rknpu2/examples/rknn_dynamic_shape_input_demo/README.md

# RKNN C API Dynamic Shape Input Demo

This is a demo that uses the RKNN C API for dynamic shape input inference. In this demo, you can see how to use the RKNN dynamic shape C API to perform image classification.

## How to Use

1. Clone or download this code repository: https://github.com/rockchip-linux/rknn-toolkit2/tree/master/rknpu2.
2. Navigate to the dynamic shape inference demo directory in your terminal.

```shell
cd examples/rknn_dynamic_shape_input_demo
```

3. Compile the application by running the shell script based on the chip platform. For example, for the RK3562 Android system, run the following command:

```shell
./build-android_RK3562.sh
```

4. Push the demo program directory to the target board's system using the adb command. For example:

```shell
#If using Android system, make sure to run adb root & adb remount first.
adb push ./install/rknn_dynshape_demo_Android/ /data
```

5. Set the runtime library path.

```
export LD_LIBRARY_PATH=./lib
```

6. Run the program. For example, on the RK3562 platform, use the command

   ```shell
   ./rknn_dynshape_inference model/RK3562/mobilenet_v2.rknn images/dog_224x224.jpg
   ```

   ,where `mobilenet_v2.rknn` is the name of the neural network model file, and `dog_224x224.jpg` is the name of the image file to classify.

## Compilation Instructions

### Arm Linux

First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute:

```
./build-linux.sh -t <target> -a <arch> -b <build_type>]

# such as: 
./build-linux.sh -t rk3588 -a aarch64 -b Release
```

### Android

First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute:

```
./build-android.sh -t <target> -a <arch> [-b <build_type>]

# sush as: 
./build-android.sh -t rk3568 -a arm64-v8a -b Release
```

## Included Features

This demonstration application includes the following features:

- Creating a neural network model with dynamic shape inputs. Please refer to the examples/functions/dynamic_input directory in the https://github.com/rockchip-linux/rknn-toolkit2 repository for more information.
- Reading an image from a file and performing classification using the neural network model. The program follows these steps:

1. Initialize the RKNN context using the `rknn_init()` function.
2. Set the shape information of all the model inputs using the `rknn_set_input_shapes()` function, including shape and layout.
3. Query the current model input and output information, including shape, data type, and size, using the `rknn_query()` function.
4. Set the input data of the model using the `rknn_inputs_set()` function, including data pointer and size.
5. Run the model using the `rknn_run()` function.
6. Retrieve the output data by using the `rknn_outputs_get()` function, specifying the need for float-type results.
7. Process the output data to obtain the classification results and probabilities.
8. Release the RKNN context using the `rknn_release()` function.