A SelectiveKernelBasicBlock for more experiments

timm/models/resnet.py

@@ -265,7 +265,7 @@ class SelectiveKernelAttn(nn.Module):
 class SelectiveKernelConv(nn.Module):
 
     def __init__(self, in_channels, out_channels, kernel_size=[3, 5], attn_reduction=16,
-                 min_attn_feat=32, stride=1, dilation=1, groups=1, keep_3x3=True, use_attn=True,
+                 min_attn_feat=16, stride=1, dilation=1, groups=1, keep_3x3=True, use_attn=True,
                  act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
         super(SelectiveKernelConv, self).__init__()
         if not isinstance(kernel_size, list):
@@ -316,6 +316,53 @@ class SelectiveKernelConv(nn.Module):
         return x
 
 
+class SelectiveKernelBasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None,
+                 cardinality=1, base_width=64, use_se=False,
+                 reduce_first=1, dilation=1, previous_dilation=1, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
+        super(SelectiveKernelBasicBlock, self).__init__()
+
+        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
+        outplanes = planes * self.expansion
+
+        self.conv1 = nn.Conv2d(
+            inplanes, first_planes, kernel_size=3, stride=stride, padding=dilation,
+            dilation=dilation, bias=False)
+        self.bn1 = norm_layer(first_planes)
+        self.act1 = act_layer(inplace=True)
+        self.conv2 = SelectiveKernelConv(first_planes, outplanes, dilation=previous_dilation)
+        self.bn2 = norm_layer(outplanes)
+        self.se = SEModule(outplanes, planes // 4) if use_se else None
+        self.act2 = act_layer(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+        self.dilation = dilation
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.act1(out)
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.se is not None:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.act2(out)
+
+        return out
+
+
 class SelectiveKernelBottleneck(nn.Module):
     expansion = 4
 
@@ -581,6 +628,18 @@ def resnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     return model
 
 
+@register_model
+def skresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Constructs a ResNet-18 model.
+    """
+    default_cfg = default_cfgs['resnet18']
+    model = ResNet(SelectiveKernelBasicBlock, [2, 2, 2, 2], 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)
+    return model
+
+
 @register_model
 def resnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """Constructs a ResNet-34 model.