static public ITensorData CreateFromTexture(Texture tex, TensorShape shape)
{
Assert.AreEqual(tex.width, shape.width);
Assert.AreEqual(tex.height, shape.height);
Assert.IsTrue(shape.channels < 4);
// @TODO: implement proper GPU storage
var data = new ArrayTensorData(shape);
if (tex is Texture2D)
{
Texture2D tex2d = tex as Texture2D;
var pixels = tex2d.GetPixels();
for (int i = 0; i < data.array.Length && i < pixels.Length * shape.channels; ++i)
{
data.array[i] = pixels[i / shape.channels][i % shape.channels];
}
}
else
{
throw new NotImplementedException();
}
return(data);
}
public Tensor GetInputAsTensor(int idx = 0, int batchCount = -1, int fromBatch = 0)
{
if (rawTestSet != null)
{
throw new Exception("GetInputAsTensor is not supported for RAW test suites");
}
var shape = GetInputShape(idx);
var array = GetInputData(idx);
var maxBatchCount = array.Length / (shape[1] * shape[2] * shape[3]);
fromBatch = Math.Min(fromBatch, maxBatchCount - 1);
if (batchCount < 0)
{
batchCount = maxBatchCount - fromBatch;
}
// pad data with 0s, if test-set doesn't have enough batches:
// 1) new ArrayTensorData() will initialize to 0
// 2) Upload will copy as much data as test-set has into ArrayTensorData
var tensorShape = new TensorShape(batchCount, shape[1], shape[2], shape[3]);
var data = new ArrayTensorData(tensorShape.length);
data.Upload(array, fromBatch * tensorShape.flatWidth, Math.Min(batchCount, maxBatchCount - fromBatch) * tensorShape.flatWidth);
var res = new Tensor(tensorShape, data);
res.name = GetInputName(idx);
return(res);
}
/// <summary>
/// Create a Tensor with specified `shape`, an array of data `srcData` and an optional debug `name`.
/// `srcData` must be of size `shape.length`.
/// </summary>
public Tensor(TensorShape shape, float[][] srcData, string name = "")
{
this.name = name;
this.shape = shape;
var arrayTensorData = new ArrayTensorData(shape);
for (var i = 0; i < Math.Min(flatHeight, srcData.Length); ++i)
{
var src = srcData[i];
var dstOffset = i * flatWidth;
Array.Copy(src, 0, arrayTensorData.array, dstOffset, Math.Min(flatWidth, src.Length));
}
m_TensorOnDevice = arrayTensorData;
m_TensorAllocator = null;
m_Cache = null;
m_CacheIsDirty = false;
}
/// <summary>
/// Create a Tensor of shape `s`, an array of data `srcData` and an optional name `n`
/// `srcData` should be of size `s.length`.
/// </summary>
public Tensor(TensorShape s, float[][] srcData, string n = "")
{
//;;UnityEngine.Debug.Log("Tensor::Tensor " + n + " " + s + " [][]-> " + srcData);
name = n;
shape = s;
var arrayTensorData = new ArrayTensorData(shape);
for (var i = 0; i < Math.Min(flatHeight, srcData.Length); ++i)
{
var src = srcData[i];
var dstOffset = i * flatWidth;
Array.Copy(src, 0, arrayTensorData.array, dstOffset, Math.Min(flatWidth, src.Length));
}
m_TensorOnDevice = arrayTensorData;
m_TensorAllocator = null;
m_Cache = null;
m_CacheIsDirty = false;
}
/// <summary>
/// Get input as `Tensor`
/// </summary>
/// <param name="idx">input index</param>
/// <param name="batchCount">max batch count</param>
/// <param name="fromBatch">start from batch</param>
/// <returns>`Tensor`</returns>
/// <exception cref="Exception">thrown if called on raw test set (only JSON test set is supported)</exception>
public Tensor GetInputAsTensor(int idx = 0, int batchCount = -1, int fromBatch = 0)
{
if (rawTestSet != null)
{
throw new Exception("GetInputAsTensor is not supported for RAW test suites");
}
TensorShape shape = GetInputShape(idx);
Assert.IsTrue(shape.sequenceLength == 1 && shape.numberOfDirections == 1);
var array = GetInputData(idx);
var maxBatchCount = array.Length / shape.flatWidth;
fromBatch = Math.Min(fromBatch, maxBatchCount - 1);
if (batchCount < 0)
{
batchCount = maxBatchCount - fromBatch;
}
// pad data with 0s, if test-set doesn't have enough batches
var shapeArray = shape.ToArray();
shapeArray[TensorShape.DataBatch] = batchCount;
var tensorShape = new TensorShape(shapeArray);
var managedBufferStartIndex = fromBatch * tensorShape.flatWidth;
var count = Math.Min(batchCount, maxBatchCount - fromBatch) * tensorShape.flatWidth;
float[] dataToUpload = new float[tensorShape.length];
Array.Copy(array, managedBufferStartIndex, dataToUpload, 0, count);
var data = new ArrayTensorData(tensorShape.length);
data.Upload(dataToUpload, tensorShape, 0);
var res = new Tensor(tensorShape, data);
res.name = GetInputName(idx);
res.name = res.name.EndsWith(":0") ? res.name.Remove(res.name.Length - 2) : res.name;
return(res);
}
/// <summary>
/// Uses shared array
/// </summary>
/// <param name="sharedArray">shared array</param>
public BurstTensorData(ArrayTensorData sharedArray) : base(sharedArray)
{
}