Bingyuan Liu, Jérôme Rony, Adrian Galdran, Jose Dolz, Ismail Ben Ayed
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 .
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.
python tools/train_net.py --help
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 helpOurs
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=10Baselines:
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=ceMbLS (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_marginOurs:
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=1Baselines:
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=ceMbLS (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@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},
}