以上)
# macOS
brew install protobuf
其它平台请参考官方安装步骤
cd MNN
mkdir build
cd build
cmake .. -DMNN_BUILD_CONVERTER=true
make
无法正常编译参考
模型转换的使用
Usage:MNNConvert [OPTION...]-h, --help Convert Other Model Format To MNN Model-v, --version show current version-f, --framework arg model type, ex: [TF,CAFFE,ONNX,TFLITE,MNN]--modelFile arg tensorflow Pb or caffeModel, ex: *.pb,*caffemodel--prototxt arg only used for caffe, ex: *.prototxt--MNNModel arg MNN model, ex: *.mnn--benchmarkModel Do NOT save big size data, such as Conv's weight,BN'sgamma,beta,mean and variance etc. Only used to testthe cost of the model--bizCode arg MNN Model Flag, ex: MNN--debug Enable debugging mode.
说明: 选项benchmarkModel将模型中例如卷积的weight，BN的mean，var等参数移除，减小转换后模型文件大小，在运行时随机初始化参数，以方便测试模型的性能。
tensorflow/ONNX/tflite
./MNNConvert -f TF/ONNX/TFLITE --modelFile XXX.pb/XXX.onnx/XXX.tflite --MNNModel XXX.XX --bizCode XXX
三个选项是必须的！
例如:
./MNNConvert -f TF --modelFile path/to/mobilenetv1.pb --MNNModel model.mnn --bizCode MNN
caffe
./MNNConvert -f CAFFE --modelFile XXX.caffemodel --prototxt XXX.prototxt --MNNModel XXX.XX --bizCode XXX
四个选项是必须的！
例如:
./MNNConvert -f CAFFE --modelFile path/to/mobilenetv1.caffemodel --prototxt path/to/mobilenetv1.prototxt --MNNModel model.mnn --bizCode MNN
MNN
./MNNConvert -f MNN --modelFile XXX.mnn --MNNModel XXX.XX --bizCode XXX
查看版本号
./MNNConvert --version
MNNDump2Json
将MNN模型bin文件 dump 成可读的类json格式文件，以方便对比原始模型参数
Pytorch 模型转换
- 用Pytorch的 onnx.export 接口转换 Onnx 模型文件（参考：https://pytorch.org/docs/stable/onnx.html）
import torch
import torchvisiondummy_input = torch.randn(10, 3, 224, 224, device='cuda')
model = torchvision.models.alexnet(pretrained=True).cuda()# Providing input and output names sets the display names for values
# within the model's graph. Setting these does not change the semantics
# of the graph; it is only for readability.
#
# The inputs to the network consist of the flat list of inputs (i.e.
# the values you would pass to the forward() method) followed by the
# flat list of parameters. You can partially specify names, i.e. provide
# a list here shorter than the number of inputs to the model, and we will
# only set that subset of names, starting from the beginning.
input_names = [ "actual_input_1" ] + [ "learned_%d" % i for i in range(16) ]
output_names = [ "output1" ]torch.onnx.export(model, dummy_input, "alexnet.onnx", verbose=True, input_names=input_names, output_names=output_names, do_constant_folding=True)
- 将 Onnx 模型文件转成 MNN 模型
./MNNConvert -f ONNX --modelFile alexnet.onnx --MNNModel alexnet.mnn --bizCode MNN
