self.mlp = nn.Sequential(OrderedDict([ ("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), ("c_proj", nn.Linear(d_model * 4, d_model)) ]))

这段代码是一个 MLP（多层感知器）的实现，用于在 Transformer 中的每个 Encoder 和 Decoder 层中进行位置前馈网络（Position-wise Feedforward Networks）操作。它包含三个层:

1. `nn.Linear(d_model, d_model * 4)`: 输入层是d_model维，输出层是d_model * 4维，即将输入向量映射到一个更高维度的空间。
2. `QuickGELU()`: 快速的GeLU激活函数，将线性变换的结果进行非线性变换。
3. `nn.Linear(d_model * 4, d_model)`: 最后一个线性层将高维向量映射回d_model维，即将向量的维度降低到原始的维度。

通过这个 MLP，Transformer 可以更好地捕捉不同位置的不同特征，提高模型的性能。

相关问题

class MLP(nn.Module): def __init__( self, input_size: int, output_size: int, n_hidden: int, classes: int, dropout: float, normalize_before: bool = True ): super(MLP, self).__init__() self.input_size = input_size self.dropout = dropout self.n_hidden = n_hidden self.classes = classes self.output_size = output_size self.normalize_before = normalize_before self.model = nn.Sequential( nn.Linear(self.input_size, n_hidden), nn.Dropout(self.dropout), nn.ReLU(), nn.Linear(n_hidden, self.output_size), nn.Dropout(self.dropout), nn.ReLU(), ) self.after_norm = torch.nn.LayerNorm(self.input_size, eps=1e-5) self.fc = nn.Sequential( nn.Dropout(self.dropout), nn.Linear(self.input_size, self.classes) ) self.output_layer = nn.Linear(self.output_size, self.classes) def forward(self, x): self.device = torch.device('cuda') # x = self.model(x) if self.normalize_before: x = self.after_norm(x) batch_size, length, dimensions = x.size(0), x.size(1), x.size(2) output = self.model(x) return output.mean(dim=1) class LabelSmoothingLoss(nn.Module): def __init__(self, size: int, smoothing: float, ): super(LabelSmoothingLoss, self).__init__() self.size = size self.criterion = nn.KLDivLoss(reduction="none") self.confidence = 1.0 - smoothing self.smoothing = smoothing def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: batch_size = x.size(0) if self.smoothing == None: return nn.CrossEntropyLoss()(x, target.view(-1)) true_dist = torch.zeros_like(x) true_dist.fill_(self.smoothing / (self.size - 1)) true_dist.scatter_(1, target.view(-1).unsqueeze(1), self.confidence) kl = self.criterion(torch.log_softmax(x, dim=1), true_dist) return kl.sum() / batch_size

这段代码中定义了一个 MLP 模型以及一个 LabelSmoothingLoss 损失函数。MLP 模型包含了多个线性层和 ReLU 激活函数，以及一个 LayerNorm 层和一个 dropout 层。LabelSmoothingLoss 损失函数主要用于解决分类问题中的过拟合问题，它通过对真实标签进行平滑处理来减少模型对噪声的敏感度。这段代码的 forward 方法实现了 MLP 模型的前向传播，以及 LabelSmoothingLoss 的计算。其中，true_dist 是经过平滑处理后的真实标签分布，kl 是计算 KL 散度的结果，最终返回的是 kl 的平均值。

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)}")