# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 1  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # 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
   # Mobilenetv3-small backbone
   # MobileNetV3_InvertedResidual [out_ch, hid_ch, k_s, stride, SE, HardSwish]
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv_BN_HSwish, [16, 2]],                              # 0-p1/2
   [-1, 1, MobileNetV3_InvertedResidual, [16,  16, 3, 2, 1, 0]],  # 1-p2/4
   [-1, 1, MobileNetV3_InvertedResidual, [24,  72, 3, 2, 0, 0]],  # 2-p3/8
   [-1, 1, MobileNetV3_InvertedResidual, [24,  88, 3, 1, 0, 0]],  # 3
   [-1, 1, MobileNetV3_InvertedResidual, [40,  96, 5, 2, 1, 1]],  # 4-p4/16
   [-1, 1, MobileNetV3_InvertedResidual, [40, 240, 5, 1, 1, 1]],  # 5
   [-1, 1, MobileNetV3_InvertedResidual, [40, 240, 5, 1, 1, 1]],  # 6
   [-1, 1, MobileNetV3_InvertedResidual, [48, 120, 5, 1, 1, 1]],  # 7
   [-1, 1, MobileNetV3_InvertedResidual, [48, 144, 5, 1, 1, 1]],  # 8
   [-1, 1, MobileNetV3_InvertedResidual, [96, 288, 5, 2, 1, 1]],  # 9-p5/32
   [-1, 1, MobileNetV3_InvertedResidual, [96, 576, 5, 1, 1, 1]],  # 10
   [-1, 1, MobileNetV3_InvertedResidual, [96, 576, 5, 1, 1, 1]],  # 11
  ]
# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [96, 1, 1]],  # 12
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [144, False]],  # 15
   [-1, 1, Conv, [144, 1, 1]], # 16
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 3], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [168, False]],  # 19 (P3/8-small)
   [-1, 1, Conv, [168, 3, 2]],
   [[-1, 16], 1, Concat, [1]], # cat head P4
   [-1, 3, C3, [312, False]],  # 22 (P4/16-medium)
   [-1, 1, Conv, [312, 3, 2]],
   [[-1, 12], 1, Concat, [1]], # cat head P5
   [-1, 3, C3, [408, False]],  # 25 (P5/32-large)
   [[19, 22, 25], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

# Mobilenetv3Small
class SeBlock(nn.Module):
    def __init__(self, in_channel, reduction=4):
        super().__init__()
        self.Squeeze = nn.AdaptiveAvgPool2d(1)
        self.Excitation = nn.Sequential()
        self.Excitation.add_module('FC1', nn.Conv2d(in_channel, in_channel // reduction, kernel_size=1))  # 1*1卷积与此效果相同
        self.Excitation.add_module('ReLU', nn.ReLU())
        self.Excitation.add_module('FC2', nn.Conv2d(in_channel // reduction, in_channel, kernel_size=1))
        self.Excitation.add_module('Sigmoid', nn.Sigmoid())
    def forward(self, x):
        y = self.Squeeze(x)
        ouput = self.Excitation(y)
        return x * (ouput.expand_as(x))
class Conv_BN_HSwish(nn.Module):
    """
    This equals to
    def conv_3x3_bn(inp, oup, stride):
        return nn.Sequential(
            nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
            nn.BatchNorm2d(oup),
            h_swish()
        )
    """
    def __init__(self, c1, c2, stride):
        super(Conv_BN_HSwish, self).__init__()
        self.conv = nn.Conv2d(c1, c2, 3, stride, 1, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.Hardswish()
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))
class MobileNetV3_InvertedResidual(nn.Module):
    def __init__(self, inp, oup, hidden_dim, kernel_size, stride, use_se, use_hs):
        super(MobileNetV3_InvertedResidual, self).__init__()
        assert stride in [1, 2]
        self.identity = stride == 1 and inp == oup
        if inp == hidden_dim:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                          bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.Hardswish() if use_hs else nn.ReLU(),
                # Squeeze-and-Excite
                SeBlock(hidden_dim) if use_se else nn.Sequential(),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.Hardswish() if use_hs else nn.ReLU(),
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                          bias=False),
                nn.BatchNorm2d(hidden_dim),
                # Squeeze-and-Excite
                SeBlock(hidden_dim) if use_se else nn.Sequential(),
                nn.Hardswish() if use_hs else nn.ReLU(),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
    def forward(self, x):
        y = self.conv(x)
        if self.identity:
            return x + y
        else:
            return y