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--------------------------------------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
<
登录后即可复制

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)
  ]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
<
登录后即可复制