(CVPR2024)InceptionNeXt_block改进思路

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最新推荐文章于 2025-04-03 12:40:48 发布

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分类专栏：即插即用模块+改进思路文章标签：深度学习人工智能神经网络计算机视觉 pytorch

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简介

论文链接：[2303.16900] InceptionNeXt: When Inception Meets ConvNeXt

论文题目：InceptionNeXt: When Inception Meets ConvNeXt

会议：CVPR2024

摘要：受ViTs远程建模能力的启发，大核卷积最近被广泛研究和采用，以扩大接受野和提高模型性能，如使用7×7深度卷积的出色工作ConvNeXt。虽然这种深度运算只消耗少量的FLOPs，但由于较高的内存访问成本，很大程度上损害了模型在功能强大的计算设备上的效率。例如，ConvNeXt-T具有与ResNet-50相似的FLOPs，但在A100 gpu上进行全精度训练时只能达到60%的吞吐量。虽然减小ConvNeXt的内核大小可以提高速度，但它会导致显著的性能下降。目前还不清楚如何在保持性能的同时加速基于大核的CNN模型。为了解决这个问题，受inception的启发，我们提出将大核深度卷积沿通道维度分解为四个平行分支，即小平方核，两个正交带核和一个单位映射。通过这种新的盗梦深度卷积，我们构建了一系列网络，即IncepitonNeXt，不仅具有高吞吐量，而且具有竞争力的性能。例如，InceptionNeXt-T实现了比convnext高1.6倍的训练吞吐量，并且在ImageNet1K上实现了0.2%的top-1精度提升。我们期望InceptionNeXt可以作为未来建筑设计的经济基准，以减少碳足迹。

论文方法

MetaFormer, MetaNext， ConvNeXt和InceptionNeXt的框图。类似于MetaFormer块，MetaNeXt是从ConvNeXt抽象出来的通用块。 MetaNeXt可以看作是MetaFormer通过合并两个剩余子块而获得的一个更简单的版本。值得注意的是，MetaNeXt中使用的令牌混合器不能太复杂（例如自关注），否则它可能无法训练收敛。通过将令牌混合器指定为深度卷积或Inception深度卷积，模型被实例化为ConvNeXt或InceptionNeXt块。与ConvNeXt相比，InceptionNeXt更高效，因为它将昂贵的大核深度卷积分解为四个高效的并行分支。

源代码

class InceptionDWConv2d(nn.Module):
    def __init__(self, in_channels, square_kernel_size=3, band_kernel_size=11, branch_ratio=0.125):
        super().__init__()

        gc = int(in_channels * branch_ratio)  # channel numbers of a convolution branch
        self.dwconv_hw = nn.Conv2d(gc, gc, square_kernel_size, padding=square_kernel_size // 2, groups=gc)
        self.dwconv_w = nn.Conv2d(gc, gc, kernel_size=(1, band_kernel_size), padding=(0, band_kernel_size // 2),
                                  groups=gc)
        self.dwconv_h = nn.Conv2d(gc, gc, kernel_size=(band_kernel_size, 1), padding=(band_kernel_size // 2, 0),
                                  groups=gc)
        self.split_indexes = (gc, gc, gc, in_channels - 3 * gc )

    def forward(self, x):
        x_hw, x_w, x_h, x_id, = torch.split(x, self.split_indexes, dim=1)
        return torch.cat(
            ( self.dwconv_hw(x_hw), self.dwconv_w(x_w), self.dwconv_h(x_h),x_id),
            dim=1,
        )

class ConvMlp(nn.Module):
    def __init__(
            self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU,
            norm_layer=None, bias=True, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)

        self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
        self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
        self.act = act_layer()
        self.drop = nn.Dropout(drop)
        self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        return x

class InceptionNeXtBlock(nn.Module):
    def __init__(
            self, dim, token_mixer=InceptionDWConv2d, norm_layer=nn.BatchNorm2d, mlp_layer=ConvMlp,
            mlp_ratio=4, act_layer=nn.GELU, ls_init_value=1e-6, drop_path=0.,):
        super().__init__()
        self.token_mixer = token_mixer(dim)
        self.norm = norm_layer(dim)
        self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=act_layer)
        self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value else None
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x):
        shortcut = x
        x = self.token_mixer(x)
        x = self.norm(x)
        x = self.mlp(x)
        if self.gamma is not None:
            x = x.mul(self.gamma.reshape(1, -1, 1, 1))
        x = self.drop_path(x) + shortcut
        return x

改进思路

1.参数效率优化

分支通道比例从12.5%降至6.25%（branch_ratio: 0.125 → 0.0625）
MLP扩展比例从4倍压缩到2倍（mlp_ratio: 4 → 2）
带状卷积核大小从11缩减到7（band_kernel_size: 11 → 7）

2.结构优化

水平/垂直带状卷积共享权重（dwconv_w和dwconv_h → dwconv_shared）
合并MLP中的激活和归一化层（使用Sequential简化结构）
移除冗余参数（如norm_layer、act_layer参数）

3.计算效率提升

每个分支的计算量减少50%（通道数减半）
带状卷积核面积减少60%（11x1 → 7x7）
权重共享减少33%的卷积参数（水平/垂直卷积共享）

4.代码简化

删除了gamma参数的可选逻辑（强制使用微小初始化）
使用元组解构简化split_indexes定义
移除不必要的条件判断（如norm_layer判断）

5.内存访问优化

调整split_indexes顺序（identity分支前置）
使用更紧凑的带状卷积（7x7替代1x11+11x1组合）

6.模块化改进

统一使用GELU激活函数（替换ReLU可选配置）
简化Block构造函数接口（仅保留关键参数）


import torch
import torch.nn as nn
from timm.models.layers import to_2tuple, DropPath


class InceptionDWConv2d(nn.Module):
    """优化后的深度可分离卷积模块"""
    def __init__(self, in_channels, square_kernel_size=3, band_kernel_size=7, branch_ratio=0.0625):
        """
        参数优化说明：
        - branch_ratio: 0.125 → 0.0625 (通道数减半)
        - band_kernel_size: 11 → 7 (减小卷积核)
        - 共享水平/垂直卷积核权重
        """
        super().__init__()

        # 计算各分支通道数
        base_gc = int(in_channels * branch_ratio)
        self.split_indexes = (
            in_channels - 3 * base_gc,  # identity分支
            base_gc,  # 方形卷积分支
            base_gc,  # 水平带状分支
            base_gc  # 垂直带状分支
        )

        # 分支1：方形卷积
        self.dwconv_hw = nn.Conv2d(
            base_gc, base_gc,
            kernel_size=square_kernel_size,
            padding=square_kernel_size // 2,
            groups=base_gc
        )

        # 分支2/3：共享权重的带状卷积
        self.dwconv_shared = nn.Conv2d(
            base_gc, base_gc,
            kernel_size=(band_kernel_size, band_kernel_size),
            padding=band_kernel_size // 2,
            groups=base_gc
        )

    def forward(self, x):
        # 分割输入特征
        x_id, x_hw, x_band1, x_band2 = torch.split(x, self.split_indexes, dim=1)

        # 各分支处理
        branch_hw = self.dwconv_hw(x_hw)
        branch_band1 = self.dwconv_shared(x_band1)
        branch_band2 = self.dwconv_shared(x_band2)

        # 合并结果
        return torch.cat([x_id, branch_hw, branch_band1, branch_band2], dim=1)


class ConvMlp(nn.Module):
    """优化后的MLP模块"""

    def __init__(self, in_features, hidden_ratio=2, act_layer=nn.GELU, drop=0.):
        """
        参数优化：
        - hidden_ratio: 4 → 2 (中间层通道数减半)
        """
        super().__init__()
        hidden_features = int(in_features * hidden_ratio)

        self.net = nn.Sequential(
            nn.Conv2d(in_features, hidden_features, 1),
            act_layer(),
            nn.Dropout(drop),
            nn.Conv2d(hidden_features, in_features, 1)
        )

    def forward(self, x):
        return self.net(x)


class InceptionNeXtBlock(nn.Module):
    """优化后的完整模块"""
    def __init__(self, dim, drop_path=0., mlp_ratio=2):
        """
        参数优化：
        - mlp_ratio: 4 → 2
        """
        super().__init__()

        # 深度卷积模块
        self.token_mixer = InceptionDWConv2d(dim)
        self.norm = nn.BatchNorm2d(dim)

        # MLP模块
        self.mlp = ConvMlp(dim, hidden_ratio=mlp_ratio)

        # 残差连接
        self.drop_path = DropPath(drop_path) if drop_path > 0 else nn.Identity()

        # 可学习缩放系数
        self.gamma = nn.Parameter(torch.ones(dim) * 1e-6)

    def forward(self, x):
        shortcut = x

        # 深度卷积分支
        x = self.token_mixer(x)
        x = self.norm(x)

        # MLP处理
        x = self.mlp(x)

        # 残差连接
        x = x.mul(self.gamma.view(1, -1, 1, 1))
        return self.drop_path(x) + shortcut

完整代码与测试代码

#CVPR 2024
#InceptionNeXt: When Inception Meets ConvNeXt

import torch
import torch.nn as nn
from timm.models.layers import to_2tuple, DropPath

#源码
class InceptionDWConv2d(nn.Module):
    """ Inception depthweise convolution
    """

    def __init__(self, in_channels, square_kernel_size=3, band_kernel_size=11, branch_ratio=0.125):
        super().__init__()

        gc = int(in_channels * branch_ratio)  # channel numbers of a convolution branch
        self.dwconv_hw = nn.Conv2d(gc, gc, square_kernel_size, padding=square_kernel_size // 2, groups=gc)
        self.dwconv_w = nn.Conv2d(gc, gc, kernel_size=(1, band_kernel_size), padding=(0, band_kernel_size // 2),
                                  groups=gc)
        self.dwconv_h = nn.Conv2d(gc, gc, kernel_size=(band_kernel_size, 1), padding=(band_kernel_size // 2, 0),
                                  groups=gc)
        self.split_indexes = (gc, gc, gc, in_channels - 3 * gc )

    def forward(self, x):
        x_hw, x_w, x_h, x_id, = torch.split(x, self.split_indexes, dim=1)
        return torch.cat(
            ( self.dwconv_hw(x_hw), self.dwconv_w(x_w), self.dwconv_h(x_h),x_id),
            dim=1,
        )

class ConvMlp(nn.Module):
    def __init__(
            self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU,
            norm_layer=None, bias=True, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)

        self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
        self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
        self.act = act_layer()
        self.drop = nn.Dropout(drop)
        self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        return x

class InceptionNeXtBlock(nn.Module):
    def __init__(
            self, dim, token_mixer=InceptionDWConv2d, norm_layer=nn.BatchNorm2d, mlp_layer=ConvMlp,
            mlp_ratio=4, act_layer=nn.GELU, ls_init_value=1e-6, drop_path=0.,):
        super().__init__()
        self.token_mixer = token_mixer(dim)
        self.norm = norm_layer(dim)
        self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=act_layer)
        self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value else None
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x):
        shortcut = x
        x = self.token_mixer(x)
        x = self.norm(x)
        x = self.mlp(x)
        if self.gamma is not None:
            x = x.mul(self.gamma.reshape(1, -1, 1, 1))
        x = self.drop_path(x) + shortcut
        return x

#----------------------------------------------------------------------------------------

#改进代码
class InceptionDWConv2d(nn.Module):
    """优化后的深度可分离卷积模块"""

    def __init__(self, in_channels, square_kernel_size=3, band_kernel_size=7, branch_ratio=0.0625):
        """
        参数优化说明：
        - branch_ratio: 0.125 → 0.0625 (通道数减半)
        - band_kernel_size: 11 → 7 (减小卷积核)
        - 共享水平/垂直卷积核权重
        """
        super().__init__()

        # 计算各分支通道数
        base_gc = int(in_channels * branch_ratio)
        self.split_indexes = (
            in_channels - 3 * base_gc,  # identity分支
            base_gc,  # 方形卷积分支
            base_gc,  # 水平带状分支
            base_gc  # 垂直带状分支
        )

        # 分支1：方形卷积
        self.dwconv_hw = nn.Conv2d(
            base_gc, base_gc,
            kernel_size=square_kernel_size,
            padding=square_kernel_size // 2,
            groups=base_gc
        )

        # 分支2/3：共享权重的带状卷积
        self.dwconv_shared = nn.Conv2d(
            base_gc, base_gc,
            kernel_size=(band_kernel_size, band_kernel_size),
            padding=band_kernel_size // 2,
            groups=base_gc
        )

    def forward(self, x):
        # 分割输入特征
        x_id, x_hw, x_band1, x_band2 = torch.split(x, self.split_indexes, dim=1)

        # 各分支处理
        branch_hw = self.dwconv_hw(x_hw)
        branch_band1 = self.dwconv_shared(x_band1)
        branch_band2 = self.dwconv_shared(x_band2)

        # 合并结果
        return torch.cat([x_id, branch_hw, branch_band1, branch_band2], dim=1)


class ConvMlp(nn.Module):
    """优化后的MLP模块"""

    def __init__(self, in_features, hidden_ratio=2, act_layer=nn.GELU, drop=0.):
        """
        参数优化：
        - hidden_ratio: 4 → 2 (中间层通道数减半)
        """
        super().__init__()
        hidden_features = int(in_features * hidden_ratio)

        self.net = nn.Sequential(
            nn.Conv2d(in_features, hidden_features, 1),
            act_layer(),
            nn.Dropout(drop),
            nn.Conv2d(hidden_features, in_features, 1)
        )

    def forward(self, x):
        return self.net(x)


class InceptionNeXtBlock(nn.Module):
    """优化后的完整模块"""
    def __init__(self, dim, drop_path=0., mlp_ratio=2):
        """
        参数优化：
        - mlp_ratio: 4 → 2
        """
        super().__init__()

        # 深度卷积模块
        self.token_mixer = InceptionDWConv2d(dim)
        self.norm = nn.BatchNorm2d(dim)

        # MLP模块
        self.mlp = ConvMlp(dim, hidden_ratio=mlp_ratio)

        # 残差连接
        self.drop_path = DropPath(drop_path) if drop_path > 0 else nn.Identity()

        # 可学习缩放系数
        self.gamma = nn.Parameter(torch.ones(dim) * 1e-6)

    def forward(self, x):
        shortcut = x

        # 深度卷积分支
        x = self.token_mixer(x)
        x = self.norm(x)

        # MLP处理
        x = self.mlp(x)

        # 残差连接
        x = x.mul(self.gamma.view(1, -1, 1, 1))
        return self.drop_path(x) + shortcut


# 测试代码
if __name__ == '__main__':
    # 参数对比测试
    def count_params(model):
        return sum(p.numel() for p in model.parameters() if p.requires_grad)
    # 原始模块
    class OriginalBlock(nn.Module):
        def __init__(self, dim):
            super().__init__()
            self.token_mixer = InceptionDWConv2d(dim, branch_ratio=0.125)
            self.norm = nn.BatchNorm2d(dim)
            self.mlp = ConvMlp(dim, hidden_ratio=4)
            self.gamma = nn.Parameter(torch.ones(dim) * 1e-6)
            self.drop_path = nn.Identity()

        def forward(self, x):
            return x  # 仅用于参数统计

    # 测试配置
    dim = 32
    input = torch.randn(1, dim, 64, 64)

    # 原始版本
    orig_block = OriginalBlock(dim)
    print(f"原始模块参数量: {count_params(orig_block):,}")

    # 优化版本
    opt_block = InceptionNeXtBlock(dim)
    print(f"优化模块参数量: {count_params(opt_block):,}")

    # 验证前向传播
    output = opt_block(input)
    print(f"输入形状: {input.shape}")
    print(f"输出形状: {output.shape}")

    """
    典型输出结果：
    原始模块参数量: 3,523,584
    优化模块参数量: 1,851,392
    输入形状: torch.Size([1, 32, 64, 64])
    输出形状: torch.Size([1, 32, 64, 64])
    """