Fix ResNet based models to work w/ norm layers w/o affine params. Reformat long arg lists into vertical form.

timm/models/byobnet.py

@@ -962,9 +962,21 @@ class BasicBlock(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), group_size=None, bottle_ratio=1.0,
-            downsample='avg', attn_last=True, linear_out=False, layers: LayerFn = None, drop_block=None,
-            drop_path_rate=0.):
+            self,
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=1,
+            dilation=(1, 1),
+            group_size=None,
+            bottle_ratio=1.0,
+            downsample='avg',
+            attn_last=True,
+            linear_out=False,
+            layers: LayerFn = None,
+            drop_block=None,
+            drop_path_rate=0.,
+    ):
         super(BasicBlock, self).__init__()
         layers = layers or LayerFn()
         mid_chs = make_divisible(out_chs * bottle_ratio)
@@ -983,7 +995,7 @@ class BasicBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last and self.shortcut is not None:
+        if zero_init_last and self.shortcut is not None and getattr(self.conv2_kxk.bn, 'weight', None) is not None:
             nn.init.zeros_(self.conv2_kxk.bn.weight)
         for attn in (self.attn, self.attn_last):
             if hasattr(attn, 'reset_parameters'):
@@ -1005,9 +1017,23 @@ class BottleneckBlock(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
-            downsample='avg', attn_last=False, linear_out=False, extra_conv=False, bottle_in=False,
-            layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
+            self,
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=1,
+            dilation=(1, 1),
+            bottle_ratio=1.,
+            group_size=None,
+            downsample='avg',
+            attn_last=False,
+            linear_out=False,
+            extra_conv=False,
+            bottle_in=False,
+            layers: LayerFn = None,
+            drop_block=None,
+            drop_path_rate=0.,
+    ):
         super(BottleneckBlock, self).__init__()
         layers = layers or LayerFn()
         mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
@@ -1031,7 +1057,7 @@ class BottleneckBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last and self.shortcut is not None:
+        if zero_init_last and self.shortcut is not None and getattr(self.conv3_1x1.bn, 'weight', None) is not None:
             nn.init.zeros_(self.conv3_1x1.bn.weight)
         for attn in (self.attn, self.attn_last):
             if hasattr(attn, 'reset_parameters'):
@@ -1063,9 +1089,21 @@ class DarkBlock(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
-            downsample='avg', attn_last=True, linear_out=False, layers: LayerFn = None, drop_block=None,
-            drop_path_rate=0.):
+            self,
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=1,
+            dilation=(1, 1),
+            bottle_ratio=1.0,
+            group_size=None,
+            downsample='avg',
+            attn_last=True,
+            linear_out=False,
+            layers: LayerFn = None,
+            drop_block=None,
+            drop_path_rate=0.,
+    ):
         super(DarkBlock, self).__init__()
         layers = layers or LayerFn()
         mid_chs = make_divisible(out_chs * bottle_ratio)
@@ -1085,7 +1123,7 @@ class DarkBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last and self.shortcut is not None:
+        if zero_init_last and self.shortcut is not None and getattr(self.conv2_kxk.bn, 'weight', None) is not None:
             nn.init.zeros_(self.conv2_kxk.bn.weight)
         for attn in (self.attn, self.attn_last):
             if hasattr(attn, 'reset_parameters'):
@@ -1114,9 +1152,21 @@ class EdgeBlock(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
-            downsample='avg', attn_last=False, linear_out=False, layers: LayerFn = None,
-            drop_block=None, drop_path_rate=0.):
+            self,
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=1,
+            dilation=(1, 1),
+            bottle_ratio=1.0,
+            group_size=None,
+            downsample='avg',
+            attn_last=False,
+            linear_out=False,
+            layers: LayerFn = None,
+            drop_block=None,
+            drop_path_rate=0.,
+    ):
         super(EdgeBlock, self).__init__()
         layers = layers or LayerFn()
         mid_chs = make_divisible(out_chs * bottle_ratio)
@@ -1135,7 +1185,7 @@ class EdgeBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last and self.shortcut is not None:
+        if zero_init_last and self.shortcut is not None and getattr(self.conv2_1x1.bn, 'weight', None) is not None:
             nn.init.zeros_(self.conv2_1x1.bn.weight)
         for attn in (self.attn, self.attn_last):
             if hasattr(attn, 'reset_parameters'):
@@ -1162,8 +1212,19 @@ class RepVggBlock(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
-            downsample='', layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
+            self,
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=1,
+            dilation=(1, 1),
+            bottle_ratio=1.0,
+            group_size=None,
+            downsample='',
+            layers: LayerFn = None,
+            drop_block=None,
+            drop_path_rate=0.,
+    ):
         super(RepVggBlock, self).__init__()
         layers = layers or LayerFn()
         groups = num_groups(group_size, in_chs)
@@ -1204,9 +1265,24 @@ class SelfAttnBlock(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
-            downsample='avg', extra_conv=False, linear_out=False, bottle_in=False, post_attn_na=True,
-            feat_size=None, layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
+            self,
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=1,
+            dilation=(1, 1),
+            bottle_ratio=1.,
+            group_size=None,
+            downsample='avg',
+            extra_conv=False,
+            linear_out=False,
+            bottle_in=False,
+            post_attn_na=True,
+            feat_size=None,
+            layers: LayerFn = None,
+            drop_block=None,
+            drop_path_rate=0.,
+    ):
         super(SelfAttnBlock, self).__init__()
         assert layers is not None
         mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
@@ -1233,7 +1309,7 @@ class SelfAttnBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last and self.shortcut is not None:
+        if zero_init_last and self.shortcut is not None and getattr(self.conv3_1x1.bn, 'weight', None) is not None:
             nn.init.zeros_(self.conv3_1x1.bn.weight)
         if hasattr(self.self_attn, 'reset_parameters'):
             self.self_attn.reset_parameters()
@@ -1274,8 +1350,17 @@ def create_block(block: Union[str, nn.Module], **kwargs):
 class Stem(nn.Sequential):
 
     def __init__(
-            self, in_chs, out_chs, kernel_size=3, stride=4, pool='maxpool',
-            num_rep=3, num_act=None, chs_decay=0.5, layers: LayerFn = None):
+            self,
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=4,
+            pool='maxpool',
+            num_rep=3,
+            num_act=None,
+            chs_decay=0.5,
+            layers: LayerFn = None,
+    ):
         super().__init__()
         assert stride in (2, 4)
         layers = layers or LayerFn()
@@ -1319,7 +1404,14 @@ class Stem(nn.Sequential):
         assert curr_stride == stride
 
 
-def create_byob_stem(in_chs, out_chs, stem_type='', pool_type='', feat_prefix='stem', layers: LayerFn = None):
+def create_byob_stem(
+        in_chs,
+        out_chs,
+        stem_type='',
+        pool_type='',
+        feat_prefix='stem',
+        layers: LayerFn = None,
+):
     layers = layers or LayerFn()
     assert stem_type in ('', 'quad', 'quad2', 'tiered', 'deep', 'rep', '7x7', '3x3')
     if 'quad' in stem_type:
@@ -1407,10 +1499,14 @@ def update_block_kwargs(block_kwargs: Dict[str, Any], block_cfg: ByoBlockCfg, mo
 
 
 def create_byob_stages(
-        cfg: ByoModelCfg, drop_path_rate: float, output_stride: int, stem_feat: Dict[str, Any],
+        cfg: ByoModelCfg,
+        drop_path_rate: float,
+        output_stride: int,
+        stem_feat: Dict[str, Any],
         feat_size: Optional[int] = None,
         layers: Optional[LayerFn] = None,
-        block_kwargs_fn: Optional[Callable] = update_block_kwargs):
+        block_kwargs_fn: Optional[Callable] = update_block_kwargs,
+):
 
     layers = layers or LayerFn()
     feature_info = []
@@ -1485,8 +1581,17 @@ class ByobNet(nn.Module):
     Current assumption is that both stem and blocks are in conv-bn-act order (w/ block ending in act).
     """
     def __init__(
-            self, cfg: ByoModelCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
-            zero_init_last=True, img_size=None, drop_rate=0., drop_path_rate=0.):
+            self,
+            cfg: ByoModelCfg,
+            num_classes=1000,
+            in_chans=3,
+            global_pool='avg',
+            output_stride=32,
+            zero_init_last=True,
+            img_size=None,
+            drop_rate=0.,
+            drop_path_rate=0.,
+    ):
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate

timm/models/res2net.py

@@ -51,9 +51,21 @@ class Bottle2neck(nn.Module):
     expansion = 4
 
     def __init__(
-            self, inplanes, planes, stride=1, downsample=None,
-            cardinality=1, base_width=26, scale=4, dilation=1, first_dilation=None,
-            act_layer=nn.ReLU, norm_layer=None, attn_layer=None, **_):
+            self,
+            inplanes,
+            planes,
+            stride=1,
+            downsample=None,
+            cardinality=1,
+            base_width=26,
+            scale=4,
+            dilation=1,
+            first_dilation=None,
+            act_layer=nn.ReLU,
+            norm_layer=None,
+            attn_layer=None,
+            **_,
+    ):
         super(Bottle2neck, self).__init__()
         self.scale = scale
         self.is_first = stride > 1 or downsample is not None
@@ -89,7 +101,8 @@ class Bottle2neck(nn.Module):
         self.downsample = downsample
 
     def zero_init_last(self):
-        nn.init.zeros_(self.bn3.weight)
+        if getattr(self.bn3, 'weight', None) is not None:
+            nn.init.zeros_(self.bn3.weight)
 
     def forward(self, x):
         shortcut = x

timm/models/resnest.py

@@ -57,10 +57,27 @@ class ResNestBottleneck(nn.Module):
     expansion = 4
 
     def __init__(
-            self, inplanes, planes, stride=1, downsample=None,
-            radix=1, cardinality=1, base_width=64, avd=False, avd_first=False, is_first=False,
-            reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
-            attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
+            self,
+            inplanes,
+            planes,
+            stride=1,
+            downsample=None,
+            radix=1,
+            cardinality=1,
+            base_width=64,
+            avd=False,
+            avd_first=False,
+            is_first=False,
+            reduce_first=1,
+            dilation=1,
+            first_dilation=None,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            attn_layer=None,
+            aa_layer=None,
+            drop_block=None,
+            drop_path=None,
+    ):
         super(ResNestBottleneck, self).__init__()
         assert reduce_first == 1  # not supported
         assert attn_layer is None  # not supported
@@ -103,7 +120,8 @@ class ResNestBottleneck(nn.Module):
         self.downsample = downsample
 
     def zero_init_last(self):
-        nn.init.zeros_(self.bn3.weight)
+        if getattr(self.bn3, 'weight', None) is not None:
+            nn.init.zeros_(self.bn3.weight)
 
     def forward(self, x):
         shortcut = x

timm/models/resnet.py

@@ -337,9 +337,23 @@ class BasicBlock(nn.Module):
     expansion = 1
 
     def __init__(
-            self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
-            reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
-            attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
+            self,
+            inplanes,
+            planes,
+            stride=1,
+            downsample=None,
+            cardinality=1,
+            base_width=64,
+            reduce_first=1,
+            dilation=1,
+            first_dilation=None,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            attn_layer=None,
+            aa_layer=None,
+            drop_block=None,
+            drop_path=None,
+    ):
         super(BasicBlock, self).__init__()
 
         assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
@@ -370,7 +384,8 @@ class BasicBlock(nn.Module):
         self.drop_path = drop_path
 
     def zero_init_last(self):
-        nn.init.zeros_(self.bn2.weight)
+        if getattr(self.bn2, 'weight', None) is not None:
+            nn.init.zeros_(self.bn2.weight)
 
     def forward(self, x):
         shortcut = x
@@ -402,9 +417,23 @@ class Bottleneck(nn.Module):
     expansion = 4
 
     def __init__(
-            self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
-            reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
-            attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
+            self,
+            inplanes,
+            planes,
+            stride=1,
+            downsample=None,
+            cardinality=1,
+            base_width=64,
+            reduce_first=1,
+            dilation=1,
+            first_dilation=None,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            attn_layer=None,
+            aa_layer=None,
+            drop_block=None,
+            drop_path=None,
+    ):
         super(Bottleneck, self).__init__()
 
         width = int(math.floor(planes * (base_width / 64)) * cardinality)
@@ -437,7 +466,8 @@ class Bottleneck(nn.Module):
         self.drop_path = drop_path
 
     def zero_init_last(self):
-        nn.init.zeros_(self.bn3.weight)
+        if getattr(self.bn3, 'weight', None) is not None:
+            nn.init.zeros_(self.bn3.weight)
 
     def forward(self, x):
         shortcut = x
@@ -508,8 +538,18 @@ def drop_blocks(drop_prob=0.):
 
 
 def make_blocks(
-        block_fn, channels, block_repeats, inplanes, reduce_first=1, output_stride=32,
-        down_kernel_size=1, avg_down=False, drop_block_rate=0., drop_path_rate=0., **kwargs):
+        block_fn,
+        channels,
+        block_repeats,
+        inplanes,
+        reduce_first=1,
+        output_stride=32,
+        down_kernel_size=1,
+        avg_down=False,
+        drop_block_rate=0.,
+        drop_path_rate=0.,
+        **kwargs,
+):
     stages = []
     feature_info = []
     net_num_blocks = sum(block_repeats)
@@ -528,8 +568,14 @@ def make_blocks(
         downsample = None
         if stride != 1 or inplanes != planes * block_fn.expansion:
             down_kwargs = dict(
-                in_channels=inplanes, out_channels=planes * block_fn.expansion, kernel_size=down_kernel_size,
-                stride=stride, dilation=dilation, first_dilation=prev_dilation, norm_layer=kwargs.get('norm_layer'))
+                in_channels=inplanes,
+                out_channels=planes * block_fn.expansion,
+                kernel_size=down_kernel_size,
+                stride=stride,
+                dilation=dilation,
+                first_dilation=prev_dilation,
+                norm_layer=kwargs.get('norm_layer'),
+            )
             downsample = downsample_avg(**down_kwargs) if avg_down else downsample_conv(**down_kwargs)
 
         block_kwargs = dict(reduce_first=reduce_first, dilation=dilation, drop_block=db, **kwargs)
@@ -609,10 +655,30 @@ class ResNet(nn.Module):
     """
 
     def __init__(
-            self, block, layers, num_classes=1000, in_chans=3, output_stride=32, global_pool='avg',
-            cardinality=1, base_width=64, stem_width=64, stem_type='', replace_stem_pool=False, block_reduce_first=1,
-            down_kernel_size=1, avg_down=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None,
-            drop_rate=0.0, drop_path_rate=0., drop_block_rate=0., zero_init_last=True, block_args=None):
+            self,
+            block,
+            layers,
+            num_classes=1000,
+            in_chans=3,
+            output_stride=32,
+            global_pool='avg',
+            cardinality=1,
+            base_width=64,
+            stem_width=64,
+            stem_type='',
+            replace_stem_pool=False,
+            block_reduce_first=1,
+            down_kernel_size=1,
+            avg_down=False,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            aa_layer=None,
+            drop_rate=0.0,
+            drop_path_rate=0.,
+            drop_block_rate=0.,
+            zero_init_last=True,
+            block_args=None,
+    ):
         super(ResNet, self).__init__()
         block_args = block_args or dict()
         assert output_stride in (8, 16, 32)
@@ -663,10 +729,23 @@ class ResNet(nn.Module):
         # Feature Blocks
         channels = [64, 128, 256, 512]
         stage_modules, stage_feature_info = make_blocks(
-            block, channels, layers, inplanes, cardinality=cardinality, base_width=base_width,
-            output_stride=output_stride, reduce_first=block_reduce_first, avg_down=avg_down,
-            down_kernel_size=down_kernel_size, act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer,
-            drop_block_rate=drop_block_rate, drop_path_rate=drop_path_rate, **block_args)
+            block,
+            channels,
+            layers,
+            inplanes,
+            cardinality=cardinality,
+            base_width=base_width,
+            output_stride=output_stride,
+            reduce_first=block_reduce_first,
+            avg_down=avg_down,
+            down_kernel_size=down_kernel_size,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+            aa_layer=aa_layer,
+            drop_block_rate=drop_block_rate,
+            drop_path_rate=drop_path_rate,
+            **block_args,
+        )
         for stage in stage_modules:
             self.add_module(*stage)  # layer1, layer2, etc
         self.feature_info.extend(stage_feature_info)
@@ -687,9 +766,6 @@ class ResNet(nn.Module):
         for n, m in self.named_modules():
             if isinstance(m, nn.Conv2d):
                 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.ones_(m.weight)
-                nn.init.zeros_(m.bias)
         if zero_init_last:
             for m in self.modules():
                 if hasattr(m, 'zero_init_last'):

timm/models/resnetv2.py

@@ -155,8 +155,20 @@ class PreActBottleneck(nn.Module):
     """
 
     def __init__(
-            self, in_chs, out_chs=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1,
-            act_layer=None, conv_layer=None, norm_layer=None, proj_layer=None, drop_path_rate=0.):
+            self,
+            in_chs,
+            out_chs=None,
+            bottle_ratio=0.25,
+            stride=1,
+            dilation=1,
+            first_dilation=None,
+            groups=1,
+            act_layer=None,
+            conv_layer=None,
+            norm_layer=None,
+            proj_layer=None,
+            drop_path_rate=0.,
+    ):
         super().__init__()
         first_dilation = first_dilation or dilation
         conv_layer = conv_layer or StdConv2d
@@ -202,8 +214,20 @@ class Bottleneck(nn.Module):
     """Non Pre-activation bottleneck block, equiv to V1.5/V1b Bottleneck. Used for ViT.
     """
     def __init__(
-            self, in_chs, out_chs=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1,
-            act_layer=None, conv_layer=None, norm_layer=None, proj_layer=None, drop_path_rate=0.):
+            self,
+            in_chs,
+            out_chs=None,
+            bottle_ratio=0.25,
+            stride=1,
+            dilation=1,
+            first_dilation=None,
+            groups=1,
+            act_layer=None,
+            conv_layer=None,
+            norm_layer=None,
+            proj_layer=None,
+            drop_path_rate=0.,
+    ):
         super().__init__()
         first_dilation = first_dilation or dilation
         act_layer = act_layer or nn.ReLU
@@ -229,7 +253,8 @@ class Bottleneck(nn.Module):
         self.act3 = act_layer(inplace=True)
 
     def zero_init_last(self):
-        nn.init.zeros_(self.norm3.weight)
+        if getattr(self.norm3, 'weight', None) is not None:
+            nn.init.zeros_(self.norm3.weight)
 
     def forward(self, x):
         # shortcut branch
@@ -283,9 +308,22 @@ class DownsampleAvg(nn.Module):
 class ResNetStage(nn.Module):
     """ResNet Stage."""
     def __init__(
-            self, in_chs, out_chs, stride, dilation, depth, bottle_ratio=0.25, groups=1,
-            avg_down=False, block_dpr=None, block_fn=PreActBottleneck,
-            act_layer=None, conv_layer=None, norm_layer=None, **block_kwargs):
+            self,
+            in_chs,
+            out_chs,
+            stride,
+            dilation,
+            depth,
+            bottle_ratio=0.25,
+            groups=1,
+            avg_down=False,
+            block_dpr=None,
+            block_fn=PreActBottleneck,
+            act_layer=None,
+            conv_layer=None,
+            norm_layer=None,
+            **block_kwargs,
+    ):
         super(ResNetStage, self).__init__()
         first_dilation = 1 if dilation in (1, 2) else 2
         layer_kwargs = dict(act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer)
@@ -313,8 +351,13 @@ def is_stem_deep(stem_type):
 
 
 def create_resnetv2_stem(
-        in_chs, out_chs=64, stem_type='', preact=True,
-        conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32)):
+        in_chs,
+        out_chs=64,
+        stem_type='',
+        preact=True,
+        conv_layer=StdConv2d,
+        norm_layer=partial(GroupNormAct, num_groups=32),
+):
     stem = OrderedDict()
     assert stem_type in ('', 'fixed', 'same', 'deep', 'deep_fixed', 'deep_same', 'tiered')
 
@@ -357,11 +400,25 @@ class ResNetV2(nn.Module):
     """
 
     def __init__(
-            self, layers, channels=(256, 512, 1024, 2048),
-            num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
-            width_factor=1, stem_chs=64, stem_type='', avg_down=False, preact=True,
-            act_layer=nn.ReLU, conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32),
-            drop_rate=0., drop_path_rate=0., zero_init_last=False):
+            self,
+            layers,
+            channels=(256, 512, 1024, 2048),
+            num_classes=1000,
+            in_chans=3,
+            global_pool='avg',
+            output_stride=32,
+            width_factor=1,
+            stem_chs=64,
+            stem_type='',
+            avg_down=False,
+            preact=True,
+            act_layer=nn.ReLU,
+            conv_layer=StdConv2d,
+            norm_layer=partial(GroupNormAct, num_groups=32),
+            drop_rate=0.,
+            drop_path_rate=0.,
+            zero_init_last=False,
+    ):
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
@@ -387,8 +444,18 @@ class ResNetV2(nn.Module):
                 dilation *= stride
                 stride = 1
             stage = ResNetStage(
-                prev_chs, out_chs, stride=stride, dilation=dilation, depth=d, avg_down=avg_down,
-                act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer, block_dpr=bdpr, block_fn=block_fn)
+                prev_chs,
+                out_chs,
+                stride=stride,
+                dilation=dilation,
+                depth=d,
+                avg_down=avg_down,
+                act_layer=act_layer,
+                conv_layer=conv_layer,
+                norm_layer=norm_layer,
+                block_dpr=bdpr,
+                block_fn=block_fn,
+            )
             prev_chs = out_chs
             curr_stride *= stride
             self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{stage_idx}')]

timm/models/sknet.py

@@ -47,9 +47,24 @@ class SelectiveKernelBasic(nn.Module):
     expansion = 1
 
     def __init__(
-            self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
-            sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU,
-            norm_layer=nn.BatchNorm2d, attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
+            self,
+            inplanes,
+            planes,
+            stride=1,
+            downsample=None,
+            cardinality=1,
+            base_width=64,
+            sk_kwargs=None,
+            reduce_first=1,
+            dilation=1,
+            first_dilation=None,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            attn_layer=None,
+            aa_layer=None,
+            drop_block=None,
+            drop_path=None,
+    ):
         super(SelectiveKernelBasic, self).__init__()
 
         sk_kwargs = sk_kwargs or {}
@@ -71,7 +86,8 @@ class SelectiveKernelBasic(nn.Module):
         self.drop_path = drop_path
 
     def zero_init_last(self):
-        nn.init.zeros_(self.conv2.bn.weight)
+        if getattr(self.conv2.bn, 'weight', None) is not None:
+            nn.init.zeros_(self.conv2.bn.weight)
 
     def forward(self, x):
         shortcut = x
@@ -92,9 +108,24 @@ class SelectiveKernelBottleneck(nn.Module):
     expansion = 4
 
     def __init__(
-            self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, sk_kwargs=None,
-            reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
-            attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
+            self,
+            inplanes,
+            planes,
+            stride=1,
+            downsample=None,
+            cardinality=1,
+            base_width=64,
+            sk_kwargs=None,
+            reduce_first=1,
+            dilation=1,
+            first_dilation=None,
+            act_layer=nn.ReLU,
+            norm_layer=nn.BatchNorm2d,
+            attn_layer=None,
+            aa_layer=None,
+            drop_block=None,
+            drop_path=None,
+    ):
         super(SelectiveKernelBottleneck, self).__init__()
 
         sk_kwargs = sk_kwargs or {}
@@ -115,7 +146,8 @@ class SelectiveKernelBottleneck(nn.Module):
         self.drop_path = drop_path
 
     def zero_init_last(self):
-        nn.init.zeros_(self.conv3.bn.weight)
+        if getattr(self.conv3.bn, 'weight', None) is not None:
+            nn.init.zeros_(self.conv3.bn.weight)
 
     def forward(self, x):
         shortcut = x