Position-Aware Recalibration Module: Learning From Feature Semantics and Feature Position, by Xu Ma, Song Fu, is a novel plug-in module for improve CNN model capability with minimal computational overheads, and further improve the performances of other high-level visual tasks, like detection.
1. Download repo
git clone https://github.com/13952522076/PRM.git
cd PRM2. Requirements
- Python3.6
- PyTorch 1.3+
- CUDA 10+
- GCC 5.0+
pip install -r requirements.txt3. Install DALI and Apex （For ImageNet Training）
DALI Installation:
cd ~
# For CUDA10
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-cuda100
# or
# For CUDA11
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-cuda110For more details, please see Nvidia DALI installation.
Apex Installation:
cd ~
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./For more details, please see Apex or Apex Full API documentation.
# change the parameters accordingly if necessary
# e.g, If you have 4 GPUs, set the nproc_per_node to 4. If you want to train with 32FP, remove ----fp16.
python3 -m torch.distributed.launch --nproc_per_node=8 imagenet.py -a prm_resnet50 --fp16 --b 32If you find this work useful in your research, you can cite the corresponding papers listed below:
@inproceedings{ma2020position,
title={Position-Aware Recalibration Module: Learning From Feature Semantics and Feature Position},
author={Ma, Xu and Fu, Song},
booktitle={Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence},
year={2020}
}