public void OverflowTest()
        {
            foreach (bool gradmode in new bool[] { false, true })
            {
                foreach (int batch in new int[] { 1, 2, 3 })
                {
                    foreach (int inchannels in new int[] { 3, 6, 9, 12 })
                    {
                        foreach (int outchannels in new int[] { 3, 6, 9, 12 })
                        {
                            float[] xval = (new float[inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
                            float[] wval = (new float[inchannels * outchannels / 9 * 4]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();

                            OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch), xval);
                            OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3), wval);

                            OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch));

                            TrivectorDense ope = new TrivectorDense(inchannels, outchannels, gradmode, batch);

                            ope.Execute(x_tensor, w_tensor, y_tensor);

                            CollectionAssert.AreEqual(xval, x_tensor.State);
                            CollectionAssert.AreEqual(wval, w_tensor.State);

                            y_tensor.CheckOverflow();

                            Console.WriteLine($"pass: {inchannels},{outchannels},{batch},{gradmode}");
                        }
                    }
                }
            }
        }
        public void SpeedTest()
        {
            int inchannels = 33, outchannels = 33;

            OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels));
            OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3));

            OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels));

            TrivectorDense ope             = new TrivectorDense(inchannels, outchannels);

            Stopwatch sw = new Stopwatch();

            sw.Start();

            ope.Execute(x_tensor, w_tensor, y_tensor);
            ope.Execute(x_tensor, w_tensor, y_tensor);
            ope.Execute(x_tensor, w_tensor, y_tensor);
            ope.Execute(x_tensor, w_tensor, y_tensor);

            sw.Stop();

            Console.WriteLine($"{sw.ElapsedMilliseconds / 4} msec");
        }
        public void ExecuteTest()
        {
            float max_err = 0;

            foreach (int batch in new int[] { 1, 2, 3 })
            {
                foreach (int inchannels in new int[] { 3, 6, 9, 12 })
                {
                    foreach (int outchannels in new int[] { 3, 6, 9, 12 })
                    {
                        float[] xval = (new float[inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
                        float[] wval = (new float[inchannels * outchannels / 9 * 4]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();

                        Trivector[] xcval = (new Trivector[xval.Length / 3])
                                            .Select((_, idx) => new Trivector(xval[idx * 3], xval[idx * 3 + 1], xval[idx * 3 + 2])).ToArray();

                        Quaternion.Quaternion[] wcval = (new Quaternion.Quaternion[wval.Length / 4])
                                                        .Select((_, idx) => new Quaternion.Quaternion(wval[idx * 4], wval[idx * 4 + 1], wval[idx * 4 + 2], wval[idx * 4 + 3])).ToArray();

                        TrivectorMap0D x = new TrivectorMap0D(inchannels / 3, batch, xcval);
                        Quaternion.QuaternionFilter0D w = new Quaternion.QuaternionFilter0D(inchannels / 3, outchannels / 3, wcval);

                        TrivectorMap0D y = Reference(x, w);

                        OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch), xval);
                        OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3), wval);

                        OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch));

                        TrivectorDense ope = new TrivectorDense(inchannels, outchannels, gradmode: false, batch);

                        ope.Execute(x_tensor, w_tensor, y_tensor);

                        float[] y_expect = y.ToArray();
                        float[] y_actual = y_tensor.State;

                        CollectionAssert.AreEqual(xval, x_tensor.State);
                        CollectionAssert.AreEqual(wval, w_tensor.State);

                        AssertError.Tolerance(y_expect, y_actual, 1e-7f, 1e-5f, ref max_err, $"mismatch value {inchannels},{outchannels},{batch}");

                        Console.WriteLine($"pass: {inchannels},{outchannels},{batch}");
                    }
                }
            }

            Console.WriteLine($"maxerr:{max_err}");
        }