File size: 3,982 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 | // 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/convolutiondepthwise3d.h"
#include "testutil.h"
static int test_convolutiondepthwise3d(int w, int h, int d, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group)
{
ncnn::Mat a = RandomMat(w, h, d, 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 * 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 * kernel * group);
weights[1] = RandomMat(outch);
int ret = test_layer<ncnn::ConvolutionDepthWise3D>("ConvolutionDepthWise3D", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_convolutiondepthwise3d failed w=%d h=%d d=%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, d, c, outch, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]);
}
return ret;
}
static int test_convolutiondepthwise3d_0()
{
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_convolutiondepthwise3d(11, 10, 9, 1, 1, k, d, s, p, 1, 1)
|| test_convolutiondepthwise3d(11, 10, 9, 2, 2, k, d, s, p, 0, 1)
|| test_convolutiondepthwise3d(11, 10, 9, 2, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise3d(11, 10, 9, 3, 3, k, d, s, p, 0, 3)
|| test_convolutiondepthwise3d(11, 10, 9, 4, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise3d(11, 10, 9, 4, 4, k, d, s, p, 0, 4)
|| test_convolutiondepthwise3d(11, 10, 9, 7, 7, k, d, s, p, 1, 7)
|| test_convolutiondepthwise3d(11, 10, 9, 8, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise3d(11, 10, 9, 8, 8, k, d, s, p, 1, 8)
|| test_convolutiondepthwise3d(11, 10, 9, 12, 12, k, d, s, p, 0, 4)
|| test_convolutiondepthwise3d(11, 10, 9, 15, 15, k, d, s, p, 1, 15)
|| test_convolutiondepthwise3d(11, 10, 9, 16, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise3d(11, 10, 9, 16, 16, k, d, s, p, 1, 16);
if (ret != 0)
return -1;
}
return 0;
}
int main()
{
SRAND(7767517);
return test_convolutiondepthwise3d_0();
}
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