1. BoTNet（Bottleneck Transformer Network）

UC伯克利，谷歌研究院(Ashish Vaswani, 大名鼎鼎的Transformer一作）

论文：https://arxiv.org/abs/2101.11605
Github：https://github.com/leaderj1001/BottleneckTransformers

BoTNet（Bottleneck Transformer Network）：一种基于Transformer的新骨干架构。BoTNet同时使用卷积和自注意力机制，即在ResNet的最后3个bottleneck blocks中使用全局多头自注意力（Multi-Head Self-Attention, MHSA）替换3 × 3空间卷积。

2.Yolov5/Yolov7加入BoTNet、MHSA

2.1 BoTNet、MHSA加入common.py中

class MHSA(nn.Module):
    def __init__(self, n_dims, width=14, height=14, heads=4, pos_emb=False):
        super(MHSA, self).__init__()
 
        self.heads = heads
        self.query = nn.Conv2d(n_dims, n_dims, kernel_size=1)
        self.key = nn.Conv2d(n_dims, n_dims, kernel_size=1)
        self.value = nn.Conv2d(n_dims, n_dims, kernel_size=1)
        self.pos = pos_emb
        if self.pos:
            self.rel_h_weight = nn.Parameter(torch.randn([1, heads, (n_dims) // heads, 1, int(height)]),
                                             requires_grad=True)
            self.rel_w_weight = nn.Parameter(torch.randn([1, heads, (n_dims) // heads, int(width), 1]),
                                             requires_grad=True)
        self.softmax = nn.Softmax(dim=-1)
 
    def forward(self, x):
        n_batch, C, width, height = x.size()
        q = self.query(x).view(n_batch, self.heads, C // self.heads, -1)
        k = self.key(x).view(n_batch, self.heads, C // self.heads, -1)
        v = self.value(x).view(n_batch, self.heads, C // self.heads, -1)
        # print('q shape:{},k shape:{},v shape:{}'.format(q.shape,k.shape,v.shape))  #1,4,64,256
        content_content = torch.matmul(q.permute(0, 1, 3, 2), k)  # 1,C,h*w,h*w
        # print("qkT=",content_content.shape)
        c1, c2, c3, c4 = content_content.size()
        if self.pos:
            # print("old content_content shape",content_content.shape) #1,4,256,256
            content_position = (self.rel_h_weight + self.rel_w_weight).view(1, self.heads, C // self.heads, -1).permute(
                0, 1, 3, 2)  # 1,4,1024,64
 
            content_position = torch.matmul(content_position, q)  # ([1, 4, 1024, 256])
            content_position = content_position if (
                        content_content.shape == content_position.shape) else content_position[:, :, :c3, ]
            assert (content_content.shape == content_position.shape)
            # print('new pos222-> shape:',content_position.shape)
            # print('new content222-> shape:',content_content.shape)
            energy = content_content + content_position
        else:
            energy = content_content
        attention = self.softmax(energy)
        out = torch.matmul(v, attention.permute(0, 1, 3, 2))  # 1,4,256,64
        out = out.view(n_batch, C, width, height)
        return out
 
 
class BottleneckTransformer(nn.Module):
    # Transformer bottleneck
    # expansion = 1
 
    def __init__(self, c1, c2, stride=1, heads=4, mhsa=True, resolution=None, expansion=1):
        super(BottleneckTransformer, self).__init__()
        c_ = int(c2 * expansion)
        self.cv1 = Conv(c1, c_, 1, 1)
        # self.bn1 = nn.BatchNorm2d(c2)
        if not mhsa:
            self.cv2 = Conv(c_, c2, 3, 1)
        else:
            self.cv2 = nn.ModuleList()
            self.cv2.append(MHSA(c2, width=int(resolution[0]), height=int(resolution[1]), heads=heads))
            if stride == 2:
                self.cv2.append(nn.AvgPool2d(2, 2))
            self.cv2 = nn.Sequential(*self.cv2)
        self.shortcut = c1 == c2
        if stride != 1 or c1 != expansion * c2:
            self.shortcut = nn.Sequential(
                nn.Conv2d(c1, expansion * c2, kernel_size=1, stride=stride),
                nn.BatchNorm2d(expansion * c2)
            )
        self.fc1 = nn.Linear(c2, c2)
 
    def forward(self, x):
        out = x + self.cv2(self.cv1(x)) if self.shortcut else self.cv2(self.cv1(x))
        return out
 
 
class BoT3(nn.Module):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, e=0.5, e2=1, w=20, h=20):  # ch_in, ch_out, number, , expansion,w,h
        super(BoT3, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
        self.m = nn.Sequential(
            *[BottleneckTransformer(c_, c_, stride=1, heads=4, mhsa=True, resolution=(w, h), expansion=e2) for _ in
              range(n)])
        # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
 
    def forward(self, x):
        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))

 2.3 BoTNet、MHSA加入yolo.py中

 if m in {
                Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, C2f,
                EVCBlock, ODConv_3rd, ConvNextBlock, SEAM, RFEM, C3RFEM, ConvMixer, MultiSEAM,MLPBlock,Partial_conv3,CBAM,GAM_Attention,MHSA,BoT3}:
            c1, c2 = ch[f], args[0]
            if c2 != no:  # if not output
                c2 = make_divisible(c2 * gw, 8)

            args = [c1, c2, *args[1:]]
            if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x, C2f,EVCBlock,C3RFEM,BoT3}:
                args.insert(2, n)  # number of repeats
                n = 1

2.4修改 yolov5s_botnet.yaml

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 1  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, BoT3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

2.5 修改 yolov5s_mhsa.yaml

# parameters
nc: 1  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple

# anchors
anchors:
  #- [5,6, 7,9, 12,10]      # P2/4
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 backbone
backbone:
  # [from, number, module, args]               # [c=channels,module,kernlsize,strides]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2           [c=3,64*0.5=32,3]
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4    
   [-1, 3, C3, [128]],                                
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8            
   [-1, 6, C3, [256]],                         
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16       
   [-1, 9, C3, [512]],                     
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]], 
   [-1, 1, MHSA, [1024,1024]], #9
   [-1, 1, SPPF, [1024,5]], #10
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]], 
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 14
  

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 18 (P3/8-small)
   [-1, 1, MHSA, [256,256]],   #19
   

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 15], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 22 (P4/16-medium)       [256, 256, 1, False]  
   [-1, 1, MHSA, [512,512]],


   [-1, 1, Conv, [512, 3, 2]],                           #[256, 256, 3, 2] 
   [[-1, 11], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 25 (P5/32-large)       [512, 512, 1, False]
   [-1, 1, MHSA, [1024,1024]], #
 

   [[19, 23, 27], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]