This folder contains the implementation of the Swin Transformer for image classification.
Please refer to MODEL HUB for more pre-trained models.
We recommend using the pytorch docker nvcr>=21.05
by
nvidia: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch.
- Clone this repo:
git clone https://github.com/microsoft/Swin-Transformer.git
cd Swin-Transformer
- Create a conda virtual environment and activate it:
conda create -n swin python=3.7 -y
conda activate swin
- Install
CUDA>=10.2
withcudnn>=7
following the official installation instructions - Install
PyTorch>=1.8.0
andtorchvision>=0.9.0
withCUDA>=10.2
:
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch
- Install
timm==0.4.12
:
pip install timm==0.4.12
- Install other requirements:
pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8 pyyaml scipy
- Install fused window process for acceleration, activated by passing
--fused_window_process
in the running script
cd kernels/window_process
python setup.py install #--user
We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to load data:
-
For standard folder dataset, move validation images to labeled sub-folders. The file structure should look like:
$ tree data imagenet ├── train │ ├── class1 │ │ ├── img1.jpeg │ │ ├── img2.jpeg │ │ └── ... │ ├── class2 │ │ ├── img3.jpeg │ │ └── ... │ └── ... └── val ├── class1 │ ├── img4.jpeg │ ├── img5.jpeg │ └── ... ├── class2 │ ├── img6.jpeg │ └── ... └── ...
-
To boost the slow speed when reading images from massive small files, we also support zipped ImageNet, which includes four files:
train.zip
,val.zip
: which store the zipped folder for train and validate splits.train_map.txt
,val_map.txt
: which store the relative path in the corresponding zip file and ground truth label. Make sure the data folder looks like this:
$ tree data data └── ImageNet-Zip ├── train_map.txt ├── train.zip ├── val_map.txt └── val.zip $ head -n 5 data/ImageNet-Zip/val_map.txt ILSVRC2012_val_00000001.JPEG 65 ILSVRC2012_val_00000002.JPEG 970 ILSVRC2012_val_00000003.JPEG 230 ILSVRC2012_val_00000004.JPEG 809 ILSVRC2012_val_00000005.JPEG 516 $ head -n 5 data/ImageNet-Zip/train_map.txt n01440764/n01440764_10026.JPEG 0 n01440764/n01440764_10027.JPEG 0 n01440764/n01440764_10029.JPEG 0 n01440764/n01440764_10040.JPEG 0 n01440764/n01440764_10042.JPEG 0
-
For ImageNet-22K dataset, make a folder named
fall11_whole
and move all images to labeled sub-folders in this folder. Then download the train-val split file (ILSVRC2011fall_whole_map_train.txt & ILSVRC2011fall_whole_map_val.txt) , and put them in the parent directory offall11_whole
. The file structure should look like:$ tree imagenet22k/ imagenet22k/ ├── ILSVRC2011fall_whole_map_train.txt ├── ILSVRC2011fall_whole_map_val.txt └── fall11_whole ├── n00004475 ├── n00005787 ├── n00006024 ├── n00006484 └── ...
To evaluate a pre-trained Swin Transformer
on ImageNet val, run:
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py --eval \
--cfg <config-file> --resume <checkpoint> --data-path <imagenet-path>
For example, to evaluate the Swin-B
with a single GPU:
python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --eval \
--cfg configs/swin/swin_base_patch4_window7_224.yaml --resume swin_base_patch4_window7_224.pth --data-path <imagenet-path>
To train a Swin Transformer
on ImageNet from scratch, run:
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py \
--cfg <config-file> --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
Notes:
- To use zipped ImageNet instead of folder dataset, add
--zip
to the parameters.- To cache the dataset in the memory instead of reading from files every time, add
--cache-mode part
, which will shard the dataset into non-overlapping pieces for different GPUs and only load the corresponding one for each GPU.
- To cache the dataset in the memory instead of reading from files every time, add
- When GPU memory is not enough, you can try the following suggestions:
- Use gradient accumulation by adding
--accumulation-steps <steps>
, set appropriate<steps>
according to your need. - Use gradient checkpointing by adding
--use-checkpoint
, e.g., it saves about 60% memory when trainingSwin-B
. Please refer to this page for more details. - We recommend using multi-node with more GPUs for training very large models, a tutorial can be found in this page.
- Use gradient accumulation by adding
- To change config options in general, you can use
--opts KEY1 VALUE1 KEY2 VALUE2
, e.g.,--opts TRAIN.EPOCHS 100 TRAIN.WARMUP_EPOCHS 5
will change total epochs to 100 and warm-up epochs to 5. - For additional options, see config and run
python main.py --help
to get detailed message.
For example, to train Swin Transformer
with 8 GPU on a single node for 300 epochs, run:
Swin-T
:
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_tiny_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 128
Swin-S
:
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_small_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 128
Swin-B
:
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_base_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 64 \
--accumulation-steps 2 [--use-checkpoint]
For example, to pre-train a Swin-B
model on ImageNet-22K:
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_base_patch4_window7_224_22k.yaml --data-path <imagenet22k-path> --batch-size 64 \
--accumulation-steps 8 [--use-checkpoint]
For example, to fine-tune a Swin-B
model pre-trained on 224x224 resolution to 384x384 resolution:
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_base_patch4_window12_384_finetune.yaml --pretrained swin_base_patch4_window7_224.pth \
--data-path <imagenet-path> --batch-size 64 --accumulation-steps 2 [--use-checkpoint]
For example, to fine-tune a Swin-B
model pre-trained on ImageNet-22K(21K):
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_base_patch4_window7_224_22kto1k_finetune.yaml --pretrained swin_base_patch4_window7_224_22k.pth \
--data-path <imagenet-path> --batch-size 64 --accumulation-steps 2 [--use-checkpoint]
To measure the throughput, run:
python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py \
--cfg <config-file> --data-path <imagenet-path> --batch-size 64 --throughput --disable_amp
Install Tutel
python3 -m pip uninstall tutel -y
python3 -m pip install --user --upgrade git+https://github.com/microsoft/tutel@main
For example, to train a Swin-MoE-S
model with 32 experts on ImageNet-22K with 32 GPUs (4 nodes):
python -m torch.distributed.launch --nproc_per_node 8 --nnode=4 \
--node_rank=<node-rank> --master_addr=<master-ip> --master_port 12345 main_moe.py \
--cfg configs/swinmoe/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml --data-path <imagenet22k-path> --batch-size 128
To evaluate a Swin-MoE-S
with 32 experts on ImageNet-22K with 32 GPUs (4 nodes):
- Download the zip file swin_moe_small_patch4_window12_192_32expert_32gpu_22k.zip which contains the pre-trained models for each rank, and unzip them to the folder "swin_moe_small_patch4_window12_192_32expert_32gpu_22k".
- Run the following evaluation command, note the checkpoint path should not contain the ".rank<x>" suffix.
python -m torch.distributed.launch --nproc_per_node 8 --nnode=4 \
--node_rank=<node-rank> --master_addr=<master-ip> --master_port 12345 main_moe.py \
--cfg configs/swinmoe/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml --data-path <imagenet22k-path> --batch-size 128 \
--resume swin_moe_small_patch4_window12_192_32expert_32gpu_22k/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.pth
More Swin-MoE models can be found in MODEL HUB
To evaluate a provided model on ImageNet validation set, run:
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_simmim_ft.py \
--eval --cfg <config-file> --resume <checkpoint> --data-path <imagenet-path>
For example, to evaluate the Swin Base
model on a single GPU, run:
python -m torch.distributed.launch --nproc_per_node 1 main_simmim_ft.py \
--eval --cfg configs/simmim/simmim_finetune__swin_base__img224_window7__800ep.yaml --resume simmim_finetune__swin_base__img224_window7__800ep.pth --data-path <imagenet-path>
To pre-train models with SimMIM
, run:
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_simmim_pt.py \
--cfg <config-file> --data-path <imagenet-path>/train [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
For example, to pre-train Swin Base
for 800 epochs on one DGX-2 server, run:
python -m torch.distributed.launch --nproc_per_node 16 main_simmim_pt.py \
--cfg configs/simmim/simmim_pretrain__swin_base__img192_window6__800ep.yaml --batch-size 128 --data-path <imagenet-path>/train [--output <output-directory> --tag <job-tag>]
To fine-tune models pre-trained by SimMIM
, run:
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_simmim_ft.py \
--cfg <config-file> --data-path <imagenet-path> --pretrained <pretrained-ckpt> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
For example, to fine-tune Swin Base
pre-trained by SimMIM
on one DGX-2 server, run:
python -m torch.distributed.launch --nproc_per_node 16 main_simmim_ft.py \
--cfg configs/simmim/simmim_finetune__swin_base__img224_window7__800ep.yaml --batch-size 128 --data-path <imagenet-path> --pretrained <pretrained-ckpt> [--output <output-directory> --tag <job-tag>]