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Commit105dfb0

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author
Anastasia Murzova
committed
Added Steps support in DNN Slice layer
1 parent1d6a1e5 commit105dfb0

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4 files changed

+142
-9
lines changed

4 files changed

+142
-9
lines changed

‎modules/dnn/include/opencv2/dnn/all_layers.hpp‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -364,6 +364,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
364364
* Inner vector has slice ranges for the first number of input dimensions.
365365
*/
366366
std::vector<std::vector<Range> > sliceRanges;
367+
std::vector<std::vector<int> > sliceSteps;
367368
int axis;
368369
int num_split;
369370

‎modules/dnn/src/layers/slice_layer.cpp‎

Lines changed: 66 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -64,6 +64,7 @@ class SliceLayerImpl : public SliceLayer
6464
SliceLayerImpl(const LayerParams& params)
6565
{
6666
setParamsFrom(params);
67+
hasSteps =false;
6768
axis = params.get<int>("axis",1);
6869
num_split = params.get<int>("num_split",0);
6970
hasDynamicShapes = params.get<bool>("has_dynamic_shapes",false);
@@ -112,6 +113,22 @@ class SliceLayerImpl : public SliceLayer
112113
sliceRanges[0][i].end = end;// We'll finalize a negative value later.
113114
}
114115
}
116+
117+
if (params.has("steps"))
118+
{
119+
const DictValue &steps = params.get("steps");
120+
sliceSteps.resize(1);
121+
sliceSteps[0].resize(steps.size());
122+
123+
for (int i =0; i < steps.size(); ++i)
124+
{
125+
int step = steps.get<int>(i);
126+
CV_Assert(step >=1);
127+
if (step >1)
128+
hasSteps =true;
129+
sliceSteps[0][i] = step;
130+
}
131+
}
115132
}
116133
}
117134

@@ -120,11 +137,11 @@ class SliceLayerImpl : public SliceLayer
120137
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
121138
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
122139
returnINF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
123-
sliceRanges.size() ==1 && sliceRanges[0].size() ==4;
140+
sliceRanges.size() ==1 && sliceRanges[0].size() ==4 && !hasSteps;
124141
#endif
125142
#ifdef HAVE_DNN_NGRAPH
126143
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
127-
return sliceRanges.size() ==1;
144+
return sliceRanges.size() ==1 && !hasSteps;
128145
#endif
129146
return backendId == DNN_BACKEND_OPENCV;
130147
}
@@ -147,6 +164,9 @@ class SliceLayerImpl : public SliceLayer
147164
{
148165
if (shapesInitialized || inpShape[j] >0)
149166
outputs[i][j] =normalize_axis_range(sliceRanges[i][j], inpShape[j]).size();
167+
168+
if (! sliceSteps.empty() && sliceSteps[i][j] >1)
169+
outputs[i][j] = (outputs[i][j] + sliceSteps[i][j] -1) / sliceSteps[i][j];
150170
}
151171
}
152172
}
@@ -181,6 +201,7 @@ class SliceLayerImpl : public SliceLayer
181201
const MatSize& inpShape = inputs[0].size;
182202

183203
finalSliceRanges = sliceRanges;
204+
184205
if (sliceRanges.empty())
185206
{
186207
// Divide input blob on equal parts by axis.
@@ -213,6 +234,9 @@ class SliceLayerImpl : public SliceLayer
213234
}
214235
}
215236

237+
if (!sliceSteps.empty() && sliceSteps[0].size() != inputs[0].dims)
238+
sliceSteps[0].resize(inputs[0].dims,1);
239+
216240
#if0
217241
std::cout << "DEBUG: DNN/Slice: " << outputs.size() << " inpShape=" << inpShape << std::endl;
218242
for (int i = 0; i < outputs.size(); ++i)
@@ -478,9 +502,24 @@ class SliceLayerImpl : public SliceLayer
478502

479503
const Mat& inpMat = inputs[0];
480504
CV_Assert(outputs.size() == finalSliceRanges.size());
481-
for (size_t i =0; i < outputs.size(); i++)
505+
506+
if (!hasSteps)
507+
{
508+
for (size_t i =0; i < outputs.size(); i++)
509+
{
510+
inpMat(finalSliceRanges[i]).copyTo(outputs[i]);
511+
}
512+
}
513+
else
482514
{
483-
inpMat(finalSliceRanges[i]).copyTo(outputs[i]);
515+
int dimsNum = inpMat.dims;
516+
517+
for (size_t i =0; i < outputs.size(); i++)
518+
{
519+
std::vector<int>inpIdx(dimsNum,0);
520+
std::vector<int>outIdx(dimsNum,0);
521+
getSliceRecursive(inpMat, inpIdx, finalSliceRanges[i], sliceSteps[i],0, dimsNum, outputs[i], outIdx);
522+
}
484523
}
485524
}
486525

@@ -570,11 +609,34 @@ class SliceLayerImpl : public SliceLayer
570609
}
571610
#endif// HAVE_DNN_NGRAPH
572611

612+
private:
613+
voidgetSliceRecursive(const Mat &inpMat, std::vector<int> &inpIdx,
614+
const std::vector<Range> &sliceRanges,
615+
const std::vector<int> &sliceSteps,int dim,int dimsNum,
616+
Mat &outputs, std::vector<int> &outIdx)
617+
{
618+
int begin = sliceRanges[dim].start;
619+
int end = sliceRanges[dim].end;
620+
int step = !sliceSteps.empty() ? sliceSteps[dim] :1;
621+
622+
for (int k = begin, j =0; k < end; k += step, j++)
623+
{
624+
inpIdx[dim] = k;
625+
outIdx[dim] = j;
626+
627+
if (dim +1 < dimsNum)
628+
getSliceRecursive(inpMat, inpIdx, sliceRanges, sliceSteps, dim +1, dimsNum, outputs, outIdx);
629+
else
630+
outputs.at<float>(outIdx.data()) = inpMat.at<float>(inpIdx.data());
631+
}
632+
}
633+
573634
protected:
574635
// The actual non-negative values determined from @p sliceRanges depends on input size.
575636
std::vector<std::vector<Range> > finalSliceRanges;
576637
bool hasDynamicShapes;
577638
bool shapesInitialized;
639+
bool hasSteps;
578640
};
579641

580642
classCropLayerImpl CV_FINAL : public SliceLayerImpl

‎modules/dnn/src/onnx/onnx_importer.cpp‎

Lines changed: 15 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -641,18 +641,20 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
641641
int axis =0;
642642
std::vector<int> begin;
643643
std::vector<int> end;
644+
std::vector<int> steps;
644645
int inp_size = node_proto.input_size();
645646

646647
if (inp_size ==1)
647648
{
648649
if (layerParams.has("steps"))
649650
{
650-
DictValuesteps = layerParams.get("steps");
651-
for (int i =0; i <steps.size(); ++i)
651+
DictValuesteps_dict = layerParams.get("steps");
652+
for (int i =0; i <steps_dict.size(); ++i)
652653
{
653-
if (steps.get<int>(i) !=1)
654+
if (steps_dict.get<int>(i) !=1) {
654655
CV_Error(Error::StsNotImplemented,
655656
"Slice layer only supports steps = 1");
657+
}
656658
}
657659
}
658660
if (layerParams.has("axes")) {
@@ -677,7 +679,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
677679
int finish = ends.get<int>(i);
678680
end.push_back((finish <0) ? --finish : finish);// numpy doesn't include last dim
679681
}
680-
}else {
682+
}else {// inp_size > 1
681683
CV_Assert(inp_size >=3);
682684
for (int i =1; i < inp_size; i++) {
683685
CV_Assert(constBlobs.find(node_proto.input(i)) != constBlobs.end());
@@ -711,6 +713,12 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
711713
if (inp_size ==5) {
712714
CV_Assert(constBlobs.find(node_proto.input(4)) != constBlobs.end());
713715
Mat step_blob =getBlob(node_proto,4);
716+
constint* steps_ptr = step_blob.ptr<int>();
717+
718+
if (axis >0)
719+
steps.resize(axis,1);
720+
721+
std::copy(steps_ptr, steps_ptr + step_blob.total(),std::back_inserter(steps));
714722

715723
// Very strange application for Slice op with tensor reversing.
716724
// We just workaround it for 2d constants.
@@ -728,13 +736,15 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
728736
return;
729737
}
730738
}
731-
CV_CheckEQ(countNonZero(step_blob !=1),0,"Slice layer only supports steps = 1");
732739
}
733740
}
734741
layerParams.set("begin",DictValue::arrayInt(&begin[0], begin.size()));
735742
layerParams.set("end",DictValue::arrayInt(&end[0], end.size()));
736743
layerParams.set("axis", axis);
737744

745+
if (!steps.empty())
746+
layerParams.set("steps",DictValue::arrayInt(&steps[0], steps.size()));
747+
738748
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
739749
{
740750
Mat inp =getBlob(node_proto,0);

‎modules/dnn/test/test_onnx_importer.cpp‎

Lines changed: 60 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -615,6 +615,66 @@ TEST_P(Test_ONNX_layers, Slice)
615615
#endif
616616
}
617617

618+
TEST_P(Test_ONNX_layers, Slice_Steps_2DInput)
619+
{
620+
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
621+
{
622+
if (target == DNN_TARGET_MYRIAD)applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
623+
}
624+
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL)
625+
{
626+
if (backend == DNN_BACKEND_OPENCV)
627+
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_OPENCL : CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
628+
}
629+
630+
testONNXModels("slice_opset_11_steps_2d");
631+
}
632+
633+
TEST_P(Test_ONNX_layers, Slice_Steps_3DInput)
634+
{
635+
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
636+
{
637+
if (target == DNN_TARGET_MYRIAD)applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
638+
}
639+
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL)
640+
{
641+
if (backend == DNN_BACKEND_OPENCV)
642+
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_OPENCL : CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
643+
}
644+
645+
testONNXModels("slice_opset_11_steps_3d");
646+
}
647+
648+
TEST_P(Test_ONNX_layers, Slice_Steps_4DInput)
649+
{
650+
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
651+
{
652+
if (target == DNN_TARGET_MYRIAD)applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
653+
}
654+
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL)
655+
{
656+
if (backend == DNN_BACKEND_OPENCV)
657+
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_OPENCL : CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
658+
}
659+
660+
testONNXModels("slice_opset_11_steps_4d");
661+
}
662+
663+
TEST_P(Test_ONNX_layers, Slice_Steps_5DInput)
664+
{
665+
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
666+
{
667+
if (target == DNN_TARGET_MYRIAD)applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
668+
}
669+
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL)
670+
{
671+
if (backend == DNN_BACKEND_OPENCV)
672+
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_OPENCL : CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
673+
}
674+
675+
testONNXModels("slice_opset_11_steps_5d");
676+
}
677+
618678
TEST_P(Test_ONNX_layers, Softmax)
619679
{
620680
testONNXModels("softmax");

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