using System;
using static Tensorflow.Binding;

namespace Tensorflow
{
 class Program
 {
 static void Main(string[] args)
 {
 var diag = new Diagnostician();
 // diag.Diagnose(@"D:\memory.txt");

 // 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);
 }
 }
}