常用例子
本文依照以下常用例子展开:
private class DownloadFilesTask extends AsyncTask<URL, Integer, Long> {
protected Long doInBackground(URL... urls) {
int count = urls.length;
long totalSize = 0;
for (int i = 0; i < count; i++) {
totalSize += Downloader.downloadFile(urls[i]);
publishProgress((int) ((i / (float) count) * 100));
// Escape early if cancel() is called
if (isCancelled()) break;
}
return totalSize;
}
protected void onProgressUpdate(Integer... progress) {
setProgressPercent(progress[0]);
}
protected void onPostExecute(Long result) {
showDialog("Downloaded " + result + " bytes");
}
}
new DownloadFilesTask().execute(url1, url2, url3);
AsyncTask的使用方法太常见了,这里不做详述。值得注意的是AsyncTask有以下用法:
DownloadFilesTask mDownloadFilesTask = new DownloadFilesTask().execute(url1, url2, url3);
Long result = mDownloadFilesTask.get(); // 如果doInBackground还没执行完毕,那么堵塞直到它返回结果为止。
可以通过get()方法来获得AsyncTask的运行结果,这是通过java.util.concurrent的FutureTask类来完成的。AsyncTask类依赖于java.util.concurrent,task的执行和调度都由FutureTask、Executor、LinkedBlockingQueue等类来完成,下文会对其展开介绍。
源码解析
AsyncTask的状态
AsyncTask有三种状态:
对象初始化完毕后为PENDING状态,execute()被执行时转变成RUNNING状态,onPostExecute执行后变成FINISHED状态。execute()方法仅可以在task为PENDING状态下调用,否则会抛出IllegalStateException。
AsyncTask的初始化
AsyncTask有很多静态属性:
public abstract class AsyncTask<Params, Progress, Result> {
...
private static final ThreadFactory sThreadFactory = new ThreadFactory() {
private final AtomicInteger mCount = new AtomicInteger(1);
public Thread newThread(Runnable r) {
return new Thread(r, "AsyncTask #" + mCount.getAndIncrement());
}
};
private static final BlockingQueue<Runnable> sPoolWorkQueue =
new LinkedBlockingQueue<Runnable>(128);
public static final Executor THREAD_POOL_EXECUTOR
= new ThreadPoolExecutor(CORE_POOL_SIZE, MAXIMUM_POOL_SIZE, KEEP_ALIVE,
TimeUnit.SECONDS, sPoolWorkQueue, sThreadFactory);
public static final Executor SERIAL_EXECUTOR = new SerialExecutor();
...
private static volatile Executor sDefaultExecutor = SERIAL_EXECUTOR;
private static InternalHandler sHandler;
...
private static class SerialExecutor implements Executor {
...
}
其中:
sThreadFactory:供THREAD_POOL_EXECUTOR使用。为每个thread设定自增的name。
sPoolWorkQueue:供THREAD_POOL_EXECUTOR使用。
THREAD_POOL_EXECUTOR:供SerialExecutor使用,task真正执行的场所。
SERIAL_EXECUTOR:默认executor的实现,下文会对其详述。
sHandler:初始化时注入主线程的Looper,接收MESSAGE_POST_RESULT和MESSAGE_POST_PROGRESS消息。
其中SerialExecutor的实现如下所示:
private static class SerialExecutor implements Executor {
final ArrayDeque<Runnable> mTasks = new ArrayDeque<Runnable>();
Runnable mActive;
public synchronized void execute(final Runnable r) {
mTasks.offer(new Runnable() {
public void run() {
try {
r.run();
} finally {
scheduleNext();
}
}
});
if (mActive == null) {
scheduleNext();
}
}
protected synchronized void scheduleNext() {
if ((mActive = mTasks.poll()) != null) {
THREAD_POOL_EXECUTOR.execute(mActive);
}
}
}
代码逻辑如下所示:
- 当SerialExecutor对象的execute(runnable)执行时,首先会将runnable对象包装一下,然后放到一个FIFO的队列mTasks当中。如果是首次执行,那么scheduleNext()方法会被调用。
- 在scheduleNext()方法中,mTasks中的runnable被取出并赋给mActive变量,然后将mActive放到THREAD_POOL_EXECUTOR中执行。
- 在THREAD_POOL_EXECUTOR中调用run()方法,然后再次调用scheduleNext()方法取出下一个runnable。
接下来看一下AsyncTask的初始化方法:
public AsyncTask() {
mWorker = new WorkerRunnable<Params, Result>() {
public Result call() throws Exception {
mTaskInvoked.set(true);
Process.setThreadPriority(Process.THREAD_PRIORITY_BACKGROUND);
//noinspection unchecked
return postResult(doInBackground(mParams));
}
};
mFuture = new FutureTask<Result>(mWorker) {
@Override
protected void done() {
try {
postResultIfNotInvoked(get());
} catch (InterruptedException e) {
android.util.Log.w(LOG_TAG, e);
} catch (ExecutionException e) {
throw new RuntimeException("An error occured while executing doInBackground()",
e.getCause());
} catch (CancellationException e) {
postResultIfNotInvoked(null);
}
}
};
}
WorkerRunnable的源码如下所示:
private static abstract class WorkerRunnable<Params
, Result> implements Callable<Result> {
Params[] mParams;
}
可以看到WorkerRunnable继承于Callable,因此可以放到Executor中执行。因此mWorker这个匿名类的call方法会在Executor中调用。
首先将mTaskInvoked设置为true。mTaskInvoked用于检查当前的mWorker是否已经被执行(有可能还没执行就cancel了)。然后设置一下线程优先级,最后调用postResult()方法向sHandler发送MESSAGE_POST_RESULT消息。
接下来看mFuture。mFuture是一个FutureTask对象,可以通过调用mFuture.get()来堵塞式地等待task的执行结果,详情可以参考这里。done()方法会在mWorker结束(无论是cancel还是正常finish)时调用。其中postResultIfNotInvoked()源码如下所示:
private void postResultIfNotInvoked(Result result) {
final boolean wasTaskInvoked = mTaskInvoked.get();
if (!wasTaskInvoked) {
postResult(result);
}
}
private Result postResult(Result result) {
@SuppressWarnings("unchecked")
Message message = getHandler().obtainMessage(MESSAGE_POST_RESULT,
new AsyncTaskResult<Result>(this, result));
message.sendToTarget();
return result;
}
private static class InternalHandler extends Handler {
public InternalHandler() {
super(Looper.getMainLooper());
}
@SuppressWarnings({"unchecked", "RawUseOfParameterizedType"})
@Override
public void handleMessage(Message msg) {
AsyncTaskResult<?> result = (AsyncTaskResult<?>) msg.obj;
switch (msg.what) {
case MESSAGE_POST_RESULT:
// There is only one result
result.mTask.finish(result.mData[0]);
break;
case MESSAGE_POST_PROGRESS:
result.mTask.onProgressUpdate(result.mData);
break;
}
}
}
如果当前mWorker没有被执行过,则执行postResult()方法。这里保证了无论mWorker是否正常结束,主线程的回调依然会正常执行。
AsyncTask的执行
通过类似调用new DownloadFilesTask().execute(url1, url2, url3);
可以执行一个AsyncTask。execute方法的源码如下所示:
public final AsyncTask<Params, Progress, Result> execute(Params... params) {
return executeOnExecutor(sDefaultExecutor, params);
}
public final AsyncTask<Params, Progress, Result> executeOnExecutor(Executor exec,
Params... params) {
//1. 检查和设置task的状态
if (mStatus != Status.PENDING) {
switch (mStatus) {
case RUNNING:
throw new IllegalStateException("Cannot execute task:" + " the task is already running.");
case FINISHED:
throw new IllegalStateException("Cannot execute task:" + " the task has already been executed " + "(a task can be executed only once)");
}
}
mStatus = Status.RUNNING;
//2. 回调api
onPreExecute();
//3. 设置params和在sDefaultExecutor上执行mFuture
mWorker.mParams = params;
exec.execute(mFuture);
return this;
}
代码逻辑见注释。结合前文对mFuture和mWorker的讲解,可以画出AsyncTask的执行流程图:
+----------+ +--------+ +---------+ +---------------+ +--------------------+ +------------+ +--------------+
|MainThread| |sHandler| |AsyncTask| |SERIAL_EXECUTOR| |THREAD_POOL_EXECUTOR| | FutureTask | |WorkerRunnable|
+----+-----+ +---+----+ +----+----+ +-------+-------+ +----------+---------+ +------+-----+ +-------+------+
| | new | | | | |
+---------------------------------------> | | | | |
| | | | | | |
| | +----------+ | | | |
| | set mWorker and mFuture | | | | |
| | | | | | | |
| | | <--------+ | | | |
| | | | | | |
| | | | | | |
| execute(Params... params) | | | | |
+---------------------------------------> | | | | |
| | | | | | |
| | onPreExecute() | | | | |
| <---------------------------------------+ exec.execute(mFuture)| | | |
| | +---------------------> | scheduleNext() | | |
| | | +------+ | | |
| | | | | | | |
| | | | | | | |
| | | | <----+ | | |
| | | | execute(mActive) | | |
| | | +-----------------------> | run() | |
| | | | +------------------> | call() |
| | | | | +-----------------> |
| | | doInBackground() | | | |
| | | <----------------------------------------------------------------------------------------+
| | MESSAGE_POST_RESULT| | | | |
| | <-------------------------------------------------------------------------------------------------------------+
| | finish() | | | | done() |
| +------------------> | | | | <-----------------+
| onPostExecute(result) | | | | |
| <---------------------------------------+ | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
+ + + + + + +
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So, how does Tencent’s AI benchmark work? Initial, an AI is foreordained a resourceful reprimand from a catalogue of as over-abundant 1,800 challenges, from edifice frolic visualisations and царствование завинтившему возможностей apps to making interactive mini-games.
Intermittently the AI generates the jus civile ‘formal law’, ArtifactsBench gets to work. It automatically builds and runs the practices in a wanton and sandboxed environment.
To upwards how the germaneness behaves, it captures a series of screenshots upwards time. This allows it to unite correct to the event that things like animations, excellence changes after a button click, and other high-powered consumer feedback.
In the limits, it hands settled all this declare – the starting in demand, the AI’s cryptogram, and the screenshots – to a Multimodal LLM (MLLM), to feigning as a judge.
This MLLM judicator isn’t no more than giving a blurry тезис and as an alternative uses a particularized, per-task checklist to iota the conclude across ten diverse metrics. Scoring includes functionality, bloke conclusion, and aid aesthetic quality. This ensures the scoring is light-complexioned, to equal’s limitation, and thorough.
The beneficent matter is, does this automated authority earnestly posteriors wary taste? The results introduce it does.
When the rankings from ArtifactsBench were compared to WebDev Arena, the gold-standard person circuitous where bona fide humans prefer on the choicest AI creations, they matched up with a 94.4% consistency. This is a titanic move it from older automated benchmarks, which solely managed hither 69.4% consistency.
On summit of this, the framework’s judgments showed across 90% concord with documented cordial developers.
https://www.artificialintelligence-news.com/
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