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ncnn/tests/test_requantize.cpp
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nihui 706c4d1964 support 4d mat in more operators (#6737) 
Add 4d Mat handling across generic and optimized backends, extend operator tests, and document supported input and output Mat dimensions.
2026-05-22 15:31:01 +08:00

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// Copyright 2021 Tencent
// SPDX-License-Identifier: BSD-3-Clause
#include "testutil.h"
static int test_requantize(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta)
{
ncnn::ParamDict pd;
pd.set(0, scale_in_data_size);
pd.set(1, scale_out_data_size);
pd.set(2, bias_data_size);
ncnn::Mat activation_params(2);
activation_params[0] = alpha;
activation_params[1] = beta;
pd.set(3, activation_type);
pd.set(4, activation_params);
std::vector<ncnn::Mat> weights(bias_data_size ? 3 : 2);
weights[0] = RandomMat(scale_in_data_size);
weights[1] = RandomMat(scale_out_data_size);
if (bias_data_size)
weights[2] = RandomMat(bias_data_size);
Randomize(weights[0], 0.0001, 0.001);
Randomize(weights[1], 10, 100);
int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING;
int ret = test_layer("Requantize", pd, weights, a, 1, flag);
if (ret != 0)
{
fprintf(stderr, "test_requantize failed a.dims=%d a=(%d %d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.d, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]);
}
return ret;
}
static int test_requantize(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size)
{
return 0
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f)
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f)
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f)
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1))
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f)
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f);
}
static int test_requantize_relu_empty_activation_params(const ncnn::Mat& a)
{
ncnn::ParamDict pd;
pd.set(0, 1);
pd.set(1, 1);
pd.set(2, 0);
pd.set(3, 1);
std::vector<ncnn::Mat> weights(2);
weights[0] = RandomMat(1);
weights[1] = RandomMat(1);
Randomize(weights[0], 0.0001, 0.001);
Randomize(weights[1], 10, 100);
int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING | TEST_LAYER_DISABLE_AUTO_INPUT_PACKING;
int ret = test_layer("Requantize", pd, weights, a, 1, flag);
if (ret != 0)
{
fprintf(stderr, "test_requantize_relu_empty_activation_params failed a.dims=%d a=(%d %d %d %d)\n", a.dims, a.w, a.h, a.d, a.c);
}
return ret;
}
static int test_requantize_pack1(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta)
{
ncnn::ParamDict pd;
pd.set(0, scale_in_data_size);
pd.set(1, scale_out_data_size);
pd.set(2, bias_data_size);
ncnn::Mat activation_params(2);
activation_params[0] = alpha;
activation_params[1] = beta;
pd.set(3, activation_type);
pd.set(4, activation_params);
std::vector<ncnn::Mat> weights(bias_data_size ? 3 : 2);
weights[0] = RandomMat(scale_in_data_size);
weights[1] = RandomMat(scale_out_data_size);
if (bias_data_size)
weights[2] = RandomMat(bias_data_size);
Randomize(weights[0], 0.0001, 0.001);
Randomize(weights[1], 10, 100);
int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING | TEST_LAYER_DISABLE_AUTO_INPUT_PACKING;
int ret = test_layer("Requantize", pd, weights, a, 1, flag);
if (ret != 0)
{
fprintf(stderr, "test_requantize_pack1 failed a.dims=%d a=(%d %d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.d, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]);
}
return ret;
}
static int test_requantize_pack1(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size)
{
return 0
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f)
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f)
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f)
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1))
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f)
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f);
}
static int test_requantize_pack8(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta)
{
ncnn::ParamDict pd;
pd.set(0, scale_in_data_size);
pd.set(1, scale_out_data_size);
pd.set(2, bias_data_size);
ncnn::Mat activation_params(2);
activation_params[0] = alpha;
activation_params[1] = beta;
pd.set(3, activation_type);
pd.set(4, activation_params);
std::vector<ncnn::Mat> weights(bias_data_size ? 3 : 2);
weights[0] = RandomMat(scale_in_data_size);
weights[1] = RandomMat(scale_out_data_size);
if (bias_data_size)
weights[2] = RandomMat(bias_data_size);
Randomize(weights[0], 0.0001, 0.001);
Randomize(weights[1], 10, 100);
int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING;
#if !__riscv
flag |= TEST_LAYER_ENABLE_FORCE_INPUT_PACK8;
#endif
int ret = test_layer("Requantize", pd, weights, a, 1, flag);
if (ret != 0)
{
fprintf(stderr, "test_requantize_pack8 failed a.dims=%d a=(%d %d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.d, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]);
}
return ret;
}
static int test_requantize_pack8(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size)
{
return 0
|| test_requantize_pack8(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f)
|| test_requantize_pack8(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f)
|| test_requantize_pack8(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f)
|| test_requantize_pack8(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1))
|| test_requantize_pack8(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f)
|| test_requantize_pack8(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f);
}
static int test_requantize_0()
{
return 0
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 1, 12)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 12, 12)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 12, 1)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 12, 0)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 12, 12)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 12, 1)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 12, 0)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 1, 12)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 1, 1)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 1, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 1, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 13, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 13, 1)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 13, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 13, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 13, 1)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 13, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 1, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 1, 1)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 1, 0);
}
static int test_requantize_1()
{
return 0
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 1, 12)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 12, 12)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 12, 1)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 12, 0)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 12, 12)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 12, 1)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 12, 0)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 1, 12)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 1, 1)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 1, 0)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 1, 15)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 15, 15)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 15, 1)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 15, 0)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 15, 15)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 15, 1)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 15, 0)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 1, 15)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 1, 1)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 1, 0);
}
static int test_requantize_2()
{
return 0
|| test_requantize_pack1(RandomIntMat(124), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(124), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(127), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(127), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(127), 1, 1, 0, 2, 0.f, 0.f)
|| test_requantize_pack1(RandomIntMat(127), 1, 1, 0, 2, RandomFloat(-1, 0), 0.f)
|| test_requantize_relu_empty_activation_params(RandomIntMat(127));
}
static int test_requantize_3()
{
return 0
#ifndef __riscv
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 1, 1, 24)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 1, 1, 1)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 1, 1, 0)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 24, 24, 24)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 24, 24, 1)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 24, 24, 0)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 1, 24, 24)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 1, 24, 1)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 1, 24, 0)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 24, 1, 24)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 24, 1, 1)
|| test_requantize_pack8(RandomIntMat(5, 7, 24), 24, 1, 0)
|| test_requantize_pack8(RandomIntMat(15, 24), 1, 1, 24)
|| test_requantize_pack8(RandomIntMat(15, 24), 1, 1, 1)
|| test_requantize_pack8(RandomIntMat(15, 24), 1, 1, 0)
|| test_requantize_pack8(RandomIntMat(15, 24), 24, 24, 24)
|| test_requantize_pack8(RandomIntMat(15, 24), 24, 24, 1)
|| test_requantize_pack8(RandomIntMat(15, 24), 24, 24, 0)
|| test_requantize_pack8(RandomIntMat(15, 24), 1, 24, 24)
|| test_requantize_pack8(RandomIntMat(15, 24), 1, 24, 1)
|| test_requantize_pack8(RandomIntMat(15, 24), 1, 24, 0)
|| test_requantize_pack8(RandomIntMat(15, 24), 24, 1, 24)
|| test_requantize_pack8(RandomIntMat(15, 24), 24, 1, 1)
|| test_requantize_pack8(RandomIntMat(15, 24), 24, 1, 0)
|| test_requantize_pack8(RandomIntMat(128), 1, 1, 1)
|| test_requantize_pack8(RandomIntMat(128), 1, 1, 0);
#else
|| test_requantize(RandomIntMat(5, 7, 24), 1, 1, 24)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 1, 1)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 1, 0)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 24, 24)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 24, 1)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 24, 0)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 24, 24)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 24, 1)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 24, 0)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 1, 24)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 1, 1)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 1, 0)
|| test_requantize(RandomIntMat(15, 24), 1, 1, 24)
|| test_requantize(RandomIntMat(15, 24), 1, 1, 1)
|| test_requantize(RandomIntMat(15, 24), 1, 1, 0)
|| test_requantize(RandomIntMat(15, 24), 24, 24, 24)
|| test_requantize(RandomIntMat(15, 24), 24, 24, 1)
|| test_requantize(RandomIntMat(15, 24), 24, 24, 0)
|| test_requantize(RandomIntMat(15, 24), 1, 24, 24)
|| test_requantize(RandomIntMat(15, 24), 1, 24, 1)
|| test_requantize(RandomIntMat(15, 24), 1, 24, 0)
|| test_requantize(RandomIntMat(15, 24), 24, 1, 24)
|| test_requantize(RandomIntMat(15, 24), 24, 1, 1)
|| test_requantize(RandomIntMat(15, 24), 24, 1, 0)
|| test_requantize(RandomIntMat(128), 1, 1, 1)
|| test_requantize(RandomIntMat(128), 1, 1, 0)
|| test_requantize(RandomIntMat(127), 1, 1, 0, 2, RandomFloat(1, 2), 0.f);
#endif // __riscv
}
static int test_requantize_4()
{
return 0
|| test_requantize_pack1(RandomIntMat(5, 3, 2, 12), 1, 1, 12)
|| test_requantize_pack1(RandomIntMat(5, 3, 2, 12), 12, 12, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 3, 13), 1, 13, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 3, 13), 13, 1, 0)
|| test_requantize_pack8(RandomIntMat(5, 3, 2, 24), 1, 1, 24)
|| test_requantize_pack8(RandomIntMat(5, 3, 2, 24), 24, 24, 0);
}
int main()
{
SRAND(7767517);
return 0
|| test_requantize_4()
|| test_requantize_0()
|| test_requantize_1()
|| test_requantize_2()
|| test_requantize_3();
}
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