public void SetArrayTensor()
{
using (var tensor = new TFTensor(new [] { 123, 456 }))
{
tensor.SetValue(new [] { 234, 567 });
Assert.Equal((uint)sizeof(int) * 2, tensor.TensorByteSize.ToUInt32());
Assert.Equal(new [] { 234, 567 }, tensor.GetValue());
}
}
public void SetByteTensor()
{
using (var tensor = new TFTensor((byte)123))
{
tensor.SetValue((byte)234);
Assert.Equal((uint)sizeof(byte), tensor.TensorByteSize.ToUInt32());
Assert.Equal((byte)234, tensor.GetValue());
}
}
public void SetBoolTensor()
{
using (var tensor = new TFTensor(true))
{
tensor.SetValue(false);
Assert.Equal((uint)sizeof(bool), tensor.TensorByteSize.ToUInt32());
Assert.Equal(false, tensor.GetValue());
}
}
public void CreateTenso()
{
// Some input matrices
var inp = new float[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 96, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 35, 55, 82, 115, 135, 140, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 63, 77, 91, 106, 122, 136, 138, 127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 112, 103, 95, 107, 121, 131, 131, 111, 90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 111, 112, 101, 91, 102, 121, 126, 126, 108, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 101, 107, 96, 88, 90, 108, 125, 130, 138, 173, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 102, 98, 88, 91, 96, 101, 120, 138, 146, 185, 243, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 88, 96, 93, 91, 92, 98, 111, 135, 153, 172, 221, 242, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 90, 91, 93, 100, 107, 125, 153, 185, 210, 226, 237, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 126, 131, 140, 163, 188, 206, 216, 223, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 180, 188, 196, 202, 208, 216, 246, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 203, 203, 193, 200, 210, 233, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 227, 232, 222, 216, 231, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 233, 247, 242, 236, 246, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 151, 155, 0, 183, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
Debug.WriteLine("TEST");
for (int i = 0; i < inp.Length; i++)
{
if (inp[i] == 0)
{
inp[i] = 255;
}
// inp[i] = inp[i] / 255;
//Debug.WriteLine(inp[i]);
}
// Debug.WriteLine("len : " + inp.Length);
var tensor = TFTensor.FromBuffer(new TFShape(1, 30, 30, 1), inp, 0, inp.Length);
// Debug.WriteLine("input: " + tensor);
using (var graph = new TFGraph())
{
// Load the model
// graph.Import(File.ReadAllBytes(@"C:\Users\Public\TestFolder\my_model.pb"));
string model_file = Path.Combine(Environment.CurrentDirectory, @"model\gesture_model.pb");
Debug.WriteLine("file path " + model_file);
graph.Import(File.ReadAllBytes(model_file));
using (var session = new TFSession(graph))
{
var runner = session.GetRunner();
//Debug.WriteLine(graph);
runner.AddInput(graph[INPUT_NAME][0], tensor);
runner.Fetch(graph[OUTPUT_NAME][0]);
Debug.WriteLine(System.DateTime.Now);
var output = runner.Run();
TFTensor result = output[0];
var re = result.GetValue() as float[, ];
foreach (float f in re)
{
Debug.WriteLine(f);
}
Debug.WriteLine("o: " + re[0, 1]);
Debug.WriteLine(result);
Debug.WriteLine(System.DateTime.Now);
}
}
}
public double[] DetectObjects(string file)
{
sw.Restart();
TFTensor tensor = CreateTensorFromImageFile(file);
Rectangle[] rect;
Rectangle size = new Rectangle(10, 10, 10, 10);
return(DetectObjectsFromTensor(tensor, size, out rect));
}
public double[] DetectObjects(Image img)
{
sw.Restart();
TFTensor tensor = CreateTensorFromBitmap(img);
Rectangle[] rect;
Rectangle size = new Rectangle(10, 10, 10, 10);
return(DetectObjectsFromTensor(tensor, size, out rect));
}
public IEnumerable <TFTensor> GetFeaturesTensors()
{
byte[] sourceAsciiBytes = Encoding.ASCII.GetBytes(Source);
byte[] targetAsciiBytes = Encoding.ASCII.GetBytes(Target);
TFTensor sourceTensor = new TFTensor(sourceAsciiBytes);
TFTensor targetTensor = new TFTensor(sourceAsciiBytes);
return(new[] { sourceTensor, targetTensor });
}
public void SetArrayTensorWithWrongDimensions()
{
using (var tensor = new TFTensor(new [, ] {
{ 123 }
}))
{
var exception = Assert.Throws <ArgumentException> (() => tensor.SetValue(new [] { 234 }));
Assert.Equal("This tensor has 2 dimensions, the given array has 1", exception.Message);
}
}
public void LoadTFModel(Texture2D tex)
{
var shape = new TFShape(1, inputWidth, inputHeight, 3);
var input = graph[inputName][0];
TFTensor inputTensor = null;
int angle = 90;
Flip flip = Flip.NONE;
if (input.OutputType == TFDataType.Float)
{
float[] imgData = Utils.DecodeTexture(tex, inputWidth, inputHeight,
inputMean, inputStd, angle, flip);
inputTensor = TFTensor.FromBuffer(shape, imgData, 0, imgData.Length);
}
else if (input.OutputType == TFDataType.UInt8)
{
byte[] imgData = Utils.DecodeTexture(tex, inputWidth, inputHeight, angle, flip);
inputTensor = TFTensor.FromBuffer(shape, imgData, 0, imgData.Length);
}
else
{
throw new Exception($"Input date type {input.OutputType} is not supported.");
}
var runner = session.GetRunner();
runner.AddInput(input, inputTensor).Fetch(graph[outputName][0]);
var output = runner.Run()[0];
var outputs = output.GetValue() as float[, ];
inputTensor.Dispose();
output.Dispose();
var list = new List <KeyValuePair <string, float> >();
for (int i = 0; i < labels.Length; i++)
{
var confidence = outputs[0, i];
if (confidence < 0.05f)
{
continue;
}
list.Add(new KeyValuePair <string, float>(labels[i], confidence));
}
var results = list.OrderByDescending(i => i.Value).Take(3).ToList();
foreach (KeyValuePair <string, float> value in results)
{
Debug.Log("my model: " + value.Key);
}
}
private TFTensor GenerateTensor(byte[] image)
{
#if UNITY_ANDROID
TFShape tshape = new TFShape(1, 128, 128, 3);
return(TFTensor.FromBuffer(tshape, image, 0, image.Length));
#endif
#if UNITY_EDITOR_WIN
// TODO: This dosen't work
return(ImageUtil.CreateTensorFromImageFile(image));
#endif
}
public unsafe static Tensor <float> ToNCHW(this TFTensor tensor)
{
var span = new Span <float>(tensor.Data.ToPointer(), (int)tensor.TensorByteSize / 4);
Tensor <float> dense = new DenseTensor <float>(span.ToArray(), tensor.Shape.Select(x => (int)x).ToArray());
if (dense.Dimensions.Length == 4)
{
dense = dense.Transpose(new[] { 0, 3, 1, 2 });
}
return(dense);
}
public void SetArrayTensorWithWrongLength()
{
using (var tensor = new TFTensor(new [, ] {
{ 123 }
}))
{
var exception = Assert.Throws <ArgumentException> (() => tensor.SetValue(new [, ] {
{ 234, 567 }
}));
Assert.Equal("This tensor has shape [1,1], the given array has shape [1,2]", exception.Message);
}
}
public float[,] GetValue(string inputNode, string outputNode, byte[] inputData)
{
if (!_bModelLoaded)
return null;
var runner = _session.GetRunner();
TFTensor tensor = TFUtils.CreateTensor(inputData, TFDataType.Float);
runner.AddInput(_tfGraph[inputNode][0], tensor);
runner.Fetch(_tfGraph[outputNode][0]);
TFTensor[] output = runner.Run();
float[,] value = (float[,])output[0].GetValue();
return value;
}
private static (TFTensor, TFTensor) CreateTestTensors()
{
var x = new int[] {
1061, 19693, 974, 980, 981, 811, 11474, 589, 39, 1058, 39, 57,
1057, 4934, 46, 1, 1, 1, 1, 1, 1, 1, 1
};
var l = new int[] { 15 };
var tensor = TFTensor.FromBuffer(new TFShape(1, x.Length), x, 0, x.Length);
var lengths = TFTensor.FromBuffer(new TFShape(1), l, 0, l.Length);
return(tensor, lengths);
}
private IEnumerator GetCamPics()
{
if (search)
{
while (tempsearch)
{
tensor3 = TransformInput(camFeed.GetImage(), INPUT_SIZE, INPUT_SIZE);
search = false;
yield return(new WaitForSeconds(2.0f));
}
}
}
static void Main(string[] args)
{
var g = new TFGraph();
var hello = g.Const(TFTensor.CreateString(Encoding.UTF8.GetBytes("Hello,world!")));
var sess = new TFSession(g);
var result = sess.GetRunner().Run(hello);
Console.WriteLine(result.GetValue());
}
public void Export(string newModelDir)
{
if (newModelDir.Last() != '/' && newModelDir.Last() != '\\')
{
newModelDir += "/";
}
TFOutput NodeSaver, NodeSaverPath;
//if (!ForTraining)
{
NodeSaver = Graph["save_1/control_dependency"][0];
NodeSaverPath = Graph["save_1/Const"][0];
}
//else
//{
// NodeSaver = Graph["save_2/control_dependency"][0];
// NodeSaverPath = Graph["save_2/Const"][0];
//}
Directory.CreateDirectory(newModelDir);
if (Directory.Exists(newModelDir + "variables"))
{
Directory.Delete(newModelDir + "variables", true);
}
Directory.CreateDirectory(newModelDir + "variables");
foreach (var fileName in Directory.EnumerateFiles(ModelDir))
{
File.Copy(fileName, newModelDir + Helper.PathToNameWithExtension(fileName), true);
}
TFTensor TensorPath = TFTensor.CreateString(Encoding.ASCII.GetBytes(newModelDir + "variables/variables"));
var Runner = Session.GetRunner().AddInput(NodeSaverPath, TensorPath);
Runner.Run(NodeSaver);
if (Directory.EnumerateDirectories(newModelDir + "variables", "variables_temp*").Count() > 0)
{
string TempName = Directory.EnumerateDirectories(newModelDir + "variables", "variables_temp*").First();
foreach (var oldPath in Directory.EnumerateFiles(TempName))
{
string OldName = Helper.PathToNameWithExtension(oldPath);
string NewName = "variables" + OldName.Substring(OldName.IndexOf("."));
string NewPath = newModelDir + "variables/" + NewName;
File.Move(oldPath, NewPath);
}
Directory.Delete(TempName, true);
}
TensorPath.Dispose();
}
public void TestSession()
{
var status = new TFStatus();
using (var graph = new TFGraph()) {
var feed = Placeholder(graph, status);
var two = ScalarConst(2, graph, status);
var add = Add(feed, two, graph, status);
Assert(status);
// Create a session for this graph
using (var session = new TFSession(graph, status)) {
Assert(status);
// Run the graph
var inputs = new TFOutput [] {
new TFOutput(feed, 0)
};
var input_values = new TFTensor [] {
3
};
var add_output = new TFOutput(add, 0);
var outputs = new TFOutput [] {
add_output
};
var results = session.Run(runOptions: null,
inputs: inputs,
inputValues: input_values,
outputs: outputs,
targetOpers: null,
runMetadata: null,
status: status);
Assert(status);
var res = results [0];
Assert(res.TensorType == TFDataType.Int32);
Assert(res.NumDims == 0); // Scalar
Assert(res.TensorByteSize == (UIntPtr)4);
Assert(Marshal.ReadInt32(res.Data) == 3 + 2);
// Use runner API
var runner = session.GetRunner();
runner.AddInput(new TFOutput(feed, 0), 3);
runner.Fetch(add_output);
results = runner.Run(status: status);
res = results [0];
Assert(res.TensorType == TFDataType.Int32);
Assert(res.NumDims == 0); // Scalar
Assert(res.TensorByteSize == (UIntPtr)4);
Assert(Marshal.ReadInt32(res.Data) == 3 + 2);
}
}
}
TFOperation ScalarConst(TFTensor v, TFGraph graph, TFStatus status, string name = null)
{
var desc = new TFOperationDesc(graph, "Const", name == null ? "scalar" : name);
desc.SetAttr("value", v, status);
if (status.StatusCode != TFCode.Ok)
{
return(null);
}
desc.SetAttrType("dtype", TFDataType.Int32);
return(desc.FinishOperation());
}
private void ReadFrame(object sender, EventArgs arg)//捕获摄像头画面的事件
{
DateTime TimeStart = DateTime.Now;
// VideoCapture捕获一帧图像
try
{
capture.Retrieve(mat, 0);
}
catch { }
// 创建Tensor作为网络输入
TFTensor tensor = Mat2Tensor(mat);
// 前向推理
TFSession.Runner runner = session.GetRunner();
runner.AddInput(graph["image_tensor"][0], tensor);
runner.Fetch(graph["num_detections"][0]);
runner.Fetch(graph["detection_scores"][0]);
runner.Fetch(graph["detection_boxes"][0]);
runner.Fetch(graph["detection_classes"][0]);
TFTensor[] outputs = runner.Run();
// 解析结果
float num = ((float[])outputs[0].GetValue(jagged: true))[0];
float[] scores = ((float[][])outputs[1].GetValue(jagged: true))[0];
float[][] boxes = ((float[][][])outputs[2].GetValue(jagged: true))[0];
float[] classes = ((float[][])outputs[3].GetValue(jagged: true))[0];
// 显示检测框和类别
for (int i = 0; i < (int)num; i++)
{
if (scores[i] > 0.8)
{
int left = (int)(boxes[i][1] * frameWidth);
int top = (int)(boxes[i][0] * frameHeight);
int right = (int)(boxes[i][3] * frameWidth);
int bottom = (int)(boxes[i][2] * frameHeight);
CvInvoke.PutText(mat, labels[(int)classes[i]] + ": " + scores[i].ToString("0.00"), new Point(left, top), FontFace.HersheyDuplex, (right - left) / 200, colorGreen);
CvInvoke.Rectangle(mat, new Rectangle(left, top, right - left, bottom - top), colorRed, 2);
}
}
// 在imageBox上显示经过缩放的图像
Mat dst = new Mat();
CvInvoke.Resize(mat, dst, new Size(imageBox.Width, imageBox.Height));
imageBox.Image = cameraFlag ? dst : null;
TimeSpan TimeCount = DateTime.Now - TimeStart;
textFPS.Text = (1000 / TimeCount.TotalMilliseconds).ToString("0.00");
}
private TFTensor[] ProcessImage(TFTensor input)
{
var runner = Session.GetRunner();
runner = runner.AddInput(Graph[InputParameter][0], input);
foreach (string output in OutputParameters)
{
runner.Fetch(Graph[output][0]);
}
return(runner.Run());
}
private TFTensor Predict(TFTensor input)
{
var runner = _session.GetRunner();
var graph = _session.Graph;
runner.AddInput(graph["Image"][0], input);
runner.Fetch(graph["Predict"][0]);
var output = runner.Run();
return(output[0]);
}
public IList Classify(Texture2D texture, int numResults = 5, float threshold = 0.1f,
int angle = 0, Flip flip = Flip.NONE)
{
var shape = new TFShape(1, inputWidth, inputHeight, 3);
var input = graph[inputName][0];
TFTensor inputTensor = null;
if (input.OutputType == TFDataType.Float)
{
float[] imgData = Utils.DecodeTexture(texture, inputWidth, inputHeight,
inputMean, inputStd, angle, flip);
inputTensor = TFTensor.FromBuffer(shape, imgData, 0, imgData.Length);
}
else if (input.OutputType == TFDataType.UInt8)
{
byte[] imgData = Utils.DecodeTexture(texture, inputWidth, inputHeight, angle, flip);
inputTensor = TFTensor.FromBuffer(shape, imgData, 0, imgData.Length);
}
else
{
throw new Exception($"Input date type {input.OutputType} is not supported.");
}
var runner = session.GetRunner();
runner.AddInput(input, inputTensor).Fetch(graph[outputName][0]);
var output = runner.Run()[0];
var outputs = output.GetValue() as float[, ];
inputTensor.Dispose();
output.Dispose();
var list = new List <KeyValuePair <string, float> >();
for (int i = 0; i < labels.Length; i++)
{
var confidence = outputs[0, i];
if (confidence < threshold)
{
continue;
}
list.Add(new KeyValuePair <string, float>(labels[i], confidence));
}
var results = list.OrderByDescending(i => i.Value).Take(numResults).ToList();
//Utils.Log(results);
return(results);
}
public void ConstructDoubleTensor()
{
double value = 123.456;
using (var tensor = new TFTensor(value))
{
Assert.Equal(TFDataType.Double, tensor.TensorType);
Assert.Equal(0, tensor.NumDims);
Assert.Equal(new long [0], tensor.Shape);
Assert.Equal((uint)sizeof(double), tensor.TensorByteSize.ToUInt32());
Assert.Equal(value, tensor.GetValue());
}
}
public void ConstructUnsignedShortTensor()
{
ushort value = 123;
using (var tensor = new TFTensor(value))
{
Assert.Equal(TFDataType.UInt16, tensor.TensorType);
Assert.Equal(0, tensor.NumDims);
Assert.Equal(new long [0], tensor.Shape);
Assert.Equal((uint)sizeof(ushort), tensor.TensorByteSize.ToUInt32());
Assert.Equal(value, tensor.GetValue());
}
}
public void ConstructLongTensor()
{
long value = 123L;
using (var tensor = new TFTensor(value))
{
Assert.Equal(TFDataType.Int64, tensor.TensorType);
Assert.Equal(0, tensor.NumDims);
Assert.Equal(new long [0], tensor.Shape);
Assert.Equal((uint)sizeof(long), tensor.TensorByteSize.ToUInt32());
Assert.Equal(value, tensor.GetValue());
}
}
public void SetMultiDimArrayTensorWithJagged()
{
using (var tensor = new TFTensor(new [, ] {
{ 123, 456 }
}))
{
tensor.SetValue(new [] { new [] { 234, 567 } });
Assert.Equal((uint)sizeof(int) * 2, tensor.TensorByteSize.ToUInt32());
Assert.Equal(new [, ] {
{ 234, 567 }
}, tensor.GetValue());
}
}
public void ConstructFloatTensor()
{
float value = 123.456f;
using (var tensor = new TFTensor(value))
{
Assert.Equal(TFDataType.Float, tensor.TensorType);
Assert.Equal(0, tensor.NumDims);
Assert.Equal(new long [0], tensor.Shape);
Assert.Equal((uint)sizeof(float), tensor.TensorByteSize.ToUInt32());
Assert.Equal(value, tensor.GetValue());
}
}
public void ConstructComplexTensor()
{
Complex value = new Complex(1, 2);
using (var tensor = new TFTensor(value))
{
Assert.Equal(TFDataType.Complex128, tensor.TensorType);
Assert.Equal(0, tensor.NumDims);
Assert.Equal(new long [0], tensor.Shape);
Assert.Equal(16u, tensor.TensorByteSize.ToUInt32());
Assert.Equal(value, tensor.GetValue());
}
}
public void GetArrayValueInPlace(Array array)
{
using (var tensor = new TFTensor(array))
{
var type = array.GetType().GetElementType();
var value = Array.CreateInstance(type, tensor.Shape);
Assert.NotEqual(array, value);
tensor.GetValue(value);
Assert.Equal(array, value);
}
}
