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社区首页 >专栏 >RT-DETR改进:新的设计范式Slim Neck助力RT-DETR | Neck改进

RT-DETR改进:新的设计范式Slim Neck助力RT-DETR | Neck改进

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AI小怪兽
发布2023-11-20 16:05:12
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发布2023-11-20 16:05:12
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文章被收录于专栏:YOLO大作战YOLO大作战

本文独家改进: 采用 GSConv 方法的 Slim-Neck 可缓解 DSC 缺陷对模型的负面影响,并充分利用深度可分离卷积 DSC 的优势,引入RT-DETR二次创新;

1)代替Upsample进行使用;

推荐指数:五星

1.GSConv+Slim Neck介绍

论文地址:https://arxiv.org/ftp/arxiv/papers/2206/2206.02424.pdf

github: GitHub - AlanLi1997/slim-neck-by-gsconv: Pytorch implementation of the 'Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles'

本文贡献:

  1. 引入了一种新方法 GSConv 来代替 SC 操作。该方法使卷积计算的输出尽可能接近 SC,同时降低计算成本;
  2. 提供了一种新的设计范式,即带有标准 BackboneSlim-Neck 设计;

图2(a)和(b)展示了深度可分离卷积(DSC )和标准卷积(SC)的网络结构,但是深度可分离卷积这种设陷导致特征提取和融合能力比 SC 低得多。

1.2 Slim-Neck

采用 GSConv 方法的 Slim-Neck 可缓解 DSC 缺陷对模型的负面影响,并充分利用深度可分离卷积 DSC 的优势。

2. GSConv+Slim Neck引入RT-DETR

2.1 加入ultralytics/nn/neck/SlimNeck.py

核心代码:

代码语言:javascript
复制

######################   slim-neck-by-gsconv ####     start ###############################
import torch
import torch.nn as nn
import math

from ..modules.conv import Conv, DWConv, GhostConv, LightConv, RepConv

class GSConv(nn.Module):
    # GSConv https://github.com/AlanLi1997/slim-neck-by-gsconv
    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
        super().__init__()
        c_ = c2 // 2
        self.cv1 = Conv(c1, c_, k, s, None, g, 1,  act)
        self.cv2 = Conv(c_, c_, 5, 1, None, c_, 1 , act)

    def forward(self, x):
        x1 = self.cv1(x)
        x2 = torch.cat((x1, self.cv2(x1)), 1)
        # shuffle
        # y = x2.reshape(x2.shape[0], 2, x2.shape[1] // 2, x2.shape[2], x2.shape[3])
        # y = y.permute(0, 2, 1, 3, 4)
        # return y.reshape(y.shape[0], -1, y.shape[3], y.shape[4])

        b, n, h, w = x2.data.size()
        b_n = b * n // 2
        y = x2.reshape(b_n, 2, h * w)
        y = y.permute(1, 0, 2)
        y = y.reshape(2, -1, n // 2, h, w)

        return torch.cat((y[0], y[1]), 1)

class GSConvns(GSConv):
    # GSConv with a normative-shuffle https://github.com/AlanLi1997/slim-neck-by-gsconv
    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
        super().__init__(c1, c2, k=1, s=1, g=1, act=True)
        c_ = c2 // 2
        self.shuf = nn.Conv2d(c_ * 2, c2, 1, 1, 0, bias=False)

    def forward(self, x):
        x1 = self.cv1(x)
        x2 = torch.cat((x1, self.cv2(x1)), 1)
        # normative-shuffle, TRT supported
        return nn.ReLU(self.shuf(x2))


class GSBottleneck(nn.Module):
    # GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconv
    def __init__(self, c1, c2, k=3, s=1, e=0.5):
        super().__init__()
        c_ = int(c2 *e)
        # for lighting
        self.conv_lighting = nn.Sequential(
            GSConv(c1, c_, 1, 1),
            GSConv(c_, c2, 3, 1, act=False))
        self.shortcut = Conv(c1, c2, 1, 1, act=False)

    def forward(self, x):
        return self.conv_lighting(x) + self.shortcut(x)


class DWConv(Conv):
    # Depth-wise convolution class
    def __init__(self, c1, c2, k=1, s=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)


class GSBottleneckC(GSBottleneck):
    # cheap GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconv
    def __init__(self, c1, c2, k=3, s=1):
        super().__init__(c1, c2, k, s)
        self.shortcut = DWConv(c1, c2, k, s, act=False)


class VoVGSCSP(nn.Module):
    # VoVGSCSP module with GSBottleneck
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        # self.gc1 = GSConv(c_, c_, 1, 1)
        # self.gc2 = GSConv(c_, c_, 1, 1)
        # self.gsb = GSBottleneck(c_, c_, 1, 1)
        self.gsb = nn.Sequential(*(GSBottleneck(c_, c_, e=1.0) for _ in range(n)))
        self.res = Conv(c_, c_, 3, 1, act=False)
        self.cv3 = Conv(2 * c_, c2, 1)  #


    def forward(self, x):
        x1 = self.gsb(self.cv1(x))
        y = self.cv2(x)
        return self.cv3(torch.cat((y, x1), dim=1))


class VoVGSCSPC(VoVGSCSP):
    # cheap VoVGSCSP module with GSBottleneck
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2)
        c_ = int(c2 * 0.5)  # hidden channels
        self.gsb = GSBottleneckC(c_, c_, 1, 1)


######################   slim-neck-by-gsconv ####     end ###############################

详见:

https://blog.csdn.net/m0_63774211/article/details/134407373

​我正在参与2023腾讯技术创作特训营第三期有奖征文,组队打卡瓜分大奖!

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

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目录
  • 1.GSConv+Slim Neck介绍
    • 1.2 Slim-Neck
    • 2. GSConv+Slim Neck引入RT-DETR
      • 2.1 加入ultralytics/nn/neck/SlimNeck.py
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