Add more experimental darknet and 'cs2' darknet variants (different cross stage setup, closer to newer YOLO backbones) for train trials.

timm/models/cspnet.py

@@ -16,6 +16,7 @@ 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, named_apply, MATCH_PREV_GROUP
@@ -46,11 +47,21 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnext50_ra_224-648b4713.pth',
         input_size=(3, 224, 224), pool_size=(7, 7), crop_pct=0.875  # FIXME I trained this at 224x224, not 256 like ref impl
     ),
-    'cspresnext50_iabn': _cfg(url=''),
     'cspdarknet53': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspdarknet53_ra_256-d05c7c21.pth'),
-    'cspdarknet53_iabn': _cfg(url=''),
+
+    'darknet17': _cfg(url=''),
+    'darknet21': _cfg(url=''),
     'darknet53': _cfg(url=''),
+
+    'cs2darknet_m': _cfg(
+        url=''),
+    'cs2darknet_l': _cfg(
+        url=''),
+    'cs2darknet_f_m': _cfg(
+        url=''),
+    'cs2darknet_f_l': _cfg(
+        url=''),
 }
 
 
@@ -116,6 +127,37 @@ model_cfgs = dict(
             down_growth=True,
         )
     ),
+    darknet17=dict(
+        stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
+        stage=dict(
+            out_chs=(64, 128, 256, 512, 1024),
+            depth=(1,) * 5,
+            stride=(2,) * 5,
+            bottle_ratio=(0.5,) * 5,
+            block_ratio=(1.,) * 5,
+        )
+    ),
+    darknet21=dict(
+        stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
+        stage=dict(
+            out_chs=(64, 128, 256, 512, 1024),
+            depth=(1, 1, 1, 2, 2),
+            stride=(2,) * 5,
+            bottle_ratio=(0.5,) * 5,
+            block_ratio=(1.,) * 5,
+        )
+    ),
+    sedarknet21=dict(
+        stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
+        stage=dict(
+            out_chs=(64, 128, 256, 512, 1024),
+            depth=(1, 1, 1, 2, 2),
+            stride=(2,) * 5,
+            bottle_ratio=(0.5,) * 5,
+            block_ratio=(1.,) * 5,
+            attn_layer=('se',) * 5,
+        )
+    ),
     darknet53=dict(
         stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
         stage=dict(
@@ -125,13 +167,81 @@ model_cfgs = dict(
             bottle_ratio=(0.5,) * 5,
             block_ratio=(1.,) * 5,
         )
+    ),
+
+    darknetaa53=dict(
+        stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
+        stage=dict(
+            out_chs=(64, 128, 256, 512, 1024),
+            depth=(1, 2, 8, 8, 4),
+            stride=(2,) * 5,
+            bottle_ratio=(0.5,) * 5,
+            block_ratio=(1.,) * 5,
+            avg_down=True,
+        ),
+    ),
+
+    cs2darknet_m=dict(
+        stem=dict(out_chs=(24, 48), kernel_size=3, stride=2, pool=''),
+        stage=dict(
+            out_chs=(96, 192, 384, 768),
+            depth=(2, 4, 6, 2),
+            stride=(2,) * 4,
+            bottle_ratio=(1.,) * 4,
+            block_ratio=(0.5,) * 4,
+            avg_down=False,
+        ),
+    ),
+
+    cs2darknet_f_m=dict(
+        stem=dict(out_chs=48, kernel_size=6, stride=2, padding=2, pool=''),
+        stage=dict(
+            out_chs=(96, 192, 384, 768),
+            depth=(2, 4, 6, 2),
+            stride=(2,) * 4,
+            bottle_ratio=(1.,) * 4,
+            block_ratio=(0.5,) * 4,
+            avg_down=False,
+        ),
+    ),
+
+    cs2darknet_l=dict(
+        stem=dict(out_chs=(32, 64), kernel_size=3, stride=2, pool=''),
+        stage=dict(
+            out_chs=(128, 256, 512, 1024),
+            depth=(3, 6, 9, 3),
+            stride=(2,) * 4,
+            bottle_ratio=(1.,) * 4,
+            block_ratio=(0.5,) * 4,
+            avg_down=False,
+        ),
+    ),
+
+    cs2darknet_f_l=dict(
+        stem=dict(out_chs=64, kernel_size=6, stride=2, padding=2, pool=''),
+        stage=dict(
+            out_chs=(128, 256, 512, 1024),
+            depth=(3, 6, 9, 3),
+            stride=(2,) * 4,
+            bottle_ratio=(1.,) * 4,
+            block_ratio=(0.5,) * 4,
+            avg_down=False,
+        ),
     )
 )
 
 
 def create_stem(
-        in_chans=3, out_chs=32, kernel_size=3, stride=2, pool='',
+        in_chans=3,
-        act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None):
+        out_chs=32,
+        kernel_size=3,
+        stride=2,
+        pool='',
+        padding='',
+        act_layer=nn.ReLU,
+        norm_layer=nn.BatchNorm2d,
+        aa_layer=None
+):
     stem = nn.Sequential()
     if not isinstance(out_chs, (tuple, list)):
         out_chs = [out_chs]
@@ -140,8 +250,12 @@ def create_stem(
     for i, out_c in enumerate(out_chs):
         conv_name = f'conv{i + 1}'
         stem.add_module(conv_name, ConvNormAct(
-            in_c, out_c, kernel_size, stride=stride if i == 0 else 1,
+            in_c, out_c, kernel_size,
-            act_layer=act_layer, norm_layer=norm_layer))
+            stride=stride if i == 0 else 1,
+            padding=padding if i == 0 else '',
+            act_layer=act_layer,
+            norm_layer=norm_layer
+        ))
         in_c = out_c
         last_conv = conv_name
     if pool:
@@ -158,9 +272,20 @@ class ResBottleneck(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, dilation=1, bottle_ratio=0.25, groups=1,
+            self,
-            act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_last=False,
+            in_chs,
-            attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
+            out_chs,
+            dilation=1,
+            bottle_ratio=0.25,
+            groups=1,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            attn_last=False,
+            attn_layer=None,
+            aa_layer=None,
+            drop_block=None,
+            drop_path=None
+    ):
         super(ResBottleneck, self).__init__()
         mid_chs = int(round(out_chs * bottle_ratio))
         ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
@@ -173,7 +298,7 @@ class ResBottleneck(nn.Module):
         self.conv3 = ConvNormAct(mid_chs, out_chs, kernel_size=1, apply_act=False, **ckwargs)
         self.attn3 = create_attn(attn_layer, channels=out_chs) if attn_last else None
         self.drop_path = drop_path
-        self.act3 = act_layer(inplace=True)
+        self.act3 = act_layer()
 
     def zero_init_last(self):
         nn.init.zeros_(self.conv3.bn.weight)
@@ -201,9 +326,19 @@ class DarkBlock(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, dilation=1, bottle_ratio=0.5, groups=1,
+            self,
-            act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None, aa_layer=None,
+            in_chs,
-            drop_block=None, drop_path=None):
+            out_chs,
+            dilation=1,
+            bottle_ratio=0.5,
+            groups=1,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            attn_layer=None,
+            aa_layer=None,
+            drop_block=None,
+            drop_path=None
+    ):
         super(DarkBlock, self).__init__()
         mid_chs = int(round(out_chs * bottle_ratio))
         ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
@@ -211,7 +346,7 @@ class DarkBlock(nn.Module):
         self.conv2 = ConvNormActAa(
             mid_chs, out_chs, kernel_size=3, dilation=dilation, groups=groups,
             aa_layer=aa_layer, drop_layer=drop_block, **ckwargs)
-        self.attn = create_attn(attn_layer, channels=out_chs)
+        self.attn = create_attn(attn_layer, channels=out_chs, act_layer=act_layer)
         self.drop_path = drop_path
 
     def zero_init_last(self):
@@ -232,23 +367,44 @@ class DarkBlock(nn.Module):
 class CrossStage(nn.Module):
     """Cross Stage."""
     def __init__(
-            self, in_chs, out_chs, stride, dilation, depth, block_ratio=1., bottle_ratio=1., exp_ratio=1.,
+            self,
-            groups=1, first_dilation=None, down_growth=False, cross_linear=False, block_dpr=None,
+            in_chs,
-            block_fn=ResBottleneck, **block_kwargs):
+            out_chs,
+            stride,
+            dilation,
+            depth,
+            block_ratio=1.,
+            bottle_ratio=1.,
+            exp_ratio=1.,
+            groups=1,
+            first_dilation=None,
+            avg_down=False,
+            down_growth=False,
+            cross_linear=False,
+            block_dpr=None,
+            block_fn=ResBottleneck,
+            **block_kwargs
+    ):
         super(CrossStage, self).__init__()
         first_dilation = first_dilation or dilation
         down_chs = out_chs if down_growth else in_chs  # grow downsample channels to output channels
-        exp_chs = int(round(out_chs * exp_ratio))
+        self.exp_chs = exp_chs = int(round(out_chs * exp_ratio))
         block_out_chs = int(round(out_chs * block_ratio))
         conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
 
         if stride != 1 or first_dilation != dilation:
-            self.conv_down = ConvNormActAa(
+            if avg_down:
-                in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
+                self.conv_down = nn.Sequential(
-                aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
+                    nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(),  # FIXME dilation handling
+                    ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
+                )
+            else:
+                self.conv_down = ConvNormActAa(
+                    in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
+                    aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
             prev_chs = down_chs
         else:
-            self.conv_down = None
+            self.conv_down = nn.Identity()
             prev_chs = in_chs
 
         # FIXME this 1x1 expansion is pushed down into the cross and block paths in the darknet cfgs. Also,
@@ -269,30 +425,115 @@ class CrossStage(nn.Module):
         self.conv_transition = ConvNormAct(exp_chs, out_chs, kernel_size=1, **conv_kwargs)
 
     def forward(self, x):
-        if self.conv_down is not None:
+        x = self.conv_down(x)
-            x = self.conv_down(x)
         x = self.conv_exp(x)
-        split = x.shape[1] // 2
+        xs, xb = x.split(self.exp_chs // 2, dim=1)
-        xs, xb = x[:, :split], x[:, split:]
         xb = self.blocks(xb)
         xb = self.conv_transition_b(xb).contiguous()
         out = self.conv_transition(torch.cat([xs, xb], dim=1))
         return out
 
 
+class CrossStage2(nn.Module):
+    """Cross Stage v2.
+    Similar to CrossStage, but with one transition conv for the concat output.
+    """
+    def __init__(
+            self,
+            in_chs,
+            out_chs,
+            stride,
+            dilation,
+            depth,
+            block_ratio=1.,
+            bottle_ratio=1.,
+            exp_ratio=1.,
+            groups=1,
+            first_dilation=None,
+            avg_down=False,
+            down_growth=False,
+            cross_linear=False,
+            block_dpr=None,
+            block_fn=ResBottleneck,
+            **block_kwargs
+    ):
+        super(CrossStage2, self).__init__()
+        first_dilation = first_dilation or dilation
+        down_chs = out_chs if down_growth else in_chs  # grow downsample channels to output channels
+        self.exp_chs = exp_chs = int(round(out_chs * exp_ratio))
+        block_out_chs = int(round(out_chs * block_ratio))
+        conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
+
+        if stride != 1 or first_dilation != dilation:
+            if avg_down:
+                self.conv_down = nn.Sequential(
+                    nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(),  # FIXME dilation handling
+                    ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
+                )
+            else:
+                self.conv_down = ConvNormActAa(
+                    in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
+                    aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
+            prev_chs = down_chs
+        else:
+            self.conv_down = None
+            prev_chs = in_chs
+
+        # expansion conv
+        self.conv_exp = ConvNormAct(prev_chs, exp_chs, kernel_size=1, apply_act=not cross_linear, **conv_kwargs)
+        prev_chs = exp_chs // 2  # expanded output is split in 2 for blocks and cross stage
+
+        self.blocks = nn.Sequential()
+        for i in range(depth):
+            drop_path = DropPath(block_dpr[i]) if block_dpr and block_dpr[i] else None
+            self.blocks.add_module(str(i), block_fn(
+                prev_chs, block_out_chs, dilation, bottle_ratio, groups, drop_path=drop_path, **block_kwargs))
+            prev_chs = block_out_chs
+
+        # transition convs
+        self.conv_transition = ConvNormAct(exp_chs, out_chs, kernel_size=1, **conv_kwargs)
+
+    def forward(self, x):
+        x = self.conv_down(x)
+        x = self.conv_exp(x)
+        x1, x2 = x.split(self.exp_chs // 2, dim=1)
+        x1 = self.blocks(x1)
+        out = self.conv_transition(torch.cat([x1, x2], dim=1))
+        return out
+
+
 class DarkStage(nn.Module):
     """DarkNet stage."""
 
     def __init__(
-            self, in_chs, out_chs, stride, dilation, depth, block_ratio=1., bottle_ratio=1., groups=1,
+            self,
-            first_dilation=None, block_fn=ResBottleneck, block_dpr=None, **block_kwargs):
+            in_chs,
+            out_chs,
+            stride,
+            dilation,
+            depth,
+            block_ratio=1.,
+            bottle_ratio=1.,
+            groups=1,
+            first_dilation=None,
+            avg_down=False,
+            block_fn=ResBottleneck,
+            block_dpr=None,
+            **block_kwargs
+    ):
         super(DarkStage, self).__init__()
         first_dilation = first_dilation or dilation
+        conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
 
-        self.conv_down = ConvNormActAa(
+        if avg_down:
-            in_chs, out_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
+            self.conv_down = nn.Sequential(
-            act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'),
+                nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(),   # FIXME dilation handling
-            aa_layer=block_kwargs.get('aa_layer', None))
+                ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
+            )
+        else:
+            self.conv_down = ConvNormActAa(
+                in_chs, out_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
+                aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
 
         prev_chs = out_chs
         block_out_chs = int(round(out_chs * block_ratio))
@@ -318,6 +559,8 @@ def _cfg_to_stage_args(cfg, curr_stride=2, output_stride=32, drop_path_rate=0.):
         cfg['down_growth'] = (cfg['down_growth'],) * num_stages
     if 'cross_linear' in cfg and not isinstance(cfg['cross_linear'], (list, tuple)):
         cfg['cross_linear'] = (cfg['cross_linear'],) * num_stages
+    if 'avg_down' in cfg and not isinstance(cfg['avg_down'], (list, tuple)):
+        cfg['avg_down'] = (cfg['avg_down'],) * num_stages
     cfg['block_dpr'] = [None] * num_stages if not drop_path_rate else \
         [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg['depth'])).split(cfg['depth'])]
     stage_strides = []
@@ -352,9 +595,20 @@ class CspNet(nn.Module):
     """
 
     def __init__(
-            self, cfg, in_chans=3, num_classes=1000, output_stride=32, global_pool='avg', drop_rate=0.,
+            self,
-            act_layer=nn.LeakyReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_path_rate=0.,
+            cfg,
-            zero_init_last=True, stage_fn=CrossStage, block_fn=ResBottleneck):
+            in_chans=3,
+            num_classes=1000,
+            output_stride=32,
+            global_pool='avg',
+            act_layer=nn.LeakyReLU,
+            norm_layer=nn.BatchNorm2d,
+            aa_layer=None,
+            drop_rate=0.,
+            drop_path_rate=0.,
+            zero_init_last=True,
+            stage_fn=CrossStage,
+            block_fn=ResBottleneck):
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
@@ -427,23 +681,22 @@ class CspNet(nn.Module):
 def _init_weights(module, name, zero_init_last=False):
     if isinstance(module, nn.Conv2d):
         nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
-    elif isinstance(module, nn.BatchNorm2d):
+        if module.bias is not None:
-        nn.init.ones_(module.weight)
+            nn.init.zeros_(module.bias)
-        nn.init.zeros_(module.bias)
     elif isinstance(module, nn.Linear):
         nn.init.normal_(module.weight, mean=0.0, std=0.01)
-        nn.init.zeros_(module.bias)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
     elif zero_init_last and hasattr(module, 'zero_init_last'):
         module.zero_init_last()
 
 
 def _create_cspnet(variant, pretrained=False, **kwargs):
-    cfg_variant = variant.split('_')[0]
     # NOTE: DarkNet is one of few models with stride==1 features w/ 6 out_indices [0..5]
     out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5) if 'darknet' in variant else (0, 1, 2, 3, 4))
     return build_model_with_cfg(
         CspNet, variant, pretrained,
-        model_cfg=model_cfgs[cfg_variant],
+        model_cfg=model_cfgs[variant],
         feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
         **kwargs)
 
@@ -469,22 +722,55 @@ def cspresnext50(pretrained=False, **kwargs):
 
 
 @register_model
-def cspresnext50_iabn(pretrained=False, **kwargs):
+def cspdarknet53(pretrained=False, **kwargs):
-    norm_layer = get_norm_act_layer('iabn', act_layer='leaky_relu')
+    return _create_cspnet('cspdarknet53', pretrained=pretrained, block_fn=DarkBlock, **kwargs)
-    return _create_cspnet('cspresnext50_iabn', pretrained=pretrained, norm_layer=norm_layer, **kwargs)
 
 
 @register_model
-def cspdarknet53(pretrained=False, **kwargs):
+def darknet17(pretrained=False, **kwargs):
-    return _create_cspnet('cspdarknet53', pretrained=pretrained, block_fn=DarkBlock, **kwargs)
+    return _create_cspnet('darknet17', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
 
 
 @register_model
-def cspdarknet53_iabn(pretrained=False, **kwargs):
+def darknet21(pretrained=False, **kwargs):
-    norm_layer = get_norm_act_layer('iabn', act_layer='leaky_relu')
+    return _create_cspnet('darknet21', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
-    return _create_cspnet('cspdarknet53_iabn', pretrained=pretrained, block_fn=DarkBlock, norm_layer=norm_layer, **kwargs)
+
+
+@register_model
+def sedarknet21(pretrained=False, **kwargs):
+    return _create_cspnet('sedarknet21', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
 
 
 @register_model
 def darknet53(pretrained=False, **kwargs):
     return _create_cspnet('darknet53', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
+
+
+@register_model
+def darknetaa53(pretrained=False, **kwargs):
+    return _create_cspnet(
+        'darknetaa53', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
+
+
+@register_model
+def cs2darknet_m(pretrained=False, **kwargs):
+    return _create_cspnet(
+        'cs2darknet_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
+
+
+@register_model
+def cs2darknet_l(pretrained=False, **kwargs):
+    return _create_cspnet(
+        'cs2darknet_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
+
+
+@register_model
+def cs2darknet_f_m(pretrained=False, **kwargs):
+    return _create_cspnet(
+        'cs2darknet_f_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
+
+
+@register_model
+def cs2darknet_f_l(pretrained=False, **kwargs):
+    return _create_cspnet(
+        'cs2darknet_f_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)

timm/models/layers/conv_bn_act.py

@@ -2,6 +2,7 @@
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
+import functools
 from torch import nn as nn
 
 from .create_conv2d import create_conv2d
@@ -40,12 +41,26 @@ class ConvNormAct(nn.Module):
 ConvBnAct = ConvNormAct
 
 
+def create_aa(aa_layer, channels, stride=2, enable=True):
+    if not aa_layer or not enable:
+        return nn.Identity()
+    if isinstance(aa_layer, functools.partial):
+        if issubclass(aa_layer.func, nn.AvgPool2d):
+            return aa_layer()
+        else:
+            return aa_layer(channels)
+    elif issubclass(aa_layer, nn.AvgPool2d):
+        return aa_layer(stride)
+    else:
+        return aa_layer(channels=channels, stride=stride)
+
+
 class ConvNormActAa(nn.Module):
     def __init__(
             self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1,
             bias=False, apply_act=True, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, aa_layer=None, drop_layer=None):
         super(ConvNormActAa, self).__init__()
-        use_aa = aa_layer is not None
+        use_aa = aa_layer is not None and stride == 2
 
         self.conv = create_conv2d(
             in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
@@ -56,7 +71,7 @@ class ConvNormActAa(nn.Module):
         # NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
         norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
         self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
-        self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else nn.Identity()
+        self.aa = create_aa(aa_layer, out_channels, stride=stride, enable=use_aa)
 
     @property
     def in_channels(self):