batch_norm layer ocl update

use a batch_norm ocl kernel to do the work

Signed-off-by: Li Peng <peng.li@intel.com>

diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp
index 5284e4d..eca30f4 100644 (file)
--- a/modules/dnn/src/layers/batch_norm_layer.cpp
+++ b/modules/dnn/src/layers/batch_norm_layer.cpp
@@ -12,6 +12,7 @@ Implementation of Batch Normalization layer.
 #include "../precomp.hpp"
 #include "op_halide.hpp"
 #include <opencv2/dnn/shape_utils.hpp>
+#include "opencl_kernels_dnn.hpp"
 
 namespace cv
 {
@@ -22,7 +23,7 @@ class BatchNormLayerImpl : public BatchNormLayer
 {
 public:
     Mat weights_, bias_;
-    Mat weightMat, biasMat;
+    UMat umat_weight, umat_bias;
 
     BatchNormLayerImpl(const LayerParams& params)
     {
@@ -80,6 +81,9 @@ public:
             dstWeightsData[i] = w;
             dstBiasData[i] = (hasBias ? biasData[i] : 0.0f) - w * meanData[i] * varMeanScale;
         }
+
+        umat_weight = weights_.getUMat(ACCESS_READ);
+        umat_bias = bias_.getUMat(ACCESS_READ);
     }
 
     void getScaleShift(Mat& scale, Mat& shift) const
@@ -97,25 +101,6 @@ public:
         return true;
     }
 
-    void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
-    {
-        if (inputs[0]->dims == 4)
-        {
-            int groups = inputs[0]->size[0];
-            int channels = inputs[0]->size[1];
-            int rows = inputs[0]->size[2];
-            int cols = inputs[0]->size[3];
-            MatShape s = shape(groups * channels, rows * cols);
-            weightMat = Mat(s[0], s[1], CV_32FC1);
-            biasMat = Mat(s[0], s[1], CV_32FC1);
-            for (int n = 0; n < s[0]; n++)
-            {
-                weightMat.row(n).setTo(weights_.at<float>(n % channels));
-                biasMat.row(n).setTo(bias_.at<float>(n % channels));
-            }
-        }
-    }
-
     virtual bool supportBackend(int backendId)
     {
         return backendId == DNN_BACKEND_DEFAULT ||
@@ -155,8 +140,23 @@ public:
                 MatShape s = shape(groups * channels, rows * cols);
                 UMat src = inputs[ii].reshape(1, s.size(), &s[0]);
                 UMat dst = outputs[ii].reshape(1, s.size(), &s[0]);
-                multiply(src, weightMat, dst);
-                add(dst, biasMat, dst);
+                int number = (s[1] % 8 == 0) ? 8 : ((s[1] % 4 == 0) ? 4 : 1);
+                String buildopt = format("-DNUM=%d ", number);
+                String kname = format("batch_norm%d", number);
+                ocl::Kernel kernel(kname.c_str(), ocl::dnn::batchnorm_oclsrc, buildopt);
+                if (kernel.empty())
+                    return false;
+                size_t global[] = { (size_t)s[0], (size_t)(s[1] / number) };
+                kernel.set(0, ocl::KernelArg::PtrReadOnly(src));
+                kernel.set(1, (int)s[0]);
+                kernel.set(2, (int)s[1]);
+                kernel.set(3, (int)channels);
+                kernel.set(4, ocl::KernelArg::PtrReadOnly(umat_weight));
+                kernel.set(5, ocl::KernelArg::PtrReadOnly(umat_bias));
+                kernel.set(6, ocl::KernelArg::PtrWriteOnly(dst));
+                bool ret = kernel.run(2, global, NULL, false);
+                if (!ret)
+                    return false;
             }
         }
         return true;

diff --git a/modules/dnn/src/opencl/batchnorm.cl b/modules/dnn/src/opencl/batchnorm.cl
index 3f9401c..e0072c9 100644 (file)
--- a/modules/dnn/src/opencl/batchnorm.cl
+++ b/modules/dnn/src/opencl/batchnorm.cl
@@ -1,26 +1,84 @@
+/*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) 2017, Intel Corporation, all rights reserved.
+// Copyright (c) 2016-2017 Fabian David Tschopp, 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*/
 
-__kernel void batchnorm(__global const T *src, int src_offset,
-                        __global const float *meanMat,
-                        float varMeanScale,
-                        __global const float *invStdMat,
-                        __global const float *weight,
-                        __global const float *bias,
-                        int hasWeight, int hasBias,
-                        int width, int height, int channel,
-                        __global T *dst, int dst_offset)
+#define Dtype float
+#define Dtype4 float4
+#define Dtype8 float8
+
+#if NUM == 8
+    #define load(src, index) vload8(0, src + index)
+    #define store(vec, dst, index) vstore8(vec, 0, dst + index)
+    #define vec_type Dtype8
+    #define BATCH_NORM batch_norm8
+#elif NUM == 4
+    #define load(src, index) vload4(0, src + index)
+    #define store(vec, dst, index) vstore4(vec, 0, dst + index)
+    #define vec_type Dtype4
+    #define BATCH_NORM batch_norm4
+#elif NUM == 1
+    #define load(src, index) src[index]
+    #define store(vec, dst, index) dst[index] = vec
+    #define vec_type Dtype
+    #define BATCH_NORM batch_norm1
+#endif
+
+__kernel void BATCH_NORM(__global const Dtype* src,
+                         const int rows,
+                         const int cols,
+                         const int channels,
+                         __global const Dtype* weight,
+                         __global const Dtype* bias,
+                         __global Dtype* dst)
 {
     int x = get_global_id(0);
-    int y = get_global_id(1);
-    int c = get_global_id(2);
+    int y = get_global_id(1) * NUM;
+    int index = x * cols + y;
 
-    if (x >= width || y >= height || c >= channel)
+    if (x >= rows || y >= cols)
         return;
 
-    float mean = meanMat[c] * varMeanScale;
-    float invstd = invStdMat[c];
-    float w = hasWeight ? weight[c] : 1;
-    float b = hasBias ? bias[c] : 0;
-    int index = y * width + x + c * width * height;
-    T val = (src[index + src_offset] - mean) * w * invstd + b;
-    dst[index + dst_offset] = val;
+    Dtype w = weight[x % channels];
+    Dtype b = bias[x % channels];
+    vec_type src_vec = load(src, index);
+    vec_type dst_vec = src_vec * w + (vec_type)b;
+    store(dst_vec, dst, index);
 }