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using Microsoft.VisualStudio.TestTools.UnitTesting;
using NumSharp;
using System;
using System.Collections.Generic;
using System.Reflection;
using System.Runtime.CompilerServices;
using System.Text;
using FluentAssertions;
using Google.Protobuf;
using NumSharp.Backends;
using Tensorflow;
using Tensorflow.Util;
using static Tensorflow.Binding;
namespace TensorFlowNET.UnitTest
{
[TestClass]
public class SessionTest : CApiTest
{
/// <summary>
/// tensorflow\c\c_api_test.cc
/// `TEST(CAPI, Session)`
/// </summary>
[TestMethod, Ignore]
public void Session()
{
lock (Locks.ProcessWide)
{
var s = new Status();
var graph = new Graph().as_default();
// Make a placeholder operation.
var feed = c_test_util.Placeholder(graph, s);
// Make a constant operation with the scalar "2".
var two = c_test_util.ScalarConst(2, graph, s);
// Add operation.
var add = c_test_util.Add(feed, two, graph, s);
var csession = new CSession(graph, s);
ASSERT_EQ(TF_Code.TF_OK, s.Code);
// Run the graph.
var inputs = new Dictionary<Operation, Tensor>();
inputs.Add(feed, new Tensor(3));
csession.SetInputs(inputs);
var outputs = new TF_Output[] {new TF_Output(add, 0)};
csession.SetOutputs(outputs);
csession.Run(s);
Tensor outTensor = csession.output_tensor(0);
EXPECT_EQ(TF_DataType.TF_INT32, outTensor.dtype);
EXPECT_EQ(0, outTensor.NDims);
ASSERT_EQ((ulong) sizeof(uint), outTensor.bytesize);
var output_contents = outTensor.ToArray<int>();
EXPECT_EQ(3 + 2, output_contents[0]);
// Add another operation to the graph.
var neg = c_test_util.Neg(add, graph, s);
ASSERT_EQ(TF_Code.TF_OK, s.Code);
// Run up to the new operation.
inputs = new Dictionary<Operation, Tensor>();
inputs.Add(feed, new Tensor(7));
csession.SetInputs(inputs);
outputs = new TF_Output[] {new TF_Output(neg, 0)};
csession.SetOutputs(outputs);
csession.Run(s);
ASSERT_EQ(TF_Code.TF_OK, s.Code);
outTensor = csession.output_tensor(0);
ASSERT_TRUE(outTensor != IntPtr.Zero);
EXPECT_EQ(TF_DataType.TF_INT32, outTensor.dtype);
EXPECT_EQ(0, outTensor.NDims); // scalar
ASSERT_EQ((ulong) sizeof(uint), outTensor.bytesize);
output_contents = outTensor.ToArray<int>();
EXPECT_EQ(-(7 + 2), output_contents[0]);
// Clean up
csession.CloseAndDelete(s);
ASSERT_EQ(TF_Code.TF_OK, s.Code);
}
}
[TestMethod]
public void EvalTensor()
{
lock (this)
{
var a = constant_op.constant(np.array(3.0).reshape(1, 1));
var b = constant_op.constant(np.array(2.0).reshape(1, 1));
var c = math_ops.matmul(a, b, name: "matmul");
using (var sess = tf.Session())
{
var result = c.eval(sess);
Assert.AreEqual(6, result.GetAtIndex<double>(0));
}
}
}
[TestMethod]
public void Eval_SmallString_Scalar()
{
lock (this)
{
var a = constant_op.constant("123 heythere 123 ", TF_DataType.TF_STRING);
var c = tf.strings.substr(a, 4, 8);
using (var sess = tf.Session())
{
var result = (string) c.eval(sess);
Console.WriteLine(result);
result.Should().Be("heythere");
}
}
}
[TestMethod]
public void Eval_LargeString_Scalar()
{
lock (this)
{
const int size = 30_000;
var a = constant_op.constant(new string('a', size), TF_DataType.TF_STRING);
var c = tf.strings.substr(a, 0, size - 5000);
using (var sess = tf.Session())
{
var result = (string) c.eval(sess);
Console.WriteLine((string) result);
result.Should().HaveLength(size - 5000).And.ContainAll("a");
}
}
}
[TestMethod]
public void Autocast_Case1()
{
var sess = tf.Session().as_default();
var input = tf.placeholder(tf.float64, shape: new TensorShape(6));
var op = tf.reshape(input, new int[] {2, 3});
sess.run(tf.global_variables_initializer());
var ret = sess.run(op, feed_dict: (input, np.array(1, 2, 3, 4, 5, 6)));
ret.Should().BeOfType<double>().And.BeShaped(2, 3).And.BeOfValues(1, 2, 3, 4, 5, 6);
print(ret.dtype);
print(ret);
}
[TestMethod]
public void Autocast_Case2()
{
var sess = tf.Session().as_default();
var input = tf.placeholder(tf.float64, shape: new TensorShape(6));
var op = tf.reshape(input, new int[] {2, 3});
sess.run(tf.global_variables_initializer());
var ret = sess.run(op, feed_dict: (input, np.array(1, 2, 3, 4, 5, 6).astype(NPTypeCode.Single) + 0.1f));
ret.Should().BeOfType<double>().And.BeShaped(2, 3).And.BeOfValuesApproximately(0.001d, 1.1, 2.1, 3.1, 4.1, 5.1, 6.1);
print(ret.dtype);
print(ret);
}
[TestMethod]
public void Autocast_Case3()
{
var sess = tf.Session().as_default();
var input = tf.placeholder(tf.int16, shape: new TensorShape(6));
var op = tf.reshape(input, new int[] {2, 3});
sess.run(tf.global_variables_initializer());
var ret = sess.run(op, feed_dict: (input, np.array(1, 2, 3, 4, 5, 6).astype(NPTypeCode.Single) + 0.1f));
ret.Should().BeOfType<short>().And.BeShaped(2, 3).And.BeOfValues(1, 2, 3, 4, 5, 6);
print(ret.dtype);
print(ret);
}
[TestMethod]
public void Autocast_Case4()
{
var sess = tf.Session().as_default();
var input = tf.placeholder(tf.@byte, shape: new TensorShape(6));
var op = tf.reshape(input, new int[] {2, 3});
sess.run(tf.global_variables_initializer());
var ret = sess.run(op, feed_dict: (input, np.array(1, 2, 3, 4, 5, 6).astype(NPTypeCode.Single) + 0.1f));
ret.Should().BeOfType<byte>().And.BeShaped(2, 3).And.BeOfValues(1, 2, 3, 4, 5, 6);
print(ret.dtype);
print(ret);
}
}
}