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Fix Proposal deep learning layer

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This commit is contained in:
Dmitry Kurtaev
2018-04-04 14:48:29 +03:00
parent 7bc980edaf
commit ef1aaf12c9
2 changed files with 39 additions and 41 deletions
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modules/dnn/test/test_layers.cpp
+16 -34
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@@ -602,54 +602,36 @@ TEST(Layer_Test_ROIPooling, Accuracy)
normAssert(out, ref);
}
TEST(Layer_Test_FasterRCNN_Proposal, Accuracy)
typedef testing::TestWithParam<DNNTarget> Test_Caffe_layers;
TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
{
Net net = readNetFromCaffe(_tf("net_faster_rcnn_proposal.prototxt"));
net.setPreferableTarget(GetParam());
Mat scores = blobFromNPY(_tf("net_faster_rcnn_proposal.scores.npy"));
Mat deltas = blobFromNPY(_tf("net_faster_rcnn_proposal.deltas.npy"));
Mat imInfo = (Mat_<float>(1, 3) << 600, 800, 1.6f);
Mat ref = blobFromNPY(_tf("net_faster_rcnn_proposal.npy"));
net.setInput(scores, "rpn_cls_prob_reshape");
net.setInput(deltas, "rpn_bbox_pred");
net.setInput(imInfo, "im_info");
Mat out = net.forward();
std::vector<Mat> outs;
net.forward(outs, "output");
const int numDets = ref.size[0];
EXPECT_LE(numDets, out.size[0]);
normAssert(out.rowRange(0, numDets), ref);
for (int i = 0; i < 2; ++i)
{
Mat ref = blobFromNPY(_tf(i == 0 ? "net_faster_rcnn_proposal.out_rois.npy" :
"net_faster_rcnn_proposal.out_scores.npy"));
const int numDets = ref.size[0];
EXPECT_LE(numDets, outs[i].size[0]);
normAssert(outs[i].rowRange(0, numDets), ref);
if (numDets < out.size[0])
EXPECT_EQ(countNonZero(out.rowRange(numDets, out.size[0])), 0);
}
OCL_TEST(Layer_Test_FasterRCNN_Proposal, Accuracy)
{
Net net = readNetFromCaffe(_tf("net_faster_rcnn_proposal.prototxt"));
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
Mat scores = blobFromNPY(_tf("net_faster_rcnn_proposal.scores.npy"));
Mat deltas = blobFromNPY(_tf("net_faster_rcnn_proposal.deltas.npy"));
Mat imInfo = (Mat_<float>(1, 3) << 600, 800, 1.6f);
Mat ref = blobFromNPY(_tf("net_faster_rcnn_proposal.npy"));
net.setInput(scores, "rpn_cls_prob_reshape");
net.setInput(deltas, "rpn_bbox_pred");
net.setInput(imInfo, "im_info");
Mat out = net.forward();
const int numDets = ref.size[0];
EXPECT_LE(numDets, out.size[0]);
normAssert(out.rowRange(0, numDets), ref);
if (numDets < out.size[0])
EXPECT_EQ(countNonZero(out.rowRange(numDets, out.size[0])), 0);
if (numDets < outs[i].size[0])
EXPECT_EQ(countNonZero(outs[i].rowRange(numDets, outs[i].size[0])), 0);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Caffe_layers, availableDnnTargets());
typedef testing::TestWithParam<tuple<Vec4i, Vec2i, bool> > Scale_untrainable;
TEST_P(Scale_untrainable, Accuracy)