​

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 = nn.Parameter(torch.randn([1, heads, (n_dims ) // heads, 1, int(height)]), requires_grad=True)
            self.rel_w = 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 + self.rel_w).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.bn2 = nn.BatchNorm2d(planes)
        #self.cv3 = nn.Conv2d(planes, expansion * planes, kernel_size=1, bias=False)
        #self.bn3 = nn.BatchNorm2d(expansion * planes)
        #self.shortcut = nn.Sequential() 
        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):
        #print("transforme  input bottleck shape:",x.shape)
        # out = F.relu(self.bn1(self.conv1(x)))
        # out = F.relu(self.bn2(self.conv2(out)))
        # out = self.bn3(self.conv3(out))
        # out += self.shortcut(x)
        # out = F.relu(out)
        out=x + self.cv2(self.cv1(x)) if self.shortcut else self.cv2(self.cv1(x))
        return out
        
class CTR3(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(CTR3, 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):
        #print("CTR3-INPUT:",x.shape)
       # return self.cv3
        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))

if m in [Conv,MobileNetV3_InvertedResidual,ShuffleNetV2_InvertedResidual，ghostc3，DepthSepConv,CTR3
]:

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 80  # 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, CTR3, [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)
  ]