/// <inheritdoc/>
Tensor IOps.Reshape(Tensor X, TensorShape shape)
{
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(m_Ops.Reshape(X, shape));
}
/// <inheritdoc/>
Tensor IOps.Flatten(Tensor X)
{
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(m_Ops.Flatten(X));
}
/// <inheritdoc/>
Tensor IOps.SpaceToDepth(Tensor X, int[] scale)
{
var O = m_Ops.SpaceToDepth(X, scale);
m_Mem += (long)X.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.DepthToSpace(Tensor X, int[] scale, Layer.DepthToSpaceMode mode)
{
var O = m_Ops.DepthToSpace(X, scale, mode);
m_Mem += (long)X.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Dense3(Tensor X, Tensor W, Tensor B)
{
var O = m_Ops.Dense3(X, W, B);
m_Alu += (long)X.height * (long)X.width * (long)W.width * 2L * (long)X.batch * (long)X.channels;
m_Mem += (long)X.length + (long)W.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.NonMaxSuppression(Tensor[] tensors, int maxOutputBoxesPerClass, float iouThreshold, float scoreThreshold, int centerPointBox)
{
var O = m_Ops.NonMaxSuppression(tensors, maxOutputBoxesPerClass, iouThreshold, scoreThreshold, centerPointBox);
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Expand(Tensor X, TensorShape shape)
{
var O = m_Ops.Expand(X, shape);
m_Alu += 0;
m_Mem += (long)X.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.GlobalAvgVariancePool2D(Tensor X)
{
var O = m_Ops.GlobalAvgVariancePool2D(X);
m_Alu += (long)X.length * 2L + (long)O.length;
m_Mem += (long)X.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Upsample3D(Tensor X, int[] scale, bool trilinear)
{
var O = m_Ops.Upsample3D(X, scale, trilinear);
m_Alu += (long)O.length * (trilinear ? 18 : 1);
m_Mem += (long)X.length * (trilinear ? 8 : 1) + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Resample2D(Tensor X, int[] size, bool bilinear)
{
var O = m_Ops.Resample2D(X, size, bilinear);
m_Alu += (long)O.length * (bilinear ? 8 : 1);
m_Mem += (long)X.length * (bilinear ? 4 : 1) + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.MatMul(Tensor X, bool xTranspose, Tensor Y, bool yTranspose)
{
var O = m_Ops.MatMul(X, xTranspose, Y, yTranspose);
m_Alu += (long)X.flatHeight * (long)X.flatWidth * (long)Y.flatWidth * 2L;
m_Mem += (long)X.length + (long)Y.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Border3D(Tensor X, int[] pad, float value)
{
var O = m_Ops.Border3D(X, pad, value);
m_Alu += 0;
m_Mem += (long)X.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.RoiAlign(Tensor X, Tensor rois, Tensor indices, int outputHeight, int outputWidth, int samplingRatio, float spatialScale)
{
var O = m_Ops.RoiAlign(X, rois, indices, outputHeight, outputWidth, samplingRatio, spatialScale);
m_Alu += 4 * outputHeight * outputWidth * samplingRatio * samplingRatio;
m_Mem += 4 * outputHeight * outputWidth * samplingRatio * samplingRatio;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Normalization(Tensor X, Tensor S, Tensor B, int pool, int axis, float epsilon, Layer.FusedActivation fusedActivation)
{
var O = m_Ops.Normalization(X, S, B, pool, axis, epsilon, fusedActivation);
m_Alu += (long)X.length * 4L + (long)O.length * 2L;
m_Mem += (long)X.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.MatMul(Tensor X, int rankX, Tensor Y, int rankY)
{
var O = m_Ops.MatMul(X, rankX, Y, rankY);
m_Alu += (long)X.height * (long)X.width * (long)Y.width * 2L * (long)X.batch * (long)X.channels;
m_Mem += (long)X.length + (long)Y.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Pad2DEdge(Tensor X, int[] pad)
{
var O = m_Ops.Pad2DEdge(X, pad);
m_Alu += 0;
m_Mem += (long)X.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Dense(Tensor X, Tensor W, Tensor B, Layer.FusedActivation fusedActivation)
{
var O = m_Ops.Dense(X, W, B, fusedActivation);
m_Alu += (long)X.flatHeight * (long)X.flatWidth * (long)W.flatWidth * 2L;
m_Mem += (long)X.length + (long)W.length + (long)B.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
public Tensor TopKValues(Tensor X, Tensor I, int axis)
{
var O = m_Ops.TopKValues(X, I, axis);
// @TODO: not implemented
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.TopKIndices(Tensor X, int k, int axis, bool largest, bool sorted)
{
var O = m_Ops.TopKIndices(X, k, axis, largest, sorted);
// @TODO: not implemented
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.OneHot(Tensor X, int depth, float onValue, float offValue)
{
var O = m_Ops.OneHot(X, depth, onValue, offValue);
// @TODO: not implemented
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Multinomial(Tensor X, int count, int seed)
{
var O = m_Ops.Multinomial(X, count, seed);
// @TODO: not implemented
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.RandomUniform(TensorShape s, float mean, float scale, int seed)
{
var O = m_Ops.RandomUniform(s, mean, scale, seed);
// @TODO: not implemented
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
public Tensor NonZero(Tensor X)
{
var O = m_Ops.NonZero(X);
// @TODO: not implemented
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
public Tensor[] LSTM(Tensor X, Tensor[] W, Tensor[] R, Tensor[] Wb, Tensor[] Rb, Tensor hidden, Tensor cell)
{
var O = m_Ops.LSTM(X, W, R, Wb, Rb, hidden, cell);
// @TODO: not implemented
m_Alu += 0;
m_Mem += 0;
RegisterLayerStats();
return(O);
}
internal void ElementwiseBroadcast(Tensor[] tensors, Tensor X, long aluOperationsPerElement = 1L)
{
m_Alu += (long)X.length * aluOperationsPerElement;
long mem = (long)X.length;
foreach (var t in tensors)
{
mem += (long)t.length;
}
m_Mem += mem;
}
/// <inheritdoc/>
Tensor IOps.LRN(Tensor X, float alpha, float beta, float bias, int size)
{
var O = m_Ops.LRN(X, alpha, beta, bias, size);
//A bit over conservative. Number of read/alu is lower than `size` when normalisation windows is too large for data at current index.
long sizeL = size;
m_Alu += (long)X.length * (5L + sizeL * 2L);
m_Mem += (long)X.length * (sizeL + 2L);
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.DepthwiseConv2D(Tensor X, Tensor K, Tensor B, int[] stride, int[] pad, Layer.FusedActivation fusedActivation)
{
var O = m_Ops.DepthwiseConv2D(X, K, B, stride, pad, fusedActivation);
long m = (long)O.batch * (long)O.width * (long)O.height;
long n = (long)X.channels;
long k = (long)K.kernelWidth * (long)K.kernelHeight;
m_Alu += m * n * k * 2L;
m_Mem += (long)X.length + (long)K.length + (long)B.length + (long)O.length;
RegisterLayerStats();
return(O);
}
/// <inheritdoc/>
Tensor IOps.Conv2DTrans(Tensor X, Tensor K, Tensor B, int[] stride, int[] pad, int[] outputAdjustment, Layer.FusedActivation fusedActivation)
{
var O = m_Ops.Conv2DTrans(X, K, B, stride, pad, outputAdjustment, fusedActivation);
long m = (long)O.batch * (long)O.width * (long)O.height;
long n = (long)X.channels;
long k = (long)(K.kernelWidth / stride[1]) * (long)(K.kernelHeight / stride[0]) * (long)K.channels;
m_Alu += m * n * k * 2L;
m_Mem += (long)X.length + (long)K.length + (long)B.length + (long)O.length;
RegisterLayerStats();
return(O);
}
internal void Reduce(Tensor X, Tensor O, long aluOperationsPerElement = 1L)
{
m_Alu += (long)X.length * aluOperationsPerElement;
m_Mem += (long)X.length + (long)O.length;
}
/// <summary>
/// Create `StatsOps`
/// </summary>
/// <param name="ops">target ops</param>
public StatsOps(IOps ops)
{
m_Ops = ops;
m_Alu = new LayerStat(0L, 0L);
m_Mem = new LayerStat(0L, 0L);
}