Awesome
Pytorch 1.9.0 code for:
F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling (https://arxiv.
org/abs/2109.07069)
WACV 2022: [Slides] [Video presentation] [Poster]
Citation:
@InProceedings{belharbi2022fcam,
title={F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling},
author={Belharbi, S. and Sarraf, A. and Pedersoli, M. and Ben Ayed, I. and McCaffrey, L. and Granger, E.},
booktitle = {WACV},
year={2022}
}
Issues:
Please create a github issue.
Content:
<a name='reqs'> Requirements</a>:
- Python 3.7.10
- Pytorch 1.9.0
- torchvision 0.10.0
- Full dependencies
- Build and install CRF:
- Install Swig
- CRF
cd dlib/crf/crfwrapper/bilateralfilter
swig -python -c++ bilateralfilter.i
python setup.py install
<a name="datasets"> Download datasets </a>:
See folds/wsol-done-right-splits/dataset-scripts. For more details, see wsol-done-right repo.
Once you download the datasets, you need to explicitly set 'baseurl' in get_root_wsol_dataset() to point to the folder parent containing your data. Inside the 'baseurl' folder there should be your dataset in folder named with the same name as your dataset. The function configure_data_paths() will setup the exact path to the dataset using 'baseurl' and the dataset name.
<a name="datasets"> Run code </a>:
- WSOL baselines: CAM over CUB using ResNet50:
time python main_wsol.py --task STD_CL \
--encoder_name resnet50 \
--arch STDClassifier \
--opt__name_optimizer sgd \
--batch_size 32 \
--opt__step_size 15 \
--opt__gamma 0.1 \
--max_epochs 50 \
--freeze_cl False \
--support_background True \
--method CAM \
--spatial_pooling WGAP \
--dataset CUB \
--box_v2_metric False \
--cudaid $cudaid \
--debug_subfolder None \
--opt__lr 0.0017 \
--exp_id 08_19_2021_14_05_20_620912__6229687
- Once you trained a WSOL baseline, copy the best model from the exp folder
into the folder ./pretrained. The best model is located in
a folder with the form name
CUB-resnet50-CAM-WGAP-cp_best-boxv2_False. Copy the whole folder. - F-CAM: to train with F-CAM, a pretrained WSOL model needs to be prepared as in the previous step. Run for training with F-CAM:
time python main_wsol.py --task F_CL \
--encoder_name resnet50 \
--arch UnetFCAM \
--opt__name_optimizer sgd \
--batch_size 32 \
--eval_checkpoint_type best \
--opt__step_size 1000 \
--opt__gamma 0.1 \
--max_epochs 50 \
--freeze_cl True \
--support_background True \
--method CAM \
--spatial_pooling WGAP \
--dataset CUB \
--box_v2_metric False \
--cudaid $cudaid \
--debug_subfolder None \
--opt__lr 0.01 \
--elb_init_t 1.0 \
--elb_max_t 10.0 \
--elb_mulcoef 1.01 \
--sl_fc True \
--sl_fc_lambda 1.0 \
--sl_start_ep 0 \
--sl_end_ep -1 \
--sl_min 1 \
--sl_max 1 \
--sl_ksz 3 \
--sl_min_p 0.1 \
--sl_fg_erode_k 11 \
--sl_fg_erode_iter 1 \
--crf_fc True \
--crf_lambda 2e-09 \
--crf_sigma_rgb 15.0 \
--crf_sigma_xy 100.0 \
--crf_scale 1.0 \
--crf_start_ep 0 \
--crf_end_ep -1 \
--max_sizepos_fc True \
--max_sizepos_fc_lambda 0.1 \
--max_sizepos_fc_start_ep 0 \
--max_sizepos_fc_end_ep -1 \
--entropy_fc False \
--exp_id 08_19_2021_14_09_48_915565__1492324