一、本文介绍

本文给大家带来的改进机制是 MobileViT系列的V2版本 ，其作为 MobileNet 网络的挑战者，其效果自然不用多说，MobileViT 模型 是为 移动设备 设计的轻量级、通用目的视觉变换器。它融合了卷积 神经网络 （CNN）和视觉变换器（ViT）的优势，旨在在保持高效性能的同时减少模型参数和降低延迟。通过其创新的 MobileViT Block和多尺度训练方法，MobileViT在多个视觉任务上取得了优异的结果， 欢迎大家订阅本专栏，本专栏每周更新3-5篇最新机制，更有包含我所有改进的文件和交流群提供给大家。

欢迎大家订阅我的专栏一起学习YOLO！

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

​

​

MobileViTv2论文《Separable Self-attention for Mobile Vision Transformers》由Sachin Mehta和Mohammad Rastegari撰写，旨在通过引入一种线性复杂度的可分离自注意力方法，解决MobileViT模型在资源受限设备上的高延迟问题。该论文展示了MobileViTv2在多项移动视觉任务中的领先性能，包括ImageNet对象分类和MS-COCO对象检测。使用约三百万参数，MobileViTv2在ImageNet数据集上实现了75.6%的Top-1准确率，比MobileViT提高了约1%，同时在移动设备上的运行速度提高了3.2倍。

主要贡献和特点

1. 可分离自注意力：引入了一种具有线性复杂度（O(k)）的自注意力方法，通过元素级操作计算自注意力，适合资源受限设备。

2. 提高效率：与传统的多头自注意力（MHA）相比，该方法降低了计算复杂度，减少了运算成本，加快了模型在资源受限设备上的推理速度。

3. 卓越性能：MobileViTv2在不同的移动视觉任务上取得了优异的性能，证明了其作为轻量级视觉变换器的有效性和实用性。

总结： MobileViTv2通过其创新的可分离自注意力机制，在保持轻量级的同时，实现了在多个移动视觉任务上的高性能，特别适合在计算能力受限的移动设备上部署。论文提供的开 源代码 进一步促进了该领域的研究和发展。

三、核心代码

代码的使用方式看章节四！

"""
original code from apple:
https://github.com/apple/ml-cvnets/blob/main/cvnets/models/classification/mobilevit.py
"""
import math
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
from typing import Tuple,  Dict, Sequence
from typing import Union, Optional
__all__ = ['mobile_vit2_xx_small']
def make_divisible(
        v: Union[float, int],
        divisor: Optional[int] = 8,
        min_value: Optional[Union[float, int]] = None,
) -> Union[float, int]:
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v
def bound_fn(
        min_val: Union[float, int], max_val: Union[float, int], value: Union[float, int]
) -> Union[float, int]:
    return max(min_val, min(max_val, value))
def get_config(mode: str = "xxs") -> dict:
    width_multiplier = 0.5
    ffn_multiplier = 2
    layer_0_dim = bound_fn(min_val=16, max_val=64, value=32 * width_multiplier)
    layer_0_dim = int(make_divisible(layer_0_dim, divisor=8, min_value=16))
    # print("layer_0_dim: ", layer_0_dim)
    if mode == "xx_small":
        mv2_exp_mult = 2
        config = {
            "layer1": {
                "out_channels": 16,
                "expand_ratio": mv2_exp_mult,
                "num_blocks": 1,
                "stride": 1,
                "block_type": "mv2",
            },
            "layer2": {
                "out_channels": 24,
                "expand_ratio": mv2_exp_mult,
                "num_blocks": 3,
                "stride": 2,
                "block_type": "mv2",
            },
            "layer3": {  # 28x28
                "out_channels": 48,
                "transformer_channels": 64,
                "ffn_dim": 128,
                "transformer_blocks": 2,
                "patch_h": 2,  # 8,
                "patch_w": 2,  # 8,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "layer4": {  # 14x14
                "out_channels": 64,
                "transformer_channels": 80,
                "ffn_dim": 160,
                "transformer_blocks": 4,
                "patch_h": 2,  # 4,
                "patch_w": 2,  # 4,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "layer5": {  # 7x7
                "out_channels": 80,
                "transformer_channels": 96,
                "ffn_dim": 192,
                "transformer_blocks": 3,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "last_layer_exp_factor": 4,
            "cls_dropout": 0.1
        }
    elif mode == "x_small":
        mv2_exp_mult = 4
        config = {
            "layer1": {
                "out_channels": 32,
                "expand_ratio": mv2_exp_mult,
                "num_blocks": 1,
                "stride": 1,
                "block_type": "mv2",
            },
            "layer2": {
                "out_channels": 48,
                "expand_ratio": mv2_exp_mult,
                "num_blocks": 3,
                "stride": 2,
                "block_type": "mv2",
            },
            "layer3": {  # 28x28
                "out_channels": 64,
                "transformer_channels": 96,
                "ffn_dim": 192,
                "transformer_blocks": 2,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "layer4": {  # 14x14
                "out_channels": 80,
                "transformer_channels": 120,
                "ffn_dim": 240,
                "transformer_blocks": 4,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "layer5": {  # 7x7
                "out_channels": 96,
                "transformer_channels": 144,
                "ffn_dim": 288,
                "transformer_blocks": 3,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "last_layer_exp_factor": 4,
            "cls_dropout": 0.1
        }
    elif mode == "small":
        mv2_exp_mult = 4
        config = {
            "layer1": {
                "out_channels": 32,
                "expand_ratio": mv2_exp_mult,
                "num_blocks": 1,
                "stride": 1,
                "block_type": "mv2",
            },
            "layer2": {
                "out_channels": 64,
                "expand_ratio": mv2_exp_mult,
                "num_blocks": 3,
                "stride": 2,
                "block_type": "mv2",
            },
            "layer3": {  # 28x28
                "out_channels": 96,
                "transformer_channels": 144,
                "ffn_dim": 288,
                "transformer_blocks": 2,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "layer4": {  # 14x14
                "out_channels": 128,
                "transformer_channels": 192,
                "ffn_dim": 384,
                "transformer_blocks": 4,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "layer5": {  # 7x7
                "out_channels": 160,
                "transformer_channels": 240,
                "ffn_dim": 480,
                "transformer_blocks": 3,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "num_heads": 4,
                "block_type": "mobilevit",
            },
            "last_layer_exp_factor": 4,
            "cls_dropout": 0.1
        }
    elif mode == "2xx_small":
        mv2_exp_mult = 2
        config = {
            "layer0": {
                "img_channels": 3,
                "out_channels": layer_0_dim,
            },
            "layer1": {
                "out_channels": int(make_divisible(64 * width_multiplier, divisor=16)),
                "expand_ratio": mv2_exp_mult,
                "num_blocks": 1,
                "stride": 1,
                "block_type": "mv2",
            },
            "layer2": {
                "out_channels": int(make_divisible(128 * width_multiplier, divisor=8)),
                "expand_ratio": mv2_exp_mult,
                "num_blocks": 2,
                "stride": 2,
                "block_type": "mv2",
            },
            "layer3": {  # 28x28
                "out_channels": int(make_divisible(256 * width_multiplier, divisor=8)),
                "attn_unit_dim": int(make_divisible(128 * width_multiplier, divisor=8)),
                "ffn_multiplier": ffn_multiplier,
                "attn_blocks": 2,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "block_type": "mobilevit",
            },
            "layer4": {  # 14x14
                "out_channels": int(make_divisible(384 * width_multiplier, divisor=8)),
                "attn_unit_dim": int(make_divisible(192 * width_multiplier, divisor=8)),
                "ffn_multiplier": ffn_multiplier,
                "attn_blocks": 4,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "block_type": "mobilevit",
            },
            "layer5": {  # 7x7
                "out_channels": int(make_divisible(512 * width_multiplier, divisor=8)),
                "attn_unit_dim": int(make_divisible(256 * width_multiplier, divisor=8)),
                "ffn_multiplier": ffn_multiplier,
                "attn_blocks": 3,
                "patch_h": 2,
                "patch_w": 2,
                "stride": 2,
                "mv_expand_ratio": mv2_exp_mult,
                "block_type": "mobilevit",
            },
            "last_layer_exp_factor": 4,
        }
    else:
        raise NotImplementedError
    for k in ["layer1", "layer2", "layer3", "layer4", "layer5"]:
        config[k].update({"dropout": 0.1, "ffn_dropout": 0.0, "attn_dropout": 0.0})
    return config
class ConvLayer(nn.Module):
    """
    Applies a 2D convolution over an input
    Args:
        in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H_{in}, W_{in})`
        out_channels (int): :math:`C_{out}` from an expected output of size :math:`(N, C_{out}, H_{out}, W_{out})`
        kernel_size (Union[int, Tuple[int, int]]): Kernel size for convolution.
        stride (Union[int, Tuple[int, int]]): Stride for convolution. Default: 1
        groups (Optional[int]): Number of groups in convolution. Default: 1
        bias (Optional[bool]): Use bias. Default: ``False``
        use_norm (Optional[bool]): Use normalization layer after convolution. Default: ``True``
        use_act (Optional[bool]): Use activation layer after convolution (or convolution and normalization).
                                Default: ``True``
    Shape:
        - Input: :math:`(N, C_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, H_{out}, W_{out})`
    .. note::
        For depth-wise convolution, `groups=C_{in}=C_{out}`.
    """
    def __init__(
            self,
            in_channels: int,  # 输入通道数
            out_channels: int,  # 输出通道数
            kernel_size: Union[int, Tuple[int, int]],  # 卷积核大小
            stride: Optional[Union[int, Tuple[int, int]]] = 1,  # 步长
            groups: Optional[int] = 1,  # 分组卷积
            bias: Optional[bool] = False,  # 是否使用偏置
            use_norm: Optional[bool] = True,  # 是否使用归一化
            use_act: Optional[bool] = True,  # 是否使用激活函数
    ) -> None:
        super().__init__()
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size, kernel_size)
        if isinstance(stride, int):
            stride = (stride, stride)
        assert isinstance(kernel_size, Tuple)
        assert isinstance(stride, Tuple)
        padding = (
            int((kernel_size[0] - 1) / 2),
            int((kernel_size[1] - 1) / 2),
        )
        block = nn.Sequential()
        conv_layer = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            groups=groups,
            padding=padding,
            bias=bias
        )
        block.add_module(name="conv", module=conv_layer)
        if use_norm:
            norm_layer = nn.BatchNorm2d(num_features=out_channels, momentum=0.1)  # BatchNorm2d
            block.add_module(name="norm", module=norm_layer)
        if use_act:
            act_layer = nn.SiLU()  # Swish activation
            block.add_module(name="act", module=act_layer)
        self.block = block
    def forward(self, x: Tensor) -> Tensor:
        return self.block(x)
class MultiHeadAttention(nn.Module):
    """
    This layer applies a multi-head self- or cross-attention as described in
    `Attention is all you need <https://arxiv.org/abs/1706.03762>`_ paper
    Args:
        embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(N, P, C_{in})`
        num_heads (int): Number of heads in multi-head attention
        attn_dropout (float): Attention dropout. Default: 0.0
        bias (bool): Use bias or not. Default: ``True``
    Shape:
        - Input: :math:`(N, P, C_{in})` where :math:`N` is batch size, :math:`P` is number of patches,
        and :math:`C_{in}` is input embedding dim
        - Output: same shape as the input
    """
    def __init__(
            self,
            embed_dim: int,
            num_heads: int,
            attn_dropout: float = 0.0,
            bias: bool = True,
            *args,
            **kwargs
    ) -> None:
        super().__init__()
        if embed_dim % num_heads != 0:
            raise ValueError(
                "Embedding dim must be divisible by number of heads in {}. Got: embed_dim={} and num_heads={}".format(
                    self.__class__.__name__, embed_dim, num_heads
                )
            )
        self.qkv_proj = nn.Linear(in_features=embed_dim, out_features=3 * embed_dim, bias=bias)
        self.attn_dropout = nn.Dropout(p=attn_dropout)
        self.out_proj = nn.Linear(in_features=embed_dim, out_features=embed_dim, bias=bias)
        self.head_dim = embed_dim // num_heads
        self.scaling = self.head_dim ** -0.5
        self.softmax = nn.Softmax(dim=-1)
        self.num_heads = num_heads
        self.embed_dim = embed_dim
    def forward(self, x_q: Tensor) -> Tensor:
        # [N, P, C]
        b_sz, n_patches, in_channels = x_q.shape
        # self-attention
        # [N, P, C] -> [N, P, 3C] -> [N, P, 3, h, c] where C = hc
        qkv = self.qkv_proj(x_q).reshape(b_sz, n_patches, 3, self.num_heads, -1)
        # [N, P, 3, h, c] -> [N, h, 3, P, C]
        qkv = qkv.transpose(1, 3).contiguous()
        # [N, h, 3, P, C] -> [N, h, P, C] x 3
        query, key, value = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
        query = query * self.scaling
        # [N h, P, c] -> [N, h, c, P]
        key = key.transpose(-1, -2)
        # QK^T
        # [N, h, P, c] x [N, h, c, P] -> [N, h, P, P]
        attn = torch.matmul(query, key)
        attn = self.softmax(attn)
        attn = self.attn_dropout(attn)
        # weighted sum
        # [N, h, P, P] x [N, h, P, c] -> [N, h, P, c]
        out = torch.matmul(attn, value)
        # [N, h, P, c] -> [N, P, h, c] -> [N, P, C]
        out = out.transpose(1, 2).reshape(b_sz, n_patches, -1)
        out = self.out_proj(out)
        return out
class TransformerEncoder(nn.Module):
    """
    This class defines the pre-norm `Transformer encoder <https://arxiv.org/abs/1706.03762>`_
    Args:
        embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(N, P, C_{in})`
        ffn_latent_dim (int): Inner dimension of the FFN
        num_heads (int) : Number of heads in multi-head attention. Default: 8
        attn_dropout (float): Dropout rate for attention in multi-head attention. Default: 0.0
        dropout (float): Dropout rate. Default: 0.0
        ffn_dropout (float): Dropout between FFN layers. Default: 0.0
    Shape:
        - Input: :math:`(N, P, C_{in})` where :math:`N` is batch size, :math:`P` is number of patches,
        and :math:`C_{in}` is input embedding dim
        - Output: same shape as the input
    """
    def __init__(
            self,
            embed_dim: int,
            ffn_latent_dim: int,
            num_heads: Optional[int] = 8,
            attn_dropout: Optional[float] = 0.0,
            dropout: Optional[float] = 0.0,
            ffn_dropout: Optional[float] = 0.0,
            *args,
            **kwargs
    ) -> None:
        super().__init__()
        attn_unit = MultiHeadAttention(
            embed_dim,
            num_heads,
            attn_dropout=attn_dropout,
            bias=True
        )
        self.pre_norm_mha = nn.Sequential(
            nn.LayerNorm(embed_dim),
            attn_unit,
            nn.Dropout(p=dropout)
        )
        self.pre_norm_ffn = nn.Sequential(
            nn.LayerNorm(embed_dim),
            nn.Linear(in_features=embed_dim, out_features=ffn_latent_dim, bias=True),
            nn.SiLU(),
            nn.Dropout(p=ffn_dropout),
            nn.Linear(in_features=ffn_latent_dim, out_features=embed_dim, bias=True),
            nn.Dropout(p=dropout)
        )
        self.embed_dim = embed_dim
        self.ffn_dim = ffn_latent_dim
        self.ffn_dropout = ffn_dropout
        self.std_dropout = dropout
    def forward(self, x: Tensor) -> Tensor:
        # multi-head attention
        res = x
        x = self.pre_norm_mha(x)
        x = x + res
        # feed forward network
        x = x + self.pre_norm_ffn(x)
        return x
class LinearSelfAttention(nn.Module):
    """
    This layer applies a self-attention with linear complexity, as described in `MobileViTv2 <https://arxiv.org/abs/2206.02680>`_ paper.
    This layer can be used for self- as well as cross-attention.
    Args:
        opts: command line arguments
        embed_dim (int): :math:`C` from an expected input of size :math:`(N, C, H, W)`
        attn_dropout (Optional[float]): Dropout value for context scores. Default: 0.0
        bias (Optional[bool]): Use bias in learnable layers. Default: True
    Shape:
        - Input: :math:`(N, C, P, N)` where :math:`N` is the batch size, :math:`C` is the input channels,
        :math:`P` is the number of pixels in the patch, and :math:`N` is the number of patches
        - Output: same as the input
    .. note::
        For MobileViTv2, we unfold the feature map [B, C, H, W] into [B, C, P, N] where P is the number of pixels
        in a patch and N is the number of patches. Because channel is the first dimension in this unfolded tensor,
        we use point-wise convolution (instead of a linear layer). This avoids a transpose operation (which may be
        expensive on resource-constrained devices) that may be required to convert the unfolded tensor from
        channel-first to channel-last format in case of a linear layer.
    """
    def __init__(self,
                 embed_dim: int,
                 attn_dropout: Optional[float] = 0.0,
                 bias: Optional[bool] = True,
                 *args,
                 **kwargs) -> None:
        super().__init__()
        self.attn_dropout = nn.Dropout(p=attn_dropout)
        self.qkv_proj = ConvLayer(
            in_channels=embed_dim,
            out_channels=embed_dim * 2 + 1,
            kernel_size=1,
            bias=bias,
            use_norm=False,
            use_act=False
        )
        self.out_proj = ConvLayer(
            in_channels=embed_dim,
            out_channels=embed_dim,
            bias=bias,
            kernel_size=1,
            use_norm=False,
            use_act=False,
        )
        self.embed_dim = embed_dim
    def forward(self, x: Tensor, x_prev: Optional[Tensor] = None, *args, **kwargs) -> Tensor:
        if x_prev is None:
            return self._forward_self_attn(x, *args, **kwargs)
        else:
            return self._forward_cross_attn(x, x_prev, *args, **kwargs)
    def _forward_self_attn(self, x: Tensor, *args, **kwargs) -> Tensor:
        # [B, C, P, N] --> [B, h + 2d, P, N]
        qkv = self.qkv_proj(x)
        # [B, h + 2d, P, N] --> [B, h, P, N], [B, d, P, N], [B, 1, P, N]
        # Query --> [B, 1, P ,N]
        # Value, key --> [B, d, P, N]
        query, key, value = torch.split(
            qkv, [1, self.embed_dim, self.embed_dim], dim=1
        )
        # 在M通道上做softmax
        context_scores = F.softmax(query, dim=-1)
        context_scores = self.attn_dropout(context_scores)
        # Compute context vector
        # [B, d, P, N] x [B, 1, P, N] -> [B, d, P, N]
        context_vector = key * context_scores
        # [B, d, P, N] --> [B, d, P, 1]
        context_vector = context_vector.sum(dim=-1, keepdim=True)
        # combine context vector with values
        # [B, d, P, N] * [B, d, P, 1] --> [B, d, P, N]
        out = F.relu(value) * context_vector.expand_as(value)
        out = self.out_proj(out)
        return out
    def _forward_cross_attn(
            self, x: Tensor, x_prev: Optional[Tensor] = None, *args, **kwargs):
        # x --> [B, C, P, N]
        # x_prev --> [B, C, P, N]
        batch_size, in_dim, kv_patch_area, kv_num_patches = x.shape
        q_patch_area, q_num_patches = x.shape[-2:]
        assert (
                kv_patch_area == q_patch_area
        ), "The number of patches in the query and key-value tensors must be the same"
        # compute query, key, and value
        # [B, C, P, M] --> [B, 1 + d, P, M]
        qk = F.conv2d(
            x_prev,
            weight=self.qkv_proj.block.conv.weight[: self.embed_dim + 1, ...],
            bias=self.qkv_proj.block.conv.bias[: self.embed_dim + 1, ...],
        )
        # [B, 1 + d, P, M] --> [B, 1, P, M], [B, d, P, M]
        query, key = torch.split(qk, split_size_or_sections=[1, self.embed_dim], dim=1)
        # [B, C, P, N] --> [B, d, P, N]
        value = F.conv2d(
            x,
            weight=self.qkv_proj.block.conv.weight[self.embed_dim + 1:, ...],
            bias=self.qkv_proj.block.conv.bias[self.embed_dim + 1:, ...],
        )
        context_scores = F.softmax(query, dim=-1)
        context_scores = self.attn_dropout(context_scores)
        context_vector = key * context_scores
        context_vector = torch.sum(context_vector, dim=-1, keepdim=True)
        out = F.relu(value) * context_vector.expand_as(value)
        out = self.out_proj(out)
        return out
class LinearAttnFFN(nn.Module):
    """
    This class defines the pre-norm transformer encoder with linear self-attention in `MobileViTv2 <https://arxiv.org/abs/2206.02680>`_ paper
    Args:
        embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, P, N)`
        ffn_latent_dim (int): Inner dimension of the FFN
        attn_dropout (Optional[float]): Dropout rate for attention in multi-head attention. Default: 0.0
        dropout (Optional[float]): Dropout rate. Default: 0.0
        ffn_dropout (Optional[float]): Dropout between FFN layers. Default: 0.0
        norm_layer (Optional[str]): Normalization layer. Default: layer_norm_2d
    Shape:
        - Input: :math:`(B, C_{in}, P, N)` where :math:`B` is batch size, :math:`C_{in}` is input embedding dim,
            :math:`P` is number of pixels in a patch, and :math:`N` is number of patches,
        - Output: same shape as the input
    """
    def __init__(
            self,
            embed_dim: int,
            ffn_latent_dim: int,
            attn_dropout: Optional[float] = 0.0,
            dropout: Optional[float] = 0.1,
            ffn_dropout: Optional[float] = 0.0,
            *args,
            **kwargs
    ) -> None:
        super().__init__()
        attn_unit = LinearSelfAttention(
            embed_dim=embed_dim, attn_dropout=attn_dropout, bias=True
        )
        self.pre_norm_attn = nn.Sequential(
            nn.GroupNorm(num_channels=embed_dim, num_groups=1),
            attn_unit,
            nn.Dropout(p=dropout)
        )
        self.pre_norm_ffn = nn.Sequential(
            nn.GroupNorm(num_channels=embed_dim, num_groups=1),
            ConvLayer(
                in_channels=embed_dim,
                out_channels=ffn_latent_dim,
                kernel_size=1,
                stride=1,
                bias=True,
                use_norm=False,
                use_act=True,
            ),
            nn.Dropout(p=ffn_dropout),
            ConvLayer(
                in_channels=ffn_latent_dim,
                out_channels=embed_dim,
                kernel_size=1,
                stride=1,
                bias=True,
                use_norm=False,
                use_act=False,
            ),
            nn.Dropout(p=dropout)
        )
        self.embed_dim = embed_dim
        self.ffn_dim = ffn_latent_dim
        self.ffn_dropout = ffn_dropout
        self.std_dropout = dropout
    def forward(self,
                x: Tensor, x_prev: Optional[Tensor] = None, *args, **kwargs
                ) -> Tensor:
        if x_prev is None:
            # self-attention
            x = x + self.pre_norm_attn(x)
        else:
            # cross-attention
            res = x
            x = self.pre_norm_attn[0](x)  # norm
            x = self.pre_norm_attn[1](x, x_prev)  # attn
            x = self.pre_norm_attn[2](x)  # drop
            x = x + res  # residual
        x = x + self.pre_norm_ffn(x)
        return x
def make_divisible(
        v: Union[float, int],
        divisor: Optional[int] = 8,
        min_value: Optional[Union[float, int]] = None,
) -> Union[float, int]:
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v
class Identity(nn.Module):
    """
    This is a place-holder and returns the same tensor.
    """
    def __init__(self):
        super(Identity, self).__init__()
    def forward(self, x: Tensor) -> Tensor:
        return x
    def profile_module(self, x: Tensor) -> Tuple[Tensor, float, float]:
        return x, 0.0, 0.0
class InvertedResidual(nn.Module):
    """
    This class implements the inverted residual block, as described in `MobileNetv2 <https://arxiv.org/abs/1801.04381>`_ paper
    Args:
        in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H_{in}, W_{in})`
        out_channels (int): :math:`C_{out}` from an expected output of size :math:`(N, C_{out}, H_{out}, W_{out)`
        stride (int): Use convolutions with a stride. Default: 1
        expand_ratio (Union[int, float]): Expand the input channels by this factor in depth-wise conv
        skip_connection (Optional[bool]): Use skip-connection. Default: True
    Shape:
        - Input: :math:`(N, C_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, H_{out}, W_{out})`
    .. note::
        If `in_channels =! out_channels` and `stride > 1`, we set `skip_connection=False`
    """
    def __init__(
            self,
            in_channels: int,
            out_channels: int,
            stride: int,
            expand_ratio: Union[int, float],  # 扩张因子，到底要在隐层将通道数扩张多少倍
            skip_connection: Optional[bool] = True,  # 是否使用跳跃连接
    ) -> None:
        assert stride in [1, 2]
        hidden_dim = make_divisible(int(round(in_channels * expand_ratio)), 8)
        super().__init__()
        block = nn.Sequential()
        if expand_ratio != 1:
            block.add_module(
                name="exp_1x1",
                module=ConvLayer(
                    in_channels=in_channels,
                    out_channels=hidden_dim,
                    kernel_size=1
                ),
            )
        block.add_module(
            name="conv_3x3",
            module=ConvLayer(
                in_channels=hidden_dim,
                out_channels=hidden_dim,
                stride=stride,
                kernel_size=3,
                groups=hidden_dim  # depth-wise convolution
            ),
        )
        block.add_module(
            name="red_1x1",
            module=ConvLayer(
                in_channels=hidden_dim,
                out_channels=out_channels,
                kernel_size=1,
                use_act=False,  # 最后一层不使用激活函数
                use_norm=True,
            ),
        )
        self.block = block
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.exp = expand_ratio
        self.stride = stride
        self.use_res_connect = (
                self.stride == 1 and in_channels == out_channels and skip_connection
        )
    def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
        if self.use_res_connect:  # 如果需要使用残差连接
            return x + self.block(x)
        else:
            return self.block(x)
class MobileViTBlock(nn.Module):
    """
    This class defines the `MobileViT block <https://arxiv.org/abs/2110.02178?context=cs.LG>`_
    Args:
        opts: command line arguments
        in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H, W)`
        transformer_dim (int): Input dimension to the transformer unit
        ffn_dim (int): Dimension of the FFN block
        n_transformer_blocks (int): Number of transformer blocks. Default: 2
        head_dim (int): Head dimension in the multi-head attention. Default: 32
        attn_dropout (float): Dropout in multi-head attention. Default: 0.0
        dropout (float): Dropout rate. Default: 0.0
        ffn_dropout (float): Dropout between FFN layers in transformer. Default: 0.0
        patch_h (int): Patch height for unfolding operation. Default: 8
        patch_w (int): Patch width for unfolding operation. Default: 8
        transformer_norm_layer (Optional[str]): Normalization layer in the transformer block. Default: layer_norm
        conv_ksize (int): Kernel size to learn local representations in MobileViT block. Default: 3
        no_fusion (Optional[bool]): Do not combine the input and output feature maps. Default: False
    """
    def __init__(
            self,
            in_channels: int,  # 输入通道数
            transformer_dim: int,  # 输入到transformer的每个token序列长度
            ffn_dim: int,  # feed forward network的维度
            n_transformer_blocks: int = 2,  # transformer block的个数
            head_dim: int = 32,
            attn_dropout: float = 0.0,
            dropout: float = 0.0,
            ffn_dropout: float = 0.0,
            patch_h: int = 8,
            patch_w: int = 8,
            conv_ksize: Optional[int] = 3,  # 卷积核大小
            *args,
            **kwargs
    ) -> None:
        super().__init__()
        conv_3x3_in = ConvLayer(
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=conv_ksize,
            stride=1
        )
        conv_1x1_in = ConvLayer(
            in_channels=in_channels,
            out_channels=transformer_dim,
            kernel_size=1,
            stride=1,
            use_norm=False,
            use_act=False
        )
        conv_1x1_out = ConvLayer(
            in_channels=transformer_dim,
            out_channels=in_channels,
            kernel_size=1,
            stride=1
        )
        conv_3x3_out = ConvLayer(
            in_channels=2 * in_channels,
            out_channels=in_channels,
            kernel_size=conv_ksize,
            stride=1
        )
        self.local_rep = nn.Sequential()
        self.local_rep.add_module(name="conv_3x3", module=conv_3x3_in)
        self.local_rep.add_module(name="conv_1x1", module=conv_1x1_in)
        assert transformer_dim % head_dim == 0  # 验证transformer_dim是否可以被head_dim整除
        num_heads = transformer_dim // head_dim
        global_rep = [
            TransformerEncoder(
                embed_dim=transformer_dim,
                ffn_latent_dim=ffn_dim,
                num_heads=num_heads,
                attn_dropout=attn_dropout,
                dropout=dropout,
                ffn_dropout=ffn_dropout
            )
            for _ in range(n_transformer_blocks)
        ]
        global_rep.append(nn.LayerNorm(transformer_dim))
        self.global_rep = nn.Sequential(*global_rep)
        self.conv_proj = conv_1x1_out
        self.fusion = conv_3x3_out
        self.patch_h = patch_h
        self.patch_w = patch_w
        self.patch_area = self.patch_w * self.patch_h
        self.cnn_in_dim = in_channels
        self.cnn_out_dim = transformer_dim
        self.n_heads = num_heads
        self.ffn_dim = ffn_dim
        self.dropout = dropout
        self.attn_dropout = attn_dropout
        self.ffn_dropout = ffn_dropout
        self.n_blocks = n_transformer_blocks
        self.conv_ksize = conv_ksize
    def unfolding(self, x: Tensor) -> Tuple[Tensor, Dict]:
        patch_w, patch_h = self.patch_w, self.patch_h
        patch_area = patch_w * patch_h
        batch_size, in_channels, orig_h, orig_w = x.shape
        new_h = int(math.ceil(orig_h / self.patch_h) * self.patch_h)  # 为后文判断是否需要插值做准备
        new_w = int(math.ceil(orig_w / self.patch_w) * self.patch_w)  # 为后文判断是否需要插值做准备
        interpolate = False
        if new_w != orig_w or new_h != orig_h:
            # Note: Padding can be done, but then it needs to be handled in attention function.
            x = F.interpolate(x, size=(new_h, new_w), mode="bilinear", align_corners=False)
            interpolate = True
        # number of patches along width and height
        num_patch_w = new_w // patch_w  # n_w
        num_patch_h = new_h // patch_h  # n_h
        num_patches = num_patch_h * num_patch_w  # N
        # [B, C, H, W] -> [B * C * n_h, p_h, n_w, p_w]
        x = x.reshape(batch_size * in_channels * num_patch_h, patch_h, num_patch_w, patch_w)
        # [B * C * n_h, p_h, n_w, p_w] -> [B * C * n_h, n_w, p_h, p_w]
        x = x.transpose(1, 2)
        # [B * C * n_h, n_w, p_h, p_w] -> [B, C, N, P] where P = p_h * p_w and N = n_h * n_w
        x = x.reshape(batch_size, in_channels, num_patches, patch_area)
        # [B, C, N, P] -> [B, P, N, C]
        x = x.transpose(1, 3)
        # [B, P, N, C] -> [BP, N, C]
        x = x.reshape(batch_size * patch_area, num_patches, -1)
        info_dict = {
            "orig_size": (orig_h, orig_w),
            "batch_size": batch_size,
            "interpolate": interpolate,
            "total_patches": num_patches,
            "num_patches_w": num_patch_w,
            "num_patches_h": num_patch_h,
        }
        return x, info_dict
    def folding(self, x: Tensor, info_dict: Dict) -> Tensor:
        n_dim = x.dim()
        assert n_dim == 3, "Tensor should be of shape BPxNxC. Got: {}".format(
            x.shape
        )
        # [BP, N, C] --> [B, P, N, C]
        # 将x变成连续的张量，以便进行重塑操作
        x = x.contiguous().view(
            # 重塑x的第一个维度为批量大小
            info_dict["batch_size"],
            # 重塑x的第二个维度为每个图像块的像素数
            self.patch_area,
            # 重塑x的第三个维度为每个批次中的图像块总数
            info_dict["total_patches"],
            # 保持x的最后一个维度不变
            -1
        )
        batch_size, pixels, num_patches, channels = x.size()
        num_patch_h = info_dict["num_patches_h"]
        num_patch_w = info_dict["num_patches_w"]
        # [B, P, N, C] -> [B, C, N, P]
        x = x.transpose(1, 3)
        # [B, C, N, P] -> [B*C*n_h, n_w, p_h, p_w]
        x = x.reshape(batch_size * channels * num_patch_h, num_patch_w, self.patch_h, self.patch_w)
        # [B*C*n_h, n_w, p_h, p_w] -> [B*C*n_h, p_h, n_w, p_w]
        x = x.transpose(1, 2)
        # [B*C*n_h, p_h, n_w, p_w] -> [B, C, H, W]
        x = x.reshape(batch_size, channels, num_patch_h * self.patch_h, num_patch_w * self.patch_w)
        if info_dict["interpolate"]:
            x = F.interpolate(
                x,
                size=info_dict["orig_size"],
                mode="bilinear",
                align_corners=False,
            )
        return x
    def forward(self, x: Tensor) -> Tensor:
        res = x
        fm = self.local_rep(x)  # [4, 64, 28, 28]
        # convert feature map to patches
        patches, info_dict = self.unfolding(fm)  # [16, 196, 64]
        # print(patches.shape)
        # learn global representations
        for transformer_layer in self.global_rep:
            patches = transformer_layer(patches)
        # [B x Patch x Patches x C] -> [B x C x Patches x Patch]
        # Patch 所有的条状Patch的数量
        # Patches 每个条状Patch的长度
        fm = self.folding(x=patches, info_dict=info_dict)
        fm = self.conv_proj(fm)
        fm = self.fusion(torch.cat((res, fm), dim=1))
        return fm
class MobileViTBlockV2(nn.Module):
    """
    This class defines the `MobileViTv2 <https://arxiv.org/abs/2206.02680>`_ block
    Args:
        opts: command line arguments
        in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H, W)`
        attn_unit_dim (int): Input dimension to the attention unit
        ffn_multiplier (int): Expand the input dimensions by this factor in FFN. Default is 2.
        n_attn_blocks (Optional[int]): Number of attention units. Default: 2
        attn_dropout (Optional[float]): Dropout in multi-head attention. Default: 0.0
        dropout (Optional[float]): Dropout rate. Default: 0.0
        ffn_dropout (Optional[float]): Dropout between FFN layers in transformer. Default: 0.0
        patch_h (Optional[int]): Patch height for unfolding operation. Default: 8
        patch_w (Optional[int]): Patch width for unfolding operation. Default: 8
        conv_ksize (Optional[int]): Kernel size to learn local representations in MobileViT block. Default: 3
        dilation (Optional[int]): Dilation rate in convolutions. Default: 1
        attn_norm_layer (Optional[str]): Normalization layer in the attention block. Default: layer_norm_2d
    """
    def __init__(self,
                 in_channels: int,
                 attn_unit_dim: int,
                 ffn_multiplier: Optional[Union[Sequence[Union[int, float]], int, float]] = 2.0,
                 n_transformer_blocks: Optional[int] = 2,
                 attn_dropout: Optional[float] = 0.0,
                 dropout: Optional[float] = 0.0,
                 ffn_dropout: Optional[float] = 0.0,
                 patch_h: Optional[int] = 8,
                 patch_w: Optional[int] = 8,
                 conv_ksize: Optional[int] = 3,
                 *args,
                 **kwargs) -> None:
        super(MobileViTBlockV2, self).__init__()
        cnn_out_dim = attn_unit_dim
        conv_3x3_in = ConvLayer(
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=conv_ksize,
            stride=1,
            use_norm=True,
            use_act=True,
            groups=in_channels,
        )
        conv_1x1_in = ConvLayer(
            in_channels=in_channels,
            out_channels=cnn_out_dim,
            kernel_size=1,
            stride=1,
            use_norm=False,
            use_act=False,
        )
        self.local_rep = nn.Sequential(conv_3x3_in, conv_1x1_in)
        self.global_rep, attn_unit_dim = self._build_attn_layer(
            d_model=attn_unit_dim,
            ffn_mult=ffn_multiplier,
            n_layers=n_transformer_blocks,
            attn_dropout=attn_dropout,
            dropout=dropout,
            ffn_dropout=ffn_dropout,
        )
        self.conv_proj = ConvLayer(
            in_channels=cnn_out_dim,
            out_channels=in_channels,
            kernel_size=1,
            stride=1,
            use_norm=True,
            use_act=False,
        )
        self.patch_h = patch_h
        self.patch_w = patch_w
        self.patch_area = self.patch_w * self.patch_h
        self.cnn_in_dim = in_channels
        self.cnn_out_dim = cnn_out_dim
        self.transformer_in_dim = attn_unit_dim
        self.dropout = dropout
        self.attn_dropout = attn_dropout
        self.ffn_dropout = ffn_dropout
        self.n_blocks = n_transformer_blocks
        self.conv_ksize = conv_ksize
    def _build_attn_layer(self,
                          d_model: int,
                          ffn_mult: Union[Sequence, int, float],
                          n_layers: int,
                          attn_dropout: float,
                          dropout: float,
                          ffn_dropout: float,
                          attn_norm_layer: str = "layer_norm_2d",
                          *args,
                          **kwargs) -> Tuple[nn.Module, int]:
        if isinstance(ffn_mult, Sequence) and len(ffn_mult) == 2:
            ffn_dims = (
                    np.linspace(ffn_mult[0], ffn_mult[1], n_layers, dtype=float) * d_model
            )
        elif isinstance(ffn_mult, Sequence) and len(ffn_mult) == 1:
            ffn_dims = [ffn_mult[0] * d_model] * n_layers
        elif isinstance(ffn_mult, (int, float)):
            ffn_dims = [ffn_mult * d_model] * n_layers
        else:
            raise NotImplementedError
        ffn_dims = [int((d // 16) * 16) for d in ffn_dims]
        global_rep = [
            LinearAttnFFN(
                embed_dim=d_model,
                ffn_latent_dim=ffn_dims[block_idx],
                attn_dropout=attn_dropout,
                dropout=dropout,
                ffn_dropout=ffn_dropout,
            )
            for block_idx in range(n_layers)
        ]
        global_rep.append(nn.GroupNorm(1, d_model))
        return nn.Sequential(*global_rep), d_model
    def forward(
            self, x: Union[Tensor, Tuple[Tensor]], *args, **kwargs
    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
        if isinstance(x, Tuple) and len(x) == 2:
            # for spatio-temporal data (e.g., videos)
            return self.forward_temporal(x=x[0], x_prev=x[1])
        elif isinstance(x, Tensor):
            # for image data
            return self.forward_spatial(x)
        else:
            raise NotImplementedError
    def forward_spatial(self, x: Tensor, *args, **kwargs) -> Tensor:
        x = self.resize_input_if_needed(x)
        # learn global representations on all patches
        fm = self.local_rep(x)
        patches, output_size = self.unfolding_pytorch(fm)
        # print(f"original x.shape = {patches.shape}")
        patches = self.global_rep(patches)
        # [B x Patch x Patches x C] --> [B x C x Patches x Patch]
        fm = self.folding_pytorch(patches=patches, output_size=output_size)
        fm = self.conv_proj(fm)
        return fm
    def forward_temporal(
            self, x: Tensor, x_prev: Optional[Tensor] = None
    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
        x = self.resize_input_if_needed(x)
        fm = self.local_rep(x)
        patches, output_size = self.unfolding_pytorch(fm)
        for global_layer in self.global_rep:
            if isinstance(global_layer, LinearAttnFFN):
                patches = global_layer(x=patches, x_prev=x_prev)
            else:
                patches = global_layer(patches)
        fm = self.folding_pytorch(patches=patches, output_size=output_size)
        fm = self.conv_proj(fm)
        return fm, patches
    def resize_input_if_needed(self, x: Tensor) -> Tensor:
        # print(f"original x.shape = {x.shape}")
        batch_size, in_channels, orig_h, orig_w = x.shape
        if orig_h % self.patch_h != 0 or orig_w % self.patch_w != 0:
            new_h = int(math.ceil(orig_h / self.patch_h) * self.patch_h)
            new_w = int(math.ceil(orig_w / self.patch_w) * self.patch_w)
            x = F.interpolate(
                x, size=(new_h, new_w), mode="bilinear", align_corners=True
            )
        # print(f"changed x.shape = {x.shape}")
        return x
    def unfolding_pytorch(self, feature_map: Tensor) -> Tuple[Tensor, Tuple[int, int]]:
        batch_size, in_channels, img_h, img_w = feature_map.shape
        # [B, C, H, W] --> [B, C, P, N]
        patches = F.unfold(
            feature_map,
            kernel_size=(self.patch_h, self.patch_w),
            stride=(self.patch_h, self.patch_w),
        )
        patches = patches.reshape(
            batch_size, in_channels, self.patch_h * self.patch_w, -1
        )
        return patches, (img_h, img_w)
    def folding_pytorch(self, patches: Tensor, output_size: Tuple[int, int]) -> Tensor:
        batch_size, in_dim, patch_size, n_patches = patches.shape
        # [B, C, P, N]
        patches = patches.reshape(batch_size, in_dim * patch_size, n_patches)
        feature_map = F.fold(
            patches,
            output_size=output_size,
            kernel_size=(self.patch_h, self.patch_w),
            stride=(self.patch_h, self.patch_w),
        )
        return feature_map
class MobileViTV2(nn.Module):
    """
    This class defines the `MobileViTv2 <https://arxiv.org/abs/2206.02680>`_ architecture
    """
    def __init__(self, model_cfg: Dict, num_classes: int = 1000):
        super().__init__()
        image_channels = model_cfg["layer0"]["img_channels"]
        out_channels = model_cfg["layer0"]["out_channels"]
        self.model_conf_dict = dict()
        self.conv_1 = ConvLayer(
            in_channels=image_channels,
            out_channels=out_channels,
            kernel_size=3,
            stride=2,
            use_norm=True,
            use_act=True,
        )
        self.model_conf_dict["conv1"] = {"in": image_channels, "out": out_channels}
        in_channels = out_channels
        self.layer_1, out_channels = self._make_layer(
            input_channel=in_channels, cfg=model_cfg["layer1"]
        )
        self.model_conf_dict["layer1"] = {"in": in_channels, "out": out_channels}
        in_channels = out_channels
        self.layer_2, out_channels = self._make_layer(
            input_channel=in_channels, cfg=model_cfg["layer2"]
        )
        self.model_conf_dict["layer2"] = {"in": in_channels, "out": out_channels}
        in_channels = out_channels
        self.layer_3, out_channels = self._make_layer(
            input_channel=in_channels, cfg=model_cfg["layer3"]
        )
        self.model_conf_dict["layer3"] = {"in": in_channels, "out": out_channels}
        in_channels = out_channels
        self.layer_4, out_channels = self._make_layer(
            input_channel=in_channels, cfg=model_cfg["layer4"]
        )
        in_channels = out_channels
        self.layer_5, out_channels = self._make_layer(
            input_channel=in_channels, cfg=model_cfg["layer5"] # 有可能会被冻结，来进行网络微调
        )
        self.model_conf_dict["layer5"] = {"in": in_channels, "out": out_channels}
        self.conv_1x1_exp = Identity()
        self.model_conf_dict["exp_before_cls"] = {
            "in": out_channels,
            "out": out_channels,
        }
        self.classifier = nn.Sequential()  # 有可能会被冻结，来进行网络微调
        self.classifier.add_module(name="global_pool", module=nn.AdaptiveAvgPool2d(1))
        self.classifier.add_module(name="flatten", module=nn.Flatten())
        self.classifier.add_module(name="fc", module=nn.Linear(in_features=out_channels, out_features=num_classes))
        self.apply(self.init_parameters)
        self.width_list = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]
    def _make_layer(self, input_channel, cfg: Dict, dilate: Optional[bool] = False) -> Optional[
        tuple[nn.Sequential, int]]:
        block_type = cfg.get("block_type", "mobilevit")
        if block_type.lower() == "mobilevit":
            return self._make_mit_layer(
                input_channel=input_channel, cfg=cfg
            )
        else:
            return self._make_mobilenet_layer(
                input_channel=input_channel, cfg=cfg
            )
    @staticmethod
    def _make_mobilenet_layer(
            input_channel: int, cfg: Dict) -> Tuple[nn.Sequential, int]:
        output_channels = cfg.get("out_channels")
        num_blocks = cfg.get("num_blocks", 2)
        expand_ratio = cfg.get("expand_ratio", 4)
        block = []
        for i in range(num_blocks):
            stride = cfg.get("stride", 1) if i == 0 else 1
            layer = InvertedResidual(
                in_channels=input_channel,
                out_channels=output_channels,
                stride=stride,
                expand_ratio=expand_ratio
            )
            block.append(layer)
            input_channel = output_channels
        return nn.Sequential(*block), input_channel
    @staticmethod
    def _make_mit_layer(input_channel: int, cfg: Dict) -> [nn.Sequential, int]:
        block = []
        stride = cfg.get("stride", 1)
        if stride == 2:
            layer = InvertedResidual(
                in_channels=input_channel,
                out_channels=cfg.get("out_channels"),
                stride=stride,
                expand_ratio=cfg.get("mv_expand_ratio", 4),
            )
            block.append(layer)
            input_channel = cfg.get("out_channels")
        attn_unit_dim = cfg["attn_unit_dim"]
        ffn_multiplier = cfg.get("ffn_multiplier")
        block.append(
            MobileViTBlockV2(
                in_channels=input_channel,
                out_channels=cfg.get("out_channels"),
                attn_unit_dim=attn_unit_dim,
                ffn_multiplier=ffn_multiplier,
                n_transformer_blocks=cfg.get("attn_blocks", 1),
                patch_h=cfg.get("patch_h", 2),
                patch_w=cfg.get("patch_w", 2),
                dropout=cfg.get("dropout", 0.1),
                attn_dropout=cfg.get("attn_dropout", 0.1),
                ff_dropout=cfg.get("ff_dropout", 0.1),
                conv_ksize=3,
            )
        )
        return nn.Sequential(*block), input_channel
        pass
    @staticmethod
    def init_parameters(m):
        if isinstance(m, nn.Conv2d):
            if m.weight is not None:
                nn.init.kaiming_normal_(m.weight, mode="fan_out")
            if m.bias is not None:
                nn.init.zeros_(m.bias)
        elif isinstance(m, (nn.GroupNorm, nn.BatchNorm2d, nn.LayerNorm)):
            if m.weight is not None:
                nn.init.ones_(m.weight)
            if m.bias is not None:
                nn.init.zeros_(m.bias)
        elif isinstance(m, (nn.Linear,)):
            if m.weight is not None:
                nn.init.trunc_normal_(m.weight, mean=0.0, std=0.02)
            if m.bias is not None:
                nn.init.zeros_(m.bias)
        else:
            pass
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        unique_tensors = {}
        x = self.conv_1(x)
        width, height = x.shape[2], x.shape[3]
        unique_tensors[(width, height)] = x
        x = self.layer_1(x)
        width, height = x.shape[2], x.shape[3]
        unique_tensors[(width, height)] = x
        x = self.layer_2(x)
        width, height = x.shape[2], x.shape[3]
        unique_tensors[(width, height)] = x
        x = self.layer_3(x)
        width, height = x.shape[2], x.shape[3]
        unique_tensors[(width, height)] = x
        x = self.layer_4(x)
        width, height = x.shape[2], x.shape[3]
        unique_tensors[(width, height)] = x
        x = self.layer_5(x)
        width, height = x.shape[2], x.shape[3]
        unique_tensors[(width, height)] = x
        result_list = list(unique_tensors.values())[-4:]
        return result_list
def mobile_vit2_xx_small(num_classes: int = 1000):
    # pretrain weight link
    # https://docs-assets.developer.apple.com/ml-research/models/cvnets/classification/mobilevit2_xxs.pt
    config = get_config("2xx_small")
    m = MobileViTV2(config, num_classes=num_classes)
    return m
if __name__ == "__main__":
    # Generating Sample image
    image_size = (1, 3, 640, 640)
    image = torch.rand(*image_size)
    # Model
    model = mobile_vit2_xx_small()
    out = model(image)
    print(out)

四、手把手教你添加MobileViTv2

4.1 修改一

第一步还是建立文件，我们找到如下ultralytics/nn文件夹下建立一个目录名字呢就是'Addmodules'文件夹 (用群内的文件的话已经有了无需新建)！ 然后在其内部建立一个新的py文件将核心代码复制粘贴进去即可

​

4.2 修改二

第二步我们在该目录下创建一个新的py文件名字为'__init__.py'( 用群内的文件的话已经有了无需新建) ，然后在其内部导入我们的检测头如下图所示。

​

4.3 修改三

第三步我门中到如下文件'ultralytics/nn/tasks.py'进行导入和注册我们的模块( 用群内的文件的话已经有了无需重新导入直接开始第四步即可) ！

从今天开始以后的教程就都统一成这个样子了，因为我默认大家用了我群内的文件来进行修改！！

​

4.4 修改四

添加如下两行代码！！！

​​

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。

​

        elif m in {自行添加对应的模型即可，下面都是一样的}:
            m = m(*args)
            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_ = 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

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。

        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)

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。

​​​

代码如下->

    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

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！！！

注意！！！ 额外的修改！

关注我的其实都知道，我大部分的修改都是一样的，这个网络需要额外的修改一步，就是s一个参数，将下面的s改为640！！！即可完美运行！！

​

打印计算量问题解决方案

我们找到如下文件'ultralytics/utils/torch_utils.py'按照如下的图片进行修改，否则容易打印不出来计算量。

​​

注意事项！！！

如果大家在验证的时候报错形状不匹配的错误可以固定验证集的图片尺寸，方法如下 ->

找到下面这个文件ultralytics/ models /yolo/detect/train.py然后其中有一个类是DetectionTrainer class中的build_dataset函数中的一个参数rect=mode == 'val'改为rect=False

​

五、MobileViTv2的yaml文件

5.1 MobileViTv2的yaml文件

此版本的训练信息：YOLO11-mobileNetV2 summary: 575 layers, 3,022,364 parameters, 3,022,348 gradients, 9.8 GFLOPs

# 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
# 共四个版本  "mobile_vit_small, mobile_vit_x_small, mobile_vit_xx_small"
# YOLO11n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, mobile_vit2_xx_small, []] # 0-4 P1/2
  - [-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)

5.2 训练文件的代码

可以复制我的运行文件进行运行。

import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO
if __name__ == '__main__':
    model = YOLO('yolov8-MLLA.yaml')
    # 如何切换模型版本, 上面的ymal文件可以改为 yolov8s.yaml就是使用的v8s,
    # 类似某个改进的yaml文件名称为yolov8-XXX.yaml那么如果想使用其它版本就把上面的名称改为yolov8l-XXX.yaml即可（改的是上面YOLO中间的名字不是配置文件的）！
    # model.load('yolov8n.pt') # 是否加载预训练权重,科研不建议大家加载否则很难提升精度
    model.train(data=r"C:\Users\Administrator\PycharmProjects\yolov5-master\yolov5-master\Construction Site Safety.v30-raw-images_latestversion.yolov8\data.yaml",
                # 如果大家任务是其它的'ultralytics/cfg/default.yaml'找到这里修改task可以改成detect, segment, classify, pose
                cache=False,
                imgsz=640,
                epochs=150,
                single_cls=False,  # 是否是单类别检测
                batch=16,
                close_mosaic=0,
                workers=0,
                device='0',
                optimizer='SGD', # using SGD
                # resume='runs/train/exp21/weights/last.pt', # 如过想续训就设置last.pt的地址
                amp=True,  # 如果出现训练损失为Nan可以关闭amp
                project='runs/train',
                name='exp',
                )

六、成功运行记录

下面是成功运行的截图，已经完成了有1个epochs的训练，图片太大截不全第2个epochs了。

​

​

七、本文总结

到此本文的正式分享内容就结束了，在这里给大家推荐我的YOLOv11改进有效涨点专栏，本专栏目前为新开的平均质量分98分，后期我会根据各种最新的前沿顶会进行论文复现，也会对一些老的改进机制进行补充 ， 如果大家觉得本文帮助到你了，订阅本专栏，关注后续更多的更新~

​​​