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Merge pull request #19428 from alalek:dnn_drop_misbehaved_clamp

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Alexander Alekhin
2021-02-04 11:57:19 +00:00
parent 09d2ca17cf 83aa711346
commit 4f08bb5046
11 changed files with 57 additions and 52 deletions
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modules/dnn/src/layers/concat_layer.cpp
+5 -5
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@@ -72,7 +72,7 @@ public:
{
CV_Assert(inputs.size() > 0);
outputs.resize(1, inputs[0]);
int cAxis = clamp(axis, inputs[0]);
int cAxis = normalize_axis(axis, inputs[0]);
int axisSum = 0;
for (size_t i = 0; i < inputs.size(); i++)
@@ -192,7 +192,7 @@ public:
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
int cAxis = clamp(axis, inputs[0].dims);
int cAxis = normalize_axis(axis, inputs[0].dims);
if (padding)
return false;
@@ -246,7 +246,7 @@ public:
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
int cAxis = clamp(axis, inputs[0].dims);
int cAxis = normalize_axis(axis, inputs[0].dims);
Mat& outMat = outputs[0];
if (padding)
@@ -306,7 +306,7 @@ public:
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::Builder::ConcatLayer ieLayer(name);
ieLayer.setAxis(clamp(axis, input->getDims().size()));
ieLayer.setAxis(normalize_axis(axis, input->getDims().size()));
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
@@ -319,7 +319,7 @@ public:
{
InferenceEngine::DataPtr data = ngraphDataNode(inputs[0]);
const int numDims = data->getDims().size();
const int cAxis = clamp(axis, numDims);
const int cAxis = normalize_axis(axis, numDims);
std::vector<size_t> maxDims(numDims, 0);
CV_Assert(inputs.size() == nodes.size());
modules/dnn/src/layers/flatten_layer.cpp
+6 -6
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@@ -82,8 +82,8 @@ public:
}
int numAxes = inputs[0].size();
int startAxis = clamp(_startAxis, numAxes);
int endAxis = clamp(_endAxis, numAxes);
int startAxis = normalize_axis(_startAxis, numAxes);
int endAxis = normalize_axis(_endAxis, numAxes);
CV_Assert(startAxis >= 0);
CV_Assert(endAxis >= startAxis && endAxis < (int)numAxes);
@@ -113,8 +113,8 @@ public:
inputs_arr.getMatVector(inputs);
int numAxes = inputs[0].dims;
_startAxis = clamp(_startAxis, numAxes);
_endAxis = clamp(_endAxis, numAxes);
_startAxis = normalize_axis(_startAxis, numAxes);
_endAxis = normalize_axis(_endAxis, numAxes);
}
#ifdef HAVE_OPENCL
@@ -186,8 +186,8 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
std::vector<size_t> dims = ieInpNode->get_shape();
int numAxes = dims.size();
int startAxis = clamp(_startAxis, numAxes);
int endAxis = clamp(_endAxis, numAxes);
int startAxis = normalize_axis(_startAxis, numAxes);
int endAxis = normalize_axis(_endAxis, numAxes);
CV_Assert(startAxis >= 0);
CV_Assert(endAxis >= startAxis && endAxis < numAxes);
modules/dnn/src/layers/fully_connected_layer.cpp
+3 -3
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@@ -129,7 +129,7 @@ public:
CV_CheckEQ(blobs[0].dims, 2, "");
numOutput = blobs[0].size[0];
CV_Assert(!bias || (size_t)numOutput == blobs[1].total());
cAxis = clamp(axis, inputs[0]);
cAxis = normalize_axis(axis, inputs[0]);
}
MatShape outShape(cAxis + 1);
@@ -352,7 +352,7 @@ public:
return true;
}
int axisCan = clamp(axis, inputs[0].dims);
int axisCan = normalize_axis(axis, inputs[0].dims);
int numOutput = blobs[0].size[0];
int innerSize = blobs[0].size[1];
int outerSize = total(shape(inputs[0]), 0, axisCan);
@@ -473,7 +473,7 @@ public:
if (!blobs.empty())
{
int axisCan = clamp(axis, input[0].dims);
int axisCan = normalize_axis(axis, input[0].dims);
int outerSize = input[0].total(0, axisCan);
for (size_t i = 0; i < input.size(); i++)
modules/dnn/src/layers/normalize_bbox_layer.cpp
+4 -4
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@@ -118,8 +118,8 @@ public:
const UMat& inp0 = inputs[0];
UMat& buffer = internals[0];
startAxis = clamp(startAxis, inp0.dims);
endAxis = clamp(endAxis, inp0.dims);
startAxis = normalize_axis(startAxis, inp0.dims);
endAxis = normalize_axis(endAxis, inp0.dims);
size_t num = total(shape(inp0.size), 0, startAxis);
size_t numPlanes = total(shape(inp0.size), startAxis, endAxis + 1);
@@ -203,8 +203,8 @@ public:
const Mat& inp0 = inputs[0];
Mat& buffer = internals[0];
startAxis = clamp(startAxis, inp0.dims);
endAxis = clamp(endAxis, inp0.dims);
startAxis = normalize_axis(startAxis, inp0.dims);
endAxis = normalize_axis(endAxis, inp0.dims);
const float* inpData = inp0.ptr<float>();
float* outData = outputs[0].ptr<float>();
modules/dnn/src/layers/reshape_layer.cpp
+1 -8
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@@ -60,14 +60,7 @@ static void computeShapeByReshapeMask(const MatShape &srcShape,
int srcShapeSize = (int)srcShape.size();
int maskShapeSize = (int)maskShape.size();
if (srcRange == Range::all())
srcRange = Range(0, srcShapeSize);
else
{
int sz = srcRange.size();
srcRange.start = clamp(srcRange.start, srcShapeSize);
srcRange.end = srcRange.end == INT_MAX ? srcShapeSize : srcRange.start + sz;
}
srcRange = normalize_axis_range(srcRange, srcShapeSize);
bool explicitMask = !maskShape.empty(); // All mask values are positive.
for (int i = 0, n = maskShape.size(); i < n && explicitMask; ++i)
modules/dnn/src/layers/scale_layer.cpp
+1 -1
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@@ -240,7 +240,7 @@ public:
numChannels = blobs[0].total();
std::vector<size_t> shape(ieInpNode0->get_shape().size(), 1);
int cAxis = clamp(axis, shape.size());
int cAxis = normalize_axis(axis, shape.size());
shape[cAxis] = numChannels;
auto node = ieInpNode0;
modules/dnn/src/layers/slice_layer.cpp
+4 -4
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@@ -146,7 +146,7 @@ public:
for (int j = 0; j < sliceRanges[i].size(); ++j)
{
if (shapesInitialized || inpShape[j] > 0)
outputs[i][j] = clamp(sliceRanges[i][j], inpShape[j]).size();
outputs[i][j] = normalize_axis_range(sliceRanges[i][j], inpShape[j]).size();
}
}
}
@@ -209,7 +209,7 @@ public:
// Clamp.
for (int j = 0; j < finalSliceRanges[i].size(); ++j)
{
finalSliceRanges[i][j] = clamp(finalSliceRanges[i][j], inpShape[j]);
finalSliceRanges[i][j] = normalize_axis_range(finalSliceRanges[i][j], inpShape[j]);
}
}
@@ -601,7 +601,7 @@ public:
CV_Assert(inputs.size() == 2);
MatShape dstShape = inputs[0];
int start = clamp(axis, dstShape);
int start = normalize_axis(axis, dstShape);
for (int i = start; i < dstShape.size(); i++)
{
dstShape[i] = inputs[1][i];
@@ -620,7 +620,7 @@ public:
const Mat &inpSzBlob = inputs[1];
int dims = inpBlob.dims;
int start_axis = clamp(axis, dims);
int start_axis = normalize_axis(axis, dims);
std::vector<int> offset_final(dims, 0);
if (offset.size() == 1)
modules/dnn/src/layers/softmax_layer.cpp
+5 -5
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@@ -82,7 +82,7 @@ public:
{
bool inplace = Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
MatShape shape = inputs[0];
int cAxis = clamp(axisRaw, shape.size());
int cAxis = normalize_axis(axisRaw, shape.size());
shape[cAxis] = 1;
internals.assign(1, shape);
return inplace;
@@ -115,7 +115,7 @@ public:
UMat& src = inputs[0];
UMat& dstMat = outputs[0];
int axis = clamp(axisRaw, src.dims);
int axis = normalize_axis(axisRaw, src.dims);
if (softmaxOp.empty())
{
@@ -207,7 +207,7 @@ public:
const Mat &src = inputs[0];
Mat &dst = outputs[0];
int axis = clamp(axisRaw, src.dims);
int axis = normalize_axis(axisRaw, src.dims);
size_t outerSize = src.total(0, axis), channels = src.size[axis],
innerSize = src.total(axis + 1);
@@ -318,7 +318,7 @@ public:
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::Builder::SoftMaxLayer ieLayer(name);
ieLayer.setAxis(clamp(axisRaw, input->getDims().size()));
ieLayer.setAxis(normalize_axis(axisRaw, input->getDims().size()));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
@@ -329,7 +329,7 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
int axis = clamp(axisRaw, ieInpNode->get_shape().size());
int axis = normalize_axis(axisRaw, ieInpNode->get_shape().size());
auto softmax = std::make_shared<ngraph::op::v1::Softmax>(ieInpNode, axis);
if (logSoftMax)
return Ptr<BackendNode>(new InfEngineNgraphNode(std::make_shared<ngraph::op::v0::Log>(softmax)));