mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-19 14:32:15 +08:00
nihui
706c4d1964
Add 4d Mat handling across generic and optimized backends, extend operator tests, and document supported input and output Mat dimensions.
124 lines
4.8 KiB
C++
124 lines
4.8 KiB
C++
// Copyright 2020 Tencent
|
|
// SPDX-License-Identifier: BSD-3-Clause
|
|
|
|
#include "testutil.h"
|
|
|
|
static int test_layernorm(const ncnn::Mat& a, int affine_size, float eps, int affine)
|
|
{
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, affine_size);
|
|
pd.set(1, eps);
|
|
pd.set(2, affine);
|
|
|
|
std::vector<ncnn::Mat> weights(2);
|
|
weights[0] = RandomMat(affine_size);
|
|
weights[1] = RandomMat(affine_size);
|
|
|
|
int ret = test_layer("LayerNorm", pd, weights, a);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_layernorm failed a.dims=%d a=(%d %d %d %d) affine_size=%d eps=%f affine=%d\n", a.dims, a.w, a.h, a.d, a.c, affine_size, eps, affine);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
static int test_layernorm_0()
|
|
{
|
|
return 0
|
|
|| test_layernorm(RandomMat(6, 4, 2), 6, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(4, 5, 6), 4, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(3, 3, 8), 3, 0.002f, 0)
|
|
|| test_layernorm(RandomMat(5, 6, 12), 5, 0.02f, 0)
|
|
|| test_layernorm(RandomMat(4, 7, 16), 4, 0.02f, 0)
|
|
|| test_layernorm(RandomMat(6, 7, 24), 6, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(5, 8, 32), 5, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(6, 4, 2), 6, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(4, 5, 6), 4, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(3, 3, 8), 3, 0.002f, 1)
|
|
|| test_layernorm(RandomMat(5, 6, 12), 5, 0.02f, 1)
|
|
|| test_layernorm(RandomMat(4, 7, 16), 4, 0.02f, 1)
|
|
|| test_layernorm(RandomMat(6, 7, 24), 6, 0.001f, 1)
|
|
|| test_layernorm(RandomMat(5, 8, 32), 5, 0.001f, 1);
|
|
}
|
|
|
|
static int test_layernorm_1()
|
|
{
|
|
return 0
|
|
|| test_layernorm(RandomMat(6, 4, 2), 24, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(4, 5, 6), 20, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(3, 3, 8), 9, 0.002f, 0)
|
|
|| test_layernorm(RandomMat(5, 6, 12), 30, 0.02f, 0)
|
|
|| test_layernorm(RandomMat(4, 7, 16), 28, 0.02f, 0)
|
|
|| test_layernorm(RandomMat(6, 7, 24), 42, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(5, 8, 32), 40, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(6, 4, 2), 24, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(4, 5, 6), 20, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(3, 3, 8), 9, 0.002f, 1)
|
|
|| test_layernorm(RandomMat(5, 6, 12), 30, 0.02f, 1)
|
|
|| test_layernorm(RandomMat(4, 7, 16), 28, 0.02f, 1)
|
|
|| test_layernorm(RandomMat(6, 7, 24), 42, 0.001f, 1)
|
|
|| test_layernorm(RandomMat(5, 8, 32), 40, 0.001f, 1);
|
|
}
|
|
|
|
static int test_layernorm_2()
|
|
{
|
|
return 0
|
|
|| test_layernorm(RandomMat(4, 2), 4, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(5, 6), 5, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(3, 8), 3, 0.002f, 0)
|
|
|| test_layernorm(RandomMat(6, 12), 6, 0.02f, 0)
|
|
|| test_layernorm(RandomMat(4, 16), 4, 0.02f, 0)
|
|
|| test_layernorm(RandomMat(7, 24), 7, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(8, 32), 8, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(4, 2), 4, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(5, 6), 5, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(3, 8), 3, 0.002f, 1)
|
|
|| test_layernorm(RandomMat(6, 12), 6, 0.02f, 1)
|
|
|| test_layernorm(RandomMat(4, 16), 4, 0.02f, 1)
|
|
|| test_layernorm(RandomMat(7, 24), 7, 0.001f, 1)
|
|
|| test_layernorm(RandomMat(8, 32), 8, 0.001f, 1);
|
|
}
|
|
|
|
static int test_layernorm_3()
|
|
{
|
|
return 0
|
|
|| test_layernorm(RandomMat(2), 2, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(6), 6, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(8), 8, 0.002f, 0)
|
|
|| test_layernorm(RandomMat(12), 12, 0.02f, 0)
|
|
|| test_layernorm(RandomMat(16), 16, 0.02f, 0)
|
|
|| test_layernorm(RandomMat(24), 24, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(32), 32, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(2), 2, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(6), 6, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(8), 8, 0.002f, 1)
|
|
|| test_layernorm(RandomMat(12), 12, 0.02f, 1)
|
|
|| test_layernorm(RandomMat(16), 16, 0.02f, 1)
|
|
|| test_layernorm(RandomMat(24), 24, 0.001f, 1)
|
|
|| test_layernorm(RandomMat(32), 32, 0.001f, 1);
|
|
}
|
|
|
|
static int test_layernorm_4()
|
|
{
|
|
return 0
|
|
|| test_layernorm(RandomMat(5, 3, 2, 8), 5, 0.01f, 0)
|
|
|| test_layernorm(RandomMat(4, 3, 2, 5), 4, 0.01f, 1)
|
|
|| test_layernorm(RandomMat(5, 3, 2, 8), 15, 0.002f, 0)
|
|
|| test_layernorm(RandomMat(4, 3, 2, 5), 12, 0.002f, 1)
|
|
|| test_layernorm(RandomMat(5, 3, 2, 8), 30, 0.001f, 0)
|
|
|| test_layernorm(RandomMat(4, 3, 2, 5), 24, 0.001f, 1);
|
|
}
|
|
|
|
int main()
|
|
{
|
|
SRAND(7767517);
|
|
|
|
return 0
|
|
|| test_layernorm_0()
|
|
|| test_layernorm_1()
|
|
|| test_layernorm_2()
|
|
|| test_layernorm_3()
|
|
|| test_layernorm_4();
|
|
}
|