TensorFlow.NET/tools/TensorFlowNET.Console/MemoryMonitor.cs at master · ShuchenQ/TensorFlow.NET · GitHub

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using System;

using System.Diagnostics;

using System.Threading;

using System.Threading.Tasks;

using Tensorflow.NumPy;

using static Tensorflow.Binding;

using static Tensorflow.KerasApi;

namespace Tensorflow

{

public class MemoryMonitor

{

public void WarmUp()

{

var x1 = tf.Variable(10, name: "x");

tf.compat.v1.disable_eager_execution();

var input = np.array(4);

var nd = tf.reshape(input, new int[] { 1, 1});

var z = nd[0, 0];

while (true)

{

var x = tf.placeholder(tf.float64, shape: (1024, 1024));

var log = tf.log(x);

var sess = tf.Session();

var ones = np.ones((1024, 1024), dtype: np.float64);

var o = sess.run(log, new FeedItem(x, ones));

// Thread.Sleep(1);

}

Shape shape = (1, 32, 32, 3);

np.arange(shape.size).astype(np.float32).reshape(shape.dims);

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

tf.Context.ensure_initialized();

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.Collect();

GC.WaitForPendingFinalizers();

}

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

{

GC.Collect();

GC.WaitForPendingFinalizers();

var initialTotalMemory = Process.GetCurrentProcess().PrivateMemorySize64;

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

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

{

var initialMemory = Process.GetCurrentProcess().PrivateMemorySize64;

for (int j = 0; j < iterate; j++)

process(i, j);

keras.backend.clear_session();

GC.Collect();

GC.WaitForPendingFinalizers();

var finalMemory = Process.GetCurrentProcess().PrivateMemorySize64;

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

}

var finalTotalMemory = Process.GetCurrentProcess().PrivateMemorySize64;

print($"Memory usage difference: {Format(finalTotalMemory - initialTotalMemory)} / {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";

}

TensorFlow.NET/tools/TensorFlowNET.Console/MemoryMonitor.cs at master · ShuchenQ/TensorFlow.NET · GitHub

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