public static bool DoesTransposeChangeTensorLayout(TensorShape shape, int[] permutations)
{
var activeDimLayout = new List <int>();
for (int i = 0; i < 8; i++)
{
if (shape[i] != 1)
{
activeDimLayout.Add(i);
}
}
if (permutations.Length == 4)
{
permutations = TensorExtensions.Get8DPermutationsForNHWCPermutationsAndShape(shape, permutations);
}
var transposedLayout = TensorExtensions.Permute(new[] { 0, 1, 2, 3, 4, 5, 6, 7 }, permutations);
var permutedShape = shape.Permute(permutations);
var premutedActiveDimLayout = new List <int>();
for (int i = 0; i < 8; i++)
{
if (permutedShape[i] != 1)
{
premutedActiveDimLayout.Add(transposedLayout[i]);
}
}
return(activeDimLayout.SequenceEqual(premutedActiveDimLayout));
}
private static int ConvertLayerAxisFor8DShapeSupportIfNeeded(int axis, long version, Layer.Type layerType)
{
if (version > Model.LastVersionWithout8DSupport)
{
return(axis);
}
//Prior to version 17, 8D tensors were not supported thus axis was expressed in NCHW format for Gather, Concat and Reduce layers.
if (layerType == Layer.Type.ReduceL2 ||
layerType == Layer.Type.ReduceLogSum ||
layerType == Layer.Type.ReduceLogSumExp ||
layerType == Layer.Type.ReduceMax ||
layerType == Layer.Type.ReduceMean ||
layerType == Layer.Type.ReduceMin ||
layerType == Layer.Type.ReduceProd ||
layerType == Layer.Type.ReduceSum ||
layerType == Layer.Type.ReduceSumSquare ||
layerType == Layer.Type.Gather ||
layerType == Layer.Type.Concat)
{
axis = TensorExtensions.Convert4DTo8DAxis(axis);
}
return(axis);
}
/// <summary>
/// Elementwise broadcast for specified kernel
/// </summary>
/// <param name="kernelName">kernel name</param>
/// <param name="tensors">input tensors</param>
/// <returns>output `Tensor`</returns>
/// <exception cref="NotImplementedException">thrown if input `Tensor` is not compatible with 4D shape</exception>
protected virtual Tensor ElementwiseWithBroadcast(string kernelName, Tensor[] tensors)
{
var Oshape = TensorExtensions.MaxShape(tensors);
var O = NewTensor(Oshape, AllocScope.LayerOutput, "O");
Assert.IsTrue(tensors.Length > 0);
var X = tensors[0];
Material material = new Material(PixelShaderSingleton.Instance.FindShader(kernelName));
for (int t = 1; t < tensors.Length; ++t)
{
var B = tensors[t];
Assert.IsTrue(B.shape.Is4D());
SetTensor(material, "X", X);
SetTensor(material, "B", B);
var pinO = Pin(O);
material.SetVector("OdeclShape", new Vector4(O.batch, O.height, O.width, O.channels));
Graphics.Blit(null, pinO.bufferAsTexture, material);
X = O;
}
return(X);
}
/// <inheritdoc/>
public override Tensor StridedSlice(Tensor X, int[] starts, int[] ends, int[] strides)
{
if (X.shape.Is4D())
{
return(base.StridedSlice(X, starts, ends, strides));
}
var Oshape = X.shape.ApplyStridedSlice(starts, ends, strides);
Vector4 starts4d = new Vector4();
starts4d[0] = Math.Min(TensorExtensions.WrapIndex(starts[TensorShape.DataBatch], X.batch), X.batch - 1);
starts4d[1] = Math.Min(TensorExtensions.WrapIndex(starts[TensorShape.H], X.height), X.height - 1);
starts4d[2] = Math.Min(TensorExtensions.WrapIndex(starts[TensorShape.W], X.width), X.width - 1);
starts4d[3] = Math.Min(TensorExtensions.WrapIndex(starts[TensorShape.C], X.channels), X.channels - 1);
Vector4 strides4d = new Vector4();
strides4d[0] = strides[TensorShape.DataBatch];
strides4d[1] = strides[TensorShape.H];
strides4d[2] = strides[TensorShape.W];
strides4d[3] = strides[TensorShape.C];
Material material = new Material(PixelShaderSingleton.Instance.FindShader("Barracuda/StridedSlice"));
SetTensor(material, "X", X);
material.SetVector("_Stride", new Vector4(strides4d[0], strides4d[1], strides4d[2], strides4d[3]));
material.SetVector("_Starts", new Vector4(starts4d[0], starts4d[1], starts4d[2], starts4d[3]));
return(Dispatch(material, Oshape));
}
static internal unsafe TensorShape ApplyStridedSlice8DUnsafeNoAlloc(this TensorShape shape, int *starts, int *ends,
int *stride)
{
if (!shape.hasNamedDimensions)
{
shape = shape.AsNamed();
}
TensorShape sliced = shape;
for (int i = 0; i < shape.rank; ++i)
{
// NOTE: begin=0, end=0, stride=1 <= full range from the existing axis
// begin=0, end=X, stride=1 <= full range from the existing axis, if X==last element on this axis
// begin=0, end=0, stride=0 <= new axis OR shrink axis to a single 1st element
// begin=N, end=N, stride=0 <= shrink axis to a single Nth element
// take + 1 is si > shape[i]
int ei = TensorExtensions.WrapIndex(ends[i], shape[i]);
int si = TensorExtensions.WrapIndex(starts[i], shape[i]);
// Barracuda convetion (non ONNX), t[0:0] => t[:]
if (si == 0 && ei == 0)
{
ei = shape[i];
}
if (stride[i] > 0)
{
sliced[i] = (int)Math.Round((double)(Math.Min(ei, shape[i]) - Math.Min(si, shape[i] - 1)) / (double)(Mathf.Abs(stride[i])), MidpointRounding.AwayFromZero);
}
else if (stride[i] < 0)
{
bool inclusive = ends[i] < -shape[i]; // edge case when ends is negative and bigger than nchwShape
sliced[i] = (int)Math.Round((double)(Math.Min(si, shape[i] - 1) - Math.Min(ei, shape[i]) + (inclusive ? 1 : 0)) / (double)(Mathf.Abs(stride[i])), MidpointRounding.AwayFromZero);
}
else
{
// Assert.IsTrue(stride[i] != 0); // 0 strides not allowed
// breaks legacy implementations
D.LogWarning("StridedSlice with 0 strides, not supported! Slicing to 1D dimension");
sliced[i] = 1;
}
}
return(sliced);
}
/// <inheritdoc/>
public override Tensor Concat(Tensor[] tensors, int axis)
{
if (tensors.Any(x => !x.shape.Is4D()))
{
return(base.Concat(tensors, axis));
}
var Oshape = TensorExtensions.Concat(tensors, axis);
axis = Oshape.Axis(axis);
var axisNCHW = TensorExtensions.Convert8DAxisTo4D(axis);
Vector4 offsets = Vector4.zero;
Material material = new Material(PixelShaderSingleton.Instance.FindShader("Barracuda/Copy"));
var O = NewTensor(Oshape, AllocScope.LayerOutput, "O");
var Opred = NewTensor(Oshape, AllocScope.LayerOutput, "O");
bool pingPong = true;
bool isFirstPass = true;
foreach (var inputTensor in tensors)
{
Assert.IsTrue(inputTensor.shape.Is4D());
SetTensor(material, "X", inputTensor);
SetTensor(material, "OPred", pingPong ? O : Opred);
material.SetVector("_Pad", offsets);
material.SetInt("_IsFirstPass", isFirstPass ? 1 : 0);
var pinO = pingPong ? Pin(Opred) : Pin(O);
material.SetVector("OdeclShape", new Vector4(O.batch, O.height, O.width, O.channels));
Graphics.Blit(null, pinO.bufferAsTexture, material);
offsets[axisNCHW] += inputTensor.shape[axis];
isFirstPass = false;
pingPong = !pingPong;
}
return(pingPong ? O : Opred);
}
public override Tensor ElementwiseWithBroadcast(string kernelName, Tensor[] tensors)
{
if (m_Compiled.kernel.shader == null)
{
return(base.ElementwiseWithBroadcast(kernelName, tensors));
}
Assert.IsNotNull(m_Compiled.kernel.shader);
var O = NewTensor(m_Compiled.shape);
var fn = m_Compiled.kernel;
Assert.IsTrue(tensors.Length > 0);
var X = tensors[0];
Tensor outputTensor1 = NewTensor(TensorExtensions.MaxShape(tensors));
Tensor outputTensor2 = null;
if (tensors.Length > 2)
{
outputTensor2 = NewTensor(TensorExtensions.MaxShape(tensors));
}
bool isFirstDispatch = true;
for (int t = 1; t < tensors.Length; ++t)
{
var B = tensors[t];
O = (t % 2 == 1) ? outputTensor1 : outputTensor2;
fn.SetTensor(_DeclX, _DataX, X.shape, Pin(X).buffer);
fn.SetTensor(_DeclO, _DataO, O.shape, Pin(O).buffer);
fn.SetTensor(_DeclB, _DataB, B.shape, Pin(B).buffer, Pin(B).offset);
fn.shader.SetFloat("_Alpha", 1.0f / (float)tensors.Length);
fn.shader.SetInt("_IsFirstDispatch", isFirstDispatch ? 1 : 0);
fn.Dispatch();
X = O;
isFirstDispatch = false;
}
return(O);
}
public static TensorShape?[] ListTemporaryTensorShapes(Model model, IDictionary <string, TensorShape> inputShapes,
out IDictionary <string, TensorShape?> shapesByName)
{
Profiler.BeginSample("Barracuda.ListTemporaryTensorShapes");
var shapes = new List <TensorShape?>();
shapesByName = new Dictionary <string, TensorShape?>();
foreach (var entry in inputShapes)
{
shapesByName.Add(entry.Key, entry.Value);
}
TensorShape?Xn;
shapesByName.TryGetValue(GetDefaultInputName(model), out Xn); // default input
TensorShape?O = Xn;
foreach (var l in model.layers)
{
if (l.inputs.Length > 0 && shapesByName.ContainsKey(l.inputs[0]))
{
Xn = shapesByName[l.inputs[0]];
}
else
{
Xn = O; // previous output is used, if-and-only-if layer has no explicit inputs
}
if (Xn == null)
{
shapes.Add(Xn);
shapesByName.Add(l.name, Xn);
continue;
}
TensorShape X = Xn.Value;
if (l.type == Layer.Type.Dense)
{
Assert.IsNotNull(l.datasets);
var W = l.datasets[0].shape;
O = new TensorShape(X.flatHeight, W.flatWidth);
}
else if (
l.type == Layer.Type.Conv2D ||
l.type == Layer.Type.DepthwiseConv2D)
{
var K = l.datasets[0].shape;
Assert.IsNotNull(l.stride);
Assert.IsNotNull(l.pad);
var pad = X.AdjustPadToKernel(K, l.stride, l.pad);
O = X.ApplyKernel(K, l.stride, pad);
}
else if (
l.type == Layer.Type.Conv2DTrans)
{
var K = l.datasets[0].shape;
Assert.IsNotNull(l.stride);
Assert.IsNotNull(l.pad);
// pool size is treated as output_adjustment aka output_padding here
var outputAdjustment = l.pool;
var pad = X.AdjustPadToKernel(K, l.stride, l.pad);
O = X.ApplyKernelInverse(K, l.stride, pad, outputAdjustment);
}
else if (
l.type == Layer.Type.Upsample2D)
{
if (inputShapes.Count > 1)
{
O = null;
}
else
{
// pool size is treated as upsample coefficient here
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 2);
O = new TensorShape(X.batch, X.height * l.pool[1], X.width * l.pool[0], X.channels);
}
}
else if (
l.type == Layer.Type.Resample2D)
{
if (inputShapes.Count > 1)
{
O = null;
}
else
{
// pool is treated as resample size here
var size = l.pool;
Assert.IsNotNull(size);
Assert.AreEqual(size.Length, 2);
O = new TensorShape(X.batch, size[1], size[0], X.channels);
}
}
else if (
l.type == Layer.Type.DepthToSpace)
{
// pool size is treated as blocksize here
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 2);
Assert.AreEqual(X.channels % (l.pool[0] * l.pool[1]), 0);
O = new TensorShape(X.batch, X.height * l.pool[1], X.width * l.pool[0], X.channels / (l.pool[0] * l.pool[1]));
}
else if (
l.type == Layer.Type.SpaceToDepth)
{
// pool size is treated as blocksize here
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 2);
O = new TensorShape(X.batch, X.height / l.pool[1], X.width / l.pool[0], X.channels * (l.pool[0] * l.pool[1]));
}
else if (
l.type == Layer.Type.MaxPool2D ||
l.type == Layer.Type.AvgPool2D)
{
Assert.IsNotNull(l.pool);
Assert.IsNotNull(l.stride);
Assert.IsNotNull(l.pad);
var pad = X.AdjustPadToPool(l.pool, l.stride, l.pad);
O = X.ApplyPool(l.pool, l.stride, pad);
}
else if (
l.type == Layer.Type.GlobalMaxPool2D ||
l.type == Layer.Type.GlobalAvgPool2D)
{
O = new TensorShape(X.batch, 1, 1, X.channels);
}
else if (
l.type == Layer.Type.Border2D ||
l.type == Layer.Type.Pad2DReflect ||
l.type == Layer.Type.Pad2DSymmetric ||
l.type == Layer.Type.Pad2DEdge)
{
Assert.IsNotNull(l.pad);
O = X.ApplyBorder(l.pad);
}
else if (
l.type == Layer.Type.Conv3D ||
l.type == Layer.Type.Conv3DTrans ||
l.type == Layer.Type.Upsample3D ||
l.type == Layer.Type.MaxPool3D ||
l.type == Layer.Type.AvgPool3D ||
l.type == Layer.Type.GlobalMaxPool3D ||
l.type == Layer.Type.GlobalAvgPool3D ||
l.type == Layer.Type.Border3D)
{
throw new NotImplementedException();
}
else if (
l.type == Layer.Type.RandomNormal ||
l.type == Layer.Type.RandomUniform)
{
Assert.IsNotNull(l.pool);
// pool size is treated as shape constant, if not empty
// otherwise shape of the previous tensor is used
if (l.pool.Length > 0)
{
O = new TensorShape(l.pool);
}
else
{
O = X;
}
}
else if (
l.type == Layer.Type.Multinomial)
{
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 1);
O = new TensorShape(X.batch, l.pool[0]);
}
else if (
l.type == Layer.Type.OneHot)
{
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 1);
int features = X.flatWidth;
int depth = l.pool[0];
O = new TensorShape(X.batch, 1, features, depth);
}
else if (
l.type == Layer.Type.Add ||
l.type == Layer.Type.Sub ||
l.type == Layer.Type.Mul ||
l.type == Layer.Type.Div ||
l.type == Layer.Type.Pow ||
l.type == Layer.Type.Min ||
l.type == Layer.Type.Max ||
l.type == Layer.Type.Mean ||
l.type == Layer.Type.Greater ||
l.type == Layer.Type.GreaterEqual ||
l.type == Layer.Type.Less ||
l.type == Layer.Type.LessEqual ||
l.type == Layer.Type.Equal ||
l.type == Layer.Type.LogicalOr ||
l.type == Layer.Type.LogicalAnd ||
l.type == Layer.Type.LogicalXor)
{
// gather shapes by names
var list = new List <TensorShape>(l.inputs.Length);
bool allShapesKnown = true;
foreach (var i in l.inputs)
{
if (!shapesByName.ContainsKey(i))
{
continue;
}
TensorShape?shape = shapesByName[i];
if (shape == null)
{
allShapesKnown = false;
continue;
}
list.Add(shapesByName[i].Value);
}
O = allShapesKnown ? TensorExtensions.Max(list.ToArray()) : default(TensorShape?);
}
else if (
l.type == Layer.Type.ReduceL1 ||
l.type == Layer.Type.ReduceL2 ||
l.type == Layer.Type.ReduceLogSum ||
l.type == Layer.Type.ReduceLogSumExp ||
l.type == Layer.Type.ReduceMax ||
l.type == Layer.Type.ReduceMean ||
l.type == Layer.Type.ReduceMin ||
l.type == Layer.Type.ReduceProd ||
l.type == Layer.Type.ReduceSum ||
l.type == Layer.Type.ReduceSumSquare)
{
O = X.Reduce(l.axis);
}
else if (
l.type == Layer.Type.Flatten)
{
O = X.Flatten();
}
else if (
l.type == Layer.Type.Reshape)
{
// pool size is treated as reshape coefficient, if not empty
// otherwise shape of the 2nd input tensor is used
var size = l.pool;
Assert.IsNotNull(size);
if (size.Length == 0 && l.inputs.Length > 1)
{
if (shapesByName[l.inputs[1]] == null)
{
O = null;
break;
}
size = shapesByName[l.inputs[1]].Value.ToArray();
}
Assert.AreEqual(size.Length, 4);
O = X.Reshape(size);
}
else if (
l.type == Layer.Type.Expand)
{
// pool size is treated as new shape
var newShape = l.pool;
Assert.IsNotNull(newShape);
Assert.AreEqual(newShape.Length, 4);
O = new TensorShape(newShape);
}
else if (
l.type == Layer.Type.Transpose)
{
O = new TensorShape(X.flatWidth, X.flatHeight);
}
else if (
l.type == Layer.Type.Gather)
{
if (shapesByName[l.inputs[0]] == null || shapesByName[l.inputs[1]] == null)
{
O = null;
break;
}
int[] shape = shapesByName[l.inputs[0]].Value.ToArray();
shape[l.axis] = shapesByName[l.inputs[1]].Value.flatWidth;
O = new TensorShape(shape);
}
else if (
l.type == Layer.Type.Squeeze ||
l.type == Layer.Type.Unsqueeze)
{
throw new NotImplementedException();
}
else if (
l.type == Layer.Type.Concat)
{
// gather shapes by names
var list = new List <TensorShape>(l.inputs.Length);
bool allShapesKnown = true;
foreach (var i in l.inputs)
{
if (!shapesByName.ContainsKey(i))
{
continue;
}
if (shapesByName[i] == null)
{
allShapesKnown = false;
continue;
}
list.Add(shapesByName[i].Value);
}
O = allShapesKnown ? TensorExtensions.Concat(list.ToArray(), l.axis) : default(TensorShape?);
}
else if (
l.type == Layer.Type.StridedSlice)
{
Assert.IsNotNull(l.pad);
Assert.IsNotNull(l.pool);
Assert.IsNotNull(l.stride);
O = X.ApplyStridedSlice(l.pad, l.pool, l.stride);
}
else if (
l.type == Layer.Type.Tile)
{
// pool size is treated as tiling coefficient here
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 4);
var scale = l.pool;
O = X.Scale(scale);
}
else if (
l.type == Layer.Type.Load)
{
O = l.datasets[0].shape;
}
else if (// elementwise operations
l.type == Layer.Type.Nop ||
l.type == Layer.Type.Activation ||
l.type == Layer.Type.ScaleBias ||
l.type == Layer.Type.Normalization ||
l.type == Layer.Type.LRN ||
l.type == Layer.Type.Dropout ||
l.type == Layer.Type.LogicalNot ||
l.activation == Layer.Activation.PRelu)
{
// works in place, keeps the same shape size
O = X;
}
else if (
l.type == Layer.Type.Conv3D ||
l.type == Layer.Type.Conv3DTrans ||
l.type == Layer.Type.Upsample3D ||
l.type == Layer.Type.MaxPool3D ||
l.type == Layer.Type.AvgPool3D ||
l.type == Layer.Type.GlobalMaxPool3D ||
l.type == Layer.Type.GlobalAvgPool3D ||
l.type == Layer.Type.Border3D)
{
throw new NotImplementedException("3D operations are not implemented yet!");
}
else
{
Assert.AreEqual(l.activation, Layer.Activation.None);
O = X;
}
shapes.Add(O);
shapesByName.Add(l.name, O);
}
Profiler.EndSample();
return(shapes.ToArray());
}
public static TensorShape?[] ListTemporaryTensorShapes(Model model, IDictionary <string, TensorShape> inputShapes,
out IDictionary <string, TensorShape?> shapesByName)
{
Profiler.BeginSample("Barracuda.ListTemporaryTensorShapes");
var shapes = new List <TensorShape?>();
shapesByName = new Dictionary <string, TensorShape?>();
foreach (var entry in inputShapes)
{
shapesByName.Add(entry.Key, entry.Value);
}
TensorShape?Xn;
shapesByName.TryGetValue(GetDefaultInputName(model), out Xn); // default input
TensorShape?O = Xn;
foreach (var l in model.layers)
{
if (l.inputs.Length > 0 && shapesByName.TryGetValue(l.inputs[0], out TensorShape? xShape))
{
Xn = xShape;
}
else
{
Xn = O; // previous output is used, if-and-only-if layer has no explicit inputs
}
if (Xn == null)
{
shapes.Add(Xn);
shapesByName.Add(l.name, Xn);
continue;
}
TensorShape X = Xn.Value;
if (l.type == Layer.Type.Dense)
{
Assert.IsNotNull(l.datasets);
var W = l.datasets[0].shape;
O = new TensorShape(X.flatHeight, W.flatWidth);
}
else if (l.type == Layer.Type.Dense3)
{
Assert.IsNotNull(l.datasets);
var W = l.datasets[0].shape;
O = new TensorShape(X.batch, 1, W.channels, X.channels);
}
else if (l.type == Layer.Type.MatMul)
{
if (!shapesByName.ContainsKey(l.inputs[1]) || shapesByName[l.inputs[1]] == null)
{
O = null;
break;
}
var Y = shapesByName[l.inputs[1]].Value;
int rankX;
int rankY;
List <int> onnxXshape;
List <int> onnxYshape;
if (l.pool == null || l.pool.Length == 0)
{
LegacyGetXYRanks(X, Y, out rankX, out rankY);
}
else
{
rankX = l.pool[0];
rankY = l.pool[1];
}
onnxXshape = Compiler.IRShapeInferenceHelper.ShapeInference.BarracudaShapeToOnnxLayout(X, rankX);
onnxYshape = Compiler.IRShapeInferenceHelper.ShapeInference.BarracudaShapeToOnnxLayout(Y, rankY);
int rankO = Math.Max(rankX, rankY);
// pad 1 on front of shape to both be rankO shape
for (int i = 0; i < (rankX - rankY); i++)
{
onnxYshape.Insert(0, 1);
}
for (int i = 0; i < (rankY - rankX); i++)
{
onnxXshape.Insert(0, 1);
}
if (rankO == 2)
{
O = new TensorShape(onnxXshape[0], 1, 1, onnxYshape[1]);
}
else if (rankO == 3)
{
O = new TensorShape(Math.Max(onnxXshape[0], onnxYshape[0]), 1, onnxYshape[2], onnxXshape[1]);
}
else
{
O = new TensorShape(Math.Max(onnxXshape[0], onnxYshape[0]), onnxXshape[2], onnxYshape[3], Math.Max(onnxXshape[1], onnxYshape[1]));
}
}
else if (
l.type == Layer.Type.Conv2D ||
l.type == Layer.Type.Conv3D ||
l.type == Layer.Type.DepthwiseConv2D)
{
var K = l.datasets[0].shape;
Assert.IsNotNull(l.stride);
Assert.IsNotNull(l.pad);
var pad = X.AdjustPadToKernel(K, l.stride, l.pad);
O = X.ApplyKernel(K, l.stride, pad);
}
else if (
l.type == Layer.Type.Conv2DTrans)
{
var K = l.datasets[0].shape;
Assert.IsNotNull(l.stride);
Assert.IsNotNull(l.pad);
// pool size is treated as output_adjustment aka output_padding here
var outputAdjustment = l.pool;
var pad = X.AdjustPadToKernel(K, l.stride, l.pad);
O = X.ApplyKernelInverse(K, l.stride, pad, outputAdjustment);
}
else if (
l.type == Layer.Type.Upsample2D)
{
if (inputShapes.Count > 1)
{
O = null;
}
else
{
// pool size is treated as upsample coefficient here
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 2);
O = new TensorShape(X.batch, X.height * l.pool[1], X.width * l.pool[0], X.channels);
}
}
else if (
l.type == Layer.Type.Upsample3D)
{
if (inputShapes.Count > 1)
{
O = null;
}
else
{
// pool size is treated as upsample coefficient here
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 3);
O = new TensorShape(1, 1, X.batch, 1, X.depth * l.pool[2], X.height * l.pool[1], X.width * l.pool[0], X.channels);
}
}
else if (
l.type == Layer.Type.Resample2D)
{
if (inputShapes.Count > 1)
{
O = null;
}
else
{
// pool is treated as resample size here
var size = l.pool;
Assert.IsNotNull(size);
Assert.AreEqual(size.Length, 2);
O = new TensorShape(X.batch, size[1], size[0], X.channels);
}
}
else if (
l.type == Layer.Type.DepthToSpace)
{
// pool size is treated as blocksize here
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 2);
Assert.AreEqual(X.channels % (l.pool[0] * l.pool[1]), 0);
O = new TensorShape(X.batch, X.height * l.pool[1], X.width * l.pool[0], X.channels / (l.pool[0] * l.pool[1]));
}
else if (
l.type == Layer.Type.SpaceToDepth)
{
// pool size is treated as blocksize here
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 2);
O = new TensorShape(X.batch, X.height / l.pool[1], X.width / l.pool[0], X.channels * (l.pool[0] * l.pool[1]));
}
else if (
l.type == Layer.Type.MaxPool2D ||
l.type == Layer.Type.AvgPool2D)
{
Assert.IsNotNull(l.pool);
Assert.IsNotNull(l.stride);
Assert.IsNotNull(l.pad);
var pad = X.AdjustPadToPool(l.pool, l.stride, l.pad);
O = X.ApplyPool(l.pool, l.stride, pad);
}
else if (
l.type == Layer.Type.GlobalMaxPool2D ||
l.type == Layer.Type.GlobalAvgPool2D)
{
O = new TensorShape(X.batch, 1, 1, X.channels);
}
else if (
l.type == Layer.Type.Border2D ||
l.type == Layer.Type.Border3D ||
l.type == Layer.Type.Pad2DReflect ||
l.type == Layer.Type.Pad2DSymmetric ||
l.type == Layer.Type.Pad2DEdge)
{
Assert.IsNotNull(l.pad);
O = X.ApplyBorder(l.pad);
}
else if (
l.type == Layer.Type.Conv3D ||
l.type == Layer.Type.Conv3DTrans ||
l.type == Layer.Type.Upsample3D ||
l.type == Layer.Type.MaxPool3D ||
l.type == Layer.Type.AvgPool3D ||
l.type == Layer.Type.GlobalMaxPool3D ||
l.type == Layer.Type.GlobalAvgPool3D ||
l.type == Layer.Type.Border3D)
{
throw new NotImplementedException();
}
else if (
l.type == Layer.Type.RandomNormal ||
l.type == Layer.Type.RandomUniform)
{
Assert.IsNotNull(l.pool);
// pool size is treated as shape constant, if not empty
// otherwise shape of the previous tensor is used
if (l.pool.Length > 0)
{
O = new TensorShape(l.pool);
}
else
{
O = X;
}
}
else if (l.type == Layer.Type.ConstantOfShape)
{
if (l.axis != 1)
{
O = null;
}
else
{
O = X;
}
}
else if (
l.type == Layer.Type.Multinomial)
{
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 1);
O = new TensorShape(X.batch, l.pool[0]);
}
else if (
l.type == Layer.Type.OneHot)
{
Assert.IsNotNull(l.pool);
Assert.AreEqual(l.pool.Length, 1);
int features = X.flatWidth;
int depth = l.pool[0];
if (X.flatWidth == 1) // 1D input
{
O = new TensorShape(X.batch, depth);
}
else
{
O = new TensorShape(X.batch, 1, depth, features);
}
}
else if (
l.type == Layer.Type.Add ||
l.type == Layer.Type.Sub ||
l.type == Layer.Type.Mul ||
l.type == Layer.Type.Div ||
l.type == Layer.Type.Pow ||
l.type == Layer.Type.Min ||
l.type == Layer.Type.Max ||
l.type == Layer.Type.Mean ||
l.type == Layer.Type.Greater ||
l.type == Layer.Type.GreaterEqual ||
l.type == Layer.Type.Less ||
l.type == Layer.Type.LessEqual ||
l.type == Layer.Type.Equal ||
l.type == Layer.Type.LogicalOr ||
l.type == Layer.Type.LogicalAnd ||
l.type == Layer.Type.LogicalXor)
{
// gather shapes by names
var list = new List <TensorShape>(l.inputs.Length);
bool allShapesKnown = true;
foreach (var i in l.inputs)
{
if (shapesByName.TryGetValue(i, out TensorShape? shape) && shape != null)
{
list.Add(shape.Value);
}
else
{
allShapesKnown = false;
}
}
O = allShapesKnown ? TensorExtensions.Max(list.ToArray()) : default(TensorShape?);
}
else if (
l.type == Layer.Type.ReduceL1 ||
l.type == Layer.Type.ReduceL2 ||
l.type == Layer.Type.ReduceLogSum ||
l.type == Layer.Type.ReduceLogSumExp ||
l.type == Layer.Type.ReduceMax ||
l.type == Layer.Type.ReduceMean ||
l.type == Layer.Type.ReduceMin ||
l.type == Layer.Type.ReduceProd ||
l.type == Layer.Type.ReduceSum ||
l.type == Layer.Type.ReduceSumSquare ||
l.type == Layer.Type.ArgMax ||
l.type == Layer.Type.ArgMin)
{
O = X.Reduce(l.axis);
}
else if (
l.type == Layer.Type.Flatten)
{
O = X.Flatten();
}
else if (
l.type == Layer.Type.Reshape)
{
// pool size is treated as the shape, if not empty
var size = l.pool;
Assert.IsNotNull(size);
if (size.Length == 0 && l.inputs.Length > 1)
{
switch (l.axis)
{
// Legacy - use the shape of the input tensor as the shape
case -1:
if (shapesByName.TryGetValue(l.inputs[1], out TensorShape? shape))
{
size = shape.Value.ToArray();
}
break;
// Use the tensor values as the shape; Calculated at runtime
case 1:
O = null;
break;
}
if (O == null)
{
break;
}
}
Assert.IsTrue((size.Length == 4) || (size.Length == 8));
O = X.Reshape(size);
}
else if (
l.type == Layer.Type.Expand)
{
// pool size is treated as new shape
var newShape = l.pool;
Assert.IsNotNull(newShape);
Assert.IsTrue(newShape.Length == 8 || newShape.Length == 4);
O = new TensorShape(newShape);
}
else if (
l.type == Layer.Type.Transpose)
{
var permutations = l.pool;
if (permutations == null)
{
O = new TensorShape(X.flatWidth, X.flatHeight);
}
else
{
Assert.IsTrue(permutations.Length == 8 || permutations.Length == 4);
O = X.Permute(permutations);
}
}
else if (
l.type == Layer.Type.Gather)
{
if (!shapesByName.TryGetValue(l.inputs[0], out TensorShape? input0Shape) || input0Shape == null ||
!shapesByName.TryGetValue(l.inputs[1], out TensorShape? input1Shape) || input1Shape == null)
{
O = null;
break;
}
int[] shape = input0Shape.Value.ToArray();
shape[l.axis] = input1Shape.Value.length;
O = new TensorShape(shape);
}
else if (
l.type == Layer.Type.Squeeze ||
l.type == Layer.Type.Unsqueeze)
{
O = X;
}
else if (
l.type == Layer.Type.Concat)
{
// gather shapes by names
var list = new List <TensorShape>(l.inputs.Length);
bool allShapesKnown = true;
foreach (var i in l.inputs)
{
if (!shapesByName.TryGetValue(i, out var shape) || shape == null)
{
allShapesKnown = false;
continue;
}
list.Add(shape.Value);
}
O = allShapesKnown ? TensorExtensions.Concat(list.ToArray(), l.axis) : default(TensorShape?);
}
else if (
l.type == Layer.Type.StridedSlice)
{
Assert.IsNotNull(l.pad);
Assert.IsNotNull(l.pool);
Assert.IsNotNull(l.stride);
O = X.ApplyStridedSlice(l.pad, l.pool, l.stride);
}
else if (
l.type == Layer.Type.Tile)
{
// pool size is treated as tiling coefficient here
Assert.IsNotNull(l.pool);
var scale = l.pool;
O = X.Scale(scale);
}
else if (
l.type == Layer.Type.Load)
{
O = l.datasets[0].shape;
}
else if (// elementwise operations
l.type == Layer.Type.Nop ||
l.type == Layer.Type.Activation ||
l.type == Layer.Type.ScaleBias ||
l.type == Layer.Type.Normalization ||
l.type == Layer.Type.LRN ||
l.type == Layer.Type.Dropout ||
l.type == Layer.Type.LogicalNot ||
l.type == Layer.Type.Sign ||
l.type == Layer.Type.Where)
{
// works in place, keeps the same shape size
O = X;
}
else if (
l.type == Layer.Type.TopKIndices ||
l.type == Layer.Type.TopKValues ||
l.type == Layer.Type.NonMaxSuppression ||
l.type == Layer.Type.LSTM ||
l.type == Layer.Type.NonZero)
{
// Calculated at runtime
O = null;
}
else if (l.type == Layer.Type.Shape)
{
int shapeRank = l.axis > 0 ? 1 : X.length;
O = new TensorShape(shapeRank, 1, 1, 1);
}
else if (
l.type == Layer.Type.Conv3D ||
l.type == Layer.Type.Conv3DTrans ||
l.type == Layer.Type.Upsample3D ||
l.type == Layer.Type.MaxPool3D ||
l.type == Layer.Type.AvgPool3D ||
l.type == Layer.Type.GlobalMaxPool3D ||
l.type == Layer.Type.GlobalAvgPool3D ||
l.type == Layer.Type.Border3D)
{
throw new NotImplementedException("3D operations are not implemented yet!");
}
else
{
throw new NotImplementedException($"Layer type {l.type} needs to be explicitly handled");
}
shapes.Add(O);
shapesByName.Add(l.name, O);
}
Profiler.EndSample();
return(shapes.ToArray());
}
