爆改YOLOv8|利用图像分割网络UNetV2改进yolov8主干-即插即用

1，本文介绍

U-Net v2 通过引入创新的跳跃连接设计来提升医学图像分割的精度。这一版本专注于更有效地融合不同层级的特征，包括高级特征中的语义信息和低级特征中的细节信息。通过这种优化，U-Net v2 能够在低级特征中注入丰富的语义，同时细化高级特征，从而实现更精准的对象边界描绘和小结构提取。

其主要技术创新包括：

多级特征提取：使用深度神经网络编码器从输入图像中提取不同层次的特征。
语义与细节融合(Semantics and Detail Infusion, SDI)模块：通过哈达玛积操作，将高级特征中的语义信息与低级特征中的细节信息融合到各层级的特征图中。
改进的跳跃连接：这些新型跳跃连接增强了各层特征的语义和细节表现，从而在解码器阶段实现更高精度的分割。

关于UNetV2的详细介绍可以看论文：https://arxiv.org/abs/2311.17791

本文将讲解如何将UNetV2融合进yolov8

话不多说，上代码！

2，将UNetV2融合进yolov8

2.1 步骤一

找到如下的目录'ultralytics/nn/modules'，然后在这个目录下创建一个UNetV2.py文件，文件名字可以根据你自己的习惯起，然后将UNetV2的核心代码复制进去


import os.path
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
import math__all__ = ['pvt_v2_b0', 'pvt_v2_b1', 'pvt_v2_b2', 'pvt_v2_b3', 'pvt_v2_b4', 'pvt_v2_b5']class ChannelAttention(nn.Module):def __init__(self, in_planes, ratio=16):super(ChannelAttention, self).__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)self.max_pool = nn.AdaptiveMaxPool2d(1)self.fc1 = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)self.relu1 = nn.ReLU()self.fc2 = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)self.sigmoid = nn.Sigmoid()def forward(self, x):avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))out = avg_out + max_outreturn self.sigmoid(out)class SpatialAttention(nn.Module):def __init__(self, kernel_size=7):super(SpatialAttention, self).__init__()assert kernel_size in (3, 7), 'kernel size must be 3 or 7'padding = 3 if kernel_size == 7 else 1self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)self.sigmoid = nn.Sigmoid()def forward(self, x):avg_out = torch.mean(x, dim=1, keepdim=True)max_out, _ = torch.max(x, dim=1, keepdim=True)x = torch.cat([avg_out, max_out], dim=1)x = self.conv1(x)return self.sigmoid(x)class BasicConv2d(nn.Module):def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1):super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_planes, out_planes,kernel_size=kernel_size, stride=stride,padding=padding, dilation=dilation, bias=False)self.bn = nn.BatchNorm2d(out_planes)self.relu = nn.ReLU(inplace=True)def forward(self, x):x = self.conv(x)x = self.bn(x)return xclass Encoder(nn.Module):def __init__(self, pretrain_path):super().__init__()self.backbone = pvt_v2_b2()if pretrain_path is None:warnings.warn('please provide the pretrained pvt model. Not using pretrained model.')elif not os.path.isfile(pretrain_path):warnings.warn(f'path: {pretrain_path} does not exists. Not using pretrained model.')else:print(f"using pretrained file: {pretrain_path}")save_model = torch.load(pretrain_path)model_dict = self.backbone.state_dict()state_dict = {k: v for k, v in save_model.items() if k in model_dict.keys()}model_dict.update(state_dict)self.backbone.load_state_dict(model_dict)def forward(self, x):f1, f2, f3, f4 = self.backbone(x)  # (x: 3, 352, 352)return f1, f2, f3, f4class SDI(nn.Module):def __init__(self, channel):super().__init__()self.convs = nn.ModuleList([nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1) for _ in range(4)])def forward(self, xs, anchor):ans = torch.ones_like(anchor)target_size = anchor.shape[-1]for i, x in enumerate(xs):if x.shape[-1] > target_size:x = F.adaptive_avg_pool2d(x, (target_size, target_size))elif x.shape[-1] < target_size:x = F.interpolate(x, size=(target_size, target_size),mode='bilinear', align_corners=True)ans = ans * self.convs[i](x)return ansclass UNetV2(nn.Module):"""use SpatialAtt + ChannelAtt"""def __init__(self, channel=3, n_classes=1, deep_supervision=True, pretrained_path=None):super().__init__()self.deep_supervision = deep_supervisionself.encoder = Encoder(pretrained_path)self.ca_1 = ChannelAttention(64)self.sa_1 = SpatialAttention()self.ca_2 = ChannelAttention(128)self.sa_2 = SpatialAttention()self.ca_3 = ChannelAttention(320)self.sa_3 = SpatialAttention()self.ca_4 = ChannelAttention(512)self.sa_4 = SpatialAttention()self.Translayer_1 = BasicConv2d(64, channel, 1)self.Translayer_2 = BasicConv2d(128, channel, 1)self.Translayer_3 = BasicConv2d(320, channel, 1)self.Translayer_4 = BasicConv2d(512, channel, 1)self.sdi_1 = SDI(channel)self.sdi_2 = SDI(channel)self.sdi_3 = SDI(channel)self.sdi_4 = SDI(channel)self.seg_outs = nn.ModuleList([nn.Conv2d(channel, n_classes, 1, 1) for _ in range(4)])self.deconv2 = nn.ConvTranspose2d(channel, channel, kernel_size=4, stride=2, padding=1,bias=False)self.deconv3 = nn.ConvTranspose2d(channel, channel, kernel_size=4, stride=2,padding=1, bias=False)self.deconv4 = nn.ConvTranspose2d(channel, channel, kernel_size=4, stride=2,padding=1, bias=False)self.deconv5 = nn.ConvTranspose2d(channel, channel, kernel_size=4, stride=2,padding=1, bias=False)self.width_list = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]def forward(self, x):seg_outs = []f1, f2, f3, f4 = self.encoder(x)f1 = self.ca_1(f1) * f1f1 = self.sa_1(f1) * f1f1 = self.Translayer_1(f1)f2 = self.ca_2(f2) * f2f2 = self.sa_2(f2) * f2f2 = self.Translayer_2(f2)f3 = self.ca_3(f3) * f3f3 = self.sa_3(f3) * f3f3 = self.Translayer_3(f3)f4 = self.ca_4(f4) * f4f4 = self.sa_4(f4) * f4f4 = self.Translayer_4(f4)f41 = self.sdi_4([f1, f2, f3, f4], f4)f31 = self.sdi_3([f1, f2, f3, f4], f3)f21 = self.sdi_2([f1, f2, f3, f4], f2)f11 = self.sdi_1([f1, f2, f3, f4], f1)seg_outs.append(self.seg_outs[0](f41))y = self.deconv2(f41) + f31seg_outs.append(self.seg_outs[1](y))y = self.deconv3(y) + f21seg_outs.append(self.seg_outs[2](y))y = self.deconv4(y) + f11seg_outs.append(self.seg_outs[3](y))for i, o in enumerate(seg_outs):seg_outs[i] = F.interpolate(o, scale_factor=4, mode='bilinear')if self.deep_supervision:return seg_outs[::-1]else:return seg_outs[-1]class Mlp(nn.Module):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_featureshidden_features = hidden_features or in_featuresself.fc1 = nn.Linear(in_features, hidden_features)self.dwconv = DWConv(hidden_features)self.act = act_layer()self.fc2 = nn.Linear(hidden_features, out_features)self.drop = nn.Dropout(drop)self.apply(self._init_weights)def _init_weights(self, m):if isinstance(m, nn.Linear):trunc_normal_(m.weight, std=.02)if isinstance(m, nn.Linear) and m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.LayerNorm):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1.0)elif isinstance(m, nn.Conv2d):fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsfan_out //= m.groupsm.weight.data.normal_(0, math.sqrt(2.0 / fan_out))if m.bias is not None:m.bias.data.zero_()def forward(self, x, H, W):x = self.fc1(x)x = self.dwconv(x, H, W)x = self.act(x)x = self.drop(x)x = self.fc2(x)x = self.drop(x)return xclass Attention(nn.Module):def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):super().__init__()assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."self.dim = dimself.num_heads = num_headshead_dim = dim // num_headsself.scale = qk_scale or head_dim ** -0.5self.q = nn.Linear(dim, dim, bias=qkv_bias)self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)self.attn_drop = nn.Dropout(attn_drop)self.proj = nn.Linear(dim, dim)self.proj_drop = nn.Dropout(proj_drop)self.sr_ratio = sr_ratioif sr_ratio > 1:self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)self.norm = nn.LayerNorm(dim)self.apply(self._init_weights)def _init_weights(self, m):if isinstance(m, nn.Linear):trunc_normal_(m.weight, std=.02)if isinstance(m, nn.Linear) and m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.LayerNorm):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1.0)elif isinstance(m, nn.Conv2d):fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsfan_out //= m.groupsm.weight.data.normal_(0, math.sqrt(2.0 / fan_out))if m.bias is not None:m.bias.data.zero_()def forward(self, x, H, W):B, N, C = x.shapeq = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)if self.sr_ratio > 1:x_ = x.permute(0, 2, 1).reshape(B, C, H, W)x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)x_ = self.norm(x_)kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)else:kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)k, v = kv[0], kv[1]attn = (q @ k.transpose(-2, -1)) * self.scaleattn = attn.softmax(dim=-1)attn = self.attn_drop(attn)x = (attn @ v).transpose(1, 2).reshape(B, N, C)x = self.proj(x)x = self.proj_drop(x)return xclass Block(nn.Module):def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):super().__init__()self.norm1 = norm_layer(dim)self.attn = Attention(dim,num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)# NOTE: drop path for stochastic depth, we shall see if this is better than dropout hereself.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()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)self.apply(self._init_weights)def _init_weights(self, m):if isinstance(m, nn.Linear):trunc_normal_(m.weight, std=.02)if isinstance(m, nn.Linear) and m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.LayerNorm):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1.0)elif isinstance(m, nn.Conv2d):fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsfan_out //= m.groupsm.weight.data.normal_(0, math.sqrt(2.0 / fan_out))if m.bias is not None:m.bias.data.zero_()def forward(self, x, H, W):x = x + self.drop_path(self.attn(self.norm1(x), H, W))x = x + self.drop_path(self.mlp(self.norm2(x), H, W))return xclass OverlapPatchEmbed(nn.Module):""" Image to Patch Embedding"""def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):super().__init__()img_size = to_2tuple(img_size)patch_size = to_2tuple(patch_size)self.img_size = img_sizeself.patch_size = patch_sizeself.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]self.num_patches = self.H * self.Wself.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,padding=(patch_size[0] // 2, patch_size[1] // 2))self.norm = nn.LayerNorm(embed_dim)self.apply(self._init_weights)def _init_weights(self, m):if isinstance(m, nn.Linear):trunc_normal_(m.weight, std=.02)if isinstance(m, nn.Linear) and m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.LayerNorm):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1.0)elif isinstance(m, nn.Conv2d):fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsfan_out //= m.groupsm.weight.data.normal_(0, math.sqrt(2.0 / fan_out))if m.bias is not None:m.bias.data.zero_()def forward(self, x):x = self.proj(x)_, _, H, W = x.shapex = x.flatten(2).transpose(1, 2)x = self.norm(x)return x, H, Wclass PyramidVisionTransformerImpr(nn.Module):def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):super().__init__()self.num_classes = num_classesself.depths = depths# patch_embedself.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans,embed_dim=embed_dims[0])self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0],embed_dim=embed_dims[1])self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1],embed_dim=embed_dims[2])self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2],embed_dim=embed_dims[3])# transformer encoderdpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rulecur = 0self.block1 = nn.ModuleList([Block(dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,sr_ratio=sr_ratios[0])for i in range(depths[0])])self.norm1 = norm_layer(embed_dims[0])cur += depths[0]self.block2 = nn.ModuleList([Block(dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,sr_ratio=sr_ratios[1])for i in range(depths[1])])self.norm2 = norm_layer(embed_dims[1])cur += depths[1]self.block3 = nn.ModuleList([Block(dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,sr_ratio=sr_ratios[2])for i in range(depths[2])])self.norm3 = norm_layer(embed_dims[2])cur += depths[2]self.block4 = nn.ModuleList([Block(dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,sr_ratio=sr_ratios[3])for i in range(depths[3])])self.norm4 = norm_layer(embed_dims[3])# classification head# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()self.apply(self._init_weights)self.width_list = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]def _init_weights(self, m):if isinstance(m, nn.Linear):trunc_normal_(m.weight, std=.02)if isinstance(m, nn.Linear) and m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.LayerNorm):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1.0)elif isinstance(m, nn.Conv2d):fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsfan_out //= m.groupsm.weight.data.normal_(0, math.sqrt(2.0 / fan_out))if m.bias is not None:m.bias.data.zero_()def init_weights(self, pretrained=None):if isinstance(pretrained, str):logger = 1#load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)def reset_drop_path(self, drop_path_rate):dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]cur = 0for i in range(self.depths[0]):self.block1[i].drop_path.drop_prob = dpr[cur + i]cur += self.depths[0]for i in range(self.depths[1]):self.block2[i].drop_path.drop_prob = dpr[cur + i]cur += self.depths[1]for i in range(self.depths[2]):self.block3[i].drop_path.drop_prob = dpr[cur + i]cur += self.depths[2]for i in range(self.depths[3]):self.block4[i].drop_path.drop_prob = dpr[cur + i]def freeze_patch_emb(self):self.patch_embed1.requires_grad = False@torch.jit.ignoredef no_weight_decay(self):return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'}  # has pos_embed may be betterdef get_classifier(self):return self.headdef reset_classifier(self, num_classes, global_pool=''):self.num_classes = num_classesself.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()# def _get_pos_embed(self, pos_embed, patch_embed, H, W):#     if H * W == self.patch_embed1.num_patches:#         return pos_embed#     else:#         return F.interpolate(#             pos_embed.reshape(1, patch_embed.H, patch_embed.W, -1).permute(0, 3, 1, 2),#             size=(H, W), mode="bilinear").reshape(1, -1, H * W).permute(0, 2, 1)def forward_features(self, x):B = x.shape[0]outs = []# stage 1x, H, W = self.patch_embed1(x)for i, blk in enumerate(self.block1):x = blk(x, H, W)x = self.norm1(x)x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()outs.append(x)# stage 2x, H, W = self.patch_embed2(x)for i, blk in enumerate(self.block2):x = blk(x, H, W)x = self.norm2(x)x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()outs.append(x)# stage 3x, H, W = self.patch_embed3(x)for i, blk in enumerate(self.block3):x = blk(x, H, W)x = self.norm3(x)x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()outs.append(x)# stage 4x, H, W = self.patch_embed4(x)for i, blk in enumerate(self.block4):x = blk(x, H, W)x = self.norm4(x)x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()outs.append(x)return outs# return x.mean(dim=1)def forward(self, x):x = self.forward_features(x)# x = self.head(x)return xclass DWConv(nn.Module):def __init__(self, dim=768):super(DWConv, self).__init__()self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)def forward(self, x, H, W):B, N, C = x.shapex = x.transpose(1, 2).view(B, C, H, W)x = self.dwconv(x)x = x.flatten(2).transpose(1, 2)return xdef _conv_filter(state_dict, patch_size=16):""" convert patch embedding weight from manual patchify + linear proj to conv"""out_dict = {}for k, v in state_dict.items():if 'patch_embed.proj.weight' in k:v = v.reshape((v.shape[0], 3, patch_size, patch_size))out_dict[k] = vreturn out_dictclass pvt_v2_b0(PyramidVisionTransformerImpr):def __init__(self, **kwargs):super(pvt_v2_b0, self).__init__(patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],drop_rate=0.0, drop_path_rate=0.1)class pvt_v2_b1(PyramidVisionTransformerImpr):def __init__(self, **kwargs):super(pvt_v2_b1, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],drop_rate=0.0, drop_path_rate=0.1)class pvt_v2_b2(PyramidVisionTransformerImpr):def __init__(self, **kwargs):super(pvt_v2_b2, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],drop_rate=0.0, drop_path_rate=0.1)class pvt_v2_b3(PyramidVisionTransformerImpr):def __init__(self, **kwargs):super(pvt_v2_b3, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],drop_rate=0.0, drop_path_rate=0.1)class pvt_v2_b4(PyramidVisionTransformerImpr):def __init__(self, **kwargs):super(pvt_v2_b4, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],drop_rate=0.0, drop_path_rate=0.1)class pvt_v2_b5(PyramidVisionTransformerImpr):def __init__(self, **kwargs):super(pvt_v2_b5, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],drop_rate=0.0, drop_path_rate=0.1)

2.2 步骤二

在task.py导入我们的模块

from .modules.UNetV2 import *

2.3 步骤三

按下图所示进行修改，在task.py的parse_model方法中

2.4 步骤四

在task.py的parse_model方法中，添加如下代码

    elif m in {pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b3, pvt_v2_b4, pvt_v2_b5}:m = m(*args)c2 = m.width_list backbone = True

2.5 步骤五

在task.py的parse_model方法中，对如下代码进行修改，修改为如图所示

if isinstance(c2, list):m_ = mm_.backbone = Trueelse:m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # modulet = str(m)[8:-2].replace('__main__.', '')  # module typem.np = sum(x.numel() for x in m_.parameters())  # number paramsm_.i, m_.f, m_.type = i + 4 if backbone else i, f, t  # attach index, 'from' index, type

2.6 步骤六

在task.py的parse_model方法中，对如下代码进行修改，修改为如图所示，在上图的紧邻后方

2.7 步骤七

在task.py的BaseModel类中，对如下代码进行修改，修改为如图所示

 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 = [], [], []  # outputsfor m in self.model:if m.f != -1:  # if not from previous layerx = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layersif profile:self._profile_one_layer(m, x, dt)if hasattr(m, 'backbone'):x = m(x)if len(x) != 5:  # 0 - 5x.insert(0, None)for index, i in enumerate(x):if index in self.save:y.append(i)else:y.append(None)x = x[-1]  # 最后一个输出传给下一层else:x = m(x)  # runy.append(x if m.i in self.save else None)  # save outputif 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))  # flattenif m.i == max(embed):return torch.unbind(torch.cat(embeddings, 1), dim=0)return x

2.8 步骤八

在task.py的大概200多行detectionmodel中，对如下参数进修改为640

2.9 步骤九

额外的步骤-如果计算量打印不下来，可以找到utils/torch_utils.py,修改以下参数值

注意！！！，这个步骤比较多，容易搞错，修改过程一定要多检查

到此修改完成，复制下面的yaml文件运行即可

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: 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 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [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, pvt_v2_b1, []]  # 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)

# 关于主干网络大家可以自行替换，数据集不同效果不同

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