mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-19 23:03:17 +08:00
nihui
211 lines
7.8 KiB
C++
211 lines
7.8 KiB
C++
// Copyright 2021 Tencent
|
|
// SPDX-License-Identifier: BSD-3-Clause
|
|
|
|
#include "testutil.h"
|
|
|
|
static int test_convolution1d(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias)
|
|
{
|
|
ncnn::Mat a = RandomMat(w, h);
|
|
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, outh); // num_output
|
|
pd.set(1, kernel); // kernel_w
|
|
pd.set(2, dilation); // dilation_w
|
|
pd.set(3, stride); // stride_w
|
|
pd.set(4, pad); // pad_w
|
|
pd.set(5, bias); // bias_term
|
|
pd.set(6, outh * h * kernel);
|
|
|
|
int activation_type = RAND() % 6; // 0 1 2 3 4 5
|
|
ncnn::Mat activation_params(2);
|
|
activation_params[0] = RandomFloat(-1, 0); // alpha
|
|
activation_params[1] = RandomFloat(0, 1); // beta
|
|
pd.set(9, activation_type);
|
|
pd.set(10, activation_params);
|
|
|
|
std::vector<ncnn::Mat> weights(bias ? 2 : 1);
|
|
weights[0] = RandomMat(outh * h * kernel);
|
|
if (bias)
|
|
weights[1] = RandomMat(outh);
|
|
|
|
int ret = test_layer("Convolution1D", pd, weights, a);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_convolution1d failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, outh, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
static int test_convolution1d_0()
|
|
{
|
|
static const int kdsp[16][4] = {
|
|
{1, 1, 1, 0},
|
|
{1, 1, 2, 0},
|
|
{2, 1, 1, 1},
|
|
{2, 1, 2, -233},
|
|
{3, 1, 1, 1},
|
|
{3, 1, 2, 1},
|
|
{3, 2, 1, 1},
|
|
{4, 1, 1, 2},
|
|
{4, 1, 2, -233},
|
|
{4, 2, 1, -234},
|
|
{5, 1, 1, -234},
|
|
{5, 1, 2, 2},
|
|
{5, 2, 2, 2},
|
|
{7, 1, 1, 3},
|
|
{7, 1, 2, 3},
|
|
{7, 2, 1, -233},
|
|
};
|
|
|
|
for (int i = 0; i < 16; i++)
|
|
{
|
|
const int k = kdsp[i][0];
|
|
const int d = kdsp[i][1];
|
|
const int s = kdsp[i][2];
|
|
const int p = kdsp[i][3];
|
|
const int b0 = i % 2;
|
|
const int b1 = 1 - b0;
|
|
|
|
int ret = 0
|
|
|| test_convolution1d(9, 1, 1, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 1, 3, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 1, 7, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 1, 15, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 1, 31, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 3, 1, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 3, 3, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 3, 7, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 3, 15, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 3, 31, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 7, 1, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 7, 3, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 7, 7, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 7, 15, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 7, 31, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 15, 1, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 15, 3, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 15, 7, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 15, 15, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 15, 31, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 31, 1, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 31, 3, k, d, s, p, b1)
|
|
|| test_convolution1d(9, 31, 7, k, d, s, p, b0)
|
|
|| test_convolution1d(9, 31, 15, k, d, s, p, b1)
|
|
|| test_convolution1d(25, 28, 31, k, d, s, p, b0)
|
|
|| test_convolution1d(25, 31, 28, k, d, s, p, b1)
|
|
|| test_convolution1d(25, 28, 28, k, d, s, p, b0)
|
|
|| test_convolution1d(25, 24, 28, k, d, s, p, b1)
|
|
|| test_convolution1d(25, 24, 31, k, d, s, p, b0)
|
|
|| test_convolution1d(25, 28, 24, k, d, s, p, b1)
|
|
|| test_convolution1d(25, 31, 24, k, d, s, p, b0)
|
|
|| test_convolution1d(25, 24, 24, k, d, s, p, b1)
|
|
|| test_convolution1d(25, 28, 48, k, d, s, p, b0)
|
|
|| test_convolution1d(25, 31, 48, k, d, s, p, b1)
|
|
|| test_convolution1d(25, 24, 48, k, d, s, p, b0)
|
|
|| test_convolution1d(25, 48, 28, k, d, s, p, b1)
|
|
|| test_convolution1d(25, 48, 31, k, d, s, p, b0)
|
|
|| test_convolution1d(25, 48, 24, k, d, s, p, b1)
|
|
|| test_convolution1d(25, 31, 31, k, d, s, p, b0)
|
|
|| test_convolution1d(25, 48, 48, k, d, s, p, b1);
|
|
|
|
if (ret != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0
|
|
|| test_convolution1d(7, 1, 4, 3, 1, 1, 1, 1)
|
|
|| test_convolution1d(14, 1, 4, 3, 1, 2, 1, 1)
|
|
|| test_convolution1d(15, 4, 4, 3, 1, 1, 1, 1)
|
|
|| test_convolution1d(15, 8, 8, 3, 1, 1, 1, 1)
|
|
|| test_convolution1d(11, 8, 16, 3, 1, 1, 1, 1)
|
|
|| test_convolution1d(13, 16, 24, 3, 1, 1, 1, 1)
|
|
|| test_convolution1d(8, 16, 24, 3, 1, 1, 1, 0)
|
|
|| test_convolution1d(4, 16, 24, 3, 1, 1, 1, 1)
|
|
|| test_convolution1d(4, 16, 24, 3, 1, 1, 1, 0)
|
|
|| test_convolution1d(6, 64, 64, 3, 1, 2, 0, 1);
|
|
}
|
|
|
|
static int test_convolution1d_dynamic(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias)
|
|
{
|
|
ncnn::Mat a = RandomMat(w, h);
|
|
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, 0);
|
|
pd.set(1, 0);
|
|
pd.set(2, dilation);
|
|
pd.set(3, stride);
|
|
pd.set(4, pad);
|
|
pd.set(5, bias);
|
|
pd.set(6, 0);
|
|
pd.set(19, 1); // dynamic weight
|
|
|
|
int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
|
|
ncnn::Mat activation_params(2);
|
|
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
|
|
activation_params[1] = RandomFloat(0, 1); // beta
|
|
pd.set(9, activation_type);
|
|
pd.set(10, activation_params);
|
|
|
|
std::vector<ncnn::Mat> as(bias ? 3 : 2);
|
|
as[0] = a;
|
|
as[1] = RandomMat(kernel, h, outh);
|
|
if (bias)
|
|
as[2] = RandomMat(outh);
|
|
|
|
std::vector<ncnn::Mat> weights(0);
|
|
|
|
int ret = test_layer("Convolution1D", pd, weights, as);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_convolution1d_dynamic failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, outh, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
static int test_convolution1d_1()
|
|
{
|
|
static const int kdsp[7][4] = {
|
|
{1, 1, 1, 0},
|
|
{1, 1, 2, 0},
|
|
{2, 1, 1, 1},
|
|
{2, 1, 2, -233},
|
|
{3, 1, 1, 1},
|
|
{3, 1, 2, 1},
|
|
{3, 2, 1, -234},
|
|
};
|
|
|
|
for (int i = 0; i < 7; i++)
|
|
{
|
|
const int k = kdsp[i][0];
|
|
const int d = kdsp[i][1];
|
|
const int s = kdsp[i][2];
|
|
const int p = kdsp[i][3];
|
|
|
|
int ret = 0
|
|
|| test_convolution1d_dynamic(11, 1, 1, k, d, s, p, 1)
|
|
|| test_convolution1d_dynamic(11, 4, 13, k, d, s, p, 0)
|
|
|| test_convolution1d_dynamic(11, 13, 4, k, d, s, p, 1)
|
|
|| test_convolution1d_dynamic(11, 12, 12, k, d, s, p, 0)
|
|
|| test_convolution1d_dynamic(11, 8, 12, k, d, s, p, 1)
|
|
|| test_convolution1d_dynamic(11, 8, 13, k, d, s, p, 0)
|
|
|| test_convolution1d_dynamic(11, 13, 8, k, d, s, p, 1)
|
|
|| test_convolution1d_dynamic(11, 12, 16, k, d, s, p, 0)
|
|
|| test_convolution1d_dynamic(11, 15, 15, k, d, s, p, 0)
|
|
|| test_convolution1d_dynamic(11, 16, 16, k, d, s, p, 0);
|
|
|
|
if (ret != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
int main()
|
|
{
|
|
SRAND(7767517);
|
|
|
|
return test_convolution1d_0() || test_convolution1d_1();
|
|
}
|