pytorch transpose实例代码

以下是 PyTorch 中 transpose 的示例代码：

import torch

# 创建一个 2x3 的张量
x = torch.tensor([[1, 2, 3], [4, 5, 6]])

# 对张量进行转置操作
y = x.transpose(, 1)

# 打印转置后的张量
print(y)

这段代码创建了一个 2x3 的张量 x，然后对其进行了转置操作，将第 维和第 1 维进行了交换。最后打印出转置后的张量 y。

相关问题

基于pytorch的Transformer代码实例

Transformer 是一种非常流行的深度学习模型，被广泛应用于自然语言处理和语音识别等领域。在 PyTorch 中，可以使用 PyTorch 实现 Transformer 模型，下面是一个简单的代码实例：

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


class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        self.depth = d_model // num_heads
        self.wq = nn.Linear(d_model, d_model)
        self.wk = nn.Linear(d_model, d_model)
        self.wv = nn.Linear(d_model, d_model)
        self.fc = nn.Linear(d_model, d_model)

    def scaled_dot_product_attention(self, query, key, value):
        matmul_qk = torch.matmul(query, key.transpose(-2, -1))
        dk = torch.tensor(self.depth, dtype=torch.float32)
        scaled_attention_logits = matmul_qk / torch.sqrt(dk)
        attention_weights = F.softmax(scaled_attention_logits, dim=-1)
        output = torch.matmul(attention_weights, value)
        return output

    def split_heads(self, x, batch_size):
        x = x.reshape(batch_size, -1, self.num_heads, self.depth)
        return x.transpose(1, 2)

    def forward(self, query, key, value):
        batch_size = query.shape

        query = self.wq(query)
        key = self.wk(key)
        value = self.wv(value)

        query = self.split_heads(query, batch_size)
        key = self.split_heads(key, batch_size)
        value = self.split_heads(value, batch_size)

        scaled_attention = self.scaled_dot_product_attention(query, key, value)

        scaled_attention = scaled_attention.transpose(1, 2)
        concat_attention = scaled_attention.reshape(batch_size, -1, self.d_model)

        output = self.fc(concat_attention)

        return output


class TransformerBlock(nn.Module):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(TransformerBlock, self).__init__()

        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = nn.Sequential(
            nn.Linear(d_model, dff),
            nn.ReLU(),
            nn.Linear(dff, d_model),
        )

        self.layernorm1 = nn.LayerNorm(d_model)
        self.layernorm2 = nn.LayerNorm(d_model)

        self.dropout1 = nn.Dropout(rate)
        self.dropout2 = nn.Dropout(rate)

    def forward(self, x):
        attn_output = self.mha(x, x, x)
        attn_output = self.dropout1(attn_output)
        out1 = self.layernorm1(x + attn_output)

        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output)
        out2 = self.layernorm2(out1 + ffn_output)

        return out2


class Transformer(nn.Module):
    def __init__(self,
                 input_vocab_size,
                 target_vocab_size,
                 max_len_input,
                 max_len_target,
                 num_layers=4,
                 d_model=128,
                 num_heads=8,
                 dff=512,
                 rate=0.1):
        super(Transformer, self).__init__()

        self.encoder_embedding = nn.Embedding(input_vocab_size, d_model)
        self.decoder_embedding = nn.Embedding(target_vocab_size, d_model)

        self.pos_encoding_input = PositionalEncoding(max_len_input, d_model)
        self.pos_encoding_target = PositionalEncoding(max_len_target, d_model)

        self.encoder_layers = nn.ModuleList([TransformerBlock(d_model,
                                                               num_heads,
                                                               dff,
                                                               rate) for _ in range(num_layers)])
        self.decoder_layers = nn.ModuleList([TransformerBlock(d_model,
                                                               num_heads,
                                                               dff,
                                                               rate) for _ in range(num_layers)])

        self.final_layer = nn.Linear(d_model, target_vocab_size)

    def forward(self,
                input_seq,
                target_seq,
                input_mask=None,
                target_mask=None):

        input_seq_embd = self.encoder_embedding(input_seq)
        input_seq_embd *= torch.sqrt(torch.tensor(self.d_model))
        input_seq_embd += self.pos_encoding_input(input_seq_embd)

        target_seq_embd = self.decoder_embedding(target_seq)
        target_seq_embd *= torch.sqrt(torch.tensor(self.d_model))
        target_seq_embd += self.pos_encoding_target(target_seq_embd)

        enc_output = input_seq_embd

        for i in range(self.num_layers):
            enc_output = self.encoder_layers[i](enc_output)

        dec_output = target_seq_embd

        for i in range(self.num_layers):
            dec_output = self.decoder_layers[i](dec_output)

        final_output = self.final_layer(dec_output)

        return final_output

class PositionalEncoding(nn.Module):
    def __init__(self, max_len, d_model):
        super(PositionalEncoding, self).__init__()
        
        pe = torch.zeros(max_len, d_model)
        
        position = torch.arange(0, max_len).unsqueeze(1).float()
        
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-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).transpose(0, 1)
        
        self.register_buffer('pe', pe)

    def forward(self, x):
        x += self.pe[:x.size(0), :]
        
        return x

这个代码实例中包括了 Multi-Head Attention、Transformer Block 和 Transformer 等模块，用于实现一个 Transformer 模型。你可以根据需要修改参数和模型结构来适应你的应用场景。

ViT pytorch的代码

ViT（Vision Transformer）是一种基于Transformer的图像分类模型，它将图像分割成一系列的图像块，并使用Transformer编码器来处理这些图像块。以下是一个使用PyTorch实现ViT的简单代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from torch.utils.data import DataLoader
from torchsummary import summary

# 定义ViT模型
class ViT(nn.Module):
    def __init__(self, image_size, patch_size, num_classes, dim):
        super(ViT, self).__init__()
        num_patches = (image_size // patch_size) ** 2
        self.patch_embedding = nn.Conv2d(3, dim, kernel_size=patch_size, stride=patch_size)
        self.positional_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=dim, nhead=8), num_layers=12)
        self.fc = nn.Linear(dim, num_classes)

    def forward(self, x):
        x = self.patch_embedding(x)
        x = x.flatten(2).transpose(1, 2)
        x = torch.cat([self.positional_embedding, x], dim=1)
        x = self.transformer(x)
        x = x[:, 0]
        x = self.fc(x)
        return x

# 数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# 创建模型实例
model = ViT(image_size=224, patch_size=16, num_classes=10, dim=256)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
for epoch in range(10):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f"Epoch {epoch+1} loss: {running_loss/len(train_loader)}")

# 测试模型
model.eval()
correct = 0
total = 0
with torch.no_grad():
    for inputs, labels in test_loader:
        inputs, labels = inputs.to(device), labels.to(device)
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
print(f"Test accuracy: {accuracy}%")

# 打印模型结构
summary(model, (3, 224, 224))

这段代码实现了一个简单的ViT模型，使用CIFAR-10数据集进行训练和测试。代码中包括了模型的定义、数据预处理、数据加载、训练过程、测试过程以及模型结构的打印。