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Command Line Script Tools (Deprecated)

0. Introduction

Since toolchain version v0.14.0, we introduce Python API and recommend using it for the general work flow. This document provides the usage of all the script utilities. Note that 530 only has the IP evaluator script. For other tools, please refer to the toolchain document.

Since toolchain version v0.21.0, most of the command line scripts are removed or deprecated. Please use the Python API instead.

Since toolchain version v0.21.0, most of the command line scripts are removed or deprecated. Please use the Python API instead.

Since toolchain version v0.21.0, most of the command line scripts are removed or deprecated. Please use the Python API instead.

1. IP Evaluator

The tool described in this section is mainly used to check your model's performance estimation and whether your model is supported by the toolchain. It can be used with both .bie and .onnx file. (The .bie file is the result of FP analyser which described in the next section.)

1.1 Running the programs

To run the compile and the ip evaluator, you need to have an .onnx or a .bie file as the input. Here we take the LittleNet as the example which is already inside the docker:

# For KDP520
cd /workspace/scripts && ./compilerIpevaluator_520.sh /workspace/examples/LittleNet/LittleNet.onnx

# For KDP720
cd /workspace/scripts && ./compilerIpevaluator_720.sh /workspace/examples/LittleNet/LittleNet.onnx

# For KDP530
cd /workspace/scripts && ./compilerIpevaluator_530.sh /workspace/examples/LittleNet/LittleNet.onnx

This part uses the default configuration for the hardware and not available for fine-tuning.

1.2 Get the result

A folder called compiler will be generated in /data1, which stores the result of the compiler and IP Evaluator. If you are using this script with an onnx file, please ignore the binary files since they are not quantized and unable to be used.

We can find the following files after running the script above:

2 Batch-Compile

This part is the instructions for batch-compile, which will generate the binary file requested by firmware with the bie files given.

2.1 Fill the input parameters

Fill the input parameters in the /data1/batch_input_params.json under /data1. Please refer to the appendix B to fill the related parameters.

For the LittleNet example, if you already follow the instructions in the section 2. You should already have LittleNet.quan.wqbi.bie under /data1/fpAnalyser/

Please make sure you are generated the bie file with the correct hardware version. For example, if you want to batch-compile for the 520 hardware, you need to generate the bie with the 520 FP-analyzer

Here is the config /data1/batch_input_params.json we need to batch compile the LittleNet:

{
    "models": [
        {
            "id": 32769,
            "version": "1",
            "path": "/data1/fpAnalyser/LittleNet.quan.wqbi.bie"
        }
    ]
}

We can also do batch compile with multiple models and with encryption. Please check the appendix fB or more details about the config fields.

4.2 Running the programs

For running the compiler and ip evaluator:

# For KDP520
# python /workspace/scripts/batchCompile_520.py
python /workspace/scripts/batchCompile_520.py

# For KDP720
# python /workspace/scripts/batchCompile_720.py
python /workspace/scripts/batchCompile_720.py

4.3 Get the result

Under /data1, you’ll find a folder called batch_compile, which contains the output files of batch compile. There so many outputs but not all of them are useful for the user. Here are the main outputs you may need:

Difference between *.bin and models_*.nef

weight.bin, setup.bin and weight.bin are for hardware simulator. They only contains information for a single model. They are mostly for debugging and testing usages.

model_*.nef is for the firmware to load onto the chip. It could contain information for multiple models. It's mainly used for firmware testing and deployment.

4.4 Run batch compile with multiple models

The name 'batch compile' comes from its ability to compile multiple models together.

The /data1/batch_input_params.json is just like in section 4.1, but with one more model. (This model is not provided. You need to prepare one yourself.)

{
    "models": [
        {
            "id": 32769,
            "version": "1",
            "path": "/data1/fpAnalyser/LittleNet.quan.wqbi.bie"
        },
        {
            "id": 32770,
            "version": "1",
            "path": "/data1/ssd7_0.8_epoch-97_loss-0.1407_val_loss-0.0825_opt.piano.kdp520.scaled.quan.wqbi.bie"
        }
    ]
}

The command is the same as in section 4.2 and the results are .bin files for each model and on .nef file like mentioned in section 4.3.

3 Other utilities

3.1 Convert bin file to png

cd /workspace/scripts/utils && python bintoPng.py -i input_rgb565_file_path –o output_png_file_path –he rgb565_height –w rgb565_width -f bin_format

3.2 Post process

cd /workspace/scripts/utils && python post_process.py -i emulator_result_folder -m model_type

3.3 Combine NEF files

You can combine multiple generated NEF files into one with /workspace/libs/compiler/kneron_nef_utils. This is very useful when you already have multiple nef files from different versions of the toolchain. The usage is as below:

/workspace/libs/compiler/kneron_nef_utils -c "nef_file_1 nef_file_2 ..." -O output_folder_name

In the command above, the nef file list after -c are the nef files you want to compiler. And the result will be saved in the folder given after -O. The result file is names as models_<platform>.def, e.g. models_520.nef. If the output folder is not given in the argument, the result is save in an output folder under the current path.

Appendix

A. How to configure the batch_input_params.json?

By following the above instructions, the batch_input_params.json will be saved under /data1. Please do not change the parameters’ names.

Here is an example JSON with comments. Please remove all the comments in the real configuration file.

{
    // [optional]
    // The encryption setting for the batch compiler. Default is not enabled.
    "encryption": {
        // Whether enable encrytion
        "whether_encryption": false,
        // Encrytion mode selection
        // Options: 1, 2
        "encryption mode": 1,
        // Encrytion key. A hex string. Required in mode 1.
        "encryption_key": "0x12345678",
        // Encrytion file. An absolute path. Required in mode 1.
        "key_file": "/data1/enc.txt",
        // Encrytion key. A hex string. Required in mode 2, optional in mode 1.
        "encryption_efuse_key": "0x12345678"
    },
    // [optional]
    // Whether seperate buffers for each model output. Default is true.
    "dedicated_output_buffer": true,
    // [optional]
    // Whether compress weight for saving space.
    "weight_compress": false,
    // Batch compile model list
    "models": [
        {
            // Model ID
            "id": 32769,
            // Model version. should be an hex code at most 4 digit.
            "version": "1",
            // The path to the model.
            // If you are running fpAnalyserBatchCompile, this field should be an onnx.
            // If you are running batchCompile, this field could be an onnx or a bie.
            // If onnx is provided in the second case, you also need to provide the
            // `radix_json`.
            "path": "models/output_0.onnx",
            // [optional]
            // Only needed when you are running batchCompile_*.py with an onnx.
            "radix_json": ".json",
        }
    ]
}

B. What’s the meaning of IP Evaluator’s output?

C. What’s the meaning of the output files?

If you find the cpu node in ioinfo.csv, whose format is c,\**,**”`, you need to implement and register this function in SDK.