A direct translation of the official PyTorch implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" to TensorFlow 2.

The official Pytorch implementation can be found here.

This Fork is a packaged version of the original repo.

pip install git+https://github.com/johnypark/Swin-Transformer-Tensorflow@main

Still under construction. -Update readme -Make things work

Swin Transformer (the name Swin stands for Shifted window) is initially described in arxiv, which capably serves as a general-purpose backbone for computer vision. It is basically a hierarchical Transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection.

Swin Transformer achieves strong performance on COCO object detection (58.7 box AP and 51.1 mask AP on test-dev) and ADE20K semantic segmentation (53.5 mIoU on val), surpassing previous models by a large margin.

Swin-T:

python main.py --cfg configs/swin_tiny_patch4_window7_224.yaml --include_top 1 --resume 1 --weights_type imagenet_1k

Swin-S:

python main.py --cfg configs/swin_small_patch4_window7_224.yaml --include_top 1 --resume 1 --weights_type imagenet_1k

Swin-B:

python main.py --cfg configs/swin_base_patch4_window7_224.yaml --include_top 1 --resume 1 --weights_type imagenet_1k

The possible options for cfg and weights_type are:

To create a custom classification model:

import argparse

import tensorflow as tf

from config import get_config
from models.build import build_model

parser = argparse.ArgumentParser('Custom Swin Transformer')

parser.add_argument(
    '--cfg',
    type=str,
    metavar="FILE",
    help='path to config file',
    default="CUSTOM_YAML_FILE_PATH"
)
parser.add_argument(
    '--resume',
    type=int,
    help='Whether or not to resume training from pretrained weights',
    choices={0, 1},
    default=1,
)
parser.add_argument(
    '--weights_type',
    type=str,
    help='Type of pretrained weight file to load including number of classes',
    choices={"imagenet_1k", "imagenet_22k", "imagenet_22kto1k"},
    default="imagenet_1k",
)

args = parser.parse_args()
custom_config = get_config(args, include_top=False)

swin_transformer = tf.keras.Sequential([
    build_model(config=custom_config, load_pretrained=args.resume, weights_type=args.weights_type),
    tf.keras.layers.Dense(CUSTOM_NUM_CLASSES)
)

Model ouputs are logits, so don't forget to include softmax in training/inference!!

You can easily customize the model configs with custom YAML files. Predefined YAML files provided by Microsoft are located in the configs directory.

We provide a python script with which we convert official PyTorch weights into Tensorflow checkpoints.

$ python convert_weights.py --cfg config_file --weights the_path_to_pytorch_weights --weights_type type_of_pretrained_weights --output the_path_to_output_tf_weights

• Translate model code over to TensorFlow
• Load PyTorch pretrained weights into TensorFlow model
• Write trainer code
• Reproduce results presented in paper
  • Object Detection
• Reproduce training efficiency of official code in TensorFlow

@misc{liu2021swin,
      title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, 
      author={Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo},
      year={2021},
      eprint={2103.14030},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}