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Implementation of Conv-based and Vit-based networks designed for CIFAR.

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A PyTorch Implementation of CIFAR Tricks

CIFAR10数据集上CNN模型、Transformer模型以及Tricks,数据增强,正则化方法等,并进行了实现。欢迎提issue或者进行PR。

0. Requirements

  • Python 3.6+
  • torch=1.8.0+cu111
  • torchvision+0.9.0+cu111
  • tqdm=4.26.0
  • PyYAML=6.0
  • einops
  • torchsummary

1. Implements

1.0 Models

vision Transformer:

Model GPU Mem Top1:train Top1:val weight:M
vision_transformer 2869M 68.96 69.02 47.6
mobilevit_s 2009M 98.83 92.50 19.2
mobilevit_xs 1681M 98.22 91.77 7.78
mobilevit_xxs 1175M 96.40 90.17 4.0
coatnet_0 1433M 99.94 90.15 64.9
coatnet_1 2089M 99.97 90.09 123
coatnet_2 2405M 99.99 90.86 208
cvt 2593M 94.64 84.74 75
swin_t 3927M 93.24 86.09 104
swin_s 6707M 90.27 83.68 184

1.1 Tricks

  • Warmup
  • Cosine LR Decay
  • SAM
  • Label Smooth
  • KD
  • Adabound
  • Xavier Kaiming init
  • lr finder

1.2 Augmentation

  • Auto Augmentation
  • Cutout
  • Mixup
  • RICAP
  • Random Erase
  • ShakeDrop

2. Training

2.1 CIFAR-10训练示例

WideResNet28-10 baseline on CIFAR-10:

python train.py --dataset cifar10

WideResNet28-10 +RICAP on CIFAR-10:

python train.py --dataset cifar10 --ricap True

WideResNet28-10 +Random Erasing on CIFAR-10:

python train.py --dataset cifar10 --random-erase True

WideResNet28-10 +Mixup on CIFAR-10:

python train.py --dataset cifar10 --mixup True

更多脚本可以参考 scripts/run.sh

3. Results

3.1 原pytorch-ricap的结果

Model Error rate Loss Error rate (paper)
WideResNet28-10 baseline 3.82(96.18) 0.158 3.89
WideResNet28-10 +RICAP 2.82(97.18) 0.141 2.85
WideResNet28-10 +Random Erasing 3.18(96.82) 0.114 4.65
WideResNet28-10 +Mixup 3.02(96.98) 0.158 3.02

3.2 Reimplementation结果

Model Error rate Loss Error rate (paper)
WideResNet28-10 baseline 3.78(96.22) 3.89
WideResNet28-10 +RICAP 2.81(97.19) 2.85
WideResNet28-10 +Random Erasing 3.03(96.97) 0.113 4.65
WideResNet28-10 +Mixup 2.93(97.07) 0.158 3.02

3.3 Half data快速训练验证各网络结构

reimplementation models(no augmentation, half data,epoch200,bs128)

Model Error rate Loss
lenet(cpu爆炸) (70.76)
wideresnet 3.78(96.22)
resnet20 (89.72)
senet (92.34)
resnet18 (92.08)
resnet34 (92.48)
resnet50 (91.72)
regnet (92.58)
nasnet out of mem
shake_resnet26_2x32d (93.06)
shake_resnet26_2x64d (94.14)
densenet (92.06)
dla (92.58)
googlenet (91.90) 0.2675
efficientnetb0(利用率低且慢) (86.82) 0.5024
mobilenet(利用率低) (89.18)
mobilenetv2 (91.06)
pnasnet (90.44)
preact_resnet (90.76)
resnext (92.30)
vgg(cpugpu利用率都高) (88.38)
inceptionv3 (91.84)
inceptionv4 (91.10)
inception_resnet_v2 (83.46)
rir (92.34) 0.3932
squeezenet(CPU利用率高) (89.16) 0.4311
stochastic_depth_resnet18 (90.22)
xception
dpn (92.06) 0.3002
ge_resnext29_8x64d (93.86) 巨慢

3.4 测试cpu gpu影响

TEST: scale/kernel ToyNet

修改网络的卷积层深度,并进行训练,可以得到以下结论:

结论:lenet这种卷积量比较少,只有两层的,cpu利用率高,gpu利用率低。在这个基础上增加深度,用vgg那种直筒方式增加深度,发现深度越深,cpu利用率越低,gpu利用率越高。

修改训练过程的batch size,可以得到以下结论:

结论:bs会影响收敛效果。

3.5 StepLR优化下测试cutout和mixup

architecture epoch cutout mixup C10 test acc (%)
shake_resnet26_2x64d 200 96.33
shake_resnet26_2x64d 200 96.99
shake_resnet26_2x64d 200 96.60
shake_resnet26_2x64d 200 96.46

3.6 测试SAM,ASAM,Cosine,LabelSmooth

architecture epoch SAM ASAM Cosine LR Decay LabelSmooth C10 test acc (%)
shake_resnet26_2x64d 200 96.51
shake_resnet26_2x64d 200 96.80
shake_resnet26_2x64d 200 96.61
shake_resnet26_2x64d 200 96.57

PS:其他库在加长训练过程(epoch=1800)情况下可以实现 shake_resnet26_2x64d achieved 97.71% test accuracy with cutout and mixup!!

3.7 测试cosine lr + shake

architecture epoch cutout mixup C10 test acc (%)
shake_resnet26_2x64d 300 96.66
shake_resnet26_2x64d 300 97.21
shake_resnet26_2x64d 300 96.90
shake_resnet26_2x64d 300 96.73

1800 epoch CIFAR ZOO中结果,由于耗时过久,未进行复现。

architecture epoch cutout mixup C10 test acc (%)
shake_resnet26_2x64d 1800 96.94(cifar zoo)
shake_resnet26_2x64d 1800 97.20(cifar zoo)
shake_resnet26_2x64d 1800 97.42(cifar zoo)
shake_resnet26_2x64d 1800 97.71(cifar zoo)

3.8 Divide and Co-training方案研究

  • lr:
    • warmup (20 epoch)
    • cosine lr decay
    • lr=0.1
    • total epoch(300 epoch)
  • bs=128
  • aug:
    • Random Crop and resize
    • Random left-right flipping
    • AutoAugment
    • Normalization
    • Random Erasing
    • Mixup
  • weight decay=5e-4 (bias and bn undecayed)
  • kaiming weight init
  • optimizer: nesterov

复现:((v100:gpu1) 4min*300/60=20h) top1: 97.59% 本项目目前最高值。

python train.py --model 'pyramidnet272' \
                --name 'divide-co-train' \
                --autoaugmentation True \
                --random-erase True \
                --mixup True \
                --epochs 300 \
                --sched 'warmcosine' \
                --optims 'nesterov' \
                --bs 128 \
                --root '/home/dpj/project/data'

3.9 测试多种数据增强

architecture epoch cutout mixup autoaugment random-erase C10 test acc (%)
shake_resnet26_2x64d 200 96.42
shake_resnet26_2x64d 200 96.49
shake_resnet26_2x64d 200 96.17
shake_resnet26_2x64d 200 96.25
shake_resnet26_2x64d 200 96.20
shake_resnet26_2x64d 200 95.82
shake_resnet26_2x64d 200 96.02
shake_resnet26_2x64d 200 96.00
shake_resnet26_2x64d 200 95.83
shake_resnet26_2x64d 200 95.89
shake_resnet26_2x64d 200 96.25
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_orgin' --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_c' --cutout True --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_m' --mixup True --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_a' --autoaugmentation True  --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_r' --random-erase True  --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_cm'  --cutout True --mixup True --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_ca' --cutout True --autoaugmentation True --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_cr' --cutout True --random-erase True --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_ma' --mixup True --autoaugmentation True --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_mr' --mixup True --random-erase True --bs 64
python train.py --model 'shake_resnet26_2x64d' --name 'ss64_ar' --autoaugmentation True --random-erase True  --bs 64

3.10 测试注意力机制

Model Top1:train Top1:val weight:M
spp_d11_pN 100 86.79 7.36
spp_d11_pA 100 85.83 7.36
spp_d11_pB 100 85.66 7.36
spp_d11_pC 100 85.56 7.36
spp_d11_pD 100 85.73 7.36
spp_d20_pN 100 90.59 13.4
spp_d20_pA 100 89.96 13.4
spp_d20_pB 100 89.26 13.4
spp_d20_pC 100 89.69 13.4
spp_d20_pD 100 89.93 13.4
spp_d29_pN 99.99 89.56 19.4
spp_d29_pA 100 90.13 19.4
spp_d29_pB 100 90.16 19.4
spp_d29_pC 100 90.09 19.4
spp_d29_pD 100 90.06 19.4

4. Reference

[1] https://github.com/BIGBALLON/CIFAR-ZOO

[2] https://github.com/pprp/MutableNAS

[3] https://github.com/clovaai/CutMix-PyTorch

[4] https://github.com/4uiiurz1/pytorch-ricap

[5] https://github.com/NUDTNASLab/pytorch-image-models

[6] https://github.com/facebookresearch/LaMCTS

[7] https://github.com/Alibaba-MIIL/ImageNet21K

[8] https://myrtle.ai/learn/how-to-train-your-resnet/