YOLOv5使用ICCV2023蛇形卷积DSConv

【提醒大家：模块只进行了简单封装测试，每个人数据不同，是否能涨点需多做实验，大家可以多尝试不同的位置，或者做进一步改进工作】

DSConv 是ICCV2023 提出的一种受变形卷积启发提出的融合拓扑控制的卷积，其主要针对血管类似的细长形态目标的分割，具体内容可以去阅读论文：https://arxiv.org/abs/2307.08388

所以如果你的项目中有类似血管这种细长形态的目标，可以尝试一下蛇形卷积

代码链接 ：https://github.com/YaoleiQi/DSCNet

主要文件就是 DSConv.py，首先测试了经过DSConv的维度前后变化，

from models.DSConv_dev import My_DSConv,DSConv
from models.yolo import Conv

import os
import numpy as np
import torch

img = torch.randn(2,3,64,64)

stdconv = Conv(3,64,5,2)
dsconv = DSConv(3,64,5,2,0,True)
my_dsconv = My_DSConv(3,64,5,2)


os.environ["CUDA_VISIBLE_DEVICES"] = '0'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

print(next(stdconv.parameters()).is_cuda)

if torch.cuda.is_available():
    img = img.to(device)
    stdconv = stdconv.to(device)
    dsconv = dsconv.to(device)
    my_dsconv = my_dsconv.to(device)

print(dsconv(img).shape)
print(stdconv(img).shape)
print(my_dsconv(img).shape)
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输出结果

可以看到输入 (2，3，64，64）
dsconv 输出为 （2，64，64，64）长宽未发生变化

DSConv的构造函数如下

class DSConv(nn.Module):
    def __init__(self, in_ch, out_ch, kernel_size, extend_scope, morph,
                 if_offset, device):
        """
        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
        """
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其中
extend_scope 为形态的控制范围 这里是 -1~1
morph 蛇形卷积的拓扑形式 0 ,1
if_offset 是否使用蛇形卷积，默认为True 使用

由于经过 dsconv 输出不改变 长宽

但是在YOLOv5中，卷积常设置stride=2，来降低特征图尺寸，为了让DSConv也能实现这一效果，我进行了一次封装，并向外提供一个参数 s，是否降低，具体方式就是将 DSConv的输出经过一次MaxPooling【大家有什么好的想法可以讨论一下】，封装代码如下：

class My_DSConv(nn.Module):
    def __init__(self, c1, c2, k, s,p=None):
        super().__init__()
        self.dsconv = DSConv(c1, c2, kernel_size=k, extend_scope=1,morph=0,if_offset=True)
        self.pool = nn.MaxPool2d(s)

    def forward(self, x):
        return self.pool(self.dsconv(x))
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除此之外，还设置了 p=None 这一参数，是为了和Conv一致【这里的处理有一点问题】

还有一个要处理的问题是，官方提供的代码 GPU/CPU 的设定是通过 一个参数来确定，大家如果直接这样封装后使用会发现 出现 error 提示数据 不在同一个设备上
这里具体处理就不展开了，直接提供我处理后的文件，复制后文件命名为 DSConv_dev.py 放在models文件夹下

# -*- coding: utf-8 -*-
import os
import torch
import numpy as np
from torch import nn
import warnings

warnings.filterwarnings("ignore")

"""
This code is mainly the deformation process of our DSConv
"""


# conv   (ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
# dsconv (in_ch, out_ch, kernel_size, extend_scope, morph,if_offset)

class My_DSConv(nn.Module):
    def __init__(self, c1, c2, k, s,p=None):
        super().__init__()
        self.dsconv = DSConv(c1, c2, kernel_size=k, extend_scope=1,morph=0,if_offset=True)
        self.pool = nn.MaxPool2d(s)

    def forward(self, x):
        return self.pool(self.dsconv(x))


class DSConv(nn.Module):

    def __init__(self, in_ch, out_ch, kernel_size, extend_scope, morph,
                 if_offset):
        """
        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.relu = nn.ReLU(inplace=True)

        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)
        # We need a range of deformation between -1 and 1 to mimic the snake's swing
        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)
            x = self.gn(x)
            x = self.relu(x)
            return x
        else:
            x = self.dsc_conv_y(deformed_feature)
            x = self.gn(x)
            x = self.relu(x)
            return x


# Core code, for ease of understanding, we mark the dimensions of input and output next to the code
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  # offset (-1 ~ 1) * extend_scope

        # define feature map shape
        """
        B: Batch size  C: Channel  W: Width  H: Height
        """
        self.num_batch = input_shape[0]
        self.num_channels = input_shape[1]

    """
    input: offset [B,2*K,W,H]  K: Kernel size (2*K: 2D image, deformation contains <x_offset> and <y_offset>)
    output_x: [B,1,W,K*H]   coordinate map
    output_y: [B,1,K*W,H]   coordinate map
    """

    def _coordinate_map_3D(self, offset, if_offset):
        # offset
        device = offset.device
        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)

                # The center position remains unchanged and the rest of the positions begin to swing
                # This part is quite simple. The main idea is that "offset is an iterative process"
                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))
                #
                # test = y_offset_new.mul(self.extend_scope)
                # y_new = y_new.to(test.device)
                # y_new = y_new.add(test)
                #
            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:
            """
            Initialize the kernel and flatten the kernel
                y: -num_points//2 ~ num_points//2 (Determined by the kernel size)
                x: only need 0
            """
            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.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] + x_offset[center + index])
                    x_offset_new[center - index] = (x_offset_new[center - index + 1] + x_offset[center - index])
                x_offset_new = x_offset_new.permute(1, 0, 2, 3).to(device)
                x_new = x_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 = y.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 + 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


# Code for testing the DSConv
if __name__ == '__main__':
    # os.environ["CUDA_VISIBLE_DEVICES"] = '0'
    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    A = np.random.rand(4, 5, 6, 7)
    # A = np.ones(shape=(3, 2, 2, 3), dtype=np.float32)
    # print(A)
    A = A.astype(dtype=np.float32)
    A = torch.from_numpy(A)
    # print(A.shape)
    conv0 = DSConv(
        in_ch=5,
        out_ch=10,
        kernel_size=15,
        extend_scope=1,
        morph=0,
        if_offset=True)  # [5,10,15,1,True,0]
    # if torch.cuda.is_available():
    #     A = A.to(device)
    #     conv0 = conv0.to(device)
    out = conv0(A)
    print(out.shape)
    # print(out)

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至此，有关DSConv修改的部分已经完成了，接下来就是修改YOLO的代码，修改的地方有三处 1 yolo.py 2 Yolov5m-dscon.yaml 3 train.py
1 这里建议直接拷贝一份 yolo.py 命名为 yolo_dsconv.py
然后导入

然后修改这里

2 建立配置文件 替换Conv或其他【这里给出我的一个配置文件，有涨点效果】

backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, My_DSConv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, My_DSConv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]
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3 train.py
把yolo原来的给注释掉，然后从我们的 yolo_dsconv导入

讨论：

1. 最好不要改动太大，效果可能会很差
2. 我的模型配置在我的数据集上有涨点效果，但是感觉相较于原有模型，其参数量 增加不少，感觉有点 空间换效率的味道
3. 大家可以多尝试修改不同的配置，一起讨论

最后，感谢论文原作者的工作，欢迎大家一起来讨论，有问题留言或加群【群号：392784757】！

群内已经提供修改过的工程文件，测试没有问题，如有疑问，可加群联系