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