This project is an implementation of ``Cross-domain Detection via Graph-induced Prototype Alignment'' in PyTorch, which is accepted by CVPR 2020. We would like to appreciate for the excellent work of jwyang/faster-rcnn.pytorch which lays a solid foundation for our work.
More details of this work can be found in our paper: [Paper (arxiv)].
First of all, clone the code
git clone https://github.com/ChrisAllenMing/GPA-detection.git
Then, create a folder:
cd GPA-detection && mkdir data
- Python 2.7 or 3.6
- Pytorch 0.4.1
- CUDA 8.0 or higher
-
p.s. Transform original segmentation annotations to detection formats using these codes.
In all experiments, we use a ResNet-50 model pretrained on ImageNet as the backbone. You can download this model from:
- ResNet-50: Jbox
This model should be put into ./data/pretrained_model/.
NOTE. We experimentally found that the Caffe pretrained model outperforms the PyTorch pretrained one. If you would like to evaluate our method with other backbones, a converted model from Caffe to PyTorch maybe favored.
Install all the python dependencies using pip:
pip install -r requirements.txt
Compile the cuda depended modules, e.g. NMS, ROI Pooling, ROI Align and ROI Crop, using following simple commands:
cd lib
sh make.sh
Some issues about compilation can be found here.
To train the Faster R-CNN baseline, simply run:
CUDA_VISIBLE_DEVICES=$GPU_ID python train_baseline.py --dataset sim10k \
--model_config baseline --net res50 --bs 3 --nw 1 \
--epochs 10 --lr 0.001 --lr_decay_step 6 \
--lr_decay_gamma 0.1 --cuda
To train the proposed model without graph-based aggregation, simply run:
CUDA_VISIBLE_DEVICES=$GPU_ID python train_GPA.py --dataset sim10k \
--tgt_dataset city --model_config adapt --mode adapt \
--rpn_mode adapt --net res50 --bs 3 --nw 1 --epochs 10 \
--lr 0.001 --lr_decay_step 6 --lr_decay_gamma 0.1 \
--warm_up 200 --da_weight 1.0 --rpn_da_weight 1.0 --cuda
To train the proposed model with graph-based aggregation, simply run:
CUDA_VISIBLE_DEVICES=$GPU_ID python train_GPA.py --dataset sim10k \
--tgt_dataset city --model_config gcn_adapt \
--mode gcn_adapt --rpn_mode gcn_adapt --net res50 --bs 3 \
--nw 1 --epochs 10 --lr 0.001 --lr_decay_step 6 \
--lr_decay_gamma 0.1 --warm_up 200 --da_weight 1.0 \
--rpn_da_weight 1.0 --cuda
In our experiments, we train the model with two GPUs as follows:
CUDA_VISIBLE_DEVICES=$GPU_IDs python train_GPA.py --dataset sim10k \
--tgt_dataset city --model_config gcn_adapt_multi \
--mode gcn_adapt --rpn_mode gcn_adapt --net res50 --bs 6 \
--nw 2 --epochs 10 --lr 0.001 --lr_decay_step 6 \
--lr_decay_gamma 0.1 --warm_up 200 --da_weight 1.0 \
--rpn_da_weight 1.0 --cuda --mGPUs
In order to test the model of every epoch, for the baseline, you can run:
python iterative_test.py --dataset city --model_config baseline --net res50 \
--checksession $session_num --checkpoint $iter_num \
--start_epoch 1 --end_epoch 10 --gpu_id $GPU_ID \
--test_mode baseline --cuda
To evaluate the proposed GPA model, you can run:
python iterative_test.py --dataset city --model_config gcn_adapt --net res50 \
--checksession $session_num --checkpoint $iter_num \
--start_epoch 1 --end_epoch 10 --gpu_id $GPU_ID \
--test_mode GPA --cuda
If this work helps your research, please cite the following paper.
inproceedings{xu2020cross-domain,
author={Minghao Xu and Hang Wang and Bingbing Ni and Qi Tian and Wenjun Zhang},
title={Cross-domain Detection via Graph-induced Prototype Alignment},
booktitle={{IEEE} Conference on Computer Vision and Pattern Recognition},
pages={12355-12364},
year={2020}
}
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