mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-24 09:45:20 +08:00
nihui
11cffce114
* runtime cpu dispatch * force thread one * disable openmp for coverage * simplify test layer * print NCNN_TARGET_ARCH * less ci build variants * weight fp16 storage option * test convdw int8 * apple a12 a13 * ncnn_add_layer ncnn_add_shader cmake macro
118 lines
3.2 KiB
C++
118 lines
3.2 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
|
|
//
|
|
// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
|
|
//
|
|
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
|
|
// in compliance with the License. You may obtain a copy of the License at
|
|
//
|
|
// https://opensource.org/licenses/BSD-3-Clause
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software distributed
|
|
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
|
|
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
|
// specific language governing permissions and limitations under the License.
|
|
|
|
#include "layer/scale.h"
|
|
#include "testutil.h"
|
|
|
|
static int test_scale(const ncnn::Mat& a, int scale_data_size, int bias)
|
|
{
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, scale_data_size);
|
|
pd.set(1, bias);
|
|
|
|
std::vector<ncnn::Mat> weights(bias ? 2 : 1);
|
|
weights[0] = RandomMat(scale_data_size);
|
|
if (bias)
|
|
weights[1] = RandomMat(scale_data_size);
|
|
|
|
int ret = test_layer<ncnn::Scale>("Scale", pd, weights, a);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_scale failed a.dims=%d a=(%d %d %d) scale_data_size=%d bias=%d\n", a.dims, a.w, a.h, a.c, scale_data_size, bias);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
static int test_scale_attention(const ncnn::Mat& a, int scale_data_size)
|
|
{
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, -233);
|
|
|
|
std::vector<ncnn::Mat> weights(0);
|
|
|
|
std::vector<ncnn::Mat> ab(2);
|
|
ab[0] = a;
|
|
ab[1] = RandomMat(scale_data_size);
|
|
|
|
int ret = test_layer<ncnn::Scale>("Scale", pd, weights, ab, 2);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_scale_attention failed a.dims=%d a=(%d %d %d) scale_data_size=%d\n", a.dims, a.w, a.h, a.c, scale_data_size);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
static int test_scale_0()
|
|
{
|
|
return 0
|
|
|| test_scale(RandomMat(5, 7, 16), 16, 0)
|
|
|| test_scale(RandomMat(5, 7, 16), 16, 1)
|
|
|| test_scale(RandomMat(3, 5, 13), 13, 0)
|
|
|| test_scale(RandomMat(3, 5, 13), 13, 1);
|
|
}
|
|
|
|
static int test_scale_1()
|
|
{
|
|
return 0
|
|
|| test_scale(RandomMat(6, 16), 16, 0)
|
|
|| test_scale(RandomMat(6, 16), 16, 1)
|
|
|| test_scale(RandomMat(7, 15), 15, 0)
|
|
|| test_scale(RandomMat(7, 15), 15, 1);
|
|
}
|
|
|
|
static int test_scale_2()
|
|
{
|
|
return 0
|
|
|| test_scale(RandomMat(128), 128, 0)
|
|
|| test_scale(RandomMat(128), 128, 1)
|
|
|| test_scale(RandomMat(127), 127, 0)
|
|
|| test_scale(RandomMat(127), 127, 1);
|
|
}
|
|
|
|
static int test_scale_3()
|
|
{
|
|
return 0
|
|
|| test_scale_attention(RandomMat(5, 7, 16), 16)
|
|
|| test_scale_attention(RandomMat(3, 5, 13), 13);
|
|
}
|
|
|
|
static int test_scale_4()
|
|
{
|
|
return 0
|
|
|| test_scale_attention(RandomMat(6, 16), 16)
|
|
|| test_scale_attention(RandomMat(7, 15), 15);
|
|
}
|
|
|
|
static int test_scale_5()
|
|
{
|
|
return 0
|
|
|| test_scale_attention(RandomMat(128), 128)
|
|
|| test_scale_attention(RandomMat(127), 127);
|
|
}
|
|
|
|
int main()
|
|
{
|
|
SRAND(7767517);
|
|
|
|
return 0
|
|
|| test_scale_0()
|
|
|| test_scale_1()
|
|
|| test_scale_2()
|
|
|| test_scale_3()
|
|
|| test_scale_4()
|
|
|| test_scale_5();
|
|
}
|