YOLOv5更换骨干网络之 PP-LCNet

news2024/11/16 3:37:01

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论文地址：https://arxiv.org/abs/2109.15099
代码地址：https://github.com/ngnquan/PP-LCNet

我们提出了一种基于MKLDNN加速策略的轻量级CPU网络，名为PP LCNet，它提高了轻量级模型在多个任务上的性能。本文列出了在延迟几乎不变的情况下可以提高网络准确性的技术。通过这些改进，PP LCNet的精度可以在相同的分类推理时间下大大超过以前的网络结构。如图1所示，它优于最先进的模型。对于计算机视觉的下游任务，它也表现得很好，例如对象检测、语义分割等。我们所有的实验都是基于PaddlePaddle1实现的。PaddleClas2提供代码和预训练模型。

PP-LCNet网路结构

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将YOLOv5主干网络替换为PP-LCNet：
yolov5lPP-LC.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

#  PP-LCNet backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [16, 3, 2, 1]],              # 0-P1/2  ch_out, kernel, stride, padding
   [-1, 1, DepthSepConv, [32, 3, 1, False]],  # 1

   [-1, 1, DepthSepConv, [64, 3, 2, False]],  # 2-P2/4
   [-1, 1, DepthSepConv, [64, 3, 1, False]],  # 3

   [-1, 1, DepthSepConv, [128, 3, 2, False]], # 4-P3/8
   [-1, 1, DepthSepConv, [128, 3, 1, False]], # 5

   [-1, 1, DepthSepConv, [256, 3, 2, False]], # 6-P4/16
   [-1, 5, DepthSepConv, [256, 5, 1, False]], # 7

   [-1, 1, DepthSepConv, [512, 5, 2, True]],  # 8-P5/32
   [-1, 1, DepthSepConv, [512, 5, 1, True]],  # 9
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]], # 11
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 7], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 14

   [-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]],  # 18 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 13], 1, Concat, [1]], # cat head P4
   [-1, 3, C3, [512, False]],  # 21 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 24 (P5/32-large)

   [[17, 20, 23], 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：

class DepthSepConv(nn.Module):
    def __init__(self, inp, oup, dw_size, stride, use_se):
        super(DepthSepConv, self).__init__()
        self.stride = stride
        self.inp = inp
        self.oup = oup
        self.dw_size = dw_size
        self.dw_sp = nn.Sequential(
            nn.Conv2d(self.inp, self.inp, kernel_size=self.dw_size, stride=self.stride, padding=(dw_size - 1) // 2, groups=self.inp, bias=False),
            nn.BatchNorm2d(self.inp),
            nn.Hardswish(),

            SeBlock(self.inp, reduction=16) if use_se else nn.Sequential(),

            nn.Conv2d(self.inp, self.oup, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(self.oup),
            nn.Hardswish())

    def forward(self, x):
        y = self.dw_sp(x)
        return y