Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Last update: Dec 29, 2022

Overview

Swin-Transformer-Tensorflow

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.

Introduction:

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.

Usage:

1. To Run a Pre-trained Swin Transformer

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:

cfg	weights_type	22K model	1K Model
configs/swin_tiny_patch4_window7_224.yaml	imagenet_1k	-	github
configs/swin_small_patch4_window7_224.yaml	imagenet_1k	-	github
configs/swin_base_patch4_window7_224.yaml	imagenet_1k	-	github
configs/swin_base_patch4_window12_384.yaml	imagenet_1k	-	github
configs/swin_base_patch4_window7_224.yaml	imagenet_22kto1k	-	github
configs/swin_base_patch4_window12_384.yaml	imagenet_22kto1k	-	github
configs/swin_large_patch4_window7_224.yaml	imagenet_22kto1k	-	github
configs/swin_large_patch4_window12_384.yaml	imagenet_22kto1k	-	github
configs/swin_base_patch4_window7_224.yaml	imagenet_22k	github	-
configs/swin_base_patch4_window12_384.yaml	imagenet_22k	github	-
configs/swin_large_patch4_window7_224.yaml	imagenet_22k	github	-
configs/swin_large_patch4_window12_384.yaml	imagenet_22k	github	-

2. Create custom models

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.

3. Convert PyTorch pretrained weights into Tensorflow checkpoints

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

TODO:

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

Citations:

@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}
}

This is an official implementation of our CVPR 2021 paper "Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression" (https://arxiv.org/abs/2104.02300)

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression Introduction In this paper, we are interested in the bottom-up paradigm of estima

367 Dec 27, 2022

Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2 Description Pytorch implementation of the paper Face Ide

58 Dec 24, 2022

Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

PAWS-TF 🐾 Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

43 Jan 8, 2023

A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

35 Jul 29, 2022

Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

163 Dec 26, 2022

Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Transfer Learning for Text Classification with Tensorflow Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01

82 Oct 22, 2022

PyTorch implementation of Asymmetric Siamese (https://arxiv.org/abs/2204.00613)

Asym-Siam: On the Importance of Asymmetry for Siamese Representation Learning This is a PyTorch implementation of the Asym-Siam paper, CVPR 2022: @inp

89 Dec 18, 2022

This repository contains the code used for Predicting Patient Outcomes with Graph Representation Learning (https://arxiv.org/abs/2101.03940).

Predicting Patient Outcomes with Graph Representation Learning This repository contains the code used for Predicting Patient Outcomes with Graph Repre

76 Dec 22, 2022

https://arxiv.org/abs/2102.11005

LogME LogME: Practical Assessment of Pre-trained Models for Transfer Learning How to use Just feed the features f and labels y to the function, and yo

149 Dec 19, 2022

Comments

Custom Swin Transformer: error: unrecognized arguments

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

parser.add_argument( '--cfg', type=str, metavar="FILE", help='/content/Swin-Transformer-Tensorflow/configs/swin_tiny_patch4_window7_224.yaml', default="CUSTOM_YAML_FILE_PATH" ) parser.add_argument( '--resume', type=int, help=1, choices={0, 1}, default=1, ) parser.add_argument( '--weights_type', type=str, help='imagenet_22k', choices={"imagenet_1k", "imagenet_22k", "imagenet_22kto1k"}, default="imagenet_1k", )

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

i am trying to use it but it throws an error below

usage: Custom Swin Transformer [-h] [--cfg FILE] [--resume {0,1}] [--weights_type {imagenet_22kto1k,imagenet_1k,imagenet_22k}] Custom Swin Transformer: error: unrecognized arguments: -f /root/.local/share/jupyter/runtime/kernel-ee309a98-1f20-4bb7-aa12-c2980aea076c.json An exception has occurred, use %tb to see the full traceback.

SystemExit: 2

opened by AliKayhanAtay 1
train dataset

Thank you for Thank you for providing your code. I've been running the pretrained model, and I'd like to know how to learn about custom data from the code you provided and how to transfer learning to custom data using the pretrained model. Thank you.

opened by hoyeoung 1

Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Related tags

Overview

Swin-Transformer-Tensorflow

Introduction:

Usage:

1. To Run a Pre-trained Swin Transformer

2. Create custom models

3. Convert PyTorch pretrained weights into Tensorflow checkpoints

TODO:

Citations:

You might also like...

This is an official implementation of our CVPR 2021 paper "Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression" (https://arxiv.org/abs/2104.02300)

Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286

Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

PyTorch implementation of Asymmetric Siamese (https://arxiv.org/abs/2204.00613)

This repository contains the code used for Predicting Patient Outcomes with Graph Representation Learning (https://arxiv.org/abs/2101.03940).

https://arxiv.org/abs/2102.11005

Comments

Custom Swin Transformer: error: unrecognized arguments

train dataset

Releases(v1.0)

v1.0(Jun 27, 2021)

Owner

An introduction to bioimage analysis - http://bioimagebook.github.io

Baseline of DCASE 2020 task 4

Repo for our ICML21 paper Unsupervised Learning of Visual 3D Keypoints for Control

Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion

CodeContests is a competitive programming dataset for machine-learning

Multi-View Radar Semantic Segmentation

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.

AirCode: A Robust Object Encoding Method

Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization

[RSS 2021] An End-to-End Differentiable Framework for Contact-Aware Robot Design

Simple embedding based text classifier inspired by fastText, implemented in tensorflow

A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

利用yolov5和TensorRT从0到1实现目标检测的模型训练到模型部署全过程

Labelbox is the fastest way to annotate data to build and ship artificial intelligence applications

Warning: This project does not have any current developer. See bellow.

Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

ToFFi - Toolbox for Frequency-based Fingerprinting of Brain Signals

A PyTorch-based library for fast prototyping and sharing of deep neural network models.