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dnn: apply CV_OVERRIDE/CV_FINAL

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This commit is contained in:
Alexander Alekhin
2018-03-15 16:16:56 +03:00
parent bdbd0129af
commit 1060c0f439
39 changed files with 313 additions and 277 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+25 -25
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@@ -79,14 +79,14 @@ public:
adjustPad.height < stride.height);
}
virtual bool supportBackend(int backendId)
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
CV_Assert(inputs.size() > 0);
@@ -123,7 +123,7 @@ public:
virtual void applyHalideScheduler(Ptr<BackendNode>& node,
const std::vector<Mat*> &inputs,
const std::vector<Mat> &outputs,
int targetId) const
int targetId) const CV_OVERRIDE
{
#ifdef HAVE_HALIDE
if (targetId != DNN_TARGET_CPU)
@@ -165,7 +165,7 @@ public:
#define IS_POWER_LAYER(layer) \
(!layer.empty() && !layer->type.compare("Power"))
//TODO: simultaneously convolution and bias addition for cache optimization
class ConvolutionLayerImpl : public BaseConvolutionLayerImpl
class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
{
public:
enum { VEC_ALIGN = 8, DFT_TYPE = CV_32F };
@@ -194,7 +194,7 @@ public:
#endif
}
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const CV_OVERRIDE
{
Size out(outShape[3], outShape[2]);
int inpGroupCn = blobs[0].size[1];
@@ -205,7 +205,7 @@ public:
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(blobs.size() != 0);
CV_Assert(!hasBias() || blobs[1].total() == (size_t)blobs[0].size[0]);
@@ -239,7 +239,7 @@ public:
return false;
}
virtual void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
virtual void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
BaseConvolutionLayerImpl::finalize(inputs, outputs);
@@ -278,7 +278,7 @@ public:
#endif
}
bool setActivation(const Ptr<ActivationLayer>& layer)
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
activ = layer;
if (activ.empty())
@@ -312,7 +312,7 @@ public:
return !activ.empty();
}
virtual bool tryFuse(Ptr<Layer>& top)
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
Mat w, b;
top->getScaleShift(w, b);
@@ -357,7 +357,7 @@ public:
biasvec[outCn] = biasvec[outCn+1] = biasvec[outCn-1];
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
@@ -405,7 +405,7 @@ public:
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs)
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@@ -517,7 +517,7 @@ public:
parallel_for_(Range(0, nstripes), p, nstripes);
}
virtual void operator ()(const Range &r0) const
virtual void operator ()(const Range &r0) const CV_OVERRIDE
{
const int valign = ConvolutionLayerImpl::VEC_ALIGN;
int ngroups = ngroups_, batchSize = input_->size[0]*ngroups;
@@ -937,7 +937,7 @@ public:
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
@@ -949,7 +949,7 @@ public:
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
@@ -993,7 +993,7 @@ public:
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_Assert(inputs.size() == outputs.size());
@@ -1007,7 +1007,7 @@ public:
}
};
class DeConvolutionLayerImpl : public BaseConvolutionLayerImpl
class DeConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
{
public:
Mat weightsMat, biasesMat;
@@ -1016,7 +1016,7 @@ public:
DeConvolutionLayerImpl(const LayerParams& params) : BaseConvolutionLayerImpl(params) {}
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const CV_OVERRIDE
{
int inpCn = inpShape[1];
int inpH = inpShape[2];
@@ -1031,7 +1031,7 @@ public:
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(!hasBias() || blobs[1].total() == (size_t)numOutput);
CV_Assert(inputs.size() != 0);
@@ -1080,7 +1080,7 @@ public:
return false;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
BaseConvolutionLayerImpl::finalize(inputs, outputs);
getConvPoolPaddings(Size(outputs[0].size[3], outputs[0].size[2]),
@@ -1102,7 +1102,7 @@ public:
useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX;
}
void operator()(const Range& range_) const
void operator()(const Range& range_) const CV_OVERRIDE
{
int stripeSize = (int)alignSize((b_->cols + nstripes_ - 1)/nstripes_, 16);
Range range(range_.start*stripeSize, std::min(range_.end*stripeSize, b_->cols));
@@ -1277,7 +1277,7 @@ public:
parallel_for_(Range(0, nstripes), t, nstripes);
}
virtual void operator ()(const Range &r) const
virtual void operator ()(const Range &r) const CV_OVERRIDE
{
const float* data_col_ = data_col;
float* data_im_ = data_im;
@@ -1423,7 +1423,7 @@ public:
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
@@ -1435,7 +1435,7 @@ public:
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
void forward(std::vector<Mat *> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
void forward(std::vector<Mat *> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
@@ -1489,7 +1489,7 @@ public:
}
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
@@ -1543,7 +1543,7 @@ public:
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_Assert(inputs.size() == outputs.size());