Improve RegVGG block identity/vs non for clariy and fix attn usage. Add comments.

timm/models/byobnet.py

@@ -32,7 +32,6 @@ from functools import partial
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg
@@ -443,7 +442,7 @@ class RepVggBlock(nn.Module):
 
     Adapted from impl at https://github.com/DingXiaoH/RepVGG
 
-    This version does not currently support the deploy optimization. It is currently fixed in 'train' model.
+    This version does not currently support the deploy optimization. It is currently fixed in 'train' mode.
     """
 
     def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
@@ -461,8 +460,8 @@ class RepVggBlock(nn.Module):
             in_chs, out_chs, kernel_size, stride=stride, dilation=dilation[0],
             groups=groups, drop_block=drop_block, apply_act=False, **layer_args)
         self.conv_1x1 = ConvBnAct(in_chs, out_chs, 1, stride=stride, groups=groups, apply_act=False, **layer_args)
-        self.attn = None if attn_layer is None else attn_layer(out_chs)
-        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+        self.attn = nn.Identity() if attn_layer is None else attn_layer(out_chs)
+        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. and use_ident else nn.Identity()
         self.act = act_layer(inplace=True)
 
     def init_weights(self, zero_init_last_bn=False):
@@ -474,14 +473,14 @@ class RepVggBlock(nn.Module):
 
     def forward(self, x):
         if self.identity is None:
-            identity = 0
+            x = self.conv_1x1(x) + self.conv_kxk(x)
         else:
             identity = self.identity(x)
             x = self.conv_1x1(x) + self.conv_kxk(x)
-        if self.attn is not None:
-            x = self.attn(x)
-        x = self.drop_path(x)
-        x = self.act(x + identity)
+            x = self.drop_path(x)  # not in the paper / official impl, experimental
+            x = x + identity
+        x = self.attn(x)  # no attn in the paper / official impl, experimental
+        x = self.act(x)
         return x
 
 
@@ -654,54 +653,87 @@ def _create_byobnet(variant, pretrained=False, **kwargs):
 
 @register_model
 def gernet_l(pretrained=False, **kwargs):
+    """ GEResNet-Large (GENet-Large from official impl)
+    `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
+    """
     return _create_byobnet('gernet_l', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def gernet_m(pretrained=False, **kwargs):
+    """ GEResNet-Medium (GENet-Normal from official impl)
+    `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
+    """
     return _create_byobnet('gernet_m', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def gernet_s(pretrained=False, **kwargs):
+    """ EResNet-Small (GENet-Small from official impl)
+    `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
+    """
     return _create_byobnet('gernet_s', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def repvgg_a2(pretrained=False, **kwargs):
+    """ RepVGG-A2
+    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
+    """
     return _create_byobnet('repvgg_a2', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def repvgg_b0(pretrained=False, **kwargs):
+    """ RepVGG-B0
+    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
+    """
     return _create_byobnet('repvgg_b0', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def repvgg_b1(pretrained=False, **kwargs):
+    """ RepVGG-B1
+    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
+    """
     return _create_byobnet('repvgg_b1', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def repvgg_b1g4(pretrained=False, **kwargs):
+    """ RepVGG-B1g4
+    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
+    """
     return _create_byobnet('repvgg_b1g4', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def repvgg_b2(pretrained=False, **kwargs):
+    """ RepVGG-B2
+    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
+    """
     return _create_byobnet('repvgg_b2', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def repvgg_b2g4(pretrained=False, **kwargs):
+    """ RepVGG-B2g4
+    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
+    """
     return _create_byobnet('repvgg_b2g4', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def repvgg_b3(pretrained=False, **kwargs):
+    """ RepVGG-B3
+    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
+    """
     return _create_byobnet('repvgg_b3', pretrained=pretrained, **kwargs)
 
 
 @register_model
 def repvgg_b3g4(pretrained=False, **kwargs):
+    """ RepVGG-B3g4
+    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
+    """
     return _create_byobnet('repvgg_b3g4', pretrained=pretrained, **kwargs)