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@ -127,21 +127,14 @@ class BasicBlock(nn.Module):
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first_planes = planes // reduce_first
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outplanes = planes * self.expansion
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first_dilation = first_dilation or dilation
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self.blur = blur
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if blur and stride==2:
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self.conv1 = nn.Conv2d(
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inplanes, first_planes, kernel_size=3, stride=1, padding=first_dilation,
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inplanes, first_planes, kernel_size=3, stride=1 if blur else stride, padding=first_dilation,
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dilation=first_dilation, bias=False)
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self.blurpool=BlurPool2d(channels=first_planes)
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else:
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self.conv1 = nn.Conv2d(
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inplanes, first_planes, kernel_size=3, stride=stride, padding=first_dilation,
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dilation=first_dilation, bias=False)
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self.blurpool = None
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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.blurpool = BlurPool2d(channels=first_planes) if stride == 2 and blur else None
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self.conv2 = nn.Conv2d(
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first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
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self.bn2 = norm_layer(outplanes)
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@ -165,11 +158,9 @@ class BasicBlock(nn.Module):
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x = self.bn1(x)
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if self.drop_block is not None:
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x = self.drop_block(x)
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if self.blurpool is not None:
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x = self.act1(x)
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if self.blurpool is not None:
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x = self.blurpool(x)
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else:
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x = self.act1(x)
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x = self.conv2(x)
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x = self.bn2(x)
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@ -209,19 +200,13 @@ class Bottleneck(nn.Module):
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self.bn1 = norm_layer(first_planes)
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self.act1 = act_layer(inplace=True)
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if blur and stride==2:
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self.conv2 = nn.Conv2d(
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first_planes, width, kernel_size=3, stride=1,
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first_planes, width, kernel_size=3, stride=1 if blur else stride,
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padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
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self.blurpool = BlurPool2d(channels=width)
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else:
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self.conv2 = nn.Conv2d(
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first_planes, width, kernel_size=3, stride=stride,
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padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
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self.blurpool = None
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self.bn2 = norm_layer(width)
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self.act2 = act_layer(inplace=True)
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self.blurpool = BlurPool2d(channels=width) if stride == 2 and blur else None
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self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
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self.bn3 = norm_layer(outplanes)
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@ -251,6 +236,8 @@ class Bottleneck(nn.Module):
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if self.drop_block is not None:
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x = self.drop_block(x)
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x = self.act2(x)
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if self.blurpool is not None:
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x = self.blurpool(x)
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x = self.conv3(x)
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x = self.bn3(x)
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@ -412,11 +399,12 @@ class ResNet(nn.Module):
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self.conv1 = nn.Conv2d(in_chans, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = norm_layer(self.inplanes)
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self.act1 = act_layer(inplace=True)
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# Stem Blur
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# Stem Pooling
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if 'max' in blur :
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self.maxpool = nn.Sequential(*[
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nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
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BlurPool2d(channels=self.inplanes)])
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BlurPool2d(channels=self.inplanes, stride=2)
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])
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else :
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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@ -470,8 +458,8 @@ class ResNet(nn.Module):
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block_kwargs = dict(
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cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
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dilation=dilation, **kwargs)
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layers = [block(self.inplanes, planes, stride, downsample, first_dilation=first_dilation, blur=self.blur, **block_kwargs)]
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dilation=dilation, blur=self.blur, **kwargs)
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layers = [block(self.inplanes, planes, stride, downsample, first_dilation=first_dilation, **block_kwargs)]
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self.inplanes = planes * block.expansion
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layers += [block(self.inplanes, planes, **block_kwargs) for _ in range(1, blocks)]
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@ -1075,7 +1063,7 @@ def resnetblur18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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def resnetblur50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a ResNet-50 model. With assembled-cnn style blur
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"""
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default_cfg = default_cfgs['resnetblur18']
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model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, blur='strided', **kwargs)
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default_cfg = default_cfgs['resnetblur50']
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model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, blur='max_strided', **kwargs)
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model.default_cfg = default_cfg
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return model
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