x-unet

Install

$ pip install x-unet

Usage

importtorchfromx_unetimportXUnetunet=XUnet(dim=64,dim_mults= (1,2,4,8),nested_unet_depths= (7,4,2,1),# nested unet depths, from unet-squared paperconsolidate_upsample_fmaps=True,# whether to consolidate outputs from all upsample blocks, used in unet-squared paper)img=torch.randn(1,3,256,256)out=unet(img)# (1, 3, 256, 256)

importtorchfromx_unetimportXUnetunet=XUnet(dim=64,frame_kernel_size=3,# set this to greater than 1dim_mults= (1,2,4,8),nested_unet_depths= (5,4,2,1),# nested unet depths, from unet-squared paperconsolidate_upsample_fmaps=True,# whether to consolidate outputs from all upsample blocks, used in unet-squared paperweight_standardize=True)video=torch.randn(1,3,10,128,128)# (batch, channels, frames, height, width)out=unet(video)# (1, 3, 10, 128, 128)

Todo

Citations

@article{Ronneberger2015UNetCN,title   ={U-Net: Convolutional Networks for Biomedical Image Segmentation},author  ={Olaf Ronneberger and Philipp Fischer and Thomas Brox},journal ={ArXiv},year    ={2015},volume  ={abs/1505.04597}}

@article{Qin2020U2NetGD,title   ={U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection},author  ={Xuebin Qin and Zichen Vincent Zhang and Chenyang Huang and Masood Dehghan and Osmar R Zaiane and Martin J{\"a}gersand},journal ={ArXiv},year    ={2020},volume  ={abs/2005.09007}}

@inproceedings{Henry2020QueryKeyNF,title   ={Query-Key Normalization for Transformers},author  ={Alex Henry and Prudhvi Raj Dachapally and Shubham Vivek Pawar and Yuxuan Chen},booktitle ={FINDINGS},year    ={2020}}

@article{Qiao2019WeightS,title   ={Weight Standardization},author  ={Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Loddon Yuille},journal ={ArXiv},year    ={2019},volume  ={abs/1903.10520}}

@article{Shleifer2021NormFormerIT,title   ={NormFormer: Improved Transformer Pretraining with Extra Normalization},author  ={Sam Shleifer and Jason Weston and Myle Ott},journal ={ArXiv},year    ={2021},volume  ={abs/2110.09456}}

@article{Sunkara2022NoMS,title   ={No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects},author  ={Raja Sunkara and Tie Luo},journal ={ArXiv},year    ={2022},volume  ={abs/2208.03641}}