Additional scripts are provided on top of mmdetection to ease the training and testing process. Several complexity optimized configurations are provided in the folder configs/edgeailite. Scripts for training or evaluation with these configs are given in the scripts folder. They can be executed by running the shell scripts provided.
Select the appropriate config file in detection_configs.py
Start the training by running run_detection_train.sh
After doing the floating point training, it is possible to run Qunatization Aware Training (QAT) starting from the trained checkpoint. For this, set quantize = True in the config file (see the line where it is set to False and change it to True) and run the training again. This will run a small number epochs of fine tuning with QAT at a lower learning rate.
Make sure that the appropriate config file is selected in detection_configs.py
Start evaluation by running run_detection_test.sh.
Note: If you did QAT, then the flag quantize in teh config file must be set to True even at this stage.
Make sure that the appropriate config file is selected in detection_configs.py
Start export by running run_detection_export.sh.
Note: If you did QAT, then the flag quantize in the config file must be set to True even at this stage.
We now have a tool that can automatically replace some of embedded un-friendly layers. This tool will run automatically if you set the parameter convert_to_lite_model in the config file. Please see our edgeailite config files for some example usage. Examples:
- convert_to_lite_model = dict(group_size_dw=1)
- convert_to_lite_model = dict(group_size_dw=None)
- where group_size_dw indicates the (convolution) group size to be used for convolution layers with kernel size larger than 1.