materials/external/Swin-Transformer/get_started.md

Swin Transformer for Image Classification

This folder contains the implementation of the Swin Transformer for image classification.

Model Zoo

Please refer to MODEL HUB for more pre-trained models.

Usage

Install

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 with cudnn>=7 following the official installation instructions
• Install PyTorch>=1.8.0 and torchvision>=0.9.0 with CUDA>=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

Data preparation

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 of fall11_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
        └── ...
  

Evaluation

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>

Training from scratch on ImageNet-1K

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.
• 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 training Swin-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.
• 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]

Pre-training on ImageNet-22K

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]

Fine-tuning on higher resolution

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]

Fine-tuning from a ImageNet-22K(21K) pre-trained model

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]

Throughput

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

Mixture-of-Experts Support

Install Tutel

python3 -m pip uninstall tutel -y 
python3 -m pip install --user --upgrade git+https://github.com/microsoft/tutel@main

Training Swin-MoE

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

Evaluating Swin-MoE

To evaluate a Swin-MoE-S with 32 experts on ImageNet-22K with 32 GPUs (4 nodes):

1. 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".
2. 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

SimMIM Support

Evaluating provided models

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>

Pre-training with SimMIM

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>]

Fine-tuning pre-trained models

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>]