Removed LPNormalize layer.

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

@@ -263,14 +263,6 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         static Ptr<SoftmaxLayer> create(const LayerParams& params);
     };
 
-    class CV_EXPORTS LPNormalizeLayer : public Layer
-    {
-    public:
-        float pnorm, epsilon;
-
-        static Ptr<LPNormalizeLayer> create(const LayerParams& params);
-    };
-
     class CV_EXPORTS InnerProductLayer : public Layer
     {
     public:
@@ -545,9 +537,37 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         static Ptr<DetectionOutputLayer> create(const LayerParams& params);
     };
 
+    /**
+     * @brief \f$ L_p \f$ - normalization layer.
+     * @param p Normalization factor. The most common `p = 1` for \f$ L_1 \f$ -
+     *          normalization or `p = 2` for \f$ L_2 \f$ - normalization or a custom one.
+     * @param eps Parameter \f$ \epsilon \f$ to prevent a division by zero.
+     * @param across_spatial If true, normalize an input across all non-batch dimensions.
+     *                       Otherwise normalize an every channel separately.
+     *
+     * Across spatial:
+     * @f[
+     * norm = \sqrt[p]{\epsilon + \sum_{x, y, c} |src(x, y, c)|^p } \\
+     * dst(x, y, c) = \frac{ src(x, y, c) }{norm}
+     * @f]
+     *
+     * Channel wise normalization:
+     * @f[
+     * norm(c) = \sqrt[p]{\epsilon + \sum_{x, y} |src(x, y, c)|^p } \\
+     * dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)}
+     * @f]
+     *
+     * Where `x, y` - spatial cooridnates, `c` - channel.
+     *
+     * An every sample in the batch is normalized separately. Optionally,
+     * output is scaled by the trained parameters.
+     */
     class NormalizeBBoxLayer : public Layer
     {
     public:
+        float pnorm, epsilon;
+        bool acrossSpatial;
+
         static Ptr<NormalizeBBoxLayer> create(const LayerParams& params);
     };
 

modules/dnn/src/init.cpp

@@ -92,7 +92,6 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(InnerProduct,   InnerProductLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Softmax,        SoftmaxLayer);
     CV_DNN_REGISTER_LAYER_CLASS(MVN,            MVNLayer);
-    CV_DNN_REGISTER_LAYER_CLASS(LPNormalize,    LPNormalizeLayer);
 
     CV_DNN_REGISTER_LAYER_CLASS(ReLU,           ReLULayer);
     CV_DNN_REGISTER_LAYER_CLASS(ReLU6,          ReLU6Layer);

modules/dnn/src/layers/lp_normalize_layer.cpp

@@ -1,78 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-//
-// Copyright (C) 2017, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-#include "../precomp.hpp"
-#include "layers_common.hpp"
-#include <iostream>
-namespace cv { namespace dnn {
-
-class LPNormalizeLayerImpl : public LPNormalizeLayer
-{
-public:
-
-    LPNormalizeLayerImpl(const LayerParams& params)
-    {
-        setParamsFrom(params);
-        pnorm = params.get<float>("p", 2);
-        epsilon = params.get<float>("eps", 1e-10f);
-        CV_Assert(pnorm > 0);
-    }
-
-    bool getMemoryShapes(const std::vector<MatShape> &inputs,
-                         const int requiredOutputs,
-                         std::vector<MatShape> &outputs,
-                         std::vector<MatShape> &internals) const
-    {
-        Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
-        if (pnorm != 1 && pnorm != 2)
-        {
-            internals.resize(1, inputs[0]);
-        }
-        return true;
-    }
-
-    virtual bool supportBackend(int backendId)
-    {
-        return backendId == DNN_BACKEND_DEFAULT;
-    }
-
-    void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
-    {
-        CV_TRACE_FUNCTION();
-        CV_TRACE_ARG_VALUE(name, "name", name.c_str());
-
-        CV_Assert(inputs[0]->total() == outputs[0].total());
-        float norm;
-        if (pnorm == 1)
-            norm = cv::norm(*inputs[0], NORM_L1);
-        else if (pnorm == 2)
-            norm = cv::norm(*inputs[0], NORM_L2);
-        else
-        {
-            cv::pow(abs(*inputs[0]), pnorm, internals[0]);
-            norm = pow((float)sum(internals[0])[0], 1.0f / pnorm);
-        }
-        multiply(*inputs[0], 1.0f / (norm + epsilon), outputs[0]);
-    }
-
-    int64 getFLOPS(const std::vector<MatShape> &inputs,
-                  const std::vector<MatShape> &) const
-    {
-        int64 flops = 0;
-        for (int i = 0; i < inputs.size(); i++)
-            flops += 3 * total(inputs[i]);
-        return flops;
-    }
-};
-
-Ptr<LPNormalizeLayer> LPNormalizeLayer::create(const LayerParams& params)
-{
-    return Ptr<LPNormalizeLayer>(new LPNormalizeLayerImpl(params));
-}
-
-}  // namespace dnn
-}  // namespace cv

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -43,83 +43,18 @@
 #include "../precomp.hpp"
 #include "layers_common.hpp"
 
-#include <float.h>
+namespace cv { namespace dnn {
-#include <algorithm>
-
-namespace cv
-{
-namespace dnn
-{
-
-namespace
-{
-    const std::string layerName = "NormalizeBBox";
-}
 
 class NormalizeBBoxLayerImpl : public NormalizeBBoxLayer
 {
-    float _eps;
-    bool _across_spatial;
-    bool _channel_shared;
 public:
-    bool getParameterDict(const LayerParams &params,
-                          const std::string &parameterName,
-                          DictValue& result)
-    {
-        if (!params.has(parameterName))
-        {
-            return false;
-        }
-
-        result = params.get(parameterName);
-        return true;
-    }
-
-    template<typename T>
-    T getParameter(const LayerParams &params,
-                   const std::string &parameterName,
-                   const size_t &idx=0,
-                   const bool required=true,
-                   const T& defaultValue=T())
-    {
-        DictValue dictValue;
-        bool success = getParameterDict(params, parameterName, dictValue);
-        if(!success)
-        {
-            if(required)
-            {
-                std::string message = layerName;
-                message += " layer parameter does not contain ";
-                message += parameterName;
-                message += " parameter.";
-                CV_Error(Error::StsBadArg, message);
-            }
-            else
-            {
-                return defaultValue;
-            }
-        }
-        return dictValue.get<T>(idx);
-    }
-
     NormalizeBBoxLayerImpl(const LayerParams& params)
     {
-        _eps = getParameter<float>(params, "eps", 0, false, 1e-10f);
-        _across_spatial = getParameter<bool>(params, "across_spatial");
-        _channel_shared = getParameter<bool>(params, "channel_shared");
         setParamsFrom(params);
-    }
+        pnorm = params.get<float>("p", 2);
-
+        epsilon = params.get<float>("eps", 1e-10f);
-    void checkInputs(const std::vector<Mat*> &inputs)
+        acrossSpatial = params.get<bool>("across_spatial", true);
-    {
+        CV_Assert(pnorm > 0);
-        CV_Assert(inputs.size() > 0);
-        CV_Assert(inputs[0]->dims == 4 && inputs[0]->type() == CV_32F);
-        for (size_t i = 1; i < inputs.size(); i++)
-        {
-            CV_Assert(inputs[i]->dims == 4 && inputs[i]->type() == CV_32F);
-            CV_Assert(inputs[i]->size == inputs[0]->size);
-        }
-        CV_Assert(inputs[0]->dims > 2);
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -127,17 +62,11 @@ public:
                          std::vector<MatShape> &outputs,
                          std::vector<MatShape> &internals) const
     {
-        bool inplace = Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
+        CV_Assert(inputs.size() == 1);
-        size_t channels = inputs[0][1];
+        Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
-        size_t rows = inputs[0][2];
+        internals.resize(1, inputs[0]);
-        size_t cols = inputs[0][3];
+        internals[0][0] = 1;  // Batch size.
-        size_t channelSize = rows * cols;
+        return true;
-
-        internals.assign(1, shape(channels, channelSize));
-        internals.push_back(shape(channels, 1));
-        internals.push_back(shape(1, channelSize));
-
-        return inplace;
     }
 
     void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
@@ -145,60 +74,46 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        checkInputs(inputs);
+        CV_Assert(inputs.size() == 1 && outputs.size() == 1);
-
+        CV_Assert(inputs[0]->total() == outputs[0].total());
-        Mat& buffer = internals[0], sumChannelMultiplier = internals[1],
-                sumSpatialMultiplier = internals[2];
-
-        sumChannelMultiplier.setTo(1.0);
-        sumSpatialMultiplier.setTo(1.0);
 
         const Mat& inp0 = *inputs[0];
+        Mat& buffer = internals[0];
         size_t num = inp0.size[0];
         size_t channels = inp0.size[1];
-        size_t channelSize = inp0.size[2] * inp0.size[3];
+        size_t channelSize = inp0.total() / (num * channels);
-
-        Mat zeroBuffer(channels, channelSize, CV_32F, Scalar(0));
-        Mat absDiff;
-        Mat scale = blobs[0];
-        for (size_t j = 0; j < inputs.size(); j++)
-        {
         for (size_t n = 0; n < num; ++n)
         {
-                Mat src = Mat(channels, channelSize, CV_32F, inputs[j]->ptr<float>(n));
+            Mat src = Mat(channels, channelSize, CV_32F, (void*)inp0.ptr<float>(n));
-                Mat dst = Mat(channels, channelSize, CV_32F, outputs[j].ptr<float>(n));
+            Mat dst = Mat(channels, channelSize, CV_32F, (void*)outputs[0].ptr<float>(n));
 
-                buffer = src.mul(src);
+            cv::pow(abs(src), pnorm, buffer);
 
-                if (_across_spatial)
+            if (acrossSpatial)
             {
-                    absdiff(buffer, zeroBuffer, absDiff);
-
                 // add eps to avoid overflow
-                    double absSum = sum(absDiff)[0] + _eps;
+                float absSum = sum(buffer)[0] + epsilon;
-
+                float norm = pow(absSum, 1.0f / pnorm);
-                    float norm = sqrt(absSum);
+                multiply(src, 1.0f / norm, dst);
-                    dst = src / norm;
             }
             else
             {
-                    Mat norm(channelSize, 1, buffer.type()); // 1 x channelSize
+                Mat norm;
-
+                reduce(buffer, norm, 0, REDUCE_SUM);
-                    // (_channels x channelSize)T * _channels x 1 -> channelSize x 1
+                norm += epsilon;
-                    gemm(buffer, sumChannelMultiplier, 1, norm, 0, norm, GEMM_1_T);
 
-                    // compute norm
+                // compute inverted norm to call multiply instead divide
-                    pow(norm, 0.5f, norm);
+                cv::pow(norm, -1.0f / pnorm, norm);
 
-                    // scale the layer
+                repeat(norm, channels, 1, buffer);
-                    // _channels x 1 * (channelSize x 1)T -> _channels x channelSize
+                multiply(src, buffer, dst);
-                    gemm(sumChannelMultiplier, norm, 1, buffer, 0, buffer, GEMM_2_T);
-
-                    dst = src / buffer;
             }
 
+            if (!blobs.empty())
+            {
                 // scale the output
-                if (_channel_shared)
+                Mat scale = blobs[0];
+                if (scale.total() == 1)
                 {
                     // _scale: 1 x 1
                     dst *= scale.at<float>(0, 0);
@@ -206,15 +121,13 @@ public:
                 else
                 {
                     // _scale: _channels x 1
-                    // _channels x 1 * 1 x channelSize -> _channels x channelSize
+                    CV_Assert(scale.total() == channels);
-                    gemm(scale, sumSpatialMultiplier, 1, buffer, 0, buffer);
+                    repeat(scale, 1, dst.cols, buffer);
-
+                    multiply(dst, buffer, dst);
-                    dst = dst.mul(buffer);
                 }
             }
         }
     }
-
 };
 
 

modules/dnn/src/torch/torch_importer.cpp

@@ -706,7 +706,7 @@ struct TorchImporter : public ::cv::dnn::Importer
                 if (scalarParams.has("eps"))
                     layerParams.set("eps", scalarParams.get<float>("eps"));
 
-                newModule->apiType = "LPNormalize";
+                newModule->apiType = "Normalize";
                 curModule->modules.push_back(newModule);
             }
             else if (nnName == "Padding")