YOLOv8改进 更换多层池化操作主干网络PoolFormer

一、多层池化操作主干网络PoolFormer论文

论文地址：2111.11418.pdf (arxiv.org)

二、PoolFormer的网络结构

PoolFormer采用自注意力机制和池化操作相结合的方式，同时考虑了局部和全局的特征关系。

PoolFormer的网络结构包括以下几个主要组件：

1. 输入特征图（Input Feature Map）：PoolFormer的输入是一个特征图，它可以是一个图像或其他形式的特征表示。输入特征图包含了原始数据的信息。

2. 编码器（Encoder）：编码器是PoolFormer的核心组件，用于提取特征并学习特征表示。编码器采用了自注意力机制，通过自注意力头（Self-Attention Heads）对输入特征图进行多尺度的特征提取。自注意力机制能够自动学习输入特征图中不同位置之间的关系，从而捕捉到更丰富的特征信息。

3. 位置编码器（Positional Encoder）：位置编码器用于将输入特征图的位置信息嵌入到特征表示中。它可以是一个简单的线性映射，也可以是一个更复杂的非线性函数。位置编码器的作用是引入位置信息，使得特征表示具有空间上的连续性。

4. 高级编码器（High-Level Encoder）：高级编码器用于进一步提取输入特征图的高级语义信息。它可以是一个或多个卷积层，用于对编码器的输出进行细化和抽象。高级编码器的作用是提高特征表示的表达能力，使得模型能够更好地理解输入数据。

5. 池化操作（Pooling Operation）：PoolFormer还采用了池化操作，用于进一步减少特征图的尺寸和维度。池化操作可以是平均池化或最大池化，它通过对输入特征图的局部区域进行汇聚，得到特征图的下采样表示。池化操作的作用是降低特征图的维度，提高计算效率。

6. 全局特征（Global Features）：在PoolFormer中，全局特征指的是对整个输入特征图进行池化操作得到的特征表示。全局特征可以捕捉到整个输入的整体信息，对于图像分类等任务非常重要。

7. 分类器（Classifier）：分类器用于将池化操作得到的特征表示映射到类别概率分布。分类器可以是一个全连接层或其他类型的线性映射函数。它的作用是将深层特征映射为对应的类别概率，从而进行分类任务。

PoolFormer的优点包括模型复杂度低、计算速度快，特别适用于实时图像分割任务。此外，通过适当调整分块大小和池化操作的参数，PoolFormer还能够在不同的图像分辨率下保持较好的分割性能。?

三、代码实现

1、在ultralytics\ultralytics\nn路径下新建一个文件夹命名为backbone，用于存放网络结构修改的代码。

并在该?backbone文件夹路径下新建py文件PoolFormer.py，并在该文件里添加PoolFormer网络结构的代码：

"""
PoolFormer implementation
"""
import os
import copy
import torch
import torch.nn as nn
import numpy as np

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from timm.models.registry import register_model

__all__ = ['poolformer_s12', 'poolformer_s24', 'poolformer_s36', 'poolformer_m48', 'poolformer_m36']

def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'pool_size': None,
        'crop_pct': .95, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'classifier': 'head',
        **kwargs
    }


default_cfgs = {
    'poolformer_s': _cfg(crop_pct=0.9),
    'poolformer_m': _cfg(crop_pct=0.95),
}


class PatchEmbed(nn.Module):
    """
    Patch Embedding that is implemented by a layer of conv.
    Input: tensor in shape [B, C, H, W]
    Output: tensor in shape [B, C, H/stride, W/stride]
    """
    def __init__(self, patch_size=16, stride=16, padding=0,
                 in_chans=3, embed_dim=768, norm_layer=None):
        super().__init__()
        patch_size = to_2tuple(patch_size)
        stride = to_2tuple(stride)
        padding = to_2tuple(padding)
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,
                              stride=stride, padding=padding)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        x = self.proj(x)
        x = self.norm(x)
        return x


class LayerNormChannel(nn.Module):
    """
    LayerNorm only for Channel Dimension.
    Input: tensor in shape [B, C, H, W]
    """
    def __init__(self, num_channels, eps=1e-05):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(num_channels))
        self.bias = nn.Parameter(torch.zeros(num_channels))
        self.eps = eps

    def forward(self, x):
        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.unsqueeze(-1).unsqueeze(-1) * x \
            + self.bias.unsqueeze(-1).unsqueeze(-1)
        return x


class GroupNorm(nn.GroupNorm):
    """
    Group Normalization with 1 group.
    Input: tensor in shape [B, C, H, W]
    """
    def __init__(self, num_channels, **kwargs):
        super().__init__(1, num_channels, **kwargs)


class Pooling(nn.Module):
    """
    Implementation of pooling for PoolFormer
    --pool_size: pooling size
    """
    def __init__(self, pool_size=3):
        super().__init__()
        self.pool = nn.AvgPool2d(
            pool_size, stride=1, padding=pool_size//2, count_include_pad=False)

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


class Mlp(nn.Module):
    """
    Implementation of MLP with 1*1 convolutions.
    Input: tensor with shape [B, C, H, W]
    """
    def __init__(self, in_features, hidden_features=None,
                 out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
        self.act = act_layer()
        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
        self.drop = nn.Dropout(drop)
        self.apply(self._init_weights)

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

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class PoolFormerBlock(nn.Module):
    """
    Implementation of one PoolFormer block.
    --dim: embedding dim
    --pool_size: pooling size
    --mlp_ratio: mlp expansion ratio
    --act_layer: activation
    --norm_layer: normalization
    --drop: dropout rate
    --drop path: Stochastic Depth,
        refer to https://arxiv.org/abs/1603.09382
    --use_layer_scale, --layer_scale_init_value: LayerScale,
        refer to https://arxiv.org/abs/2103.17239
    """
    def __init__(self, dim, pool_size=3, mlp_ratio=4.,
                 act_layer=nn.GELU, norm_layer=GroupNorm,
                 drop=0., drop_path=0.,
                 use_layer_scale=True, layer_scale_init_value=1e-5):

        super().__init__()

        self.norm1 = norm_layer(dim)
        self.token_mixer = Pooling(pool_size=pool_size)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
                       act_layer=act_layer, drop=drop)

        # The following two techniques are useful to train deep PoolFormers.
        self.drop_path = DropPath(drop_path) if drop_path > 0. \
            else nn.Identity()
        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale_1 = nn.Parameter(
                layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            self.layer_scale_2 = nn.Parameter(
                layer_scale_init_value * torch.ones((dim)), requires_grad=True)

    def forward(self, x):
        if self.use_layer_scale:
            x = x + self.drop_path(
                self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)
                * self.token_mixer(self.norm1(x)))
            x = x + self.drop_path(
                self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
                * self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.token_mixer(self.norm1(x)))
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


def basic_blocks(dim, index, layers,
                 pool_size=3, mlp_ratio=4.,
                 act_layer=nn.GELU, norm_layer=GroupNorm,
                 drop_rate=.0, drop_path_rate=0.,
                 use_layer_scale=True, layer_scale_init_value=1e-5):
    """
    generate PoolFormer blocks for a stage
    return: PoolFormer blocks
    """
    blocks = []
    for block_idx in range(layers[index]):
        block_dpr = drop_path_rate * (
            block_idx + sum(layers[:index])) / (sum(layers) - 1)
        blocks.append(PoolFormerBlock(
            dim, pool_size=pool_size, mlp_ratio=mlp_ratio,
            act_layer=act_layer, norm_layer=norm_layer,
            drop=drop_rate, drop_path=block_dpr,
            use_layer_scale=use_layer_scale,
            layer_scale_init_value=layer_scale_init_value,
            ))
    blocks = nn.Sequential(*blocks)

    return blocks


class PoolFormer(nn.Module):
    """
    PoolFormer, the main class of our model
    --layers: [x,x,x,x], number of blocks for the 4 stages
    --embed_dims, --mlp_ratios, --pool_size: the embedding dims, mlp ratios and
        pooling size for the 4 stages
    --downsamples: flags to apply downsampling or not
    --norm_layer, --act_layer: define the types of normalization and activation
    --num_classes: number of classes for the image classification
    --in_patch_size, --in_stride, --in_pad: specify the patch embedding
        for the input image
    --down_patch_size --down_stride --down_pad:
        specify the downsample (patch embed.)
    --fork_feat: whether output features of the 4 stages, for dense prediction
    --init_cfg, --pretrained:
        for mmdetection and mmsegmentation to load pretrained weights
    """
    def __init__(self, layers, embed_dims=None,
                 mlp_ratios=None, downsamples=None,
                 pool_size=3,
                 norm_layer=GroupNorm, act_layer=nn.GELU,
                 num_classes=1000,
                 in_patch_size=7, in_stride=4, in_pad=2,
                 down_patch_size=3, down_stride=2, down_pad=1,
                 drop_rate=0., drop_path_rate=0.,
                 use_layer_scale=True, layer_scale_init_value=1e-5,
                 fork_feat=True,
                 init_cfg=None,
                 pretrained=None,
                 **kwargs):

        super().__init__()

        if not fork_feat:
            self.num_classes = num_classes
        self.fork_feat = fork_feat

        self.patch_embed = PatchEmbed(
            patch_size=in_patch_size, stride=in_stride, padding=in_pad,
            in_chans=3, embed_dim=embed_dims[0])

        # set the main block in network
        network = []
        for i in range(len(layers)):
            stage = basic_blocks(embed_dims[i], i, layers,
                                 pool_size=pool_size, mlp_ratio=mlp_ratios[i],
                                 act_layer=act_layer, norm_layer=norm_layer,
                                 drop_rate=drop_rate,
                                 drop_path_rate=drop_path_rate,
                                 use_layer_scale=use_layer_scale,
                                 layer_scale_init_value=layer_scale_init_value)
            network.append(stage)
            if i >= len(layers) - 1:
                break
            if downsamples[i] or embed_dims[i] != embed_dims[i+1]:
                # downsampling between two stages
                network.append(
                    PatchEmbed(
                        patch_size=down_patch_size, stride=down_stride,
                        padding=down_pad,
                        in_chans=embed_dims[i], embed_dim=embed_dims[i+1]
                        )
                    )

        self.network = nn.ModuleList(network)

        if self.fork_feat:
            # add a norm layer for each output
            self.out_indices = [0, 2, 4, 6]
            for i_emb, i_layer in enumerate(self.out_indices):
                if i_emb == 0 and os.environ.get('FORK_LAST3', None):
                    # TODO: more elegant way
                    """For RetinaNet, `start_level=1`. The first norm layer will not used.
                    cmd: `FORK_LAST3=1 python -m torch.distributed.launch ...`
                    """
                    layer = nn.Identity()
                else:
                    layer = norm_layer(embed_dims[i_emb])
                layer_name = f'norm{i_layer}'
                self.add_module(layer_name, layer)
        else:
            # Classifier head
            self.norm = norm_layer(embed_dims[-1])
            self.head = nn.Linear(
                embed_dims[-1], num_classes) if num_classes > 0 \
                else nn.Identity()
        self.init_cfg = copy.deepcopy(init_cfg)
        self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 224, 224))]

    def reset_classifier(self, num_classes):
        self.num_classes = num_classes
        self.head = nn.Linear(
            self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_embeddings(self, x):
        x = self.patch_embed(x)
        return x

    def forward_tokens(self, x):
        outs = []
        for idx, block in enumerate(self.network):
            x = block(x)
            if self.fork_feat and idx in self.out_indices:
                norm_layer = getattr(self, f'norm{idx}')
                x_out = norm_layer(x)
                outs.append(x_out)
        return outs

    def forward(self, x):
        # input embedding
        x = self.forward_embeddings(x)
        # through backbone
        x = self.forward_tokens(x)
        return x


model_urls = {
    "poolformer_s12": "https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s12.pth.tar",
    "poolformer_s24": "https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s24.pth.tar",
    "poolformer_s36": "https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s36.pth.tar",
    "poolformer_m36": "https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_m36.pth.tar",
    "poolformer_m48": "https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_m48.pth.tar",
}

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 poolformer_s12(pretrained=False, **kwargs):
    """
    PoolFormer-S12 model, Params: 12M
    --layers: [x,x,x,x], numbers of layers for the four stages
    --embed_dims, --mlp_ratios:
        embedding dims and mlp ratios for the four stages
    --downsamples: flags to apply downsampling or not in four blocks
    """
    layers = [2, 2, 6, 2]
    embed_dims = [64, 128, 320, 512]
    mlp_ratios = [4, 4, 4, 4]
    downsamples = [True, True, True, True]
    model = PoolFormer(
        layers, embed_dims=embed_dims,
        mlp_ratios=mlp_ratios, downsamples=downsamples,
        **kwargs)
    model.default_cfg = default_cfgs['poolformer_s']
    if pretrained:
        url = model_urls['poolformer_s12']
        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
        model.load_state_dict(update_weight(model.state_dict(), checkpoint))
    return model

def poolformer_s24(pretrained=False, **kwargs):
    """
    PoolFormer-S24 model, Params: 21M
    """
    layers = [4, 4, 12, 4]
    embed_dims = [64, 128, 320, 512]
    mlp_ratios = [4, 4, 4, 4]
    downsamples = [True, True, True, True]
    model = PoolFormer(
        layers, embed_dims=embed_dims,
        mlp_ratios=mlp_ratios, downsamples=downsamples,
        **kwargs)
    model.default_cfg = default_cfgs['poolformer_s']
    if pretrained:
        url = model_urls['poolformer_s24']
        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
        model.load_state_dict(update_weight(model.state_dict(), checkpoint))
    return model

def poolformer_s36(pretrained=False, **kwargs):
    """
    PoolFormer-S36 model, Params: 31M
    """
    layers = [6, 6, 18, 6]
    embed_dims = [64, 128, 320, 512]
    mlp_ratios = [4, 4, 4, 4]
    downsamples = [True, True, True, True]
    model = PoolFormer(
        layers, embed_dims=embed_dims,
        mlp_ratios=mlp_ratios, downsamples=downsamples,
        layer_scale_init_value=1e-6,
        **kwargs)
    model.default_cfg = default_cfgs['poolformer_s']
    if pretrained:
        url = model_urls['poolformer_s36']
        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
        model.load_state_dict(update_weight(model.state_dict(), checkpoint))
    return model

def poolformer_m36(pretrained=False, **kwargs):
    """
    PoolFormer-M36 model, Params: 56M
    """
    layers = [6, 6, 18, 6]
    embed_dims = [96, 192, 384, 768]
    mlp_ratios = [4, 4, 4, 4]
    downsamples = [True, True, True, True]
    model = PoolFormer(
        layers, embed_dims=embed_dims,
        mlp_ratios=mlp_ratios, downsamples=downsamples,
        layer_scale_init_value=1e-6,
        **kwargs)
    model.default_cfg = default_cfgs['poolformer_m']
    if pretrained:
        url = model_urls['poolformer_m36']
        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
        model.load_state_dict(update_weight(model.state_dict(), checkpoint))
    return model


@register_model
def poolformer_m48(pretrained=False, **kwargs):
    """
    PoolFormer-M48 model, Params: 73M
    """
    layers = [8, 8, 24, 8]
    embed_dims = [96, 192, 384, 768]
    mlp_ratios = [4, 4, 4, 4]
    downsamples = [True, True, True, True]
    model = PoolFormer(
        layers, embed_dims=embed_dims,
        mlp_ratios=mlp_ratios, downsamples=downsamples,
        layer_scale_init_value=1e-6,
        **kwargs)
    model.default_cfg = default_cfgs['poolformer_m']
    if pretrained:
        url = model_urls['poolformer_m48']
        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
        model.load_state_dict(update_weight(model.state_dict(), checkpoint))
    return model

if __name__ == '__main__':
    model = poolformer_s12(pretrained=True)
    inputs = torch.randn((1, 3, 640, 640))
    for i in model(inputs):
        print(i.size())

2、在ultralytics\ultralytics\nn\tasks.py文件中加入PoolFormer模块

开头先从新建的文件夹引入PoolFormer的包：

from ultralytics.nn.backbone.PoolFormer import *

并且文件的def _predict_once函数模块要替换为更换网络结构后的预测模块：

替换为：

    def _predict_once(self, x, profile=False, visualize=False):
        """
        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.

        Returns:
            (torch.Tensor): The last output of the model.
        """
        y, dt = [], []  # outputs
        for m in 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)
                # for i in x:
                #     if i is not None:
                #         print(i.size())
                x = x[-1]
            else:
                x = m(x)  # run
                y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

然后在def parse_model函数模块中进行修改：

由于是更换yolov8原始的网路结构，所以需要在该parse_model函数模块中加入更改网络模块的代码，更改后完整的def parse_model模块代码为：

def parse_model(d, ch, verbose=True, warehouse_manager=None):  # 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
    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, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, RepC3,
                 C2f_DBB,C2f_DySnakeConv):
            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)

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


            if m in (
            BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x, RepC3, C2f_DBB,C2f_DySnakeConv):
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is AIFI:
            args = [ch[f], *args]
            ##### 更换网络lsknet  ####
        elif m in {lsknet_s, lsknet_t}:
            m = m(*args)
            c2 = m.channel
        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 in {GAM_Attention}:
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if not output
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            args = [c1, c2, *args[1:]]
        elif m in {ShuffleAttention}:
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if not output
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            args = [c1, c2, *args[1:]]
        elif m in (
        Detect, DetectAux, Pose,Detect_DyHead):
            args.append([ch[x] for x in f])

        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])
        elif m in {poolformer_s12, poolformer_s12, poolformer_s36, poolformer_m36, poolformer_m48}:
            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:<45}{str(args):<30}')  # 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)

由于我的模型中包含着其他多种改进，所以该?parse_model函数模块中也包含其他改进的代码，如果出现标红，把标红的改进模块删除即可。

3、创建yolov8+PoolFormer.yaml文件：

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 2  # 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

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
  - [-1, 3, C2f, [128, True]]
  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f, [256, True]]
  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 9
  - [-1, 1, SegNext_Attention, []] # 10

# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 13

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 16 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 13], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 19 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 10], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 22 (P5/32-large)

  - [[16, 19, 22], 1, Detect, [nc]]  # Detect(P3, P4, P5)

四、运行验证

可以看出模型结构已经变成PoolFormer的主干网络。