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提交 6f8f468f 编写于 作者: G gaoyuan
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Add priorbox layer gpu unit test.

上级 7dfe3bdf
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Showing with 92 addition and 35 deletion
paddle/gserver/tests/test_PriorBox.cpp
浏览文件 @ 6f8f468f
......@@ -24,14 +24,15 @@ using namespace std; // NOLINT
// Do one forward pass of priorBox layer and check to see if its output
// matches the given result
void doOnePriorBoxTest(size_t featureMapWidth,
size_t featureMapHeight,
size_t imageWidth,
size_t imageHeight,
vector<int> minSize,
vector<int> maxSize,
vector<float> aspectRatio,
void doOnePriorBoxTest(size_t feature_map_width,
size_t feature_map_height,
size_t image_width,
size_t image_height,
vector<int> min_size,
vector<int> max_size,
vector<float> aspect_ratio,
vector<float> variance,
bool use_gpu,
MatrixPtr& result) {
// Setting up the priorbox layer
TestConfig configt;
......@@ -42,28 +43,27 @@ void doOnePriorBoxTest(size_t featureMapWidth,
configt.inputDefs.push_back({INPUT_DATA, "image", 1, 0});
configt.layerConfig.add_inputs();
PriorBoxConfig* pb = input->mutable_priorbox_conf();
for (size_t i = 0; i < minSize.size(); i++) pb->add_min_size(minSize[i]);
for (size_t i = 0; i < maxSize.size(); i++) pb->add_max_size(maxSize[i]);
for (size_t i = 0; i < aspectRatio.size(); i++)
pb->add_aspect_ratio(aspectRatio[i]);
for (size_t i = 0; i < min_size.size(); i++) pb->add_min_size(min_size[i]);
for (size_t i = 0; i < max_size.size(); i++) pb->add_max_size(max_size[i]);
for (size_t i = 0; i < variance.size(); i++) pb->add_variance(variance[i]);
for (size_t i = 0; i < aspect_ratio.size(); i++)
pb->add_aspect_ratio(aspect_ratio[i]);
// data layer initialize
std::vector<DataLayerPtr> dataLayers;
LayerMap layerMap;
vector<Argument> datas;
initDataLayer(
configt, &dataLayers, &datas, &layerMap, "priorbox", 1, false, false);
dataLayers[0]->getOutput().setFrameHeight(featureMapHeight);
dataLayers[0]->getOutput().setFrameWidth(featureMapWidth);
dataLayers[1]->getOutput().setFrameHeight(imageHeight);
dataLayers[1]->getOutput().setFrameWidth(imageWidth);
configt, &dataLayers, &datas, &layerMap, "priorbox", 1, false, use_gpu);
dataLayers[0]->getOutput().setFrameHeight(feature_map_height);
dataLayers[0]->getOutput().setFrameWidth(feature_map_width);
dataLayers[1]->getOutput().setFrameHeight(image_height);
dataLayers[1]->getOutput().setFrameWidth(image_width);
// test layer initialize
std::vector<ParameterPtr> parameters;
LayerPtr priorboxLayer;
initTestLayer(configt, &layerMap, &parameters, &priorboxLayer);
priorboxLayer->forward(PASS_GC);
checkMatrixEqual(priorboxLayer->getOutputValue(), result);
}
......@@ -73,6 +73,7 @@ TEST(Layer, priorBoxLayerFwd) {
vector<int> maxSize;
vector<float> aspectRatio;
vector<float> variance;
bool useGpu = false;
minSize.push_back(276);
maxSize.push_back(330);
......@@ -81,9 +82,8 @@ TEST(Layer, priorBoxLayerFwd) {
variance.push_back(0.2);
variance.push_back(0.2);
// CPU case 1.
MatrixPtr result;
result = Matrix::create(1, 2 * 8, false, false);
float resultData[] = {0.04,
0.04,
0.96,
......@@ -100,52 +100,109 @@ TEST(Layer, priorBoxLayerFwd) {
0.1,
0.2,
0.2};
result = Matrix::create(1, 2 * 8, false, useGpu);
result->setData(resultData);
doOnePriorBoxTest(/* featureMapWidth */ 1,
/* featureMapHeight */ 1,
/* imageWidth */ 300,
/* imageHeight */ 300,
doOnePriorBoxTest(/* feature_map_width */ 1,
/* feature_map_height */ 1,
/* image_width */ 300,
/* image_height */ 300,
minSize,
maxSize,
aspectRatio,
variance,
useGpu,
result);
// CPU case 2.
variance[1] = 0.2;
variance[3] = 0.1;
maxSize.pop_back();
Matrix::resizeOrCreate(result, 1, 4 * 8, false, false);
float resultData2[] = {0, 0, 0.595, 0.595, 0.1, 0.2, 0.2, 0.1,
0.405, 0, 1, 0.595, 0.1, 0.2, 0.2, 0.1,
0, 0.405, 0.595, 1, 0.1, 0.2, 0.2, 0.1,
0.405, 0.405, 1, 1, 0.1, 0.2, 0.2, 0.1};
Matrix::resizeOrCreate(result, 1, 4 * 8, false, useGpu);
result->setData(resultData2);
doOnePriorBoxTest(/* featureMapWidth */ 2,
/* featureMapHeight */ 2,
/* imageWidth */ 400,
/* imageHeight */ 400,
doOnePriorBoxTest(/* feature_map_width */ 2,
/* feature_map_height */ 2,
/* image_width */ 400,
/* image_height */ 400,
minSize,
maxSize,
aspectRatio,
variance,
useGpu,
result);
// CPU case 3.
aspectRatio.push_back(2);
Matrix::resizeOrCreate(result, 1, 3 * 8, false, false);
float resultData3[] = {0.04, 0.04, 0.96, 0.96, 0.1, 0.2,
0.2, 0.1, 0, 0.17473088, 1, 0.825269,
0.1, 0.2, 0.2, 0.1, 0.17473088, 0,
0.825269, 1, 0.1, 0.2, 0.2, 0.1};
Matrix::resizeOrCreate(result, 1, 3 * 8, false, useGpu);
result->setData(resultData3);
doOnePriorBoxTest(/* featureMapWidth */ 1,
/* featureMapHeight */ 1,
/* imageWidth */ 300,
/* imageHeight */ 300,
doOnePriorBoxTest(/* feature_map_width */ 1,
/* feature_map_height */ 1,
/* image_width */ 300,
/* image_height */ 300,
minSize,
maxSize,
aspectRatio,
variance,
useGpu,
result);
#ifndef PADDLE_ONLY_CPU
// reset the input parameters
variance[1] = 0.1;
variance[3] = 0.2;
maxSize.push_back(330);
aspectRatio.pop_back();
MatrixPtr resultGpu;
useGpu = true;
// GPU case 1.
resultGpu = Matrix::create(1, 2 * 8, false, useGpu);
resultGpu->copyFrom(resultData, 2 * 8);
doOnePriorBoxTest(/* feature_map_width */ 1,
/* feature_map_height */ 1,
/* image_width */ 300,
/* image_height */ 300,
minSize,
maxSize,
aspectRatio,
variance,
useGpu,
resultGpu);
// GPU case 2.
variance[1] = 0.2;
variance[3] = 0.1;
maxSize.pop_back();
Matrix::resizeOrCreate(resultGpu, 1, 4 * 8, false, useGpu);
resultGpu->copyFrom(resultData2, 4 * 8);
doOnePriorBoxTest(/* feature_map_width */ 2,
/* feature_map_height */ 2,
/* image_width */ 400,
/* image_height */ 400,
minSize,
maxSize,
aspectRatio,
variance,
useGpu,
resultGpu);
// GPU case 3.
aspectRatio.push_back(2);
Matrix::resizeOrCreate(resultGpu, 1, 3 * 8, false, useGpu);
resultGpu->copyFrom(resultData3, 3 * 8);
doOnePriorBoxTest(/* feature_map_width */ 1,
/* feature_map_height */ 1,
/* image_width */ 300,
/* image_height */ 300,
minSize,
maxSize,
aspectRatio,
variance,
useGpu,
resultGpu);
#endif
}
int main(int argc, char** argv) {
......
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