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https://github.com/opencv/opencv.git
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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.
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#include "../precomp.hpp"
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#include "op_halide.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#include "opencl_kernels_dnn.hpp"
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namespace cv
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{
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@ -22,7 +23,7 @@ class BatchNormLayerImpl : public BatchNormLayer
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{
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public:
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Mat weights_, bias_;
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Mat weightMat, biasMat;
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UMat umat_weight, umat_bias;
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BatchNormLayerImpl(const LayerParams& params)
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{
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@ -80,6 +81,9 @@ public:
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dstWeightsData[i] = w;
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dstBiasData[i] = (hasBias ? biasData[i] : 0.0f) - w * meanData[i] * varMeanScale;
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}
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umat_weight = weights_.getUMat(ACCESS_READ);
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umat_bias = bias_.getUMat(ACCESS_READ);
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}
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void getScaleShift(Mat& scale, Mat& shift) const
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@ -97,25 +101,6 @@ public:
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return true;
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}
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void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
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{
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if (inputs[0]->dims == 4)
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{
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int groups = inputs[0]->size[0];
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int channels = inputs[0]->size[1];
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int rows = inputs[0]->size[2];
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int cols = inputs[0]->size[3];
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MatShape s = shape(groups * channels, rows * cols);
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weightMat = Mat(s[0], s[1], CV_32FC1);
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biasMat = Mat(s[0], s[1], CV_32FC1);
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for (int n = 0; n < s[0]; n++)
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{
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weightMat.row(n).setTo(weights_.at<float>(n % channels));
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biasMat.row(n).setTo(bias_.at<float>(n % channels));
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}
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}
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}
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virtual bool supportBackend(int backendId)
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{
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return backendId == DNN_BACKEND_DEFAULT ||
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@ -155,8 +140,23 @@ public:
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MatShape s = shape(groups * channels, rows * cols);
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UMat src = inputs[ii].reshape(1, s.size(), &s[0]);
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UMat dst = outputs[ii].reshape(1, s.size(), &s[0]);
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multiply(src, weightMat, dst);
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add(dst, biasMat, dst);
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int number = (s[1] % 8 == 0) ? 8 : ((s[1] % 4 == 0) ? 4 : 1);
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String buildopt = format("-DNUM=%d ", number);
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String kname = format("batch_norm%d", number);
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ocl::Kernel kernel(kname.c_str(), ocl::dnn::batchnorm_oclsrc, buildopt);
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if (kernel.empty())
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return false;
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size_t global[] = { (size_t)s[0], (size_t)(s[1] / number) };
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kernel.set(0, ocl::KernelArg::PtrReadOnly(src));
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kernel.set(1, (int)s[0]);
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kernel.set(2, (int)s[1]);
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kernel.set(3, (int)channels);
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kernel.set(4, ocl::KernelArg::PtrReadOnly(umat_weight));
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kernel.set(5, ocl::KernelArg::PtrReadOnly(umat_bias));
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kernel.set(6, ocl::KernelArg::PtrWriteOnly(dst));
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bool ret = kernel.run(2, global, NULL, false);
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if (!ret)
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return false;
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}
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}
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return true;
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@ -1,26 +1,84 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Copyright (c) 2016-2017 Fabian David Tschopp, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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__kernel void batchnorm(__global const T *src, int src_offset,
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__global const float *meanMat,
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float varMeanScale,
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__global const float *invStdMat,
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__global const float *weight,
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__global const float *bias,
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int hasWeight, int hasBias,
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int width, int height, int channel,
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__global T *dst, int dst_offset)
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#define Dtype float
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#define Dtype4 float4
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#define Dtype8 float8
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#if NUM == 8
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#define load(src, index) vload8(0, src + index)
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#define store(vec, dst, index) vstore8(vec, 0, dst + index)
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#define vec_type Dtype8
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#define BATCH_NORM batch_norm8
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#elif NUM == 4
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#define load(src, index) vload4(0, src + index)
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#define store(vec, dst, index) vstore4(vec, 0, dst + index)
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#define vec_type Dtype4
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#define BATCH_NORM batch_norm4
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#elif NUM == 1
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#define load(src, index) src[index]
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#define store(vec, dst, index) dst[index] = vec
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#define vec_type Dtype
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#define BATCH_NORM batch_norm1
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#endif
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__kernel void BATCH_NORM(__global const Dtype* src,
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const int rows,
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const int cols,
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const int channels,
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__global const Dtype* weight,
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__global const Dtype* bias,
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__global Dtype* dst)
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{
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int x = get_global_id(0);
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int y = get_global_id(1);
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int c = get_global_id(2);
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int y = get_global_id(1) * NUM;
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int index = x * cols + y;
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if (x >= width || y >= height || c >= channel)
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if (x >= rows || y >= cols)
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return;
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float mean = meanMat[c] * varMeanScale;
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float invstd = invStdMat[c];
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float w = hasWeight ? weight[c] : 1;
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float b = hasBias ? bias[c] : 0;
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int index = y * width + x + c * width * height;
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T val = (src[index + src_offset] - mean) * w * invstd + b;
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dst[index + dst_offset] = val;
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Dtype w = weight[x % channels];
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Dtype b = bias[x % channels];
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vec_type src_vec = load(src, index);
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vec_type dst_vec = src_vec * w + (vec_type)b;
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store(dst_vec, dst, index);
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}
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