File size: 3,505 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 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2022 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/deconvolution1d.h"
#include "testutil.h"
static int test_deconvolution1d(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias, int output_pad_right, int output_w)
{
ncnn::Mat a = RandomMat(w, h);
if (output_w > 0 && pad != -233 && pad != -234)
{
pad = -233;
}
ncnn::ParamDict pd;
pd.set(0, outh);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outh * h * kernel);
int activation_type = RAND() % 5; // 0 1 2 3 4
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);
pd.set(18, output_pad_right);
pd.set(20, output_w);
std::vector<ncnn::Mat> weights(2);
weights[0] = RandomMat(outh * h * kernel);
weights[1] = RandomMat(outh);
int ret = test_layer<ncnn::Deconvolution1D>("Deconvolution1D", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_deconvolution1d failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f] output_pad_right=%d output_w=%d\n", w, h, outh, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1], output_pad_right, output_w);
}
return ret;
}
static int test_deconvolution1d_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, -233},
{4, 1, 2, -234},
{4, 2, 1, -234},
{5, 1, 1, 2},
{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_deconvolution1d(9, 1, 1, k, d, s, p, 1, 0, 0)
|| test_deconvolution1d(9, 4, 13, k, d, s, p, 0, 1, 7)
|| test_deconvolution1d(9, 13, 4, k, d, s, p, 1, 1, 0)
|| test_deconvolution1d(9, 4, 8, k, d, s, p, 0, 0, 0)
|| test_deconvolution1d(9, 8, 4, k, d, s, p, 1, 0, 7)
|| test_deconvolution1d(9, 8, 13, k, d, s, p, 0, 2, 0)
|| test_deconvolution1d(9, 13, 8, k, d, s, p, 1, 2, 0)
|| test_deconvolution1d(9, 16, 16, k, d, s, p, 0, 0, 7);
if (ret != 0)
return -1;
}
return 0;
}
int main()
{
SRAND(7767517);
return test_deconvolution1d_0();
}
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