Merge pull request #9058 from alalek:dnn_minor_fixes

modules/dnn/CMakeLists.txt

@@ -9,6 +9,8 @@ endif()
 
 set(the_description "Deep neural network module. It allows to load models from different frameworks and to make forward pass")
 
+ocv_add_dispatched_file("layers/layers_common" AVX AVX2)
+
 ocv_add_module(dnn opencv_core opencv_imgproc WRAP python matlab java)
 ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-shadow -Wno-parentheses -Wmaybe-uninitialized -Wsign-promo
                                      -Wmissing-declarations -Wmissing-prototypes

modules/dnn/include/opencv2/dnn.hpp

@@ -44,7 +44,7 @@
 
 // This is an umbrealla header to include into you project.
 // We are free to change headers layout in dnn subfolder, so please include
-// this header for future compartibility
+// this header for future compatibility
 
 
 /** @defgroup dnn Deep Neural Network module

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

@@ -152,7 +152,19 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         int outputNameToIndex(String outputName);
     };
 
-    //! Classical recurrent layer
+    /** @brief Classical recurrent layer
+
+    Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$.
+
+    - input: should contain packed input @f$x_t@f$.
+    - output: should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true).
+
+    input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively.
+
+    output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix.
+
+    If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix.
+    */
     class CV_EXPORTS RNNLayer : public Layer
     {
     public:
@@ -180,17 +192,6 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          */
         virtual void setProduceHiddenOutput(bool produce = false) = 0;
 
-        /** Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$.
-
-        @param input should contain packed input @f$x_t@f$.
-        @param output should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true).
-
-        @p input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively.
-
-        @p output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix.
-
-        If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix.
-        */
     };
 
     class CV_EXPORTS BaseConvolutionLayer : public Layer

modules/dnn/src/dnn.cpp

@@ -969,9 +969,6 @@ struct Net::Impl
         }
     }
 
-    #define CV_RETHROW_ERROR(err, newmsg)\
-        cv::error(err.code, newmsg, err.func.c_str(), err.file.c_str(), err.line)
-
     void allocateLayer(int lid, const LayersShapesMap& layersShapes)
     {
         CV_TRACE_FUNCTION();

modules/dnn/src/layers/convolution_layer.cpp

@@ -506,13 +506,13 @@ public:
                         int bsz = ofs1 - ofs0;
                     #if CV_TRY_AVX2
                         if(useAVX2)
-                            fastConv_avx2(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
+                            opt_AVX2::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
                                           outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
                         else
                     #endif
                     #if CV_TRY_AVX
                         if(useAVX)
-                            fastConv_avx(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
+                            opt_AVX::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
                                          outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
                         else
                     #endif
@@ -824,12 +824,12 @@ public:
 
         #if CV_TRY_AVX2
             if( useAVX2 )
-                fastGEMM_avx2( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
+                opt_AVX2::fastGEMM( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
             else
         #endif
         #if CV_TRY_AVX
             if( useAVX )
-                fastGEMM_avx( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
+                opt_AVX::fastGEMM( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
             else
         #endif
             for( m = 0; m < mmax; m += 2 )

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -177,12 +177,12 @@ public:
 
             #if CV_TRY_AVX2
                 if( useAVX2 )
-                    fastGEMM1T_avx2( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
+                    opt_AVX2::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
                 else
             #endif
             #if CV_TRY_AVX
                 if( useAVX )
-                    fastGEMM1T_avx( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
+                    opt_AVX::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
                 else
             #endif
                 {
@@ -191,19 +191,19 @@ public:
             #if CV_SIMD128
                     for( ; i <= nw - 4; i += 4, wptr += 4*wstep )
                     {
-                        vfloat32x4 vs0 = v_setall_f32(0.f), vs1 = v_setall_f32(0.f);
-                        vfloat32x4 vs2 = v_setall_f32(0.f), vs3 = v_setall_f32(0.f);
+                        v_float32x4 vs0 = v_setall_f32(0.f), vs1 = v_setall_f32(0.f);
+                        v_float32x4 vs2 = v_setall_f32(0.f), vs3 = v_setall_f32(0.f);
 
                         for( k = 0; k < vecsize; k += 4 )
                         {
-                            vfloat32x4 v = v_load_aligned(sptr + k);
+                            v_float32x4 v = v_load_aligned(sptr + k);
                             vs0 += v*v_load_aligned(wptr + k);
                             vs1 += v*v_load_aligned(wptr + wstep + k);
                             vs2 += v*v_load_aligned(wptr + wstep*2 + k);
                             vs3 += v*v_load_aligned(wptr + wstep*3 + k);
                         }
 
-                        vfloat32x4 s = v_reduce_sum4(vs0, vs1, vs2, vs3);
+                        v_float32x4 s = v_reduce_sum4(vs0, vs1, vs2, vs3);
                         s += v_load(biasptr + i);
                         v_store(dptr + i, s);
                     }

modules/dnn/src/layers/layers_common.avx.cpp

-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2017, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "precomp.hpp"
-#include "layers_common.hpp"
-#include "opencv2/core/hal/intrin.hpp"
-
-#define fastConv_some_avx fastConv_avx
-#define fastGEMM1T_some_avx fastGEMM1T_avx
-#define fastGEMM_some_avx fastGEMM_avx
-
-#undef _mm256_fmadd_ps
-#define _mm256_fmadd_ps(a, b, c) _mm256_add_ps(c, _mm256_mul_ps(a, b))
-
-#include "layers_common.simd.hpp"

modules/dnn/src/layers/layers_common.avx2.cpp

-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2017, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "precomp.hpp"
-#include "layers_common.hpp"
-#include "opencv2/core/hal/intrin.hpp"
-
-#define fastConv_some_avx fastConv_avx2
-#define fastGEMM1T_some_avx fastGEMM1T_avx2
-#define fastGEMM_some_avx fastGEMM_avx2
-
-#include "layers_common.simd.hpp"

modules/dnn/src/layers/layers_common.hpp

@@ -45,6 +45,10 @@
 #include <opencv2/dnn.hpp>
 #include <opencv2/dnn/shape_utils.hpp>
 
+// dispatched AVX/AVX2 optimizations
+#include "layers/layers_common.simd.hpp"
+#include "layers/layers_common.simd_declarations.hpp"
+
 namespace cv
 {
 namespace dnn
@@ -64,32 +68,6 @@ void getConvPoolPaddings(const Size& inp, const Size& out,
                          const Size &kernel, const Size &stride,
                          const String &padMode, Size &pad);
 
-#if CV_TRY_AVX
-void fastConv_avx(const float* weights, size_t wstep, const float* bias,
-                   const float* rowbuf, float* output, const int* outShape,
-                   int blockSize, int vecsize, int vecsize_aligned,
-                   const float* relu, bool initOutput);
-void fastGEMM1T_avx( const float* vec, const float* weights,
-                     size_t wstep, const float* bias,
-                     float* dst, int nvecs, int vecsize );
-void fastGEMM_avx( const float* aptr, size_t astep, const float* bptr0,
-                   size_t bstep, float* cptr, size_t cstep,
-                   int ma, int na, int nb );
-#endif
-
-#if CV_TRY_AVX2
-void fastConv_avx2(const float* weights, size_t wstep, const float* bias,
-                   const float* rowbuf, float* output, const int* outShape,
-                   int blockSize, int vecsize, int vecsize_aligned,
-                   const float* relu, bool initOutput);
-void fastGEMM1T_avx2( const float* vec, const float* weights,
-                     size_t wstep, const float* bias,
-                     float* dst, int nvecs, int vecsize );
-void fastGEMM_avx2( const float* aptr, size_t astep, const float* bptr0,
-                   size_t bstep, float* cptr, size_t cstep,
-                   int ma, int na, int nb );
-#endif
-
 }
 }
 

modules/dnn/src/layers/layers_common.simd.hpp

@@ -40,16 +40,34 @@
 //
 //M*/
 
-#ifndef __DNN_LAYERS_COMMON_SIMD_HPP__
-#define __DNN_LAYERS_COMMON_SIMD_HPP__
+#include "opencv2/core/hal/intrin.hpp"
 
 namespace cv {
 namespace dnn {
+CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
+
+void fastConv( const float* weights, size_t wstep, const float* bias,
+               const float* rowbuf, float* output, const int* outShape,
+               int blockSize, int vecsize, int vecsize_aligned,
+               const float* relu, bool initOutput );
+void fastGEMM1T( const float* vec, const float* weights,
+                 size_t wstep, const float* bias,
+                 float* dst, int nvecs, int vecsize );
+void fastGEMM( const float* aptr, size_t astep, const float* bptr,
+               size_t bstep, float* cptr, size_t cstep,
+               int ma, int na, int nb );
+
+#if !defined(CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY) && CV_AVX
+
+#if !CV_FMA // AVX workaround
+#undef _mm256_fmadd_ps
+#define _mm256_fmadd_ps(a, b, c) _mm256_add_ps(c, _mm256_mul_ps(a, b))
+#endif
 
-void fastConv_some_avx( const float* weights, size_t wstep, const float* bias,
-                        const float* rowbuf, float* output, const int* outShape,
-                        int blockSize, int vecsize, int vecsize_aligned,
-                        const float* relu, bool initOutput )
+void fastConv( const float* weights, size_t wstep, const float* bias,
+               const float* rowbuf, float* output, const int* outShape,
+               int blockSize, int vecsize, int vecsize_aligned,
+               const float* relu, bool initOutput )
 {
     int outCn = outShape[1];
     size_t outPlaneSize = outShape[2]*outShape[3];
@@ -214,9 +232,9 @@ void fastConv_some_avx( const float* weights, size_t wstep, const float* bias,
 }
 
 // dst = vec * weights^t + bias
-void fastGEMM1T_some_avx( const float* vec, const float* weights,
-                          size_t wstep, const float* bias,
-                          float* dst, int nvecs, int vecsize )
+void fastGEMM1T( const float* vec, const float* weights,
+                 size_t wstep, const float* bias,
+                 float* dst, int nvecs, int vecsize )
 {
     int i = 0;
 
@@ -276,9 +294,9 @@ void fastGEMM1T_some_avx( const float* vec, const float* weights,
     _mm256_zeroupper();
 }
 
-void fastGEMM_some_avx( const float* aptr, size_t astep, const float* bptr,
-                        size_t bstep, float* cptr, size_t cstep,
-                        int ma, int na, int nb )
+void fastGEMM( const float* aptr, size_t astep, const float* bptr,
+               size_t bstep, float* cptr, size_t cstep,
+               int ma, int na, int nb )
 {
     int n = 0;
     for( ; n <= nb - 16; n += 16 )
@@ -346,7 +364,7 @@ void fastGEMM_some_avx( const float* aptr, size_t astep, const float* bptr,
     _mm256_zeroupper();
 }
 
-}
-}
+#endif // CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
 
-#endif
+CV_CPU_OPTIMIZATION_NAMESPACE_END
+}} // namespace