从零搭建CBAM、SENet、STN、transformer、mobile_vit、simple_vit、vit模型（Pytorch代码示例）

qq7422349842024-12-20 18:08

从零搭建深度学习模型（Pytorch代码示例二）

    • CBAM
      • [CBAM 的关键特点](#CBAM 的关键特点)
      • [CBAM 的基本结构](#CBAM 的基本结构)
    • SENet
      • [SENet 的关键特点](#SENet 的关键特点)
      • [SENet 的基本结构](#SENet 的基本结构)
    • STN
      • [STN 的关键特点](#STN 的关键特点)
      • [STN 的基本结构](#STN 的基本结构)
    • transformer
      • [Transformer 的关键特点](#Transformer 的关键特点)
      • [Transformer 的基本结构](#Transformer 的基本结构)
    • mobile_vit
      • [MobileViT 的关键特点](#MobileViT 的关键特点)
      • [MobileViT 的基本结构](#MobileViT 的基本结构)
      • [MobileViT Block 的详细结构](#MobileViT Block 的详细结构)
    • simple_vit
      • [SimpleViT 的关键特点](#SimpleViT 的关键特点)
      • [SimpleViT 的基本结构](#SimpleViT 的基本结构)
      • [Patch Embedding](#Patch Embedding)
      • [Positional Encoding](#Positional Encoding)
      • [Transformer Encoder](#Transformer Encoder)
      • [Classification Head](#Classification Head)
    • vit

CBAM

CBAM（Convolutional Block Attention Module）是一种注意力机制，可以在现有的卷积神经网络（CNN）中插入，以增强模型对重要特征的关注。CBAM 通过同时考虑通道维度和空间维度的注意力，提高了模型的表征能力和性能。以下是 CBAM 的一些关键特点和实现细节：

CBAM 的关键特点

  • 通道注意力（Channel Attention）：通过计算每个通道的重要性权重，增强或抑制特定通道的特征。
  • 空间注意力（Spatial Attention）：通过计算每个位置的重要性权重，增强或抑制特定区域的特征。
  • 轻量级：CBAM 可以轻松地插入到现有的 CNN 架构中，而不会显著增加计算复杂度。

CBAM 的基本结构

CBAM 包括两个主要模块：通道注意力模块和空间注意力模块。

  1. 通道注意力模块（Channel Attention Module）
    最大池化：对输入特征图进行最大池化操作。
    平均池化：对输入特征图进行平均池化操作。
    共享多层感知机（MLP）：通过两个全连接层（FC）来学习通道的重要性权重。
    Sigmoid 激活：将 MLP 的输出通过 Sigmoid 函数转换为权重。
  2. 空间注意力模块（Spatial Attention Module）
    最大池化：对输入特征图进行最大池化操作。
    平均池化：对输入特征图进行平均池化操作。
    卷积层：通过一个 7x7 的卷积层来学习空间的重要性权重。
    Sigmoid 激活：将卷积层的输出通过 Sigmoid 函数转换为权重。
python 复制代码 
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)

## 通道注意力模块
class ChannelAttention(nn.Module):
    def __init__(self, in_planes, ratio=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
           
        self.fc = nn.Sequential(nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False),
                               nn.ReLU(),
                               nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False))
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc(self.avg_pool(x))
        max_out = self.fc(self.max_pool(x))
        out = avg_out + max_out
        return self.sigmoid(out)

## 空间注意力模块
class SpatialAttention(nn.Module):
    def __init__(self, kernel_size=7):
        super(SpatialAttention, self).__init__()

        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size//2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        x = torch.cat([avg_out, max_out], dim=1)
        x = self.conv1(x)
        return self.sigmoid(x)

## ResNet18与ResNet34使用的残差模块
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)

        self.ca = ChannelAttention(planes) # 通道注意力
        self.sa = SpatialAttention() # 空间注意力

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out = self.ca(out) * out
        out = self.sa(out) * out

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

        out += residual
        out = self.relu(out)

        return out

## ResNet50,ResNet101与ResNet152使用的残差模块
class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)

        self.ca = ChannelAttention(planes * 4)
        self.sa = SpatialAttention()

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        out = self.ca(out) * out
        out = self.sa(out) * out

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

        out += residual
        out = self.relu(out)

        return out

## 不同残差网络
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, 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.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

if __name__ == '__main__':
    resnet18 = ResNet(BasicBlock, [2, 2, 2, 2])
    resnet34 = ResNet(BasicBlock, [3, 4, 6, 3])
    resnet50 = ResNet(Bottleneck, [3, 4, 6, 3])
    resnet101 = ResNet(Bottleneck, [3, 4, 23, 3])
    resnet152 = ResNet(Bottleneck, [3, 8, 36, 3])

    x = torch.randn(1, 3, 224, 224)
    output = resnet18(x)
    torch.onnx.export(resnet18,x,'resnet18_cbam.onnx')

    # 使用预训练权重
    pretrained_state_dict = model_zoo.load_url(model_urls['resnet18'])
    new_state_dict = resnet18.state_dict()
    new_state_dict.update(pretrained_state_dict)
    resnet18.load_state_dict(new_state_dict)

SENet

SENet（Squeeze-and-Excitation Network）是一种通过引入注意力机制来增强卷积神经网络（CNN）性能的方法。SENet 通过动态地重新校准特征通道的重要性，使得模型能够更好地关注重要的特征，从而提高模型的表征能力和泛化能力。以下是 SENet 的一些关键特点和实现细节：

SENet 的关键特点

  • Squeeze 操作：通过全局平均池化（Global Average Pooling）将每个特征通道压缩成一个全局描述符，捕获每个通道的全局信息。
  • Excitation 操作：通过两个全连接层（FC）学习每个通道的重要性权重，这些权重通过 Sigmoid 激活函数转换为 [0, 1] 范围内的值。
  • Scale 操作：将学习到的权重与原始特征图相乘，增强或抑制特定通道的特征。

SENet 的基本结构

SENet 通过在传统的卷积块中插入 SE 模块来实现注意力机制。SE 模块包括以下步骤：

  • Squeeze 操作：对输入特征图进行全局平均池化，得到每个通道的全局描述符。
  • Excitation 操作：通过两个全连接层学习每个通道的重要性权重。
  • Scale 操作：将学习到的权重与原始特征图相乘，得到增强后的特征图。
python 复制代码 
#coding:utf8
import torch
import torch.nn as nn

## SE模块搭建
class SELayer(nn.Module):
    def __init__(self, channel,reduction = 16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(in_features=channel, out_features=channel // reduction)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(in_features=channel // reduction, out_features=channel)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        identity = x
        n,c,_,_ = x.size() # [N,C,H,W]
        x = self.avg_pool(x)
        x = x.view(n,c)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        x = x.view(n,c,1,1)
        x = identity * x

        return x

## 残差模块
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, outchannels):
        super(ResidualBlock, self).__init__()

        self.channel_equal_flag = True
        if in_channels == outchannels:
            self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=outchannels, kernel_size=3, padding=1, stride=1, bias=False)
        else:
            self.conv1x1 = nn.Conv2d(in_channels=in_channels, out_channels=outchannels, kernel_size=1, stride=2, bias=False)
            self.bn1x1 = nn.BatchNorm2d(num_features=outchannels)
            self.channel_equal_flag = False

            self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=outchannels,kernel_size=3,padding=1, stride=2, bias=False)

        self.bn1 = nn.BatchNorm2d(num_features=outchannels)
        self.relu = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(in_channels=outchannels, out_channels=outchannels, kernel_size=3,padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(num_features=outchannels)

        self.selayer = SELayer(channel=outchannels)

    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        if self.channel_equal_flag == True:
            pass
        else:
            identity = self.conv1x1(identity)
            identity = self.bn1x1(identity)
            identity = self.relu(identity)

        x = self.selayer(x) ## 即插即用模块

        out = identity + x
        return out

## SENet-18
class Model(nn.Module):
    def __init__(self, num_classes=1000):
        super(Model, self).__init__()
        # conv1
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64,kernel_size=7,stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(num_features=64)
        self.relu = nn.ReLU(inplace=True)

        # conv2_x
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2,padding=1)
        self.conv2_1 = ResidualBlock(in_channels=64, outchannels=64)
        self.conv2_2 = ResidualBlock(in_channels=64, outchannels=64)

        # conv3_x
        self.conv3_1 = ResidualBlock(in_channels=64, outchannels=128)
        self.conv3_2 = ResidualBlock(in_channels=128, outchannels=128)

        # conv4_x
        self.conv4_1 = ResidualBlock(in_channels=128, outchannels=256)
        self.conv4_2 = ResidualBlock(in_channels=256, outchannels=256)

        # conv5_x
        self.conv5_1 = ResidualBlock(in_channels=256, outchannels=512)
        self.conv5_2 = ResidualBlock(in_channels=512, outchannels=512)

        # avg_pool
        self.avg_pool = nn.AdaptiveAvgPool2d(output_size=(1,1)) # [N, C, H, W] = [N, 512, 1, 1]

        # fc
        self.fc = nn.Linear(in_features=512, out_features=num_classes)  # [N, num_classes]

    def forward(self, x):
        # conv1
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        # conv2_x
        x = self.maxpool(x)
        x = self.conv2_1(x)
        x = self.conv2_2(x)

        # conv3_x
        x = self.conv3_1(x)
        x = self.conv3_2(x)

        # conv4_x
        x = self.conv4_1(x)
        x = self.conv4_2(x)

        # conv5_x
        x = self.conv5_1(x)
        x = self.conv5_2(x)

        # avgpool + fc
        x = self.avg_pool(x)

        x = torch.flatten(x, 1)

        x = self.fc(x)

        return x


if __name__ == '__main__':
    # 定义模型
    model = Model(num_classes=10)

    # 定义输入 [N, C, H, W]
    input = torch.ones([10, 3, 224, 224])
    output = model(input)
    torch.onnx.export(model,input,'senet.onnx')

STN

STN（Spatial Transformer Networks）是一种用于在卷积神经网络（CNN）中进行空间变换的技术。STN 可以动态地对输入图像进行空间变换，如平移、旋转、缩放等，从而提高模型的鲁棒性和泛化能力。STN 通过引入一个可学习的空间变换模块，使得模型能够自适应地调整输入图像的位置和姿态，从而更好地捕捉特征。

STN 的关键特点

  • 局部化网络（Localization Network）：用于预测变换参数的网络，通常是一个小的 CNN。
  • 网格生成器（Grid Generator）：根据预测的变换参数生成采样网格。
  • 采样器（Sampler）：根据生成的采样网格对输入图像进行采样，生成变换后的图像。

STN 的基本结构

STN 的基本结构包括三个主要部分：

  • 局部化网络：接收输入图像并输出变换参数。
  • 网格生成器：根据变换参数生成采样网格。
  • 采样器：根据采样网格对输入图像进行采样，生成变换后的图像。
python 复制代码 
import torch
from torch import nn
from torch.nn import functional as F

class STN(nn.Module):
    def __init__(self, c,h,w,mode='stn'):
        assert mode in ['stn', 'cnn']

        super(STN, self).__init__()
        self.mode = mode
        self.local_net = LocalNetwork(c,h,w)
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
        )

        self.fc = nn.Sequential(
            nn.Linear(in_features=16*8*8, out_features=1024),
            nn.ReLU(),
            nn.Linear(in_features=1024, out_features=10)
        )

    def forward(self, img):
        '''
        :param img: (b, c, h, w)
        :return: (b, c, h, w), (b,)
        '''
        batch_size,c,h,w = img.shape
        img = self.local_net(img)

        conv_output = self.conv(img).view(batch_size, -1)
        predict = self.fc(conv_output)
        return img, predict


class LocalNetwork(nn.Module):
    def __init__(self,c,h,w):
        super(LocalNetwork, self).__init__()
        self.fc = nn.Sequential(
            nn.Linear(in_features=c*h*w,
                      out_features=20),
            nn.Tanh(),
            nn.Linear(in_features=20, out_features=6),
            nn.Tanh(),
        )

    def forward(self, img):
        '''
        :param img: (b, c, h, w)
        :return: (b, c, h, w)
        '''
        batch_size,c,w,h = img.shape

        theta = self.fc(img.view(batch_size, -1)).view(batch_size, 2, 3)

        ## 仿射变换采样函数
        grid = F.affine_grid(theta, torch.Size((batch_size,c,h,w)))
        img_transform = F.grid_sample(img, grid)

        return img_transform


if __name__ == '__main__':
    net = STN(3, 32, 32)
    x = torch.randn(1, 3, 32, 32)

    feature,predict = net(x)

    print(feature.shape)

transformer

Transformer 是一种基于自注意力机制（Self-Attention Mechanism）的深度学习模型，最初由 Vaswani 等人在 2017 年的论文《Attention is All You Need》中提出。Transformer 模型在自然语言处理（NLP）任务中取得了显著的成功，尤其是在机器翻译、文本生成、问答系统等领域。以下是 Transformer 的一些关键特点和实现细节：

Transformer 的关键特点

  • 自注意力机制（Self-Attention Mechanism）：允许模型在处理序列数据时，关注序列中的不同位置，从而捕捉长距离依赖关系。
  • 前馈神经网络（Feed-Forward Neural Network）：每个位置的特征经过相同的前馈神经网络进行处理。
  • 位置编码（Positional Encoding）：由于 Transformer 模型本身不包含序列顺序信息，因此需要添加位置编码来保留序列的顺序信息。
  • 多头注意力机制（Multi-Head Attention）：通过多个不同的注意力头来捕捉不同类型的依赖关系，提高模型的表达能力。

Transformer 的基本结构

Transformer 模型主要由编码器（Encoder）和解码器（Decoder）组成。每个编码器和解码器都包含多个相同的层。

  1. 编码器（Encoder）
    多头自注意力机制（Multi-Head Self-Attention）：对输入序列进行自注意力计算。
    前馈神经网络（Feed-Forward Neural Network）：对每个位置的特征进行非线性变换。
    残差连接（Residual Connections）：在每个子层之后添加残差连接，以缓解梯度消失问题。
    层归一化（Layer Normalization）：在每个子层之后进行层归一化，以稳定训练过程。
  2. 解码器（Decoder）
    多头自注意力机制（Multi-Head Self-Attention）：对目标序列进行自注意力计算。
    多头编码器-解码器注意力机制（Multi-Head Encoder-Decoder Attention）：对编码器的输出和目标序列进行交叉注意力计算。
    前馈神经网络（Feed-Forward Neural Network）：对每个位置的特征进行非线性变换。
    残差连接（Residual Connections）：在每个子层之后添加残差连接。
    层归一化（Layer Normalization）：在每个子层之后进行层归一化。
python 复制代码 
# coding:utf8
# 第一步：导入需要的库
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy

import matplotlib.pyplot as plt

# 第二步：定义Transformer类,标准的Encoder-Decoder架构
class Transformer(nn.Module):
    def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
        super(Transformer, self).__init__()
        # encoder和decoder都是构造的时候传入的，这样会非常灵活
        self.encoder = encoder
        self.decoder = decoder
        # 输入和输出的embedding
        self.src_embed = src_embed
        self.tgt_embed = tgt_embed
        # Decoder部分最后的Linear+softmax
        self.generator = generator

    def forward(self, src, tgt, src_mask, tgt_mask):
        # 接收并处理屏蔽src和目标序列
        # 首先调用encode方法对输入进行编码，然后调用decode方法进行解码
        return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask)

    def encode(self, src, src_mask):
        # 传入的参数包括src的embedding和src_mask
        return self.encoder(self.src_embed(src), src_mask)

    def decode(self, memory, src_mask, tgt, tgt_mask):
        # 传入的参数包括目标的embedding，Encoder的输出memory，及两种掩码
        return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)

# 第三步：创建Generator类，最终的输出层,全连接（linear）+ softmax,根据Decoder的隐状态输出一个词
class Generator(nn.Module):
    """d_model是Decoder输出的大小，vocab是词典大小"""

    def __init__(self, d_model, vocab):
        super(Generator, self).__init__()
        self.proj = nn.Linear(d_model, vocab)

    # 全连接再加上一个softmax
    def forward(self, x):
        return F.log_softmax(self.proj(x), dim=-1)

# 第四步：创建LayerNorm类，SublayerConnection类，Feedforward类
class LayerNorm(nn.Module):
    def __init__(self, features, eps=1e-6):
        super(LayerNorm, self).__init__()
        # pytorch中各层权重的数据类型是nn.Parameter，而不是Tensor。故需对初始化后参数（Tensor型）进行类型转换。
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2

# 不管是Self-Attention还是全连接层，都首先是LayerNorm，然后是Self-Attention/Dense，然后是Dropout，最后是残差连接。这里把它封装成SublayerConnection
class SublayerConnection(nn.Module):
    """
    LayerNorm + sublayer(Self-Attenion/Dense) + dropout + 残差连接
    为了简单，把LayerNorm放到了前面，这和原始论文稍有不同，原始论文LayerNorm在最后
    """
    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        # 将残差连接应用于具有相同大小的任何子层
        return x + self.dropout(sublayer(self.norm(x)))

# Feedforward层
class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.w_2(self.dropout(F.relu(self.w_1(x))))

# 第五步：构建HeadedAttention，Scaled Dot Product Attention
def attention(query, key, value, mask=None, dropout=None):
    d_k = query.size(-1) # 特征维度
    # 矩阵(-1,-1,n,d_k)与矩阵(-1,-1,d_k,n)相乘，得到大小为(-1,-1,n,n)的矩阵，n为输入词的长度
    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
    if mask is not None: # 掩码为0的地方
        scores = scores.masked_fill(mask == 0, -1e9)
    p_attn = F.softmax(scores, dim=-1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    return torch.matmul(p_attn, value), p_attn # 矩阵(-1,-1,n,n)与矩阵(-1,-1,n,d_k)相乘，得到大小为(-1,-1,n,d_k)的矩阵

# 计算MultiHeadedAttention，传入head个数及所有head拼接后的model维度
class MultiHeadedAttention(nn.Module):
    def __init__(self, h, d_model, dropout=0.1):
        super(MultiHeadedAttention, self).__init__()
        assert d_model % h == 0
        # 这里假设d_v=d_k
        self.d_k = d_model // h  # 计算每一个头的输入维度，如512/8=64
        self.h = h
        self.linears = clones(nn.Linear(d_model, d_model), 4)  ## 定义线性变换矩阵wq,wk,wv
        self.attn = None
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        if mask is not None:
            # 相同的mask适应所有的head.
            mask = mask.unsqueeze(1)
        nbatches = query.size(0)

        # 1) 首先使用线性变换，然后把d_model分配给h个Head，每个head为d_k=d_model/h，矩阵格式(nbatches, self.h, n, self.d_k)
        query, key, value = \
            [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
             for l, x in zip(self.linears, (query, key, value))]

        # 2) 使用attention函数计算scaled-Dot-product-attention
        # Q=矩阵(nbatches, self.h, n, self.d_k),K=矩阵(nbatches, self.h, self.d_k,n)相乘，
        # A=矩阵(nbatches, self.h, n, n),V=矩阵(nbatches, self.h,n, self.d_k),B=矩阵(nbatches, self.h, n, self.d_k)
        x, self.attn = attention(query, key, value, mask=mask,
                                 dropout=self.dropout)

        # 3) 实现Multi-head attention，用view函数把8个head的64维向量拼接成一个512的向量。
        # 然后再使用一个线性变换(512,512)，shape不变.
        x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
        return self.linears[-1](x)

# 第六步：创建Encoder,Encoder是N个EncoderLayer的堆积而成
def clones(module, N):
    "克隆N个完全相同的SubLayer，使用了copy.deepcopy"
    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])

class Encoder(nn.Module):
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        # clone N个layer
        self.layers = clones(layer, N)
        # 再加一个LayerNorm层
        self.norm = LayerNorm(layer.size)

    def forward(self, x, mask):
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)  # N个EncoderLayer处理完成之后还需要一个LayerNorm

# 创建EncoderLayer，由self-attn and feed forward构成
class EncoderLayer(nn.Module):
    def __init__(self, size, self_attn, feed_forward, dropout):
        super(EncoderLayer, self).__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        # 第一个SublayerConnection是multi attention模块，第一个SublayerConnection是feed forward模块
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x, mask):
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
        return self.sublayer[1](x, self.feed_forward)

# 第七步：定义Decoder，构建N个完全相同的Decoder层
class Decoder(nn.Module):
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        return self.norm(x)

# 创建DecoderLayer，由self-attn, src-attn和feed forward构成
class DecoderLayer(nn.Module):
    def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
        super(DecoderLayer, self).__init__()
        self.size = size
        self.self_attn = self_attn
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 3)

    def forward(self, x, memory, src_mask, tgt_mask):
        m = memory
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
        x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
        return self.sublayer[2](x, self.feed_forward)

# 构建上三角掩膜矩阵
def subsequent_mask(size):
    "Mask out subsequent positions."
    attn_shape = (1, size, size)
    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
    return torch.from_numpy(subsequent_mask) == 0

#----------绘制掩膜矩阵图------------#
plt.figure(figsize=(5,5))
plt.imshow(subsequent_mask(6)[0])
plt.savefig('mask.png')

# 第八步：输入数据
# 词嵌入
class Embeddings(nn.Module):
    def __init__(self, d_model, vocab):
        super(Embeddings, self).__init__()
        self.lut = nn.Embedding(vocab, d_model) #vocab为词表大小
        self.d_model = d_model

    def forward(self, x):
        return self.lut(x) * math.sqrt(self.d_model)

# 位置编码
class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
        # 在对数空间中计算，保证数值稳定性
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) *
                             -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:, :x.size(1)].clone().detach()
        return self.dropout(x)

#----------绘制位置编码图------------#
## 语句长度为100，假设d_model=20，
plt.figure(figsize=(15, 5))
pe = PositionalEncoding(20, 0)
y = pe.forward(torch.zeros(1, 100, 20))  

plt.plot(np.arange(100), y[0, :, :].data.numpy())
plt.legend(["dim %d"%p for p in [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]])

plt.savefig('positioncode.png')

# 第九步：构建完整网络
def make_model(src_vocab, tgt_vocab, N=6, d_model=512, d_ff=2048, h=8, dropout=0.1):
    c = copy.deepcopy
    attn = MultiHeadedAttention(h, d_model)
    ff = PositionwiseFeedForward(d_model, d_ff, dropout)
    position = PositionalEncoding(d_model, dropout)
    model = Transformer(
        Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
        Decoder(DecoderLayer(d_model, c(attn), c(attn),
                             c(ff), dropout), N),
        nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
        nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),
        Generator(d_model, tgt_vocab))
    return model

# 测试一个简单模型，输入、目标语句长度分别为10，Encoder、Decoder各2层。
if __name__ == '__main__':
    model = make_model(11, 11, N=2)
    src = torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) # 本质上是一个索引向量
    src_mask = torch.ones(1, 1, 10)
    torch.onnx._export(model, (src, src, src_mask, src_mask), 'transformer.onnx')
    print(model)

mobile_vit

MobileViT 是一种轻量级的视觉 Transformer 模型，旨在在移动设备上高效运行。它结合了卷积神经网络（CNN）和 Transformer 的优点，通过引入一种新的模块------MobileViT Block，实现了在保持高性能的同时降低计算复杂度。MobileViT 在图像分类、目标检测和语义分割等任务中表现出色，特别适合资源受限的设备。

MobileViT 的关键特点

  • MobileViT Block：结合了卷积和 Transformer 的优点，通过局部和全局信息的融合来提高模型的表达能力。
  • 轻量级设计：通过使用高效的卷积操作和 Transformer 结构，使得模型在保持高性能的同时具有较低的计算复杂度。
  • 多尺度特征提取：通过多尺度的特征提取，提高了模型对不同尺度特征的捕捉能力。

MobileViT 的基本结构

MobileViT 主要由以下几个部分组成：

  • Stem：初始的卷积层，用于提取低级别的特征。
  • MobileViT Blocks：核心模块，结合了卷积和 Transformer 的优点。
  • Global Pooling：全局池化层，用于将特征图降维。
  • Classification Head：最终的分类头，用于输出分类结果。

MobileViT Block 的详细结构

MobileViT Block 是 MobileViT 的核心模块，其结构如下：

  • Local Representation：通过卷积操作提取局部特征。
  • Global Representation：通过 Transformer 提取全局特征。
  • Fusion：将局部特征和全局特征进行融合。

具体步骤如下：

局部特征提取：

  • 使用卷积层提取局部特征。

全局特征提取：

  • 将局部特征展平并重塑为序列形式。
  • 使用 Transformer 进行全局特征提取。

特征融合：

  • 将全局特征重塑回特征图形式。
  • 与局部特征进行融合。
python 复制代码 
import torch
import torch.nn as nn

from einops import rearrange
from einops.layers.torch import Reduce

# helpers
def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.SiLU()
    )

def conv_nxn_bn(inp, oup, kernel_size=3, stride=1):
    return nn.Sequential(
        nn.Conv2d(inp, oup, kernel_size, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        nn.SiLU()
    )

class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn

    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout=0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )

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

class Attention(nn.Module):
    def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
        super().__init__()
        inner_dim = dim_head * heads
        self.heads = heads
        self.scale = dim_head ** -0.5

        self.attend = nn.Softmax(dim=-1)
        self.dropout = nn.Dropout(dropout)

        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        qkv = self.to_qkv(x).chunk(3, dim=-1)
        q, k, v = map(lambda t: rearrange(
            t, 'b p n (h d) -> b p h n d', h=self.heads), qkv)

        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)
        attn = self.dropout(attn)

        out = torch.matmul(attn, v)
        out = rearrange(out, 'b p h n d -> b p n (h d)')
        return self.to_out(out)

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads, dim_head, dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout))
            ]))

    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x
        return x

# MobileNetV2 Block
class MV2Block(nn.Module):
    def __init__(self, inp, oup, stride=1, expansion=4):
        super().__init__()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = int(inp * expansion)
        self.use_res_connect = self.stride == 1 and inp == oup

        if expansion == 1:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride,
                          1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.SiLU(),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.SiLU(),
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride,
                          1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.SiLU(),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )

    def forward(self, x):
        out = self.conv(x)
        if self.use_res_connect:
            out = out + x
        return out

class MobileViTBlock(nn.Module):
    def __init__(self, dim, depth, channel, kernel_size, patch_size, mlp_dim, dropout=0.):
        super().__init__()
        self.ph, self.pw = patch_size

        self.conv1 = conv_nxn_bn(channel, channel, kernel_size)
        self.conv2 = conv_1x1_bn(channel, dim)

        self.transformer = Transformer(dim, depth, 4, 8, mlp_dim, dropout)

        self.conv3 = conv_1x1_bn(dim, channel)
        self.conv4 = conv_nxn_bn(2 * channel, channel, kernel_size)

    def forward(self, x):
        y = x.clone()

        # Local representations
        x = self.conv1(x)
        x = self.conv2(x)

        # Global representations
        _, _, h, w = x.shape
        x = rearrange(x, 'b d (h ph) (w pw) -> b (ph pw) (h w) d',
                      ph=self.ph, pw=self.pw)
        x = self.transformer(x)
        x = rearrange(x, 'b (ph pw) (h w) d -> b d (h ph) (w pw)',
                      h=h//self.ph, w=w//self.pw, ph=self.ph, pw=self.pw)

        # Fusion
        x = self.conv3(x)
        x = torch.cat((x, y), 1)
        x = self.conv4(x)
        return x

# MobileViT模型定义
class MobileViT(nn.Module):
    def __init__(
        self,
        image_size,
        dims,
        channels,
        num_classes,
        expansion=4,
        kernel_size=3,
        patch_size=(2, 2),
        depths=(2, 4, 3)
    ):
        super().__init__()
        assert len(dims) == 3, 'dims must be a tuple of 3'
        assert len(depths) == 3, 'depths must be a tuple of 3'

        ih, iw = image_size
        ph, pw = patch_size
        assert ih % ph == 0 and iw % pw == 0

        init_dim, *_, last_dim = channels

        self.conv1 = conv_nxn_bn(3, init_dim, stride=2)

        self.stem = nn.ModuleList([])
        self.stem.append(MV2Block(channels[0], channels[1], 1, expansion))
        self.stem.append(MV2Block(channels[1], channels[2], 2, expansion))
        self.stem.append(MV2Block(channels[2], channels[3], 1, expansion))
        self.stem.append(MV2Block(channels[2], channels[3], 1, expansion))

        self.trunk = nn.ModuleList([])
        self.trunk.append(nn.ModuleList([
            MV2Block(channels[3], channels[4], 2, expansion),
            MobileViTBlock(dims[0], depths[0], channels[5],
                           kernel_size, patch_size, int(dims[0] * 2))
        ]))

        self.trunk.append(nn.ModuleList([
            MV2Block(channels[5], channels[6], 2, expansion),
            MobileViTBlock(dims[1], depths[1], channels[7],
                           kernel_size, patch_size, int(dims[1] * 4))
        ]))

        self.trunk.append(nn.ModuleList([
            MV2Block(channels[7], channels[8], 2, expansion),
            MobileViTBlock(dims[2], depths[2], channels[9],
                           kernel_size, patch_size, int(dims[2] * 4))
        ]))

        self.to_logits = nn.Sequential(
            conv_1x1_bn(channels[-2], last_dim),
            Reduce('b c h w -> b c', 'mean'),
            nn.Linear(channels[-1], num_classes, bias=False)
        )

    def forward(self, x):
        x = self.conv1(x)

        for conv in self.stem:
            x = conv(x)

        for conv, attn in self.trunk:
            x = conv(x)
            x = attn(x)

        return self.to_logits(x)

if __name__ == '__main__':
    mbvit_xs = MobileViT(
        image_size=(256, 256),
        dims=[96, 120, 144],
        channels=[16, 32, 48, 48, 64, 64, 80, 80, 96, 96, 384],
        num_classes=1000
    )

    img = torch.randn(1, 3, 256, 256)
    pred = mbvit_xs(img)
    print(pred.shape)
    torch.onnx.export(mbvit_xs, img, 'mbvit_xs.onnx')

simple_vit

SimpleViT 是一个简化版的 Vision Transformer (ViT) 模型，旨在降低原始 ViT 的复杂性和计算成本，同时保持一定的性能。ViT 模型的核心思想是将图像视为一系列的 patch，然后通过 Transformer 架构进行处理。SimpleViT 通常会减少层数、隐藏维度或其他参数，以适应更小的计算资源或更快的推理速度。

SimpleViT 的关键特点

  • 简化架构：减少了原始 ViT 的层数和隐藏维度，降低了模型的复杂度。
  • 高效性：适合在资源受限的环境中运行，如移动设备或嵌入式系统。
  • 易于实现：代码相对简单，便于理解和修改。

SimpleViT 的基本结构

SimpleViT 主要由以下几个部分组成：

  • Patch Embedding：将图像分割成一系列 patch，并将每个 patch 转换为嵌入向量。
  • Positional Encoding：为每个 patch 添加位置信息，以便模型能够理解 patch 的顺序。
  • Transformer Encoder：通过多个 Transformer 编码器层对 patch 嵌入进行处理。
  • Classification Head：最终的分类头，用于输出分类结果。

Patch Embedding

Patch Embedding 将图像分割成一系列固定大小的 patch，并将每个 patch 转换为一个嵌入向量。具体步骤如下：

  • 分割图像：将图像分割成一系列固定大小的 patch。
  • 线性变换：将每个 patch 展平并通过线性变换转换为嵌入向量。
  • 添加类别标记：在嵌入向量序列的开头添加一个类别标记（cls token），用于最终的分类任务。

Positional Encoding

由于 Transformer 模型本身不包含序列顺序信息，因此需要添加位置编码来保留 patch 的顺序信息。位置编码可以通过正弦和余弦函数生成。

Transformer Encoder

Transformer Encoder 包含多个相同的编码器层，每个编码器层包含多头自注意力机制和前馈神经网络。具体步骤如下：

  • 多头自注意力机制：对 patch 嵌入进行自注意力计算。
  • 前馈神经网络：对每个位置的特征进行非线性变换。
  • 残差连接：在每个子层之后添加残差连接，以缓解梯度消失问题。
  • 层归一化：在每个子层之后进行层归一化，以稳定训练过程。

Classification Head

Classification Head 通过一个线性变换将最终的类别标记嵌入转换为分类结果。具体步骤如下：

  • 获取类别标记嵌入：从 Transformer Encoder 的输出中获取类别标记嵌入。
  • 线性变换：将类别标记嵌入通过线性变换转换为分类结果。
python 复制代码 
import torch
from torch import nn

from einops import rearrange
from einops.layers.torch import Rearrange

def pair(t):
    return t if isinstance(t, tuple) else (t, t)

## 位置编码向量
def posemb_sincos_2d(patches, temperature = 10000, dtype = torch.float32):
    _, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
    # 假设h=8,w=8,dim=1024
    y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij')
    assert (dim % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb'
    omega = torch.arange(dim // 4, device = device) / (dim // 4 - 1) # shape=1024/4=256
    omega = 1. / (temperature ** omega)

    print('y.shape',y.shape) # [8,8]
    print('x.shape',x.shape) # [8,8]
    y = y.flatten()[:, None] * omega[None, :]
    x = x.flatten()[:, None] * omega[None, :] 
    print('omega.shape',omega.shape) # [256]
    print('y.shape',y.shape) # [64, 256]
    print('x.shape',x.shape) # [64, 256]

    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim = 1)
    return pe.type(dtype)

## 前向计算模块定义，包括两个全连接层
class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, dim),
        )
    def forward(self, x):
        return self.net(x)

## 注意力模块定义
class Attention(nn.Module):
    def __init__(self, dim, heads = 8, dim_head = 64):
        super().__init__()
        inner_dim = dim_head *  heads
        self.heads = heads
        self.scale = dim_head ** -0.5
        self.norm = nn.LayerNorm(dim)

        self.attend = nn.Softmax(dim = -1)

        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
        self.to_out = nn.Linear(inner_dim, dim, bias = False)

    def forward(self, x):
        x = self.norm(x)

        qkv = self.to_qkv(x).chunk(3, dim = -1)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)

        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)

        out = torch.matmul(attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)

## Transformer模型定义
class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                Attention(dim, heads = heads, dim_head = dim_head),
                FeedForward(dim, mlp_dim)
            ]))
    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x
        return x

## SimpleViT模型定义
class SimpleViT(nn.Module):
    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64):
        super().__init__()
        image_height, image_width = pair(image_size)
        patch_height, patch_width = pair(patch_size)

        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'

        num_patches = (image_height // patch_height) * (image_width // patch_width)
        patch_dim = channels * patch_height * patch_width

        self.to_patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b h w (p1 p2 c)', p1 = patch_height, p2 = patch_width),
            nn.Linear(patch_dim, dim),
        )

        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)

        self.to_latent = nn.Identity()
        self.linear_head = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, num_classes)
        )

    def forward(self, img):
        *_, h, w, dtype = *img.shape, img.dtype

        x = self.to_patch_embedding(img)
        pe = posemb_sincos_2d(x)

        print('x shape',x.shape) # [1,8,8,1024]
        print('pe shape',pe.shape) # [64,1024]

        x = rearrange(x, 'b ... d -> b (...) d') + pe

        x = self.transformer(x)
        x = x.mean(dim = 1)

        x = self.to_latent(x)
        return self.linear_head(x)

model = SimpleViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024, ## token维度
    depth = 6, ## 模块数量
    heads = 16, ## 头的数量
    mlp_dim = 2048 ## mlp隐藏层维度
)

if __name__ == '__main__':
    img = torch.randn(1, 3, 256, 256)
    preds = model(img)
    print(preds.shape)

    torch.onnx.export(model, img, 'Simple_ViT.onnx')

vit

python 复制代码 
#coding:utf8
import torch
from torch import nn

from einops import rearrange, repeat
from einops.layers.torch import Rearrange

def pair(t):
    return t if isinstance(t, tuple) else (t, t)

## 预标准化方法
class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn
    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)

## 前向计算模块定义，包括两个全连接层
class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x):
        return self.net(x)

## 注意力模块定义
class Attention(nn.Module):
    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
        super().__init__()
        inner_dim = dim_head *  heads ## 8*64=512
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head ** -0.5 ## 1/sqrt(64)=1/8

        self.attend = nn.Softmax(dim = -1)
        self.dropout = nn.Dropout(dropout)

        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) ## 默认dim=1024，inner_dim * 3 = 512*3

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()

    def forward(self, x):
        qkv = self.to_qkv(x).chunk(3, dim = -1) ## 从输入x，生成q,k,v，每一个维度 = inner_dim = dim_head * heads = 512
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)

        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale ## q*k/sqrt(d)
        attn = self.attend(dots) ## 得到softmax(q*k/sqrt(d))
        attn = self.dropout(attn)

        out = torch.matmul(attn, v) ## 得到softmax(q*k/sqrt(d))*v
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)

## Transformer模型定义
class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))
    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x ## 自注意力模块
            x = ff(x) + x ## feedforward模块
        return x

## ViT模型定义
class ViT(nn.Module):
    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
        super().__init__()
        image_height, image_width = pair(image_size) ## 图像尺寸
        patch_height, patch_width = pair(patch_size) ## 裁剪子图尺寸

        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'

        num_patches = (image_height // patch_height) * (image_width // patch_width) ## 子图数量
        patch_dim = channels * patch_height * patch_width ## 展平后子图维度
        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'

        ## 把图片切分为patch，然后拉成序列，假设输入图片大小是256x256（b,3,256,256），打算分成64个patch，每个patch是32x32像素，则rearrange操作是先变成(b,3,8x32,8x32)，最后变成(b,8x8,32x32x3)即(b,64,3072)
        self.to_patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
            nn.Linear(patch_dim, dim), ## 把子图维度映射到特定维度dim，比如32*32*3 -> 1024
        )

        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) ## num_patches=64，dim=1024,+1是因为多了一个cls开启解码标志
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) ## 额外的分类token
        self.dropout = nn.Dropout(emb_dropout)

        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)

        self.pool = pool
        self.to_latent = nn.Identity()

        ## 在编码器后接fc分类器head即可
        self.mlp_head = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, num_classes)
        )

    def forward(self, img):
        x = self.to_patch_embedding(img)
        print('x shape', x.shape)
        b, n, _ = x.shape
        cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b)
        x = torch.cat((cls_tokens, x), dim=1) ## 在输入token数量维度进行拼接,## 额外追加token，变成b,65,1024
        x += self.pos_embedding[:, :(n + 1)]
        x = self.dropout(x)

        x = self.transformer(x)

        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] ## 取第0个token的特征，或者所有特征的平均值

        x = self.to_latent(x)
        return self.mlp_head(x)


model = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024, ## token维度
    depth = 6, ## 模块数量
    heads = 16, ## 头的数量
    mlp_dim = 2048 ## mlp隐藏层维度
)

if __name__ == '__main__':
    img = torch.randn(1, 3, 256, 256)
    preds = model(img)
    print(preds.shape)

    torch.onnx.export(model, img, 'ViT.onnx')
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