【YOLOv8】YOLOv8改进系列（10）----替换主干网络之UniRepLKNet

主页：HABUO🍁主页：HABUO

🍁YOLOv8入门+改进专栏🍁

🍁如果再也不能见到你，祝你早安，午安，晚安🍁

---

  【YOLOv8改进系列】： 

 

YOLOv8改进系列（2）----替换主干网络之FasterNet

YOLOv8改进系列（3）----替换主干网络之ConvNeXt V2

YOLOv8改进系列（4）----替换C2f之FasterNet中的FasterBlock替换C2f中的Bottleneck 

YOLOv8改进系列（5）----替换主干网络之EfficientFormerV2 

YOLOv8改进系列（6）----替换主干网络之VanillaNet

YOLOv8改进系列（7）----替换主干网络之LSKNet

YOLOv8改进系列（8）----替换主干网络之Swin Transformer 

YOLOv8改进系列（9）----替换主干网络之RepViT

---

目录

💯一、UniRepLKNet介绍

1. 简介

2. UniRepLKNet 架构设计

背景知识

研究方法

Dilated Reparam Block

3. 实验与结果

4. 关键结论

💯二、具体添加方法 

第①步：创建UniRepLKNet.py

第②步：修改task.py 

(1)引入创建的UniRepLKNet文件 

(2)修改_predict_once函数 

(3)修改parse_model函数

第③步：yolov8.yaml文件修改   

第④步：验证是否加入成功  

---

💯一、UniRepLKNet介绍

• 论文题目：《UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition》
• 论文地址：https://arxiv.org/pdf/2311.15599

1. 简介

论文介绍了一种名为 UniRepLKNet 的新型大核卷积神经网络（ConvNet），它在图像识别、音频、视频、点云、时间序列等多种模态的任务上表现出色，展示了卷积神经网络在多模态领域的巨大潜力。

2. UniRepLKNet 架构设计

背景知识

• 大核卷积神经网络（ConvNet）：近年来，大核卷积神经网络因其在图像识别等任务中的出色表现而受到广泛关注。然而，现有的大核 ConvNet 架构大多沿用传统 ConvNet 或 Transformer 的设计原则，针对大核 ConvNet 的架构设计仍待深入研究。

• 多模态任务：Transformer 在多个模态的任务中表现出色，但卷积网络在非视觉领域的表现尚未充分挖掘。作者试图探索 ConvNet 在多模态任务中的潜力，尤其是那些传统上不擅长的领域，如音频和时间序列。

研究方法

• 核心理念：利用大核的特性（能够“看宽而不必加深”）来设计架构，通过解耦大核卷积层的三种效果（扩大感受野、增加空间模式的抽象层次、提升模型的泛化能力），实现更高效的特征提取和更高的性能。

• 架构指南：

  1. 使用高效的结构（如 SE 块）增加模型深度。

  2. 使用提出的 Dilated Reparam Block 重新参数化大核卷积层，以提升性能且不增加推理成本。

  3. 根据下游任务决定核大小，通常在中高层使用大核。

  4. 在扩展模型深度时，添加的块应使用小核。

Dilated Reparam Block

• 核心思想：通过并行的小核卷积层（包括扩张卷积）来增强大核卷积层，这些小核卷积层在训练时捕捉小尺度和稀疏模式，在推理时可以等效地合并到大核卷积层中，从而不增加推理成本。

• 等效转换：通过转置卷积操作，将扩张卷积层的权重转换为非扩张的稀疏大核卷积层的权重，从而在推理时将整个块等效为一个单一的大核卷积层。

​

​​

---

3. 实验与结果

• 图像识别：

  • 在 ImageNet 数据集上，UniRepLKNet 实现了 88.0% 的准确率。

  • 在 ADE20K 语义分割任务上，mIoU 达到 55.6%。

  • 在 COCO 目标检测任务上，box AP 达到 56.4%。

  • 与现有的大核 ConvNet（如 RepLKNet、SLaK）和强大的架构（如 ConvNeXt V2、FastViT、Swin V2）相比，UniRepLKNet 在准确性和效率上均表现出色。

• 多模态任务：

  • 时间序列预测：在大规模时间序列预测任务中，UniRepLKNet 击败了最新的 Transformer 定制模型，实现了最低的预测误差。

  • 音频识别：在 Speech Commands V2 数据集上，UniRepLKNet 达到了 98.5% 的准确率，且无需预训练。

  • 点云分析：在 ModelNet-40 数据集上，UniRepLKNet 实现了 93.2% 的总体准确率，超越了现有的 ConvNet 专业模型。

  • 视频识别：在 Kinetics-400 数据集上，UniRepLKNet 达到了 54.8% 的准确率，尽管与最先进模型仍有差距，但作为通用模型且无需预训练，表现已相当出色。

---

4. 关键结论

• 大核的重要性：大核是解锁 ConvNet 在多模态任务中卓越性能的关键，尤其是在那些传统上不擅长的领域。

• 架构设计的有效性：通过提出的架构指南和 Dilated Reparam Block，UniRepLKNet 在图像识别和多模态任务中均取得了领先性能，证明了其架构设计的有效性。

• 通用感知能力：UniRepLKNet 展示了卷积网络在多模态任务中的通用感知能力，为未来在更多领域的应用提供了新的可能性。

---

💯二、具体添加方法 

第①步：创建UniRepLKNet.py

​

创建完成后，将下面代码直接复制粘贴进去：

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np

__all__ = ['unireplknet_a', 'unireplknet_f', 'unireplknet_p', 'unireplknet_n', 'unireplknet_t', 'unireplknet_s', 'unireplknet_b', 'unireplknet_l', 'unireplknet_xl']

class GRNwithNHWC(nn.Module):
    """ GRN (Global Response Normalization) layer
    Originally proposed in ConvNeXt V2 (https://arxiv.org/abs/2301.00808)
    This implementation is more efficient than the original (https://github.com/facebookresearch/ConvNeXt-V2)
    We assume the inputs to this layer are (N, H, W, C)
    """
    def __init__(self, dim, use_bias=True):
        super().__init__()
        self.use_bias = use_bias
        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
        if self.use_bias:
            self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))

    def forward(self, x):
        Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        if self.use_bias:
            return (self.gamma * Nx + 1) * x + self.beta
        else:
            return (self.gamma * Nx + 1) * x


class NCHWtoNHWC(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return x.permute(0, 2, 3, 1)


class NHWCtoNCHW(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return x.permute(0, 3, 1, 2)

#================== This function decides which conv implementation (the native or iGEMM) to use
#   Note that iGEMM large-kernel conv impl will be used if
#       -   you attempt to do so (attempt_to_use_large_impl=True), and
#       -   it has been installed (follow https://github.com/AILab-CVC/UniRepLKNet), and
#       -   the conv layer is depth-wise, stride = 1, non-dilated, kernel_size > 5, and padding == kernel_size // 2
def get_conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias,
               attempt_use_lk_impl=True):
    kernel_size = to_2tuple(kernel_size)
    if padding is None:
        padding = (kernel_size[0] // 2, kernel_size[1] // 2)
    else:
        padding = to_2tuple(padding)
    need_large_impl = kernel_size[0] == kernel_size[1] and kernel_size[0] > 5 and padding == (kernel_size[0] // 2, kernel_size[1] // 2)

    # if attempt_use_lk_impl and need_large_impl:
    #     print('---------------- trying to import iGEMM implementation for large-kernel conv')
    #     try:
    #         from depthwise_conv2d_implicit_gemm import DepthWiseConv2dImplicitGEMM
    #         print('---------------- found iGEMM implementation ')
    #     except:
    #         DepthWiseConv2dImplicitGEMM = None
    #         print('---------------- found no iGEMM. use original conv. follow https://github.com/AILab-CVC/UniRepLKNet to install it.')
    #     if DepthWiseConv2dImplicitGEMM is not None and need_large_impl and in_channels == out_channels \
    #             and out_channels == groups and stride == 1 and dilation == 1:
    #         print(f'===== iGEMM Efficient Conv Impl, channels {in_channels}, kernel size {kernel_size} =====')
    #         return DepthWiseConv2dImplicitGEMM(in_channels, kernel_size, bias=bias)
    return nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
                     padding=padding, dilation=dilation, groups=groups, bias=bias)


def get_bn(dim, use_sync_bn=False):
    if use_sync_bn:
        return nn.SyncBatchNorm(dim)
    else:
        return nn.BatchNorm2d(dim)

class SEBlock(nn.Module):
    """
    Squeeze-and-Excitation Block proposed in SENet (https://arxiv.org/abs/1709.01507)
    We assume the inputs to this layer are (N, C, H, W)
    """
    def __init__(self, input_channels, internal_neurons):
        super(SEBlock, self).__init__()
        self.down = nn.Conv2d(in_channels=input_channels, out_channels=internal_neurons,
                              kernel_size=1, stride=1, bias=True)
        self.up = nn.Conv2d(in_channels=internal_neurons, out_channels=input_channels,
                            kernel_size=1, stride=1, bias=True)
        self.input_channels = input_channels
        self.nonlinear = nn.ReLU(inplace=True)

    def forward(self, inputs):
        x = F.adaptive_avg_pool2d(inputs, output_size=(1, 1))
        x = self.down(x)
        x = self.nonlinear(x)
        x = self.up(x)
        x = F.sigmoid(x)
        return inputs * x.view(-1, self.input_channels, 1, 1)

def fuse_bn(conv, bn):
    conv_bias = 0 if conv.bias is None else conv.bias
    std = (bn.running_var + bn.eps).sqrt()
    return conv.weight * (bn.weight / std).reshape(-1, 1, 1, 1), bn.bias + (conv_bias - bn.running_mean) * bn.weight / std

def convert_dilated_to_nondilated(kernel, dilate_rate):
    identity_kernel = torch.ones((1, 1, 1, 1)).to(kernel.device)
    if kernel.size(1) == 1:
        #   This is a DW kernel
        dilated = F.conv_transpose2d(kernel, identity_kernel, stride=dilate_rate)
        return dilated
    else:
        #   This is a dense or group-wise (but not DW) kernel
        slices = []
        for i in range(kernel.size(1)):
            dilated = F.conv_transpose2d(kernel[:,i:i+1,:,:], identity_kernel, stride=dilate_rate)
            slices.append(dilated)
        return torch.cat(slices, dim=1)

def merge_dilated_into_large_kernel(large_kernel, dilated_kernel, dilated_r):
    large_k = large_kernel.size(2)
    dilated_k = dilated_kernel.size(2)
    equivalent_kernel_size = dilated_r * (dilated_k - 1) + 1
    equivalent_kernel = convert_dilated_to_nondilated(dilated_kernel, dilated_r)
    rows_to_pad = large_k // 2 - equivalent_kernel_size // 2
    merged_kernel = large_kernel + F.pad(equivalent_kernel, [rows_to_pad] * 4)
    return merged_kernel


class DilatedReparamBlock(nn.Module):
    """
    Dilated Reparam Block proposed in UniRepLKNet (https://github.com/AILab-CVC/UniRepLKNet)
    We assume the inputs to this block are (N, C, H, W)
    """
    def __init__(self, channels, kernel_size, deploy, use_sync_bn=False, attempt_use_lk_impl=True):
        super().__init__()
        self.lk_origin = get_conv2d(channels, channels, kernel_size, stride=1,
                                    padding=kernel_size//2, dilation=1, groups=channels, bias=deploy,
                                    attempt_use_lk_impl=attempt_use_lk_impl)
        self.attempt_use_lk_impl = attempt_use_lk_impl

        #   Default settings. We did not tune them carefully. Different settings may work better.
        if kernel_size == 17:
            self.kernel_sizes = [5, 9, 3, 3, 3]
            self.dilates = [1, 2, 4, 5, 7]
        elif kernel_size == 15:
            self.kernel_sizes = [5, 7, 3, 3, 3]
            self.dilates = [1, 2, 3, 5, 7]
        elif kernel_size == 13:
            self.kernel_sizes = [5, 7, 3, 3, 3]
            self.dilates = [1, 2, 3, 4, 5]
        elif kernel_size == 11:
            self.kernel_sizes = [5, 5, 3, 3, 3]
            self.dilates = [1, 2, 3, 4, 5]
        elif kernel_size == 9:
            self.kernel_sizes = [5, 5, 3, 3]
            self.dilates = [1, 2, 3, 4]
        elif kernel_size == 7:
            self.kernel_sizes = [5, 3, 3]
            self.dilates = [1, 2, 3]
        elif kernel_size == 5:
            self.kernel_sizes = [3, 3]
            self.dilates = [1, 2]
        else:
            raise ValueError('Dilated Reparam Block requires kernel_size >= 5')

        if not deploy:
            self.origin_bn = get_bn(channels, use_sync_bn)
            for k, r in zip(self.kernel_sizes, self.dilates):
                self.__setattr__('dil_conv_k{}_{}'.format(k, r),
                                 nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=k, stride=1,
                                           padding=(r * (k - 1) + 1) // 2, dilation=r, groups=channels,
                                           bias=False))
                self.__setattr__('dil_bn_k{}_{}'.format(k, r), get_bn(channels, use_sync_bn=use_sync_bn))

    def forward(self, x):
        if not hasattr(self, 'origin_bn'):      # deploy mode
            return self.lk_origin(x)
        out = self.origin_bn(self.lk_origin(x))
        for k, r in zip(self.kernel_sizes, self.dilates):
            conv = self.__getattr__('dil_conv_k{}_{}'.format(k, r))
            bn = self.__getattr__('dil_bn_k{}_{}'.format(k, r))
            out = out + bn(conv(x))
        return out

    def merge_dilated_branches(self):
        if hasattr(self, 'origin_bn'):
            origin_k, origin_b = fuse_bn(self.lk_origin, self.origin_bn)
            for k, r in zip(self.kernel_sizes, self.dilates):
                conv = self.__getattr__('dil_conv_k{}_{}'.format(k, r))
                bn = self.__getattr__('dil_bn_k{}_{}'.format(k, r))
                branch_k, branch_b = fuse_bn(conv, bn)
                origin_k = merge_dilated_into_large_kernel(origin_k, branch_k, r)
                origin_b += branch_b
            merged_conv = get_conv2d(origin_k.size(0), origin_k.size(0), origin_k.size(2), stride=1,
                                    padding=origin_k.size(2)//2, dilation=1, groups=origin_k.size(0), bias=True,
                                    attempt_use_lk_impl=self.attempt_use_lk_impl)
            merged_conv.weight.data = origin_k
            merged_conv.bias.data = origin_b
            self.lk_origin = merged_conv
            self.__delattr__('origin_bn')
            for k, r in zip(self.kernel_sizes, self.dilates):
                self.__delattr__('dil_conv_k{}_{}'.format(k, r))
                self.__delattr__('dil_bn_k{}_{}'.format(k, r))


class UniRepLKNetBlock(nn.Module):

    def __init__(self,
                 dim,
                 kernel_size,
                 drop_path=0.,
                 layer_scale_init_value=1e-6,
                 deploy=False,
                 attempt_use_lk_impl=True,
                 with_cp=False,
                 use_sync_bn=False,
                 ffn_factor=4):
        super().__init__()
        self.with_cp = with_cp
        # if deploy:
        #     print('------------------------------- Note: deploy mode')
        # if self.with_cp:
        #     print('****** note with_cp = True, reduce memory consumption but may slow down training ******')

        self.need_contiguous = (not deploy) or kernel_size >= 7

        if kernel_size == 0:
            self.dwconv = nn.Identity()
            self.norm = nn.Identity()
        elif deploy:
            self.dwconv = get_conv2d(dim, dim, kernel_size=kernel_size, stride=1, padding=kernel_size // 2,
                                     dilation=1, groups=dim, bias=True,
                                     attempt_use_lk_impl=attempt_use_lk_impl)
            self.norm = nn.Identity()
        elif kernel_size >= 7:
            self.dwconv = DilatedReparamBlock(dim, kernel_size, deploy=deploy,
                                              use_sync_bn=use_sync_bn,
                                              attempt_use_lk_impl=attempt_use_lk_impl)
            self.norm = get_bn(dim, use_sync_bn=use_sync_bn)
        elif kernel_size == 1:
            self.dwconv = nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, padding=kernel_size // 2,
                                    dilation=1, groups=1, bias=deploy)
            self.norm = get_bn(dim, use_sync_bn=use_sync_bn)
        else:
            assert kernel_size in [3, 5]
            self.dwconv = nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, padding=kernel_size // 2,
                                    dilation=1, groups=dim, bias=deploy)
            self.norm = get_bn(dim, use_sync_bn=use_sync_bn)

        self.se = SEBlock(dim, dim // 4)

        ffn_dim = int(ffn_factor * dim)
        self.pwconv1 = nn.Sequential(
            NCHWtoNHWC(),
            nn.Linear(dim, ffn_dim))
        self.act = nn.Sequential(
            nn.GELU(),
            GRNwithNHWC(ffn_dim, use_bias=not deploy))
        if deploy:
            self.pwconv2 = nn.Sequential(
                nn.Linear(ffn_dim, dim),
                NHWCtoNCHW())
        else:
            self.pwconv2 = nn.Sequential(
                nn.Linear(ffn_dim, dim, bias=False),
                NHWCtoNCHW(),
                get_bn(dim, use_sync_bn=use_sync_bn))

        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(dim),
                                  requires_grad=True) if (not deploy) and layer_scale_init_value is not None \
                                                         and layer_scale_init_value > 0 else None
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, inputs):

        def _f(x):
            if self.need_contiguous:
                x = x.contiguous()
            y = self.se(self.norm(self.dwconv(x)))
            y = self.pwconv2(self.act(self.pwconv1(y)))
            if self.gamma is not None:
                y = self.gamma.view(1, -1, 1, 1) * y
            return self.drop_path(y) + x

        if self.with_cp and inputs.requires_grad:
            return checkpoint.checkpoint(_f, inputs)
        else:
            return _f(inputs)

    def reparameterize(self):
        if hasattr(self.dwconv, 'merge_dilated_branches'):
            self.dwconv.merge_dilated_branches()
        if hasattr(self.norm, 'running_var') and hasattr(self.dwconv, 'lk_origin'):
            std = (self.norm.running_var + self.norm.eps).sqrt()
            self.dwconv.lk_origin.weight.data *= (self.norm.weight / std).view(-1, 1, 1, 1)
            self.dwconv.lk_origin.bias.data = self.norm.bias + (self.dwconv.lk_origin.bias - self.norm.running_mean) * self.norm.weight / std
            self.norm = nn.Identity()
        if self.gamma is not None:
            final_scale = self.gamma.data
            self.gamma = None
        else:
            final_scale = 1
        if self.act[1].use_bias and len(self.pwconv2) == 3:
            grn_bias = self.act[1].beta.data
            self.act[1].__delattr__('beta')
            self.act[1].use_bias = False
            linear = self.pwconv2[0]
            grn_bias_projected_bias = (linear.weight.data @ grn_bias.view(-1, 1)).squeeze()
            bn = self.pwconv2[2]
            std = (bn.running_var + bn.eps).sqrt()
            new_linear = nn.Linear(linear.in_features, linear.out_features, bias=True)
            new_linear.weight.data = linear.weight * (bn.weight / std * final_scale).view(-1, 1)
            linear_bias = 0 if linear.bias is None else linear.bias.data
            linear_bias += grn_bias_projected_bias
            new_linear.bias.data = (bn.bias + (linear_bias - bn.running_mean) * bn.weight / std) * final_scale
            self.pwconv2 = nn.Sequential(new_linear, self.pwconv2[1])



default_UniRepLKNet_A_F_P_kernel_sizes = ((3, 3),
                                      (13, 13),
                                      (13, 13, 13, 13, 13, 13),
                                      (13, 13))
default_UniRepLKNet_N_kernel_sizes = ((3, 3),
                                      (13, 13),
                                      (13, 13, 13, 13, 13, 13, 13, 13),
                                      (13, 13))
default_UniRepLKNet_T_kernel_sizes = ((3, 3, 3),
                                      (13, 13, 13),
                                      (13, 3, 13, 3, 13, 3, 13, 3, 13, 3, 13, 3, 13, 3, 13, 3, 13, 3),
                                      (13, 13, 13))
default_UniRepLKNet_S_B_L_XL_kernel_sizes = ((3, 3, 3),
                                             (13, 13, 13),
                                             (13, 3, 3, 13, 3, 3, 13, 3, 3, 13, 3, 3, 13, 3, 3, 13, 3, 3, 13, 3, 3, 13, 3, 3, 13, 3, 3),
                                             (13, 13, 13))
UniRepLKNet_A_F_P_depths = (2, 2, 6, 2)
UniRepLKNet_N_depths = (2, 2, 8, 2)
UniRepLKNet_T_depths = (3, 3, 18, 3)
UniRepLKNet_S_B_L_XL_depths = (3, 3, 27, 3)

default_depths_to_kernel_sizes = {
    UniRepLKNet_A_F_P_depths: default_UniRepLKNet_A_F_P_kernel_sizes,
    UniRepLKNet_N_depths: default_UniRepLKNet_N_kernel_sizes,
    UniRepLKNet_T_depths: default_UniRepLKNet_T_kernel_sizes,
    UniRepLKNet_S_B_L_XL_depths: default_UniRepLKNet_S_B_L_XL_kernel_sizes
}

class UniRepLKNet(nn.Module):
    r""" UniRepLKNet
        A PyTorch impl of UniRepLKNet

    Args:
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        depths (tuple(int)): Number of blocks at each stage. Default: (3, 3, 27, 3)
        dims (int): Feature dimension at each stage. Default: (96, 192, 384, 768)
        drop_path_rate (float): Stochastic depth rate. Default: 0.
        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
        kernel_sizes (tuple(tuple(int))): Kernel size for each block. None means using the default settings. Default: None.
        deploy (bool): deploy = True means using the inference structure. Default: False
        with_cp (bool): with_cp = True means using torch.utils.checkpoint to save GPU memory. Default: False
        init_cfg (dict): weights to load. The easiest way to use UniRepLKNet with for OpenMMLab family. Default: None
        attempt_use_lk_impl (bool): try to load the efficient iGEMM large-kernel impl. Setting it to False disabling the iGEMM impl. Default: True
        use_sync_bn (bool): use_sync_bn = True means using sync BN. Use it if your batch size is small. Default: False
    """
    def __init__(self,
                 in_chans=3,
                 num_classes=1000,
                 depths=(3, 3, 27, 3),
                 dims=(96, 192, 384, 768),
                 drop_path_rate=0.,
                 layer_scale_init_value=1e-6,
                 head_init_scale=1.,
                 kernel_sizes=None,
                 deploy=False,
                 with_cp=False,
                 init_cfg=None,
                 attempt_use_lk_impl=True,
                 use_sync_bn=False,
                 **kwargs
                 ):
        super().__init__()

        depths = tuple(depths)
        if kernel_sizes is None:
            if depths in default_depths_to_kernel_sizes:
                # print('=========== use default kernel size ')
                kernel_sizes = default_depths_to_kernel_sizes[depths]
            else:
                raise ValueError('no default kernel size settings for the given depths, '
                                 'please specify kernel sizes for each block, e.g., '
                                 '((3, 3), (13, 13), (13, 13, 13, 13, 13, 13), (13, 13))')
        # print(kernel_sizes)
        for i in range(4):
            assert len(kernel_sizes[i]) == depths[i], 'kernel sizes do not match the depths'

        self.with_cp = with_cp

        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
        # print('=========== drop path rates: ', dp_rates)

        self.downsample_layers = nn.ModuleList()
        self.downsample_layers.append(nn.Sequential(
            nn.Conv2d(in_chans, dims[0] // 2, kernel_size=3, stride=2, padding=1),
            LayerNorm(dims[0] // 2, eps=1e-6, data_format="channels_first"),
            nn.GELU(),
            nn.Conv2d(dims[0] // 2, dims[0], kernel_size=3, stride=2, padding=1),
            LayerNorm(dims[0], eps=1e-6, data_format="channels_first")))

        for i in range(3):
            self.downsample_layers.append(nn.Sequential(
                nn.Conv2d(dims[i], dims[i + 1], kernel_size=3, stride=2, padding=1),
                LayerNorm(dims[i + 1], eps=1e-6, data_format="channels_first")))

        self.stages = nn.ModuleList()

        cur = 0
        for i in range(4):
            main_stage = nn.Sequential(
                *[UniRepLKNetBlock(dim=dims[i], kernel_size=kernel_sizes[i][j], drop_path=dp_rates[cur + j],
                                   layer_scale_init_value=layer_scale_init_value, deploy=deploy,
                                   attempt_use_lk_impl=attempt_use_lk_impl,
                                   with_cp=with_cp, use_sync_bn=use_sync_bn) for j in
                  range(depths[i])])
            self.stages.append(main_stage)
            cur += depths[i]

        self.output_mode = 'features'
        norm_layer = partial(LayerNorm, eps=1e-6, data_format="channels_first")
        for i_layer in range(4):
            layer = norm_layer(dims[i_layer])
            layer_name = f'norm{i_layer}'
            self.add_module(layer_name, layer)
        self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=.02)
            if hasattr(m, 'bias') and m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        if self.output_mode == 'logits':
            for stage_idx in range(4):
                x = self.downsample_layers[stage_idx](x)
                x = self.stages[stage_idx](x)
            x = self.norm(x.mean([-2, -1]))
            x = self.head(x)
            return x
        elif self.output_mode == 'features':
            outs = []
            for stage_idx in range(4):
                x = self.downsample_layers[stage_idx](x)
                x = self.stages[stage_idx](x)
                outs.append(self.__getattr__(f'norm{stage_idx}')(x))
            return outs
        else:
            raise ValueError('Defined new output mode?')

    def switch_to_deploy(self):
        for m in self.modules():
            if hasattr(m, 'reparameterize'):
                m.reparameterize()



class LayerNorm(nn.Module):
    r""" LayerNorm implementation used in ConvNeXt
    LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
    shape (batch_size, height, width, channels) while channels_first corresponds to inputs
    with shape (batch_size, channels, height, width).
    """

    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last", reshape_last_to_first=False):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError
        self.normalized_shape = (normalized_shape,)
        self.reshape_last_to_first = reshape_last_to_first

    def forward(self, x):
        if self.data_format == "channels_last":
            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x

def update_weight(model_dict, weight_dict):
    idx, temp_dict = 0, {}
    for k, v in weight_dict.items():
        if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):
            temp_dict[k] = v
            idx += 1
    model_dict.update(temp_dict)
    print(f'loading weights... {idx}/{len(model_dict)} items')
    return model_dict

def unireplknet_a(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_A_F_P_depths, dims=(40, 80, 160, 320), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

def unireplknet_f(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_A_F_P_depths, dims=(48, 96, 192, 384), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

def unireplknet_p(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_A_F_P_depths, dims=(64, 128, 256, 512), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

def unireplknet_n(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_N_depths, dims=(80, 160, 320, 640), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

def unireplknet_t(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_T_depths, dims=(80, 160, 320, 640), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

def unireplknet_s(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_S_B_L_XL_depths, dims=(96, 192, 384, 768), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

def unireplknet_b(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_S_B_L_XL_depths, dims=(128, 256, 512, 1024), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

def unireplknet_l(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_S_B_L_XL_depths, dims=(192, 384, 768, 1536), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

def unireplknet_xl(weights='', **kwargs):
    model = UniRepLKNet(depths=UniRepLKNet_S_B_L_XL_depths, dims=(256, 512, 1024, 2048), **kwargs)
    if weights:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)))
    return model

if __name__ == '__main__':
    inputs = torch.randn((1, 3, 640, 640))
    model = unireplknet_a('unireplknet_a_in1k_224_acc77.03.pth')
    res = model(inputs)[-1]
    model.switch_to_deploy()
    res_fuse = model(inputs)[-1]
    print(torch.mean(res_fuse - res))

第②步：修改task.py 

(1)引入创建的UniRepLKNet文件 

​

from ultralytics.nn.backbone.UniRepLKNet import *

(2)修改_predict_once函数 

 可直接将下述代码替换对应位置 

​

 def _predict_once(self, x, profile=False, visualize=False, embed=None):
        """
        Perform a forward pass through the network.
        Args:
            x (torch.Tensor): The input tensor to the model.
            profile (bool):  Print the computation time of each layer if True, defaults to False.
            visualize (bool): Save the feature maps of the model if True, defaults to False.
            embed (list, optional): A list of feature vectors/embeddings to return.
        Returns:
            (torch.Tensor): The last output of the model.
        """
        y, dt, embeddings = [], [], []  # outputs
        for idx, m in enumerate(self.model):
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            if hasattr(m, 'backbone'):
                x = m(x)
                for _ in range(5 - len(x)):
                    x.insert(0, None)
                for i_idx, i in enumerate(x):
                    if i_idx in self.save:
                        y.append(i)
                    else:
                        y.append(None)
                # print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x if x_ is not None])}')
                x = x[-1]
            else:
                x = m(x)  # run
                y.append(x if m.i in self.save else None)  # save output
            
            # if type(x) in {list, tuple}:
            #     if idx == (len(self.model) - 1):
            #         if type(x[1]) is dict:
            #             print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x[1]["one2one"]])}')
            #         else:
            #             print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x[1]])}')
            #     else:
            #         print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x if x_ is not None])}')
            # elif type(x) is dict:
            #     print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x["one2one"]])}')
            # else:
            #     if not hasattr(m, 'backbone'):
            #         print(f'layer id:{idx:>2} {m.type:>50} output shape:{x.size()}')
            
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
            if embed and m.i in embed:
                embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max(embed):
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        return x

(3)修改parse_model函数

可以直接把下面的代码粘贴到对应的位置中

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

    Args:
        d (dict): Model dictionary.
        ch (int): Input channels.
        verbose (bool): Whether to print model details.

    Returns:
        (tuple): Tuple containing the PyTorch model and sorted list of output layers.
    """
    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}'.")
        if len(scales[scale]) == 3:
            depth, width, max_channels = scales[scale]
        elif len(scales[scale]) == 4:
            depth, width, max_channels, threshold = 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':<60}{'arguments':<50}")
    ch = [ch]
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    is_backbone = False
    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
        try:
            if m == 'node_mode':
                m = d[m]
                if len(args) > 0:
                    if args[0] == 'head_channel':
                        args[0] = int(d[args[0]])
            t = m
            m = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m]  # get module
        except:
            pass
        for j, a in enumerate(args):
            if isinstance(a, str):
                with contextlib.suppress(ValueError):
                    try:
                        args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
                    except:
                        args[j] = a
        n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
        if m in {
            Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
            BottleneckCSP, C1, C2, C2f, ELAN1, AConv, SPPELAN, C2fAttn, C3, C3TR,
            C3Ghost, nn.Conv2d, nn.ConvTranspose2d, DWConvTranspose2d, C3x, RepC3, PSA, SCDown, C2fCIB

        }:
            if args[0] == 'head_channel':
                args[0] = d[args[0]]
            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)
            if m is C2fAttn:
                args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)  # embed channels
                args[2] = int(
                    max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2]
                )  # num heads

            args = [c1, c2, *args[1:]]


        elif m in {AIFI}:
            args = [ch[f], *args]
            c2 = args[0]
        elif m in (HGStem, HGBlock):
            c1, cm, c2 = ch[f], args[0], args[1]
            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)
                cm = make_divisible(min(cm, max_channels) * width, 8)
            args = [c1, cm, c2, *args[2:]]
            if m in (HGBlock):
                args.insert(4, n)  # number of repeats
                n = 1
        elif m is ResNetLayer:
            c2 = args[1] if args[3] else args[1] * 4
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        elif m in frozenset({Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}):
            args.append([ch[x] for x in f])
        elif m is RTDETRDecoder:  # special case, channels arg must be passed in index 1
            args.insert(1, [ch[x] for x in f])
        elif m is CBLinear:
            c2 = make_divisible(min(args[0][-1], max_channels) * width, 8)
            c1 = ch[f]
            args = [c1, [make_divisible(min(c2_, max_channels) * width, 8) for c2_ in args[0]], *args[1:]]
        elif m is CBFuse:
            c2 = ch[f[-1]]
        elif isinstance(m, str):
            t = m
            if len(args) == 2:
                m = timm.create_model(m, pretrained=args[0], pretrained_cfg_overlay={'file': args[1]},
                                      features_only=True)
            elif len(args) == 1:
                m = timm.create_model(m, pretrained=args[0], features_only=True)
            c2 = m.feature_info.channels()
        elif m in {unireplknet_a, unireplknet_f, unireplknet_p, unireplknet_n, unireplknet_t, unireplknet_s, unireplknet_b, unireplknet_l, unireplknet_xl
                   }:
            m = m(*args)
            c2 = m.channel
        else:
            c2 = ch[f]


        if isinstance(c2, list):
            is_backbone = True
            m_ = m
            m_.backbone = True
        else:
            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 + 4 if is_backbone else 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:<60}{str(args):<50}")  # print
        save.extend(x % (i + 4 if is_backbone else i) for x in ([f] if isinstance(f, int) else f) if
                    x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        if isinstance(c2, list):
            ch.extend(c2)
            for _ in range(5 - len(ch)):
                ch.insert(0, 0)
        else:
            ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

具体改进差别如下图所示: ​​​​​

​

第③步：yolov8.yaml文件修改   

在下述文件夹中创立yolov8-unireplknet.yaml

​​

# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

# 0-P1/2
# 1-P2/4
# 2-P3/8
# 3-P4/16
# 4-P5/32

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, unireplknet_a, []]  # 4
  - [-1, 1, SPPF, [1024, 5]]  # 5

# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 6
  - [[-1, 3], 1, Concat, [1]]  # 7 cat backbone P4
  - [-1, 3, C2f, [512]]  # 8

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 9
  - [[-1, 2], 1, Concat, [1]]  # 10 cat backbone P3
  - [-1, 3, C2f, [256]]  # 11 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]] # 12
  - [[-1, 8], 1, Concat, [1]]  # 13 cat head P4
  - [-1, 3, C2f, [512]]  # 14 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]] # 15
  - [[-1, 5], 1, Concat, [1]]  # 16 cat head P5
  - [-1, 3, C2f, [1024]]  # 17 (P5/32-large)

  - [[11, 14, 17], 1, Detect, [nc]]  # Detect(P3, P4, P5)

第④步：验证是否加入成功   

将train.py中的配置文件进行修改，并运行  ​

​

---

🏋不是每一粒种子都能开花，但播下种子就比荒芜的旷野强百倍🏋

🍁YOLOv8入门+改进专栏🍁

---

  【YOLOv8改进系列】： 

 

YOLOv8改进系列（2）----替换主干网络之FasterNet

YOLOv8改进系列（3）----替换主干网络之ConvNeXt V2

YOLOv8改进系列（4）----替换C2f之FasterNet中的FasterBlock替换C2f中的Bottleneck 

YOLOv8改进系列（5）----替换主干网络之EfficientFormerV2 

YOLOv8改进系列（6）----替换主干网络之VanillaNet

YOLOv8改进系列（7）----替换主干网络之LSKNet

YOLOv8改进系列（8）----替换主干网络之Swin Transformer 

YOLOv8改进系列（9）----替换主干网络之RepViT

---

​