using System;

using System.Diagnostics;

using System.Threading;

using System.Threading.Tasks;

using NumSharp;

using static Tensorflow.Binding;

namespace Tensorflow

{

public class MemoryMonitor

{

public void WarmUp()

{

print($"tensorflow native version: v{tf.VERSION}");

var a = tf.constant(np.ones(10, 10));

var b = tf.Variable(a);

var c = tf.Variable(b);

var d = b * c;

print(d.numpy());

GC.WaitForPendingFinalizers();

GC.Collect();

Thread.Sleep(1000);

}

public void Execute(int epoch, int iterate, Action<int> process)

{

print($"{process.Method.Name} started...");

// new thread to run

Task.Run(() =>

{

for (int i = 0; i < epoch; i++)

{

var initialMemory = Process.GetCurrentProcess().PrivateMemorySize64;// GC.GetTotalMemory(true);

process(iterate);

var finalMemory = Process.GetCurrentProcess().PrivateMemorySize64; //GC.GetTotalMemory(true);

print($"Epoch {i}: {Format(finalMemory - initialMemory)}.");

GC.Collect();

GC.WaitForPendingFinalizers();

}

}).Wait();

print($"Total {process.Method.Name} usage {Format(Process.GetCurrentProcess().PrivateMemorySize64)}");

}

private string Format(long usage)

{

if (usage < 0)

return $"-{Format(0 - usage)}";

if (usage <= 1024 && usage >= 0)

return $"{usage} Bytes";

else if (usage > 1024 && usage <= 1024 * 1024)

return $"{usage / 1024} KB";

else

return $"{usage / 1024 / 1024} MB";

}

}

}