YOLOv5更换骨干网络之 EfficientNet-B0

news2024/11/16 3:47:20

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论文地址：https://arxiv.org/abs/1905.11946
代码地址：https：//githeb.com/TensorFlow/tpu/tree/master/Models/Offical/Efficientnet

卷积神经网络(ConvNet)通常是在固定的资源预算下开发的，如果有更多的资源可用，则会扩大规模以获得更好的精度。在本文中，我们系统地研究了模型缩放，发现仔细平衡网络深度、宽度和分辨率可以带来更好的性能。基于这一观察结果，我们提出了一种新的缩放方法，该方法使用一个简单而高效的复合系数来统一缩放所有维度的深度/宽度/分辨率。我们演示了该方法在扩展移动网和ResNet上的有效性。更进一步，我们使用神经结构搜索来设计一个新的基线网络，并将其放大以获得一系列称为EfficientNets的模型，这些模型获得了比以前的ConvNets更高的精度和效率。特别是，我们的EfficientNet-B7在ImageNet上达到了最先进的84.3%的TOP-1准确率，同时比现有最好的ConvNet小8.4倍，推理速度快6.1倍。我们的EfficientNets在CIFAR-100(91.7%)、Flowers(98.8%)和其他3个迁移学习数据集上的迁移效果也很好，达到了最先进的准确率，参数减少了一个数量级。源代码在https：//githeb.com/TensorFlow/tpu/tree/master/Models/Offical/Efficientnet

EfficientNet-B0网络结构

EfficientNet提供了多个版本满足各种应用场景，本文提供的是EfficientNet-B0版本
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将YOLOv5主干网络替换为Efficient-B0：
yolov5lEfficient-B0.yaml

# 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

#  EfficientNet-B0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, stem, [32, 'ReLU6']],             # 0-P1/2  ch_out, act
   [-1, 1, MBConvBlock, [16, 3, 1, 1, 0]],   # 1 ch_out, k_size, s, expand

   [-1, 1, MBConvBlock, [24, 3, 2, 6, 0.028, True]],   # 2-P2/4 ch_out, k_size, s, expand, drop_connect_rate, se
   [-1, 1, MBConvBlock, [24, 3, 1, 6, 0.057]],

   [-1, 1, MBConvBlock, [40, 5, 2, 6, 0.085]],   # 4-P3/8 ch_out, k_size, s, expand, drop_connect_rate, se
   [-1, 1, MBConvBlock, [40, 5, 1, 6, 0.114]],

   [-1, 1, MBConvBlock, [80, 3, 2, 6, 0.142]],   # 6-P4/16 ch_out, k_size, s, expand, drop_connect_rate, se
   [-1, 1, MBConvBlock, [80, 3, 1, 6, 0.171]],
   [-1, 1, MBConvBlock, [80, 3, 1, 6, 0.200]],
   [-1, 1, MBConvBlock, [112, 5, 1, 6, 0.228]],  # 9
   [-1, 1, MBConvBlock, [112, 5, 1, 6, 0.257]],
   [-1, 1, MBConvBlock, [112, 5, 1, 6, 0.285]],

   [-1, 1, MBConvBlock, [192, 5, 2, 6, 0.314]],  # 12-P5/32 ch_out, k_size, s, expand, drop_connect_rate, se
   [-1, 1, MBConvBlock, [192, 5, 1, 6, 0.342]],
   [-1, 1, MBConvBlock, [192, 5, 1, 6, 0.371]],
   [-1, 1, MBConvBlock, [192, 5, 1, 6, 0.400]],
   [-1, 1, MBConvBlock, [320, 3, 1, 6, 0.428]],  # 16
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 11], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 21

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 5], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 25 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 21], 1, Concat, [1]], # cat head P4
   [-1, 3, C3, [512, False]],  # 28 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 17], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 31 (P5/32-large)

   [[24, 27, 30], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

在YOLOv5项目中添加方式：

common.py中加入以下代码：

# EfficientNetLite
class drop_connect:
    def __init__(self, drop_connect_rate):
        self.drop_connect_rate = drop_connect_rate

    def forward(self, x, training):
        if not training:
            return x
        keep_prob = 1.0 - self.drop_connect_rate
        batch_size = x.shape[0]
        random_tensor = keep_prob
        random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=x.dtype, device=x.device)
        binary_mask = torch.floor(random_tensor)  # 1
        x = (x / keep_prob) * binary_mask
        return x


class stem(nn.Module):
    def __init__(self, c1, c2, act='ReLU6'):
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn = nn.BatchNorm2d(num_features=c2)
        if act == 'ReLU6':
            self.act = nn.ReLU6(inplace=True)

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))


class MBConvBlock(nn.Module):
    def __init__(self, inp, final_oup, k, s, expand_ratio, drop_connect_rate, has_se=False):
        super(MBConvBlock, self).__init__()

        self._momentum = 0.01
        self._epsilon = 1e-3
        self.input_filters = inp
        self.output_filters = final_oup
        self.stride = s
        self.expand_ratio = expand_ratio
        self.has_se = has_se
        self.id_skip = True  # skip connection and drop connect
        se_ratio = 0.25

        # Expansion phase
        oup = inp * expand_ratio  # number of output channels
        if expand_ratio != 1:
            self._expand_conv = nn.Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False)
            self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._momentum, eps=self._epsilon)

        # Depthwise convolution phase
        self._depthwise_conv = nn.Conv2d(
            in_channels=oup, out_channels=oup, groups=oup,  # groups makes it depthwise
            kernel_size=k, padding=(k - 1) // 2, stride=s, bias=False)
        self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._momentum, eps=self._epsilon)

        # Squeeze and Excitation layer, if desired
        if self.has_se:
            num_squeezed_channels = max(1, int(inp * se_ratio))
            self.se = SeBlock(oup, 4)

        # Output phase
        self._project_conv = nn.Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)
        self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._momentum, eps=self._epsilon)
        self._relu = nn.ReLU6(inplace=True)

        self.drop_connect = drop_connect(drop_connect_rate)

    def forward(self, x, drop_connect_rate=None):
        """
        :param x: input tensor
        :param drop_connect_rate: drop connect rate (float, between 0 and 1)
        :return: output of block
        """

        # Expansion and Depthwise Convolution
        identity = x
        if self.expand_ratio != 1:
            x = self._relu(self._bn0(self._expand_conv(x)))
        x = self._relu(self._bn1(self._depthwise_conv(x)))

        # Squeeze and Excitation
        if self.has_se:
            x = self.se(x)

        x = self._bn2(self._project_conv(x))

        # Skip connection and drop connect
        if self.id_skip and self.stride == 1 and self.input_filters == self.output_filters:
            if drop_connect_rate:
                x = self.drop_connect(x, training=self.training)
            x += identity  # skip connection
        return x