YOLOv5 6.0/6.1结合ASFF

YOLOv5 6.0/6.1结合ASFF

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前言

YOLO小白纯干货分享！！！

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一、主要修改代码

二、使用步骤

1. models/common.py：加入要修改的代码, 类ASFFV5 class ASFFV5(nn.Module):

 class ASFFV5(nn.Module):
 	 def __init__(self, level, multiplier=1, rfb=False, vis=False, act_cfg=True):
        """
        ASFF version for YoloV5 only.
        Since YoloV5 outputs 3 layer of feature maps with different channels
        which is different than YoloV3
        normally, multiplier should be 1, 0.5
        which means, the channel of ASFF can be 
        512, 256, 128 -> multiplier=1
        256, 128, 64 -> multiplier=0.5
        For even smaller, you gonna need change code manually.
        """
        super(ASFFV5, self).__init__()
        self.level = level
        self.dim = [int(1024*multiplier), int(512*multiplier),
                    int(256*multiplier)]
        #print("dim:",self.dim)
        
        self.inter_dim = self.dim[self.level]
        if level == 0:
            self.stride_level_1 = Conv(int(512*multiplier), self.inter_dim, 3, 2)
            #print(self.dim)
            self.stride_level_2 = Conv(int(256*multiplier), self.inter_dim, 3, 2)
                
            self.expand = Conv(self.inter_dim, int(
                1024*multiplier), 3, 1)
        elif level == 1:
            self.compress_level_0 = Conv(
                int(1024*multiplier), self.inter_dim, 1, 1)
            self.stride_level_2 = Conv(
                int(256*multiplier), self.inter_dim, 3, 2)
            self.expand = Conv(self.inter_dim, int(512*multiplier), 3, 1)
        elif level == 2:
            self.compress_level_0 = Conv(
                int(1024*multiplier), self.inter_dim, 1, 1)
            self.compress_level_1 = Conv(
                int(512*multiplier), self.inter_dim, 1, 1)
            self.expand = Conv(self.inter_dim, int(
                256*multiplier), 3, 1)

        # when adding rfb, we use half number of channels to save memory
        compress_c = 8 if rfb else 16

        self.weight_level_0 = Conv(
            self.inter_dim, compress_c, 1, 1)
        self.weight_level_1 = Conv(
            self.inter_dim, compress_c, 1, 1)
        self.weight_level_2 = Conv(
            self.inter_dim, compress_c, 1, 1)

        self.weight_levels = Conv(
            compress_c*3, 3, 1, 1)
        self.vis = vis

    def forward(self, x_level_0, x_level_1, x_level_2): #s,m,l
        """
        # 128, 256, 512
        512, 256, 128
        from small -> large
        """
        # print('x_level_0: ', x_level_0.shape)
        # print('x_level_1: ', x_level_1.shape)
        # print('x_level_2: ', x_level_2.shape)
        x_level_0=x[2]
        x_level_1=x[1]
        x_level_2=x[0]
        if self.level == 0:
            level_0_resized = x_level_0
            level_1_resized = self.stride_level_1(x_level_1)

            level_2_downsampled_inter = F.max_pool2d(
                x_level_2, 3, stride=2, padding=1)
            
            level_2_resized = self.stride_level_2(level_2_downsampled_inter)
            #print('X——level_0: ', level_2_downsampled_inter.shape)
        elif self.level == 1:
            level_0_compressed = self.compress_level_0(x_level_0)
            level_0_resized = F.interpolate(
                level_0_compressed, scale_factor=2, mode='nearest')
            level_1_resized = x_level_1
            level_2_resized = self.stride_level_2(x_level_2)
        elif self.level == 2:
            level_0_compressed = self.compress_level_0(x_level_0)
            level_0_resized = F.interpolate(
                level_0_compressed, scale_factor=4, mode='nearest')
            x_level_1_compressed = self.compress_level_1(x_level_1)
            level_1_resized = F.interpolate(
                x_level_1_compressed, scale_factor=2, mode='nearest')
            level_2_resized = x_level_2

        # print('level: {}, l1_resized: {}, l2_resized: {}'.format(self.level,
            #  level_1_resized.shape, level_2_resized.shape))
        level_0_weight_v = self.weight_level_0(level_0_resized)
        level_1_weight_v = self.weight_level_1(level_1_resized)
        level_2_weight_v = self.weight_level_2(level_2_resized)
        # print('level_0_weight_v: ', level_0_weight_v.shape)
        # print('level_1_weight_v: ', level_1_weight_v.shape)
        # print('level_2_weight_v: ', level_2_weight_v.shape)

        levels_weight_v = torch.cat(
            (level_0_weight_v, level_1_weight_v, level_2_weight_v), 1)
        levels_weight = self.weight_levels(levels_weight_v)
        levels_weight = F.softmax(levels_weight, dim=1)

        fused_out_reduced = level_0_resized * levels_weight[:, 0:1, :, :] +\
            level_1_resized * levels_weight[:, 1:2, :, :] +\
            level_2_resized * levels_weight[:, 2:, :, :]

        out = self.expand(fused_out_reduced)

        if self.vis:
            return out, levels_weight, fused_out_reduced.sum(dim=1)
        else:
            return out

2. models/yolo.py：添加 类ASFF_Detect

然后在yolo.py 中 Detect 类下面，添加一个ASFF_Detect类

class ASFF_Detect(nn.Module):   #add ASFFV5 layer and Rfb 
    stride = None  # strides computed during build
    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), multiplier=0.5,rfb=False,ch=()):  # detection layer
        super(ASFF_Detect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        self.l0_fusion = ASFFV5(level=0, multiplier=multiplier,rfb=rfb)
        self.l1_fusion = ASFFV5(level=1, multiplier=multiplier,rfb=rfb)
        self.l2_fusion = ASFFV5(level=2, multiplier=multiplier,rfb=rfb)
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
        self.register_buffer('anchors', a)  # shape(nl,na,2)
        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv

接着在 yolo.py的parse_model 中把函数放到模型的代码里：
（大概在283行左右）

 if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,CBAM,ResBlock_CBAM,
                 C3]:
            c1, c2 = ch[f], args[0]
            if c2 != no:  # if not output
                c2 = make_divisible(c2 * gw, 8)

            args = [c1, c2, *args[1:]]
            if m in [BottleneckCSP, C3]:
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum([ch[x] for x in f])
        elif m is ASFF_Detect:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
        elif m is Contract:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        elif m is ASFFV5:
            c2=args[1]
        else:
            c2 = ch[f]

3.models/yolov5s-asff.yaml

在models文件夹下新建对应的yolov5s-asff.yaml 文件
然后将yolov5s.yaml的内容复制过来，将 head 部分的最后一行进行修改；
将[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ]
修改成下面：

 [[17, 20, 23], 1, ASFF_Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

4.查看网络结构

修改 models/yolo.py --cfg models/yolov5s-asff.yaml

接下来run yolo.py 即可查看网络结构

5.将train.py 中 --cfg中的 yaml 文件修改成本文文件即可，开始训练

总结

本人在多个数据集上做了大量实验，针对不同的数据集效果不同，需要大家进行实验。有效果有提升的情况占大多数。

最后，希望能互粉一下，做个朋友，一起学习交流。