mirror of
https://github.com/Tencent/ncnn.git
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LI
149 lines
4.1 KiB
C++
149 lines
4.1 KiB
C++
// Copyright 2020 Tencent
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// SPDX-License-Identifier: BSD-3-Clause
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#include "testutil.h"
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#include "layer_type.h"
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#include <limits.h>
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static int test_lrn(const ncnn::Mat& a, int region_type, int local_size, float alpha, float beta, float bias, int flag = 0)
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{
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ncnn::ParamDict pd;
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pd.set(0, region_type);
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pd.set(1, local_size);
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pd.set(2, alpha);
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pd.set(3, beta);
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pd.set(4, bias);
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std::vector<ncnn::Mat> weights(0);
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int ret = test_layer("LRN", pd, weights, a, 0.001, flag);
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if (ret != 0)
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{
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fprintf(stderr, "test_lrn failed a.dims=%d a=(%d %d %d) region_type=%d local_size=%d alpha=%f beta=%f bias=%f\n", a.dims, a.w, a.h, a.c, region_type, local_size, alpha, beta, bias);
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}
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return ret;
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}
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static int test_lrn_0()
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{
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ncnn::Mat a = RandomMat(11, 7, 12);
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return 0
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|| test_lrn(a, 0, 2, 1.f, 0.75f, 1.f)
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|| test_lrn(a, 0, 4, 2.f, 0.12f, 1.33f)
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|| test_lrn(a, 0, 1, 1.f, 0.75f, 1.f)
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|| test_lrn(a, 0, 5, 2.f, 0.12f, 1.33f)
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|| test_lrn(a, 1, 1, 0.6f, 0.4f, 2.4f)
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|| test_lrn(a, 1, 3, 1.f, 0.75f, 0.5f);
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}
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static int test_lrn_1()
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{
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ncnn::Mat a = RandomMat(10, 8, 16);
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return 0
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|| test_lrn(a, 0, 2, 1.f, 0.75f, 1.f, TEST_LAYER_DISABLE_GPU_TESTING)
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|| test_lrn(a, 0, 4, 2.f, 0.12f, 1.33f, TEST_LAYER_DISABLE_GPU_TESTING)
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|| test_lrn(a, 0, 1, 1.f, 0.75f, 1.f, TEST_LAYER_DISABLE_GPU_TESTING)
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|| test_lrn(a, 0, 5, 2.f, 0.12f, 1.33f, TEST_LAYER_DISABLE_GPU_TESTING)
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|| test_lrn(a, 1, 1, 0.6f, 0.4f, 2.4f, TEST_LAYER_DISABLE_GPU_TESTING)
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|| test_lrn(a, 1, 3, 1.f, 0.75f, 0.5f, TEST_LAYER_DISABLE_GPU_TESTING);
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}
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static int test_lrn_2()
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{
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ncnn::Mat a = RandomMat(12, 10, 9);
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return 0
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|| test_lrn(a, 0, 2, 1.f, 0.75f, 1.f)
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|| test_lrn(a, 0, 4, 2.f, 0.12f, 1.33f)
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|| test_lrn(a, 0, 1, 1.f, 0.75f, 1.f)
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|| test_lrn(a, 0, 5, 2.f, 0.12f, 1.33f)
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|| test_lrn(a, 1, 1, 0.6f, 0.4f, 2.4f)
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|| test_lrn(a, 1, 3, 1.f, 0.75f, 0.5f);
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}
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static int test_lrn_even_window()
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{
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ncnn::Mat a(1, 1, 5);
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for (int q = 0; q < 5; q++)
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a.channel(q)[0] = q + 1.f;
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const int local_sizes[] = {2, 4, 8};
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// Squared channel sums for [q - size / 2, q + (size - 1) / 2],
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// with zeros outside the five channels containing 1, 2, 3, 4, 5.
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const float square_sums[][5] = {{1.f, 5.f, 13.f, 25.f, 41.f},
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{5.f, 14.f, 30.f, 54.f, 50.f},
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{30.f, 55.f, 55.f, 55.f, 55.f}
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};
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for (int i = 0; i < 3; i++)
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{
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ncnn::ParamDict pd;
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pd.set(0, 0);
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pd.set(1, local_sizes[i]);
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pd.set(2, 1.f);
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pd.set(3, 1.f);
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pd.set(4, 1.f);
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ncnn::Mat expected(1, 1, 5);
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for (int q = 0; q < 5; q++)
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expected.channel(q)[0] = (q + 1.f) / (1.f + square_sums[i][q] / local_sizes[i]);
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ncnn::Mat actual;
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std::vector<ncnn::Mat> weights;
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if (test_layer_naive(ncnn::LayerType::LRN, pd, weights, a, actual, 0) != 0 || CompareMat(expected, actual, 0.000001f) != 0)
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{
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fprintf(stderr, "test_lrn_even_window failed local_size=%d\n", local_sizes[i]);
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return -1;
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}
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if (test_lrn(a, 0, local_sizes[i], 1.f, 1.f, 1.f) != 0)
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return -1;
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}
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return 0;
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}
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#if NCNN_VALIDATION
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static int test_lrn_load_param()
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{
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ncnn::ParamDict base;
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if (test_layer_param(ncnn::LayerType::LRN, base, 0) != 0)
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return -1;
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for (int i = 0; i <= 1; i++)
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{
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if (test_layer_param(ncnn::LayerType::LRN, base, 0, i, 0) != 0)
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return -1;
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}
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const int invalid[] = {-1, 2, INT_MIN, INT_MAX};
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for (int i = 0; i < 4; i++)
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{
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if (test_layer_param(ncnn::LayerType::LRN, base, 0, invalid[i], -1) != 0)
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return -1;
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}
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return 0;
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}
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#endif // NCNN_VALIDATION
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int main()
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{
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SRAND(7767517);
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return 0
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|| test_lrn_even_window()
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|| test_lrn_0()
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|| test_lrn_1()
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|| test_lrn_2()
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#if NCNN_VALIDATION
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|| test_lrn_load_param()
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#endif // NCNN_VALIDATION
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;
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}
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