deepin-ocr/3rdparty/ncnn/tests/test_yolov3detectionoutput.cpp

// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2020 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/yolov3detectionoutput.h"
#include "testutil.h"

static int test_yolov3detectionoutput(const std::vector<ncnn::Mat>& a, int num_class,
                                      int num_box, float confidence_threshold, float nms_threshold,
                                      ncnn::Mat& biases, ncnn::Mat& mask, ncnn::Mat& anchors_scale)
{
    ncnn::ParamDict pd;
    pd.set(0, num_class);
    pd.set(1, num_box);
    pd.set(2, confidence_threshold);
    pd.set(3, nms_threshold);
    pd.set(4, biases);
    pd.set(5, mask);
    pd.set(6, anchors_scale);

    std::vector<ncnn::Mat> weights(0);

    int ret = test_layer<ncnn::Yolov3DetectionOutput>("Yolov3DetectionOutput", pd, weights, a);
    if (ret != 0)
    {
        fprintf(stderr, "test_yolov3detectionoutput failed a.dims=%d a=(%d %d %d) ", a[0].dims, a[0].w, a[0].h, a[0].c);
        fprintf(stderr, " num_class=%d num_box=%d", num_class, num_box);
        fprintf(stderr, " confidence_threshold=%f nms_threshold=%f\n", confidence_threshold, nms_threshold);
    }

    return ret;
}

static ncnn::Mat create_mat_from(const float* src, int length)
{
    ncnn::Mat ret(length);
    memcpy(ret.data, src, length * sizeof(float));
    return ret;
}

static ncnn::Mat MyRandomMat(int w, int h, int c)
{
    ncnn::Mat m(w, h, c);
    Randomize(m, -15.f, 1.5f);
    return m;
}

static int test_yolov3detectionoutput_v4()
{
    const float b[] = {12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401};
    const float m[] = {0, 1, 2, 3, 4, 5, 6, 7, 8};
    const float s[] = {9.6, 17.6, 33.6};

    ncnn::Mat biases = create_mat_from(b, sizeof(b) / sizeof(b[0]));
    ncnn::Mat mask = create_mat_from(m, sizeof(m) / sizeof(m[0]));
    ncnn::Mat anchors_scale = create_mat_from(s, sizeof(s) / sizeof(s[0]));

    std::vector<ncnn::Mat> a(3);
    a[0] = MyRandomMat(76, 76, 255);
    a[1] = MyRandomMat(38, 38, 255);
    a[2] = MyRandomMat(19, 19, 255);

    return 0
           || test_yolov3detectionoutput(a, 80, 3, 0.55f, 0.45f, biases, mask, anchors_scale);
}

static int test_yolov3detectionoutput_v4tiny()
{
    const float b[] = {10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319};
    const float m[] = {3, 4, 5, 1, 2, 3};
    const float s[] = {33.6, 16.8};

    ncnn::Mat biases = create_mat_from(b, sizeof(b) / sizeof(b[0]));
    ncnn::Mat mask = create_mat_from(m, sizeof(m) / sizeof(m[0]));
    ncnn::Mat anchors_scale = create_mat_from(s, sizeof(s) / sizeof(s[0]));

    std::vector<ncnn::Mat> a(2);
    a[0] = MyRandomMat(13, 13, 255);
    a[1] = MyRandomMat(26, 26, 255);

    return 0
           || test_yolov3detectionoutput(a, 80, 3, 0.4f, 0.45f, biases, mask, anchors_scale);
}

static int test_yolov3detectionoutput_v3()
{
    const float b[] = {10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326};
    const float m[] = {6, 7, 8, 3, 4, 5, 0, 1, 2};
    const float s[] = {32, 16, 8};

    ncnn::Mat biases = create_mat_from(b, sizeof(b) / sizeof(b[0]));
    ncnn::Mat mask = create_mat_from(m, sizeof(m) / sizeof(m[0]));
    ncnn::Mat anchors_scale = create_mat_from(s, sizeof(s) / sizeof(s[0]));

    std::vector<ncnn::Mat> a(3);
    a[0] = MyRandomMat(19, 19, 255);
    a[1] = MyRandomMat(38, 38, 255);
    a[2] = MyRandomMat(76, 76, 255);

    return 0
           || test_yolov3detectionoutput(a, 80, 3, 0.6f, 0.45f, biases, mask, anchors_scale);
}

static int test_yolov3detectionoutput_v3tiny()
{
    const float b[] = {10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319};
    const float m[] = {3, 4, 5, 1, 2, 3};
    const float s[] = {32, 16};

    ncnn::Mat biases = create_mat_from(b, sizeof(b) / sizeof(b[0]));
    ncnn::Mat mask = create_mat_from(m, sizeof(m) / sizeof(m[0]));
    ncnn::Mat anchors_scale = create_mat_from(s, sizeof(s) / sizeof(s[0]));

    std::vector<ncnn::Mat> a(2);
    a[0] = MyRandomMat(13, 13, 255);
    a[1] = MyRandomMat(26, 26, 255);

    return 0
           || test_yolov3detectionoutput(a, 80, 3, 0.3f, 0.45f, biases, mask, anchors_scale);
}

int main()
{
    SRAND(7767517);

    return 0
           || test_yolov3detectionoutput_v3tiny()
           || test_yolov3detectionoutput_v3()
           || test_yolov3detectionoutput_v4tiny()
           || test_yolov3detectionoutput_v4();
}