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Swin Transformer for Image Classification

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

Model Zoo

Regular ImageNet-1K trained models

name resolution acc@1 acc@5 #params FLOPs model
Swin-T 224x224 81.2 95.5 28M 4.5G github/baidu/log
Swin-S 224x224 83.2 96.2 50M 8.7G github/baidu/log
Swin-B 224x224 83.5 96.5 88M 15.4G github/baidu/log
Swin-B 384x384 84.5 97.0 88M 47.1G github/baidu

ImageNet-22K pre-trained models

name resolution acc@1 acc@5 #params FLOPs 22K model 1K model
Swin-B 224x224 85.2 97.5 88M 15.4G github/baidu github/baidu
Swin-B 384x384 86.4 98.0 88M 47.1G github/baidu github/baidu
Swin-L 224x224 86.3 97.9 197M 34.5G github/baidu github/baidu
Swin-L 384x384 87.3 98.2 197M 103.9G github/baidu github/baidu

Note: access code for baidu is swin.

Usage

Install

  • 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
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
  • Install timm==0.3.2:
pip install timm==0.3.2
  • Install Apex:
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
  • Install other requirements:
pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8

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

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_base_patch4_window7_224.yaml --resume swin_base_patch4_window7_224.pth --data-path <imagenet-path>

Training from scratch

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_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_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_base_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 64 \
--accumulation-steps 2 [--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_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_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 --amp-opt-level O0