YoloV5+DAMOYOLO:将DAMOYOLO中的GFPN结构与Yolov5结合

        前段时间写了一篇damoYolo的训练教程，同时也对自己的数据集进行了训练，虽然效果确实不是很好，但是damoyolo的一些思想和网络结构啥的还是可以借鉴使用的，此次将damoyolo的RepGFPN结构掏出来放到v5的NECK中，测试一下对本人的数据集(小目标)效果比v5要好，大概提升2个点左右。

        放一下damoyolo的github网址：

https://github.com/tinyvision/DAMO-YOLO

damoyolo的整体结构我们是无法看到的因为他的主干网络是nas_backbones 里面是txt文件，RepGFPN是可以看到的。

import torch
import torch.nn as nn
 
from ..core.ops import ConvBNAct, CSPStage
 
 
class GiraffeNeckV2(nn.Module):
    def __init__(
        self,
        depth=1.0,
        hidden_ratio=1.0,
        in_features=[2, 3, 4],
        in_channels=[256, 512, 1024],
        out_channels=[256, 512, 1024],
        act='silu',
        spp=False,
        block_name='BasicBlock',
    ):
        super().__init__()
        self.in_features = in_features
        self.in_channels = in_channels
        self.out_channels = out_channels
        Conv = ConvBNAct
 
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
 
        # node x3: input x0, x1
        self.bu_conv13 = Conv(in_channels[1], in_channels[1], 3, 2, act=act)
        self.merge_3 = CSPStage(block_name,
                                in_channels[1] + in_channels[2],
                                hidden_ratio,
                                in_channels[2],
                                round(3 * depth),
                                act=act,
                                spp=spp)
 
        # node x4: input x1, x2, x3
        self.bu_conv24 = Conv(in_channels[0], in_channels[0], 3, 2, act=act)
        self.merge_4 = CSPStage(block_name,
                                in_channels[0] + in_channels[1] +
                                in_channels[2],
                                hidden_ratio,
                                in_channels[1],
                                round(3 * depth),
                                act=act,
                                spp=spp)
 
        # node x5: input x2, x4
        self.merge_5 = CSPStage(block_name,
                                in_channels[1] + in_channels[0],
                                hidden_ratio,
                                out_channels[0],
                                round(3 * depth),
                                act=act,
                                spp=spp)
 
        # node x7: input x4, x5
        self.bu_conv57 = Conv(out_channels[0], out_channels[0], 3, 2, act=act)
        self.merge_7 = CSPStage(block_name,
                                out_channels[0] + in_channels[1],
                                hidden_ratio,
                                out_channels[1],
                                round(3 * depth),
                                act=act,
                                spp=spp)
 
        # node x6: input x3, x4, x7
        self.bu_conv46 = Conv(in_channels[1], in_channels[1], 3, 2, act=act)
        self.bu_conv76 = Conv(out_channels[1], out_channels[1], 3, 2, act=act)
        self.merge_6 = CSPStage(block_name,
                                in_channels[1] + out_channels[1] +
                                in_channels[2],
                                hidden_ratio,
                                out_channels[2],
                                round(3 * depth),
                                act=act,
                                spp=spp)
 
    def init_weights(self):
        pass
 
    def forward(self, out_features):
        """
        Args:
            inputs: input images.
        Returns:
            Tuple[Tensor]: FPN feature.
        """
 
        #  backbone
        [x2, x1, x0] = out_features
 
        # node x3
        x13 = self.bu_conv13(x1)
        x3 = torch.cat([x0, x13], 1)
        x3 = self.merge_3(x3)
 
        # node x4
        x34 = self.upsample(x3)
        x24 = self.bu_conv24(x2)
        x4 = torch.cat([x1, x24, x34], 1)
        x4 = self.merge_4(x4)
 
        # node x5
        x45 = self.upsample(x4)
        x5 = torch.cat([x2, x45], 1)
        x5 = self.merge_5(x5)
 
        # node x8
        # x8 = x5
 
        # node x7
        x57 = self.bu_conv57(x5)
        x7 = torch.cat([x4, x57], 1)
        x7 = self.merge_7(x7)
 
        # node x6
        x46 = self.bu_conv46(x4)
        x76 = self.bu_conv76(x7)
        x6 = torch.cat([x3, x46, x76], 1)
        x6 = self.merge_6(x6)
 
        outputs = (x5, x7, x6)
        return outputs

我根据ONNX结构图和上述代码画了简易的展示图：画的相对简单了，可能有些错误，后续我都没在看了，大家还是主要看代码吧

训练自己的数据集：

YoloV5+GFPN（我没用Rep）

yolov5：

map@0.5 相比之下提升了1.7个百分点。。。。还是阔以的

再看下参数量对比：(imgsize,map@50,mAP50-95,参数量(M),FLOPs)

 

 对比之下参数量和FLOPs确实有增加，这种的增加不大，还是可以接受的，但同时map也相应地增加了。具体的话看大家自己抉择了。