AI技术前沿探索：深度学习、自然语言处理与计算机视觉的最新进展

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

class SelfAttention(nn.Module):
    def __init__(self, embed_dim, num_heads):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.query_linear = nn.Linear(embed_dim, embed_dim)
        self.key_linear = nn.Linear(embed_dim, embed_dim)
        self.value_linear = nn.Linear(embed_dim, embed_dim)
        
    def forward(self, x):
        # x shape: (batch_size, seq_len, embed_dim)
        batch_size = x.size(0)
        seq_len = x.size(1)
        
        # Compute query, key, value
        query = self.query_linear(x).view(batch_size, -1, self.num_heads, self.embed_dim // self.num_heads).transpose(1, 2)
        key = self.key_linear(x).view(batch_size, -1, self.num_heads, self.embed_dim // self.num_heads).transpose(1, 2)
        value = self.value_linear(x).view(batch_size, -1, self.num_heads, self.embed_dim // self.num_heads).transpose(1, 2)
        
        # Compute attention scores
        attention_scores = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(self.embed_dim)
        attention_weights = F.softmax(attention_scores, dim=-1)
        
        # Apply attention weights to value
        output = torch.matmul(attention_weights, value).transpose(1, 2).contiguous().view(batch_size, seq_len, self.embed_dim)
        return output

from transformers import AutoTokenizer, AutoModelForCausalLM

# 加载模型和分词器
model_name = "gpt-3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# 定义生成函数
def generate_text(prompt, max_length=100):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(inputs.input_ids, max_length=max_length)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# 生成文本
prompt = "写一段关于人工智能未来的描述。"
generated_text = generate_text(prompt)
print(generated_text)

import cv2
import torch

# 加载模型
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# 加载图像
img = cv2.imread('image.jpg')

# 进行推理
results = model(img)

# 显示结果
results.show()