After the PC and mobile phone are successfully connected, use the following command to start the model evaluation.
```
sh tools/lite/benchmark.sh ./benchmark_bin_v8 ./inference result_armv8.txt true
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.
Among them, the `--model` parameter is used to specify the model name, `--pretrained_model` parameter is used to specify the model file path, the path does not need to include the model file suffix name, and `--output_path` is used to specify the storage path of the converted model.
**Note**:
1.File prefix must be assigned in `--output_path`. If `--output_path=./inference/cls_infer`, then three files will be generated in the folder `inference`, they are `cls_infer.pdiparams`, `cls_infer.pdmodel` and `cls_infer.pdiparams.info`.
1.If `--output_path=./inference`, then three files will be generated in the folder `inference`, they are `inference.pdiparams`, `inference.pdmodel` and `inference.pdiparams.info`.
2. In the file `export_model.py:line53`, the `shape` parameter is the shape of the model input image, the default is `224*224`. Please modify it according to the actual situation, as shown below:
```python
...
...
@@ -243,20 +243,20 @@ Among them, the `--model` parameter is used to specify the model name, `--pretra
54])
```
The above command will generate the model structure file (`cls_infer.pdmodel`) and the model weight file (`cls_infer.pdiparams`), and then the inference engine can be used for inference:
The above command will generate the model structure file (`inference.pdmodel`) and the model weight file (`inference.pdiparams`), and then the inference engine can be used for inference:
```bash
python tools/infer/predict.py \
--image_file image path \
--model_file"./inference/cls_infer.pdmodel"\
--params_file"./inference/cls_infer.pdiparams"\
--model_file"./inference/inference.pdmodel"\
--params_file"./inference/inference.pdiparams"\
--use_gpu=True \
--use_tensorrt=False
```
Among them:
+`image_file`: The path of the image file to be predicted, such as `./test.jpeg`;
+`model_file`: Model file path, such as `./MobileNetV3_large_x1_0/cls_infer.pdmodel`;
+`params_file`: Weight file path, such as `./MobileNetV3_large_x1_0/cls_infer.pdiparams`;
+`model_file`: Model file path, such as `./MobileNetV3_large_x1_0/inference.pdmodel`;
+`params_file`: Weight file path, such as `./MobileNetV3_large_x1_0/inference.pdiparams`;
+`use_tensorrt`: Whether to use the TesorRT, default by `True`;
+`use_gpu`: Whether to use the GPU, default by `True`
+`enable_mkldnn`: Wheter to use `MKL-DNN`, default by `False`. When both `use_gpu` and `enable_mkldnn` are set to `True`, GPU is used to run and `enable_mkldnn` will be ignored.
