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by-liu / CALS

Code for our method CALS (Class Adaptive Label Smoothing) for network calibration. To Appear at CVPR 2023. Paper: https://arxiv.org/abs/2211.15088
MIT License
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augmented-lagrange-multipliers calibration deep-learning pytorch

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Class Adaptive Network Calibration

Bingyuan Liu, Jérôme Rony, Adrian Galdran, Jose Dolz, Ismail Ben Ayed

[CVPR 2023][arXiv][BibTeX]

overview

Install:

The pytorch version we used:

torch==1.12.1
torchvision==0.13.1

setup:

pip install -e .

Install window_process kernal for Swin-T model(Optional)

cd kernels/window_process
pip install -e .

Data preparation

For the datasets (i.e., Tiny-ImageNet, ImageNet, ImageNetLT, and VOC2012), please refer to their official citer for download. We provide the data splits we used under data_splits. Please put them to the root directory (or VOC2012/ImageSets/Segmentation for VOC2012) of your dataset.

Important Note : Before you run the code, please add the absolute path of the root directory for the related data configs in configs/data, or pass it in the running commands.

Usage:

Arguments

python tools/train_net.py --help

```python dist_train is powered by Hydra. == Configuration groups == Compose your configuration from those groups (group=option) data: imagenet, imagenet_lt, tiny_imagenet, voc2012 dist: pytorch, slurm lag: aug_lagrangian, aug_lagrangian_class loss: ce, cpc, focal, focal_adaptive, logit_margin, logit_margin_plus, ls, mmce, penalty_ent, soft_ce lr_scheduler: cosine, multi_step, one_cycle, plateau, step mixup: mixup model: SwinV2-T, deeplabv3, resnet, resnet101_tiny, resnet50_tiny optim: adam, adamw, sgd test: local, segment train: dist, dist_lag, dist_plus, lag_segment, local, segment wandb: my == Config == Override anything in the config (foo.bar=value) data: name: imagenet data_root: YOUR_DATA_ROOT input_size: 224 train_batch_size: 256 val_batch_size: 250 test_batch_size: 250 return_ind: false use_mysplit: true num_workers: 8 pin_memory: true object: trainval: _target_: calibrate.data.imagenet.build_train_val_dataset data_root: ${data.data_root} input_size: ${data.input_size} use_mysplit: ${data.use_mysplit} test: _target_: calibrate.data.imagenet.build_test_dataset data_root: ${data.data_root} input_size: ${data.input_size} use_mysplit: ${data.use_mysplit} model: name: resnet50 num_classes: 10 pretrained: false drop_rate: 0.0 object: _target_: timm.create_model model_name: ${model.name} pretrained: ${model.pretrained} num_classes: ${model.num_classes} drop_rate: ${model.drop_rate} loss: name: ce ignore_index: -100 object: _target_: torch.nn.CrossEntropyLoss ignore_index: ${loss.ignore_index} reduction: mean optim: name: adamw lr: 0.0005 weight_decay: 0.05 object: _target_: torch.optim.AdamW lr: ${optim.lr} betas: - 0.9 - 0.999 weight_decay: ${optim.weight_decay} eps: 1.0e-08 lr_scheduler: name: cosine min_lr: 5.0e-06 warmup_lr: 5.0e-07 warmup_epochs: 1 cycle_decay: 0.1 object: _target_: timm.scheduler.cosine_lr.CosineLRScheduler t_initial: ${train.max_epoch} lr_min: ${lr_scheduler.min_lr} cycle_mul: 1 cycle_decay: ${lr_scheduler.cycle_decay} warmup_lr_init: ${lr_scheduler.warmup_lr} warmup_t: ${lr_scheduler.warmup_epochs} cycle_limit: 1 t_in_epochs: true train: name: dist max_epoch: 200 clip_grad: 2.0 clip_mode: norm resume: true keep_checkpoint_num: 1 keep_checkpoint_interval: 0 use_amp: true evaluate_logits: true object: _target_: calibrate.engine.DistributedTrainer mixup: name: mixup enable: false mixup_alpha: 0.4 mode: pair label_smoothing: 0 object: _target_: timm.data.mixup.Mixup mixup_alpha: ${mixup.mixup_alpha} mode: ${mixup.mode} label_smoothing: ${mixup.label_smoothing} num_classes: ${model.num_classes} dist: launch: python backend: nccl wandb: enable: false project: NA entity: NA tags: train test: name: local checkpoint: best.pth save_logits: false object: _target_: calibrate.engine.Tester job_name: ${hydra:job.name} work_dir: ${hydra:run.dir} seed: 1 log_period: 50 calibrate: num_bins: 15 visualize: false post_temperature: enable: false learn: false grid_search_interval: 0.1 cross_validate: ece Powered by Hydra (https://hydra.cc) Use --hydra-help to view Hydra specific help ```

Example

TinyImageNet

Ours

OMP_NUM_THREADS=8 torchrun \
    --master_port 29500 --nnodes=1 --nproc_per_node=1 \
     tools/dist_train.py \
     dist=pytorch model=resnet50_tiny train.max_epoch=100 \
     data=tiny_imagenet data.train_batch_size=128 \
     optim=sgd optim.lr=0.1 lr_scheduler=multi_step \
     train=dist_lag loss=ce +lag=aug_lagrangian_class lag.margin=10

Baselines:

CE (Cross Entropy)

OMP_NUM_THREADS=8 torchrun \
    --master_port 29500 --nnodes=1 --nproc_per_node=1 \
     tools/dist_train.py \
     dist=pytorch model=resnet50_tiny train=dist train.max_epoch=100 \
     data=tiny_imagenet data.train_batch_size=128 \
     optim=sgd optim.lr=0.1 lr_scheduler=multi_step \
     train=dist loss=ce

MbLS (Margin based Label Smoothing)

OMP_NUM_THREADS=8 torchrun \
    --master_port 29500 --nnodes=1 --nproc_per_node=1 \
     tools/dist_train.py \
     dist=pytorch model=resnet50_tiny train=dist train.max_epoch=100 \
     data=tiny_imagenet data.train_batch_size=128 \
     optim=sgd optim.lr=0.1 lr_scheduler=multi_step \
     train=dist loss=logit_margin

ImageNetLT:

Ours:

OMP_NUM_THREADS=8 CUDA_VISIBLE_DEVICES=0,1 torchrun \
    --master_port 29500 --nnodes=1 --nproc_per_node=2 \
    tools/dist_train.py \
    model=resnet model.num_classes=1000 \
    data=imagenet_lt data.train_batch_size=256 \
    optim.lr=0.0005 lr_scheduler.min_lr=5e-6 lr_scheduler.warmup_lr=5e-7 \
    train=dist_lag train.max_epoch=200 \
    loss=ce +lag=aug_lagrangian_class lag.margin=10 lag.lambd_step=1

Baselines:

CE (Cross Entropy):

OMP_NUM_THREADS=8 CUDA_VISIBLE_DEVICES=0,1 torchrun \
    --master_port 29500 --nnodes=1 --nproc_per_node=2 \
    tools/dist_train.py \
    model=resnet model.num_classes=1000 \
    data=imagenet_lt data.train_batch_size=256 \
    optim.lr=0.0005 lr_scheduler.min_lr=5e-6 lr_scheduler.warmup_lr=5e-7 \
    train=dist train.max_epoch=200 \
    loss=ce

MbLS (Margin based Label Smoothing):

OMP_NUM_THREADS=8 CUDA_VISIBLE_DEVICES=0,1 torchrun \
    --master_port 29500 --nnodes=1 --nproc_per_node=2 \
    tools/dist_train.py \
    model=resnet model.num_classes=1000 \
    data=imagenet_lt data.train_batch_size=256 \
    optim.lr=0.0005 lr_scheduler.min_lr=5e-6 lr_scheduler.warmup_lr=5e-7 \
    train=dist train.max_epoch=200 \
    loss=logit_margin

Citing CALS

@inproceedings{liu2023cals,
  title = {Class Adaptive Network Calibration}, 
  author={Bingyuan Liu and Jérôme Rony and Adrian Galdran and Jose Dolz and Ismail Ben Ayed},
  booktitle = {CVPR},
  year={2023},
}