final project for mace.
The original Mace GitHub repository link is: https://github.com/XiaoMi/mace
This repository contains the final project for Mace, which includes the implementation of two deep neural networks (DNN) models, ShuffleNet v2+ small and RegNet (200M), and instructions on how to run the code successfully.
To use the RegNet DNN model, users can download and open the RegNetUp file, and follow the provided instructions in the final report to run the code. For the ShuffleNet DNN model, users can download and open the ShuffleNetUp file, and similarly follow the instructions provided in the final report to run the code.
To utilize the DNN models, users must also download the corresponding .yml file and model file. It is important for users to change the path in the .yml file based on the path of the model file and their own sha256.
I ensure the code can run successfully on both Linux and MacOs with M1 chip.
Overall, this project showcases successful implementation of these two popular DNN models, providing a valuable resource for researchers and practitioners in the field.
MACE requires the following dependencies:
| Software | Installation command |
|---|---|
| Python | 2.7 or 3.xx |
| CMake | Linux: apt-get install cmake /// Mac:brew install cmake |
| Jinja2 | pip install jinja2==2.10 |
| PyYaml | pip install pyyaml==3.12 |
| sh | pip install sh==1.12.14 |
| Numpy | pip install numpy==1.14.0 |
| six | pip install six==1.11.0 |
First of all, make sure the environment has been set up correctly already.
Before you do anything, clear the workspace used by build and test process.
tools/clear_workspace.sh [--expunge]
Please make sure you have CMake installed.
RUNTIME=GPU QUANTIZE=OFF bash tools/cmake/cmake-build-armeabi-v7a.sh
which generate libraries in build/cmake-build/armeabi-v7a, you can use either static libraries or the libmace.so shared library.
You can also build for other target abis: arm64-v8a, arm-linux-gnueabihf, aarch64-linux-gnu, host; and runtime: GPU, HEXAGON, HTA, APU.
When you have prepared your model, the first thing to do is write a model config in YAML format. For example:
library_name: mobilenet-v2_1_0
target_abis: [armeabi-v7a, arm64-v8a]
model_graph_format: file
model_data_format: file
models:
mobilenet:
platform: onnx
model_file_path: /Users/path/to/model/regnet_opt.onnx
model_sha256_checksum: 2defjieojoejabce3020r03107d3c47ce4461e390fa4
subgraphs:
- input_tensors: input
output_tensors: output
input_shapes: 1,224,224,3
output_shapes: 1,1000
runtime: cpu+gpu
limit_opencl_kernel_time: 0
nnlib_graph_mode: 0
obfuscate: 0
The following steps generate output to build directory which is the default build and test workspace. Suppose you have the model config in ../mace-models/mobilenet-v1/mobilenet-v1.yml. Then run
python tools/python/convert.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml
which generate 4 files in build/mobilenet_v1/model/
├── mobilenet_v1.pb (model file)
├── mobilenet_v1.data (param file)
├── mobilenet_v1_index.html (visualization page, you can open it in browser)
└── mobilenet_v1.pb_txt (model text file, which can be for debug use)
MACE also supports other platform: caffe, onnx. Beyond GPU, users can specify cpu, dsp to run on other target devices.
We provide simple tools to test and benchmark your model.
After model is converted, simply run
python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --validate
Or benchmark the model
python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --benchmark
It will test your model on the device configured in the model config (runtime). You can also test on other device by specify --runtime=cpu (dsp/hta/apu) when you run test if you previously build engine for the device. The log will be shown if --vlog_level=2 is specified.