Update README.md before merge. Bump version to 0.4.10

Author: rwightman

README.md

@@ -23,6 +23,14 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 
 ## What's New
 
+### May 25, 2021
+* Add LeViT, Visformer, ConViT (PR by Aman Arora), Twins (PR by paper authors) transformer models
+* Add ResMLP and gMLP MLP vision models to the existing MLP Mixer impl
+* Fix a number of torchscript issues with various vision transformer models
+* Cleanup input_size/img_size override handling and improve testing / test coverage for all vision transformer and MLP models
+* More flexible pos embedding resize (non-square) for ViT and TnT. Thanks [Alexander Soare](https://github.com/alexander-soare)
+* Add `efficientnetv2_rw_m` model and weights (started training before official code). 84.8 top-1, 53M params.
+
 ### May 14, 2021
 * Add EfficientNet-V2 official model defs w/ ported weights from official [Tensorflow/Keras](https://github.com/google/automl/tree/master/efficientnetv2) impl.
   * 1k trained variants: `tf_efficientnetv2_s/m/l`
@@ -166,30 +174,6 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 * Misc fixes for SiLU ONNX export, default_cfg missing from Feature extraction models, Linear layer w/ AMP + torchscript
 * PyPi release @ 0.3.2 (needed by EfficientDet)
 
-### Oct 30, 2020
-* Test with PyTorch 1.7 and fix a small top-n metric view vs reshape issue.
-* Convert newly added 224x224 Vision Transformer weights from official JAX repo. 81.8 top-1 for B/16, 83.1 L/16.
-* Support PyTorch 1.7 optimized, native SiLU (aka Swish) activation. Add mapping to 'silu' name, custom swish will eventually be deprecated.
-* Fix regression for loading pretrained classifier via direct model entrypoint functions. Didn't impact create_model() factory usage.
-* PyPi release @ 0.3.0 version!
-
-### Oct 26, 2020
-* Update Vision Transformer models to be compatible with official code release at https://github.com/google-research/vision_transformer
-* Add Vision Transformer weights (ImageNet-21k pretrain) for 384x384 base and large models converted from official jax impl
-  * ViT-B/16 - 84.2
-  * ViT-B/32 - 81.7
-  * ViT-L/16 - 85.2
-  * ViT-L/32 - 81.5
-
-### Oct 21, 2020
-* Weights added for Vision Transformer (ViT) models. 77.86 top-1 for 'small' and 79.35 for 'base'. Thanks to [Christof](https://www.kaggle.com/christofhenkel) for training the base model w/ lots of GPUs.
-
-### Oct 13, 2020
-* Initial impl of Vision Transformer models. Both patch and hybrid (CNN backbone) variants. Currently trying to train...
-* Adafactor and AdaHessian (FP32 only, no AMP) optimizers
-* EdgeTPU-M (`efficientnet_em`) model trained in PyTorch, 79.3 top-1
-* Pip release, doc updates pending a few more changes...
-
 
 ## Introduction
 
@@ -207,6 +191,7 @@ A full version of the list below with source links can be found in the [document
 * Bottleneck Transformers - https://arxiv.org/abs/2101.11605
 * CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
 * CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
+* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
 * CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
 * DeiT (Vision Transformer) - https://arxiv.org/abs/2012.12877
 * DenseNet - https://arxiv.org/abs/1608.06993
@@ -224,13 +209,15 @@ A full version of the list below with source links can be found in the [document
     * MobileNet-V2 - https://arxiv.org/abs/1801.04381
     * Single-Path NAS - https://arxiv.org/abs/1904.02877
 * GhostNet - https://arxiv.org/abs/1911.11907
+* gMLP - https://arxiv.org/abs/2105.08050
 * GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
 * Halo Nets - https://arxiv.org/abs/2103.12731
 * HardCoRe-NAS - https://arxiv.org/abs/2102.11646
 * HRNet - https://arxiv.org/abs/1908.07919
 * Inception-V3 - https://arxiv.org/abs/1512.00567
 * Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
 * Lambda Networks - https://arxiv.org/abs/2102.08602
+* LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
 * MLP-Mixer - https://arxiv.org/abs/2105.01601
 * MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
 * NASNet-A - https://arxiv.org/abs/1707.07012
@@ -240,6 +227,7 @@ A full version of the list below with source links can be found in the [document
 * Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302
 * RegNet - https://arxiv.org/abs/2003.13678
 * RepVGG - https://arxiv.org/abs/2101.03697
+* ResMLP - https://arxiv.org/abs/2105.03404
 * ResNet/ResNeXt
     * ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
     * ResNeXt - https://arxiv.org/abs/1611.05431
@@ -257,6 +245,7 @@ A full version of the list below with source links can be found in the [document
 * Swin Transformer - https://arxiv.org/abs/2103.14030
 * Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
 * TResNet - https://arxiv.org/abs/2003.13630
+* Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
 * Vision Transformer - https://arxiv.org/abs/2010.11929
 * VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
 * Xception - https://arxiv.org/abs/1610.02357

docs/archived_changes.md

@@ -1,5 +1,29 @@
 # Archived Changes
 
+### Oct 30, 2020
+* Test with PyTorch 1.7 and fix a small top-n metric view vs reshape issue.
+* Convert newly added 224x224 Vision Transformer weights from official JAX repo. 81.8 top-1 for B/16, 83.1 L/16.
+* Support PyTorch 1.7 optimized, native SiLU (aka Swish) activation. Add mapping to 'silu' name, custom swish will eventually be deprecated.
+* Fix regression for loading pretrained classifier via direct model entrypoint functions. Didn't impact create_model() factory usage.
+* PyPi release @ 0.3.0 version!
+
+### Oct 26, 2020
+* Update Vision Transformer models to be compatible with official code release at https://github.com/google-research/vision_transformer
+* Add Vision Transformer weights (ImageNet-21k pretrain) for 384x384 base and large models converted from official jax impl
+  * ViT-B/16 - 84.2
+  * ViT-B/32 - 81.7
+  * ViT-L/16 - 85.2
+  * ViT-L/32 - 81.5
+
+### Oct 21, 2020
+* Weights added for Vision Transformer (ViT) models. 77.86 top-1 for 'small' and 79.35 for 'base'. Thanks to [Christof](https://www.kaggle.com/christofhenkel) for training the base model w/ lots of GPUs.
+
+### Oct 13, 2020
+* Initial impl of Vision Transformer models. Both patch and hybrid (CNN backbone) variants. Currently trying to train...
+* Adafactor and AdaHessian (FP32 only, no AMP) optimizers
+* EdgeTPU-M (`efficientnet_em`) model trained in PyTorch, 79.3 top-1
+* Pip release, doc updates pending a few more changes...
+
 ### Sept 18, 2020
 * New ResNet 'D' weights. 72.7 (top-1) ResNet-18-D, 77.1 ResNet-34-D, 80.5 ResNet-50-D
 * Added a few untrained defs for other ResNet models (66D, 101D, 152D, 200/200D)

docs/changes.md

@@ -1,5 +1,33 @@
 # Recent Changes
 
+### May 25, 2021
+* Add LeViT, Visformer, Convit (PR by Aman Arora), Twins (PR by paper authors) transformer models
+* Cleanup input_size/img_size override handling and testing for all vision transformer models
+* Add `efficientnetv2_rw_m` model and weights (started training before official code). 84.8 top-1, 53M params.
+
+### May 14, 2021
+* Add EfficientNet-V2 official model defs w/ ported weights from official [Tensorflow/Keras](https://github.com/google/automl/tree/master/efficientnetv2) impl.
+  * 1k trained variants: `tf_efficientnetv2_s/m/l`
+  * 21k trained variants: `tf_efficientnetv2_s/m/l_in21k`
+  * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_in21ft1k`
+  * v2 models w/ v1 scaling: `tf_efficientnetv2_b0` through `b3`
+  * Rename my prev V2 guess `efficientnet_v2s` -> `efficientnetv2_rw_s`
+  * Some blank `efficientnetv2_*` models in-place for future native PyTorch training
+
+### May 5, 2021
+* Add MLP-Mixer models and port pretrained weights from [Google JAX impl](https://github.com/google-research/vision_transformer/tree/linen)
+* Add CaiT models and pretrained weights from [FB](https://github.com/facebookresearch/deit)
+* Add ResNet-RS models and weights from [TF](https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rs). Thanks [Aman Arora](https://github.com/amaarora)
+* Add CoaT models and weights. Thanks [Mohammed Rizin](https://github.com/morizin)
+* Add new ImageNet-21k weights & finetuned weights for TResNet, MobileNet-V3, ViT models. Thanks [mrT](https://github.com/mrT23)
+* Add GhostNet models and weights. Thanks [Kai Han](https://github.com/iamhankai)
+* Update ByoaNet attention modles
+   * Improve SA module inits
+   * Hack together experimental stand-alone Swin based attn module and `swinnet`
+   * Consistent '26t' model defs for experiments.
+* Add improved Efficientnet-V2S (prelim model def) weights. 83.8 top-1.
+* WandB logging support
+
 ### April 13, 2021
 * Add Swin Transformer models and weights from https://github.com/microsoft/Swin-Transformer
 

timm/version.py

@@ -1 +1 @@
-__version__ = '0.4.9'
+__version__ = '0.4.10'