transformer图像分类生成代码

以下是使用 Transformer 实现图像分类和生成的伪代码：

## 图像分类

1. 导入必要的库和模型

import torch
import torch.nn as nn
from transformers import BertModel

class ImageClassifier(nn.Module):
    def __init__(self, num_classes):
        super(ImageClassifier, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.classifier = nn.Linear(self.bert.config.hidden_size, num_classes)

    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs[1]
        logits = self.classifier(pooled_output)
        return logits

2. 加载数据集

from torch.utils.data import DataLoader

train_dataset = ImageDataset(train_data)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

3. 训练模型

model = ImageClassifier(num_classes=num_classes)
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    for i, batch in enumerate(train_loader):
        input_ids, attention_mask, labels = batch
        logits = model(input_ids, attention_mask)
        loss = nn.CrossEntropyLoss()(logits, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

4. 评估模型

model.eval()

with torch.no_grad():
    for batch in test_loader:
        input_ids, attention_mask, labels = batch
        logits = model(input_ids, attention_mask)
        predictions = torch.argmax(logits, dim=1)
        accuracy = torch.sum(predictions == labels) / len(labels)

## 图像生成

1. 导入必要的库和模型

import torch
import torch.nn as nn
from transformers import GPT2Model

class ImageGenerator(nn.Module):
    def __init__(self, latent_size, img_size):
        super(ImageGenerator, self).__init__()
        self.latent_size = latent_size
        self.img_size = img_size
        self.gpt2 = GPT2Model.from_pretrained('gpt2')
        self.linear = nn.Linear(self.gpt2.config.hidden_size, self.img_size)

    def forward(self, latent_vector):
        outputs = self.gpt2(inputs_embeds=latent_vector)
        logits = self.linear(outputs[0])
        return logits

2. 产生随机噪声

import torch

def generate_latent_vector(batch_size, latent_size):
    return torch.randn(batch_size, latent_size)

3. 生成图像

generator = ImageGenerator(latent_size=latent_size, img_size=img_size)

with torch.no_grad():
    latent_vector = generate_latent_vector(batch_size, latent_size)
    generated_images = generator(latent_vector)