hunk.code_window: @pytest.mark.timeout(120)
   @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS))
   @pytest.mark.parametrize('batch_size', [1])
   def test_model_forward_torchscript(model_name, batch_size):
       """Run a single forward pass with each model"""
  
hunk.labels: keep keep keep replace keep keep keep
hunk.after_edit: @pytest.mark.parametrize(
       'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
  
hunk.file_path: tests/test_models.py
hunk.type: replace
file: # Model Summaries
  
  The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below.
  
  Most included models have pretrained weights. The weights are either:
  
  1. from their original sources
  2. ported by myself from their original impl in a different framework (e.g. Tensorflow models)
  3. trained from scratch using the included training script
  
  The validation results for the pretrained weights are [here](results.md)
  
  A more exciting view (with pretty pictures) of the models within `timm` can be found at [paperswithcode](https://paperswithcode.com/lib/timm).
  
  ## Big Transfer ResNetV2 (BiT) [[resnetv2.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnetv2.py)]
  * Paper: `Big Transfer (BiT): General Visual Representation Learning` - https://arxiv.org/abs/1912.11370
  * Reference code: https://github.com/google-research/big_transfer
  
  ## Cross-Stage Partial Networks [[cspnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/cspnet.py)]
  * Paper: `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
  * Reference impl: https://github.com/WongKinYiu/CrossStagePartialNetworks
  
  ## DenseNet [[densenet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/densenet.py)]
  * Paper: `Densely Connected Convolutional Networks` - https://arxiv.org/abs/1608.06993
  * Code: https://github.com/pytorch/vision/tree/master/torchvision/models
  
  ## DLA [[dla.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py)]
  * Paper: https://arxiv.org/abs/1707.06484
  * Code: https://github.com/ucbdrive/dla
  
  ## Dual-Path Networks [[dpn.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dpn.py)]
  * Paper: `Dual Path Networks` - https://arxiv.org/abs/1707.01629
  * My PyTorch code: https://github.com/rwightman/pytorch-dpn-pretrained
  * Reference code: https://github.com/cypw/DPNs
  
  ## GPU-Efficient Networks [[byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py)]
  * Paper: `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
  * Reference code: https://github.com/idstcv/GPU-Efficient-Networks
  
  ## HRNet [[hrnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/hrnet.py)]
  * Paper: `Deep High-Resolution Representation Learning for Visual Recognition` - https://arxiv.org/abs/1908.07919
  * Code: https://github.com/HRNet/HRNet-Image-Classification
  
  ## Inception-V3 [[inception_v3.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v3.py)]
  * Paper: `Rethinking the Inception Architecture for Computer Vision` - https://arxiv.org/abs/1512.00567
  * Code: https://github.com/pytorch/vision/tree/master/torchvision/models
  
  ## Inception-V4 [[inception_v4.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v4.py)]
  * Paper: `Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning` - https://arxiv.org/abs/1602.07261
  * Code: https://github.com/Cadene/pretrained-models.pytorch
  * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets
  
  ## Inception-ResNet-V2 [[inception_resnet_v2.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_resnet_v2.py)]
  * Paper: `Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning` - https://arxiv.org/abs/1602.07261
  * Code: https://github.com/Cadene/pretrained-models.pytorch
  * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets
  
  ## NASNet-A [[nasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/nasnet.py)]
  * Papers: `Learning Transferable Architectures for Scalable Image Recognition` - https://arxiv.org/abs/1707.07012
  * Code: https://github.com/Cadene/pretrained-models.pytorch
  * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet
  
  ## PNasNet-5 [[pnasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/pnasnet.py)]
  * Papers: `Progressive Neural Architecture Search` - https://arxiv.org/abs/1712.00559
  * Code: https://github.com/Cadene/pretrained-models.pytorch
  * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet
  
  ## EfficientNet [[efficientnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py)]
  
  * Papers:
    * EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
    * EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
    * EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
    * EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
    * MixNet - https://arxiv.org/abs/1907.09595
    * MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
    * MobileNet-V2 - https://arxiv.org/abs/1801.04381
    * FBNet-C - https://arxiv.org/abs/1812.03443
    * Single-Path NAS - https://arxiv.org/abs/1904.02877
  * My PyTorch code: https://github.com/rwightman/gen-efficientnet-pytorch
  * Reference code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
  
  ## MobileNet-V3 [[mobilenetv3.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py)]
  * Paper: `Searching for MobileNetV3` - https://arxiv.org/abs/1905.02244
  * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet
  
  ## RegNet [[regnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/regnet.py)]
  * Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
  * Reference code: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
  
  ## RepVGG [[byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py)]
  * Paper: `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
  * Reference code: https://github.com/DingXiaoH/RepVGG
  
  ## ResNet, ResNeXt [[resnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnet.py)]
  
  * ResNet (V1B)
    * Paper: `Deep Residual Learning for Image Recognition` - https://arxiv.org/abs/1512.03385
    * Code: https://github.com/pytorch/vision/tree/master/torchvision/models
  * ResNeXt
    * Paper: `Aggregated Residual Transformations for Deep Neural Networks` - https://arxiv.org/abs/1611.05431
    * Code: https://github.com/pytorch/vision/tree/master/torchvision/models
  * 'Bag of Tricks' / Gluon C, D, E, S ResNet variants
    * Paper: `Bag of Tricks for Image Classification with CNNs` - https://arxiv.org/abs/1812.01187
    * Code: https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py
  * Instagram pretrained / ImageNet tuned ResNeXt101
    * Paper: `Exploring the Limits of Weakly Supervised Pretraining` - https://arxiv.org/abs/1805.00932
    * Weights: https://pytorch.org/hub/facebookresearch_WSL-Images_resnext (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)
  * Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet and ResNeXts
    * Paper: `Billion-scale semi-supervised learning for image classification` - https://arxiv.org/abs/1905.00546
    * Weights: https://github.com/facebookresearch/semi-supervised-ImageNet1K-models (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)
  * Squeeze-and-Excitation Networks
    * Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
    * Code: Added to ResNet base, this is current version going forward, old `senet.py` is being deprecated
  * ECAResNet (ECA-Net)
    * Paper: `ECA-Net: Efficient Channel Attention for Deep CNN` - https://arxiv.org/abs/1910.03151v4
    * Code: Added to ResNet base, ECA module contributed by @VRandme, reference https://github.com/BangguWu/ECANet
  
  ## Res2Net [[res2net.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/res2net.py)]
  * Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
  * Code: https://github.com/gasvn/Res2Net
  
  ## ResNeSt [[resnest.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnest.py)]
  * Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
  * Code: https://github.com/zhanghang1989/ResNeSt
  
  ## ReXNet [[rexnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/rexnet.py)]
  * Paper: `ReXNet: Diminishing Representational Bottleneck on CNN` - https://arxiv.org/abs/2007.00992
  * Code: https://github.com/clovaai/rexnet
  
  ## Selective-Kernel Networks [[sknet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/sknet.py)]
  * Paper: `Selective-Kernel Networks` - https://arxiv.org/abs/1903.06586
  * Code: https://github.com/implus/SKNet, https://github.com/clovaai/assembled-cnn
  
  ## SelecSLS [[selecsls.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/selecsls.py)]
  * Paper: `XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera` - https://arxiv.org/abs/1907.00837
  * Code: https://github.com/mehtadushy/SelecSLS-Pytorch
  
  ## Squeeze-and-Excitation Networks [[senet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/senet.py)]
  NOTE: I am deprecating this version of the networks, the new ones are part of `resnet.py`
  
  * Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
  * Code: https://github.com/Cadene/pretrained-models.pytorch 
  
  ## TResNet [[tresnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tresnet.py)]
  * Paper: `TResNet: High Performance GPU-Dedicated Architecture` - https://arxiv.org/abs/2003.13630
  * Code: https://github.com/mrT23/TResNet
  
  ## VGG [[vgg.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vgg.py)]
  * Paper: `Very Deep Convolutional Networks For Large-Scale Image Recognition` - https://arxiv.org/pdf/1409.1556.pdf
  * Reference code: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
  
  ## Vision Transformer [[vision_transformer.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py)]
  * Paper: `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929
  * Reference code and pretrained weights: https://github.com/google-research/vision_transformer
  
  ## VovNet V2 and V1 [[vovnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vovnet.py)]
  * Paper: `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
  * Reference code: https://github.com/youngwanLEE/vovnet-detectron2
  
  ## Xception [[xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/xception.py)]
  * Paper: `Xception: Deep Learning with Depthwise Separable Convolutions` - https://arxiv.org/abs/1610.02357
  * Code: https://github.com/Cadene/pretrained-models.pytorch
  
  ## Xception (Modified Aligned, Gluon) [[gluon_xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/gluon_xception.py)]
  * Paper: `Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation` - https://arxiv.org/abs/1802.02611
  * Reference code: https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo, https://github.com/jfzhang95/pytorch-deeplab-xception/
  
  ## Xception (Modified Aligned, TF) [[aligned_xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/aligned_xception.py)]
  * Paper: `Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation` - https://arxiv.org/abs/1802.02611
  * Reference code: https://github.com/tensorflow/models/tree/master/research/deeplab
  