classSKConv(nn.Module): def__init__(self, features, M=2, G=32, r=16, stride=1, L=32): """ Constructor Args: features: input channel dimensionality. M: the number of branchs. G: num of convolution groups. r: the ratio for compute d, the length of z. stride: stride, default 1. L: the minimum dim of the vector z in paper, default 32. """ super(SKConv, self).__init__() d = max(int(features/r), L) self.M = M self.features = features self.convs = nn.ModuleList([]) for i in range(M): self.convs.append(nn.Sequential( nn.Conv2d(features, features, kernel_size=3, stride=stride, padding=1+i, dilation=1+i, groups=G, bias=False), nn.BatchNorm2d(features), nn.ReLU(inplace=False) )) self.gap = nn.AdaptiveAvgPool2d((1,1)) self.fc = nn.Sequential(nn.Conv2d(features, d, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(d), nn.ReLU(inplace=False)) self.fcs = nn.ModuleList([]) for i in range(M): self.fcs.append( nn.Conv2d(d, features, kernel_size=1, stride=1) ) self.softmax = nn.Softmax(dim=1)
defforward(self, x):
batch_size = x.shape[0]
feats = [conv(x) for conv in self.convs] feats = torch.cat(feats, dim=1) feats = feats.view(batch_size, self.M, self.features, feats.shape[2], feats.shape[3])
defforward(self, x): x = self.conv1(x) if hasattr(self, 'conv1') else x b, c, _, _ = x.size() y1 = self.avg_pool1(x).view(b, c) # like resize() in numpy y2 = self.avg_pool2(x).view(b, 4 * c) y3 = self.avg_pool4(x).view(b, 16 * c) y = torch.cat((y1, y2, y3), 1) y = self.fc(y) b, out_channel = y.size() y = y.view(b, out_channel, 1, 1) return y
ECA-Net[15]
《ECANet：Efficient Channel Attention for Deep Convolutional Neural Networks》发表于CVPR 2020，是对SE-Net中特征变换部分进行了改进。SE-Net的通道信息交互方式是通过全连接实现的，在降维和升维的过程中会损害一部分的特征表达。ECA-Net则进一步地利用一维卷积来实现通道间的信息交互，相对于全连接实现的全局通道信息交互所带来的计算开销，ECA-Net提出了一种基于自适应选择卷积核大小的方法，以实现局部交互，从而显著地降低模型复杂度且保持性能。
classECALayer(nn.Module): """Constructs a ECA module. Args: channel: Number of channels of the input feature map k_size: Adaptive selection of kernel size """ def__init__(self, channel, k_size=3): super(ECALayer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1
) // 2, bias=False) self.sigmoid = nn.Sigmoid()
defforward(self, x): # feature descriptor on the global spatial information y = self.avg_pool(x) # Two different branches of ECA module y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) # Multi-scale information fusion y = self.sigmoid(y)
class DoubleAtten(nn.Module): """ A2-Nets: Double Attention Networks. NIPS 2018 """ def __init__(self,in_c): super(DoubleAtten,self).__init__() self.in_c = in_c """Convolve the same input feature map to produce three feature maps with the same scale, i.e., A, B, V (as shown in paper). """ self.convA = nn.Conv2d(in_c,in_c,kernel_size=1) self.convB = nn.Conv2d(in_c,in_c,kernel_size=1) self.convV = nn.Conv2d(in_c,in_c,kernel_size=1) def forward(self,input):
feature_maps = feature_maps.view(b, 1, self.in_c, h*w) # reshape A atten_map = atten_map.view(b, self.in_c, 1, h*w) # reshape B to generate attention map global_descriptors = torch.mean((feature_maps * F.softmax(atten_map, dim=-1)),dim=-1) # Multiply the feature map and the attention weight map to generate a global feature descriptor
v = self.convV(input) atten_vectors = F.softmax(v.view(b, self.in_c, h*w), dim=-1) # 生成 attention_vectors out = torch.bmm(atten_vectors.permute(0,2,1), global_descriptors).permute(0,2,1)
《DA-Net：Dual Attention Network for Scene Segmentation》发表于CVPR 2019，该论文将Non-local的思想同时引入到了通道域和空间域，分别将空间像素点以及通道特征作为查询语句进行上下文建模，自适应地整合局部特征和全局依赖。【代码链接可访问github[23]】
Position Attention Module
PAM将更广泛的上下文信息编码为局部特征，从而提高了它们的代表性。
Channel Attention Module
CAM通过挖掘通道图之间的相互依赖关系，可以强调相互依赖的特征图，改进特定语义的特征表示。
ANLNet[24]
《ANLNet：Asymmetric Non-local Neural Networks for Semantic Segmentation》发表于ICCV 2019，是基于Non-Local的思路往轻量化方向做改进。Non-Local模块是一种效果显著的技术，但同时也受限于过大计算量而难以很好地嵌入网络中应用。为了解决以上问题，ANLNet基于Non-Local结构并融入了金字塔采样模块，在充分考虑了长距离依赖的前提下，融入了不同层次的特征，从而在保持性能的同时极大地减少计算量。【代码链接可访问github[25]】
《GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond》发表于ICCV 2019，受SE-Net和Non-local思想的启发提出了一种更简化的空间自注意力模块。Non-local采用Self-attention机制来建模全局的像素对关系，建模长距离依赖，但这种基于全局像素点(pixel-to-pixel) 对的建模方式其计算量无疑是巨大的。SE-Net则利用GAP和MLP完成通道之间的特征重标定，虽然轻量，但未能充分利用到全局上下文信息。因此，作者提出了GC-Net可以高效的建模全局的上下文信息。【代码链接可访问github[29]】
类别注意力
OCR-Net[30]
《Object-Contextual Representations for SemanticSegmentation》发表于ECCV 2020，是一种基于自注意力对类别信息进行建模的方法。与先前的自注意力对全局上下文建模的角度（通道和空间）不同，OCR-Net是从类别的角度进行建模，其利用粗分割的结果作为建模的对象，最后加权到每一个查询点，这是一种轻量并有效的方法。【代码链接可访问github[31]】
Soft Object Regions 对Backbone倒数第二层所输出的粗分割结果进行监督;
Object Region Representations 融合粗分割和Backbone网络最后一层所输出的高级语义特征图生成对象区域语义，每一条向量代表不同的类别信息;
《IAUnet: Global Context-Aware Feature Learning forPerson Re-Identification》发表于 IEEE Trans. on Neural Networks and Learning Systems，将自注意力机制的方法扩展到时间维度并应用于行人充识别任务，有效的解决了大多数基于卷积神经网络的方法无法充分对空间-时间上下文进行建模的弊端。【代码链接可访问github[33]】
