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 x = ff(x) + x return x 每句语句什莫意思

Transformer 是一种神经网络模型，用于处理自然语言处理任务，它的架构可以提供有效的语言表示。它的核心是一系列的注意力机制，它们可以捕捉不同输入之间的关联，并使用多头注意力和前馈网络来更新输入的表示。

相关问题

Swin Transformer model代码

以下是Swin Transformer的PyTorch代码实现，包括Swin Transformer的模型定义和训练过程：

import torch
import torch.nn as nn
import torch.nn.functional as F


class SwinBlock(nn.Module):
    """Swin Transformer Block"""
    
    def __init__(self, dim, num_heads, window_size=7, shift_size=0):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = nn.MultiheadAttention(dim, num_heads)
        self.norm2 = nn.LayerNorm(dim)
        self.mlp = nn.Sequential(
            nn.Linear(dim, dim * 4),
            nn.GELU(),
            nn.Linear(dim * 4, dim),
        )
        self.window_size = window_size
        self.shift_size = shift_size
        
        if window_size == 1 and shift_size == 0:
            self.window_attn = None
        else:
            self.window_attn = nn.MultiheadAttention(dim, num_heads)
        
    def forward(self, x):
        res = x
        x = self.norm1(x)
        if self.window_attn is not None:
            b, n, d = x.shape
            assert n % self.window_size == 0, "sequence length must be divisible by window size"
            
            x = x.reshape(b, n // self.window_size, self.window_size, d)
            x = x.permute(0, 2, 1, 3)
            x = x.reshape(b * self.window_size, n // self.window_size, d)
            
            window_res = x
            x = self.window_attn(x, x, x)[0]
            
            x = x.reshape(b, self.window_size, n // self.window_size, d)
            x = x.permute(0, 2, 1, 3)
            x = x.reshape(b, n, d)
            
            x += window_res
        
        x = x + self.attn(x, x, x)[0]
        x = res + x
        res = x
        x = self.norm2(x)
        x = x + self.mlp(x)
        x = res + x
        
        if self.shift_size > 0:
            x = F.pad(x, (0, 0, 0, 0, self.shift_size, 0))
            x = x[:, :-self.shift_size, :]
        
        return x


class SwinTransformer(nn.Module):
    """Swin Transformer Model"""
    
    def __init__(self, img_size, patch_size, in_chans, num_classes, embed_dim, depths, num_heads, window_size=7, shift_size=0):
        super().__init__()
        assert img_size % patch_size == 0, "image size must be divisible by patch size"
        num_patches = (img_size // patch_size) ** 2
        patch_dim = in_chans * patch_size ** 2
        self.patch_size = patch_size
        
        # Patch Embeddings
        self.patch_embed = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
        
        # Stages
        self.stages = nn.ModuleList([
            nn.Sequential(*[
                SwinBlock(embed_dim, num_heads, window_size, shift_size)
                for _ in range(depth)
            ])
            for depth in depths
        ])
        
        # Classifier Head
        self.norm = nn.LayerNorm(embed_dim)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Linear(embed_dim, num_classes)
        
    def forward(self, x):
        x = self.patch_embed(x)
        b, c, h, w = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = x + self.pos_embed
        x = self.norm(x)
        
        for stage in self.stages:
            x = stage(x)
        
        x = self.avgpool(x.transpose(1, 2).reshape(b, c, h // self.patch_size, w // self.patch_size))
        x = x.flatten(1)
        x = self.fc(x)
        
        return x

训练过程：

import torch.optim as optim

# Define Model
model = SwinTransformer(
    img_size=224,
    patch_size=4,
    in_chans=3,
    num_classes=10,
    embed_dim=96,
    depths=[2, 2, 6, 2],
    num_heads=6,
    window_size=7,
    shift_size=2,
)
model.cuda()

# Define Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# Train Model
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.cuda(), labels.cuda()

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print(f"Epoch {epoch+1}: Training Loss = {running_loss/len(trainloader)}")

编写pytorch代码搭建Vision Transformer模型

好的，关于Vision Transformer模型的搭建，我可以给你一些指导。

首先，你需要导入PyTorch和一些其他的必要库。下面是一个简单的示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.layers.torch import Rearrange

接下来，你需要定义模型的各个模块。一个标准的Vision Transformer模型由多个Transformer层组成，每个Transformer层包含了多头自注意力机制、前馈网络和残差连接。下面是一个简单的Transformer层的示例：

class TransformerBlock(nn.Module):
    def __init__(self, embed_dim, num_heads, dropout=0.1):
        super().__init__()
        self.attention = nn.MultiheadAttention(embed_dim, num_heads)
        self.norm1 = nn.LayerNorm(embed_dim)
        self.dropout1 = nn.Dropout(dropout)
        self.fc = nn.Sequential(
            nn.Linear(embed_dim, 4 * embed_dim),
            nn.GELU(),
            nn.Linear(4 * embed_dim, embed_dim),
            nn.Dropout(dropout)
        )
        self.norm2 = nn.LayerNorm(embed_dim)
        self.dropout2 = nn.Dropout(dropout)

    def forward(self, x):
        attn_output, _ = self.attention(x, x, x)
        x = self.norm1(x + self.dropout1(attn_output))
        fc_output = self.fc(x)
        x = self.norm2(x + self.dropout2(fc_output))
        return x

接下来，你需要将多个Transformer层堆叠起来组成一个完整的Vision Transformer模型。这个示例中，我们还添加了一个可训练的分类头用于图像分类任务：

class VisionTransformer(nn.Module):
    def __init__(self, num_classes, image_size=224, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., dropout=0.1):
        super().__init__()
        assert image_size % patch_size == 0, "Image size must be divisible by patch size."
        num_patches = (image_size // patch_size) ** 2
        patch_dim = 3 * patch_size ** 2
        self.patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=patch_size, p2=patch_size),
            nn.Linear(patch_dim, embed_dim),
            nn.Dropout(dropout)
        )
        self.positional_encoding = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.transformer_blocks = nn.ModuleList([
            TransformerBlock(embed_dim, num_heads, dropout) for _ in range(depth)
        ])
        self.mlp_head = nn.Sequential(
            nn.LayerNorm(embed_dim),
            nn.Linear(embed_dim, int(embed_dim * mlp_ratio)),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(int(embed_dim * mlp_ratio), num_classes)
        )

    def forward(self, x):
        b = x.shape[0]
        x = self.patch_embedding(x)
        cls_tokens = self.cls_token.expand(b, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.positional_encoding[:, :(x.shape[1]), :]
        for transformer_block in self.transformer_blocks:
            x = transformer_block(x)
        x = x.mean(dim=1)
        x = self.mlp_head(x)
        return x

最后，你可以实例化该模型并传递输入数据来进行推理或训练：

model = VisionTransformer(num_classes=10)
input_data = torch.randn((1, 3, 224, 224))
output = model(input_data)

希望这能够帮助到你。