118 lines
3.2 KiB
C++
118 lines
3.2 KiB
C++
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// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include "layer/priorbox.h"
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#include "testutil.h"
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static int test_priorbox_caffe()
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{
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ncnn::Mat min_sizes(1);
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min_sizes[0] = 105.f;
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ncnn::Mat max_sizes(1);
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max_sizes[0] = 150.f;
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ncnn::Mat aspect_ratios(2);
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aspect_ratios[0] = 2.f;
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aspect_ratios[1] = 3.f;
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ncnn::ParamDict pd;
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pd.set(0, min_sizes);
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pd.set(1, max_sizes);
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pd.set(2, aspect_ratios);
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pd.set(3, 0.1f); // variances[0]
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pd.set(4, 0.1f); // variances[1]
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pd.set(5, 0.2f); // variances[2]
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pd.set(6, 0.2f); // variances[3]
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pd.set(7, 1); // flip
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pd.set(8, 0); // clip
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pd.set(9, -233); // image_width
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pd.set(10, -233); // image_height
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pd.set(11, -233.f); // step_width
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pd.set(12, -233.f); // step_height
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pd.set(13, 0.f); // offset
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pd.set(14, 0.f); // step_mmdetection
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pd.set(15, 0.f); // center_mmdetection
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std::vector<ncnn::Mat> weights(0);
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std::vector<ncnn::Mat> as(2);
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as[0] = RandomMat(72, 72, 1);
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as[1] = RandomMat(512, 512, 1);
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int ret = test_layer<ncnn::PriorBox>("PriorBox", pd, weights, as, 1);
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if (ret != 0)
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{
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fprintf(stderr, "test_priorbox_caffe failed\n");
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}
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return ret;
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}
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static int test_priorbox_mxnet()
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{
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ncnn::Mat min_sizes(2);
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min_sizes[0] = 0.15f;
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min_sizes[1] = 0.2121f;
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ncnn::Mat max_sizes(0);
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ncnn::Mat aspect_ratios(5);
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aspect_ratios[0] = 1.f;
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aspect_ratios[1] = 2.f;
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aspect_ratios[2] = 0.5f;
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aspect_ratios[3] = 3.f;
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aspect_ratios[4] = 0.333333;
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ncnn::ParamDict pd;
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pd.set(0, min_sizes);
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pd.set(1, max_sizes);
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pd.set(2, aspect_ratios);
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pd.set(3, 0.1f); // variances[0]
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pd.set(4, 0.1f); // variances[1]
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pd.set(5, 0.2f); // variances[2]
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pd.set(6, 0.2f); // variances[3]
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pd.set(7, 0); // flip
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pd.set(8, 0); // clip
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pd.set(9, -233); // image_width
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pd.set(10, -233); // image_height
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pd.set(11, -233.f); // step_width
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pd.set(12, -233.f); // step_height
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pd.set(13, 0.5f); // offset
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pd.set(14, 0.f); // step_mmdetection
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pd.set(15, 0.f); // center_mmdetection
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std::vector<ncnn::Mat> weights(0);
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std::vector<ncnn::Mat> as(1);
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as[0] = RandomMat(72, 72, 1);
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int ret = test_layer<ncnn::PriorBox>("PriorBox", pd, weights, as, 1);
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if (ret != 0)
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{
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fprintf(stderr, "test_priorbox_mxnet failed\n");
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}
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return ret;
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}
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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_priorbox_caffe()
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|| test_priorbox_mxnet();
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}
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