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test_priorbox.cpp
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test_priorbox.cpp
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// 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/priorbox.h"
#include "testutil.h"
static int test_priorbox_caffe()
{
ncnn::Mat min_sizes(1);
min_sizes[0] = 105.f;
ncnn::Mat max_sizes(1);
max_sizes[0] = 150.f;
ncnn::Mat aspect_ratios(2);
aspect_ratios[0] = 2.f;
aspect_ratios[1] = 3.f;
ncnn::ParamDict pd;
pd.set(0, min_sizes);
pd.set(1, max_sizes);
pd.set(2, aspect_ratios);
pd.set(3, 0.1f); // variances[0]
pd.set(4, 0.1f); // variances[1]
pd.set(5, 0.2f); // variances[2]
pd.set(6, 0.2f); // variances[3]
pd.set(7, 1); // flip
pd.set(8, 0); // clip
pd.set(9, -233); // image_width
pd.set(10, -233); // image_height
pd.set(11, -233.f); // step_width
pd.set(12, -233.f); // step_height
pd.set(13, 0.f); // offset
pd.set(14, 0.f); // step_mmdetection
pd.set(15, 0.f); // center_mmdetection
std::vector<ncnn::Mat> weights(0);
std::vector<ncnn::Mat> as(2);
as[0] = RandomMat(72, 72, 1);
as[1] = RandomMat(512, 512, 1);
int ret = test_layer<ncnn::PriorBox>("PriorBox", pd, weights, as, 1);
if (ret != 0)
{
fprintf(stderr, "test_priorbox_caffe failed\n");
}
return ret;
}
static int test_priorbox_mxnet()
{
ncnn::Mat min_sizes(2);
min_sizes[0] = 0.15f;
min_sizes[1] = 0.2121f;
ncnn::Mat max_sizes(0);
ncnn::Mat aspect_ratios(5);
aspect_ratios[0] = 1.f;
aspect_ratios[1] = 2.f;
aspect_ratios[2] = 0.5f;
aspect_ratios[3] = 3.f;
aspect_ratios[4] = 0.333333;
ncnn::ParamDict pd;
pd.set(0, min_sizes);
pd.set(1, max_sizes);
pd.set(2, aspect_ratios);
pd.set(3, 0.1f); // variances[0]
pd.set(4, 0.1f); // variances[1]
pd.set(5, 0.2f); // variances[2]
pd.set(6, 0.2f); // variances[3]
pd.set(7, 0); // flip
pd.set(8, 0); // clip
pd.set(9, -233); // image_width
pd.set(10, -233); // image_height
pd.set(11, -233.f); // step_width
pd.set(12, -233.f); // step_height
pd.set(13, 0.5f); // offset
pd.set(14, 0.f); // step_mmdetection
pd.set(15, 0.f); // center_mmdetection
std::vector<ncnn::Mat> weights(0);
std::vector<ncnn::Mat> as(1);
as[0] = RandomMat(72, 72, 1);
int ret = test_layer<ncnn::PriorBox>("PriorBox", pd, weights, as, 1);
if (ret != 0)
{
fprintf(stderr, "test_priorbox_mxnet failed\n");
}
return ret;
}
int main()
{
SRAND(7767517);
return 0
|| test_priorbox_caffe()
|| test_priorbox_mxnet();
}