Yolov8+动态蛇形卷积，助你大幅涨点！

Yolov8+动态蛇形卷积（DSCONV），助你大幅涨点！

1.简介

动态蛇形卷积（DSCONV）是一种深度学习中的卷积神经网络（CNN）技术，它旨在处理卷积操作中的某些限制，以提高网络处理不规则数据的能力。

适用场景：血管、道路等复杂网状管状结构

2.背景

传统卷积的限制：
标准的CNN卷积层通常在处理规则、网格状数据（如图像）时效果最佳。然而，在面对不规则或扭曲的数据结构时，它们可能不那么有效。
不规则数据的挑战：
在实际应用中，如医学图像、三维形状处理或自然语言处理等领域，数据常常呈现不规则形态，这给传统的CNN带来了挑战。

3.原理

动态调整：
DSCONV通过动态调整卷积核的形状和大小来适应数据的不规则性，这种调整是根据数据的特定特征和结构进行的。
灵活性：
这种方法比传统的卷积操作更加灵活，能更好地捕捉不规则数据中的局部特征。

4.具体实现

1.yolov8的主体代码来自 官方仓库
2.在ultralytics-main\ultralytics\nn\modules目录下，新增DySnakeConv.py文件

DySnakeConv.py

import torch
import torch.nn as nn

def autopad(k, p=None):
    if p is None:
        p = k//2 if isinstance(k,int) else [x//2 for x in k] #auto-pad
    return p

class Conv(nn.Module):
    #Standard convolution
    def __init__(self,c1,c2,k=1,s=1,p=None,g=1,act=True): #ch_in,ch_out,kernel,stride,padding,groups
        super(Conv,self).__init__()
        self.conv = nn.Conv2d(c1,c2,k,s,autopad(k,p),groups=g,bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act is True else (act if isinstance(act,nn.Module) else nn.Identity())

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

    def fuseforward(self,x):
        return self.act(self.conv(x))

class DySnakeConv(nn.Module):
    def __init__(self, inc, ouc, k=3, act=True) -> None:
        super().__init__()

        self.conv_0 = Conv(inc, ouc, k, act=act)
        self.conv_x = DSConv(inc, ouc, 0, k)
        self.conv_y = DSConv(inc, ouc, 1, k)
        self.conv_1x1 = Conv(ouc * 3, ouc, 1, act=act)

    def forward(self, x):
        return self.conv_1x1(torch.cat([self.conv_0(x), self.conv_x(x), self.conv_y(x)], dim=1))

class DSConv(nn.Module):
    def __init__(self,in_ch,out_ch,morph,kernel_size=3,if_offset=True,extend_scope=1,act=True):
        """
                The Dynamic Snake Convolution
                :param in_ch: input channel
                :param out_ch: output channel
                :param kernel_size: the size of kernel
                :param extend_scope: the range to expand (default 1 for this method)
                :param morph: the morphology of the convolution kernel is mainly divided into two types
                                along the x-axis (0) and the y-axis (1) (see the paper for details)
                :param if_offset: whether deformation is required, if it is False, it is the standard convolution kernel
                :param device: set on gpu
        """

        super(DSConv,self).__init__()
        #use the <offset_conv>to learn the deformable offset
        self.offset_conv = nn.Conv2d(in_ch,2 * kernel_size,3,padding=1)
        self.bn = nn.BatchNorm2d(2 * kernel_size)
        self.kernel_size = kernel_size
        #two types of the DSConv (along.x-axis and y-axis)
        self.dsc_conv_x = nn.Conv2d(
            in_ch,
            out_ch,
            kernel_size=(kernel_size,1),
            stride=(kernel_size,1),
            padding=0,
        )
        self.dsc_conv_y = nn.Conv2d(
            in_ch,
            out_ch,
            kernel_size=(1,kernel_size),
            stride=(1,kernel_size),
            padding=0,
        )
        self.gn = nn.GroupNorm(out_ch // 4, out_ch)
        self.act = nn.SiLU() if act is True else (act if isinstance(act,nn.Module)else nn.Identity())
        self.extend_scope = extend_scope
        self.morph = morph
        self.if_offset = if_offset

    def forward(self, f):
        offset = self.offset_conv(f)
        offset = self.bn(offset)
        offset = torch.tanh(offset)
        input_shape = f.shape
        dsc = DSC(input_shape, self.kernel_size, self.extend_scope, self.morph)
        deformed_feature = dsc.deform_conv(f, offset, self.if_offset)

        if self.morph == 0:
            x = self.dsc_conv_x(deformed_feature.type(f.dtype))
            x = self.gn(x)
            x = self.act(x)
            return x
        else:
            x = self.dsc_conv_y(deformed_feature.type(f.dtype))
            x = self.gn(x)
            x = self.act(x)
            return x


class DSC(object):
    def __init__(self,input_shape,kernel_size,extend_scope,morph):
        self.num_points = kernel_size
        self.width = input_shape[2]
        self.height = input_shape[3]
        self.morph = morph
        self.extend_scope = extend_scope
        self.num_batch = input_shape[0]
        self.num_channels = input_shape[1]

    def _coordinate_map_3D(self, offset, if_offset):
        device = offset.device
        # offset
        y_offset, x_offset = torch.split(offset, self.num_points, dim=1)
        y_center = torch.arange(0, self.width).repeat([self.height])
        y_center = y_center.reshape(self.height, self.width)
        y_center = y_center.permute(1, 0)
        y_center = y_center.reshape([-1, self.width, self.height])
        y_center = y_center.repeat([self.num_points, 1,1]).float()
        y_center = y_center.unsqueeze(0)

        x_center = torch.arange(0, self.height).repeat([self.width])
        x_center = x_center.reshape(self.width, self.height)
        x_center = x_center.permute(0, 1)
        x_center = x_center.reshape([-1, self.width, self.height])
        x_center = x_center.repeat([self.num_points, 1, 1]).float()
        x_center = x_center.unsqueeze(0)

        if self.morph == 0:
            """
            Initialize the kernel and flatten the kernel
            y:only need 0
            x:-num_points//2 num_points//2 (Determined by the kernel size)
            !!The related PPT will be submitted later,and the PPT will contain the whole changes of each step
            """
            y = torch.linspace(0, 0, 1)
            x = torch.linspace(
                -int(self.num_points // 2),
                int(self.num_points // 2),
                int(self.num_points),
            )
            y,x = torch.meshgrid(y,x)
            y_spread = y.reshape(-1, 1)
            x_spread = x.reshape(-1, 1)
            y_grid = y_spread.repeat([1, self.width * self.height])
            y_grid = y_grid.reshape([self.num_points, self.width, self.height])
            y_grid = y_grid.unsqueeze(0) #[B * K * K, W, H]
            x_grid = x_spread.repeat([1, self.width * self.height])
            x_grid = x_grid.reshape([self.num_points, self.width, self.height])
            x_grid = x_grid.unsqueeze(0)  # [B*K*K,W,H]

            y_new = y_center + y_grid
            x_new = x_center + x_grid

            y_new = y_new.repeat(self.num_batch, 1, 1, 1).to(device)
            x_new = x_new.repeat(self.num_batch, 1, 1, 1).to(device)
            y_offset_new = y_offset.detach().clone()

            if if_offset:
                y_offset = y_offset.permute(1,0, 2, 3)
                y_offset_new = y_offset_new.permute(1, 0, 2, 3)
                center = int(self.num_points // 2)
                y_offset_new[center] = 0
                for index in range(1, center):
                    y_offset_new[center+index] = (y_offset_new[center + index - 1] + y_offset[center + index])
                    y_offset_new[center-index] = (y_offset_new[center - index + 1] + y_offset[center - index])
                y_offset_new = y_offset_new.permute(1, 0, 2, 3).to(device)
                y_new = y_new.add(y_offset_new.mul(self.extend_scope))

            y_new = y_new.reshape(
                [self.num_batch, self.num_points, 1, self.width, self.height])
            y_new = y_new.permute(0, 3, 1, 4, 2)
            y_new = y_new.reshape([
                    self.num_batch, self.num_points * self.width, 1 * self.height
                    ])
            x_new = x_new.reshape(
                [self.num_batch, self.num_points,1,self.width,self.height])
            x_new = x_new.permute(0, 3, 1, 4, 2)
            x_new = x_new.reshape([
                self.num_batch, self.num_points * self.width, 1 * self.height
            ])
            return y_new,x_new
        else:
            y = torch.linspace(
                -int(self.num_points // 2),
                int(self.num_points // 2),
                int(self.num_points),
            )
            x = torch.linspace(0, 0, 1)
            y,x = torch.meshgrid(y,x)
            y_spread = y.reshape(-1, 1)
            x_spread = x.reshape(-1, 1)
            y_grid = y_spread.repeat([1, self.width * self.height])
            y_grid = y_grid.reshape([self.num_points, self.width, self.height])
            y_grid = y_grid.unsqueeze(0)

            x_grid = x_spread.repeat([1, self.width * self.height])
            x_grid = x_grid.reshape([self.num_points, self.width, self.height])
            x_grid = x_grid.unsqueeze(0)

            y_new = y_center + y_grid
            x_new = x_center + x_grid

            y_new = y_new.repeat(self.num_batch, 1, 1, 1)
            x_new = x_new.repeat(self.num_batch, 1, 1, 1)

            y_new = y_new.to(device)
            x_new = x_new.to(device)
            x_offset_new = x_offset.detach().clone()

            if if_offset:
                x_offset = x_offset.permute(1, 0, 2, 3)
                x_offset_new = x_offset_new.permute(1, 0, 2, 3)
                center = int(self.num_points // 2)
                x_offset_new[center] = 0
                for index in range(1, center):
                    x_offset_new[center + index] = (x_offset_new[center + index - 1] + y_offset[center + index])
                    x_offset_new[center - index] = (x_offset_new[center - index + 1] + y_offset[center - index])
                x_offset_new = x_offset_new.permute(1, 0, 2, 3).to(device)
                x_new = y_new.add(x_offset_new.mul(self.extend_scope))

            y_new = y_new.reshape(
                [self.num_batch, 1, self.num_points, self.width, self.height])
            y_new = y_new.permute(0, 3, 1, 4, 2)
            y_new = y_new.reshape([
                self.num_batch, 1 * self.width, self.num_points * self.height
            ])
            x_new = x_new.reshape(
                [self.num_batch, 1, self.num_points, self.width, self.height])
            x_new = x_new.permute(0, 3, 1, 4, 2)
            x_new = x_new.reshape([
                self.num_batch, 1 * self.width, self.num_points * self.height
            ])
            return y_new, x_new

    """
            input: input feature map [N,C,D,W,H]；coordinate map [N,K*D,K*W,K*H] 
            output: [N,1,K*D,K*W,K*H]  deformed feature map
    """

    def _bilinear_interpolate_3D(self, input_feature, y, x):
        device = input_feature.device
        y = y.reshape([-1]).float()
        x = x.reshape([-1]).float()

        zero = torch.zeros([]).int()
        max_y = self.width - 1
        max_x = self.height - 1

        # find 8 grid locations
        y0 = torch.floor(y).int()
        y1 = y0 + 1
        x0 = torch.floor(x).int()
        x1 = x0 + 1

        # clip out coordinates exceeding feature map volume
        y0 = torch.clamp(y0, zero, max_y)
        y1 = torch.clamp(y1, zero, max_y)
        x0 = torch.clamp(x0, zero, max_x)
        x1 = torch.clamp(x1, zero, max_x)

        input_feature_flat = input_feature.flatten()
        input_feature_flat = input_feature_flat.reshape(
            self.num_batch, self.num_channels, self.width, self.height)
        input_feature_flat = input_feature_flat.permute(0, 2, 3, 1)
        input_feature_flat = input_feature_flat.reshape(-1, self.num_channels)
        dimension = self.height * self.width

        base = torch.arange(self.num_batch) * dimension
        base = base.reshape([-1, 1]).float()

        repeat = torch.ones([self.num_points * self.width * self.height
                             ]).unsqueeze(0)
        repeat = repeat.float()

        base = torch.matmul(base, repeat)
        base = base.reshape([-1])

        base = base.to(device)

        base_y0 = base + y0 * self.height
        base_y1 = base + y1 * self.height

        # top rectangle of the neighbourhood volume
        index_a0 = base_y0 - base + x0
        index_c0 = base_y0 - base + x1

        # bottom rectangle of the neighbourhood volume
        index_a1 = base_y1 - base + x0
        index_c1 = base_y1 - base + x1

        # get 8 grid values
        value_a0 = input_feature_flat[index_a0.type(torch.int64)].to(device)
        value_c0 = input_feature_flat[index_c0.type(torch.int64)].to(device)
        value_a1 = input_feature_flat[index_a1.type(torch.int64)].to(device)
        value_c1 = input_feature_flat[index_c1.type(torch.int64)].to(device)

        # find 8 grid locations
        y0 = torch.floor(y).int()
        y1 = y0 + 1
        x0 = torch.floor(x).int()
        x1 = x0 + 1

        # clip out coordinates exceeding feature map volume
        y0 = torch.clamp(y0, zero, max_y)
        y1 = torch.clamp(y1, zero, max_y)
        x0 = torch.clamp(x0, zero, max_x)
        x1 = torch.clamp(x1, zero, max_x)

        input_feature_flat = input_feature.flatten()
        input_feature_flat = input_feature_flat.reshape(
            self.num_batch, self.num_channels, self.width, self.height)
        input_feature_flat = input_feature_flat.permute(0, 2, 3, 1)
        input_feature_flat = input_feature_flat.reshape(-1, self.num_channels)
        dimension = self.height * self.width
        base = torch.arange(self.num_batch) * dimension
        base = base.reshape([-1, 1]).float()
        repeat = torch.ones([self.num_points * self.width * self.height
                             ]).unsqueeze(0)
        repeat = repeat.float()

        base = torch.matmul(base, repeat)
        base = base.reshape([-1])

        base = base.to(device)

        base_y0 = base + y0 * self.height
        base_y1 = base + y1 * self.height

        # top rectangle of the neighbourhood volume
        index_a0 = base_y0 - base + x0
        index_c0 = base_y0 - base + x1

        # bottom rectangle of the neighbourhood volume
        index_a1 = base_y1 - base + x0
        index_c1 = base_y1 - base + x1

        # get 8 grid values
        value_a0 = input_feature_flat[index_a0.type(torch.int64)].to(device)
        value_c0 = input_feature_flat[index_c0.type(torch.int64)].to(device)
        value_a1 = input_feature_flat[index_a1.type(torch.int64)].to(device)
        value_c1 = input_feature_flat[index_c1.type(torch.int64)].to(device)

        # find 8 grid locations
        y0 = torch.floor(y).int()
        y1 = y0 + 1
        x0 = torch.floor(x).int()
        x1 = x0 + 1

        # clip out coordinates exceeding feature map volume
        y0 = torch.clamp(y0, zero, max_y + 1)
        y1 = torch.clamp(y1, zero, max_y + 1)
        x0 = torch.clamp(x0, zero, max_x + 1)
        x1 = torch.clamp(x1, zero, max_x + 1)

        x0_float = x0.float()
        x1_float = x1.float()
        y0_float = y0.float()
        y1_float = y1.float()

        vol_a0 = ((y1_float - y) * (x1_float - x)).unsqueeze(-1).to(device)
        vol_c0 = ((y1_float - y) * (x - x0_float)).unsqueeze(-1).to(device)
        vol_a1 = ((y - y0_float) * (x1_float - x)).unsqueeze(-1).to(device)
        vol_c1 = ((y - y0_float) * (x - x0_float)).unsqueeze(-1).to(device)

        outputs = (value_a0 * vol_a0 + value_c0 * vol_c0 + value_a1 * vol_a1 +
                   value_c1 * vol_c1)

        if self.morph == 0:
            outputs = outputs.reshape([
                self.num_batch,
                self.num_points * self.width,
                1 * self.height,
                self.num_channels,
            ])
            outputs = outputs.permute(0, 3, 1, 2)
        else:
            outputs = outputs.reshape([
                self.num_batch,
                1 * self.width,
                self.num_points * self.height,
                self.num_channels,
            ])
            outputs = outputs.permute(0, 3, 1, 2)
        return outputs

    def deform_conv(self, input, offset, if_offset):
        y, x = self._coordinate_map_3D(offset, if_offset)
        deformed_feature = self._bilinear_interpolate_3D(input, y, x)
        return deformed_feature

if __name__ == '__main__':
    input = torch.randn(1,128,8,8)
    dsconv = DySnakeConv(128,256)
    output = dsconv(input)
    print(output.shape)

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3.修改block.py配置

(a)新增C2f_DySnake类

class C2f_DySnakeConv(nn.Module):
    """Faster Implementation of CSP Bottleneck with 2 convolutions."""

    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
        """Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,
        expansion.
        """
        super().__init__()
        self.c = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
        self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)
        self.m = nn.ModuleList(Bottleneck_DySnakeConv(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))

    def forward(self, x):
        """Forward pass through C2f layer."""
        y = list(self.cv1(x).chunk(2, 1))
        y.extend(m(y[-1]) for m in self.m)
        return self.cv2(torch.cat(y, 1))

    def forward_split(self, x):
        """Forward pass using split() instead of chunk()."""
        y = list(self.cv1(x).split((self.c, self.c), 1))
        y.extend(m(y[-1]) for m in self.m)
        return self.cv2(torch.cat(y, 1))
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(b)新增Bottleneck_DySnakeConv类

class Bottleneck_DySnakeConv(nn.Module):
    def __init__(self,c1,c2,shortcut=True,g=1,k=(3,3),e=0.5):
        super().__init__()
        c_ = int(c2 * e)
        self.cv1 = DySnakeConv(c1,c_)
        self.cv2 = DySnakeConv(c_,c2)
        self.add = shortcut and c1==c2

    def forward(self,x):
        return x+self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
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©import自定义模块DySnakeConv

from ultralytics.nn.modules.DySnakeConv import DySnakeConv
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3.导入到task.py文件
(a)修改\ultralytics-main\ultralytics\nn\tasks.py文件

from ultralytics.nn.modules.block import Bottleneck_DySnakeConv,C2f_DySnakeConv
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(b)修改parse_model函数

def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
    """Parse a YOLO model.yaml dictionary into a PyTorch model."""
    import ast

    # Args
    max_channels = float('inf')
    nc, act, scales = (d.get(x) for x in ('nc', 'activation', 'scales'))
    depth, width, kpt_shape = (d.get(x, 1.0) for x in ('depth_multiple', 'width_multiple', 'kpt_shape'))
    if scales:
        scale = d.get('scale')
        if not scale:
            scale = tuple(scales.keys())[0]
            LOGGER.warning(f"WARNING ⚠️ no model scale passed. Assuming scale='{scale}'.")
        depth, width, max_channels = scales[scale]

    if act:
        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
        if verbose:
            LOGGER.info(f"{colorstr('activation:')} {act}")  # print

    if verbose:
        LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10}  {'module':<45}{'arguments':<30}")
    ch = [ch]
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
        m = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m]  # get module
        for j, a in enumerate(args):
            if isinstance(a, str):
                with contextlib.suppress(ValueError):
                    args[j] = locals()[a] if a in locals() else ast.literal_eval(a)

        n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain  """动态蛇形,增加了C2f_DySnakeConv，增加了Bottleneck_DySnakeConv"""
        if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck,Bottleneck_DySnakeConv, GhostBottleneck, SPP, SPPF, DWConv, Focus,
                 BottleneckCSP, C1, C2,C2f, C2f_DySnakeConv, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, RepC3):
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(min(c2, max_channels) * width, 8)

            args = [c1, c2, *args[1:]]
            if m in (BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x, RepC3):
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is AIFI:
            args = [ch[f], *args]
        elif m in (HGStem, HGBlock):
            c1, cm, c2 = ch[f], args[0], args[1]
            args = [c1, cm, c2, *args[2:]]
            if m is HGBlock:
                args.insert(4, 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 in (Detect, Segment, Pose):
            args.append([ch[x] for x in f])
            if m is Segment:
                args[2] = make_divisible(min(args[2], max_channels) * width, 8)
        elif m is RTDETRDecoder:  # special case, channels arg must be passed in index 1
            args.insert(1, [ch[x] for x in f])
        else:
            c2 = ch[f]

        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace('__main__.', '')  # module type
        m.np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type = i, f, t  # attach index, 'from' index, type
        if verbose:
            LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f}  {t:<45}{str(args):<30}')  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)
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4.在yaml文件中用C2f_DySnakeConv替换C2f

5.优点和挑战

优点：提供更好的灵活性和适应性，在处理复杂、不规则数据时可能比标准CNN更有效。
挑战：实现复杂，计算成本可能更高，需要更多的调优和实验来优化其性能。