yolov1_maxim

This repo covers the MAX78000 model training and synthesis pipeline for the YOLO v1 model.

This is a slightly modified version of the original repo by Guanchu Wang ([email protected]) & Yi-Wei Chen ([email protected]).

Changes:

• moved some files into the history folder that were not in use
• changed some static paths for the datasets
• added argument parser to the YOLO_V1_Test.py python script

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Role of each Python script:

• YOLO_V1_Train_QAT.py: Python script used to train the network

• YOLO_V1_Test.py: Fake INT8 test of the model; change the directory of weight file (*.pth) to test different models.

• YOLO_V1_DataSet_small.py: Preprocess the VOC2007 dataset.

• yolov1_bn_model.py: Define the structure of the deep neural network.

• YOLO_V1_LossFunction.py: Define the loss function.

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Environment setup

Note that Python >= 3.8

$ git clone [email protected]:YIWEI-CHEN/yolov1_maxim.git
$ cd yolov1_maxim
# note that ai8x-training and ai8x-synthesis should in the project root (e.g., yolov1_maxim)
$ git clone --recursive https://github.com/MaximIntegratedAI/ai8x-training.git
$ git clone --recursive https://github.com/MaximIntegratedAI/ai8x-synthesis.git

# in your virtual environment (same process with pyenv as described in ai8x-training repo)
# install pytorch for NVIDIA RTX A5000
$ pyenv local 3.8.11
$ python -m venv venv --prompt yolov1_maxim
$ source venv/bin/activate
(yolov1_maxim) $ pip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

# install distiller
$ cd yolov1_maxim/ai8x-training/distiller
# remove lines of numpy, torch, torchvision in requirements.txt
$ pip install -e .

# install other packages
$ pip install tensorboard matplotlib numpy colorama yamllint onnx PyGithub GitPython opencv-python

Its not required to setup the other packages for the ai8x-training repo that is cloned into the subfolder.

The ai8x-synthesis has to be setup as described in the repo.

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How to train the YOLO model?

After initializing the repo as described above

Run 'python YOLO_V1_Train_QAT.py'.

* You can change the hyperparameter as you want. But there is no need to do this because the current hyperparameters work for our Layer-wise QAT training.

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How to test the trained model (Fake INT8 testing)?

1. Open YOLO_V1_Test.py, revise line 27 into the directory of your trained model.

2. Run YOLO_V1_Test.py. (python3 YOLO_V1_Test.py or using Pycharm)

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How to generate the checkpoint file of our model?

Run python YOLO_V1_Test.py --gen_checkpoint to generate the distilled checkpoint file. Afterwards you can quantize the checkpoint using ai8x-synthesis.

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Trained models

The follow links contains previous trained models and logs.

1. weight_20210711

2. yolo_models_test

3. logs

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VOC 2007 Dataset

• You can download train/validation and test by the above hyperlinks.

• Or if you have an Texas A&M account, you might access the VOC2007 on datalab6.engr.tamu.edu

  • Train: /data/yiwei/VOC2007/Train
  • Test: /data/yiwei/VOC2007/Test

• Place the downloaded datasets in the dataset/Train and dataset/Test folder

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Reference

• The repo starts from https://www.dropbox.com/s/pssda2gxrqa51v9/yolov1_maxim.zip?dl=0
• The YOLOv1 train and test framework are from https://github.com/ProgrammerZhujinming/YOLOv1

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Contributors

• Guanchu Wang ([email protected])

• Yi-Wei Chen ([email protected])

• Jonas Menges ([email protected])