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using System;

using Tensorflow.Keras;

using static Tensorflow.Binding;

namespace Tensorflow

{

class Program

{

static void Main(string[] args)

{

var diag = new Diagnostician();

// diag.Diagnose(@"D:\memory.txt");

var rnn = new SimpleRnnTest();

rnn.Run();

// this class is used explor new features.

var exploring = new Exploring();

// exploring.Run();

// boot .net core 10.5M.

var mm = new MemoryMonitor();

// warm up tensorflow.net 37.3M.

mm.WarmUp();

BasicTest(mm);

KerasTest(mm);

FuncGraph(mm);

// 65M

Console.WriteLine("Finished.");

Console.ReadLine();

}

static void BasicTest(MemoryMonitor mm)

{

int batchSize = 1000;

var basic = new MemoryBasicTest();

// 1 million placeholder

/*tf.compat.v1.disable_eager_execution();

mm.Execute(10, 100 * batchSize, basic.Placeholder);

tf.enable_eager_execution();*/

// 1 million tensor

mm.Execute(10, 100 * batchSize, basic.Constant);

// explaination of constant

mm.Execute(10, 100 * batchSize, basic.Constant2x3);

mm.Execute(10, batchSize, basic.ConstantString);

// 100K float variable.

mm.Execute(10, batchSize, basic.Variable);

mm.Execute(10, batchSize, basic.VariableRead);

mm.Execute(10, batchSize, basic.VariableAssign);

// 1 million math.

mm.Execute(10, 100 * batchSize, basic.MathAdd);

// Conv2d in constant tensor

mm.Execute(10, batchSize, basic.Conv2DWithTensor);

// Conv2d in variable

mm.Execute(10, batchSize, basic.Conv2DWithVariable);

// 100K gradient 44M.

mm.Execute(10, 10 * batchSize, basic.Gradient);

// memory leak when increasing the epoch

mm.Execute(10, 10, basic.Dataset);

}

static void KerasTest(MemoryMonitor mm)

{

var keras = new MemoryKerasTest();

// +1M (10,50)

mm.Execute(10, 1, keras.Conv2DLayer);

mm.Execute(10, 50, keras.InputLayer);

mm.Execute(10, 10, keras.Prediction);

}

static void FuncGraph(MemoryMonitor mm)

{

var func = new MemoryFuncGraphTest();

mm.Execute(10, 100, func.ConcreteFunction);

}

}

}