1，本文介绍
MobileNetV4 是最新的 MobileNet 系列模型，专为移动设备优化。它引入了通用反转瓶颈（UIB）和 Mobile MQA 注意力机制，提升了推理速度和效率。通过改进的神经网络架构搜索（NAS）和蒸馏技术，MobileNetV4 在多种硬件平台上实现了高效和准确的表现，在 ImageNet-1K 数据集上达到 87% 的准确率，同时在 Pixel 8 EdgeTPU 上的运行时间为 3.8 毫秒。
关于MobileNetV4的详细介绍可以看论文：[2404.10518] MobileNetV4 - Universal Models for the Mobile Ecosystem
本文将讲解如何将MobileNetV4融合进yolov8
话不多说，上代码！
2， 将MobileNetV4融合进yolov8
2.1 步骤一
首先找到如下的目录'ultralytics/nn/modules'，然后在这个目录下创建一个MobileNetV4.py文件，文件名字可以根据你自己的习惯起，然后将MobileNetV4的核心代码复制进去。
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F__all__ = ['MobileNetV4ConvLarge', 'MobileNetV4ConvSmall', 'MobileNetV4ConvMedium', 'MobileNetV4HybridMedium', 'MobileNetV4HybridLarge']MNV4ConvSmall_BLOCK_SPECS = {"conv0": {"block_name": "convbn","num_blocks": 1,"block_specs": [[3, 32, 3, 2]]},"layer1": {"block_name": "convbn","num_blocks": 2,"block_specs": [[32, 32, 3, 2],[32, 32, 1, 1]]},"layer2": {"block_name": "convbn","num_blocks": 2,"block_specs": [[32, 96, 3, 2],[96, 64, 1, 1]]},"layer3": {"block_name": "uib","num_blocks": 6,"block_specs": [[64, 96, 5, 5, True, 2, 3],[96, 96, 0, 3, True, 1, 2],[96, 96, 0, 3, True, 1, 2],[96, 96, 0, 3, True, 1, 2],[96, 96, 0, 3, True, 1, 2],[96, 96, 3, 0, True, 1, 4],]},"layer4": {"block_name": "uib","num_blocks": 6,"block_specs": [[96, 128, 3, 3, True, 2, 6],[128, 128, 5, 5, True, 1, 4],[128, 128, 0, 5, True, 1, 4],[128, 128, 0, 5, True, 1, 3],[128, 128, 0, 3, True, 1, 4],[128, 128, 0, 3, True, 1, 4],]},"layer5": {"block_name": "convbn","num_blocks": 2,"block_specs": [[128, 960, 1, 1],[960, 1280, 1, 1]]}
}MNV4ConvMedium_BLOCK_SPECS = {"conv0": {"block_name": "convbn","num_blocks": 1,"block_specs": [[3, 32, 3, 2]]},"layer1": {"block_name": "fused_ib","num_blocks": 1,"block_specs": [[32, 48, 2, 4.0, True]]},"layer2": {"block_name": "uib","num_blocks": 2,"block_specs": [[48, 80, 3, 5, True, 2, 4],[80, 80, 3, 3, True, 1, 2]]},"layer3": {"block_name": "uib","num_blocks": 8,"block_specs": [[80, 160, 3, 5, True, 2, 6],[160, 160, 3, 3, True, 1, 4],[160, 160, 3, 3, True, 1, 4],[160, 160, 3, 5, True, 1, 4],[160, 160, 3, 3, True, 1, 4],[160, 160, 3, 0, True, 1, 4],[160, 160, 0, 0, True, 1, 2],[160, 160, 3, 0, True, 1, 4]]},"layer4": {"block_name": "uib","num_blocks": 11,"block_specs": [[160, 256, 5, 5, True, 2, 6],[256, 256, 5, 5, True, 1, 4],[256, 256, 3, 5, True, 1, 4],[256, 256, 3, 5, True, 1, 4],[256, 256, 0, 0, True, 1, 4],[256, 256, 3, 0, True, 1, 4],[256, 256, 3, 5, True, 1, 2],[256, 256, 5, 5, True, 1, 4],[256, 256, 0, 0, True, 1, 4],[256, 256, 0, 0, True, 1, 4],[256, 256, 5, 0, True, 1, 2]]},"layer5": {"block_name": "convbn","num_blocks": 2,"block_specs": [[256, 960, 1, 1],[960, 1280, 1, 1]]}
}MNV4ConvLarge_BLOCK_SPECS = {"conv0": {"block_name": "convbn","num_blocks": 1,"block_specs": [[3, 24, 3, 2]]},"layer1": {"block_name": "fused_ib","num_blocks": 1,"block_specs": [[24, 48, 2, 4.0, True]]},"layer2": {"block_name": "uib","num_blocks": 2,"block_specs": [[48, 96, 3, 5, True, 2, 4],[96, 96, 3, 3, True, 1, 4]]},"layer3": {"block_name": "uib","num_blocks": 11,"block_specs": [[96, 192, 3, 5, True, 2, 4],[192, 192, 3, 3, True, 1, 4],[192, 192, 3, 3, True, 1, 4],[192, 192, 3, 3, True, 1, 4],[192, 192, 3, 5, True, 1, 4],[192, 192, 5, 3, True, 1, 4],[192, 192, 5, 3, True, 1, 4],[192, 192, 5, 3, True, 1, 4],[192, 192, 5, 3, True, 1, 4],[192, 192, 5, 3, True, 1, 4],[192, 192, 3, 0, True, 1, 4]]},"layer4": {"block_name": "uib","num_blocks": 13,"block_specs": [[192, 512, 5, 5, True, 2, 4],[512, 512, 5, 5, True, 1, 4],[512, 512, 5, 5, True, 1, 4],[512, 512, 5, 5, True, 1, 4],[512, 512, 5, 0, True, 1, 4],[512, 512, 5, 3, True, 1, 4],[512, 512, 5, 0, True, 1, 4],[512, 512, 5, 0, True, 1, 4],[512, 512, 5, 3, True, 1, 4],[512, 512, 5, 5, True, 1, 4],[512, 512, 5, 0, True, 1, 4],[512, 512, 5, 0, True, 1, 4],[512, 512, 5, 0, True, 1, 4]]},"layer5": {"block_name": "convbn","num_blocks": 2,"block_specs": [[512, 960, 1, 1],[960, 1280, 1, 1]]}
}def mhsa(num_heads, key_dim, value_dim, px):if px == 24:kv_strides = 2elif px == 12:kv_strides = 1query_h_strides = 1query_w_strides = 1use_layer_scale = Trueuse_multi_query = Trueuse_residual = Truereturn [num_heads, key_dim, value_dim, query_h_strides, query_w_strides, kv_strides,use_layer_scale, use_multi_query, use_residual]MNV4HybridConvMedium_BLOCK_SPECS = {"conv0": {"block_name": "convbn","num_blocks": 1,"block_specs": [[3, 32, 3, 2]]},"layer1": {"block_name": "fused_ib","num_blocks": 1,"block_specs": [[32, 48, 2, 4.0, True]]},"layer2": {"block_name": "uib","num_blocks": 2,"block_specs": [[48, 80, 3, 5, True, 2, 4],[80, 80, 3, 3, True, 1, 2]]},"layer3": {"block_name": "uib","num_blocks": 8,"block_specs": [[80, 160, 3, 5, True, 2, 6],[160, 160, 0, 0, True, 1, 2],[160, 160, 3, 3, True, 1, 4],[160, 160, 3, 5, True, 1, 4, mhsa(4, 64, 64, 24)],[160, 160, 3, 3, True, 1, 4, mhsa(4, 64, 64, 24)],[160, 160, 3, 0, True, 1, 4, mhsa(4, 64, 64, 24)],[160, 160, 3, 3, True, 1, 4, mhsa(4, 64, 64, 24)],[160, 160, 3, 0, True, 1, 4]]},"layer4": {"block_name": "uib","num_blocks": 12,"block_specs": [[160, 256, 5, 5, True, 2, 6],[256, 256, 5, 5, True, 1, 4],[256, 256, 3, 5, True, 1, 4],[256, 256, 3, 5, True, 1, 4],[256, 256, 0, 0, True, 1, 2],[256, 256, 3, 5, True, 1, 2],[256, 256, 0, 0, True, 1, 2],[256, 256, 0, 0, True, 1, 4, mhsa(4, 64, 64, 12)],[256, 256, 3, 0, True, 1, 4, mhsa(4, 64, 64, 12)],[256, 256, 5, 5, True, 1, 4, mhsa(4, 64, 64, 12)],[256, 256, 5, 0, True, 1, 4, mhsa(4, 64, 64, 12)],[256, 256, 5, 0, True, 1, 4]]},"layer5": {"block_name": "convbn","num_blocks": 2,"block_specs": [[256, 960, 1, 1],[960, 1280, 1, 1]]}
}MNV4HybridConvLarge_BLOCK_SPECS = {"conv0": {"block_name": "convbn","num_blocks": 1,"block_specs": [[3, 24, 3, 2]]},"layer1": {"block_name": "fused_ib","num_blocks": 1,"block_specs": [[24, 48, 2, 4.0, True]]},"layer2": {"block_name": "uib","num_blocks": 2,"block_specs": [[48, 96, 3, 5, True, 2, 4],[96, 96, 3, 3, True, 1, 4]]},"layer3": {"block_name": "uib","num_blocks": 11,"block_specs": [[96, 192, 3, 5, True, 2, 4],[192, 192, 3, 3, True, 1, 4],[192, 192, 3, 3, True, 1, 4],[192, 192, 3, 3, True, 1, 4],[192, 192, 3, 5, True, 1, 4],[192, 192, 5, 3, True, 1, 4],[192, 192, 5, 3, True, 1, 4, mhsa(8, 48, 48, 24)],[192, 192, 5, 3, True, 1, 4, mhsa(8, 48, 48, 24)],[192, 192, 5, 3, True, 1, 4, mhsa(8, 48, 48, 24)],[192, 192, 5, 3, True, 1, 4, mhsa(8, 48, 48, 24)],[192, 192, 3, 0, True, 1, 4]]},"layer4": {"block_name": "uib","num_blocks": 14,"block_specs": [[192, 512, 5, 5, True, 2, 4],[512, 512, 5, 5, True, 1, 4],[512, 512, 5, 5, True, 1, 4],[512, 512, 5, 5, True, 1, 4],[512, 512, 5, 0, True, 1, 4],[512, 512, 5, 3, True, 1, 4],[512, 512, 5, 0, True, 1, 4],[512, 512, 5, 0, True, 1, 4],[512, 512, 5, 3, True, 1, 4],[512, 512, 5, 5, True, 1, 4, mhsa(8, 64, 64, 12)],[512, 512, 5, 0, True, 1, 4, mhsa(8, 64, 64, 12)],[512, 512, 5, 0, True, 1, 4, mhsa(8, 64, 64, 12)],[512, 512, 5, 0, True, 1, 4, mhsa(8, 64, 64, 12)],[512, 512, 5, 0, True, 1, 4]]},"layer5": {"block_name": "convbn","num_blocks": 2,"block_specs": [[512, 960, 1, 1],[960, 1280, 1, 1]]}
}MODEL_SPECS = {"MobileNetV4ConvSmall": MNV4ConvSmall_BLOCK_SPECS,"MobileNetV4ConvMedium": MNV4ConvMedium_BLOCK_SPECS,"MobileNetV4ConvLarge": MNV4ConvLarge_BLOCK_SPECS,"MobileNetV4HybridMedium": MNV4HybridConvMedium_BLOCK_SPECS,"MobileNetV4HybridLarge": MNV4HybridConvLarge_BLOCK_SPECS
}def make_divisible(value: float,divisor: int,min_value: Optional[float] = None,round_down_protect: bool = True,
) -> int:"""This function is copied from here"https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_layers.py"This is to ensure that all layers have channels that are divisible by 8.Args:value: A `float` of original value.divisor: An `int` of the divisor that need to be checked upon.min_value: A `float` of minimum value threshold.round_down_protect: A `bool` indicating whether round down more than 10%will be allowed.Returns:The adjusted value in `int` that is divisible against divisor."""if min_value is None:min_value = divisornew_value = max(min_value, int(value + divisor / 2) // divisor * divisor)# Make sure that round down does not go down by more than 10%.if round_down_protect and new_value < 0.9 * value:new_value += divisorreturn int(new_value)def conv_2d(inp, oup, kernel_size=3, stride=1, groups=1, bias=False, norm=True, act=True):conv = nn.Sequential()padding = (kernel_size - 1) // 2conv.add_module('conv', nn.Conv2d(inp, oup, kernel_size, stride, padding, bias=bias, groups=groups))if norm:conv.add_module('BatchNorm2d', nn.BatchNorm2d(oup))if act:conv.add_module('Activation', nn.ReLU6())return convclass InvertedResidual(nn.Module):def __init__(self, inp, oup, stride, expand_ratio, act=False, squeeze_excitation=False):super(InvertedResidual, self).__init__()self.stride = strideassert stride in [1, 2]hidden_dim = int(round(inp * expand_ratio))self.block = nn.Sequential()if expand_ratio != 1:self.block.add_module('exp_1x1', conv_2d(inp, hidden_dim, kernel_size=3, stride=stride))if squeeze_excitation:self.block.add_module('conv_3x3',conv_2d(hidden_dim, hidden_dim, kernel_size=3, stride=stride, groups=hidden_dim))self.block.add_module('red_1x1', conv_2d(hidden_dim, oup, kernel_size=1, stride=1, act=act))self.use_res_connect = self.stride == 1 and inp == oupdef forward(self, x):if self.use_res_connect:return x + self.block(x)else:return self.block(x)class UniversalInvertedBottleneckBlock(nn.Module):def __init__(self,inp,oup,start_dw_kernel_size,middle_dw_kernel_size,middle_dw_downsample,stride,expand_ratio):"""An inverted bottleneck block with optional depthwises.Referenced from here https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_blocks.py"""super().__init__()# Starting depthwise conv.self.start_dw_kernel_size = start_dw_kernel_sizeif self.start_dw_kernel_size:stride_ = stride if not middle_dw_downsample else 1self._start_dw_ = conv_2d(inp, inp, kernel_size=start_dw_kernel_size, stride=stride_, groups=inp, act=False)# Expansion with 1x1 convs.expand_filters = make_divisible(inp * expand_ratio, 8)self._expand_conv = conv_2d(inp, expand_filters, kernel_size=1)# Middle depthwise conv.self.middle_dw_kernel_size = middle_dw_kernel_sizeif self.middle_dw_kernel_size:stride_ = stride if middle_dw_downsample else 1self._middle_dw = conv_2d(expand_filters, expand_filters, kernel_size=middle_dw_kernel_size, stride=stride_,groups=expand_filters)# Projection with 1x1 convs.self._proj_conv = conv_2d(expand_filters, oup, kernel_size=1, stride=1, act=False)# Ending depthwise conv.# this not used# _end_dw_kernel_size = 0# self._end_dw = conv_2d(oup, oup, kernel_size=_end_dw_kernel_size, stride=stride, groups=inp, act=False)def forward(self, x):if self.start_dw_kernel_size:x = self._start_dw_(x)# print("_start_dw_", x.shape)x = self._expand_conv(x)# print("_expand_conv", x.shape)if self.middle_dw_kernel_size:x = self._middle_dw(x)# print("_middle_dw", x.shape)x = self._proj_conv(x)# print("_proj_conv", x.shape)return xclass MultiQueryAttentionLayerWithDownSampling(nn.Module):def __init__(self, inp, num_heads, key_dim, value_dim, query_h_strides, query_w_strides, kv_strides,dw_kernel_size=3, dropout=0.0):"""Multi Query Attention with spatial downsampling.Referenced from here https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_blocks.py3 parameters are introduced for the spatial downsampling:1. kv_strides: downsampling factor on Key and Values only.2. query_h_strides: vertical strides on Query only.3. query_w_strides: horizontal strides on Query only.This is an optimized version.1. Projections in Attention is explict written out as 1x1 Conv2D.2. Additional reshapes are introduced to bring a up to 3x speed up."""super().__init__()self.num_heads = num_headsself.key_dim = key_dimself.value_dim = value_dimself.query_h_strides = query_h_stridesself.query_w_strides = query_w_stridesself.kv_strides = kv_stridesself.dw_kernel_size = dw_kernel_sizeself.dropout = dropoutself.head_dim = key_dim // num_headsif self.query_h_strides > 1 or self.query_w_strides > 1:self._query_downsampling_norm = nn.BatchNorm2d(inp)self._query_proj = conv_2d(inp, num_heads * key_dim, 1, 1, norm=False, act=False)if self.kv_strides > 1:self._key_dw_conv = conv_2d(inp, inp, dw_kernel_size, kv_strides, groups=inp, norm=True, act=False)self._value_dw_conv = conv_2d(inp, inp, dw_kernel_size, kv_strides, groups=inp, norm=True, act=False)self._key_proj = conv_2d(inp, key_dim, 1, 1, norm=False, act=False)self._value_proj = conv_2d(inp, key_dim, 1, 1, norm=False, act=False)self._output_proj = conv_2d(num_heads * key_dim, inp, 1, 1, norm=False, act=False)self.dropout = nn.Dropout(p=dropout)def forward(self, x):batch_size, seq_length, _, _ = x.size()if self.query_h_strides > 1 or self.query_w_strides > 1:q = F.avg_pool2d(self.query_h_stride, self.query_w_stride)q = self._query_downsampling_norm(q)q = self._query_proj(q)else:q = self._query_proj(x)px = q.size(2)q = q.view(batch_size, self.num_heads, -1, self.key_dim) # [batch_size, num_heads, seq_length, key_dim]if self.kv_strides > 1:k = self._key_dw_conv(x)k = self._key_proj(k)v = self._value_dw_conv(x)v = self._value_proj(v)else:k = self._key_proj(x)v = self._value_proj(x)k = k.view(batch_size, self.key_dim, -1) # [batch_size, key_dim, seq_length]v = v.view(batch_size, -1, self.key_dim) # [batch_size, seq_length, key_dim]# calculate attn scoreattn_score = torch.matmul(q, k) / (self.head_dim ** 0.5)attn_score = self.dropout(attn_score)attn_score = F.softmax(attn_score, dim=-1)context = torch.matmul(attn_score, v)context = context.view(batch_size, self.num_heads * self.key_dim, px, px)output = self._output_proj(context)return outputclass MNV4LayerScale(nn.Module):def __init__(self, init_value):"""LayerScale as introduced in CaiT: https://arxiv.org/abs/2103.17239Referenced from here https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_blocks.pyAs used in MobileNetV4.Attributes:init_value (float): value to initialize the diagonal matrix of LayerScale."""super().__init__()self.init_value = init_valuedef forward(self, x):gamma = self.init_value * torch.ones(x.size(-1), dtype=x.dtype, device=x.device)return x * gammaclass MultiHeadSelfAttentionBlock(nn.Module):def __init__(self,inp,num_heads,key_dim,value_dim,query_h_strides,query_w_strides,kv_strides,use_layer_scale,use_multi_query,use_residual=True):super().__init__()self.query_h_strides = query_h_stridesself.query_w_strides = query_w_stridesself.kv_strides = kv_stridesself.use_layer_scale = use_layer_scaleself.use_multi_query = use_multi_queryself.use_residual = use_residualself._input_norm = nn.BatchNorm2d(inp)if self.use_multi_query:self.multi_query_attention = MultiQueryAttentionLayerWithDownSampling(inp, num_heads, key_dim, value_dim, query_h_strides, query_w_strides, kv_strides)else:self.multi_head_attention = nn.MultiheadAttention(inp, num_heads, kdim=key_dim)if self.use_layer_scale:self.layer_scale_init_value = 1e-5self.layer_scale = MNV4LayerScale(self.layer_scale_init_value)def forward(self, x):# Not using CPE, skipped# input normshortcut = xx = self._input_norm(x)# multi queryif self.use_multi_query:x = self.multi_query_attention(x)else:x = self.multi_head_attention(x, x)# layer scaleif self.use_layer_scale:x = self.layer_scale(x)# use residualif self.use_residual:x = x + shortcutreturn xdef build_blocks(layer_spec):if not layer_spec.get('block_name'):return nn.Sequential()block_names = layer_spec['block_name']layers = nn.Sequential()if block_names == "convbn":schema_ = ['inp', 'oup', 'kernel_size', 'stride']for i in range(layer_spec['num_blocks']):args = dict(zip(schema_, layer_spec['block_specs'][i]))layers.add_module(f"convbn_{i}", conv_2d(**args))elif block_names == "uib":schema_ = ['inp', 'oup', 'start_dw_kernel_size', 'middle_dw_kernel_size', 'middle_dw_downsample', 'stride','expand_ratio', 'msha']for i in range(layer_spec['num_blocks']):args = dict(zip(schema_, layer_spec['block_specs'][i]))msha = args.pop("msha") if "msha" in args else 0layers.add_module(f"uib_{i}", UniversalInvertedBottleneckBlock(**args))if msha:msha_schema_ = ["inp", "num_heads", "key_dim", "value_dim", "query_h_strides", "query_w_strides", "kv_strides","use_layer_scale", "use_multi_query", "use_residual"]args = dict(zip(msha_schema_, [args['oup']] + (msha)))layers.add_module(f"msha_{i}", MultiHeadSelfAttentionBlock(**args))elif block_names == "fused_ib":schema_ = ['inp', 'oup', 'stride', 'expand_ratio', 'act']for i in range(layer_spec['num_blocks']):args = dict(zip(schema_, layer_spec['block_specs'][i]))layers.add_module(f"fused_ib_{i}", InvertedResidual(**args))else:raise NotImplementedErrorreturn layersclass MobileNetV4(nn.Module):def __init__(self, model):# MobileNetV4ConvSmall MobileNetV4ConvMedium MobileNetV4ConvLarge# MobileNetV4HybridMedium MobileNetV4HybridLarge"""Params to initiate MobilenNetV4Args:model : support 5 types of models as indicated in"https://github.com/tensorflow/models/blob/master/official/vision/modeling/backbones/mobilenet.py""""super().__init__()assert model in MODEL_SPECS.keys()self.model = modelself.spec = MODEL_SPECS[self.model]# conv0self.conv0 = build_blocks(self.spec['conv0'])# layer1self.layer1 = build_blocks(self.spec['layer1'])# layer2self.layer2 = build_blocks(self.spec['layer2'])# layer3self.layer3 = build_blocks(self.spec['layer3'])# layer4self.layer4 = build_blocks(self.spec['layer4'])# layer5self.layer5 = build_blocks(self.spec['layer5'])self.width_list = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]def forward(self, x):x0 = self.conv0(x)x1 = self.layer1(x0)x2 = self.layer2(x1)x3 = self.layer3(x2)x4 = self.layer4(x3)# x5 = self.layer5(x4)# x5 = nn.functional.adaptive_avg_pool2d(x5, 1)return [x1, x2, x3, x4]def MobileNetV4ConvSmall():model = MobileNetV4('MobileNetV4ConvSmall')return modeldef MobileNetV4ConvMedium():model = MobileNetV4('MobileNetV4ConvMedium')return modeldef MobileNetV4ConvLarge():model = MobileNetV4('MobileNetV4ConvLarge')return modeldef MobileNetV4HybridMedium():model = MobileNetV4('MobileNetV4HybridMedium')return modeldef MobileNetV4HybridLarge():model = MobileNetV4('MobileNetV4HybridLarge')return model2.2 步骤二
在task.py导入我们的模块
2.3 步骤三
如下图标注框所示，添加两行代码
2.4 步骤四
在task.py如下图所示位置，添加标注框内所示代码
elif m in {MobileNetV4ConvLarge, MobileNetV4ConvSmall, \MobileNetV4ConvMedium, MobileNetV4HybridMedium, MobileNetV4HybridLarge}:m = m(*args)c2 = m.width_listbackbone = True2.5 步骤五
在task.py如下图所示位置，添加标注框内所示代码
2.6 步骤六
在task.py如下图所示位置的代码需要替换
替换为下图所示代码
if verbose:LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f} {t:<45}{str(args):<30}') # printsave.extend(x % (i + 4 if backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelistlayers.append(m_)if i == 0:ch = []if isinstance(c2, list):ch.extend(c2)if len(c2) != 5:ch.insert(0, 0)else:ch.append(c2)2.7 步骤七
这次修改在base_model的predict_once方法里面，在task.py的前面部分代码中。
在task.py如下图所示位置的代码需要替换
替换为下图所示代码
def _predict_once(self, x, profile=False, visualize=False, embed=None):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 x2.8 步骤八
将下图所示代码注释掉，在ultralytics/utils/torch_utils.py中
修改为下图所示
2.9 步骤九
将下图所示代码注释掉，在task.py中,改为s=640
到这里完成修改，但是这里面细节很多，大家一定要注意，仔细修改，步骤比较多，出现错误很难找出来
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]# MobileNetV4ConvSmall, MobileNetV4ConvLarge, MobileNetV4ConvMedium,# MobileNetV4HybridMedium, MobileNetV4HybridLarge 支持这五种版本- [-1, 1, MobileNetV4ConvSmall, []] # 4 将左面的MobileNetV4ConvSmall改为上面任意一个即替换对应的MobileNetV4版本- [-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)# 今天这个修改的地方比较多，大家一定要仔细检查
不知不觉已经看完了哦，动动小手留个点赞吧--_--
