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Update README, ensure vit excluded from all tests (not ready)
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Diff for: README.md

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## What's New
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### Oct 13, 2020
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* Initial impl of Vision Transformer models. Both patch and hybrid (CNN backbone) variants. Currently trying to train...
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* Adafactor and AdaHessian (FP32 only, no AMP) optimizers
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* EdgeTPU-M (`efficientnet_em`) model trained in PyTorch, 79.3 top-1
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* Pip release, doc updates pending a few more changes...
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### Sept 18, 2020
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* New ResNet 'D' weights. 72.7 (top-1) ResNet-18-D, 77.1 ResNet-34-D, 80.5 ResNet-50-D
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* Added a few untrained defs for other ResNet models (66D, 101D, 152D, 200/200D)
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* SelecSLS - https://arxiv.org/abs/1907.00837
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* Selective Kernel Networks - https://arxiv.org/abs/1903.06586
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* TResNet - https://arxiv.org/abs/2003.13630
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* Vision Transformer - https://openreview.net/forum?id=YicbFdNTTy
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* VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
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* Xception - https://arxiv.org/abs/1610.02357
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* Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611
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* `lookahead` adapted from impl by [Liam](https://github.com/alphadl/lookahead.pytorch) (https://arxiv.org/abs/1907.08610)
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* `fused<name>` optimizers by name with [NVIDIA Apex](https://github.com/NVIDIA/apex/tree/master/apex/optimizers) installed
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* `adamp` and `sgdp` by [Naver ClovAI](https://github.com/clovaai) (https://arxiv.org/abs/2006.08217)
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* `adafactor` adapted from [FAIRSeq impl](https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py) (https://arxiv.org/abs/1804.04235)
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* `adahessian` by [David Samuel](https://github.com/davda54/ada-hessian) (https://arxiv.org/abs/2006.00719)
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* Random Erasing from [Zhun Zhong](https://github.com/zhunzhong07/Random-Erasing/blob/master/transforms.py) (https://arxiv.org/abs/1708.04896)
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* Mixup (https://arxiv.org/abs/1710.09412)
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* CutMix (https://arxiv.org/abs/1905.04899)

Diff for: tests/test_models.py

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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models())
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=['vit_*']))
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_default_cfgs(model_name, batch_size):
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"""Run a single forward pass with each model"""

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