游标

--声明游标
declare my_cursor cursor keyset for select * from info
--删除游标资源
deallocate my_cursor

--打开游标,在游标关闭或删除前都有效
open my_cursor
--关闭游标
close my_cursor

--声明局部变量
declare @id int,@name varchar(20),@address varchar(20)
--定位到指定位置的记录
fetch absolute 56488 from my_cursor into @id,@name,@address
select @id as id,@name as name,@address as address
--定位到当前记录相对位置记录
fetch relative -88 from my_cursor into @id,@name,@address
select @id as id,@name as name,@address as address
--定位到当前记录前一条
fetch prior from my_cursor into @id,@name,@address
select @id as id,@name as name,@address as address
--定位到当前记录后一条
fetch next from my_cursor into @id,@name,@address
select @id as id,@name as name,@address as address
--定位到首记录
fetch first from my_cursor into @id,@name,@address
select @id as id,@name as name,@address as address
--定位到尾记录
fetch last from my_cursor into @id,@name,@address
select @id as id,@name as name,@address as address

 

一个例子:

-- =============================================
-- Create procedure basic template
-- =============================================
-- creating the store procedure
IF EXISTS (SELECT name
    FROM   sysobjects
    WHERE  name = N'chy'
    AND    type = 'P')
    DROP PROCEDURE chy
GO

 

CREATE PROCEDURE chy
AS
     declare @change varchar(30)
     declare @id int
    
     DECLARE curr CURSOR
     for select [id] from a
     open curr
    
     fetch next  from curr into @id
     while(@@fetch_status<>-1)
     begin
          select @change=aa from a where [id]=@id
          set @change=substring(@change,0,3)
        
          update a set
bb=@change where [id]=@id
          fetch next  from curr into @id
     end
     close curr
     DEALLOCATE curr
GO

-- =============================================
-- example to execute the store procedure
-- =============================================
EXECUTE chy
GO

 

### 使用Transformer模型进行图像分类的方法 #### 方法概述 为了使Transformer能够应用于图像分类任务,一种有效的方式是将图像分割成固定大小的小块(patches),这些小块被线性映射为向量,并加上位置编码以保留空间信息[^2]。 #### 数据预处理 在准备输入数据的过程中,原始图片会被切分成多个不重叠的patch。假设一张尺寸为\(H \times W\)的RGB图像是要处理的对象,则可以按照设定好的宽度和高度参数来划分该图像。例如,对于分辨率为\(224\times 224\)像素的图像,如果选择每边切成16个部分的话,那么最终会得到\((224/16)^2=196\)个小方格作为单独的特征表示单元。之后,每一个这样的补丁都会通过一个简单的全连接层转换成为维度固定的嵌入向量。 ```python import torch from torchvision import transforms def preprocess_image(image_path, patch_size=16): transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), # 假设目标分辨率是224x224 transforms.ToTensor(), ]) image = Image.open(image_path).convert('RGB') tensor = transform(image) patches = [] for i in range(tensor.shape[-2] // patch_size): # 高度方向上的循环 row_patches = [] for j in range(tensor.shape[-1] // patch_size): # 宽度方向上的循环 patch = tensor[:, :, i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size].flatten() row_patches.append(patch) patches.extend(row_patches) return torch.stack(patches) ``` #### 构建Transformer架构 构建Vision Transformer (ViT),通常包括以下几个组成部分: - **Patch Embedding Layer**: 将每个图像块转化为低维向量; - **Positional Encoding Layer**: 添加绝对或相对位置信息给上述获得的向量序列; - **Multiple Layers of Self-Attention and Feed Forward Networks**: 多层自注意机制与前馈神经网络交替堆叠而成的核心模块; 最后,在顶层附加一个全局平均池化层(Global Average Pooling)以及一个多类别Softmax回归器用于预测类标签。 ```python class VisionTransformer(nn.Module): def __init__(self, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0.): super().__init__() self.patch_embed = PatchEmbed(embed_dim=embed_dim) self.pos_embed = nn.Parameter(torch.zeros(1, self.patch_embed.num_patches + 1, embed_dim)) self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) dpr = [drop_rate for _ in range(depth)] self.blocks = nn.Sequential(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=dpr[i], ) for i in range(depth)]) self.norm = nn.LayerNorm(embed_dim) self.head = nn.Linear(embed_dim, num_classes) def forward(self, x): B = x.shape[0] cls_tokens = self.cls_token.expand(B, -1, -1) x = self.patch_embed(x) x = torch.cat((cls_tokens, x), dim=1) x += self.pos_embed x = self.blocks(x) x = self.norm(x) return self.head(x[:, 0]) ```
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