Merge pull request #668 from rwightman/more_attn

Add Gather-Excite, Global Context, BAT, Non-Local attn modules and refactored all attn modules and factory for improved consistency. EfficientNet / MobileNetV3 backbones able to use a wider variety of attention modules.

Author: rwightman

.github/workflows/tests.yml

@@ -36,11 +36,16 @@ jobs:
       run: pip install --no-cache-dir torch==${{ matrix.torch }} torchvision==${{ matrix.torchvision }}
     - name: Install torch on ubuntu
       if: startsWith(matrix.os, 'ubuntu')
-      run: pip install --no-cache-dir torch==${{ matrix.torch }}+cpu torchvision==${{ matrix.torchvision }}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      run: |
+        pip install --no-cache-dir torch==${{ matrix.torch }}+cpu torchvision==${{ matrix.torchvision }}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        sudo apt update
+        sudo apt install -y google-perftools
     - name: Install requirements
       run: |
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
         pip install --no-cache-dir git+https://github.com/mapillary/inplace_abn.git@v1.0.12
     - name: Run tests
+      env:
+        LD_PRELOAD: /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4
       run: |
         pytest -vv --durations=0 ./tests

README.md

@@ -295,10 +295,24 @@ Several (less common) features that I often utilize in my projects are included.
 * SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data
 * DropPath aka "Stochastic Depth" (https://arxiv.org/abs/1603.09382) 
 * DropBlock (https://arxiv.org/abs/1810.12890)
-* Efficient Channel Attention - ECA (https://arxiv.org/abs/1910.03151)
 * Blur Pooling (https://arxiv.org/abs/1904.11486)
 * Space-to-Depth by [mrT23](https://github.com/mrT23/TResNet/blob/master/src/models/tresnet/layers/space_to_depth.py) (https://arxiv.org/abs/1801.04590) -- original paper?
 * Adaptive Gradient Clipping (https://arxiv.org/abs/2102.06171, https://github.com/deepmind/deepmind-research/tree/master/nfnets)
+* An extensive selection of channel and/or spatial attention modules:
+    * Bottleneck Transformer - https://arxiv.org/abs/2101.11605
+    * CBAM - https://arxiv.org/abs/1807.06521
+    * Effective Squeeze-Excitation (ESE) - https://arxiv.org/abs/1911.06667
+    * Efficient Channel Attention (ECA) - https://arxiv.org/abs/1910.03151
+    * Gather-Excite (GE) - https://arxiv.org/abs/1810.12348
+    * Global Context (GC) - https://arxiv.org/abs/1904.11492
+    * Halo - https://arxiv.org/abs/2103.12731
+    * Involution - https://arxiv.org/abs/2103.06255
+    * Lambda Layer - https://arxiv.org/abs/2102.08602
+    * Non-Local (NL) -  https://arxiv.org/abs/1711.07971
+    * Squeeze-and-Excitation (SE) - https://arxiv.org/abs/1709.01507
+    * Selective Kernel (SK) - (https://arxiv.org/abs/1903.06586
+    * Split (SPLAT) - https://arxiv.org/abs/2004.08955
+    * Shifted Window (SWIN) - https://arxiv.org/abs/2103.14030
 
 ## Results
 

tests/test_models.py

@@ -24,7 +24,7 @@
 if 'GITHUB_ACTIONS' in os.environ:  # and 'Linux' in platform.system():
     # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
     EXCLUDE_FILTERS = [
-        '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm',
+        '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
         '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', 
         '*resnetrs350*', '*resnetrs420*']
 else:

timm/models/__init__.py

@@ -17,7 +17,6 @@
 from .inception_v3 import *
 from .inception_v4 import *
 from .levit import *
-#from .levit import *
 from .mlp_mixer import *
 from .mobilenetv3 import *
 from .nasnet import *