mirror of
https://github.com/opencv/opencv.git
synced 2024-11-25 19:50:38 +08:00
batch_norm layer ocl update
use a batch_norm ocl kernel to do the work Signed-off-by: Li Peng <peng.li@intel.com>
This commit is contained in:
Li Peng
parent
be1207e5e4
commit
4189214d04
@ -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;
|
||||
|
||||
@ -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);
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user