x unet

x-unet

تنفيذ شبكة U كاملة مع الاهتمام الفعال بالإضافة إلى أحدث نتائج الأبحاث

ثَبَّتَ

$ pip install x-unet

الاستخدام

 import torch
from x_unet import XUnet

unet = XUnet (
    dim = 64 ,
    channels = 3 ,
    dim_mults = ( 1 , 2 , 4 , 8 ),
    nested_unet_depths = ( 7 , 4 , 2 , 1 ),     # nested unet depths, from unet-squared paper
    consolidate_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)

للتصوير ثلاثي الأبعاد (الفيديو أو التصوير المقطعي/التصوير بالرنين المغناطيسي)

 import torch
from x_unet import XUnet

unet = XUnet (
    dim = 64 ,
    frame_kernel_size = 3 ,                 # set this to greater than 1
    channels = 3 ,
    dim_mults = ( 1 , 2 , 4 , 8 ),
    nested_unet_depths = ( 5 , 4 , 2 , 1 ),     # nested unet depths, from unet-squared paper
    consolidate_upsample_fmaps = True ,     # whether to consolidate outputs from all upsample blocks, used in unet-squared paper
    weight_standardize = True
)

video = torch . randn ( 1 , 3 , 10 , 128 , 128 )    # (batch, channels, frames, height, width)
out = unet ( video ) # (1, 3, 10, 128, 128) 

ما يجب القيام به

• كفاءة الذاكرة ثلاثية الأبعاد - الكتل القابلة للعكس، ونقاط التفتيش، ووحدة الذاكرة الفعالة
• عرض خيار التلافيف المحورية (وضع تلافيف الإطار في نهاية سلسلة resnet)

الاستشهادات

 @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 }
}

 @inproceedings { Woo2023ConvNeXtVC ,
    title   = { ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders } ,
    author  = { Sanghyun Woo and Shoubhik Debnath and Ronghang Hu and Xinlei Chen and Zhuang Liu and In-So Kweon and Saining Xie } ,
    year    = { 2023 }
}