yolov5l加入EfficientNetv2

yolov5l加入EfficientNetv2 backbone

步骤1：在common.py中添加EfficientNetV2模块

将下面EfficientNetV2模块的代码复制粘贴到common.py文件的末尾。

#-------------------------------------efficientnetv2 start--------------------------------------

class stem(nn.Module):
    def __init__(self, c1, c2, kernel_size=3, stride=1, groups=1):
        super().__init__()

        padding = (kernel_size - 1) // 2
        self.conv = nn.Conv2d(c1, c2, kernel_size, stride, padding=padding, groups=groups, bias=False)
        self.bn = nn.BatchNorm2d(c2, eps=1e-3, momentum=0.1)
        self.act = nn.SiLU(inplace=True)

    def forward(self, x):
        # print(x.shape)
        x = self.conv(x)
        x = self.bn(x)
        x = self.act(x)
        return x


def drop_path(x, drop_prob: float = 0., training: bool = False):
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize

    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class SqueezeExcite_efficientv2(nn.Module):
    def __init__(self, c1, c2, se_ratio=0.25, act_layer=nn.ReLU):
        super().__init__()
        self.gate_fn = nn.Sigmoid()
        reduced_chs = int(c1 * se_ratio)
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_reduce = nn.Conv2d(c1, reduced_chs, 1, bias=True)
        self.act1 = act_layer(inplace=True)
        self.conv_expand = nn.Conv2d(reduced_chs, c2, 1, bias=True)

    def forward(self, x):
        x_se = self.avg_pool(x)
        x_se = self.conv_reduce(x_se)
        x_se = self.act1(x_se)
        x_se = self.conv_expand(x_se)
        x_se = self.gate_fn(x_se)
        x = x * (x_se.expand_as(x))
        return x


class FusedMBConv(nn.Module):
    def __init__(self, c1, c2, k=3, s=1, expansion=1, se_ration=0, dropout_rate=0.2, drop_connect_rate=0.2):
        super().__init__()

        assert s in [1, 2]

        self.has_shortcut = (s == 1 and c1 == c2)
        self.has_expansion = expansion != 1
        expanded_c = c1 * expansion

        if self.has_expansion:
            self.expansion_conv = stem(c1, expanded_c, kernel_size=k, stride=s)
            self.project_conv = stem(expanded_c, c2, kernel_size=1, stride=1)

        else:
            self.project_conv = stem(c1, c2, kernel_size=k, stride=s)

        self.drop_connect_rate = drop_connect_rate
        if self.has_shortcut and drop_connect_rate > 0:
            self.dropout = DropPath(drop_connect_rate)

    def forward(self, x):
        if self.has_expansion:
            result = self.expansion_conv(x)
            result = self.project_conv(result)
        else:
            result = self.project_conv(x)

        if self.has_shortcut:
            if self.drop_connect_rate > 0:
                result = self.dropout(result)

            result += x

        return result


class MBConv(nn.Module):
    def __init__(self, c1, c2, k=3, s=1, expansion=1, se_ration=0, dropout_rate=0.2, drop_connect_rate=0.2):
        super().__init__()

        assert s in [1, 2]

        self.has_shortcut = (s == 1 and c1 == c2)
        # print(c1, c2, k, s, expansion)

        assert expansion != 1
        expanded_c = c1 * expansion

        self.expansion_conv = stem(c1, expanded_c, kernel_size=1, stride=1)

        self.dw_conv = stem(expanded_c, expanded_c, kernel_size=k, stride=s, groups=expanded_c)

        self.se = SqueezeExcite_efficientv2(c1, expanded_c, se_ration) if se_ration > 0 else nn.Identity()

        self.project_conv = stem(expanded_c, c2, kernel_size=1, stride=1)

        self.drop_connect_rate = drop_connect_rate
        if self.has_shortcut and drop_connect_rate > 0:
            self.dropout = DropPath(drop_connect_rate)

    def forward(self, x):

        # print(x.shape)
        result = self.expansion_conv(x)
        result = self.dw_conv(result)

        result = self.se(result)
        result = self.project_conv(result)

        if self.has_shortcut:
            if self.drop_connect_rate > 0:
                result = self.dropout(result)

            result += x

        return result

#-------------------------------------efficientnetv2 end--------------------------------------

步骤2：在yolo.py文件中加入类名

步骤3：创建自定义yaml文件

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 80  # 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

# YOLOv5 v6.0 backbone
backbone:
  [[-1, 1, stem, [24, 3, 2]],  # 0-P1/2

   [-1, 2, FusedMBConv, [24, 3, 1, 1, 0]], # 1-p2/4 

   [-1, 1, FusedMBConv, [48, 3, 2, 4, 0]], # 2
   [-1, 3, FusedMBConv, [48, 3, 1, 4, 0]], # 3

   [-1, 1, FusedMBConv, [64, 3, 2, 4, 0]], # 4
   [-1, 3, FusedMBConv, [64, 3, 1, 4, 0]], # 5

   [-1, 1, MBConv, [128, 3, 2, 4, 0.25]], # 6
   [-1, 5, MBConv, [128, 3, 1, 4, 0.25]], # 7

   [-1, 1, MBConv, [160, 3, 2, 6, 0.25]], # 8
   [-1, 8, MBConv, [160, 3, 1, 6, 0.25]], # 9

   [-1, 1, MBConv, [256, 3, 2, 4, 0.25]], # 10
   [-1, 14, MBConv, [256, 3, 1, 4, 0.25]], # 11

   [-1, 1, SPPF, [1024, 5]], #12
  ]
# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]], # 13
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14
   [[-1, 9], 1, Concat, [1]],  # cat backbone P4 15
   [-1, 3, C3, [512, False]],  # 16

   [-1, 1, Conv, [256, 1, 1]], #17
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], #18
   [[-1, 7], 1, Concat, [1]],  # cat backbone P3 19
   [-1, 3, C3, [256, False]],  # 20 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],  #21
   [[-1, 17], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 23 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]], #24
   [[-1, 13], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 26 (P5/32-large)

   [[20, 23, 26], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]