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ModelZoo for Pytorch

This is a model zoo project under Pytorch. In this repo I will implement some of basic classification models which have good performance on ImageNet. Then I will train them in most fair way as possible and try my best to get SOTA model on ImageNet. In this repo I'll only consider FP16.

Usage

Environment

  • OS: Ubuntu 18.04
  • CUDA: 10.1, CuDNN: 7.6
  • Devices: I use 8 * RTX 2080ti(8 * V100 should be much better /cry). This project is in FP16 precision, it's recommend to use FP16 friendly devices like RTX series, V100. If you want to totally reproduce my research, you'd better use same batch size with me.

Requirement

  • Pytorch: >= 1.6.0 (Need torch.cuda.amp in version 1.6)
  • TorchToolbox: stable version. Helper functions to make your code simpler and more readable, it's a optional tools if you don't want to use it just write them yourself.

LMDB Dataset

  • No necessary.

If you found any IO bottleneck please use LMDB format dataset. A good way is try both and find out which is more faster.

I provide conversion script here.

Train script

python distribute_train_script --params

Here is a example

python distribute_train_script.py --data-path /s4/piston/ImageNet --batch-size 256 --dtype float16 \
                                  -j 48 --epochs 360 --lr 2.6 --warmup-epochs 5 --label-smoothing \
                                  --no-wd --wd 0.00003 --model GhostNet --log-interval 150 --model-info \
                                  --dist-url tcp://127.0.0.1:26548 --world-size 1 --rank 0

ToDo

  • Resume training
  • Try Nvidia-DALI
  • Multi-node(distributed) training by Apex or BytePS Pytorch
  • I may try AutoAugment.This project aims to train models by ourselves to observe and learn, it's impossible for me to train this, just copy feels meaningless.

Baseline models

model epochs dtype batch size* gpus lr tricks Params(M)/FLOPs top1/top5 params/logs
resnet50 120 FP16 128 8 0.4 - 25.6/4.1G 77.36/- Google Drive
resnet101 120 FP16 128 8 0.4 - 44.7/7.8G 79.13/94.38 Google Drive
resnet50v2 120 FP16 128 8 0.4 - 25.6/4.1G 77.06/93.44 Google Drive
resnet101v2 120 FP16 128 8 0.4 - 44.6/7.8G 78.90/94.39 Google Drive
ResNext50_32x4d 120 FP16 128 8 0.4 - 25.1/4.2G 79.00/94.39
RegNetX4_0GF 120 FP16 128 8 0.4 - 22.2/4.0G 78.40/94.04
RegNetY4_0GF 120 FP16 128 8 0.4 - 22.1/4.0G 79.22/94.57
RegNetY6_4GF 120 FP16 128 8 0.4 - 31.2/6.4G 79.69/94.82
ResNeST50 120 FP16 128 8 0.4 - 27.5/4.1G 78.62/94.28
mobilenetv1 150 FP16 256 8 0.4 - 4.3/572.2M 72.17/90.70 Google Drive
mobilenetv2 150 FP16 256 8 0.4 - 3.5/305.3M 71.94/90.59 Google Drive
mobilenetv3 Large 360 FP16 256 8 2.6 Label smoothing No decay bias Dropout 5.5/219M 75.64/92.61 Google Drive
mobilenetv3 Small 360 FP16 256 8 2.6 Label smoothing No decay bias Dropout 3.0/57.8M 67.83/87.78
GhostNet1.3 360 FP16 400 8 2.6 Label smoothing No decay bias Dropout 7.4/230.4M 75.78/92.77 Google Drive
  • I use nesterov SGD and cosine lr decay with 5 warmup epochs by default[2][3] (to save time), it's more common and effective.
  • *Batch size is pre GPU holds. Total batch size should be (batch size * gpus).

Optimized Models(with tricks)

  • In progress.

Ablation Study on Tricks

Here are lots of tricks to improve accuracy during this years.(If you have another idea please open an issue.) I want to verify them in a fair way.

Tricks: RandomRotation, OctConv[14], Drop out, Label Smoothing[4], Sync BN, SwitchNorm[6], Mixup[17], no decay bias[7], Cutout[5], Relu6[18], swish activation[10], Stochastic Depth[9], Lookahead Optimizer[11], Pre-active(ResnetV2)[12], DCNv2[13], LIP[16].

  • Delete line means make me out of memory.

Special: Zero-initialize the last BN, just call it 'Zero γ', only for post-active model.

I'll only use 120 epochs and 128*8 batch size to train them. I know some tricks may need train more time or larger batch size but it's not fair for others. You can think of it as a performance in the current situation.

model epochs dtype batch size* gpus lr tricks degree top1/top5 improve params/logs
resnet50 120 FP16 128 8 0.4 - - 77.36/- baseline Google Drive
resnet50 120 FP16 128 8 0.4 Label smoothing smoothing=0.1 77.78/93.80 +0.42 Google Drive
resnet50 120 FP16 128 8 0.4 No decay bias - 77.28/93.61 -0.08 Google Drive
resnet50 120 FP16 128 8 0.4 Sync BN - 77.31/93.49 -0.05 Google Drive
resnet50 120 FP16 128 8 0.4 Mixup alpha=0.2 77.49/93.73 +0.13 missing
resnet50 120 FP16 128 8 0.4 RandomRotation degree=15 76.64/93.28 -1.15 Google Drive
resnet50 120 FP16 128 8 0.4 Cutout read code 77.44/93.62 +0.08 Google Drive
resnet50 120 FP16 128 8 0.4 Dropout rate=0.3 77.11/93.58 -0.25 Google Drive
resnet50 120 FP16 128 8 0.4 Lookahead-SGD - 77.23/93.39 -0.13 Google Drive
resnet50v2 120 FP16 128 8 0.4 pre-active - 77.06/93.44 -0.30 Google Drive
oct_resnet50 120 FP16 128 8 0.4 OctConv alpha=0.125 - -
resnet50 120 FP16 128 8 0.4 Relu6 77.28/93.5 -0.08 Google Drive
resnet50 120 FP16 128 8 0.4 - 77.00/- DDP baseline
resnet50 120 FP16 128 8 0.4 Gradient Centralization Conv only 77.40/93.57 +0.40
resnet50 120 FP16 128 8 0.4 Zero γ 77.24/- +0.24
resnet50 120 FP16 128 8 0.4 No decay bias 77.74/93.77 +0.74
resnet50 120 FP16 128 8 0.4 RandAugment n=2,m=9 76.44/93.18 -0.96
resnet50 120 FP16 128 8 0.4 AutoAugment 76.50/93.23 -0.50
  • More epochs for Mixup, Cutout, Dropout may get better results.
  • Auto/Rand Augment may train 180 epochs better.

Citation

@misc{ModelZoo.pytorch,
  title = {Basic deep conv neural network reproduce and explore},
  author = {X.Yang},
  URL = {https://github.com/PistonY/ModelZoo.pytorch},
  year = {2019}
  }

Reference