YOLOv5改进 | 主干篇 | 2024.5全新的移动端网络MobileNetV4改进YOLOv5（含MobileNetV4全部版本改进）

一、本文介绍

本文给大家带来的改进机制是MobileNetV4，其发布时间是2024.5月。MobileNetV4是一种高度优化的神经网络架构，专为移动设备设计。它最新的改动总结主要有两点，采用了通用反向瓶颈（UIB）和针对移动加速器优化的Mobile MQA注意力模块（一种全新的注意力机制）。这些创新有助于在不牺牲准确性的情况下，显著提高推理速度和计算效率。MobileNetV4是一种移动端的网络。

欢迎大家订阅我的专栏一起学习YOLO！

专栏回顾： YOLOv5改进专栏——持续复现各种顶会内容——内含100+创新

一、本文介绍

二、原理介绍

三、核心代码

四、手把手教你添加MobileNetv4

4.1 修改一

4.2 修改二

4.3修改三

4.4 修改四

4.5 修改五

4.6 修改六

4.7 修改七

4.8 修改八

五、MobileNetv4 的yaml文件

六、成功运行记录

七、本文总结

二、原理介绍

官方论文地址： 官方论文地址点击此处即可跳转

官方代码地址： 官方代码地址点击此处即可跳转

MobileNetV4是MobileNet系列的最新版本，专为移动设备设计，引入了多种新颖且高效的架构组件。其中最关键的是通用反转瓶颈（UIB），它结合了以前模型如MobileNetV2的反转瓶颈和新元素，例如ConvNext块和视觉变换器（ViT）中的前馈网络。这种结构允许在不过度复杂化架构搜索过程的情况下，适应性地并有效地扩展模型到各种平台。

此外，MobileNetV4还包括一种名为Mobile MQA的新型注意力机制，该机制通过优化算术运算与内存访问的比率，显著提高了移动加速器上的推理速度，这是移动性能的关键因素。该架构通过精细的神经网络架构搜索（NAS）和新颖的蒸馏技术进一步优化，使得MobileNetV4能够在多种硬件平台上达到最优性能，包括移动CPU、DSP、GPU和特定的加速器，如Apple的Neural Engine和Google的Pixel EdgeTPU。

此外，MobileNetV4还引入了改进的NAS策略，通过粗粒度和细粒度搜索相结合的方法，显著提高搜索效率并改善模型质量。通过这种方法，MobileNetV4能够实现大多数情况下的Pareto最优性能，这意味着在不同设备上都能达到效率和准确性的最佳平衡。

最后，通过一种新的蒸馏技术，MobileNetV4进一步提高了准确性，其混合型大模型在ImageNet-1K数据集上达到了87%的顶级准确率，同时在Pixel 8 EdgeTPU上的运行时间仅为3.8毫秒。这些特性使MobileNetV4成为适用于移动环境中高效视觉任务的理想选择。

主要思想提取和总结：

1. 通用反转瓶颈（UIB）:

MobileNetV4引入了一种名为通用反转瓶颈（UIB）的新架构组件。UIB是一个灵活的架构单元，融合了反转瓶颈（IB）、ConvNext、前馈网络（FFN），以及新颖的额外深度（ExtraDW）变体。

2. Mobile MQA注意力机制:

为了优化移动加速器的性能，MobileNetV4设计了一个特殊的注意力模块，名为Mobile MQA。这一模块针对移动设备的计算和存储限制进行了优化，提供了高达39%的推理速度提升。

3. 优化的神经架构搜索（NAS）配方:

通过改进的NAS配方，MobileNetV4能够更高效地搜索和优化网络架构，这有助于发现适合特定硬件的最优模型配置。

4. 模型蒸馏技术:

引入了一种新的蒸馏技术，用以提高模型的准确性。通过这种技术，MNv4-Hybrid-Large模型在ImageNet-1K上达到了87%的准确率，并且在Pixel 8 EdgeTPU上的运行时间仅为3.8毫秒。

个人总结：MobileNetV4是一个专为移动设备设计的高效深度学习模型。它通过整合多种先进技术，如通用反转瓶颈（UIB）、针对移动设备优化的注意力机制（Mobile MQA），以及先进的架构搜索方法（NAS），实现了在不同硬件上的高效运行。这些技术的融合不仅大幅提升了模型的运行速度，还显著提高了准确率。特别是，它的一个变体模型在标准图像识别测试中取得了87%的准确率，运行速度极快。

三、核心代码

核心代码的使用方式看章节四！

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 modelif __name__ == "__main__":# Generating Sample imageimage_size = (1, 3, 640, 640)image = torch.rand(*image_size)# Modelmodel = MobileNetV4HybridLarge()out = model(image)for i in range(len(out)):print(out[i].shape)

四、手把手教你添加MobileNetv4

这个主干的网络结构添加起来算是所有的改进机制里最麻烦的了，因为有一些网略结构可以用yaml文件搭建出来，有一些网络结构其中的一些细节根本没有办法用yaml文件去搭建，用yaml文件去搭建会损失一些细节部分(而且一个网络结构设计很多细节的结构修改方式都不一样，一个一个去修改大家难免会出错)，所以这里让网络直接返回整个网络，然后修改部分 yolo代码以后就都以这种形式添加了，以后我提出的网络模型基本上都会通过这种方式修改，我也会进行一些模型细节改进。创新出新的网络结构大家直接拿来用就可以的。下面开始添加教程->

(同时每一个后面都有代码，大家拿来复制粘贴替换即可，但是要看好了不要复制粘贴替换多了)

4.1 修改一

我们复制网络结构代码到“yolov5-master/models”目录下创建一个目录，我这里的名字是modules(如果将文件放在models下面随着改进机制越来越多不太好区分，所以创建一个文件目录将改进机制全部放在里面) ，然后创建一个py文件将代码复制粘贴到里面我这里起的名字是MobileNetv4 。

4.2 修改二

然后我们在我们创建的目录里面创建一个初始化文件'__init__.py'，然后在里面导入我们同级目录的所有改进机制

4.3修改三

我们找到如下文件'models/yolo.py'在开头里面导入我们的模块，这里需要注意要将代码放在common导入的文件上面，否则有一些模块会使用我们modules里面的，可能同名导致报错，这里如果你使用多个我的改进机制填写一个即可，不用重复添加。

4.4 修改四

添加如下两行代码，根据行数找相似的代码进行添加

4.5 修改五

找到七百多行大概把具体看图片，按照图片来修改就行，添加红框内的部分，注意没有()只是函数名，我这里只添加了部分的版本，大家有兴趣这个主干还有更多的版本可以添加，看我给的代码函数头即可。

        elif m in {mobile_vit2_xx_small}:m = m()c2 = m.width_list  # 返回通道列表backbone = True

4.6 修改六

下面的两个红框内都是需要改动的。

        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 typenp = sum(x.numel() for x in m_.parameters())  # number paramsm_.i, m_.f, m_.type, m_.np = i + 4 if backbone else i, f, t, np # attach index, 'from' index, type

4.7 修改七

如下的也需要修改，全部按照我的来。

代码如下把原先的代码替换了即可。

        save.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)

4.8 修改八

修改八和前面的都不太一样，需要修改前向传播中的一个部分，已经离开了parse_model方法了。

可以在图片中开代码行数，没有离开task.py文件都是同一个文件。同时这个部分有好几个前向传播都很相似，大家不要看错了，是70多行左右的！！！，同时我后面提供了代码，大家直接复制粘贴即可，有时间我针对这里会出一个视频。

找到如下的代码，这里不太好找，我给大家上传一个原始的样子。

然后我们用后面的代码进行替换，替换完之后的样子如下->

代码如下->

    def _forward_once(self, x, profile=False, visualize=False):y, dt = [], []  # 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)return x

到这里就完成了修改部分，但是这里面细节很多，大家千万要注意不要替换多余的代码，导致报错，也不要拉下任何一部，都会导致运行失败，而且报错很难排查！！！很难排查！！！

五、MobileNetv4的yaml文件

复制如下yaml文件进行运行！！！

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # layer channel multiple
anchors:- [10,13, 16,30, 33,23]  # P3/8- [30,61, 62,45, 59,119]  # P4/16- [116,90, 156,198, 373,326]  # P5/32# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args]# MobileNetV4ConvSmall, MobileNetV4ConvLarge,  MobileNetV4ConvMedium,# MobileNetV4HybridMedium, MobileNetV4HybridLarge  支持这五种版本[[-1, 1, MobileNetV4ConvSmall, []],  # 0-4 有多个版本可以使用，大家看注册的时候添加了几个这里都可以使用[-1, 1, SPPF, [1024, 5]],  # 5]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 3], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [512, False]],  # 9[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 2], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [256, False]],  # 13 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 10], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 16 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 6], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [1024, False]],  # 19 (P5/32-large)[[13, 16, 19], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]