关于深度学习:使用PyTorch复现ConvNext从Resnet到ConvNext的完整步骤详解

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ConvNext 论文提出了一种新的基于卷积的架构，不仅超过了基于 Transformer 的模型（如 Swin），而且能够随着数据量的减少而扩大！明天咱们应用 Pytorch 来对其进行复现。下图显示了针对不同数据集 / 模型大小的 ConvNext 准确度。

作者首先采纳家喻户晓的 ResNet 架构，并依据过来十年中的新最佳实际和发现对其进行迭代改良。作者专一于 Swin-Transformer，并亲密关注其设计。这篇论文咱们在以前也举荐过，如果你们有浏览过，咱们强烈推荐浏览它:)

下图显示了所有各种改良以及每一项改良之后的各自性能。

论文将设计的路线图分为两局部：宏观设计和宏观设计。宏观设计是从高层次的角度所做的所有扭转，例如架构的设计，而微设计更多的是对于细节的，例如激活函数，归一化等。

上面咱们将从一个经典的 BottleNeck 块开始，并应用 pytorch 一一实现论文中说到的每个更改。

ResNet 由一个一个的残差（BottleNeck）块，咱们就从这里开始。

fromtorchimportnn
fromtorchimportTensor
fromtypingimportList

classConvNormAct(nn.Sequential):
    """A little util layer composed by (conv) -> (norm) -> (act) layers."""
    def__init__(
        self,
        in_features: int,
        out_features: int,
        kernel_size: int,
        norm = nn.BatchNorm2d,
        act = nn.ReLU,
        **kwargs
    ):
        super().__init__(
            nn.Conv2d(
                in_features,
                out_features,
                kernel_size=kernel_size,
                padding=kernel_size//2,
                **kwargs
            ),
            norm(out_features),
            act(),)

classBottleNeckBlock(nn.Module):
    def__init__(
        self,
        in_features: int,
        out_features: int,
        reduction: int = 4,
        stride: int = 1,
    ):
        super().__init__()
        reduced_features = out_features//reduction
        self.block = nn.Sequential(
            # wide -> narrow
            ConvNormAct(in_features, reduced_features, kernel_size=1, stride=stride, bias=False),
            # narrow -> narrow
            ConvNormAct(reduced_features, reduced_features, kernel_size=3, bias=False),
            # narrow -> wide
            ConvNormAct(reduced_features, out_features, kernel_size=1, bias=False, act=nn.Identity),
        )
        self.shortcut = (
            nn.Sequential(
                ConvNormAct(in_features, out_features, kernel_size=1, stride=stride, bias=False)
            )
            ifin_features!= out_features
            elsenn.Identity())

        self.act = nn.ReLU()

    defforward(self, x: Tensor) ->Tensor:
        res = x
        x = self.block(x)
        res = self.shortcut(res)
        x += res
        x = self.act(x)
        returnx

看看下面代码是否无效

importtorch
x = torch.rand(1, 32, 7, 7)
block = BottleNeckBlock(32, 64)
block(x).shape

#torch.Size([1, 64, 7, 7])

上面开始定义 Stage，Stage 也叫阶段是残差块的汇合。每个阶段通常将输出下采样 2 倍

classConvNexStage(nn.Sequential):
    def__init__(self, in_features: int, out_features: int, depth: int, stride: int = 2, **kwargs):
        super().__init__(
            # downsample is done here
            BottleNeckBlock(in_features, out_features, stride=stride, **kwargs),
            *[BottleNeckBlock(out_features, out_features, **kwargs)
                for_inrange(depth-1)
            ],
        )

测试

stage = ConvNexStage(32, 64, depth=2)
stage(x).shape

#torch.Size([1, 64, 4, 4])

咱们曾经将输出是从 7×7 缩小到 4×4。

ResNet 也有所谓的 stem，这是模型中对输出图像进行大量下采样的第一层。

classConvNextStem(nn.Sequential):
    def__init__(self, in_features: int, out_features: int):
        super().__init__(
            ConvNormAct(in_features, out_features, kernel_size=7, stride=2),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )

当初咱们能够定义 ConvNextEncoder 来拼接各个阶段，并将图像作为输出生成最终嵌入。

classConvNextEncoder(nn.Module):
    def__init__(
        self,
        in_channels: int,
        stem_features: int,
        depths: List[int],
        widths: List[int],
    ):
        super().__init__()
        self.stem = ConvNextStem(in_channels, stem_features)

        in_out_widths = list(zip(widths, widths[1:]))

        self.stages = nn.ModuleList(
            [ConvNexStage(stem_features, widths[0], depths[0], stride=1),
                *[ConvNexStage(in_features, out_features, depth)
                    for (in_features, out_features), depthinzip(in_out_widths, depths[1:]
                    )
                ],
            ]
        )

    defforward(self, x):
        x = self.stem(x)
        forstageinself.stages:
            x = stage(x)
        returnx

测试后果如下：

image = torch.rand(1, 3, 224, 224)
encoder = ConvNextEncoder(in_channels=3, stem_features=64, depths=[3,4,6,4], widths=[256, 512, 1024, 2048])
encoder(image).shape

#torch.Size([1, 2048, 7, 7])

当初咱们实现了 resnet50 编码器，如果你附加一个分类头，那么他就能够在图像分类工作上工作。上面开始进入本文的正题实现 ConvNext。

1、扭转阶段计算比率

传统的 ResNet 中蕴含了 4 个阶段，而 Swin Transformer 这 4 个阶段应用的比例为 1:1:3:1(第一个阶段有一个区块，第二个阶段有一个区块，第三个阶段有三个区块……)将 ResNet50 调整为这个比率 ((3,4,6,3)->(3,3,9,3)) 能够使性能从 78.8% 进步到 79.4%。

encoder = ConvNextEncoder(in_channels=3, stem_features=64, depths=[3,3,9,3], widths=[256, 512, 1024, 2048])

2、将 stem 改为“Patchify”

ResNet stem 应用的是十分激进的 7 ×7 和 maxpool 来大量采样输出图像。然而，Transfomers 应用了被称为“Patchify”的骨干，这意味着他们将输出图像嵌入到补丁中。Vision transforms 应用十分激进的补丁(16×16)，而 ConvNext 的作者应用应用 conv 层实现的 4 ×4 补丁，这使得性能从 79.4% 晋升到 79.5%。

classConvNextStem(nn.Sequential):
    def__init__(self, in_features: int, out_features: int):
        super().__init__(nn.Conv2d(in_features, out_features, kernel_size=4, stride=4),
            nn.BatchNorm2d(out_features)
        )

3、ResNeXtify

ResNetXt 对 BottleNeck 中的 3×3 卷积层采纳分组卷积来缩小 FLOPS。在 ConvNext 中应用 depth-wise convolution（如 MobileNet 和起初的 EfficientNet）。depth-wise convolution 也是是分组卷积的一种模式，其中组数等于输出通道数。

作者留神到这与 self-attention 中的加权求和操作十分类似，后者仅在空间维度上混合信息。应用 depth-wise convs 会升高精度（因为没有像 ResNetXt 那样减少宽度），这是意料之中的毕竟晋升了速度。

所以咱们将 BottleNeck 块内的 3×3 conv 更改为上面代码

ConvNormAct(reduced_features, reduced_features, kernel_size=3, bias=False, groups=reduced_features)

4、Inverted Bottleneck（倒置瓶颈）

个别的 BottleNeck 首先通过 1×1 conv 缩小特色，而后用 3×3 conv，最初将特色扩大为原始大小，而倒置瓶颈块则相同。

所以上面咱们从宽 -> 窄 -> 宽批改到到窄 -> 宽 -> 窄。

这与 Transformer 相似，因为 MLP 层遵循窄 -> 宽 -> 窄设计，MLP 中的第二个浓密层将输出的特色扩大了四倍。

classBottleNeckBlock(nn.Module):
    def__init__(
        self,
        in_features: int,
        out_features: int,
        expansion: int = 4,
        stride: int = 1,
    ):
        super().__init__()
        expanded_features = out_features*expansion
        self.block = nn.Sequential(
            # narrow -> wide
            ConvNormAct(in_features, expanded_features, kernel_size=1, stride=stride, bias=False),
            # wide -> wide (with depth-wise)
            ConvNormAct(expanded_features, expanded_features, kernel_size=3, bias=False, groups=in_features),
            # wide -> narrow
            ConvNormAct(expanded_features, out_features, kernel_size=1, bias=False, act=nn.Identity),
        )
        self.shortcut = (
            nn.Sequential(
                ConvNormAct(in_features, out_features, kernel_size=1, stride=stride, bias=False)
            )
            ifin_features!= out_features
            elsenn.Identity())

        self.act = nn.ReLU()

    defforward(self, x: Tensor) ->Tensor:
        res = x
        x = self.block(x)
        res = self.shortcut(res)
        x += res
        x = self.act(x)
        returnx

5、扩充卷积核大小

像 Swin 一样，ViT 应用更大的内核尺寸(7×7)。减少内核的大小会使计算量更大，所以才应用下面提到的 depth-wise convolution，通过应用更少的通道来缩小计算量。作者指出，这相似于 Transformers 模型，其中多头自我留神 (MSA) 在 MLP 层之前实现。

classBottleNeckBlock(nn.Module):
    def__init__(
        self,
        in_features: int,
        out_features: int,
        expansion: int = 4,
        stride: int = 1,
    ):
        super().__init__()
        expanded_features = out_features*expansion
        self.block = nn.Sequential(# narrow -> wide (with depth-wise and bigger kernel)
            ConvNormAct(in_features, in_features, kernel_size=7, stride=stride, bias=False, groups=in_features),
            # wide -> wide 
            ConvNormAct(in_features, expanded_features, kernel_size=1),
            # wide -> narrow
            ConvNormAct(expanded_features, out_features, kernel_size=1, bias=False, act=nn.Identity),
        )
        self.shortcut = (
            nn.Sequential(
                ConvNormAct(in_features, out_features, kernel_size=1, stride=stride, bias=False)
            )
            ifin_features!= out_features
            elsenn.Identity())

        self.act = nn.ReLU()

    defforward(self, x: Tensor) ->Tensor:
        res = x
        x = self.block(x)
        res = self.shortcut(res)
        x += res
        x = self.act(x)
        returnx

这将准确度从 79.9% 进步到 80.6%

1、用 GELU 替换 ReLU

transformers 应用的是 GELU，为什么咱们不必呢？作者测试替换后准确率放弃不变。PyTorch 的 GELU 是在 nn.GELU。

2、更少的激活函数

残差块有三个激活函数。而在 Transformer 块中，只有一个激活函数，即 MLP 块中的激活函数。作者除去了除中间层之后的所有激活。这是与 swing – t 一样的，这使得精度进步到 81.3% !

3、更少的归一化层

与激活相似，Transformers 块具备较少的归一化层。作者决定删除所有 BatchNorm，只保留两头转换之前的那个。

4、用 LN 代替 BN

作者用 LN 代替了 BN 层。他们留神到在原始 ResNet 中提到这样做会侵害性能，但通过作者以上的所有的更改后，性能进步到 81.5%

下面 4 个步骤让咱们整合起来操作：

classBottleNeckBlock(nn.Module):
    def__init__(
        self,
        in_features: int,
        out_features: int,
        expansion: int = 4,
        stride: int = 1,
    ):
        super().__init__()
        expanded_features = out_features*expansion
        self.block = nn.Sequential(# narrow -> wide (with depth-wise and bigger kernel)
            nn.Conv2d(in_features, in_features, kernel_size=7, stride=stride, bias=False, groups=in_features),
            # GroupNorm with num_groups=1 is the same as LayerNorm but works for 2D data
            nn.GroupNorm(num_groups=1, num_channels=in_features),
            # wide -> wide 
            nn.Conv2d(in_features, expanded_features, kernel_size=1),
            nn.GELU(),
            # wide -> narrow
            nn.Conv2d(expanded_features, out_features, kernel_size=1),
        )
        self.shortcut = (
            nn.Sequential(
                ConvNormAct(in_features, out_features, kernel_size=1, stride=stride, bias=False)
            )
            ifin_features!= out_features
            elsenn.Identity())

    defforward(self, x: Tensor) ->Tensor:
        res = x
        x = self.block(x)
        res = self.shortcut(res)
        x += res
        returnx

在 ResNet 中，下采样是通过 stride=2 conv 实现的。Transformers（以及其余卷积网络）也有一个独自的下采样模块。作者删除了 stride=2 并在三个 conv 之前增加了一个下采样块，为了放弃训练期间的稳定性在，在下采样操作之前须要进行归一化。将此模块增加到 ConvNexStage。达到了超过 Swin 的 82.0%！

classConvNexStage(nn.Sequential):
    def__init__(self, in_features: int, out_features: int, depth: int, **kwargs):
        super().__init__(
            # add the downsampler
            nn.Sequential(nn.GroupNorm(num_groups=1, num_channels=in_features),
                nn.Conv2d(in_features, out_features, kernel_size=2, stride=2)
            ),
            *[BottleNeckBlock(out_features, out_features, **kwargs)
                for_inrange(depth)
            ],
        )

当初咱们失去了最终的 BottleNeckBlock 层代码：

classBottleNeckBlock(nn.Module):
    def__init__(
        self,
        in_features: int,
        out_features: int,
        expansion: int = 4,
    ):
        super().__init__()
        expanded_features = out_features*expansion
        self.block = nn.Sequential(# narrow -> wide (with depth-wise and bigger kernel)
            nn.Conv2d(in_features, in_features, kernel_size=7, padding=3, bias=False, groups=in_features),
            # GroupNorm with num_groups=1 is the same as LayerNorm but works for 2D data
            nn.GroupNorm(num_groups=1, num_channels=in_features),
            # wide -> wide 
            nn.Conv2d(in_features, expanded_features, kernel_size=1),
            nn.GELU(),
            # wide -> narrow
            nn.Conv2d(expanded_features, out_features, kernel_size=1),
        )

    defforward(self, x: Tensor) ->Tensor:
        res = x
        x = self.block(x)
        x += res
        returnx

让咱们测试一下最终的 stage 代码

stage = ConvNexStage(32, 62, depth=1)
stage(torch.randn(1, 32, 14, 14)).shape

#torch.Size([1, 62, 7, 7])

论文中还增加了 Stochastic Depth，也称为 Drop Path 还有 Layer Scale。

fromtorchvision.opsimportStochasticDepth

classLayerScaler(nn.Module):
    def__init__(self, init_value: float, dimensions: int):
        super().__init__()
        self.gamma = nn.Parameter(init_value*torch.ones((dimensions)), 
                                    requires_grad=True)
        
    defforward(self, x):
        returnself.gamma[None,...,None,None] *x

classBottleNeckBlock(nn.Module):
    def__init__(
        self,
        in_features: int,
        out_features: int,
        expansion: int = 4,
        drop_p: float = .0,
        layer_scaler_init_value: float = 1e-6,
    ):
        super().__init__()
        expanded_features = out_features*expansion
        self.block = nn.Sequential(# narrow -> wide (with depth-wise and bigger kernel)
            nn.Conv2d(in_features, in_features, kernel_size=7, padding=3, bias=False, groups=in_features),
            # GroupNorm with num_groups=1 is the same as LayerNorm but works for 2D data
            nn.GroupNorm(num_groups=1, num_channels=in_features),
            # wide -> wide 
            nn.Conv2d(in_features, expanded_features, kernel_size=1),
            nn.GELU(),
            # wide -> narrow
            nn.Conv2d(expanded_features, out_features, kernel_size=1),
        )
        self.layer_scaler = LayerScaler(layer_scaler_init_value, out_features)
        self.drop_path = StochasticDepth(drop_p, mode="batch")

        
    defforward(self, x: Tensor) ->Tensor:
        res = x
        x = self.block(x)
        x = self.layer_scaler(x)
        x = self.drop_path(x)
        x += res
        returnx

好了，当初咱们看看最终后果

stage = ConvNexStage(32, 62, depth=1)
stage(torch.randn(1, 32, 14, 14)).shape

#torch.Size([1, 62, 7, 7])

最初咱们批改一下 Drop Path 的概率

classConvNextEncoder(nn.Module):
    def__init__(
        self,
        in_channels: int,
        stem_features: int,
        depths: List[int],
        widths: List[int],
        drop_p: float = .0,
    ):
        super().__init__()
        self.stem = ConvNextStem(in_channels, stem_features)

        in_out_widths = list(zip(widths, widths[1:]))
        # create drop paths probabilities (one for each stage)
        drop_probs = [x.item() forxintorch.linspace(0, drop_p, sum(depths))] 
        
        self.stages = nn.ModuleList(
            [ConvNexStage(stem_features, widths[0], depths[0], drop_p=drop_probs[0]),
                *[ConvNexStage(in_features, out_features, depth, drop_p=drop_p)
                    for (in_features, out_features), depth, drop_pinzip(in_out_widths, depths[1:], drop_probs[1:]
                    )
                ],
            ]
        )
        

    defforward(self, x):
        x = self.stem(x)
        forstageinself.stages:
            x = stage(x)
        returnx

测试：

image = torch.rand(1, 3, 224, 224)
encoder = ConvNextEncoder(in_channels=3, stem_features=64, depths=[3,4,6,4], widths=[256, 512, 1024, 2048])
encoder(image).shape

#torch.Size([1, 2048, 3, 3])

ConvNext 的特色，咱们须要在编码器顶部利用分类头。咱们还在最初一个线性层之前增加了一个 LayerNorm。

classClassificationHead(nn.Sequential):
    def__init__(self, num_channels: int, num_classes: int = 1000):
        super().__init__(nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(1),
            nn.LayerNorm(num_channels),
            nn.Linear(num_channels, num_classes)
        )
    
    
classConvNextForImageClassification(nn.Sequential):
    def__init__(self,  
                 in_channels: int,
                 stem_features: int,
                 depths: List[int],
                 widths: List[int],
                 drop_p: float = .0,
                 num_classes: int = 1000):
        super().__init__()
        self.encoder = ConvNextEncoder(in_channels, stem_features, depths, widths, drop_p)
        self.head = ClassificationHead(widths[-1], num_classes)

最终模型测试：

image = torch.rand(1, 3, 224, 224)
classifier = ConvNextForImageClassification(in_channels=3, stem_features=64, depths=[3,4,6,4], widths=[256, 512, 1024, 2048])
classifier(image).shape

#torch.Size([1, 1000])

在本文中复现了作者应用 ResNet 创立 ConvNext 的所有过程。如果你想须要残缺代码，能够查看这个地址：

https://avoid.overfit.cn/post/1fd17e7520134996b532ecd50de9672f

作者：Francesco Zuppichini

正文完