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Added function to get timings for layers

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This commit is contained in:
Aleksandr Rybnikov
2017-08-02 17:27:58 +03:00
parent da0a36c323
commit 8b1146deb2
5 changed files with 98 additions and 52 deletions
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modules/dnn/include/opencv2/dnn/dnn.hpp
+22 -12
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@@ -483,15 +483,15 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* order is the same as in layersIds
*/
CV_WRAP void getLayersShapes(const std::vector<MatShape>& netInputShapes,
std::vector<int>* layersIds,
std::vector<std::vector<MatShape> >* inLayersShapes,
std::vector<std::vector<MatShape> >* outLayersShapes) const;
CV_OUT std::vector<int>& layersIds,
CV_OUT std::vector<std::vector<MatShape> >& inLayersShapes,
CV_OUT std::vector<std::vector<MatShape> >& outLayersShapes) const;
/** @overload */
CV_WRAP void getLayersShapes(const MatShape& netInputShape,
std::vector<int>* layersIds,
std::vector<std::vector<MatShape> >* inLayersShapes,
std::vector<std::vector<MatShape> >* outLayersShapes) const;
CV_OUT std::vector<int>& layersIds,
CV_OUT std::vector<std::vector<MatShape> >& inLayersShapes,
CV_OUT std::vector<std::vector<MatShape> >& outLayersShapes) const;
/** @brief Returns input and output shapes for layer with specified
* id in loaded model; preliminary inferencing isn't necessary.
@@ -504,14 +504,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_WRAP void getLayerShapes(const MatShape& netInputShape,
const int layerId,
std::vector<MatShape>* inLayerShapes,
std::vector<MatShape>* outLayerShapes) const;
CV_OUT std::vector<MatShape>& inLayerShapes,
CV_OUT std::vector<MatShape>& outLayerShapes) const;
/** @overload */
CV_WRAP void getLayerShapes(const std::vector<MatShape>& netInputShapes,
const int layerId,
std::vector<MatShape>* inLayerShapes,
std::vector<MatShape>* outLayerShapes) const;
CV_OUT std::vector<MatShape>& inLayerShapes,
CV_OUT std::vector<MatShape>& outLayerShapes) const;
/** @brief Computes FLOP for whole loaded model with specified input shapes.
* @param netInputShapes vector of shapes for all net inputs.
@@ -566,11 +566,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @param blobs output parameter to store resulting bytes for intermediate blobs.
*/
CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
CV_OUT std::vector<int>& layerIds,
CV_OUT std::vector<size_t>& weights,
CV_OUT std::vector<size_t>& blobs) const;
/** @overload */
CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
CV_OUT std::vector<int>& layerIds,
CV_OUT std::vector<size_t>& weights,
CV_OUT std::vector<size_t>& blobs) const;
/** @brief Enables or disables layer fusion in the network.
@@ -578,6 +580,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_WRAP void enableFusion(bool fusion);
/** @brief Returns overall time for inference and timings (in ticks) for layers.
* Indexes in returned vector correspond to layers ids. Some layers can be fused with others,
* in this case zero ticks count will be return for that skipped layers.
* @param timings vector for tick timings for all layers.
* @return overall ticks for model inference.
*/
CV_WRAP int64 getPerfProfile(CV_OUT std::vector<double>& timings);
private:
struct Impl;
Ptr<Impl> impl;