mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-28 13:10:12 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge pull request #14407 from dkurt:dnn_ie_fix_batch_detection

Browse Source
This commit is contained in:
Alexander Alekhin 2019-04-27 21:48:56 +00:00
commit e8a626d43a
2 changed files with 37 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
modules/dnn/src/layers/detection_output_layer.cpp
View File

@ -206,8 +206,9 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
const int num = inputs[0][0];
CV_Assert(inputs.size() >= 3);
CV_Assert(inputs[0][0] == inputs[1][0]);
CV_Assert(num == inputs[1][0]);
int numPriors = inputs[2][2] / 4;
CV_Assert((numPriors * _numLocClasses * 4) == total(inputs[0], 1));
@ -216,10 +217,10 @@ public:
// num() and channels() are 1.
// Since the number of bboxes to be kept is unknown before nms, we manually
// set it to maximal number of detections, [keep_top_k] parameter.
// set it to maximal number of detections, [keep_top_k] parameter multiplied by batch size.
// Each row is a 7 dimension std::vector, which stores
// [image_id, label, confidence, xmin, ymin, xmax, ymax]
outputs.resize(1, shape(1, 1, _keepTopK, 7));
outputs.resize(1, shape(1, 1, _keepTopK * num, 7));
return false;
}

54
modules/dnn/test/test_caffe_importer.cpp
View File

@ -207,60 +207,72 @@ TEST(Reproducibility_SSD, Accuracy)
normAssertDetections(ref, out);
}
typedef testing::TestWithParam<Target> Reproducibility_MobileNet_SSD;
typedef testing::TestWithParam<tuple<Backend, Target> > Reproducibility_MobileNet_SSD;
TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
{
const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
int targetId = GetParam();
const float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 1.5e-4 : 1e-5;
const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-4;
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat sample = imread(_tf("street.png"));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
net.setInput(inp);
Mat out = net.forward();
Mat out = net.forward().clone();
const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-5;
const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-3 : 1e-4;
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"));
normAssertDetections(ref, out, "", 0.0, scores_diff, boxes_iou_diff);
normAssertDetections(ref, out, "", FLT_MIN, scores_diff, boxes_iou_diff);
// Check that detections aren't preserved.
inp.setTo(0.0f);
net.setInput(inp);
out = net.forward();
out = out.reshape(1, out.total() / 7);
Mat zerosOut = net.forward();
zerosOut = zerosOut.reshape(1, zerosOut.total() / 7);
const int numDetections = out.rows;
const int numDetections = zerosOut.rows;
ASSERT_NE(numDetections, 0);
for (int i = 0; i < numDetections; ++i)
{
float confidence = out.ptr<float>(i)[2];
float confidence = zerosOut.ptr<float>(i)[2];
ASSERT_EQ(confidence, 0);
}
// There is something wrong with Reshape layer in Myriad plugin and
// regression with DLIE/OCL_FP16 target.
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
if ((targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2) ||
targetId == DNN_TARGET_OPENCL_FP16)
return;
}
// Check batching mode.
ref = ref.reshape(1, numDetections);
inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
net.setInput(inp);
Mat outBatch = net.forward();
// Output blob has a shape 1x1x2Nx7 where N is a number of detection for
// a single sample in batch. The first numbers of detection vectors are batch id.
outBatch = outBatch.reshape(1, outBatch.total() / 7);
EXPECT_EQ(outBatch.rows, 2 * numDetections);
normAssert(outBatch.rowRange(0, numDetections), ref, "", l1, lInf);
normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7),
"", l1, lInf);
// For Inference Engine backend there is -1 delimiter which points the end of detections.
const int numRealDetections = ref.size[2];
EXPECT_EQ(outBatch.size[2], 2 * numDetections);
out = out.reshape(1, numDetections).rowRange(0, numRealDetections);
outBatch = outBatch.reshape(1, 2 * numDetections);
for (int i = 0; i < 2; ++i)
{
Mat pred = outBatch.rowRange(i * numRealDetections, (i + 1) * numRealDetections);
EXPECT_EQ(countNonZero(pred.col(0) != i), 0);
normAssert(pred.colRange(1, 7), out.colRange(1, 7));
}
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD,
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD, dnnBackendsAndTargets());
typedef testing::TestWithParam<Target> Reproducibility_ResNet50;
TEST_P(Reproducibility_ResNet50, Accuracy)