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基于PaddlePaddle复现CoTNet

2025-07-31
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基于Self-Attention的Transformer结构，首先在NLP任务中被提出，最近在CV任务中展现出了非常好的效果。然而，大多数现有的Transformer直接在二维特征图上的进行Self-Attention，基于每个空间位置的query和key获得注意力矩阵，但相邻的key之间的上下文信息未得到充分利用。

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基于paddlepaddle复现cotnet -

引入

基于Self-Attention的Transformer结构，首先在NLP任务中被提出，最近在CV任务中展现出了非常好的效果。然而，大多数现有的Transformer直接在二维特征图上的进行Self-Attention，基于每个空间位置的query和key获得注意力矩阵，但相邻的key之间的上下文信息未得到充分利用。

本文设计了一种新的注意力结构CoT Block，这种结构充分利用了key的上下文信息，以指导动态注意力矩阵的学习，从而增强了视觉表示的能力。

作者将CoT Block代替了ResNet结构中的3x3卷积，来形成CoTNet，最终在一系列视觉任务（分类、检测、分割）上取得了非常好的性能，此外，CoTNet在CVPR上获得开放域图像识别竞赛冠军。

模型介绍

基于PaddlePaddle复现CoTNet -        

  • 相关资料：

    • 论文地址：【Contextual Transformer Networks for Visual Recognition】
    • 代码地址：【JDAI-CV/CoTNet】
    • 核心代码：【CoTAttention-Usage】

  • 论文摘要：

    • 传统的CV领域self-attention直接在2D特征图上获取注意力矩阵，确实很好的获取了全局上下文信息，但是忽视了卷积带来的邻近上下文。而邻近上下文确确实实可以提供很多信息。

    • 本文提供的CoTNet模块很好的继承了传统self- attention的全局上下文，也结合了卷积所带来的邻近上下文，提升了视觉表达能力。且CoTNet模块具有“即插即用”的特性，可以直接替换ResNet中Bottleneck里3*3的卷积，形成Transformer风格架构。

  • Self-attention结构在CV领域的引入,之所以在CV领域使用自注意力机制主要是有以下几点原因：

    • 在CNN中，卷积层通过卷积核获得输出特征，但是卷积核的感受野很小
    • 采用堆叠卷积层的方式来增加感受野的方式并不高效
    • 计算机视觉中的很多任务都是由于语义信息不足从而影响的性能
    • 自注意力机制可以捕获全局信息获得更大的感受野

模型介绍

1.Motivation

  • 起初，CNN由于其强大的视觉表示学习能力，被广泛使用在各种CV任务中，CNN这种局部信息建模的结构充分使用了空间局部性和平移等边性。但是同样的，CNN由于只能对局部信息建模，就缺少了长距离建模和感知的能力，而这种能力在很多视觉任务中又是非常重要的。 基于PaddlePaddle复现CoTNet -            
  • Transformer由于其强大的全局建模能力，被广泛使用在了各种NLP任务中。受到Transformer结构的启发，ViT、DETR等模型也借鉴了Transformer的结构来进行长距离的建模。然而，原始Transformer中的Self-Attention结构（如上图所示）只是根据query和key的交互来计算注意力矩阵，因此忽略了相邻key之间的联系。

基于PaddlePaddle复现CoTNet -        

  • 基于此，作者提出了这样一个问题——“有没有一种优雅的方法可以通过利用二维特征图中输入key之间的上下文来增强Transformer结构？”因此作者就提出了上面的结构CoT block。传统的Self-Attention只是根据query和key来计算注意力矩阵，从而导致没有充分利用key的上下文信息。

  • 因此作者首先在key上采用3x3的卷积来建模静态上下文信息，然后将query和上下文信息建模之后的key进行concat，再使用两个连续的1x1卷积来自我注意，生成动态上下文。静态和动态上下文信息最终被融合为输出。（简单的说，就是作者先用卷积来提取了局部了信息，从而充分发掘了key内部的静态上下文信息 ）

2.方法

2.1. Multi-head Self-attention

基于PaddlePaddle复现CoTNet -        

  • 目前在视觉的backbone中，通用的可扩展的局部多头自我注意（scalable local multi-head self-attention），如上图所示。首先用1x1的卷积上X映射到Q、K、V三个不同的空间，Q和K进行相乘获得局部的关系矩阵：基于PaddlePaddle复现CoTNet -                

  • 由于原始的Self-Attention对输入特征的位置是不敏感的，所以还需要在Q上加上位置信息，然后将结果与关系矩阵相加： 基于PaddlePaddle复现CoTNet -                

  • 接着，我们还需要对上面得到的结果进行归一化，得到Attention Map： 基于PaddlePaddle复现CoTNet -                

  • 得到Attention Map之后，我们需要将kxk的局部信息进行聚合，然后与V相乘，得到Attention之后的结果： 基于PaddlePaddle复现CoTNet -                

2.2. Contextual Transformer Block

基于PaddlePaddle复现CoTNet -        

  • 传统的Self-Attention可以很好地触发不同空间位置的特征交互。然而，在传统的Self-Attention机制中，所有的query-key关系都是通过独立的quey-key pair学习的，没有探索两者之间的丰富上下文，这极大的限制了视觉表示学习。

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  • 因此，作者提出了CoT Block，如上图所示，这个结构将上下文信息的挖掘和Self-Attention的学习聚合到了一个结构中。

  • 首先对于输入特征 ，首先定义了三个变量 （这里只是将V进行了特征的映射，Q和K还是采用了原来的X值 ）。

  • 作者首先在K上进行了kxk的分组卷积，来获得具备局部上下文信息表示的K，（记作 ），这个 可以看做是在局部信息上进行了静态的建模。

  • 接着作者将 和Q进行了concat，然后对concat的结果进行了两次连续的卷积操作：

基于PaddlePaddle复现CoTNet -        

  • 不同于传统的Self-Attention，这里的A矩阵是由query信息和局部上下文信息 交互得到的，而不只是建模了query和key之间的关系。换句话说，就是通过局部上下文建模的引导，增强了自注意力机制。

  • 然后，作者将这个Attention Map和V进行了相乘，得到了动态上下文建模的 ：

基于PaddlePaddle复现CoTNet -        

  • 最后CoT的结果为局部静态上下文建模的 和全局动态上下文建模的 fuse之后的结果。

2.3. Contextual Transformer Networks

CoT的设计是一个统一的自我关注的构建块，可以作为ConvNet中标准卷积的替代品。

因此，作者用CoT代替了ResNet和ResNeXt结构中的3x3卷积，形成了CoTNet和CoTNeXt。 基于PaddlePaddle复现CoTNet -        

可以看出，CoTNet-50的参数和计算量比ResNet-50略小。 基于PaddlePaddle复现CoTNet -  与ResNeXt-50相比，CoTNeXt-50的参数数量稍多，但与FLOPs相似。

快速使用

  • 推荐使用 【Paddle-Image-Models】 项目来快速加载本模型
  • 具体使用方法请参考：【【Paddle-Image-Models】飞桨预训练图像模型库】

模型搭建

导入必要的包

In [1] 
import paddleimport paddle.nn as nnimport paddle.nn.functional as Fimport numpy as np
    In [2] 
# 构建CoTNetLayerclass CoTNetLayer(nn.Layer):

    def __init__(self, dim=512, kernel_size=3):
        super().__init__()
        self.dim = dim
        self.kernel_size = kernel_size

        self.key_embed = nn.Sequential(            # 通过K*K的卷积提取上下文信息，视作输入X的静态上下文表达
            nn.Conv2D(dim, dim, kernel_size=kernel_size, padding=1, stride=1, bias_attr=False),
            nn.BatchNorm2D(dim),
            nn.ReLU()
        )
        self.value_embed = nn.Sequential(
            nn.Conv2D(dim, dim, kernel_size=1, stride=1, bias_attr=False),  # 1*1的卷积进行Value的编码
            nn.BatchNorm2D(dim)
        )

        factor = 4
        self.attention_embed = nn.Sequential(  # 通过连续两个1*1的卷积计算注意力矩阵
            nn.Conv2D(2 * dim, 2 * dim // factor, 1, bias_attr=False),  # 输入concat后的特征矩阵 Channel = 2*C
            nn.BatchNorm2D(2 * dim // factor),
            nn.ReLU(),
            nn.Conv2D(2 * dim // factor, kernel_size * kernel_size * dim, 1, stride=1)  # out: H * W * (K*K*C)
        )    def forward(self, x):
        bs, c, h, w = x.shape
        k1 = self.key_embed(x)  # shape：bs,c,h,w  提取静态上下文信息得到key
        # v = self.value_embed(x)  # shape：bs,c,h*w  得到value编码
        # print(v)
        flatten1 = nn.Flatten(start_axis=2, stop_axis=-1)  # shape：bs,c,h*w  得到value编码
        v = flatten1(self.value_embed(x))
        y = paddle.concat([k1, x], axis=1)
        att = self.attention_embed(y)  # shape：bs,c*k*k,h,w  计算注意力矩阵
        att = paddle.reshape(att, [bs, c, self.kernel_size * self.kernel_size, h, w])        # att = att.reshape(bs, c, self.kernel_size * self.kernel_size, h, w)
        att = att.mean(2, keepdim=False)  # shape：bs,c,h*w  求平均降低维度
        att = flatten1(att)
        k2 = F.softmax(att, axis=-1) * v  # 对每一个H*w进行softmax后
        k2 = paddle.reshape(k2, [bs, c, h, w])        return k1 + k2  # 注意力融合# if __name__ == '__main__':#     input = paddle.randn(shape=[50, 512, 7, 7])#     cot = CoTNetLayer(dim=512, kernel_size=3)#     output = cot(input)#     print(output.shape)
    In [3] 
from paddle.nn.layer.activation import ReLUfrom CoTNetBlock import CoTNetLayer

zeros_ = nn.initializer.Constant(value=0.)
ones_ = nn.initializer.Constant(value=1.)
kaiming_normal_ = nn.initializer.KaimingNormal()def get_n_params(model):
    pp = 0
    for p in list(model.parameters()):
        nn = 1
        for s in list(p.size()):
            nn = nn * s
        pp += nn    return pp# 构建Bottleneckclass Bottleneck(nn.Layer):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()        # width = int(planes * (base_width / 64.)) * groups
        self.conv1 = nn.Conv2D(in_planes, planes, kernel_size=1, bias_attr=False)
        self.bn1 = nn.BatchNorm2D(planes)
        self.cot_layer = CoTNetLayer(dim=planes, kernel_size=3)
        self.bn2 = nn.BatchNorm2D(planes)
        self.conv3 = nn.Conv2D(planes, planes * self.expansion, 1, bias_attr=False)
        self.bn3 = nn.BatchNorm2D(planes * self.expansion)
        self.relu = ReLU(True)
        self.downsample = downsample
        self.stride = stride        if stride > 1:
            self.*d = nn.AvgPool2D(3, 2, padding=1)        else:
            self.*d = None

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)        if self.*d is not None:
            out = self.*d(out)

        out = self.cot_layer(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)        return out# 构建CoTResNetclass CoTResNet(nn.Layer):

    def __init__(self, block, layers, num_classes=1000):
        super(CoTResNet, self).__init__()
        self.in_planes = 64
        self.conv1 = nn.Conv2D(3, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
        self.bn1 = nn.BatchNorm2D(64)
        self.relu = ReLU()
        self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._maker_layer(block, 64, layers[0])
        self.layer2 = self._maker_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._maker_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._maker_layer(block, 512, layers[3], stride=2)

        self.*gpool = nn.AvgPool2D(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)    def _init_weights(self, m):
        if isinstance(m, nn.Conv2D):
            kaiming_normal_(m.weight)        elif isinstance(m, nn.BatchNorm2D):
            ones_(m.weight)
            zeros_(m.bias)    def _maker_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_planes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2D(self.in_planes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias_attr=False),
                nn.BatchNorm2D(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.in_planes, planes, stride, downsample))
        self.in_planes = planes * block.expansion        for i in range(1, blocks):
            layers.append(block(self.in_planes, planes))        return nn.Sequential(*layers)    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.*gpool(x)
        flatten = nn.Flatten(start_axis=1, stop_axis=-1)
        x = flatten(x)
        x = self.fc(x)        return xdef cotnet50(**kwargs):
    model = CoTResNet(Bottleneck, [3, 4, 6, 3], **kwargs)    return model# if __name__ == '__main__':#     # x = paddle.rand(shape=[1, 3, 224, 224])#     # y = model(x)#     # print(y.shape)#     paddle.Model(cotnet50()).summary((1, 3, 224, 224))#     # paddle.Model(paddle.vision.resnet50()).summary((1, 3, 224, 224))
   

预设模型

In [4] 
def cotnet50(**kwargs):
    model = CoTResNet(Bottleneck, [3, 4, 6, 3], **kwargs)    return model
   

模型训练

  • 对构建的模型进行训练。

模型验证

In [ ] 
# !mkdir ~/data/ILSVRC2012# !tar -xf ~/data/data89857/ILSVRC2012mini.tar -C ~/data/ILSVRC2012
    In [5] 
import osimport paddleimport numpy as npfrom PIL import Imageclass ILSVRC2012(paddle.io.Dataset):
    def __init__(self, root, label_list, transform):
        self.transform = transform
        self.root = root
        self.label_list = label_list
        self.load_datas()    def load_datas(self):
        self.imgs = []
        self.labels = []        with open(self.label_list, 'r') as f:            for line in f:
                img, label = line[:-1].split(' ')
                self.imgs.append(os.path.join(self.root, img))
                self.labels.append(int(label))    def __getitem__(self, idx):
        label = self.labels[idx]
        image = self.imgs[idx]
        image = Image.open(image).convert('RGB')
        image = self.transform(image)        return image.astype('float32'), np.array(label).astype('int64')    def __len__(self):
        return len(self.imgs)
   

模型训练

In [6] 
# 1.构建模型network = cotnet50(num_classes=1000)#使用paddle高层APImodel = paddle.Model(network)
model.summary((1, 3, 224, 224))#模型可视化
       
W0426 17:09:21.611791  1204 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0426 17:09:21.615837  1204 device_context.cc:465] device: 0, cuDNN Version: 7.6.
        
---------------------------------------------------------------------------
 Layer (type)       Input Shape          Output Shape         Param #    
===========================================================================
   Conv2D-1      [[1, 3, 224, 224]]   [1, 64, 112, 112]        9,408     
 BatchNorm2D-1  [[1, 64, 112, 112]]   [1, 64, 112, 112]         256      
    ReLU-1      [[1, 64, 112, 112]]   [1, 64, 112, 112]          0       
  MaxPool2D-1   [[1, 64, 112, 112]]    [1, 64, 56, 56]           0       
   Conv2D-3      [[1, 64, 56, 56]]     [1, 64, 56, 56]         4,096     
 BatchNorm2D-3   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
    ReLU-4       [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
   Conv2D-4      [[1, 64, 56, 56]]     [1, 64, 56, 56]        36,864     
 BatchNorm2D-4   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
    ReLU-2       [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
   Conv2D-5      [[1, 64, 56, 56]]     [1, 64, 56, 56]         4,096     
 BatchNorm2D-5   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
   Conv2D-6      [[1, 128, 56, 56]]    [1, 32, 56, 56]         4,096     
 BatchNorm2D-6   [[1, 32, 56, 56]]     [1, 32, 56, 56]          128      
    ReLU-3       [[1, 32, 56, 56]]     [1, 32, 56, 56]           0       
   Conv2D-7      [[1, 32, 56, 56]]     [1, 576, 56, 56]       19,008     
 CoTNetLayer-1   [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
 BatchNorm2D-7   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
   Conv2D-8      [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384     
 BatchNorm2D-8   [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     
   Conv2D-2      [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384     
 BatchNorm2D-2   [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     
 Bottleneck-1    [[1, 64, 56, 56]]     [1, 256, 56, 56]          0       
   Conv2D-9      [[1, 256, 56, 56]]    [1, 64, 56, 56]        16,384     
 BatchNorm2D-9   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
    ReLU-7       [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
   Conv2D-10     [[1, 64, 56, 56]]     [1, 64, 56, 56]        36,864     
BatchNorm2D-10   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
    ReLU-5       [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
   Conv2D-11     [[1, 64, 56, 56]]     [1, 64, 56, 56]         4,096     
BatchNorm2D-11   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
   Conv2D-12     [[1, 128, 56, 56]]    [1, 32, 56, 56]         4,096     
BatchNorm2D-12   [[1, 32, 56, 56]]     [1, 32, 56, 56]          128      
    ReLU-6       [[1, 32, 56, 56]]     [1, 32, 56, 56]           0       
   Conv2D-13     [[1, 32, 56, 56]]     [1, 576, 56, 56]       19,008     
 CoTNetLayer-2   [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
BatchNorm2D-13   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
   Conv2D-14     [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384     
BatchNorm2D-14   [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     
 Bottleneck-2    [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
   Conv2D-15     [[1, 256, 56, 56]]    [1, 64, 56, 56]        16,384     
BatchNorm2D-15   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
    ReLU-10      [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
   Conv2D-16     [[1, 64, 56, 56]]     [1, 64, 56, 56]        36,864     
BatchNorm2D-16   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
    ReLU-8       [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
   Conv2D-17     [[1, 64, 56, 56]]     [1, 64, 56, 56]         4,096     
BatchNorm2D-17   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
   Conv2D-18     [[1, 128, 56, 56]]    [1, 32, 56, 56]         4,096     
BatchNorm2D-18   [[1, 32, 56, 56]]     [1, 32, 56, 56]          128      
    ReLU-9       [[1, 32, 56, 56]]     [1, 32, 56, 56]           0       
   Conv2D-19     [[1, 32, 56, 56]]     [1, 576, 56, 56]       19,008     
 CoTNetLayer-3   [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
BatchNorm2D-19   [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
   Conv2D-20     [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384     
BatchNorm2D-20   [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     
 Bottleneck-3    [[1, 256, 56, 56]]    [1, 256, 56, 56]          0       
   Conv2D-22     [[1, 256, 56, 56]]    [1, 128, 56, 56]       32,768     
BatchNorm2D-22   [[1, 128, 56, 56]]    [1, 128, 56, 56]         512      
    ReLU-13      [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
  AvgPool2D-1    [[1, 128, 56, 56]]    [1, 128, 28, 28]          0       
   Conv2D-23     [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,456    
BatchNorm2D-23   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
    ReLU-11      [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
   Conv2D-24     [[1, 128, 28, 28]]    [1, 128, 28, 28]       16,384     
BatchNorm2D-24   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
   Conv2D-25     [[1, 256, 28, 28]]    [1, 64, 28, 28]        16,384     
BatchNorm2D-25   [[1, 64, 28, 28]]     [1, 64, 28, 28]          256      
    ReLU-12      [[1, 64, 28, 28]]     [1, 64, 28, 28]           0       
   Conv2D-26     [[1, 64, 28, 28]]    [1, 1152, 28, 28]       74,880     
 CoTNetLayer-4   [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
BatchNorm2D-26   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
   Conv2D-27     [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536     
BatchNorm2D-27   [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
   Conv2D-21     [[1, 256, 56, 56]]    [1, 512, 28, 28]       131,072    
BatchNorm2D-21   [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
 Bottleneck-4    [[1, 256, 56, 56]]    [1, 512, 28, 28]          0       
   Conv2D-28     [[1, 512, 28, 28]]    [1, 128, 28, 28]       65,536     
BatchNorm2D-28   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
    ReLU-16      [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
   Conv2D-29     [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,456    
BatchNorm2D-29   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
    ReLU-14      [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
   Conv2D-30     [[1, 128, 28, 28]]    [1, 128, 28, 28]       16,384     
BatchNorm2D-30   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
   Conv2D-31     [[1, 256, 28, 28]]    [1, 64, 28, 28]        16,384     
BatchNorm2D-31   [[1, 64, 28, 28]]     [1, 64, 28, 28]          256      
    ReLU-15      [[1, 64, 28, 28]]     [1, 64, 28, 28]           0       
   Conv2D-32     [[1, 64, 28, 28]]    [1, 1152, 28, 28]       74,880     
 CoTNetLayer-5   [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
BatchNorm2D-32   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
   Conv2D-33     [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536     
BatchNorm2D-33   [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
 Bottleneck-5    [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
   Conv2D-34     [[1, 512, 28, 28]]    [1, 128, 28, 28]       65,536     
BatchNorm2D-34   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
    ReLU-19      [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
   Conv2D-35     [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,456    
BatchNorm2D-35   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
    ReLU-17      [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
   Conv2D-36     [[1, 128, 28, 28]]    [1, 128, 28, 28]       16,384     
BatchNorm2D-36   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
   Conv2D-37     [[1, 256, 28, 28]]    [1, 64, 28, 28]        16,384     
BatchNorm2D-37   [[1, 64, 28, 28]]     [1, 64, 28, 28]          256      
    ReLU-18      [[1, 64, 28, 28]]     [1, 64, 28, 28]           0       
   Conv2D-38     [[1, 64, 28, 28]]    [1, 1152, 28, 28]       74,880     
 CoTNetLayer-6   [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
BatchNorm2D-38   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
   Conv2D-39     [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536     
BatchNorm2D-39   [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
 Bottleneck-6    [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
   Conv2D-40     [[1, 512, 28, 28]]    [1, 128, 28, 28]       65,536     
BatchNorm2D-40   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
    ReLU-22      [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
   Conv2D-41     [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,456    
BatchNorm2D-41   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
    ReLU-20      [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
   Conv2D-42     [[1, 128, 28, 28]]    [1, 128, 28, 28]       16,384     
BatchNorm2D-42   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
   Conv2D-43     [[1, 256, 28, 28]]    [1, 64, 28, 28]        16,384     
BatchNorm2D-43   [[1, 64, 28, 28]]     [1, 64, 28, 28]          256      
    ReLU-21      [[1, 64, 28, 28]]     [1, 64, 28, 28]           0       
   Conv2D-44     [[1, 64, 28, 28]]    [1, 1152, 28, 28]       74,880     
 CoTNetLayer-7   [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
BatchNorm2D-44   [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
   Conv2D-45     [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536     
BatchNorm2D-45   [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     
 Bottleneck-7    [[1, 512, 28, 28]]    [1, 512, 28, 28]          0       
   Conv2D-47     [[1, 512, 28, 28]]    [1, 256, 28, 28]       131,072    
BatchNorm2D-47   [[1, 256, 28, 28]]    [1, 256, 28, 28]        1,024     
    ReLU-25     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
  AvgPool2D-2    [[1, 256, 28, 28]]    [1, 256, 14, 14]          0       
   Conv2D-48     [[1, 256, 14, 14]]    [1, 256, 14, 14]       589,824    
BatchNorm2D-48   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-23      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   Conv2D-49     [[1, 256, 14, 14]]    [1, 256, 14, 14]       65,536     
BatchNorm2D-49   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-50     [[1, 512, 14, 14]]    [1, 128, 14, 14]       65,536     
BatchNorm2D-50   [[1, 128, 14, 14]]    [1, 128, 14, 14]         512      
    ReLU-24      [[1, 128, 14, 14]]    [1, 128, 14, 14]          0       
   Conv2D-51     [[1, 128, 14, 14]]   [1, 2304, 14, 14]       297,216    
 CoTNetLayer-8   [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
BatchNorm2D-51   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-52     [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
BatchNorm2D-52  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
   Conv2D-46     [[1, 512, 28, 28]]   [1, 1024, 14, 14]       524,288    
BatchNorm2D-46  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
 Bottleneck-8    [[1, 512, 28, 28]]   [1, 1024, 14, 14]          0       
   Conv2D-53    [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
BatchNorm2D-53   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-28     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-54     [[1, 256, 14, 14]]    [1, 256, 14, 14]       589,824    
BatchNorm2D-54   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-26      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   Conv2D-55     [[1, 256, 14, 14]]    [1, 256, 14, 14]       65,536     
BatchNorm2D-55   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-56     [[1, 512, 14, 14]]    [1, 128, 14, 14]       65,536     
BatchNorm2D-56   [[1, 128, 14, 14]]    [1, 128, 14, 14]         512      
    ReLU-27      [[1, 128, 14, 14]]    [1, 128, 14, 14]          0       
   Conv2D-57     [[1, 128, 14, 14]]   [1, 2304, 14, 14]       297,216    
 CoTNetLayer-9   [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
BatchNorm2D-57   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-58     [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
BatchNorm2D-58  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
 Bottleneck-9   [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-59    [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
BatchNorm2D-59   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-31     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-60     [[1, 256, 14, 14]]    [1, 256, 14, 14]       589,824    
BatchNorm2D-60   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-29      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   Conv2D-61     [[1, 256, 14, 14]]    [1, 256, 14, 14]       65,536     
BatchNorm2D-61   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-62     [[1, 512, 14, 14]]    [1, 128, 14, 14]       65,536     
BatchNorm2D-62   [[1, 128, 14, 14]]    [1, 128, 14, 14]         512      
    ReLU-30      [[1, 128, 14, 14]]    [1, 128, 14, 14]          0       
   Conv2D-63     [[1, 128, 14, 14]]   [1, 2304, 14, 14]       297,216    
CoTNetLayer-10   [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
BatchNorm2D-63   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-64     [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
BatchNorm2D-64  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
 Bottleneck-10  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-65    [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
BatchNorm2D-65   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-34     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-66     [[1, 256, 14, 14]]    [1, 256, 14, 14]       589,824    
BatchNorm2D-66   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-32      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   Conv2D-67     [[1, 256, 14, 14]]    [1, 256, 14, 14]       65,536     
BatchNorm2D-67   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-68     [[1, 512, 14, 14]]    [1, 128, 14, 14]       65,536     
BatchNorm2D-68   [[1, 128, 14, 14]]    [1, 128, 14, 14]         512      
    ReLU-33      [[1, 128, 14, 14]]    [1, 128, 14, 14]          0       
   Conv2D-69     [[1, 128, 14, 14]]   [1, 2304, 14, 14]       297,216    
CoTNetLayer-11   [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
BatchNorm2D-69   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-70     [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
BatchNorm2D-70  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
 Bottleneck-11  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-71    [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
BatchNorm2D-71   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-37     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-72     [[1, 256, 14, 14]]    [1, 256, 14, 14]       589,824    
BatchNorm2D-72   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-35      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   Conv2D-73     [[1, 256, 14, 14]]    [1, 256, 14, 14]       65,536     
BatchNorm2D-73   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-74     [[1, 512, 14, 14]]    [1, 128, 14, 14]       65,536     
BatchNorm2D-74   [[1, 128, 14, 14]]    [1, 128, 14, 14]         512      
    ReLU-36      [[1, 128, 14, 14]]    [1, 128, 14, 14]          0       
   Conv2D-75     [[1, 128, 14, 14]]   [1, 2304, 14, 14]       297,216    
CoTNetLayer-12   [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
BatchNorm2D-75   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-76     [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
BatchNorm2D-76  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
 Bottleneck-12  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-77    [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144    
BatchNorm2D-77   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-40     [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-78     [[1, 256, 14, 14]]    [1, 256, 14, 14]       589,824    
BatchNorm2D-78   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
    ReLU-38      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   Conv2D-79     [[1, 256, 14, 14]]    [1, 256, 14, 14]       65,536     
BatchNorm2D-79   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-80     [[1, 512, 14, 14]]    [1, 128, 14, 14]       65,536     
BatchNorm2D-80   [[1, 128, 14, 14]]    [1, 128, 14, 14]         512      
    ReLU-39      [[1, 128, 14, 14]]    [1, 128, 14, 14]          0       
   Conv2D-81     [[1, 128, 14, 14]]   [1, 2304, 14, 14]       297,216    
CoTNetLayer-13   [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
BatchNorm2D-81   [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
   Conv2D-82     [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144    
BatchNorm2D-82  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     
 Bottleneck-13  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0       
   Conv2D-84    [[1, 1024, 14, 14]]    [1, 512, 14, 14]       524,288    
BatchNorm2D-84   [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
    ReLU-43      [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
  AvgPool2D-3    [[1, 512, 14, 14]]     [1, 512, 7, 7]           0       
   Conv2D-85      [[1, 512, 7, 7]]      [1, 512, 7, 7]       2,359,296   
BatchNorm2D-85    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
    ReLU-41       [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
   Conv2D-86      [[1, 512, 7, 7]]      [1, 512, 7, 7]        262,144    
BatchNorm2D-86    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
   Conv2D-87     [[1, 1024, 7, 7]]      [1, 256, 7, 7]        262,144    
BatchNorm2D-87    [[1, 256, 7, 7]]      [1, 256, 7, 7]         1,024     
    ReLU-42       [[1, 256, 7, 7]]      [1, 256, 7, 7]           0       
   Conv2D-88      [[1, 256, 7, 7]]     [1, 4608, 7, 7]       1,184,256   
CoTNetLayer-14    [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
BatchNorm2D-88    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
   Conv2D-89      [[1, 512, 7, 7]]     [1, 2048, 7, 7]       1,048,576   
BatchNorm2D-89   [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     
   Conv2D-83    [[1, 1024, 14, 14]]    [1, 2048, 7, 7]       2,097,152   
BatchNorm2D-83   [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     
 Bottleneck-14  [[1, 1024, 14, 14]]    [1, 2048, 7, 7]           0       
   Conv2D-90     [[1, 2048, 7, 7]]      [1, 512, 7, 7]       1,048,576   
BatchNorm2D-90    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
    ReLU-46      [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
   Conv2D-91      [[1, 512, 7, 7]]      [1, 512, 7, 7]       2,359,296   
BatchNorm2D-91    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
    ReLU-44       [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
   Conv2D-92      [[1, 512, 7, 7]]      [1, 512, 7, 7]        262,144    
BatchNorm2D-92    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
   Conv2D-93     [[1, 1024, 7, 7]]      [1, 256, 7, 7]        262,144    
BatchNorm2D-93    [[1, 256, 7, 7]]      [1, 256, 7, 7]         1,024     
    ReLU-45       [[1, 256, 7, 7]]      [1, 256, 7, 7]           0       
   Conv2D-94      [[1, 256, 7, 7]]     [1, 4608, 7, 7]       1,184,256   
CoTNetLayer-15    [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
BatchNorm2D-94    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
   Conv2D-95      [[1, 512, 7, 7]]     [1, 2048, 7, 7]       1,048,576   
BatchNorm2D-95   [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     
 Bottleneck-15   [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
   Conv2D-96     [[1, 2048, 7, 7]]      [1, 512, 7, 7]       1,048,576   
BatchNorm2D-96    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
    ReLU-49      [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
   Conv2D-97      [[1, 512, 7, 7]]      [1, 512, 7, 7]       2,359,296   
BatchNorm2D-97    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
    ReLU-47       [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
   Conv2D-98      [[1, 512, 7, 7]]      [1, 512, 7, 7]        262,144    
BatchNorm2D-98    [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
   Conv2D-99     [[1, 1024, 7, 7]]      [1, 256, 7, 7]        262,144    
BatchNorm2D-99    [[1, 256, 7, 7]]      [1, 256, 7, 7]         1,024     
    ReLU-48       [[1, 256, 7, 7]]      [1, 256, 7, 7]           0       
  Conv2D-100      [[1, 256, 7, 7]]     [1, 4608, 7, 7]       1,184,256   
CoTNetLayer-16    [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
BatchNorm2D-100   [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
  Conv2D-101      [[1, 512, 7, 7]]     [1, 2048, 7, 7]       1,048,576   
BatchNorm2D-101  [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     
 Bottleneck-16   [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0       
  AvgPool2D-4    [[1, 2048, 7, 7]]     [1, 2048, 1, 1]           0       
   Linear-1         [[1, 2048]]           [1, 1000]          2,049,000   
===========================================================================
Total params: 33,855,464
Trainable params: 33,711,464
Non-trainable params: 144,000
---------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 426.00
Params size (MB): 129.15
Estimated Total Size (MB): 555.72
---------------------------------------------------------------------------
        
{'total_params': 33855464, 'trainable_params': 33711464}
                In [7] 
# 2.数据预处理import paddle.vision.transforms as T
train_transforms = T.Compose([
    T.Resize(256, interpolation='bicubic'),
    T.CenterCrop(224),
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

train_dataset = ILSVRC2012('ILSVRC2012mini', transform=train_transforms, label_list='ILSVRC2012mini/train_list.txt')

val_transforms = T.Compose([
    T.Resize(256, interpolation='bicubic'),
    T.CenterCrop(224),
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
val_dataset = ILSVRC2012('ILSVRC2012mini', transform=val_transforms, label_list='ILSVRC2012mini/val_list.txt')
    In [ ] 
EPOCHS = 100BATCH_SIZE = 64#优化函数def create_optim(parameters):
    step_each_epoch = 40000 // BATCH_SIZE
    lr = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.01, T_max=step_each_epoch * EPOCHS)    return paddle.optimizer.Momentum(
        learning_rate=lr, 
        parameters=parameters, 
        weight_decay=paddle.regularizer.L2Decay(1.0)) #正则化来提升精度
 
 # 模型训练配置model.prepare(create_optim(network.parameters()),
    paddle.nn.CrossEntropyLoss(),        # 损失函数
    paddle.metric.Accuracy(topk=(1, 5))) # 评估指标
 # 训练可视化callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir') 
# 启动模型全流程训练model.fit(train_dataset,        # 训练数据集
    val_dataset,                # 评估数据集 
    epochs=EPOCHS,              # 总的训练轮次
    batch_size=BATCH_SIZE,      # 批次计算的样本量大小 
    shuffle=True,               # 是否打乱样本集
    verbose=1,                  # 日志展示格式
    s*e_dir='./chk_points/',   # 分阶段的训练模型存储路径
    callbacks=callback)       # 回调函数使用
   

以上就是基于PaddlePaddle复现CoTNet的详细内容，更多请关注其它相关文章！


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