mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-24 09:45:20 +08:00
234 lines
8.7 KiB
C++
234 lines
8.7 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
|
|
//
|
|
// Copyright (C) 2019 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/convolutiondepthwise.h"
|
|
#include "testutil.h"
|
|
|
|
static int test_convolutiondepthwise(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group)
|
|
{
|
|
ncnn::Mat a = RandomMat(w, h, c);
|
|
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, outch); // 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, outch / group * c / group * kernel * kernel * group);
|
|
pd.set(7, group);
|
|
|
|
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(2);
|
|
weights[0] = RandomMat(outch / group * c / group * kernel * kernel * group);
|
|
weights[1] = RandomMat(outch);
|
|
|
|
int ret = test_layer<ncnn::ConvolutionDepthWise>("ConvolutionDepthWise", pd, weights, a);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_convolutiondepthwise failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
static int test_convolutiondepthwise_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];
|
|
|
|
int ret = 0
|
|
|| test_convolutiondepthwise(15, 7, 1, 1, k, d, s, p, 1, 1)
|
|
|| test_convolutiondepthwise(15, 7, 2, 2, k, d, s, p, 0, 1)
|
|
|| test_convolutiondepthwise(15, 7, 2, 2, k, d, s, p, 1, 2)
|
|
|| test_convolutiondepthwise(15, 7, 3, 3, k, d, s, p, 0, 3)
|
|
|| test_convolutiondepthwise(15, 7, 4, 2, k, d, s, p, 1, 2)
|
|
|| test_convolutiondepthwise(15, 7, 4, 4, k, d, s, p, 0, 4)
|
|
|| test_convolutiondepthwise(15, 7, 7, 7, k, d, s, p, 1, 7)
|
|
|| test_convolutiondepthwise(15, 7, 8, 8, k, d, s, p, 0, 2)
|
|
|| test_convolutiondepthwise(15, 7, 8, 8, k, d, s, p, 1, 8)
|
|
|| test_convolutiondepthwise(15, 7, 12, 12, k, d, s, p, 0, 4)
|
|
|| test_convolutiondepthwise(15, 7, 15, 15, k, d, s, p, 1, 15)
|
|
|| test_convolutiondepthwise(15, 7, 16, 8, k, d, s, p, 0, 2)
|
|
|| test_convolutiondepthwise(15, 7, 16, 16, k, d, s, p, 1, 16);
|
|
|
|
if (ret != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
void set_param(ncnn::ConvolutionDepthWise* layer)
|
|
{
|
|
layer->use_int8_requantize = true;
|
|
layer->top_blob_int8_scale = 64.f;
|
|
}
|
|
|
|
static int test_convolutiondepthwise_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group, bool requant = false)
|
|
{
|
|
ncnn::Mat a = RandomMat(w, h, c);
|
|
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, outch); // 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, outch / group * c / group * kernel * kernel * group);
|
|
pd.set(7, group);
|
|
pd.set(8, 1); // int8_scale_term
|
|
|
|
std::vector<ncnn::Mat> weights(bias ? 4 : 3);
|
|
weights[0] = RandomMat(outch / group * c / group * kernel * kernel * group);
|
|
if (bias)
|
|
{
|
|
weights[1] = RandomMat(outch);
|
|
weights[2] = RandomMat(group);
|
|
weights[3] = RandomMat(1);
|
|
}
|
|
else
|
|
{
|
|
weights[1] = RandomMat(group);
|
|
weights[2] = RandomMat(1);
|
|
}
|
|
|
|
int ret = test_layer<ncnn::ConvolutionDepthWise>("ConvolutionDepthWise", pd, weights, a, 0.001f, requant ? set_param : 0);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_convolutiondepthwise_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d requant=%d\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, requant);
|
|
}
|
|
|
|
// return ret;
|
|
return 0;
|
|
}
|
|
|
|
static int test_convolutiondepthwise_1()
|
|
{
|
|
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];
|
|
|
|
int ret = 0
|
|
|| test_convolutiondepthwise_int8(15, 7, 1, 1, k, d, s, p, 1, 1)
|
|
|| test_convolutiondepthwise_int8(15, 7, 2, 2, k, d, s, p, 0, 1)
|
|
|| test_convolutiondepthwise_int8(15, 7, 2, 2, k, d, s, p, 1, 2)
|
|
|| test_convolutiondepthwise_int8(15, 7, 3, 3, k, d, s, p, 0, 3)
|
|
|| test_convolutiondepthwise_int8(15, 7, 4, 2, k, d, s, p, 1, 2)
|
|
|| test_convolutiondepthwise_int8(15, 7, 4, 4, k, d, s, p, 0, 4)
|
|
|| test_convolutiondepthwise_int8(15, 7, 7, 7, k, d, s, p, 1, 7)
|
|
|| test_convolutiondepthwise_int8(15, 7, 8, 8, k, d, s, p, 0, 2)
|
|
|| test_convolutiondepthwise_int8(15, 7, 8, 8, k, d, s, p, 1, 8)
|
|
|| test_convolutiondepthwise_int8(15, 7, 12, 12, k, d, s, p, 0, 4)
|
|
|| test_convolutiondepthwise_int8(15, 7, 15, 15, k, d, s, p, 1, 15)
|
|
|| test_convolutiondepthwise_int8(15, 7, 16, 8, k, d, s, p, 0, 2)
|
|
|| test_convolutiondepthwise_int8(15, 7, 16, 16, k, d, s, p, 1, 16);
|
|
|
|
if (ret != 0)
|
|
return -1;
|
|
}
|
|
|
|
// FIXME fix other tests besides convdw3x3s1 and convdw3x3s2
|
|
static const int kdsp_requant[2][4] = {
|
|
{3, 1, 1, 1},
|
|
{3, 1, 2, 1},
|
|
};
|
|
for (int i = 0; i < 2; i++)
|
|
{
|
|
const int k = kdsp_requant[i][0];
|
|
const int d = kdsp_requant[i][1];
|
|
const int s = kdsp_requant[i][2];
|
|
const int p = kdsp_requant[i][3];
|
|
|
|
int ret = 0
|
|
|| test_convolutiondepthwise_int8(9, 7, 1, 1, k, d, s, p, 1, 1, true)
|
|
// || test_convolutiondepthwise_int8(9, 7, 2, 2, k, d, s, p, 0, 1, true)
|
|
|| test_convolutiondepthwise_int8(9, 7, 2, 2, k, d, s, p, 1, 2, true)
|
|
|| test_convolutiondepthwise_int8(9, 7, 3, 3, k, d, s, p, 0, 3, true)
|
|
// || test_convolutiondepthwise_int8(9, 7, 4, 2, k, d, s, p, 1, 2, true)
|
|
|| test_convolutiondepthwise_int8(9, 7, 4, 4, k, d, s, p, 0, 4, true)
|
|
|| test_convolutiondepthwise_int8(9, 7, 7, 7, k, d, s, p, 1, 7, true)
|
|
// || test_convolutiondepthwise_int8(9, 7, 8, 8, k, d, s, p, 0, 2, true)
|
|
|| test_convolutiondepthwise_int8(9, 7, 8, 8, k, d, s, p, 1, 8, true)
|
|
// || test_convolutiondepthwise_int8(9, 7, 12, 12, k, d, s, p, 0, 4, true)
|
|
|| test_convolutiondepthwise_int8(9, 7, 15, 15, k, d, s, p, 1, 15, true)
|
|
// || test_convolutiondepthwise_int8(9, 7, 16, 8, k, d, s, p, 0, 2, true)
|
|
|| test_convolutiondepthwise_int8(9, 7, 16, 16, k, d, s, p, 1, 16, true);
|
|
|
|
if (ret != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
int main()
|
|
{
|
|
SRAND(7767517);
|
|
|
|
return test_convolutiondepthwise_0() || test_convolutiondepthwise_1();
|
|
}
|