Layer refactoring continues, ResNet downsample rewrite for proper dilation in 3x3 and avg_pool cases

* select_conv2d -> create_conv2d
* added create_attn to create attention module from string/bool/module
* factor padding helpers into own file, use in both conv2d_same and avg_pool2d_same
* add some more test eca resnet variants
* minor tweaks, naming, comments, consistency

timm/models/efficientnet.py

@@ -28,7 +28,7 @@ from .feature_hooks import FeatureHooks
 from .registry import register_model
 from .helpers import load_pretrained
 from .layers import SelectAdaptivePool2d
-from timm.models.layers import select_conv2d
+from timm.models.layers import create_conv2d
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
 
 
@@ -220,7 +220,7 @@ class EfficientNet(nn.Module):
 
     def __init__(self, block_args, num_classes=1000, num_features=1280, in_chans=3, stem_size=32,
                  channel_multiplier=1.0, channel_divisor=8, channel_min=None,
-                 pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_connect_rate=0.,
+                 output_stride=32, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_connect_rate=0.,
                  se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'):
         super(EfficientNet, self).__init__()
         norm_kwargs = norm_kwargs or {}
@@ -232,21 +232,21 @@ class EfficientNet(nn.Module):
 
         # Stem
         stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
-        self.conv_stem = select_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
+        self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
         self.bn1 = norm_layer(stem_size, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
         self._in_chs = stem_size
 
         # Middle stages (IR/ER/DS Blocks)
         builder = EfficientNetBuilder(
-            channel_multiplier, channel_divisor, channel_min, 32, pad_type, act_layer, se_kwargs,
+            channel_multiplier, channel_divisor, channel_min, output_stride, pad_type, act_layer, se_kwargs,
             norm_layer, norm_kwargs, drop_connect_rate, verbose=_DEBUG)
         self.blocks = nn.Sequential(*builder(self._in_chs, block_args))
         self.feature_info = builder.features
         self._in_chs = builder.in_chs
 
         # Head + Pooling
-        self.conv_head = select_conv2d(self._in_chs, self.num_features, 1, padding=pad_type)
+        self.conv_head = create_conv2d(self._in_chs, self.num_features, 1, padding=pad_type)
         self.bn2 = norm_layer(self.num_features, **norm_kwargs)
         self.act2 = act_layer(inplace=True)
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
@@ -314,7 +314,7 @@ class EfficientNetFeatures(nn.Module):
 
         # Stem
         stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
-        self.conv_stem = select_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
+        self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
         self.bn1 = norm_layer(stem_size, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
         self._in_chs = stem_size

timm/models/efficientnet_blocks.py

@@ -2,7 +2,7 @@ import torch
 import torch.nn as nn
 from torch.nn import functional as F
 from .layers.activations import sigmoid
-from .layers import select_conv2d
+from .layers import create_conv2d
 
 
 # Defaults used for Google/Tensorflow training of mobile networks /w RMSprop as per
@@ -129,7 +129,7 @@ class ConvBnAct(nn.Module):
                  norm_layer=nn.BatchNorm2d, norm_kwargs=None):
         super(ConvBnAct, self).__init__()
         norm_kwargs = norm_kwargs or {}
-        self.conv = select_conv2d(in_chs, out_chs, kernel_size, stride=stride, dilation=dilation, padding=pad_type)
+        self.conv = create_conv2d(in_chs, out_chs, kernel_size, stride=stride, dilation=dilation, padding=pad_type)
         self.bn1 = norm_layer(out_chs, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
 
@@ -162,7 +162,7 @@ class DepthwiseSeparableConv(nn.Module):
         self.has_pw_act = pw_act  # activation after point-wise conv
         self.drop_connect_rate = drop_connect_rate
 
-        self.conv_dw = select_conv2d(
+        self.conv_dw = create_conv2d(
             in_chs, in_chs, dw_kernel_size, stride=stride, dilation=dilation, padding=pad_type, depthwise=True)
         self.bn1 = norm_layer(in_chs, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
@@ -174,7 +174,7 @@ class DepthwiseSeparableConv(nn.Module):
         else:
             self.se = None
 
-        self.conv_pw = select_conv2d(in_chs, out_chs, pw_kernel_size, padding=pad_type)
+        self.conv_pw = create_conv2d(in_chs, out_chs, pw_kernel_size, padding=pad_type)
         self.bn2 = norm_layer(out_chs, **norm_kwargs)
         self.act2 = act_layer(inplace=True) if self.has_pw_act else nn.Identity()
 
@@ -223,12 +223,12 @@ class InvertedResidual(nn.Module):
         self.drop_connect_rate = drop_connect_rate
 
         # Point-wise expansion
-        self.conv_pw = select_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type, **conv_kwargs)
+        self.conv_pw = create_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type, **conv_kwargs)
         self.bn1 = norm_layer(mid_chs, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
 
         # Depth-wise convolution
-        self.conv_dw = select_conv2d(
+        self.conv_dw = create_conv2d(
             mid_chs, mid_chs, dw_kernel_size, stride=stride, dilation=dilation,
             padding=pad_type, depthwise=True, **conv_kwargs)
         self.bn2 = norm_layer(mid_chs, **norm_kwargs)
@@ -242,7 +242,7 @@ class InvertedResidual(nn.Module):
             self.se = None
 
         # Point-wise linear projection
-        self.conv_pwl = select_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type, **conv_kwargs)
+        self.conv_pwl = create_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type, **conv_kwargs)
         self.bn3 = norm_layer(out_chs, **norm_kwargs)
 
     def feature_module(self, location):
@@ -356,7 +356,7 @@ class EdgeResidual(nn.Module):
         self.drop_connect_rate = drop_connect_rate
 
         # Expansion convolution
-        self.conv_exp = select_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type)
+        self.conv_exp = create_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type)
         self.bn1 = norm_layer(mid_chs, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
 
@@ -368,7 +368,7 @@ class EdgeResidual(nn.Module):
             self.se = None
 
         # Point-wise linear projection
-        self.conv_pwl = select_conv2d(
+        self.conv_pwl = create_conv2d(
             mid_chs, out_chs, pw_kernel_size, stride=stride, dilation=dilation, padding=pad_type)
         self.bn2 = norm_layer(out_chs, **norm_kwargs)
 

timm/models/gluon_resnet.py

@@ -11,6 +11,7 @@ import torch.nn.functional as F
 
 from .registry import register_model
 from .helpers import load_pretrained
+from .layers import SEModule
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
 from .resnet import ResNet, Bottleneck, BasicBlock
@@ -319,8 +320,8 @@ def gluon_seresnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kw
     """
     default_cfg = default_cfgs['gluon_seresnext50_32x4d']
     model = ResNet(
-        Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, use_se=True,
+        Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
+        num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer=SEModule), **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)
@@ -333,8 +334,8 @@ def gluon_seresnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **k
     """
     default_cfg = default_cfgs['gluon_seresnext101_32x4d']
     model = ResNet(
-        Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4, use_se=True,
+        Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4,
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
+        num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer=SEModule), **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)
@@ -346,9 +347,10 @@ def gluon_seresnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **k
     """Constructs a SEResNeXt-101-64x4d model.
     """
     default_cfg = default_cfgs['gluon_seresnext101_64x4d']
+    block_args = dict(attn_layer=SEModule)
     model = ResNet(
-        Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4, use_se=True,
+        Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4,
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
+        num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)
@@ -360,10 +362,10 @@ def gluon_senet154(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """Constructs an SENet-154 model.
     """
     default_cfg = default_cfgs['gluon_senet154']
+    block_args = dict(attn_layer=SEModule)
     model = ResNet(
-        Bottleneck, [3, 8, 36, 3], cardinality=64, base_width=4, use_se=True,
+        Bottleneck, [3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep', down_kernel_size=3,
-        stem_type='deep', down_kernel_size=3, block_reduce_first=2,
+        block_reduce_first=2, num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)

timm/models/layers/__init__.py

@@ -1,8 +1,13 @@
+from .padding import get_padding
+from .avg_pool2d_same import AvgPool2dSame
+from .conv2d_same import Conv2dSame
 from .conv_bn_act import ConvBnAct
 from .mixed_conv2d import MixedConv2d
 from .cond_conv2d import CondConv2d, get_condconv_initializer
-from .select_conv2d import select_conv2d
+from .create_conv2d import create_conv2d
+from .create_attn import create_attn
 from .selective_kernel import SelectiveKernelConv
+from .se import SEModule
 from .eca import EcaModule, CecaModule
 from .activations import *
 from .adaptive_avgmax_pool import \

timm/models/layers/avg_pool2d_same.py

@@ -0,0 +1,31 @@
+""" AvgPool2d w/ Same Padding
+
+Hacked together by Ross Wightman
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import List
+import math
+
+from .helpers import tup_pair
+from .padding import pad_same
+
+
+def avg_pool2d_same(x, kernel_size: List[int], stride: List[int], padding: List[int] = (0, 0),
+                    ceil_mode: bool = False, count_include_pad: bool = True):
+    x = pad_same(x, kernel_size, stride)
+    return F.avg_pool2d(x, kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
+
+
+class AvgPool2dSame(nn.AvgPool2d):
+    """ Tensorflow like 'SAME' wrapper for 2D average pooling
+    """
+    def __init__(self, kernel_size: int, stride=None, padding=0, ceil_mode=False, count_include_pad=True):
+        kernel_size = tup_pair(kernel_size)
+        stride = tup_pair(stride)
+        super(AvgPool2dSame, self).__init__(kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
+
+    def forward(self, x):
+        return avg_pool2d_same(
+            x, self.kernel_size, self.stride, self.padding, self.ceil_mode, self.count_include_pad)

timm/models/layers/cond_conv2d.py

@@ -10,8 +10,8 @@ import torch
 from torch import nn as nn
 from torch.nn import functional as F
 
+from .helpers import tup_pair
 from .conv2d_same import get_padding_value, conv2d_same
-from .conv_helpers import tup_pair
 
 
 def get_condconv_initializer(initializer, num_experts, expert_shape):

timm/models/layers/conv2d_same.py

@@ -8,26 +8,13 @@ import torch.nn.functional as F
 from typing import Union, List, Tuple, Optional, Callable
 import math
 
-from .conv_helpers import get_padding
+from .padding import get_padding, pad_same, is_static_pad
-
-
-def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
-    return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
-
-
-def _calc_same_pad(i: int, k: int, s: int, d: int):
-    return max((math.ceil(i / s) - 1) * s + (k - 1) * d + 1 - i, 0)
 
 
 def conv2d_same(
         x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1),
         padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), groups: int = 1):
-    ih, iw = x.size()[-2:]
+    x = pad_same(x, weight.shape[-2:], stride, dilation)
-    kh, kw = weight.size()[-2:]
-    pad_h = _calc_same_pad(ih, kh, stride[0], dilation[0])
-    pad_w = _calc_same_pad(iw, kw, stride[1], dilation[1])
-    if pad_h > 0 or pad_w > 0:
-        x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
     return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
 
 
@@ -51,7 +38,7 @@ def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]:
         padding = padding.lower()
         if padding == 'same':
             # TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
-            if _is_static_pad(kernel_size, **kwargs):
+            if is_static_pad(kernel_size, **kwargs):
                 # static case, no extra overhead
                 padding = get_padding(kernel_size, **kwargs)
             else:

timm/models/layers/conv_bn_act.py

@@ -4,7 +4,7 @@ Hacked together by Ross Wightman
 """
 from torch import nn as nn
 
-from timm.models.layers.conv_helpers import get_padding
+from timm.models.layers import get_padding
 
 
 class ConvBnAct(nn.Module):

timm/models/layers/create_attn.py

@@ -0,0 +1,30 @@
+""" Select AttentionFactory Method
+
+Hacked together by Ross Wightman
+"""
+import torch
+from .se import SEModule
+from .eca import EcaModule, CecaModule
+
+
+def create_attn(attn_type, channels, **kwargs):
+    module_cls = None
+    if attn_type is not None:
+        if isinstance(attn_type, str):
+            attn_type = attn_type.lower()
+            if attn_type == 'se':
+                module_cls = SEModule
+            elif attn_type == 'eca':
+                module_cls = EcaModule
+            elif attn_type == 'eca':
+                module_cls = CecaModule
+            else:
+                assert False, "Invalid attn module (%s)" % attn_type
+        elif isinstance(attn_type, bool):
+            if attn_type:
+                module_cls = SEModule
+        else:
+            module_cls = attn_type
+    if module_cls is not None:
+        return module_cls(channels, **kwargs)
+    return None

timm/models/layers/create_conv2d.py

@@ -1,4 +1,4 @@
-""" Select Conv2d Factory Method
+""" Create Conv2d Factory Method
 
 Hacked together by Ross Wightman
 """
@@ -8,7 +8,7 @@ from .cond_conv2d import CondConv2d
 from .conv2d_same import create_conv2d_pad
 
 
-def select_conv2d(in_chs, out_chs, kernel_size, **kwargs):
+def create_conv2d(in_chs, out_chs, kernel_size, **kwargs):
     """ Select a 2d convolution implementation based on arguments
     Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.
 

timm/models/layers/drop.py

@@ -1,3 +1,9 @@
+""" DropBlock, DropPath
+
+PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
+
+Hacked together by Ross Wightman
+"""
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -6,6 +12,8 @@ import math
 
 
 def drop_block_2d(x, drop_prob=0.1, block_size=7, gamma_scale=1.0, drop_with_noise=False):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    """
     _, _, height, width = x.shape
     total_size = width * height
     clipped_block_size = min(block_size, min(width, height))
@@ -24,7 +32,7 @@ def drop_block_2d(x, drop_prob=0.1, block_size=7, gamma_scale=1.0, drop_with_noi
     block_mask = ((2 - seed_drop_rate - valid_block + uniform_noise) >= 1).float()
     block_mask = -F.max_pool2d(
         -block_mask,
-        kernel_size=clipped_block_size,  # block_size,
+        kernel_size=clipped_block_size,  # block_size, ???
         stride=1,
         padding=clipped_block_size // 2)
 
@@ -58,7 +66,8 @@ class DropBlock2d(nn.Module):
 
 
 def drop_path(x, drop_prob=0.):
-    """Drop paths (Stochastic Depth) per sample (when applied in residual blocks)."""
+    """Drop paths (Stochastic Depth) per sample (when applied in residual blocks).
+    """
     keep_prob = 1 - drop_prob
     random_tensor = keep_prob + torch.rand((x.size()[0], 1, 1, 1), dtype=x.dtype, device=x.device)
     random_tensor.floor_()  # binarize
@@ -67,6 +76,8 @@ def drop_path(x, drop_prob=0.):
 
 
 class DropPath(nn.ModuleDict):
+    """Drop paths (Stochastic Depth) per sample (when applied in residual blocks).
+    """
     def __init__(self, drop_prob=None):
         super(DropPath, self).__init__()
         self.drop_prob = drop_prob

timm/models/layers/eca.py

@@ -47,19 +47,20 @@ class EcaModule(nn.Module):
             gamma, beta: when channel is given parameters of mapping function
             refer to original paper https://arxiv.org/pdf/1910.03151.pdf
             (default=None. if channel size not given, use k_size given for kernel size.)
-        k_size: Adaptive selection of kernel size (default=3)
+        kernel_size: Adaptive selection of kernel size (default=3)
     """
-    def __init__(self, channel=None, k_size=3, gamma=2, beta=1):
+    def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1):
         super(EcaModule, self).__init__()
-        assert k_size % 2 == 1
+        assert kernel_size % 2 == 1
 
-        if channel is not None:
+        if channels is not None:
-            t = int(abs(math.log(channel, 2)+beta) / gamma)
+            t = int(abs(math.log(channels, 2) + beta) / gamma)
-            k_size = t if t % 2 else t + 1
+            kernel_size = max(t if t % 2 else t + 1, 3)
+
+        print('florg', kernel_size)
 
         self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
+        self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=False)
-        self.sigmoid = nn.Sigmoid()
 
     def forward(self, x):
         # Feature descriptor on the global spatial information
@@ -69,7 +70,7 @@ class EcaModule(nn.Module):
         # Two different branches of ECA module
         y = self.conv(y)
         # Multi-scale information fusion
-        y = self.sigmoid(y.view(x.shape[0], -1, 1, 1))
+        y = y.view(x.shape[0], -1, 1, 1).sigmoid()
         return x * y.expand_as(x)
 
 
@@ -93,22 +94,21 @@ class CecaModule(nn.Module):
         k_size: Adaptive selection of kernel size (default=3)
     """
 
-    def __init__(self, channel=None, k_size=3, gamma=2, beta=1):
+    def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1):
         super(CecaModule, self).__init__()
-        assert k_size % 2 == 1
+        assert kernel_size % 2 == 1
 
-        if channel is not None:
+        if channels is not None:
-            t = int(abs(math.log(channel, 2)+beta) / gamma)
+            t = int(abs(math.log(channels, 2) + beta) / gamma)
-            k_size = t if t % 2 else t + 1
+            kernel_size = max(t if t % 2 else t + 1, 3)
 
         self.avg_pool = nn.AdaptiveAvgPool2d(1)
         #pytorch circular padding mode is buggy as of pytorch 1.4
         #see https://github.com/pytorch/pytorch/pull/17240
 
         #implement manual circular padding
-        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=0, bias=False)
+        self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=0, bias=False)
-        self.padding = (k_size - 1) // 2
+        self.padding = (kernel_size - 1) // 2
-        self.sigmoid = nn.Sigmoid()
 
     def forward(self, x):
         # Feature descriptor on the global spatial information
@@ -121,6 +121,6 @@ class CecaModule(nn.Module):
         y = self.conv(y)
 
         # Multi-scale information fusion
-        y = self.sigmoid(y.view(x.shape[0], -1, 1, 1))
+        y = y.view(x.shape[0], -1, 1, 1).sigmoid()
 
         return x * y.expand_as(x)

timm/models/layers/helpers.py

@@ -1,4 +1,4 @@
-""" Common Helpers
+""" Layer/Module Helpers
 
 Hacked together by Ross Wightman
 """
@@ -21,7 +21,7 @@ tup_triple = _ntuple(3)
 tup_quadruple = _ntuple(4)
 
 
-# Calculate symmetric padding for a convolution
+
-def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:
+
-    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
+
-    return padding
+

timm/models/layers/padding.py

@@ -0,0 +1,33 @@
+""" Padding Helpers
+
+Hacked together by Ross Wightman
+"""
+import math
+from typing import List
+
+import torch.nn.functional as F
+
+
+# Calculate symmetric padding for a convolution
+def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:
+    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
+    return padding
+
+
+# Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution
+def get_same_padding(x: int, k: int, s: int, d: int):
+    return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
+
+
+# Can SAME padding for given args be done statically?
+def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_):
+    return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
+
+
+# Dynamically pad input x with 'SAME' padding for conv with specified args
+def pad_same(x, k: List[int], s: List[int], d: List[int] = (1, 1)):
+    ih, iw = x.size()[-2:]
+    pad_h, pad_w = get_same_padding(ih, k[0], s[0], d[0]), get_same_padding(iw, k[1], s[1], d[1])
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
+    return x

timm/models/layers/se.py

@@ -0,0 +1,21 @@
+from torch import nn as nn
+
+
+class SEModule(nn.Module):
+
+    def __init__(self, channels, reduction=16, act_layer=nn.ReLU):
+        super(SEModule, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        reduction_channels = max(channels // reduction, 8)
+        self.fc1 = nn.Conv2d(
+            channels, reduction_channels, kernel_size=1, padding=0, bias=True)
+        self.act = act_layer(inplace=True)
+        self.fc2 = nn.Conv2d(
+            reduction_channels, channels, kernel_size=1, padding=0, bias=True)
+
+    def forward(self, x):
+        x_se = self.avg_pool(x)
+        x_se = self.fc1(x_se)
+        x_se = self.act(x_se)
+        x_se = self.fc2(x_se)
+        return x * x_se.sigmoid()

timm/models/layers/test_time_pool.py

@@ -34,6 +34,8 @@ class TestTimePoolHead(nn.Module):
 
 def apply_test_time_pool(model, config, args):
     test_time_pool = False
+    if not hasattr(model, 'default_cfg') or not model.default_cfg:
+        return model, False
     if not args.no_test_pool and \
             config['input_size'][-1] > model.default_cfg['input_size'][-1] and \
             config['input_size'][-2] > model.default_cfg['input_size'][-2]:

timm/models/mobilenetv3.py

@@ -11,7 +11,7 @@ Hacked together by Ross Wightman
 from .efficientnet_builder import *
 from .registry import register_model
 from .helpers import load_pretrained
-from .layers import SelectAdaptivePool2d, select_conv2d
+from .layers import SelectAdaptivePool2d, create_conv2d
 from .layers.activations import HardSwish, hard_sigmoid
 from .feature_hooks import FeatureHooks
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
@@ -82,7 +82,7 @@ class MobileNetV3(nn.Module):
 
         # Stem
         stem_size = round_channels(stem_size, channel_multiplier)
-        self.conv_stem = select_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
+        self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
         self.bn1 = norm_layer(stem_size, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
         self._in_chs = stem_size
@@ -97,7 +97,7 @@ class MobileNetV3(nn.Module):
         
         # Head + Pooling
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
-        self.conv_head = select_conv2d(self._in_chs, self.num_features, 1, padding=pad_type, bias=head_bias)
+        self.conv_head = create_conv2d(self._in_chs, self.num_features, 1, padding=pad_type, bias=head_bias)
         self.act2 = act_layer(inplace=True)
 
         # Classifier
@@ -162,7 +162,7 @@ class MobileNetV3Features(nn.Module):
 
         # Stem
         stem_size = round_channels(stem_size, channel_multiplier)
-        self.conv_stem = select_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
+        self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
         self.bn1 = norm_layer(stem_size, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
         self._in_chs = stem_size

timm/models/res2net.py

@@ -8,10 +8,10 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .resnet import ResNet, SEModule
+from .resnet import ResNet
 from .registry import register_model
 from .helpers import load_pretrained
-from .layers import SelectAdaptivePool2d
+from .layers import SEModule
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
 __all__ = []
@@ -53,8 +53,8 @@ class Bottle2neck(nn.Module):
     expansion = 4
 
     def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 cardinality=1, base_width=26, scale=4, use_se=False,
+                 cardinality=1, base_width=26, scale=4, dilation=1, first_dilation=None,
-                 act_layer=nn.ReLU, norm_layer=None, 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
@@ -82,7 +82,7 @@ class Bottle2neck(nn.Module):
 
         self.conv3 = nn.Conv2d(width * scale, outplanes, kernel_size=1, bias=False)
         self.bn3 = norm_layer(outplanes)
-        self.se = SEModule(outplanes, planes // 4) if use_se else None
+        self.se = attn_layer(outplanes) if attn_layer is not None else None
 
         self.relu = act_layer(inplace=True)
         self.downsample = downsample

timm/models/resnet.py

@@ -7,13 +7,12 @@ ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered ste
 """
 import math
 
-import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 from .registry import register_model
 from .helpers import load_pretrained
-from .layers import EcaModule, SelectAdaptivePool2d, DropBlock2d, DropPath
+from .layers import SelectAdaptivePool2d, DropBlock2d, DropPath, AvgPool2dSame, create_attn
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
 
@@ -103,7 +102,8 @@ default_cfgs = {
     'ecaresnext26tn_32x4d': _cfg(
         url='',
         interpolation='bicubic'),
-    
+    'ecaresnet18': _cfg(),
+    'ecaresnet50': _cfg(),
 }
 
 
@@ -112,32 +112,12 @@ def get_padding(kernel_size, stride, dilation=1):
     return padding
 
 
-class SEModule(nn.Module):
-
-    def __init__(self, channels, reduction_channels):
-        super(SEModule, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc1 = nn.Conv2d(
-            channels, reduction_channels, kernel_size=1, padding=0, bias=True)
-        self.relu = nn.ReLU(inplace=True)
-        self.fc2 = nn.Conv2d(
-            reduction_channels, channels, kernel_size=1, padding=0, bias=True)
-
-    def forward(self, x):
-        x_se = self.avg_pool(x)
-        x_se = self.fc1(x_se)
-        x_se = self.relu(x_se)
-        x_se = self.fc2(x_se)
-        return x * x_se.sigmoid()
-
-
 class BasicBlock(nn.Module):
-    __constants__ = ['se', 'downsample']  # for pre 1.4 torchscript compat
     expansion = 1
 
-    def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, use_se=False,
+    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,
-                 drop_block=None, drop_path=None):
+                 attn_layer=None, drop_block=None, drop_path=None):
         super(BasicBlock, self).__init__()
 
         assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
@@ -155,7 +135,7 @@ class BasicBlock(nn.Module):
             first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
         self.bn2 = norm_layer(outplanes)
 
-        self.se = SEModule(outplanes, planes // 4) if use_se else None
+        self.se = create_attn(attn_layer, outplanes)
 
         self.act2 = act_layer(inplace=True)
         self.downsample = downsample
@@ -199,9 +179,9 @@ class Bottleneck(nn.Module):
     __constants__ = ['se', 'downsample']  # for pre 1.4 torchscript compat
     expansion = 4
 
-    def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, use_se=False,
+    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,
-                 drop_block=None, drop_path=None):
+                 attn_layer=None, drop_block=None, drop_path=None):
         super(Bottleneck, self).__init__()
 
         width = int(math.floor(planes * (base_width / 64)) * cardinality)
@@ -220,7 +200,7 @@ class Bottleneck(nn.Module):
         self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
         self.bn3 = norm_layer(outplanes)
 
-        self.se = SEModule(outplanes, planes // 4) if use_se else None
+        self.se = create_attn(attn_layer, outplanes)
 
         self.act3 = act_layer(inplace=True)
         self.downsample = downsample
@@ -266,6 +246,37 @@ class Bottleneck(nn.Module):
         return x
 
 
+def downsample_conv(
+        in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
+    norm_layer = norm_layer or nn.BatchNorm2d
+    kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
+    first_dilation = (first_dilation or dilation) if kernel_size > 1 else 1
+    p = get_padding(kernel_size, stride, first_dilation)
+
+    return nn.Sequential(*[
+        nn.Conv2d(
+            in_channels, out_channels, kernel_size, stride=stride, padding=p, dilation=first_dilation, bias=False),
+        norm_layer(out_channels)
+    ])
+
+
+def downsample_avg(
+        in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
+    norm_layer = norm_layer or nn.BatchNorm2d
+    avg_stride = stride if dilation == 1 else 1
+    if stride == 1 and dilation == 1:
+        pool = nn.Identity()
+    else:
+        avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
+        pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
+
+    return nn.Sequential(*[
+        pool,
+        nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False),
+        norm_layer(out_channels)
+    ])
+
+
 class ResNet(nn.Module):
     """ResNet / ResNeXt / SE-ResNeXt / SE-Net
 
@@ -307,8 +318,6 @@ class ResNet(nn.Module):
         Number of classification classes.
     in_chans : int, default 3
         Number of input (color) channels.
-    use_se : bool, default False
-        Enable Squeeze-Excitation module in blocks
     cardinality : int, default 1
         Number of convolution groups for 3x3 conv in Bottleneck.
     base_width : int, default 64
@@ -337,7 +346,7 @@ class ResNet(nn.Module):
     global_pool : str, default 'avg'
         Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
     """
-    def __init__(self, block, layers, num_classes=1000, in_chans=3, use_se=False, use_eca=False,
+    def __init__(self, block, layers, num_classes=1000, in_chans=3,
                  cardinality=1, base_width=64, stem_width=64, stem_type='',
                  block_reduce_first=1, down_kernel_size=1, avg_down=False, output_stride=32,
                  act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_rate=0.0, drop_path_rate=0.,
@@ -385,14 +394,14 @@ class ResNet(nn.Module):
             dilations[2:4] = [2, 4]
         else:
             assert output_stride == 32
-        llargs = list(zip(channels, layers, strides, dilations))
+        layer_args = list(zip(channels, layers, strides, dilations))
-        lkwargs = dict(
+        layer_kwargs = dict(
-            use_se=use_se, reduce_first=block_reduce_first, act_layer=act_layer, norm_layer=norm_layer,
+            reduce_first=block_reduce_first, act_layer=act_layer, norm_layer=norm_layer,
             avg_down=avg_down, down_kernel_size=down_kernel_size, drop_path=dp, **block_args)
-        self.layer1 = self._make_layer(block, *llargs[0], **lkwargs)
+        self.layer1 = self._make_layer(block, *layer_args[0], **layer_kwargs)
-        self.layer2 = self._make_layer(block, *llargs[1], **lkwargs)
+        self.layer2 = self._make_layer(block, *layer_args[1], **layer_kwargs)
-        self.layer3 = self._make_layer(block, drop_block=db_3, *llargs[2], **lkwargs)
+        self.layer3 = self._make_layer(block, drop_block=db_3, *layer_args[2], **layer_kwargs)
-        self.layer4 = self._make_layer(block, drop_block=db_4, *llargs[3], **lkwargs)
+        self.layer4 = self._make_layer(block, drop_block=db_4, *layer_args[3], **layer_kwargs)
 
         # Head (Pooling and Classifier)
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
@@ -411,31 +420,21 @@ class ResNet(nn.Module):
                     m.zero_init_last_bn()
 
     def _make_layer(self, block, planes, blocks, stride=1, dilation=1, reduce_first=1,
-                    use_se=False, use_eca=False,avg_down=False, down_kernel_size=1, **kwargs):
+                    avg_down=False, down_kernel_size=1, **kwargs):
-        norm_layer = kwargs.get('norm_layer')
         downsample = None
-        down_kernel_size = 1 if stride == 1 and dilation == 1 else down_kernel_size
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample_padding = get_padding(down_kernel_size, stride)
-            downsample_layers = []
-            conv_stride = stride
-            if avg_down:
-                avg_stride = stride if dilation == 1 else 1
-                conv_stride = 1
-                downsample_layers = [nn.AvgPool2d(avg_stride, avg_stride, ceil_mode=True, count_include_pad=False)]
-            downsample_layers += [
-                nn.Conv2d(self.inplanes, planes * block.expansion, down_kernel_size,
-                          stride=conv_stride, padding=downsample_padding, bias=False),
-                norm_layer(planes * block.expansion)]
-            downsample = nn.Sequential(*downsample_layers)
-
         first_dilation = 1 if dilation in (1, 2) else 2
-        bkwargs = dict(
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample_args = dict(
+                in_channels=self.inplanes, out_channels=planes * block.expansion, kernel_size=down_kernel_size,
+                stride=stride, dilation=dilation, first_dilation=first_dilation, norm_layer=kwargs.get('norm_layer'))
+            downsample = downsample_avg(**downsample_args) if avg_down else downsample_conv(**downsample_args)
+
+        block_kwargs = dict(
             cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
-            dilation=dilation, use_se=use_se, **kwargs)
+            dilation=dilation, **kwargs)
-        layers = [block(self.inplanes, planes, stride, downsample, first_dilation=first_dilation, **bkwargs)]
+        layers = [block(self.inplanes, planes, stride, downsample, first_dilation=first_dilation, **block_kwargs)]
         self.inplanes = planes * block.expansion
-        layers += [block(self.inplanes, planes, **bkwargs) for _ in range(1, blocks)]
+        layers += [block(self.inplanes, planes, **block_kwargs) for _ in range(1, blocks)]
 
         return nn.Sequential(*layers)
 
@@ -936,9 +935,8 @@ def seresnext26d_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     """
     default_cfg = default_cfgs['seresnext26d_32x4d']
     model = ResNet(
-        Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4,
+        Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32, stem_type='deep', avg_down=True,
-        stem_width=32, stem_type='deep', avg_down=True, use_se=True,
+        num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer='se'), **kwargs)
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)
@@ -954,8 +952,8 @@ def seresnext26t_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
     default_cfg = default_cfgs['seresnext26t_32x4d']
     model = ResNet(
         Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4,
-        stem_width=32, stem_type='deep_tiered', avg_down=True, use_se=True,
+        stem_width=32, stem_type='deep_tiered', avg_down=True,
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
+        num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer='se'), **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)
@@ -971,25 +969,55 @@ def seresnext26tn_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs
     default_cfg = default_cfgs['seresnext26tn_32x4d']
     model = ResNet(
         Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4,
-        stem_width=32, stem_type='deep_tiered_narrow', avg_down=True, use_se=True,
+        stem_width=32, stem_type='deep_tiered_narrow', avg_down=True,
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
+        num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer='se'), **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)
     return model
 
+
 @register_model
 def ecaresnext26tn_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
-    """Constructs a eca-ResNeXt-26-TN model.
+    """Constructs an ECA-ResNeXt-26-TN model.
     This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
     in the deep stem. The channel number of the middle stem conv is narrower than the 'T' variant.
     this model replaces SE module with the ECA module
     """
     default_cfg = default_cfgs['ecaresnext26tn_32x4d']
+    block_args = dict(attn_layer='eca')
     model = ResNet(
         Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4,
-        stem_width=32, stem_type='deep_tiered_narrow', avg_down=True, use_eca=True,
+        stem_width=32, stem_type='deep_tiered_narrow', avg_down=True,
-        num_classes=num_classes, in_chans=in_chans, **kwargs)
+        num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+@register_model
+def ecaresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """ Constructs an ECA-ResNet-18 model.
+    """
+    default_cfg = default_cfgs['ecaresnet18']
+    block_args = dict(attn_layer='eca')
+    model = ResNet(
+        BasicBlock, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+@register_model
+def ecaresnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Constructs an ECA-ResNet-50 model.
+    """
+    default_cfg = default_cfgs['ecaresnet50']
+    block_args = dict(attn_layer='eca')
+    model = ResNet(
+        Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)

timm/models/sknet.py

@@ -4,8 +4,8 @@ from torch import nn as nn
 
 from .registry import register_model
 from .helpers import load_pretrained
-from .layers import SelectiveKernelConv, ConvBnAct
+from .layers import SelectiveKernelConv, ConvBnAct, create_attn
-from .resnet import ResNet, SEModule
+from .resnet import ResNet
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
 
@@ -33,8 +33,8 @@ class SelectiveKernelBasic(nn.Module):
     expansion = 1
 
     def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
-                 use_se=False, sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None,
+                 sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None,
-                 drop_block=None, drop_path=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
+                 drop_block=None, drop_path=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None):
         super(SelectiveKernelBasic, self).__init__()
 
         sk_kwargs = sk_kwargs or {}
@@ -42,7 +42,7 @@ class SelectiveKernelBasic(nn.Module):
         assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
         assert base_width == 64, 'BasicBlock doest not support changing base width'
         first_planes = planes // reduce_first
-        out_planes = planes * self.expansion
+        outplanes = planes * self.expansion
         first_dilation = first_dilation or dilation
 
         _selective_first = True  # FIXME temporary, for experiments
@@ -51,14 +51,14 @@ class SelectiveKernelBasic(nn.Module):
                 inplanes, first_planes, stride=stride, dilation=first_dilation, **conv_kwargs, **sk_kwargs)
             conv_kwargs['act_layer'] = None
             self.conv2 = ConvBnAct(
-                first_planes, out_planes, kernel_size=3, dilation=dilation, **conv_kwargs)
+                first_planes, outplanes, kernel_size=3, dilation=dilation, **conv_kwargs)
         else:
             self.conv1 = ConvBnAct(
                 inplanes, first_planes, kernel_size=3, stride=stride, dilation=first_dilation, **conv_kwargs)
             conv_kwargs['act_layer'] = None
             self.conv2 = SelectiveKernelConv(
-                first_planes, out_planes, dilation=dilation, **conv_kwargs, **sk_kwargs)
+                first_planes, outplanes, dilation=dilation, **conv_kwargs, **sk_kwargs)
-        self.se = SEModule(out_planes, planes // 4) if use_se else None
+        self.se = create_attn(attn_layer, outplanes)
         self.act = act_layer(inplace=True)
         self.downsample = downsample
         self.stride = stride
@@ -88,17 +88,15 @@ class SelectiveKernelBottleneck(nn.Module):
     expansion = 4
 
     def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 cardinality=1, base_width=64, use_se=False, sk_kwargs=None,
+                 cardinality=1, base_width=64, sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None,
-                 reduce_first=1, dilation=1, first_dilation=None,
+                 drop_block=None, drop_path=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None):
-                 drop_block=None, drop_path=None,
-                 act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
         super(SelectiveKernelBottleneck, self).__init__()
 
         sk_kwargs = sk_kwargs or {}
         conv_kwargs = dict(drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer)
         width = int(math.floor(planes * (base_width / 64)) * cardinality)
         first_planes = width // reduce_first
-        out_planes = planes * self.expansion
+        outplanes = planes * self.expansion
         first_dilation = first_dilation or dilation
 
         self.conv1 = ConvBnAct(inplanes, first_planes, kernel_size=1, **conv_kwargs)
@@ -106,8 +104,8 @@ class SelectiveKernelBottleneck(nn.Module):
             first_planes, width, stride=stride, dilation=first_dilation, groups=cardinality,
             **conv_kwargs, **sk_kwargs)
         conv_kwargs['act_layer'] = None
-        self.conv3 = ConvBnAct(width, out_planes, kernel_size=1, **conv_kwargs)
+        self.conv3 = ConvBnAct(width, outplanes, kernel_size=1, **conv_kwargs)
-        self.se = SEModule(out_planes, planes // 4) if use_se else None
+        self.se = create_attn(attn_layer, outplanes)
         self.act = act_layer(inplace=True)
         self.downsample = downsample
         self.stride = stride