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gaoyuan
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Add unittest of the priorbox layer
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4 files changed

+171
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‎paddle/gserver/layers/PriorBox.cpp‎

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@@ -76,6 +76,7 @@ void PriorBoxLayer::forward(PassType passType) {
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auto image = getInput(1);
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int imageWidth = image.getFrameWidth();
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int imageHeight = image.getFrameHeight();
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float stepW = static_cast<float>(imageWidth) / layerWidth;
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float stepH = static_cast<float>(imageHeight) / layerHeight;
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int dim = layerHeight * layerWidth * numPriors_ * 4;

‎paddle/gserver/tests/CMakeLists.txt‎

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@@ -34,6 +34,14 @@ add_unittest_without_exec(test_ConvTrans
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add_test(NAME test_ConvTrans
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COMMAND test_ConvTrans)
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################# test_PriorBox #######################
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add_unittest_without_exec(test_PriorBox
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test_PriorBox.cpp
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LayerGradUtil.cpp
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TestUtil.cpp)
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add_test(NAME test_PriorBox
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COMMAND test_PriorBox)
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################# test_ConvUnify #######################
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add_unittest_without_exec(test_ConvUnify
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test_ConvUnify.cpp

‎paddle/gserver/tests/test_PriorBox.cpp‎

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <string>
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#include <vector>
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#include "LayerGradUtil.h"
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#include "TestUtil.h"
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using namespace paddle; // NOLINT
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using namespace std; // NOLINT
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P_DECLARE_bool(use_gpu);
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P_DECLARE_int32(gpu_id);
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P_DECLARE_bool(thread_local_rand_use_global_seed);
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// Do one forward pass of priorBox layer and check to see if its output
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// matches the given result
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void doOnePriorBoxTest(size_t featureMapWidth,
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size_t featureMapHeight,
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size_t imageWidth,
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size_t imageHeight,
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vector<int> minSize,
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vector<int> maxSize,
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vector<float> aspectRatio,
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vector<float> variance,
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MatrixPtr& result) {
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// Setting up the priorbox layer
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TestConfig configt;
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configt.layerConfig.set_type("priorbox");
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configt.inputDefs.push_back({INPUT_DATA, "featureMap", 1, 0});
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LayerInputConfig* input = configt.layerConfig.add_inputs();
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configt.inputDefs.push_back({INPUT_DATA, "image", 1, 0});
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configt.layerConfig.add_inputs();
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PriorBoxConfig* pb = input->mutable_priorbox_conf();
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for (size_t i = 0; i < minSize.size(); i++) pb->add_min_size(minSize[i]);
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for (size_t i = 0; i < maxSize.size(); i++) pb->add_max_size(maxSize[i]);
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for (size_t i = 0; i < aspectRatio.size(); i++)
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pb->add_aspect_ratio(aspectRatio[i]);
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for (size_t i = 0; i < variance.size(); i++) pb->add_variance(variance[i]);
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// data layer initialize
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std::vector<DataLayerPtr> dataLayers;
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LayerMap layerMap;
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vector<Argument> datas;
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initDataLayer(
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configt, &dataLayers, &datas, &layerMap, "priorbox", 1, false, true);
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dataLayers[0]->getOutput().setFrameHeight(featureMapHeight);
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dataLayers[0]->getOutput().setFrameWidth(featureMapWidth);
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dataLayers[1]->getOutput().setFrameHeight(imageHeight);
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dataLayers[1]->getOutput().setFrameWidth(imageWidth);
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// test layer initialize
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std::vector<ParameterPtr> parameters;
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LayerPtr priorboxLayer;
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initTestLayer(configt, &layerMap, &parameters, &priorboxLayer);
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priorboxLayer->forward(PASS_GC);
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checkMatrixEqual(priorboxLayer->getOutputValue(), result);
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}
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TEST(Layer, priorBoxLayerFwd) {
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vector<int> minSize;
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vector<int> maxSize;
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vector<float> aspectRatio;
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vector<float> variance;
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minSize.push_back(276);
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maxSize.push_back(330);
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variance.push_back(0.1);
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variance.push_back(0.1);
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variance.push_back(0.2);
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variance.push_back(0.2);
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MatrixPtr result;
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result = Matrix::create(1, 2 * 8, false, false);
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float resultData[] = {0.04,
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0.04,
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0.96,
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0.96,
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0.1,
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0.1,
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0.2,
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0.2,
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0,
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0,
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1,
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1,
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0.1,
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0.1,
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0.2,
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0.2};
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result->setData(resultData);
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doOnePriorBoxTest(/* featureMapWidth */ 1,
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/* featureMapHeight */ 1,
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/* imageWidth */ 300,
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/* imageHeight */ 300,
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minSize,
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maxSize,
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aspectRatio,
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variance,
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result);
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variance[1] = 0.2;
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variance[3] = 0.1;
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maxSize.pop_back();
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Matrix::resizeOrCreate(result, 1, 4 * 8, false, false);
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float resultData2[] = {0, 0, 0.595, 0.595, 0.1, 0.2, 0.2, 0.1,
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0.405, 0, 1, 0.595, 0.1, 0.2, 0.2, 0.1,
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0, 0.405, 0.595, 1, 0.1, 0.2, 0.2, 0.1,
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0.405, 0.405, 1, 1, 0.1, 0.2, 0.2, 0.1};
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result->setData(resultData2);
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doOnePriorBoxTest(/* featureMapWidth */ 2,
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/* featureMapHeight */ 2,
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/* imageWidth */ 400,
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/* imageHeight */ 400,
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minSize,
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maxSize,
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aspectRatio,
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variance,
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result);
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aspectRatio.push_back(2);
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Matrix::resizeOrCreate(result, 1, 3 * 8, false, false);
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float resultData3[] = {0.04, 0.04, 0.96, 0.96, 0.1, 0.2,
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0.2, 0.1, 0, 0.17473088, 1, 0.825269,
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0.1, 0.2, 0.2, 0.1, 0.17473088, 0,
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0.825269, 1, 0.1, 0.2, 0.2, 0.1};
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result->setData(resultData3);
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doOnePriorBoxTest(/* featureMapWidth */ 1,
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/* featureMapHeight */ 1,
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/* imageWidth */ 300,
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/* imageHeight */ 300,
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minSize,
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maxSize,
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aspectRatio,
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variance,
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result);
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}
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int main(int argc, char** argv) {
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testing::InitGoogleTest(&argc, argv);
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initMain(argc, argv);
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FLAGS_thread_local_rand_use_global_seed = true;
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srand(1);
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return RUN_ALL_TESTS();
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}

‎python/paddle/trainer/config_parser.py‎

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@@ -1583,14 +1583,15 @@ class PriorBoxLayer(LayerBase):
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def __init__(self, name, inputs, size, min_size, max_size, aspect_ratio,
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variance):
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super(PriorBoxLayer, self).__init__(name, 'priorbox', 0, inputs)
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config_assert(len(inputs) == 2, 'PriorBoxLayer must have 2 input')
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config_assert(len(inputs) == 2, 'PriorBoxLayer must have 2 inputs')
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input_layer = self.get_input_layer(1)
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config_assert(
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input_layer.type == 'data',
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'Expecting the second input layer of an priorbox layer to be '
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'a data layer')
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config_assert(input_layer.width > 0, 'The data layer must set width')
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config_assert(input_layer.height > 0, 'The data layer must set height')
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config_assert(len(variance) == 4, 'The variance must have 4 inputs')
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self.config.inputs[0].priorbox_conf.min_size.extend(min_size)
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self.config.inputs[0].priorbox_conf.max_size.extend(max_size)
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self.config.inputs[0].priorbox_conf.aspect_ratio.extend(aspect_ratio)

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