YOLOv11改进 | 主干部分 | 引入MobileNetV4结构

学术界的一枚小白

已于 2025-03-03 15:54:18 修改

阅读量4k

点赞数 55

分类专栏： YOLOv11改进文章标签： YOLO python 目标检测

于 2025-02-28 15:55:19 首次发布

本文链接：https://blog.youkuaiyun.com/m0_64858060/article/details/145929873

版权

YOLOv11改进专栏收录该内容

5 篇文章

订阅专栏

1 MobileNetV4介绍

1.1 摘要

我们推出了最新一代的 MobileNet，称为 MobileNetV4 （MNv4），具有适用于移动设备的通用高效架构设计。我们的核心是引入了通用反向瓶颈（UIB）搜索块，这是一种统一且灵活的结构，它融合了反向瓶颈（IB）、ConvNext、前馈网络（FFN）和新颖的 Extra Depthwise （ExtraDW）变体。除了 UIB，我们还推出了 Mobile MQA，这是一个为移动加速器量身定制的注意力块，可提供 39% 的显著加速。还引入了优化的神经架构搜索（NAS）配方，它提高了 MNv4 搜索的有效性。UIB、移动 MQA 和改进的 NAS 配方的集成产生了一套新的 MNv4 模型，这些模型在移动 CPU、DSP、GPU 以及 Apple Neural Engine 和 Google Pixel EdgeTPU 等专用加速器上大多是帕累托最优的——这是任何其他测试模型中都没有的特性。最后，为了进一步提高准确性，我们引入了一种新颖的蒸馏技术。通过这项技术增强，我们的 MNv4-Hybrid-Large 模型可提供 87% 的 ImageNet-1K 准确率，Pixel 8 EdgeTPU 运行时间仅为 3.8 毫秒。

论文地址：https://arxiv.org/pdf/2404.10518

1.2 核心技术

MobileNetV4 是 Google 在 MobileNet 系列中的最新升级版本（截至 2024 年 7 月），延续了轻量级、高效的设计理念，重点优化硬件适配性、推理速度与多任务性能。以下是其核心结构特点：

1. 核心架构：通用反向瓶颈（UIB Block）

MobileNetV4 提出 Universal Inverted Bottleneck（UIB） 模块，整合了传统反向残差结构与最新注意力机制：

多分支设计：并行处理不同尺度的特征，增强模型表达能力。
硬件感知优化：采用 4D 卷积分解（将传统卷积拆分为空间+通道维度操作），减少计算量同时保持精度。
动态激活函数：引入自适应参数，根据输入动态调整非线性响应，提升模型灵活性。

2. 统一主干网络（Unified Backbone）

多任务兼容性：主干网络设计同时支持分类、检测、分割等任务，减少针对不同任务调整架构的成本。
跨阶段特征融合：通过密集连接增强浅层与深层特征的交互，提升小目标检测能力。

3. 硬件级优化技术

INT8 量化友好结构：优化层间数值分布，使模型在低精度量化下仍保持高精度。
内存访问优化：通过 深度可分离卷积的硬件映射，减少内存占用，提升移动端推理速度。

4. 对比前代改进

速度提升：相比 MobileNetV3，V4 在相同精度下推理速度提升 20-30%。
多任务泛化：在 COCO 检测和 ADE20K 分割任务上，AP/mIoU 平均提升 3-5%。
部署友好：支持 TensorFlow Lite、Core ML 等框架的即时编译（JIT）优化。

1.3 结构图

2 MobileNetV4代码

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 = 2
    elif px == 12:
        kv_strides = 1
    query_h_strides = 1
    query_w_strides = 1
    use_layer_scale = True
    use_multi_query = True
    use_residual = True
    return [
        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 = divisor
    new_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 += divisor
    return 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) // 2
    conv.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 conv
 
 
class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio, act=False, squeeze_excitation=False):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert 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 == oup
 
    def 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_size
        if self.start_dw_kernel_size:
            stride_ = stride if not middle_dw_downsample else 1
            self._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_size
        if self.middle_dw_kernel_size:
            stride_ = stride if middle_dw_downsample else 1
            self._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 x
 
 
class 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.py
        3 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_heads
        self.key_dim = key_dim
        self.value_dim = value_dim
        self.query_h_strides = query_h_strides
        self.query_w_strides = query_w_strides
        self.kv_strides = kv_strides
        self.dw_kernel_size = dw_kernel_size
        self.dropout = dropout
 
        self.head_dim = key_dim // num_heads
 
        if 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 score
        attn_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 output
 
 
class MNV4LayerScale(nn.Module):
    def __init__(self, init_value):
        """LayerScale as introduced in CaiT: https://arxiv.org/abs/2103.17239
        Referenced from here https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_blocks.py
        As used in MobileNetV4.
        Attributes:
            init_value (float): value to initialize the diagonal matrix of LayerScale.
        """
        super().__init__()
        self.init_value = init_value
 
    def forward(self, x):
        gamma = self.init_value * torch.ones(x.size(-1), dtype=x.dtype, device=x.device)
        return x * gamma
 
 
class 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_strides
        self.query_w_strides = query_w_strides
        self.kv_strides = kv_strides
        self.use_layer_scale = use_layer_scale
        self.use_multi_query = use_multi_query
        self.use_residual = use_residual
 
        self._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-5
            self.layer_scale = MNV4LayerScale(self.layer_scale_init_value)
 
    def forward(self, x):
        # Not using CPE, skipped
        # input norm
        shortcut = x
        x = self._input_norm(x)
        # multi query
        if self.use_multi_query:
            x = self.multi_query_attention(x)
        else:
            x = self.multi_head_attention(x, x)
        # layer scale
        if self.use_layer_scale:
            x = self.layer_scale(x)
        # use residual
        if self.use_residual:
            x = x + shortcut
        return x
 
 
def 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 0
            layers.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 NotImplementedError
    return layers
 
 
class MobileNetV4(nn.Module):
    def __init__(self, model):
        # MobileNetV4ConvSmall  MobileNetV4ConvMedium  MobileNetV4ConvLarge
        # MobileNetV4HybridMedium  MobileNetV4HybridLarge
        """Params to initiate MobilenNetV4
        Args:
            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 = model
        self.spec = MODEL_SPECS[self.model]
 
        # conv0
        self.conv0 = build_blocks(self.spec['conv0'])
        # layer1
        self.layer1 = build_blocks(self.spec['layer1'])
        # layer2
        self.layer2 = build_blocks(self.spec['layer2'])
        # layer3
        self.layer3 = build_blocks(self.spec['layer3'])
        # layer4
        self.layer4 = build_blocks(self.spec['layer4'])
        # layer5
        self.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 model
 
def MobileNetV4ConvMedium():
    model = MobileNetV4('MobileNetV4ConvMedium')
    return model
 
def MobileNetV4ConvLarge():
    model = MobileNetV4('MobileNetV4ConvLarge')
    return model
 
def MobileNetV4HybridMedium():
    model = MobileNetV4('MobileNetV4HybridMedium')
    return model
 
def MobileNetV4HybridLarge():
    model = MobileNetV4('MobileNetV4HybridLarge')
    return model
 
 
if __name__ == "__main__":
    # Generating Sample image
    image_size = (1, 3, 640, 640)
    image = torch.rand(*image_size)
 
    # Model
    model = MobileNetV4HybridLarge()
 
    out = model(image)
    for i in range(len(out)):
        print(out[i].shape)

3 改进步骤

3.1 在ultralytics/nn下新建Addmodule文件夹，并在Addmodule里创建MobileNetV4.py

在MobileNetV4.py文件里添加给出的MobileNetV4代码

添加完MobileNetV4代码后，在ultralytics/nn/Addmodule/__init__.py文件中引用

from .MobileNetV4 import *

在ultralytics/nn/tasks.py里引用

from .Addmodule import *

3.2 在ultralytics/nn/tasks.py查找

（1）在tasks.py找到parse_model函数（ctrl+f 可以直接搜索parse_model位置），添加：

（2）仍然在parse_model函数中，添加主代码

        elif m in {MobileNetV4ConvLarge, MobileNetV4ConvSmall,MobileNetV4ConvMedium, MobileNetV4HybridMedium, MobileNetV4HybridLarge
                    }:
            m = m(*args)
            c2 = m.width_list
            backbone = True

（3）将elif m is AIFI 到parse_model函数的结尾的代码全部替换

        elif m is AIFI:
            args = [ch[f], *args]
        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 is ResNetLayer:
            c2 = args[1] if args[3] else args[1] * 4
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}:
            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 is CBLinear:
            c2 = args[0]
            c1 = ch[f]
            args = [c1, c2, *args[1:]]
        elif m is CBFuse:
            c2 = ch[f[-1]]
        else:
            c2 = ch[f]
 
        if isinstance(c2, list):
            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 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 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)
            if len(c2) != 5:
                ch.insert(0, 0)
        else:
            ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

（4）在tasks.py找到_predict_once函数，替换为：

def _predict_once(self, x, profile=False, visualize=False, embed=None):
            """
            Perform a forward pass through the network.
            Args:
                x (torch.Tensor): The input tensor to the model.
                profile (bool):  Print the computation time of each layer if True, defaults to False.
                visualize (bool): Save the feature maps of the model if True, defaults to False.
                embed (list, optional): A list of feature vectors/embeddings to return.
            Returns:
                (torch.Tensor): The last output of the model.
            """
            y, dt, embeddings = [], [], []  # outputs
            for 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)
                    if len(x) != 5:  # 0 - 5
                        x.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)  # 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)
                if embed and m.i in embed:
                    embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                    if m.i == max(embed):
                        return torch.unbind(torch.cat(embeddings, 1), dim=0)
            return x

（5）（目标检测需要，分类则不需要修改）在ultralytics/models/yolo/detect/train.py里找到build_dataset函数，替换为：

    def build_dataset(self, img_path, mode="train", batch=None):
        """
        Build YOLO Dataset.
        Args:
            img_path (str): Path to the folder containing images.
            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
        """
        gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=False, stride=gs)

修改到这里，所有内容基本完成。接下去创建yaml文件。

4 创建YOLO11_MobileNetV4.yaml

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

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

# YOLO11n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, MobileNetV4ConvSmall, []]  # 4
  - [-1, 1, SPPF, [1024, 5]]  # 5
  - [-1, 2, C2PSA, [1024]] # 6

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 3], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 9

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 2], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 12 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 9], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 15 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 6], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 18 (P5/32-large)

  - [[12, 15, 18], 1, Detect, [nc]] # Detect(P3, P4, P5)

5 训练模型

import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO


if __name__ == '__main__':
    model = YOLO('YOLO11_mobilenetv4.yaml')
    # model.load('yolo11n.pt') # loading pretrain weights
    model.train(data='dataset/data.yaml',
                cache=False,
                imgsz=640,
                epochs=300,
                batch=32,
                close_mosaic=0,
                workers=4, # Windows下出现莫名其妙卡主的情况可以尝试把workers设置为0
                # device='0',
                optimizer='SGD', # using SGD
                # patience=0, # set 0 to close earlystop.
                # resume=True, # 断点续训,YOLO初始化时选择last.pt
                # amp=False, # close amp
                # fraction=0.2,
                project='runs/train',
                name='exp',
                )

如图所示，训练成功，改进结束。