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Merge pull request #14552 from dkurt:fixed_detections_out_blob

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This commit is contained in:
Alexander Alekhin 2019-05-15 15:17:05 +00:00
commit a70e1eecd9
4 changed files with 28 additions and 48 deletions
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49
modules/dnn/src/layers/detection_output_layer.cpp
View File

@ -312,15 +312,13 @@ public:
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
outs.getUMatVector(outputs);
bool use_half = (inps.depth() == CV_16S);
if (use_half)
{
std::vector<UMat> orig_inputs;
std::vector<UMat> orig_outputs;
inps.getUMatVector(orig_inputs);
outs.getUMatVector(orig_outputs);
inputs.resize(orig_inputs.size());
for (size_t i = 0; i < orig_inputs.size(); i++)
@ -329,7 +327,6 @@ public:
else
{
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
}
std::vector<LabelBBox> allDecodedBBoxes;
@ -362,19 +359,17 @@ public:
if (numKept == 0)
{
// Set confidences to zeros.
Range ranges[] = {Range::all(), Range::all(), Range::all(), Range(2, 3)};
if (use_half)
{
std::vector<UMat> orig_outputs;
outs.getUMatVector(orig_outputs);
orig_outputs[0](ranges).setTo(0);
} else
outputs[0](ranges).setTo(0);
outputs[0].setTo(0);
return true;
}
int outputShape[] = {1, 1, (int)numKept, 7};
UMat umat = UMat(4, outputShape, CV_32F);
UMat umat = use_half ? UMat::zeros(4, outputs[0].size, CV_32F) : outputs[0];
if (!use_half)
umat.setTo(0);
// If there are valid detections
if (numKept > 0)
{
Mat mat = umat.getMat(ACCESS_WRITE);
float* outputsData = mat.ptr<float>();
@ -393,16 +388,7 @@ public:
{
UMat half_umat;
convertFp16(umat, half_umat);
std::vector<UMat> orig_outputs;
outs.getUMatVector(orig_outputs);
orig_outputs.clear();
orig_outputs.push_back(half_umat);
outs.assign(orig_outputs);
} else {
outputs.clear();
outputs.push_back(umat);
outs.assign(outputs);
outs.assign(std::vector<UMat>(1, half_umat));
}
return true;
@ -484,15 +470,12 @@ public:
numKept += processDetections_(allDecodedBBoxes[i], allConfidenceScores[i], allIndices);
}
outputs[0].setTo(0);
// If there is no detections
if (numKept == 0)
{
// Set confidences to zeros.
Range ranges[] = {Range::all(), Range::all(), Range::all(), Range(2, 3)};
outputs[0](ranges).setTo(0);
return;
}
int outputShape[] = {1, 1, (int)numKept, 7};
outputs[0].create(4, outputShape, CV_32F);
float* outputsData = outputs[0].ptr<float>();
size_t count = 0;
@ -703,8 +686,6 @@ public:
prior_width += 1.0f;
prior_height += 1.0f;
}
CV_Assert(prior_width > 0);
CV_Assert(prior_height > 0);
float prior_center_x = prior_bbox.xmin + prior_width * .5;
float prior_center_y = prior_bbox.ymin + prior_height * .5;

21
modules/dnn/src/layers/proposal_layer.cpp
View File

@ -131,6 +131,9 @@ public:
CV_Assert(layerInternals.empty());
internals.push_back(layerOutputs[0]);
// Detections layer.
internals.push_back(shape(1, 1, keepTopAfterNMS, 7));
outputs.resize(2);
outputs[0] = shape(keepTopAfterNMS, 5);
outputs[1] = shape(keepTopAfterNMS, 1);
@ -176,13 +179,14 @@ public:
internals_.getUMatVector(internals);
CV_Assert(inputs.size() == 3);
CV_Assert(internals.size() == 3);
CV_Assert(internals.size() == 4);
const UMat& scores = inputs[0];
const UMat& bboxDeltas = inputs[1];
const UMat& imInfo = inputs[2];
UMat& priorBoxes = internals[0];
UMat& permuttedScores = internals[1];
UMat& permuttedDeltas = internals[2];
UMat& detections = internals[3];
CV_Assert(imInfo.total() >= 2);
// We've chosen the smallest data type because we need just a shape from it.
@ -217,7 +221,7 @@ public:
layerInputs[2] = priorBoxes;
layerInputs[3] = umat_fakeImageBlob;
layerOutputs[0] = UMat();
layerOutputs[0] = detections;
detectionOutputLayer->forward(layerInputs, layerOutputs, internals);
// DetectionOutputLayer produces 1x1xNx7 output where N might be less or
@ -237,10 +241,6 @@ public:
dst = outputs[1].rowRange(0, numDets);
layerOutputs[0].col(2).copyTo(dst);
if (numDets < keepTopAfterNMS)
for (int i = 0; i < 2; ++i)
outputs[i].rowRange(numDets, keepTopAfterNMS).setTo(0);
return true;
}
#endif
@ -266,13 +266,14 @@ public:
internals_arr.getMatVector(internals);
CV_Assert(inputs.size() == 3);
CV_Assert(internals.size() == 3);
CV_Assert(internals.size() == 4);
const Mat& scores = inputs[0];
const Mat& bboxDeltas = inputs[1];
const Mat& imInfo = inputs[2];
Mat& priorBoxes = internals[0];
Mat& permuttedScores = internals[1];
Mat& permuttedDeltas = internals[2];
Mat& detections = internals[3];
CV_Assert(imInfo.total() >= 2);
// We've chosen the smallest data type because we need just a shape from it.
@ -302,7 +303,7 @@ public:
layerInputs[2] = priorBoxes;
layerInputs[3] = fakeImageBlob;
layerOutputs[0] = Mat();
layerOutputs[0] = detections;
detectionOutputLayer->forward(layerInputs, layerOutputs, internals);
// DetectionOutputLayer produces 1x1xNx7 output where N might be less or
@ -319,10 +320,6 @@ public:
// The scores.
dst = outputs[1].rowRange(0, numDets);
layerOutputs[0].col(2).copyTo(dst);
if (numDets < keepTopAfterNMS)
for (int i = 0; i < 2; ++i)
outputs[i].rowRange(numDets, keepTopAfterNMS).setTo(0);
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE

2
modules/dnn/test/test_backends.cpp
View File

@ -172,7 +172,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
float diffSquares = (target == DNN_TARGET_MYRIAD) ? 0.063 : 0.0;
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252 : 0.0;
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252 : FLT_MIN;
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "", diffScores, diffSquares, detectionConfThresh);
expectNoFallbacksFromIE(net);

4
modules/dnn/test/test_caffe_importer.cpp
View File

@ -204,7 +204,7 @@ TEST(Reproducibility_SSD, Accuracy)
Mat out = net.forward("detection_out");
Mat ref = blobFromNPY(_tf("ssd_out.npy"));
normAssertDetections(ref, out);
normAssertDetections(ref, out, "", FLT_MIN);
}
typedef testing::TestWithParam<tuple<Backend, Target> > Reproducibility_MobileNet_SSD;
@ -225,6 +225,8 @@ TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
net.setInput(inp);
Mat out = net.forward().clone();
ASSERT_EQ(out.size[2], 100);
const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.5e-2 : 1e-5;
const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 6.3e-2 : 1e-4;
Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));