【悉读经典】SegFormer:语义分割中的层次化Transformer网络

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本文介绍SegFormer语义分割网络，其有层次化Transformer编码器和轻量全MLP解码器两大创新。编码器生成多尺度特征，解码器融合特征。还说明基于PaddleSeg工具，用SegFormer对遥感影像地块分割进行训练、推理的过程，包括环境与数据准备、代码修改、网络训练和图片推理等步骤。

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项目说明

SegFormer是2025年发布的语义分割网络，成功地在Transformer中引入层次结构，提取不同尺度信息，在语义分割任务中，其精度与速度均不逊于OCRNet，因此发布后广受欢迎

本项目先对SegFormer原始论文的关键内容进行简单摘录，并使用PaddleSeg代码进行辅助，方便对SegFormer网络结构有详细的理解

然后基于PaddleSeg工具，使用SegFormer对常规赛：遥感影像地块分割的影像进行训练、推理

《SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers》

参考链接:

pdf; url; code

管检测:

transformer；语义分割

关键创新点:

• 提出一种 不需要位置编码的、层次化的 transformer 编码器
• 提出一种 轻量级的、全MLP 的解码器，不需要复杂计算与高计算资源，就可以的到有效的特征表达

层次化的Transformer编码器:

SegFormer主要有2个模块：

1. 层次化的transformer编码器/MiT，生成不同尺度特征
2. 轻量的全MLP解码器，融合不同层级特征

层次化的特征表示

在SegFormer的编码器MiT中，其仿照CNN结构，通过在不同阶段进行下采样，生成多尺度特征。

MiT输入的图像尺寸为 H*W*3, 经过各个阶段的特征处理得到的特征图尺寸为

$$\frac{H}{2^{i+1}}\ast \frac{W}{2^{i+1}}\ast C_{i+1},i\in \{1,2,3,4\}$$2i+1H∗2i+1W∗Ci+1,i∈{1,2,3,4}

       

代码中，各个阶段的下采样层定义如下：

# patch_embed，通过定义卷积操作的步长/stride，时相下采样self.patch_embed1 = OverlapPatchEmbed(    img_size=img_size,    patch_size=7,                   # stage1, 大卷积核7*7
    stride=4,                          # stage1, 4倍下采样
    in_chans=in_chans,    embed_dim=embed_dims[0])self.patch_embed2 = OverlapPatchEmbed(    img_size=img_size // 4,    patch_size=3,    stride=2,                          # stage2, 2倍下采样
    in_chans=embed_dims[0],    embed_dim=embed_dims[1])self.patch_embed3 = OverlapPatchEmbed(    img_size=img_size // 8,    patch_size=3,    stride=2,                          # stage3, 2倍下采样
    in_chans=embed_dims[1],    embed_dim=embed_dims[2])self.patch_embed4 = OverlapPatchEmbed(    img_size=img_size // 16,    patch_size=3,    stride=2,                          # stage4, 2倍下采样
    in_chans=embed_dims[2],    embed_dim=embed_dims[3])

       

有重叠的patch合并

SegFormer中的patch合并，仿照ViT中的池化方式，将2*2*Ci 的特征变为1*1*Ci+1，具体实现时，使用卷积下采样并进行通道变换，得到1*1*Ci+1。从而实现下采样、通道维数变化。

这一操作的设计初衷，是为了组合非重叠的图像或特征patch，因此不能保持patch周边的局部连续性。【各个patch是不重叠的，不能跨patch进行信息交互】

为了解决这一问题，本文提出重叠patch合并，并定义如下参数：
patch尺寸K、步长S、填充尺寸P，在网络中设置参了2套参数：K = 7, S = 4, P = 3 ；K = 3, S = 2, P = 1【在stage1中使用大尺寸、大步长生成的patch，可以快速压缩空间信息，实现下采样，便于进行特征计算】

       

代码中，重叠patch合并层定义如下：

class OverlapPatchEmbed(nn.Layer):
    def __init__(self,                 img_size=224,
                 patch_size=7,          # 卷积核大小
                 stride=4,                 # 下采样倍数
                 in_chans=3,            # 输入通道数
                 embed_dim=768):  # 输出通道数
        super().__init__()        img_size = to_2tuple(img_size)        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        self.H, self.W = img_size[0] // patch_size[0], img_size[            1] // patch_size[1]
        self.num_patches = self.H * self.W        # 定义投影变换所用的卷积
        self.proj = nn.Conv2D(
            in_chans,
            embed_dim,            kernel_size=patch_size,
            stride=stride,
            padding=(patch_size[0] // 2, patch_size[1] // 2))        # 定义layer norm层
        self.norm = nn.LayerNorm(embed_dim)

    def forward(self, x):        x = self.proj(x)    # 通过卷积进行特征重投影，实现下采样、通道变换
        x_shape = paddle.shape(x)
        H, W = x_shape[2], x_shape[3]        x = x.flatten(2).transpose([0, 2, 1])  # 将H*W维度压缩成1个维度
        x = self.norm(x)          # 标准化

        return x, H, W

       

高效的自关注机制

编码器部分的主要计算消耗在于 自关注层/self-attention。

原在始的自关注过程中，Q、K、C的维度均为N*C，N=H*W，自关注原始计算如下：

$$Attention(Q,K,V)=Softmax(\frac{Q{K}^T}{\sqrt{d_{head}}})V$$Attention(Q,K,V)=Softmax(dheadQKT)V

而该公式的计算复杂度为O(N2)，计算消耗高，且与图像尺寸相关，因此不适用于高分辨率图像。

本文提出一种改进方式，在计算attention时，参考CNN中的处理，使用下采样率R对K进行处理，改进的计算过程如下：

$$\widehat{K}=Reshape(\frac{N}{R},C\cdot R)(K)$$K=Reshape(RN,C⋅R)(K)

$$K^{\mathrm{\prime }}=Linear(C\cdot R,C)(\widehat{K})$$K′=Linear(C⋅R,C)(K)

其中，K是输入的映射特征，$\widehat{K}$K是K维度变换后的特征，K'是降维后的特征。
【通过将K进行reshape将空间维度N的信息转移到通道维度C上，可以得到$\widehat{K}$K；然后通过定义的线性变换层将通道为降到原始维度C上，得到K'，实现空间下采样。】

通过上述操作计算复杂度降到O(N2/ R)，大大降低了计算复杂度，在SegFormer中中，将各阶段的设置R为[64, 16, 4, 1]

       

代码中，改进后的Attention定义如下：

class Attention(nn.Layer):
    def __init__(self,                 dim,                 num_heads=8,                 qkv_bias=False,                 qk_scale=None,                 attn_drop=0.,                 proj_drop=0.,                 sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5
        self.dim = dim
        
        # 定义q映射
        self.q = nn.Linear(dim, dim, bias_attr=qkv_bias)
        # 定义kv映射
        self.kv = nn.Linear(dim, dim * 2, bias_attr=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        # 定义输入特征的残差映射
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2D(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)     
            self.norm = nn.LayerNorm(dim)

    def forward(self, x, H, W):
        x_shape = paddle.shape(x)        B, N = x_shape[0], x_shape[1]        C = self.dim
        
        # 输入特征通过映射得到q
        q = self.q(x).reshape([B, N, self.num_heads,C // self.num_heads]).transpose([0, 2, 1, 3])
        
        # 输入特征通过映射得到k v
        if self.sr_ratio > 1:
            x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])
            x_ = self.sr(x_).reshape([B, C, -1]).transpose([0, 2, 1])         # 下采样
            x_ = self.norm(x_)
            kv = self.kv(x_).reshape([B, -1, 2, self.num_heads,C // self.num_heads]).transpose([2, 0, 3, 1, 4])
        else:
            kv = self.kv(x).reshape([B, -1, 2, self.num_heads,C // self.num_heads]).transpose([2, 0, 3, 1, 4])
        k, v = kv[0], kv[1]
        
        # att计算，q*k/sqrt(d)
        attn = (q @ k.transpose([0, 1, 3, 2])) * self.scale
        attn = F.softmax(attn, axis=-1)
        attn = self.attn_drop(attn)
        
        # att权重与x融合
        x = (attn @ v).transpose([0, 2, 1, 3]).reshape([B, N, C])
        # 关注后处理
        x = self.proj(x)
        x = self.proj_drop(x)

        return x

       

Mix-FFN

ViT使用位置编码引入位置信息，但由于在测试时的分辨率发生变化时，会引起精度下降的问题。

本文任务位置信息在语义分割中不是必需的，因此提出Mix-FFN：直接使用3*3卷积对输入特征进行处理，并考虑了用0进行填充导致的局部信息泄漏。计算过程如下：

$$x_{out}=MLP(GELU(Con{v}_{3\ast 3}(MLP(x_{in}))))+x_{in}$$xout=MLP(GELU(Conv3∗3(MLP(xin))))+xin

其中$x_{in}$xin是自关注模块生成的结果，Mix-FNN混合了3*3卷积与MLP，并进一步使用了深度分离卷积减少参数量、提高效率
       

代码中，Mix-FNN定义如下：

class Mlp(nn.Layer):
    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)    def forward(self, x, H, W):
        x = self.fc1(x)                    # 线性变换/MLP
        x = self.dwconv(x, H, W)  # 卷积/Conv3*3
        x = self.act(x)                   # GELU
        x = self.drop(x)
        x = self.fc2(x)                   # 线性变换/MLP
        x = self.drop(x)        return x

       

Lightweight All-MLP Decoder：

在解码器部分，SegFormer采用了简单的结构，仅由MLP组成，减少了手动设计、计算需求高等问题，主要包括4步：

1. 对多尺度特征进行通道维度变换，统一维度：通过MLP进行维度变换
2. 对多尺度特征进行空间维度变换，统一尺寸：通过插值上采样进行尺寸变换
3. 特征拼接与通道压缩：通过MLP进行通道压缩
4. 分类预测：1*1卷积

class SegFormer(nn.Layer):

    def __init__(self,
                 num_classes,
                 backbone,
                 embedding_dim,                 align_corners=False,
                 pretrained=None):
        super(SegFormer, self).__init__()

        self.pretrained = pretrained
        self.align_corners = align_corners
        self.backbone = backbone
        self.num_classes = num_classes
        c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = self.backbone.feat_channels

        self.linear_c4 = MLP(input_dim=c4_in_channels, embed_dim=embedding_dim)
        self.linear_c3 = MLP(input_dim=c3_in_channels, embed_dim=embedding_dim)
        self.linear_c2 = MLP(input_dim=c2_in_channels, embed_dim=embedding_dim)
        self.linear_c1 = MLP(input_dim=c1_in_channels, embed_dim=embedding_dim)

        self.dropout = nn.Dropout2D(0.1)
        self.linear_fuse = layers.ConvBNReLU(            in_channels=embedding_dim * 4,            out_channels=embedding_dim,
            kernel_size=1,
            bias_attr=False)

        self.linear_pred = nn.Conv2D(
            embedding_dim, self.num_classes, kernel_size=1)

    def forward(self, x):        feats = self.backbone(x)
        c1, c2, c3, c4 = feats        ############## MLP decoder on C1-C4 ###########
        c1_shape = paddle.shape(c1)        c2_shape = paddle.shape(c2)        c3_shape = paddle.shape(c3)        c4_shape = paddle.shape(c4)        
        # 统一stage4的维度、尺寸
        _c4 = self.linear_c4(c4).transpose([0, 2, 1]).reshape([0, 0, c4_shape[2], c4_shape[3]])        _c4 = F.interpolate(
            _c4,            size=c1_shape[2:],
            mode='bilinear',
            align_corners=self.align_corners)
        
        # 统一stage3的维度、尺寸
        _c3 = self.linear_c3(c3).transpose([0, 2, 1]).reshape([0, 0, c3_shape[2], c3_shape[3]])        _c3 = F.interpolate(
            _c3,            size=c1_shape[2:],
            mode='bilinear',
            align_corners=self.align_corners)
        
        # 统一stage2的维度、尺寸
        _c2 = self.linear_c2(c2).transpose([0, 2, 1]).reshape([0, 0, c2_shape[2], c2_shape[3]])        _c2 = F.interpolate(
            _c2,            size=c1_shape[2:],
            mode='bilinear',
            align_corners=self.align_corners)
        
        # 统一stage1维度、尺寸
        _c1 = self.linear_c1(c1).transpose([0, 2, 1]).reshape(
            [0, 0, c1_shape[2], c1_shape[3]])        
        # 特征拼接与通道压缩
        _c = self.linear_fuse(paddle.concat([_c4, _c3, _c2, _c1], axis=1))
        logit = self.dropout(_c)        
        #分类预测
        logit = self.linear_pred(logit)
        return [
            F.interpolate(
                logit,                size=paddle.shape(x)[2:],
                mode='bilinear',
                align_corners=self.align_corners)
        ]

       

Effective Receptive Field Analysis

语义分割任务中，保持大感受野是关键，本文分析了不同阶段的感受野，如下图：

       

在stage4阶段，DeepLabV3+的感受野小于SegFormer

SegFormer的编码器，在浅层阶段，可以产生类似于卷积一样的局部关注，并输出非局部关注，从而有效捕获stage4的上下文信息

在上采样阶段，Head的感受野除了具有非局部关注外，还有较强的局部关注。

Experiments

       

上图是SegFormer在ADE20K、Cityscapes数据集上与不同模型的参数量、精度。

       

SegFormer B4的Cityscapes miou精度已达到84%，属于SOTA水准，大于OCRNet HRNet48的81.1

【conclusion】

之前的语义分割中常用OCRNet48，虽然精度很高，但由于多尺度、多阶段的特征处理结构，计算速度慢、网络收敛慢。

在使用了SegFormer b3后，发现其与OCRNet48精度相差无几，并且显存占用相对较少、收敛快，在相同时间、显存下，可以加大batchsize与epoch。对于数据量较多，或者对推理速度有限制的应用情境下，SegFormer 是更优选择。

虽然SegFormer在语义冯上的表现已足够优秀，编码器MiT成功借鉴了CNN的层次结构应用在transformer中，但解码器较为简单，仍然存在提高的空间。

在PaddleSeg中使用ConvNeXt进行特征提取实现语义分割

环境准备

In [1]

# pip升级!pip install --user --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple# 下载仓库，并切换到2.4版本%cd /home/aistudio/
!git clone https://gitee.com/paddlepaddle/PaddleSeg.git #该行仅在初次运行项目时运行即可，后续不需要运行改行命令%cd /home/aistudio/PaddleSeg
!git checkout -b release/2.4 origin/release/2.4# 安装依赖!pip install -r requirements.txt

       

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数据准备

In [ ]

# 耗时约35秒!unzip -oq /home/aistudio/data/data77571/train_and_label.zip -d /home/aistudio/data/src/
!unzip -oq /home/aistudio/data/data77571/img_test.zip -d /home/aistudio/data/src/

    In [ ]

# 生产数据集划分txt# 演示时使用比例0.98：0.02!python /home/aistudio/work/segmentation/data_split.py \        0.98 0.02 0 \
        /home/aistudio/data/src/img_train \
        /home/aistudio/data/src/lab_train# # 实践时使用比例0.2：0.2# !python /home/aistudio/work/segmentation/data_split.py \#         0.8 0.2 0 \#         /home/aistudio/data/src/img_train \#         /home/aistudio/data/src/lab_train

   

代码准备

In [ ]

# 修改文件!cp /home/aistudio/work/segmentation/segformerb3.yml /home/aistudio/PaddleSeg/segformerb3.yml
!cp /home/aistudio/work/segmentation/utils.py /home/aistudio/PaddleSeg/paddleseg/utils/utils.py                         # 加载tif数据与模型参数!cp /home/aistudio/work/segmentation/predict.py /home/aistudio/PaddleSeg/paddleseg/core/predict.py                      # 预测类别结果保存!cp /home/aistudio/work/segmentation/transformer_utils.py /home/aistudio/PaddleSeg/paddleseg/models/backbones/transformer_utils.py # 修复数据类型bug

   

网络训练

In [ ]

# 演示时使用的训练超参数，约5分钟!python /home/aistudio/PaddleSeg/train.py \
    --config  /home/aistudio/PaddleSeg/segformerb3.yml \
    --save_dir /home/aistudio/data/output_seg \
    --do_eval \
    --use_vdl \
    --batch_size 32 \
    --iters 100 \
    --save_interval 50 \
    --log_iters 10 \
    --fp16 

# # 实践时使用的训练超参数，约20+小时# !python /home/aistudio/PaddleSeg/train.py \#     --config  /home/aistudio/PaddleSeg/segformerb3.yml \#     --save_dir /home/aistudio/data/output_seg \#     --do_eval \#     --use_vdl \#     --batch_size 32 \#     --iters 100000 \#     --save_interval 2100 \#     --log_iters 100 \#     --fp16

    In [2]

# 将训练参数转移到best_model/seg下!mkdir /home/aistudio/best_model
!mkdir /home/aistudio/best_model/seg
!cp /home/aistudio/data/output_seg/best_model/model.pdparams /home/aistudio/best_model/seg/model.pdparams

       

mkdir: 无法创建目录"/home/aistudio/best_model/seg": 没有那个文件或目录
cp: 无法获取'/home/aistudio/data/output_seg/best_model/model.pdparams' 的文件状态(stat): 没有那个文件或目录

       

图片推理

In [ ]

# 结果保存在/home/aistudio/data/infer_seg下!python /home/aistudio/PaddleSeg/predict.py \
       --config /home/aistudio/PaddleSeg/segformerb3.yml \
       --model_path /home/aistudio/best_model/seg/model.pdparams \
       --image_path /home/aistudio/data/src/img_testA \
       --save_dir /home/aistudio/data/infer_seg

   

预测结果/训练5分钟

       

       

# 不需要  # 很高  # 相差无几  # 均为  # 转移到  # 使用了  # 但由于  # 尺寸为  # 显存  # 这一  # python  # transformer  # cnn  # for  # red  # pip升级  # yy  # ai  # 工具  # git 

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