pytorch-image-models

Bunch of changes:

DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions
VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1
Activation factory added along with new activations:
- select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export)
- hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish
- context mgr for setting exportable/scriptable/no_jit states
Norm + Activation combo layers added with initial trial support in DenseNet and VoVNet along with impl of EvoNorm and InplaceAbn wrapper that fit the interface
Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this
Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call
Prep for 0.1.28 pip release

Pruned EfficientNet B1, B2, and B3 (https://arxiv.org/abs/2002.08258) contributed by Yonathan Aflalo

Merged a number of execellent contributions in the ResNet model family over the past month
- BlurPool2D and resnetblur models initiated by Chris Ha, I trained resnetblur50 to 79.3.
- TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by mrT23
- ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (https://arxiv.org/abs/2002.08258) by Yonathan Aflalo
200 pretrained models in total now with updated results csv in results folder

Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite
- 3.5M param MobileNet-V2 100 @ 73%
- 4.5M param MobileNet-V2 110d @ 75%
- 6.1M param MobileNet-V2 140 @ 76.5%
- 5.8M param MobileNet-V2 120d @ 77.3%

Add EfficientNet-Lite models w/ weights ported from Tensorflow TPU
Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by Andrew Lavin (see Training section for hparams)

New MobileNet-V3 Large weights trained from stratch with this code to 75.77% top-1
IMPORTANT CHANGE - default weight init changed for all MobilenetV3 / EfficientNet / related models
- overall results similar to a bit better training from scratch on a few smaller models tried
- performance early in training seems consistently improved but less difference by end
- set fix_group_fanout=False in _init_weight_goog fn if you need to reproducte past behaviour
Experimental LR noise feature added applies a random perturbation to LR each epoch in specified range of training

Big refactor of model layers and addition of several attention mechanisms. Several additions motivated by 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268):
- Move layer/module impl into layers subfolder/module of models and organize in a more granular fashion
- ResNet downsample paths now properly support dilation (output stride != 32) for avg_pool ('D' variant) and 3x3 (SENets) networks
- Add Selective Kernel Nets on top of ResNet base, pretrained weights
  - skresnet18 - 73% top-1
  - skresnet34 - 76.9% top-1
  - skresnext50_32x4d (equiv to SKNet50) - 80.2% top-1
- ECA and CECA (circular padding) attention layer contributed by Chris Ha
- CBAM attention experiment (not the best results so far, may remove)
- Attention factory to allow dynamically selecting one of SE, ECA, CBAM in the .se position for all ResNets
- Add DropBlock and DropPath (formerly DropConnect for EfficientNet/MobileNetv3) support to all ResNet variants
Full dataset results updated that incl NoisyStudent weights and 2 of the 3 SK weights