75 lines
1.8 KiB
Go
75 lines
1.8 KiB
Go
// Copyright (c) 2026 Petr Balvín <opensource@petrbalvin.org> (https://petrbalvin.org)
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// SPDX-License-Identifier: MIT
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package signal
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import (
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"testing"
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core "sourcedock.dev/petrbalvin/tensor/internal/core"
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)
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// Convolution benchmarks guard the direct kernels; the 2-D pair
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// already lives in bench_test.go, these add the 1-D path and pooling
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// at inference-like sizes.
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func benchSig(b *testing.B, seed, n int, shape ...int) *core.Array {
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b.Helper()
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v := make([]float64, n)
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for i := range v {
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v[i] = float64(i%23)*float64(seed%7)*0.5 + float64(i%11) - 5
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}
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a, err := core.FromFloats(v, shape...)
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if err != nil {
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b.Fatal(err)
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}
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return a
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}
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// BenchmarkConv1D measures a 1×16×4096 input with 8 kernels of width
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// 7, stride 1.
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func BenchmarkConv1D(b *testing.B) {
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input := benchSig(b, 1, 16*4096, 1, 16, 4096)
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kernel := benchSig(b, 2, 8*16*7, 8, 16, 7)
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b.ReportAllocs()
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for b.Loop() {
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if _, err := Conv1D(input, kernel, nil, 1, 0, 1); err != nil {
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b.Fatal(err)
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}
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}
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}
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// BenchmarkMaxPool2D measures a 8×64×28×28 input through 2×2 pooling.
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func BenchmarkMaxPool2D(b *testing.B) {
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input := benchSig(b, 3, 8*64*28*28, 8, 64, 28, 28)
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b.ReportAllocs()
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for b.Loop() {
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if _, err := MaxPool2D(input, 2, 2, 0); err != nil {
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b.Fatal(err)
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}
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}
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}
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// BenchmarkAvgPool2D is the averaging twin.
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func BenchmarkAvgPool2D(b *testing.B) {
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input := benchSig(b, 4, 8*64*28*28, 8, 64, 28, 28)
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b.ReportAllocs()
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for b.Loop() {
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if _, err := AvgPool2D(input, 2, 2, 0, false); err != nil {
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b.Fatal(err)
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}
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}
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}
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// BenchmarkAutocorrelate measures the FFT-based 1-D correlation at a
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// speech-like length.
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func BenchmarkAutocorrelate(b *testing.B) {
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x := benchSig(b, 5, 8192, 8192)
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b.ReportAllocs()
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for b.Loop() {
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if _, err := Autocorrelate(x, 512); err != nil {
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b.Fatal(err)
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}
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}
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}
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