* [2.4 Model Optimization and Speed Evaluation](#2.4)
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## 1. Introduction
[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) is a set of lightweight inference engine which is fully functional, easy to use and then performs well. Lightweighting is reflected in the use of fewer bits to represent the weight and activation of the neural network, which can greatly reduce the size of the model, solve the problem of limited storage space of the mobile device, and the inference speed is better than other frameworks on the whole.
In [PaddleClas](https://github.com/PaddlePaddle/PaddleClas), we uses Paddle-Lite to [evaluate the performance on the mobile device](../models/Mobile_en.md), in this section we uses the `MobileNetV1` model trained on the `ImageNet1k` dataset as an example to introduce how to use `Paddle-Lite` to evaluate the model speed on the mobile terminal (evaluated on SD855)
* First you should transform the saved model during training to the special model which can be used to inference, the special model can be exported by `tools/export_model.py`, the specific way of transform is as follows.
Finally the `model` and `parmas` can be saved in `inference/MobileNetV1`.
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### 2.2 Download Benchmark Binary File
* Use the adb (Android Debug Bridge) tool to connect the Android phone and the PC, then develop and debug. After installing adb and ensuring that the PC and the phone are successfully connected, use the following command to view the ARM version of the phone and select the pre-compiled library based on ARM version.
After the PC and mobile phone are successfully connected, use the following command to start the model evaluation.
```
sh deploy/lite/benchmark/benchmark.sh ./benchmark_bin_v8 ./inference result_armv8.txt true
```
Where `./benchmark_bin_v8` is the path of the benchmark binary file, `./inference` is the path of all the models that need to be evaluated, `result_armv8.txt` is the result file, and the final parameter `true` means that the model will be optimized before evaluation. Eventually, the evaluation result file of `result_armv8.txt` will be saved in the current folder. The specific performances are as follows.
```
PaddleLite Benchmark
Threads=1 Warmup=10 Repeats=30
MobileNetV1 min = 30.89100 max = 30.73600 average = 30.79750
Threads=2 Warmup=10 Repeats=30
MobileNetV1 min = 18.26600 max = 18.14000 average = 18.21637
Threads=4 Warmup=10 Repeats=30
MobileNetV1 min = 10.03200 max = 9.94300 average = 9.97627
```
Here is the model inference speed under different number of threads, the unit is FPS, taking model on one threads as an example, the average speed of MobileNetV1 on SD855 is `30.79750FPS`.
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### 2.4 Model Optimization and Speed Evaluation
* In II.III section, we mention that the model will be optimized before evaluation, here you can first optimize the model, and then directly load the optimized model for speed evaluation
* Paddle-Lite
In Paddle-Lite, we provides multiple strategies to automatically optimize the original training model, which contain Quantify, Subgraph fusion, Hybrid scheduling, Kernel optimization and so on. In order to make the optimization more convenient and easy to use, we provide opt tools to automatically complete the optimization steps and output a lightweight, optimal and executable model in Paddle-Lite, which can be downloaded on [Paddle-Lite Model Optimization Page](https://paddle-lite.readthedocs.io/zh/latest/user_guides/model_optimize_tool.html). Here we take `MacOS` as our development environment, download[opt_mac](https://paddlelite-data.bj.bcebos.com/model_optimize_tool/opt_mac) model optimization tools and use the following commands to optimize the model.
```shell
model_file="../MobileNetV1/model"
param_file="../MobileNetV1/params"
opt_models_dir="./opt_models"
mkdir ${opt_models_dir}
./opt_mac --model_file=${model_file} \
--param_file=${param_file} \
--valid_targets=arm \
--optimize_out_type=naive_buffer \
--prefer_int8_kernel=false \
--optimize_out=${opt_models_dir}/MobileNetV1
```
Where the `model_file` and `param_file` are exported model file and the file address respectively, after transforming successfully, the `MobileNetV1.nb` will be saved in `opt_models`
Use the benchmark_bin file to load the optimized model for evaluation. The commands are as follows.
