应用TADOConnection、TADODataSet和TADOCommand组件

本文介绍使用 ADO (ActiveX Data Objects) 进行数据库操作的方法,包括执行 SQL 查询、更新数据等常见操作。通过两个具体示例展示了如何在 Delphi 或 C++Builder 中利用 ADOQuery 和 ADOCommand 组件实现数据检索与交互。

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unit U_AdoQuery;

interface

uses
  Windows, Messages, SysUtils, Variants, Classes, Graphics, Controls, Forms,
  Dialogs, StdCtrls, DB, ADODB, Buttons, DBClient, ExtCtrls,SQLEdit, Grids,
  DBGrids;

type
  TForm1 = class(TForm)
    DataSource1: TDataSource;
    QueryButton: TButton;
    EditCommandText: TSpeedButton;
    ADOQuery1: TADOQuery;
    SQLText: TMemo;
    Connection: TADOConnection;
    DBGrid1: TDBGrid;
    Label1: TLabel;
    procedure QueryButtonClick(Sender: TObject);
    procedure EditCommandTextClick(Sender: TObject);

  private
    { Private declarations }
  public
    { Public declarations }
  end;

var
  Form1: TForm1;

implementation

{$R *.dfm}

procedure TForm1.QueryButtonClick(Sender: TObject);
begin
  AdoQuery1.Close;
  AdoQuery1.SQL.Clear;
  AdoQuery1.SQL.Add(SQLText.Text);
  AdoQuery1.Open;
end;

procedure TForm1.EditCommandTextClick(Sender: TObject);
var
  Command: string;
begin
  Command := SQLText.Text;
  if EditSQL(Command, Connection.GetTableNames, Connection.GetFieldNames) then
      SQLText.Text := Command;
end;

end.

unit U_AdoCommand;
interface
uses
  Windows, Messages, SysUtils, Variants, Classes, Graphics, Controls, Forms,
  Dialogs, StdCtrls, DB, ADODB, Grids, DBGrids;

type
  TF_AdoCommand = class(TForm)
    ADOConnection1: TADOConnection;
    ADOCommand1: TADOCommand;
    DataSource1: TDataSource;
    DBGrid1: TDBGrid;
    ADODataSet1: TADODataSet;
    ADOCommand2: TADOCommand;
    ExcuteButton: TButton;
    CancelButton: TButton;
    DataSource2: TDataSource;
    DBGrid2: TDBGrid;
    CancelAllButton: TButton;
    ADODataSet2: TADODataSet;
    ExcuteAllButton: TButton;
    ComboBox1: TComboBox;
    Label1: TLabel;
    procedure ExcuteButtonClick(Sender: TObject);
    procedure CancelButtonClick(Sender: TObject);
    procedure CancelAllButtonClick(Sender: TObject);
    procedure ExcuteAllButtonClick(Sender: TObject);
    procedure FormCreate(Sender: TObject);
  private
    { Private declarations }
  public
    { Public declarations }
  end;

var
  F_AdoCommand: TF_AdoCommand;

implementation

{$R *.dfm}

procedure TF_AdoCommand.ExcuteButtonClick(Sender: TObject);
begin
if Combobox1.ItemIndex =0 then
//调用第一个ADO命令组件的Excute方法,并在DBGrid1中显示查询结果
  with ADOCommand1 do
  begin
      CommandType := cmdText;
      CommandText :='select *  from  country';
      ADODataSet1.Recordset := Execute;
  end
else
//调用第二个ADO命令组件的Excute方法,并在DBGrid2中显示查询结果
  with ADOCommand2 do begin
    CommandType := cmdText;
    CommandText :='select *  from  employee';
    ADODataSet2.Recordset := Execute;
  end;
end;

procedure TF_AdoCommand.CancelButtonClick(Sender: TObject);
begin
  if Combobox1.ItemIndex =0 then
   begin
     ADOCommand1.Cancel;
     ADODataSet1.Recordset :=nil ;
   end
  else
   begin
     ADOCommand2.Cancel;
     ADODataSet2.Recordset :=nil;
   end;
end;

procedure TF_AdoCommand.CancelAllButtonClick(Sender: TObject);
begin
   ADOCommand1.Cancel;
   ADOCommand2.Cancel;
   ADODataSet1.Recordset :=nil;
   ADODataSet2.Recordset :=nil;
end;

procedure TF_AdoCommand.ExcuteAllButtonClick(Sender: TObject);
var
i: Integer;
begin
  ADOCommand2.CommandText :='Update';
  for i := 0 to (ADOConnection1.CommandCount-1)  do
      begin
         if i=0 then
             begin
               ADOConnection1.Commands[i].CommandType := cmdText;
               ADOConnection1.Commands[i].CommandText :='select *  from  country';
                ADODataSet1.Recordset := ADOConnection1.Commands[i].Execute;
             end
         else  if i=1 then
              begin
               ADOConnection1.Commands[i].CommandType := cmdText;
               ADOConnection1.Commands[i].CommandText :='select *  from  employee';
               ADODataSet2.Recordset := ADOConnection1.Commands[i].Execute;

             end;
      end;
end;

procedure TF_AdoCommand.FormCreate(Sender: TObject);
var
i: Integer;
begin
  ComboBox1.Items.Clear;
  //添加的列表项的数目等于ADOConnection1组件所相关的TADOCommand组件的个数
  for i := 0 to (ADOConnection1.CommandCount-1)  do
     //添加的列表项为相关TADOCommand组件的名称
    Combobox1.Items.Add(ADOConnection1.Commands[i].Name) ;
  Combobox1.ItemIndex :=0;
end;

end.

### 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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