xuliangcs / RepLKNet-pytorch

RepLKNet-pytorch (CVPR 2022)

This is the official PyTorch implementation of RepLKNet, from the following CVPR-2022 paper:

Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs.

The paper is released on arXiv: https://arxiv.org/abs/2203.06717.

Update: training code released. testing

Other implementations

framework	link
MegEngine (official)	https://github.com/megvii-research/RepLKNet
PyTorch (official)	https://github.com/DingXiaoH/RepLKNet-pytorch
Tensorflow	https://github.com/shkarupa-alex/tfreplknet
PaddlePaddle	re-implementations are welcomed
...

More re-implementations are welcomed.

Use our efficient large-kernel convolution with PyTorch

We have released an example for PyTorch. Please check setup.py and depthwise_conv2d_implicit_gemm.py (a replacement of torch.nn.Conv2d) in https://github.com/MegEngine/cutlass/tree/master/examples/19_large_depthwise_conv2d_torch_extension.

1. Clone cutlass (https://github.com/MegEngine/cutlass), enter the directory.
2. cd examples/19_large_depthwise_conv2d_torch_extension
3. ./setup.py install --user. If you get errors, check your CUDA_HOME.
4. A quick check: python depthwise_conv2d_implicit_gemm.py
5. Add WHERE_YOU_CLONED_CUTLASS/examples/19_large_depthwise_conv2d_torch_extension into your PYTHONPATH so that you can from depthwise_conv2d_implicit_gemm import DepthWiseConv2dImplicitGEMM anywhere. Then you may use DepthWiseConv2dImplicitGEMM as a replacement of nn.Conv2d.
6. export LARGE_KERNEL_CONV_IMPL=WHERE_YOU_CLONED_CUTLASS/examples/19_large_depthwise_conv2d_torch_extension so that RepLKNet will use the efficient implementation. Or you may simply modify the related code (get_conv2d) in replknet.py.

Our implementation mentioned in the paper has been integrated into MegEngine. The engine will automatically use it. If you would like to use it in other frameworks like Tensorflow, you may need to compile our released cuda sources (the *.cu files in the above example should work with other frameworks) and use some tools to load them, just like cutlass and torch.utils.cpp_extension in the PyTorch example. Would be appreciated if you could share with us your experience.

You may refer to the MegEngine source code: https://github.com/MegEngine/MegEngine/tree/8a2e92bd6c5ac02807b27d174dce090ee391000b/dnn/src/cuda/conv_bias/chanwise. .

Pull requests (e.g., better or other implementations or implementations on other frameworks) are welcomed.

Catalog

• Model code
• PyTorch pretrained models
• PyTorch large-kernel conv impl
• PyTorch training code
• PyTorch downstream models
• PyTorch downstream code

Results and Pre-trained Models

ImageNet-1K Models

name	resolution	ImageNet-1K acc	#params	FLOPs	ImageNet-1K pretrained model
RepLKNet-31B	224x224	83.5	79M	15.3G	Google Drive, Baidu
RepLKNet-31B	384x384	84.8	79M	45.1G	Google Drive, Baidu

ImageNet-22K Models

name	resolution	ImageNet-1K acc	#params	FLOPs	22K pretrained model	1K finetuned model
RepLKNet-31B	224x224	85.2	79M	15.3G	Google Drive, Baidu	Google Drive, Baidu
RepLKNet-31B	384x384	86.0	79M	45.1G	-	Google Drive, Baidu
RepLKNet-31L	384x384	86.6	172M	96.0G	Google Drive, Baidu	Google Drive, Baidu

MegData-73M Models

(uploading)

name	resolution	ImageNet-1K acc	#params	FLOPs	MegData-73M pretrained model	1K finetuned model
RepLKNet-XL	320x320	87.8	335M	128.7G

Evaluation

Training

You may use multi-node training on a SLURM cluster with submitit. Please install:

pip install submitit

If you have limited GPU memory (e.g., 2080Ti), use --use_checkpoint True to save GPU memory.

Pretrain RepLKNet-31B on ImageNet-1K

Single machine:

python -m torch.distributed.launch --nproc_per_node=8 main.py --model RepLKNet-31B --drop_path 0.5 --batch_size 64 --lr 4e-3 --update_freq 4 --model_ema true --model_ema_eval true --data_path /path/to/imagenet-1k --warmup_epochs 10 --epochs 300 --use_checkpoint True --output_dir your_training_dir

Four machines:

python run_with_submitit.py --nodes 4 --ngpus 8 --model RepLKNet-31B --drop_path 0.5 --batch_size 64 --lr 4e-3 --update_freq 4 --model_ema true --model_ema_eval true --data_path /path/to/imagenet-1k --warmup_epochs 10 --epochs 300 --use_checkpoint True --job_dir your_training_dir

Finetune the ImageNet-1K-pretrained (224x224) RepLKNet-31B with 384x384

Single machine:

Pretrain RepLKNet-31B on ImageNet-22K

Finetune 22K-pretrained RepLKNet-31B on ImageNet-1K (224x224)

Finetune 22K-pretrained RepLKNet-31B on ImageNet-1K (384x384)

Pretrain RepLKNet-31L on ImageNet-22K

Finetune 22K-pretrained RepLKNet-31L on ImageNet-1K (224x224)

Finetune 22K-pretrained RepLKNet-31L on ImageNet-1K (384x384)

Acknowledgement

The released PyTorch training script is based on the code of ConvNeXt, which was built using the timm library, DeiT and BEiT repositories.

License

This project is released under the MIT license. Please see the LICENSE file for more information.