Converting into the MindSpore Lite Model

Overview

MindSpore Lite provides a tool for offline model conversion. It supports conversion of multiple types of models. The converted models can be used for inference. The command line parameters contain multiple personalized options, providing a convenient conversion method for users.

Currently, the following input formats are supported: MindSpore, TensorFlow Lite, Caffe, and ONNX.

Linux Environment Instructions

Environment Preparation

To use the MindSpore Lite model conversion tool, you need to prepare the environment as follows:

Compilation: Install basic and additional build dependencies and perform build. The build version is x86_64. The code of the model conversion tool is stored in the mindspore/lite/tools/converter directory of the MindSpore source code. For details about the build operations, see the Environment Requirements and Compilation Example in the build document.
Run: Obtain the converter tool and configure environment variables by referring to Output Description in the build document.

Example

First, in the root directory of the source code, run the following command to perform compilation. For details, see compile.md.

bash build.sh -I x86_64

Currently, the model conversion tool supports only the x86_64 architecture.

The following describes how to use the conversion command by using several common examples.

Take the Caffe model LeNet as an example. Run the following conversion command:
```
./converter_lite --fmk=CAFFE --modelFile=lenet.prototxt --weightFile=lenet.caffemodel --outputFile=lenet
```
In this example, the Caffe model is used. Therefore, the model structure and model weight files are required. Two more parameters fmk and outputFile are also required.

The output is as follows:
```
CONVERTER RESULT SUCCESS:0
```
This indicates that the Caffe model is successfully converted into the MindSpore Lite model and the new file lenet.ms is generated.
The following uses the MindSpore, TensorFlow Lite, ONNX and perception quantization models as examples to describe how to run the conversion command.
- MindSpore model model.mindir
```
./converter_lite --fmk=MINDIR --modelFile=model.mindir --outputFile=model
```
- TensorFlow Lite model model.tflite
```
./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model
```
- ONNX model model.onnx
```
./converter_lite --fmk=ONNX --modelFile=model.onnx --outputFile=model
```
- TensorFlow Lite aware quantization model model_quant.tflite
```
./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model --quantType=AwareTraining
```
- TensorFlow Lite aware quantization model model_quant.tflite set the input and output data type to be float
```
./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model --quantType=AwareTraining  --inferenceType=FLOAT
```
In the preceding scenarios, the following information is displayed, indicating that the conversion is successful. In addition, the target file model.ms is obtained.
```
CONVERTER RESULT SUCCESS:0
```
If fail to run the conversion command, an errorcode will be output.

Parameter Description

MindSpore Lite model conversion tool provides multiple parameters. You can enter ./converter_lite --help to obtain help information in real time.

The following describes the parameters in detail.

Parameter	Mandatory or Not	Parameter Description	Value Range	Default Value
`--help`	No	Prints all help information.	-	-
`--fmk=<FMK>`	Yes	Original format of the input model.	MINDIR, CAFFE, TFLITE, or ONNX	-
`--modelFile=<MODELFILE>`	Yes	Path of the input model.	-	-
`--outputFile=<OUTPUTFILE>`	Yes	Path of the output model. (If the path does not exist, a directory will be automatically created.) The suffix `.ms` can be automatically generated.	-	-
`--weightFile=<WEIGHTFILE>`	Yes (for Caffe models only)	Path of the weight file of the input model.	-	-
`--quantType=<QUANTTYPE>`	No	Sets the quant type of the model.	PostTraining: quantization after training AwareTraining: perceptual quantization	-
`--inferenceType=<INFERENCETYPE>`	No(supported by aware quant models only)	Sets the input and output data type of the converted model. If the types are different from the origin model, the convert tool will insert data type convert op in the inputs and outputs of the model to make sure the data types are same as origin model.	UINT8, FLOAT or INT8	FLOAT
`--stdDev=<STDDEV>`	No(supported by aware quant models only)	Sets the standard deviation of the input data.	（0，+∞）	128
`--mean=<MEAN>`	No(supported by aware quant models only)	Sets the mean value of the input data.	[-128, 127]	-0.5

The parameter name and parameter value are separated by an equal sign (=) and no space is allowed between them.

The Caffe model is divided into two files: model structure *.prototxt, corresponding to the --modelFile parameter; model weight *.caffemodel, corresponding to the --weightFile parameter

Windows Environment Instructions

Environment Preparation

To use the MindSpore Lite model conversion tool, the following environment preparations are required.

Get the toolkit: To obtain the ‘Converter’ tool, download the zip package of windows conversion tool and unzip it to the local directory.

Parameter Description

Reference description Linux environment model conversion tool parameter description.

Example

Set the log printing level to INFO.

set MSLOG=INFO

Several common examples are selected below to illustrate the use of conversion commands.

Take Caffe model LeNet as an example to execute the conversion command.
```
call converter_lite --fmk=CAFFE --modelFile=lenet.prototxt --weightFile=lenet.caffemodel --outputFile=lenet
```
In this example, because the Caffe model is used, two input files of model structure and model weight are required. Then plus fmk type and output path two parameters which are required, you can successfully execute.

The result is shown as:
```
CONVERTER RESULT SUCCESS:0
```
This means that the Caffe model has been successfully converted to the MindSpore Lite model and the new file lenet.ms has been obtained.
Take MindSpore, TensorFlow Lite, ONNX model format and perceptual quantization model as examples to execute conversion commands.
- MindSpore model model.mindir
```
call converter_lite --fmk=MINDIR --modelFile=model.mindir --outputFile=model
```
- TensorFlow Lite modelmodel.tflite
```
call converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model
```
- ONNX modelmodel.onnx
```
call converter_lite --fmk=ONNX --modelFile=model.onnx --outputFile=model
```
- TensorFlow Lite awaring quant model model_quant.tflite
```
call converter_lite --fmk=TFLITE --modelFile=model_quant.tflite --outputFile=model --quantType=AwareTraining
```
In the above cases, the following conversion success prompt is displayed, and the model.ms target file is obtained at the same time.
```
CONVERTER RESULT SUCCESS:0
```
If fail to run the conversion command, an errorcode will be output.