-`mindspore-lite-0.7.0-runtime-x86-cpu`: include `flatbuffers` (FlatBuffers header file).
-`mindspore-lite-0.7.0-runtime-x86-cpu`: `third party`include `flatbuffers` (FlatBuffers header file), `lib`include`libmindspore-lite.so`(Dynamic library of MindSpore Lite inference framework).
TODO: Add document content.
-`mindspore-lite-0.7.0-runtime-arm64-cpu`: `third party`include `flatbuffers` (FlatBuffers header file), `lib`include`libmindspore-lite.so`(Dynamic library of MindSpore Lite inference framework) and `liboptimize.so`(Dynamic library of MindSpore Lite advanced operators).
> `liboptimize.so` only exits in runtime-arm64 outputs, and only can be used in the CPU which supports armv8.2 and fp16.
> Before running the tools in the `converter`, `benchmark`, or `time_profiler` directory, you need to configure environment variables and set the paths of the dynamic libraries of MindSpore Lite and Protobuf to the paths of the system dynamic libraries. The following uses the 0.7.0-beta version as an example: `export LD_LIBRARY_PATH=./mindspore-lite-0.7.0/lib:./mindspore-lite-0.7.0/third_party/protobuf/lib:${LD_LIBRARY_PATH}`.
> Before running the tools in the `converter`, `benchmark`, or `time_profiler` directory, you need to configure environment variables and set the paths of the dynamic libraries of MindSpore Lite and Protobuf to the paths of the system dynamic libraries. The following uses the 0.7.0-beta version as an example: `export LD_LIBRARY_PATH=./mindspore-lite-0.7.0/lib:./mindspore-lite-0.7.0/third_party/protobuf/lib:${LD_LIBRARY_PATH}`.
MindSpore Lite provides a tool for offline model conversion. It supports conversion of multiple types of models and visualization of converted models. The converted models can be used for inference. The command line parameters contain multiple personalized options, providing a convenient conversion method for users.
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.
Currently, the following input formats are supported: MindSpore, TensorFlow Lite, Caffe, and ONNX.
...
@@ -47,11 +46,6 @@ The following describes the parameters in detail.
...
@@ -47,11 +46,6 @@ The following describes the parameters in detail.
> - The parameter name and parameter value are separated by an equal sign (=) and no space is allowed between them.
> - 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
> - The Caffe model is divided into two files: model structure `*.prototxt`, corresponding to the `--modelFile` parameter; model weight `*.caffemodel`, corresponding to the `--weightFile` parameter
## Model Visualization
The model visualization tool provides a method for checking the model conversion result. You can run the JSON command to generate a `*.json` file and compare it with the original model to determine the conversion effect.
TODO: This function is under development now.
## Example
## Example
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@@ -103,6 +97,4 @@ The following describes how to use the conversion command by using several commo
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@@ -103,6 +97,4 @@ The following describes how to use the conversion command by using several commo
```
```
INFO [converter/converter.cc:190] Runconverter] CONVERTER RESULT: SUCCESS!
INFO [converter/converter.cc:190] Runconverter] CONVERTER RESULT: SUCCESS!
```
```
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You can use the model visualization tool to visually check the converted MindSpore Lite model. This function is under development.
