potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 23:33:05 +04:00
Code Packages Releases Wiki Activity

Merge remote-tracking branch 'upstream/3.4' into merge-3.4

Browse Source
This commit is contained in:
Alexander Alekhin
2021-02-06 00:43:06 +00:00
parent a9c954e821 0be18f5cb0
commit 6b474c4051
23 changed files with 202 additions and 82 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/concat_layer.cpp
+7 -7
View File
@@ -79,7 +79,7 @@ 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++)
@@ -201,7 +201,7 @@ 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;
@@ -255,7 +255,7 @@ 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)
@@ -296,7 +296,7 @@ public:
auto context = reinterpret_cast<csl::CSLContext*>(context_);
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
auto concat_axis = clamp(axis, input_wrapper->getRank());
auto concat_axis = normalize_axis(axis, input_wrapper->getRank());
return make_cuda_node<cuda4dnn::ConcatOp>(preferableTarget, std::move(context->stream), concat_axis, padding);
}
#endif
@@ -305,7 +305,7 @@ public:
{
#ifdef HAVE_VULKAN
vkcom::Tensor in = VkComTensor(input[0]);
int cAxis = clamp(axis, in.dimNum());
int cAxis = normalize_axis(axis, in.dimNum());
std::shared_ptr<vkcom::OpBase> op(new vkcom::OpConcat(cAxis));
return Ptr<BackendNode>(new VkComBackendNode(input, op));
#endif // HAVE_VULKAN
@@ -341,7 +341,7 @@ 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));
}
@@ -354,7 +354,7 @@ 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
View File
@@ -89,8 +89,8 @@ 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);
@@ -120,8 +120,8 @@ 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
@@ -195,8 +195,8 @@ 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
+4 -4
View File
@@ -132,7 +132,7 @@ 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);
@@ -356,7 +356,7 @@ 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);
@@ -477,7 +477,7 @@ 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++)
@@ -525,7 +525,7 @@ public:
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
auto flatten_start_axis = clamp(axis, input_wrapper->getRank());
auto flatten_start_axis = normalize_axis(axis, input_wrapper->getRank());
auto biasMat_ = bias ? biasMat : Mat();
return make_cuda_node<cuda4dnn::InnerProductOp>(preferableTarget, std::move(context->stream), std::move(context->cublas_handle), flatten_start_axis, weightsMat, biasMat_);
modules/dnn/src/layers/normalize_bbox_layer.cpp
+6 -6
View File
@@ -126,8 +126,8 @@ 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);
@@ -211,8 +211,8 @@ 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>();
@@ -378,8 +378,8 @@ public:
NormalizeConfiguration<float> config;
config.input_shape.assign(std::begin(input_shape), std::end(input_shape));
config.axis_start = clamp(startAxis, input_shape.size());
config.axis_end = clamp(endAxis, input_shape.size()) + 1; /* +1 because NormalizeOp follows [start, end) convention */
config.axis_start = normalize_axis(startAxis, input_shape.size());
config.axis_end = normalize_axis(endAxis, input_shape.size()) + 1; /* +1 because NormalizeOp follows [start, end) convention */
config.norm = pnorm;
config.eps = epsilon;
modules/dnn/src/layers/reshape_layer.cpp
+1 -8
View File
@@ -66,14 +66,7 @@ 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
View File
@@ -305,7 +305,7 @@ 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
View File
@@ -153,7 +153,7 @@ 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();
}
}
}
@@ -216,7 +216,7 @@ 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]);
}
}
@@ -634,7 +634,7 @@ 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];
@@ -653,7 +653,7 @@ 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
+7 -7
View File
@@ -89,7 +89,7 @@ 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;
@@ -124,7 +124,7 @@ 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())
{
@@ -216,7 +216,7 @@ 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);
@@ -306,7 +306,7 @@ public:
auto context = reinterpret_cast<csl::CSLContext*>(context_);
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
auto channel_axis = clamp(axisRaw, input_wrapper->getRank());
auto channel_axis = normalize_axis(axisRaw, input_wrapper->getRank());
return make_cuda_node<cuda4dnn::SoftmaxOp>(preferableTarget, std::move(context->cudnn_handle), channel_axis, logSoftMax);
}
#endif
@@ -315,7 +315,7 @@ public:
{
#ifdef HAVE_VULKAN
vkcom::Tensor in = VkComTensor(inputs[0]);
int cAxis = clamp(axisRaw, in.dimNum());
int cAxis = normalize_axis(axisRaw, in.dimNum());
std::shared_ptr<vkcom::OpBase> op(new vkcom::OpSoftmax(cAxis, logSoftMax));
return Ptr<BackendNode>(new VkComBackendNode(inputs, op));
#endif // HAVE_VULKAN
@@ -354,7 +354,7 @@ 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));
}
@@ -365,7 +365,7 @@ 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)));