CMT实现.zip

# CMT.pytorch ## Implementation of [CMT: Convolutional Neural Networks Meet Vision Transformers](https://arxiv.org/pdf/2107.06263.pdf) ### Set up ``` - python==3.6 - cuda==10.0 # other pytorch/timm version can also work pip install torch==1.7.0 torchvision==0.8.1; pip install timm==0.3.2; pip install torchprofile; # build apex cd /your_path_to/apex-master/; pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ ``` ### Data preparation Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is: ``` │path/to/imagenet/ ├──train/ │ ├── n01440764 │ │ ├── n01440764_10026.JPEG │ │ ├── n01440764_10027.JPEG │ │ ├── ...... │ ├── ...... ├──val/ │ ├── n01440764 │ │ ├── ILSVRC2012_val_00000293.JPEG │ │ ├── ILSVRC2012_val_00002138.JPEG │ │ ├── ...... │ ├── ...... ``` #### Training To train CMT-Tiny on ImageNet-1K on a single node with 8 gpus: ``` python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model cmt_ti --batch-size 256 --apex-amp --input-size 160 --weight-decay 0.05 --drop-path 0.1 --epochs 800 --test_freq 100 --test_epoch 760 --warmup-lr 1e-7 --warmup-epochs 20 --lr 8e-4 --min-lr 1e-5 --no-model-ema ``` To train CMT-XS on ImageNet-1K on a single node with 8 gpus: ``` python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model cmt_xs --batch-size 256 --apex-amp --input-size 192 --weight-decay 0.04 --drop-path 0.08 --epochs 400 --test_freq 100 --test_epoch 360 --warmup-lr 1e-6 --warmup-epochs 20 --lr 7e-4 --min-lr 2e-5 --model-ema-decay 0.9998 ``` To train CMT-Small on ImageNet-1K on a single node with 8 gpus: ``` python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model cmt_s --batch-size 128 --apex-amp --input-size 224 --weight-decay 0.05 --drop-path 0.1 --epochs 300 --test_freq 100 --test_epoch 260 --warmup-lr 1e-7 --warmup-epochs 20 ``` To train CMT-Base on ImageNet-1K on a single node with 8 gpus: ``` python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model cmt_b --batch-size 64 --apex-amp --input-size 256 --weight-decay 0.05 --drop-path 0.25 --epochs 300 --test_freq 100 --test_epoch 260 --warmup-lr 1e-6 --min-lr 2e-5 --warmup-epochs 20 ``` ### CMT on ImageNet-1K Classification | Model | Top 1 Acc. | Log | Ckpt | | :------------------- | :--------: | :------: | :------: | | CMT-Ti | 79.0% | [github](https://github.com/ggjy/CMT.pytorch/releases/download/release-v1/log_cmt_tiny.txt) | [github](https://github.com/ggjy/CMT.pytorch/releases/download/release-v1/cmt_tiny.pth) | | CMT-XS | 81.8% | [github](https://github.com/ggjy/CMT.pytorch/releases/download/release-v1/log_cmt_xs.txt) | [github](https://github.com/ggjy/CMT.pytorch/releases/download/release-v1/cmt_xs.pth) | | CMT-Small | 83.5% | [github](https://github.com/ggjy/CMT.pytorch/releases/download/release-v1/log_cmt_small.txt) | [github](https://github.com/ggjy/CMT.pytorch/releases/download/release-v1/cmt_small.pth) | | CMT-Base | 84.5% | [github](https://github.com/ggjy/CMT.pytorch/releases/download/release-v1/log_cmt_base.txt) | [github](https://github.com/ggjy/CMT.pytorch/releases/download/release-v1/cmt_base.pth) | ## Citation If you find this project useful in your research, please consider cite: ```bibtex @article{guo2021cmt, title={Cmt: Convolutional neural networks meet vision transformers}, author={Guo, Jianyuan and Han, Kai and Wu, Han and Xu, Chang and Tang, Yehui and Xu, Chunjing and Wang, Yunhe}, journal={arXiv preprint arXiv:2107.06263}, year={2021} } ``` ## Acknowledgment This repo is based on [DeiT](https://github.com/facebookresearch/deit) and [pytorch-image-models](https://github.com/rwightman/pytorch-image-models).