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class Swish(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)


class ConcatBiFPN(nn.Module):
    # Concatenate a list of tensors along dimension
    def __init__(self, c1, c2):
        super(ConcatBiFPN, self).__init__()
        # self.relu = nn.ReLU()
        self.w1 = nn.Parameter(torch.ones(2, dtype = torch.float32), requires_grad = True)
        self.w2 = nn.Parameter(torch.ones(3, dtype = torch.float32), requires_grad = True)
        self.epsilon = 0.0001
        self.conv = nn.Conv2d(c1, c2, kernel_size = 1, stride = 1, padding = 0)
        self.swish = Swish()

    def forward(self, x):
        outs = self._forward(x)
        return outs

    def _forward(self, x):
        if len(x) == 2:
            # w = self.relu(self.w1)
            w = self.w1
            weight = w / (torch.sum(w, dim=0) + self.epsilon)
            # Connections for P6_0 and P7_0 to P6_1 respectively
            x = self.conv(self.swish(weight[0] * x[0] + weight[1] * x[1]))
        elif len(x) == 3:
            # w = self.relu(self.w2)
            w = self.w2
            weight = w / (torch.sum(w, dim=0) + self.epsilon)
            x = self.conv(self.swish(weight[0] * x[0] + weight[1] * x[1] + weight[2] * x[2]))

        return x



elif m is ConcatBiFPN:
            c2 = max([ch[x] for x in f])


# 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, C3, [1024]],
   [-1, 1, SPPFTR2, [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, [512, 3, 2]],
   [[-1, 6, 13], 1, ConcatBiFPN, [256, 256]],  # 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)
  ]