Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows. For more details, please refer to "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"

This repo is an implementation of MegEngine version Swin-Transformer. This is also a showcase for training on GPU with less memory by leveraging MegEngine DTR technique.

There is also an official PyTorch implementation.

• Clone this repo:

git clone https://github.com/MegEngine/swin-transformer.git
cd swin-transformer

• Install megengine==1.6.0

pip3 install megengine==1.6.0 -f https://megengine.org.cn/whl/mge.html

To train a Swin Transformer using random data, run:

python3 -n <num-of-gpus-to-use> -b <batch-size-per-gpu> -s <num-of-train-steps> train_random.py

To train a Swin Transformer using AMP (Auto Mix Precision), run:

python3 -n <num-of-gpus-to-use> -b <batch-size-per-gpu> -s <num-of-train-steps> --mode mp train_random.py

To train a Swin Transformer using DTR in dynamic graph mode, run:

python3 -n <num-of-gpus-to-use> -b <batch-size-per-gpu> -s <num-of-train-steps> --dtr [--dtr-thd <eviction-threshold-of-dtr>] train_random.py

To train a Swin Transformer using DTR in static graph mode, run:

python3 -n <num-of-gpus-to-use> -b <batch-size-per-gpu> -s <num-of-train-steps> --trace --symbolic --dtr --dtr-thd <eviction-threshold-of-dtr> train_random.py

For example, to train a Swin Transformer with a single GPU using DTR in static graph mode with threshold=8GB and AMP, run:

python3 -n 1 -b 340 -s 10 --trace --symbolic --dtr --dtr-thd 8 --mode mp train_random.py

For more usage, run:

python3 train_random.py -h

• Testing Devices
  • 2080Ti @ cuda-10.1-cudnn-v7.6.3-TensorRT-5.1.5.0 @ Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz
  • Reserve all CUDA memory by setting MGB_CUDA_RESERVE_MEMORY=1, in order to alleviate memory fragmentation problem

We are inspired by the Swin-Transformer repository, many thanks to microsoft!