public static NDArrayList SplitAndLoad(NDArray data, Context[] ctx_list, int batch_axis = 0,
bool even_split = true)
{
if (ctx_list.Length == 1)
{
return(data.AsInContext(ctx_list[0]));
}
var slices = SplitData(data, ctx_list.Length, batch_axis, even_split);
var result = new NDArrayList();
result = slices.Zip(ctx_list, (i, ctx) => { return(i.AsInContext(ctx)); }).ToList();
return(result.ToArray());
}
public override void Update(NDArray labels, NDArray preds)
{
float loss = 0;
var num = 0;
labels = labels.AsInContext(preds.Context).Reshape(preds.Size);
preds = nd.Pick(preds, labels.AsType(DType.Int32), Axis);
if (IgnoreLabel.HasValue)
{
var ignore = nd.EqualScalar(labels, IgnoreLabel.Value).AsType(preds.DataType);
num -= nd.Sum(ignore).AsScalar <int>();
preds = preds * (1 - ignore) + ignore;
}
loss -= nd.Sum(nd.Log(nd.MaximumScalar(preds, 1e-10f))).AsScalar <float>();
num += preds.Size;
sum_metric += loss;
global_sum_metric += loss;
num_inst += num;
global_num_inst += num;
}
internal virtual NDArray MaskTransform(NDArray mask)
{
return(mask.AsInContext(mx.Cpu()).AsType(DType.Int32));
}
internal NDArray ImgTransform(NDArray img)
{
return(img.AsInContext(mx.Cpu()));
}