public NDArray Gather(NDArray result, NDArray src, int dim, NDArray indices)
{
if (result != null && result.DimensionCount != src.DimensionCount)
{
throw new InvalidOperationException("result and src must have same number of dimensions");
}
if (result != null && dim < 0 && dim >= result.DimensionCount)
{
throw new ArgumentOutOfRangeException("dim");
}
if (indices.DimensionCount != src.DimensionCount)
{
throw new InvalidOperationException("src and indices must have same number of dimensions");
}
if (result != null && !result.IsSameSizeAs(indices))
{
throw new InvalidOperationException("result and indices must be the same size");
}
if (result != null && !TensorResultBuilder.ArrayEqualExcept(src.Shape, result.Shape, dim))
{
throw new InvalidOperationException("result and src must be the same size except in dimension dim");
}
var writeTarget = TensorResultBuilder.GetWriteTarget(result, indices.Allocator, src.ElementType, false, indices.Shape);
NativeWrapper.InvokeTypeMatch(gather_func, writeTarget, src, dim, indices);
return(writeTarget);
}
public NDArray ScatterFill(NDArray result, float value, int dim, NDArray indices)
{
if (result == null)
{
throw new ArgumentNullException("result");
}
if (dim < 0 && dim >= result.DimensionCount)
{
throw new ArgumentOutOfRangeException("dim");
}
if (indices.DimensionCount != result.DimensionCount)
{
throw new InvalidOperationException("result and indices must have same number of dimensions");
}
if (!TensorResultBuilder.ArrayEqualExcept(indices.Shape, result.Shape, dim))
{
throw new InvalidOperationException("result and indices must be the same size except in dimension dim");
}
var writeTarget = result;
NativeWrapper.InvokeTypeMatch(scatterFill_func, writeTarget, value, dim, indices);
return(writeTarget);
}
public void Cols2Im(NDArray col, NDArray im, int channels, int height, int width,
int patch_h, int patch_w, int pad_h,
int pad_w, int stride_h, int stride_w,
int dilation_h, int dilation_w)
{
int height_col = (height + 2 * pad_h - (dilation_h * (patch_h - 1) + 1))
/ stride_h + 1;
int width_col = (width + 2 * pad_w - (dilation_w * (patch_w - 1) + 1))
/ stride_w + 1;
NativeWrapper.InvokeTypeMatch(cols2im_func, col, height, width, channels, patch_h, patch_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, height_col, width_col, im);
}
public void Im2Cols(NDArray im, NDArray col, int channels,
int height, int width,
int ksize_h, int ksize_w, int pad_h,
int pad_w, int stride_h, int stride_w,
int dilation_h, int dilation_w)
{
int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1))
/ stride_h + 1;
int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1))
/ stride_w + 1;
NativeWrapper.InvokeTypeMatch(im2cols_func, im, height, width, channels, ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, height_col, width_col, col);
}
// allArgs should start with a null placeholder for the RNG object
/// <summary>
/// Invokes the with RNG.
/// </summary>
/// <param name="seed">The seed.</param>
/// <param name="method">The method.</param>
/// <param name="allArgs">All arguments.</param>
private static void InvokeWithRng(int?seed, MethodInfo method, params object[] allArgs)
{
if (!seed.HasValue)
{
seed = seedGen.Next();
}
IntPtr rng;
NativeWrapper.CheckResult(CpuOpsNative.TS_NewRNG(out rng));
NativeWrapper.CheckResult(CpuOpsNative.TS_SetRNGSeed(rng, seed.Value));
allArgs[0] = rng;
NativeWrapper.InvokeTypeMatch(method, allArgs);
NativeWrapper.CheckResult(CpuOpsNative.TS_DeleteRNG(rng));
}
public void Fill(NDArray result, float value)
{
NativeWrapper.InvokeTypeMatch(fill_func, result, value);
}