File size: 6,045 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | // 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);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch / group * c / group * kernel * kernel * group);
pd.set(7, group);
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> 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)
|| test_convolutiondepthwise(18, 17, 1, 1, k, d, s, p, 1, 1)
|| test_convolutiondepthwise(18, 17, 2, 2, k, d, s, p, 0, 1)
|| test_convolutiondepthwise(18, 17, 2, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise(18, 17, 3, 3, k, d, s, p, 0, 3)
|| test_convolutiondepthwise(18, 17, 4, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise(18, 17, 4, 4, k, d, s, p, 0, 4)
|| test_convolutiondepthwise(18, 17, 7, 7, k, d, s, p, 1, 7)
|| test_convolutiondepthwise(18, 17, 8, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise(18, 17, 8, 8, k, d, s, p, 1, 8)
|| test_convolutiondepthwise(18, 17, 12, 12, k, d, s, p, 0, 4)
|| test_convolutiondepthwise(18, 17, 15, 15, k, d, s, p, 1, 15)
|| test_convolutiondepthwise(18, 17, 16, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise(18, 17, 16, 16, k, d, s, p, 1, 16)
|| test_convolutiondepthwise(25, 33, 1, 1, k, d, s, p, 1, 1)
|| test_convolutiondepthwise(25, 33, 2, 2, k, d, s, p, 0, 1)
|| test_convolutiondepthwise(25, 33, 2, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise(25, 33, 3, 3, k, d, s, p, 0, 3)
|| test_convolutiondepthwise(25, 33, 4, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise(25, 33, 4, 4, k, d, s, p, 0, 4)
|| test_convolutiondepthwise(25, 33, 7, 7, k, d, s, p, 1, 7)
|| test_convolutiondepthwise(25, 33, 8, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise(25, 33, 8, 8, k, d, s, p, 1, 8)
|| test_convolutiondepthwise(25, 33, 12, 12, k, d, s, p, 0, 4)
|| test_convolutiondepthwise(25, 33, 15, 15, k, d, s, p, 1, 15)
|| test_convolutiondepthwise(25, 33, 16, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise(25, 33, 16, 16, k, d, s, p, 1, 16);
if (ret != 0)
return -1;
}
return 0;
}
int main()
{
SRAND(7767517);
return test_convolutiondepthwise_0();
}
|