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EPSANet：多尺度通道注意力机制

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发布： 2025-07-23 11:12:54
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本文复现了EPSANet，其核心是高效金字塔注意力分割模块（EPSA）。该模块通过SPC获取多尺度特征图，经SE提取注意力、Softmax校准后加权输出。文中给出模块及网络完整代码，构建了EPSANet50等模型，并在Cifar10上与ResNet-50对比，显示其性能更优，泛化性强，适用于多种下游任务。

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epsanet：多尺度通道注意力机制 - php中文网

前言

Hi guy！我们这下又见面了，这次是来复现 EPSANet

关于注意力机制，实际上最近我比较关注enhance一类的文章，这篇论文也可以算是划分到enhance一类，先上性能图

EPSANet：多尺度通道注意力机制 - php中文网

不得不说这个图做的还怪好看，可视化效果也蛮直观，性能还可以

不仅是图像分类，而且在下游任务比如目标检测分割上，也展现了良好的性能

EPSANet：多尺度通道注意力机制 - php中文网

  • 浅谈神经网络的各种enhance

高效金字塔注意力分割模块（EPSA）

EPSA模块可以有效地提取更细粒度的多尺度空间信息，同时可以建立更长距离的通道依赖关系

EPSANet：多尺度通道注意力机制 - php中文网

其中 SPC module 如下

EPSANet：多尺度通道注意力机制 - php中文网

PSA模块主要分四个步骤实现：

  • 通过 Split And Concat（SPC）得到多尺度特征图
  • 用SE提取多尺度特征图中不同尺度的 attention
  • 用Softmax对attention进行重新校准
  • 对重新校准的权重和相应的特征图进行乘积

输出得到一个多尺度特征信息注意力加权之后的特征图。该特征图多尺度信息表示能力更丰富。

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In [1]
import paddleimport paddle.nn as nnimport mathdef conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, groups=1):
    """standard convolution with padding"""
    return nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
                     padding=padding, dilation=dilation, groups=groups, bias_attr=False)def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2D(in_planes, out_planes, kernel_size=1, stride=stride, bias_attr=False)class SEWeightModule(nn.Layer):

    def __init__(self, channels, reduction=16):
        super(SEWeightModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2D(1)
        self.fc1 = nn.Conv2D(channels, channels//reduction, kernel_size=1, padding=0)
        self.relu = nn.ReLU()
        self.fc2 = nn.Conv2D(channels//reduction, channels, kernel_size=1, padding=0)
        self.sigmoid = nn.Sigmoid()    def forward(self, x):
        out = self.avg_pool(x)
        out = self.fc1(out)
        out = self.relu(out)
        out = self.fc2(out)
        weight = self.sigmoid(out)        return weightclass PSAModule(nn.Layer):

    def __init__(self, inplans, planes, conv_kernels=[3, 5, 7, 9], stride=1, conv_groups=[1, 4, 8, 16]):
        super(PSAModule, self).__init__()
        self.conv_1 = conv(inplans, planes//4, kernel_size=conv_kernels[0], padding=conv_kernels[0]//2,
                            stride=stride, groups=conv_groups[0])
        self.conv_2 = conv(inplans, planes//4, kernel_size=conv_kernels[1], padding=conv_kernels[1]//2,
                            stride=stride, groups=conv_groups[1])
        self.conv_3 = conv(inplans, planes//4, kernel_size=conv_kernels[2], padding=conv_kernels[2]//2,
                            stride=stride, groups=conv_groups[2])
        self.conv_4 = conv(inplans, planes//4, kernel_size=conv_kernels[3], padding=conv_kernels[3]//2,
                            stride=stride, groups=conv_groups[3])
        self.se = SEWeightModule(planes // 4)
        self.split_channel = planes // 4
        self.softmax = nn.Softmax(axis=1)    def forward(self, x):
        # stage 1
        batch_size = x.shape[0]
        x1 = self.conv_1(x)
        x2 = self.conv_2(x)
        x3 = self.conv_3(x)
        x4 = self.conv_4(x)

        feats = paddle.concat((x1, x2, x3, x4), axis=1)
        feats = feats.reshape([batch_size, 4, self.split_channel, feats.shape[2], feats.shape[3]])        
        # stage 2
        x1_se = self.se(x1)
        x2_se = self.se(x2)
        x3_se = self.se(x3)
        x4_se = self.se(x4)

        x_se = paddle.concat((x1_se, x2_se, x3_se, x4_se), axis=1)
        attention_vectors = x_se.reshape([batch_size, 4, self.split_channel, 1, 1])
        attention_vectors = self.softmax(attention_vectors) # stage 3

        # stage 4
        feats_weight = feats * attention_vectors        for i in range(4):
            x_se_weight_fp = feats_weight[:, i, :, :]            if i == 0:
                out = x_se_weight_fp            else:
                out = paddle.concat((x_se_weight_fp, out), axis=1)        return out
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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  def convert_to_list(value, n, name, dtype=np.int):
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In [3]
# 验证 PSA psa = PSAModule(64, 64)
x = paddle.randn([32, 64, 112, 112])
y = psa(x)print(y)
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EPSANet

现在搭建一个EPSANet，它基于EPSA Block，从下图可以看出EPSA Block用PSA模块代替了ResNet Block的Conv 3x3卷积

EPSANet：多尺度通道注意力机制 - php中文网

与ResNet-50对比

EPSANet：多尺度通道注意力机制 - php中文网

  • 上图SENet Block模块可以参考 浅谈神经网络的各种enhance
In [4]
class EPSABlock(nn.Layer):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=None, conv_kernels=[3, 5, 7, 9],
                 conv_groups=[1, 4, 8, 16]):
        super(EPSABlock, self).__init__()        if norm_layer is None:
            norm_layer = nn.BatchNorm2D
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = norm_layer(planes)
        self.conv2 = PSAModule(planes, planes, stride=stride, conv_kernels=conv_kernels, conv_groups=conv_groups)
        self.bn2 = norm_layer(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU()
        self.downsample = downsample
        self.stride = stride    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)        return outclass EPSANet(nn.Layer):
    def __init__(self, block, layers, num_classes=1000):
        super(EPSANet, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2D(3, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
        self.bn1 = nn.BatchNorm2D(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layers(block, 64, layers[0], stride=1)
        self.layer2 = self._make_layers(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layers(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layers(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)        
        
        for m in self.sublayers():            if isinstance(m, nn.Conv2D):
                n = m.weight.shape[2] * m.weight.shape[3] * m.weight.shape[0]
                normal_ = nn.initializer.Normal(std=math.sqrt(2. / n))
                normal_(m.weight)    def _make_layers(self, block, planes, num_blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2D(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias_attr=False),
                nn.BatchNorm2D(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion        for i in range(1, num_blocks):
            layers.append(block(self.inplanes, planes))        return nn.Sequential(*layers)    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.reshape([x.shape[0], -1])
        x = self.fc(x)        return x
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模型定义

In [5]
def epsanet50(**kwarg):
    model = EPSANet(EPSABlock, [3, 4, 6, 3], **kwarg)    return modeldef epsanet101():
    model = EPSANet(EPSABlock, [3, 4, 23, 3], **kwarg)    return model
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查看模型

In [6]
paddle.Model(epsanet50()).summary((1,3,224,224))
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--------------------------------------------------------------------------------
    Layer (type)         Input Shape          Output Shape         Param #    
================================================================================
     Conv2D-13        [[1, 3, 224, 224]]   [1, 64, 112, 112]        9,408     
   BatchNorm2D-1     [[1, 64, 112, 112]]   [1, 64, 112, 112]         256      
       ReLU-3        [[1, 64, 112, 112]]   [1, 64, 112, 112]          0       
    MaxPool2D-1      [[1, 64, 112, 112]]    [1, 64, 56, 56]           0       
     Conv2D-15        [[1, 64, 56, 56]]     [1, 64, 56, 56]         4,096     
   BatchNorm2D-3      [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
       ReLU-5         [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
     Conv2D-16        [[1, 64, 56, 56]]     [1, 16, 56, 56]         9,216     
     Conv2D-17        [[1, 64, 56, 56]]     [1, 16, 56, 56]         6,400     
     Conv2D-18        [[1, 64, 56, 56]]     [1, 16, 56, 56]         6,272     
     Conv2D-19        [[1, 64, 56, 56]]     [1, 16, 56, 56]         5,184     
AdaptiveAvgPool2D-3   [[1, 16, 56, 56]]      [1, 16, 1, 1]            0       
     Conv2D-20         [[1, 16, 1, 1]]        [1, 1, 1, 1]           17       
       ReLU-4           [[1, 1, 1, 1]]        [1, 1, 1, 1]            0       
     Conv2D-21          [[1, 1, 1, 1]]       [1, 16, 1, 1]           32       
     Sigmoid-3         [[1, 16, 1, 1]]       [1, 16, 1, 1]            0       
  SEWeightModule-3    [[1, 16, 56, 56]]      [1, 16, 1, 1]            0       
     Softmax-3        [[1, 4, 16, 1, 1]]    [1, 4, 16, 1, 1]          0       
    PSAModule-3       [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
   BatchNorm2D-4      [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
     Conv2D-22        [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384     
   BatchNorm2D-5      [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     
     Conv2D-14        [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384     
   BatchNorm2D-2      [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     
    EPSABlock-1       [[1, 64, 56, 56]]     [1, 256, 56, 56]          0       
     Conv2D-23        [[1, 256, 56, 56]]    [1, 64, 56, 56]        16,384     
   BatchNorm2D-6      [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
       ReLU-7         [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
     Conv2D-24        [[1, 64, 56, 56]]     [1, 16, 56, 56]         9,216     
     Conv2D-25        [[1, 64, 56, 56]]     [1, 16, 56, 56]         6,400     
     Conv2D-26        [[1, 64, 56, 56]]     [1, 16, 56, 56]         6,272     
     Conv2D-27        [[1, 64, 56, 56]]     [1, 16, 56, 56]         5,184     
AdaptiveAvgPool2D-4   [[1, 16, 56, 56]]      [1, 16, 1, 1]            0       
     Conv2D-28         [[1, 16, 1, 1]]        [1, 1, 1, 1]           17       
       ReLU-6           [[1, 1, 1, 1]]        [1, 1, 1, 1]            0       
     Conv2D-29          [[1, 1, 1, 1]]       [1, 16, 1, 1]           32       
     Sigmoid-4         [[1, 16, 1, 1]]       [1, 16, 1, 1]            0       
  SEWeightModule-4    [[1, 16, 56, 56]]      [1, 16, 1, 1]            0       
     Softmax-4        [[1, 4, 16, 1, 1]]    [1, 4, 16, 1, 1]          0       
    PSAModule-4       [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
   BatchNorm2D-7      [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
     Conv2D-30        [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384     
   BatchNorm2D-8      [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     
    EPSABlock-2       [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
     Conv2D-31        [[1, 256, 56, 56]]    [1, 64, 56, 56]        16,384     
   BatchNorm2D-9      [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
       ReLU-9         [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
     Conv2D-32        [[1, 64, 56, 56]]     [1, 16, 56, 56]         9,216     
     Conv2D-33        [[1, 64, 56, 56]]     [1, 16, 56, 56]         6,400     
     Conv2D-34        [[1, 64, 56, 56]]     [1, 16, 56, 56]         6,272     
     Conv2D-35        [[1, 64, 56, 56]]     [1, 16, 56, 56]         5,184     
AdaptiveAvgPool2D-5   [[1, 16, 56, 56]]      [1, 16, 1, 1]            0       
     Conv2D-36         [[1, 16, 1, 1]]        [1, 1, 1, 1]           17       
       ReLU-8           [[1, 1, 1, 1]]        [1, 1, 1, 1]            0       
     Conv2D-37          [[1, 1, 1, 1]]       [1, 16, 1, 1]           32       
     Sigmoid-5         [[1, 16, 1, 1]]       [1, 16, 1, 1]            0       
  SEWeightModule-5    [[1, 16, 56, 56]]      [1, 16, 1, 1]            0       
     Softmax-5        [[1, 4, 16, 1, 1]]    [1, 4, 16, 1, 1]          0       
    PSAModule-5       [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
   BatchNorm2D-10     [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
     Conv2D-38        [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384     
   BatchNorm2D-11     [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     
    EPSABlock-3       [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
     Conv2D-40        [[1, 256, 56, 56]]    [1, 128, 56, 56]       32,768     
   BatchNorm2D-13     [[1, 128, 56, 56]]    [1, 128, 56, 56]         512      
      ReLU-11         [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
     Conv2D-41        [[1, 128, 56, 56]]    [1, 32, 28, 28]        36,864     
     Conv2D-42        [[1, 128, 56, 56]]    [1, 32, 28, 28]        25,600     
     Conv2D-43        [[1, 128, 56, 56]]    [1, 32, 28, 28]        25,088     
     Conv2D-44        [[1, 128, 56, 56]]    [1, 32, 28, 28]        20,736     
AdaptiveAvgPool2D-6   [[1, 32, 28, 28]]      [1, 32, 1, 1]            0       
     Conv2D-45         [[1, 32, 1, 1]]        [1, 2, 1, 1]           66       
      ReLU-10           [[1, 2, 1, 1]]        [1, 2, 1, 1]            0       
     Conv2D-46          [[1, 2, 1, 1]]       [1, 32, 1, 1]           96       
     Sigmoid-6         [[1, 32, 1, 1]]       [1, 32, 1, 1]            0       
  SEWeightModule-6    [[1, 32, 28, 28]]      [1, 32, 1, 1]            0       
     Softmax-6        [[1, 4, 32, 1, 1]]    [1, 4, 32, 1, 1]          0       
    PSAModule-6       [[1, 128, 56, 56]]    [1, 128, 28, 28]          0       
   BatchNorm2D-14     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
     Conv2D-47        [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536     
   BatchNorm2D-15     [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
     Conv2D-39        [[1, 256, 56, 56]]    [1, 512, 28, 28]       131,072    
   BatchNorm2D-12     [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
    EPSABlock-4       [[1, 256, 56, 56]]    [1, 512, 28, 28]          0       
     Conv2D-48        [[1, 512, 28, 28]]    [1, 128, 28, 28]       65,536     
   BatchNorm2D-16     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
      ReLU-13         [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
     Conv2D-49        [[1, 128, 28, 28]]    [1, 32, 28, 28]        36,864     
     Conv2D-50        [[1, 128, 28, 28]]    [1, 32, 28, 28]        25,600     
     Conv2D-51        [[1, 128, 28, 28]]    [1, 32, 28, 28]        25,088     
     Conv2D-52        [[1, 128, 28, 28]]    [1, 32, 28, 28]        20,736     
AdaptiveAvgPool2D-7   [[1, 32, 28, 28]]      [1, 32, 1, 1]            0       
     Conv2D-53         [[1, 32, 1, 1]]        [1, 2, 1, 1]           66       
      ReLU-12           [[1, 2, 1, 1]]        [1, 2, 1, 1]            0       
     Conv2D-54          [[1, 2, 1, 1]]       [1, 32, 1, 1]           96       
     Sigmoid-7         [[1, 32, 1, 1]]       [1, 32, 1, 1]            0       
  SEWeightModule-7    [[1, 32, 28, 28]]      [1, 32, 1, 1]            0       
     Softmax-7        [[1, 4, 32, 1, 1]]    [1, 4, 32, 1, 1]          0       
    PSAModule-7       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
   BatchNorm2D-17     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
     Conv2D-55        [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536     
   BatchNorm2D-18     [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
    EPSABlock-5       [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
     Conv2D-56        [[1, 512, 28, 28]]    [1, 128, 28, 28]       65,536     
   BatchNorm2D-19     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
      ReLU-15         [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
     Conv2D-57        [[1, 128, 28, 28]]    [1, 32, 28, 28]        36,864     
     Conv2D-58        [[1, 128, 28, 28]]    [1, 32, 28, 28]        25,600     
     Conv2D-59        [[1, 128, 28, 28]]    [1, 32, 28, 28]        25,088     
     Conv2D-60        [[1, 128, 28, 28]]    [1, 32, 28, 28]        20,736     
AdaptiveAvgPool2D-8   [[1, 32, 28, 28]]      [1, 32, 1, 1]            0       
     Conv2D-61         [[1, 32, 1, 1]]        [1, 2, 1, 1]           66       
      ReLU-14           [[1, 2, 1, 1]]        [1, 2, 1, 1]            0       
     Conv2D-62          [[1, 2, 1, 1]]       [1, 32, 1, 1]           96       
     Sigmoid-8         [[1, 32, 1, 1]]       [1, 32, 1, 1]            0       
  SEWeightModule-8    [[1, 32, 28, 28]]      [1, 32, 1, 1]            0       
     Softmax-8        [[1, 4, 32, 1, 1]]    [1, 4, 32, 1, 1]          0       
    PSAModule-8       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
   BatchNorm2D-20     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
     Conv2D-63        [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536     
   BatchNorm2D-21     [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
    EPSABlock-6       [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
     Conv2D-64        [[1, 512, 28, 28]]    [1, 128, 28, 28]       65,536     
   BatchNorm2D-22     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
      ReLU-17         [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
     Conv2D-65        [[1, 128, 28, 28]]    [1, 32, 28, 28]        36,864     
     Conv2D-66        [[1, 128, 28, 28]]    [1, 32, 28, 28]        25,600     
     Conv2D-67        [[1, 128, 28, 28]]    [1, 32, 28, 28]        25,088     
     Conv2D-68        [[1, 128, 28, 28]]    [1, 32, 28, 28]        20,736     
AdaptiveAvgPool2D-9   [[1, 32, 28, 28]]      [1, 32, 1, 1]            0       
     Conv2D-69         [[1, 32, 1, 1]]        [1, 2, 1, 1]           66       
      ReLU-16           [[1, 2, 1, 1]]        [1, 2, 1, 1]            0       
     Conv2D-70          [[1, 2, 1, 1]]       [1, 32, 1, 1]           96       
     Sigmoid-9         [[1, 32, 1, 1]]       [1, 32, 1, 1]            0       
  SEWeightModule-9    [[1, 32, 28, 28]]      [1, 32, 1, 1]            0       
     Softmax-9        [[1, 4, 32, 1, 1]]    [1, 4, 32, 1, 1]          0       
    PSAModule-9       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
   BatchNorm2D-23     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
     Conv2D-71        [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536     
   BatchNorm2D-24     [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
    EPSABlock-7       [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
     Conv2D-73        [[1, 512, 28, 28]]    [1, 256, 28, 28]       131,072    
   BatchNorm2D-26     [[1, 256, 28, 28]]    [1, 256, 28, 28]        1,024     
      ReLU-19        [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-74        [[1, 256, 28, 28]]    [1, 64, 14, 14]        147,456    
     Conv2D-75        [[1, 256, 28, 28]]    [1, 64, 14, 14]        102,400    
     Conv2D-76        [[1, 256, 28, 28]]    [1, 64, 14, 14]        100,352    
     Conv2D-77        [[1, 256, 28, 28]]    [1, 64, 14, 14]        82,944     
AdaptiveAvgPool2D-10  [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Conv2D-78         [[1, 64, 1, 1]]        [1, 4, 1, 1]           260      
      ReLU-18           [[1, 4, 1, 1]]        [1, 4, 1, 1]            0       
     Conv2D-79          [[1, 4, 1, 1]]       [1, 64, 1, 1]           320      
     Sigmoid-10        [[1, 64, 1, 1]]       [1, 64, 1, 1]            0       
 SEWeightModule-10    [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Softmax-10       [[1, 4, 64, 1, 1]]    [1, 4, 64, 1, 1]          0       
    PSAModule-10      [[1, 256, 28, 28]]    [1, 256, 14, 14]          0       
   BatchNorm2D-27     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
     Conv2D-80        [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
   BatchNorm2D-28    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
     Conv2D-72        [[1, 512, 28, 28]]   [1, 1024, 14, 14]       524,288    
   BatchNorm2D-25    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
    EPSABlock-8       [[1, 512, 28, 28]]   [1, 1024, 14, 14]          0       
     Conv2D-81       [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
   BatchNorm2D-29     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
      ReLU-21        [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-82        [[1, 256, 14, 14]]    [1, 64, 14, 14]        147,456    
     Conv2D-83        [[1, 256, 14, 14]]    [1, 64, 14, 14]        102,400    
     Conv2D-84        [[1, 256, 14, 14]]    [1, 64, 14, 14]        100,352    
     Conv2D-85        [[1, 256, 14, 14]]    [1, 64, 14, 14]        82,944     
AdaptiveAvgPool2D-11  [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Conv2D-86         [[1, 64, 1, 1]]        [1, 4, 1, 1]           260      
      ReLU-20           [[1, 4, 1, 1]]        [1, 4, 1, 1]            0       
     Conv2D-87          [[1, 4, 1, 1]]       [1, 64, 1, 1]           320      
     Sigmoid-11        [[1, 64, 1, 1]]       [1, 64, 1, 1]            0       
 SEWeightModule-11    [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Softmax-11       [[1, 4, 64, 1, 1]]    [1, 4, 64, 1, 1]          0       
    PSAModule-11      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   BatchNorm2D-30     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
     Conv2D-88        [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
   BatchNorm2D-31    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
    EPSABlock-9      [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-89       [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
   BatchNorm2D-32     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
      ReLU-23        [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-90        [[1, 256, 14, 14]]    [1, 64, 14, 14]        147,456    
     Conv2D-91        [[1, 256, 14, 14]]    [1, 64, 14, 14]        102,400    
     Conv2D-92        [[1, 256, 14, 14]]    [1, 64, 14, 14]        100,352    
     Conv2D-93        [[1, 256, 14, 14]]    [1, 64, 14, 14]        82,944     
AdaptiveAvgPool2D-12  [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Conv2D-94         [[1, 64, 1, 1]]        [1, 4, 1, 1]           260      
      ReLU-22           [[1, 4, 1, 1]]        [1, 4, 1, 1]            0       
     Conv2D-95          [[1, 4, 1, 1]]       [1, 64, 1, 1]           320      
     Sigmoid-12        [[1, 64, 1, 1]]       [1, 64, 1, 1]            0       
 SEWeightModule-12    [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Softmax-12       [[1, 4, 64, 1, 1]]    [1, 4, 64, 1, 1]          0       
    PSAModule-12      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   BatchNorm2D-33     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
     Conv2D-96        [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
   BatchNorm2D-34    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
    EPSABlock-10     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-97       [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
   BatchNorm2D-35     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
      ReLU-25        [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-98        [[1, 256, 14, 14]]    [1, 64, 14, 14]        147,456    
     Conv2D-99        [[1, 256, 14, 14]]    [1, 64, 14, 14]        102,400    
     Conv2D-100       [[1, 256, 14, 14]]    [1, 64, 14, 14]        100,352    
     Conv2D-101       [[1, 256, 14, 14]]    [1, 64, 14, 14]        82,944     
AdaptiveAvgPool2D-13  [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Conv2D-102        [[1, 64, 1, 1]]        [1, 4, 1, 1]           260      
      ReLU-24           [[1, 4, 1, 1]]        [1, 4, 1, 1]            0       
     Conv2D-103         [[1, 4, 1, 1]]       [1, 64, 1, 1]           320      
     Sigmoid-13        [[1, 64, 1, 1]]       [1, 64, 1, 1]            0       
 SEWeightModule-13    [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Softmax-13       [[1, 4, 64, 1, 1]]    [1, 4, 64, 1, 1]          0       
    PSAModule-13      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   BatchNorm2D-36     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
     Conv2D-104       [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
   BatchNorm2D-37    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
    EPSABlock-11     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-105      [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
   BatchNorm2D-38     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
      ReLU-27        [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-106       [[1, 256, 14, 14]]    [1, 64, 14, 14]        147,456    
     Conv2D-107       [[1, 256, 14, 14]]    [1, 64, 14, 14]        102,400    
     Conv2D-108       [[1, 256, 14, 14]]    [1, 64, 14, 14]        100,352    
     Conv2D-109       [[1, 256, 14, 14]]    [1, 64, 14, 14]        82,944     
AdaptiveAvgPool2D-14  [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Conv2D-110        [[1, 64, 1, 1]]        [1, 4, 1, 1]           260      
      ReLU-26           [[1, 4, 1, 1]]        [1, 4, 1, 1]            0       
     Conv2D-111         [[1, 4, 1, 1]]       [1, 64, 1, 1]           320      
     Sigmoid-14        [[1, 64, 1, 1]]       [1, 64, 1, 1]            0       
 SEWeightModule-14    [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Softmax-14       [[1, 4, 64, 1, 1]]    [1, 4, 64, 1, 1]          0       
    PSAModule-14      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   BatchNorm2D-39     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
     Conv2D-112       [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
   BatchNorm2D-40    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
    EPSABlock-12     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-113      [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
   BatchNorm2D-41     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
      ReLU-29        [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-114       [[1, 256, 14, 14]]    [1, 64, 14, 14]        147,456    
     Conv2D-115       [[1, 256, 14, 14]]    [1, 64, 14, 14]        102,400    
     Conv2D-116       [[1, 256, 14, 14]]    [1, 64, 14, 14]        100,352    
     Conv2D-117       [[1, 256, 14, 14]]    [1, 64, 14, 14]        82,944     
AdaptiveAvgPool2D-15  [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Conv2D-118        [[1, 64, 1, 1]]        [1, 4, 1, 1]           260      
      ReLU-28           [[1, 4, 1, 1]]        [1, 4, 1, 1]            0       
     Conv2D-119         [[1, 4, 1, 1]]       [1, 64, 1, 1]           320      
     Sigmoid-15        [[1, 64, 1, 1]]       [1, 64, 1, 1]            0       
 SEWeightModule-15    [[1, 64, 14, 14]]      [1, 64, 1, 1]            0       
     Softmax-15       [[1, 4, 64, 1, 1]]    [1, 4, 64, 1, 1]          0       
    PSAModule-15      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   BatchNorm2D-42     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
     Conv2D-120       [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
   BatchNorm2D-43    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
    EPSABlock-13     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
     Conv2D-122      [[1, 1024, 14, 14]]    [1, 512, 14, 14]       524,288    
   BatchNorm2D-45     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-31         [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
     Conv2D-123       [[1, 512, 14, 14]]     [1, 128, 7, 7]        589,824    
     Conv2D-124       [[1, 512, 14, 14]]     [1, 128, 7, 7]        409,600    
     Conv2D-125       [[1, 512, 14, 14]]     [1, 128, 7, 7]        401,408    
     Conv2D-126       [[1, 512, 14, 14]]     [1, 128, 7, 7]        331,776    
AdaptiveAvgPool2D-16   [[1, 128, 7, 7]]      [1, 128, 1, 1]           0       
     Conv2D-127        [[1, 128, 1, 1]]       [1, 8, 1, 1]          1,032     
      ReLU-30           [[1, 8, 1, 1]]        [1, 8, 1, 1]            0       
     Conv2D-128         [[1, 8, 1, 1]]       [1, 128, 1, 1]         1,152     
     Sigmoid-16        [[1, 128, 1, 1]]      [1, 128, 1, 1]           0       
 SEWeightModule-16     [[1, 128, 7, 7]]      [1, 128, 1, 1]           0       
     Softmax-16      [[1, 4, 128, 1, 1]]   [1, 4, 128, 1, 1]          0       
    PSAModule-16      [[1, 512, 14, 14]]     [1, 512, 7, 7]           0       
   BatchNorm2D-46      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
     Conv2D-129        [[1, 512, 7, 7]]     [1, 2048, 7, 7]       1,048,576   
   BatchNorm2D-47     [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     
     Conv2D-121      [[1, 1024, 14, 14]]    [1, 2048, 7, 7]       2,097,152   
   BatchNorm2D-44     [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     
    EPSABlock-14     [[1, 1024, 14, 14]]    [1, 2048, 7, 7]           0       
     Conv2D-130       [[1, 2048, 7, 7]]      [1, 512, 7, 7]       1,048,576   
   BatchNorm2D-48      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
      ReLU-33         [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
     Conv2D-131        [[1, 512, 7, 7]]      [1, 128, 7, 7]        589,824    
     Conv2D-132        [[1, 512, 7, 7]]      [1, 128, 7, 7]        409,600    
     Conv2D-133        [[1, 512, 7, 7]]      [1, 128, 7, 7]        401,408    
     Conv2D-134        [[1, 512, 7, 7]]      [1, 128, 7, 7]        331,776    
AdaptiveAvgPool2D-17   [[1, 128, 7, 7]]      [1, 128, 1, 1]           0       
     Conv2D-135        [[1, 128, 1, 1]]       [1, 8, 1, 1]          1,032     
      ReLU-32           [[1, 8, 1, 1]]        [1, 8, 1, 1]            0       
     Conv2D-136         [[1, 8, 1, 1]]       [1, 128, 1, 1]         1,152     
     Sigmoid-17        [[1, 128, 1, 1]]      [1, 128, 1, 1]           0       
 SEWeightModule-17     [[1, 128, 7, 7]]      [1, 128, 1, 1]           0       
     Softmax-17      [[1, 4, 128, 1, 1]]   [1, 4, 128, 1, 1]          0       
    PSAModule-17       [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
   BatchNorm2D-49      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
     Conv2D-137        [[1, 512, 7, 7]]     [1, 2048, 7, 7]       1,048,576   
   BatchNorm2D-50     [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     
    EPSABlock-15      [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
     Conv2D-138       [[1, 2048, 7, 7]]      [1, 512, 7, 7]       1,048,576   
   BatchNorm2D-51      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
      ReLU-35         [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
     Conv2D-139        [[1, 512, 7, 7]]      [1, 128, 7, 7]        589,824    
     Conv2D-140        [[1, 512, 7, 7]]      [1, 128, 7, 7]        409,600    
     Conv2D-141        [[1, 512, 7, 7]]      [1, 128, 7, 7]        401,408    
     Conv2D-142        [[1, 512, 7, 7]]      [1, 128, 7, 7]        331,776    
AdaptiveAvgPool2D-18   [[1, 128, 7, 7]]      [1, 128, 1, 1]           0       
     Conv2D-143        [[1, 128, 1, 1]]       [1, 8, 1, 1]          1,032     
      ReLU-34           [[1, 8, 1, 1]]        [1, 8, 1, 1]            0       
     Conv2D-144         [[1, 8, 1, 1]]       [1, 128, 1, 1]         1,152     
     Sigmoid-18        [[1, 128, 1, 1]]      [1, 128, 1, 1]           0       
 SEWeightModule-18     [[1, 128, 7, 7]]      [1, 128, 1, 1]           0       
     Softmax-18      [[1, 4, 128, 1, 1]]   [1, 4, 128, 1, 1]          0       
    PSAModule-18       [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
   BatchNorm2D-52      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
     Conv2D-145        [[1, 512, 7, 7]]     [1, 2048, 7, 7]       1,048,576   
   BatchNorm2D-53     [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     
    EPSABlock-16      [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
AdaptiveAvgPool2D-19  [[1, 2048, 7, 7]]     [1, 2048, 1, 1]           0       
      Linear-1           [[1, 2048]]           [1, 1000]          2,049,000   
================================================================================
Total params: 22,614,835
Trainable params: 22,508,595
Non-trainable params: 106,240
--------------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 272.07
Params size (MB): 86.27
Estimated Total Size (MB): 358.91
--------------------------------------------------------------------------------
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{'total_params': 22614835, 'trainable_params': 22508595}
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在Cifar10数据集上进行对比

如下图所示，我们对比EPSANet-50和Paddle官方ResNet-50，看看他们谁的性能更好

EPSANet：多尺度通道注意力机制 - php中文网

数据准备

In [7]
import paddle.vision.transforms as Tfrom paddle.vision.datasets import Cifar10

paddle.set_device('gpu')#数据准备transform = T.Compose([
    T.Resize(size=(224,224)),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],data_format='HWC'),
    T.ToTensor()
])

train_dataset = Cifar10(mode='train', transform=transform)
val_dataset = Cifar10(mode='test',  transform=transform)
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Cache file /home/aistudio/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz not found, downloading https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz 
Begin to download

Download finished
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模型准备

In [8]
# ResNet-50 vs EPSANet-50resnet50=paddle.Model(paddle.vision.models.resnet50(num_classes=10))
epsanet50=paddle.Model(epsanet50(num_classes=10))
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开始训练

In [9]
# ResNet-50resnet50.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.001, parameters=resnet50.parameters()),
              loss=paddle.nn.CrossEntropyLoss(),
              metrics=paddle.metric.Accuracy())

visualdl=paddle.callbacks.VisualDL(log_dir='resnet50_log') # 开启训练可视化resnet50.fit(
    train_data=train_dataset, 
    eval_data=val_dataset, 
    batch_size=64, 
    epochs=10, 
    verbose=1,
    callbacks=[visualdl] 
)
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In [10]
# EPSANet-50epsanet50.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.001, parameters=epsanet50.parameters()),
              loss=paddle.nn.CrossEntropyLoss(),
              metrics=paddle.metric.Accuracy())

visualdl=paddle.callbacks.VisualDL(log_dir='epsanet50_log') # 开启训练可视化epsanet50.fit(
    train_data=train_dataset, 
    eval_data=val_dataset, 
    batch_size=64, 
    epochs=10, 
    verbose=1,
    callbacks=[visualdl] 
)
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训练可视化

  • 训练集上性能对比

EPSANet：多尺度通道注意力机制 - php中文网

  • 验证集上性能对比

EPSANet：多尺度通道注意力机制 - php中文网

总结

  • PSA注意力模块实现了最好的性能，超越了当前其它注意力模块

EPSANet：多尺度通道注意力机制 - php中文网

  • EPSANet具有很好的泛化性能，可以非常容易应用到其他下游任务

EPSANet：多尺度通道注意力机制 - php中文网

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