You can create your CondConv custom layer from the original impl:

  

      github.com
  

  
    aim-uofa/AdelaiDet/blob/master/adet/modeling/condinst/condinst.py 3


    # -*- coding: utf-8 -*-
import logging
from skimage import color

import torch
from torch import nn
import torch.nn.functional as F

from detectron2.structures import ImageList
from detectron2.modeling.proposal_generator import build_proposal_generator
from detectron2.modeling.backbone import build_backbone
from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
from detectron2.structures.instances import Instances
from detectron2.structures.masks import PolygonMasks, polygons_to_bitmask

from .dynamic_mask_head import build_dynamic_mask_head
from .mask_branch import build_mask_branch

from adet.utils.comm import aligned_bilinear




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Or request the off the shelf model at:

  

      github.com/tensorflow/models
  

  
    
  
	  
  

  
    
      📄 Community requests: New paper implementations
    

    
      
        opened 
        
          
        
        Jun 6, 2020
      
      


      
        
          
          jaeyounkim
        
      
    

    
        models:official
        type:support
    
  


  
    This issue contains **all open requests for paper implementations requested by t…he community**.

We cannot guarantee that we can fulfill community requests for specific paper implementations.

If you'd like to contribute, **please add a comment to the relevant GitHub issue to express your interest in providing your paper implementation**.

Awesome external contributors will be nominated for [Google Open Source Peer Bonus](https://opensource.google/docs/growing/peer-bonus/).

Please also see our [contribution guidelines](https://github.com/tensorflow/models/wiki/Research-paper-code-contribution) and [paper selection criteria](https://github.com/tensorflow/models/wiki/Research-paper-code-contribution#model-selection).

## Computer Vision

| Paper | Conference | GitHub issue | Note |
--------|------------|--------------|------|
| ResNeXt: [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431) | CVPR 2017 | #6752 | |
| DenseNet: [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) | CVPR 2017 | #8278 | |
| [Density estimation using Real NVP](https://arxiv.org/abs/1605.08803) | ICLR 2017 | #7848 | Need to migrate [TF 1 code](https://github.com/tensorflow/models/tree/master/research/real_nvp) to TF 2 |
| [Spatiotemporal Contrastive Video Representation Learning](https://arxiv.org/abs/2008.03800) |  CVPR 2021 | #9993 |  In progress (Internally) |