geoshift's Introduction

Learning Transformations to reduce Geometric Shift in Object Detection

Installation

This code is based on detectron2 and requires python>= 3.6

pip install -r requirements.txt

Dataset

Set the environ variable DETECTRON2_DATASETS to the parent folder of the datasets

/datasets
    /cityscapes
    /kitti
    /mot

Download

Cityscapes & Kitti from -- https://github.com/chengchunhsu/EveryPixelMatters#dataset
MOT sequence MOT20-02 -- https://motchallenge.net

Base Training

python train_net.py --config-file configs/<file_name>_random_crop.yaml 
.py

Aggregator Training

python train_net_only_aggregator.py --config-file configs/<file_name>_aggregator_fivestnperspective.yaml 
.py

Mean Teacher Training

We train on single V100 GPU with batch size 2 for mean teacher(in config setting which means 2 source domain and 2 target domain), steps

python train_net_student_teacher_<task>.py --config-file configs/<task>_student_teacher.yaml SOLVER.BASE_LR 1e-3 SOLVER.STEPS [10000,] MODEL.STN_ARCH FIVE_OPT_PERSPECTIVE
.py

Citation

@inproceedings{vidit2023learning,
  title={Learning Transformations To Reduce the Geometric Shift in Object Detection},
  author={Vidit, Vidit and Engilberge, Martin and Salzmann, Mathieu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={17441--17450},
  year={2023}
}

geoshift's People

Contributors

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geoshift's Issues

Figure5.ours

How can I obtain an image similar to the one labeled as "ours" in Figure 5 of your paper?

IMS_ PER_ BATCH:

hello,i want to aks a question.
In the last trainging -meaning teacher training,i wonder if IMS_ PER_ BATCH: Can 2 be changed to 1?
Looking forward to your reply。

FloatingPointError: Predicted boxes or scores contain Inf/NaN. Training has diverged.

FloatingPointError: Predicted boxes or scores contain Inf/NaN. Training has diverged.
[04/15 14:33:06 d2.engine .hooks]: Overall training speed: 2 iterations in 0:00:07(3.7250 s / it)[04/15 14:33:06 d2.engine.hooks]: Total training time: 0:00:07 (0:00:00 on hooks)
[04/15 14:33065 62.tils.events]: eta: 1 day,0:25:08 iter: 4 total_ loss: 1.289e106 loss cls: 1.162e+05 loss_box_ reg: 1.235e+04loss_rpn_cls: 3.925e+C5 loss-rp_loc: 7.65e+C5 time: 3.636 last_time:0.2101 data_time: 4.9687 last data _time: 0.0o4 lr: 3.997e-07max_mem: 4413M
Traceback (most recent call last):
File "D: \geoshift-main\geoshift-main\train_net.py", line 55，in
launch(
File "d: \temp\detectron2-main\detectron2-main\detectron2\engine\launch.py", line 84,in launchmain_func(*args)
File "D: \geoshift-main\geoshift-main\train_net.py", line 49,in maintrainer.train()
File "d:\temp\detectron2-main\detectron2-main\detectron2\engine\defaults.py"， line 486，in trainsuper().train(self.start_iter, self.max_iter)
File "d : \temp\detectron2-main\detectron2-main(detectron2\engine\train_loop.py", line 155,in trainself.run_step(
File "d: \temp\ldetectron2-main(detectron2-main(detectron2\engine\defaults.py", line 496，in run_stepself._trainer.run_step()
File "d: \temp\detectron2-main\detectron2-main(detectron2\engine\train_loop.py" ， line 310，in run_steploss_dict = self.model(data)
File "D: \anaconda\envs\geo\lib\site-packages\torch \n(modules[module.py" , line 1102,in _call_implreturn forward_call(*input，**kwargs)
File "d:\temp(detectron2-main\detectron2-main)detectron2\modeling(meta_arch\rcnn.py""line 161,in forwardproposals, proposal_losses = self. proposal_generator(images，features, gt_instances)
File"D: \anaconda\envs\geo\lib\site-packages \torch\nn\modules\module.py"， line 1102,in _call_implreturn forward_call(*input，**kwargs)
File "d:\temp(detectron2-main(detectron2-main\detectron2[modeling\proposal generator\rpn.py" line 477,in forwardproposals = self.predict_proposals(
File "d: templdetectron2-main\detectron2-main\detectron2[modelingIproposal generatorIrpn.py", line 503,in predict_proposalsreturn find_top_rpn_proposals(
File "d: temp]detectron2-main(detectron2-main detectron2[modelinglproposal generator \proposal_utils.py", line 108,in find_top_rpn_proposals
raise FloatingPointError(
FloatingPointError: Predicted boxes or scores contain Inf/NaN. Training has diverged.

when i try python train_net.py --config-file configs/base_city_rcnn_person_random_crop.yaml,i meet such error,i wonder how can i solve it.

No module named 'data.datasets'

python train_net.py --config-file configs/base_city_rcnn_person_random_crop.yaml
Traceback (most recent call last):
File "train_net.py", line 13, in
from data.datasets import builtin
ModuleNotFoundError: No module named 'data.datasets'

i wonder how can i solve such question?

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