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Open Source Computer Vision Library https://opencv.org/
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opencv/modules/dnn/src/layers/pooling_layer.cpp

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/*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.
// 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"
#include "op_halide.hpp"
#include <float.h>
#include <algorithm>
using std::max;
using std::min;
namespace cv
{
namespace dnn
{
//TODO: add ceil_mode param
class PoolingLayerImpl : public PoolingLayer
{
public:
PoolingLayerImpl(const LayerParams& params)
{
type = PoolingLayer::MAX;
computeMaxIdx = true;
if (params.has("pool"))
{
String pool = params.get<String>("pool").toLowerCase();
if (pool == "max")
type = PoolingLayer::MAX;
else if (pool == "ave")
type = PoolingLayer::AVE;
else if (pool == "stochastic")
type = PoolingLayer::STOCHASTIC;
else
CV_Error(Error::StsBadArg, "Unknown pooling type \"" + pool + "\"");
}
getPoolingKernelParams(params, kernel.height, kernel.width, globalPooling,
pad.height, pad.width, stride.height, stride.width, padMode);
setParamsFrom(params);
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
{
CV_Assert(inputs.size() == 1);
cv::Size inp(inputs[0]->size[3], inputs[0]->size[2]),
out(outputs[0].size[3], outputs[0].size[2]);
if(globalPooling)
{
kernel = inp;
}
getConvPoolPaddings(inp, out, kernel, stride, padMode, pad);
}
virtual bool supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_HALIDE && haveHalide() &&
(type == PoolingLayer::MAX ||
type == PoolingLayer::AVE && !pad.width && !pad.height);
}
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
for (size_t ii = 0; ii < inputs.size(); ii++)
{
switch (type)
{
case MAX:
maxPooling(*inputs[ii], outputs[2 * ii], outputs[2 * ii + 1]);
break;
case AVE:
avePooling(*inputs[ii], outputs[ii]);
break;
default:
CV_Error(Error::StsNotImplemented, "Not implemented");
break;
}
}
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
{
if (type == PoolingLayer::MAX)
return initMaxPoolingHalide(inputs);
else if (type == PoolingLayer::AVE)
return initAvePoolingHalide(inputs);
else
return Ptr<BackendNode>();
}
class MaxPoolingInvoker : public ParallelLoopBody
{
public:
const Mat* src_;
Mat *dst_, *mask_;
Size kernel_, stride_, pad_;
int nstripes_;
bool computeMaxIdx_;
MaxPoolingInvoker(const Mat& src, Mat& dst, Mat& mask, Size kernel,
Size stride, Size pad, int nstripes, bool computeMaxIdx)
{
src_ = &src;
dst_ = &dst;
mask_ = &mask;
kernel_ = kernel;
stride_ = stride;
pad_ = pad;
nstripes_ = nstripes;
computeMaxIdx_ = computeMaxIdx;
CV_Assert(src.isContinuous() && dst.isContinuous() &&
src.type() == CV_32F && src.type() == dst.type() &&
mask.type() == src.type() && src.dims == 4 && dst.dims == 4 &&
src.size[0] == dst.size[0] && src.size[1] == dst.size[1] &&
mask.size == dst.size);
}
void operator()(const Range& r) const
{
int nimgs = dst_->size[0], channels = dst_->size[1];
int width = dst_->size[3], height = dst_->size[2];
int inp_width = src_->size[3], inp_height = src_->size[2];
size_t total = dst_->total();
size_t stripeSize = (total + nstripes_ - 1)/nstripes_;
size_t stripeStart = r.start*stripeSize;
size_t stripeEnd = std::min(r.end*stripeSize, total);
size_t ofs = stripeStart;
int x0 = (int)(ofs % width);
ofs /= width;
int y0 = (int)(ofs % height);
ofs /= height;
int c = (int)(ofs % channels);
int n = (int)(ofs / channels);
const float *srcData = src_->ptr<float>(n, c);
float *dstData = dst_->ptr<float>(n, c, y0) + x0;
float *dstMaskData = mask_->ptr<float>(n, c, y0) + x0;
int kernel_w = kernel_.width, kernel_h = kernel_.height;
int pad_w = pad_.width, pad_h = pad_.height;
int stride_w = stride_.width, stride_h = stride_.height;
bool compMaxIdx = computeMaxIdx_;
#if CV_SIMD128
v_float32x4 idx00(0.f, (float)stride_w, (float)(stride_w*2), (float)(stride_w*3));
v_float32x4 ones = v_setall_f32(1.f);
v_float32x4 delta = v_setall_f32((float)(inp_width - kernel_w));
#endif
for( ofs = stripeStart; ofs < stripeEnd; ofs++ )
{
int ystart = y0 * stride_h - pad_h;
int xstart = x0 * stride_w - pad_w;
int yend = min(ystart + kernel_h, inp_height);
int xend = min(xstart + kernel_w, inp_width);
ystart = max(ystart, 0);
xstart = max(xstart, 0);
float max_val = -FLT_MAX;
int max_index = -1;
#if CV_SIMD128
if( xstart > 0 && (x0 + 7) * stride_w - pad_w + kernel_w < inp_width )
{
if( compMaxIdx )
{
v_float32x4 max_val0 = v_setall_f32(max_val);
v_float32x4 max_val1 = max_val0;
v_float32x4 max_idx0 = v_setall_f32(-1.f);
v_float32x4 max_idx1 = max_idx0;
int index0 = ystart * inp_width + xstart;
v_float32x4 idx0 = idx00 + v_setall_f32((float)index0);
v_float32x4 idx1 = idx0 + v_setall_f32((float)(stride_w*4));
for (int y = ystart; y < yend; ++y)
{
for (int x = xstart; x < xend; ++x, idx0 += ones, idx1 += ones)
{
const int index = y * inp_width + x;
v_float32x4 v0(srcData[index], srcData[index + stride_w],
srcData[index + stride_w*2], srcData[index + stride_w*3]);
v_float32x4 v1(srcData[index + stride_w*4], srcData[index + stride_w*5],
srcData[index + stride_w*6], srcData[index + stride_w*7]);
max_idx0 = v_select(v0 > max_val0, idx0, max_idx0);
max_idx1 = v_select(v1 > max_val1, idx1, max_idx1);
max_val0 = v_max(max_val0, v0);
max_val1 = v_max(max_val1, v1);
}
idx0 += delta;
idx1 += delta;
}
v_store(dstData, max_val0);
v_store(dstData + 4, max_val1);
v_store(dstMaskData, max_idx0);
v_store(dstMaskData + 4, max_idx1);
ofs += 7;
dstData += 8;
dstMaskData += 8;
x0 += 7;
}
else
{
v_float32x4 max_val0 = v_setall_f32(max_val);
v_float32x4 max_val1 = max_val0;
for (int y = ystart; y < yend; ++y)
{
for (int x = xstart; x < xend; ++x)
{
const int index = y * inp_width + x;
v_float32x4 v0(srcData[index], srcData[index + stride_w],
srcData[index + stride_w*2], srcData[index + stride_w*3]);
v_float32x4 v1(srcData[index + stride_w*4], srcData[index + stride_w*5],
srcData[index + stride_w*6], srcData[index + stride_w*7]);
max_val0 = v_max(max_val0, v0);
max_val1 = v_max(max_val1, v1);
}
}
v_store(dstData, max_val0);
v_store(dstData + 4, max_val1);
ofs += 7;
dstData += 8;
x0 += 7;
}
}
else
#endif
{
if( compMaxIdx )
{
for (int y = ystart; y < yend; ++y)
for (int x = xstart; x < xend; ++x)
{
const int index = y * inp_width + x;
float val = srcData[index];
if (val > max_val)
{
max_val = val;
max_index = index;
}
}
*dstData++ = max_val;
*dstMaskData++ = max_index;
}
else
{
for (int y = ystart; y < yend; ++y)
for (int x = xstart; x < xend; ++x)
{
const int index = y * inp_width + x;
float val = srcData[index];
max_val = std::max(max_val, val);
}
*dstData++ = max_val;
}
}
if( ++x0 >= width )
{
x0 = 0;
if( ++y0 >= height )
{
y0 = 0;
if( ++c >= channels )
{
c = 0;
if( ++n >= nimgs )
break;
}
srcData = src_->ptr<float>(n, c);
}
}
}
}
};
void maxPooling(Mat &src, Mat &dst, Mat &mask)
{
const int nstripes = getNumThreads();
MaxPoolingInvoker mp(src, dst, mask, kernel, stride, pad, nstripes, computeMaxIdx);
parallel_for_(Range(0, nstripes), mp, nstripes);
}
void avePooling(Mat &src, Mat &dst)
{
Size inp(src.size[3], src.size[2]),
out(dst.size[3], dst.size[2]);
for (int n = 0; n < src.size[0]; ++n)
{
for (int c = 0; c < src.size[1]; ++c)
{
const float *srcData = src.ptr<float>(n, c);
float *dstData = dst.ptr<float>(n, c);
for (int ph = 0; ph < out.height; ++ph)
{
for (int pw = 0; pw < out.width; ++pw)
{
int hstart = ph * stride.height - pad.height;
int wstart = pw * stride.width - pad.width;
int hend = min(hstart + kernel.height, inp.height + pad.height);
int wend = min(wstart + kernel.width, inp.width + pad.width);
int poolSize = (hend - hstart) * (wend - wstart);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
hend = min(hend, inp.height);
wend = min(wend, inp.width);
dstData[ph * out.width + pw] = 0.f;
for (int h = hstart; h < hend; ++h)
for (int w = wstart; w < wend; ++w)
dstData[ph * out.width + pw] += srcData[h * inp.width + w];
dstData[ph * out.width + pw] /= poolSize;
}
}
}
}
}
virtual Ptr<BackendNode> initMaxPoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
const int inWidth = inputBuffer.width();
const int inHeight = inputBuffer.height();
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
Halide::RDom r(0, kernel.width, 0, kernel.height);
Halide::Expr kx, ky;
if (pad.width || pad.height)
{
kx = clamp(x * stride.width + r.x - pad.width, 0, inWidth - 1);
ky = clamp(y * stride.height + r.y - pad.height, 0, inHeight - 1);
}
else
{
kx = min(x * stride.width + r.x, inWidth - 1);
ky = min(y * stride.height + r.y, inHeight - 1);
}
// Halide::argmax returns tuple (r.x, r.y, max).
Halide::Tuple res = argmax(inputBuffer(kx, ky, c, n));
// Compute offset from argmax in range [0, kernel_size).
Halide::Expr max_index;
if (pad.width || pad.height)
{
max_index = clamp(y * stride.height + res[1] - pad.height,
0, inHeight - 1) * inWidth +
clamp(x * stride.width + res[0] - pad.width,
0, inWidth - 1);
}
else
{
max_index = min(y * stride.height + res[1], inHeight - 1) * inWidth +
min(x * stride.width + res[0], inWidth - 1);
}
top(x, y, c, n) = { res[2], Halide::cast<float>(max_index) };
return Ptr<BackendNode>(new HalideBackendNode(top));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initAvePoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
const int inW = inputBuffer.width(), inH = inputBuffer.height();
if ((inW - kernel.width) % stride.width || (inH - kernel.height) % stride.height)
{
CV_Error(cv::Error::StsNotImplemented,
"Halide backend for average pooling with partial "
"kernels is not implemented");
}
const float norm = 1.0f / (kernel.width * kernel.height);
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
Halide::RDom r(0, kernel.width, 0, kernel.height);
top(x, y, c, n) = sum(
inputBuffer(x * stride.width + r.x,
y * stride.height + r.y, c, n)) * norm;
return Ptr<BackendNode>(new HalideBackendNode(top));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
virtual void applyHalideScheduler(Ptr<BackendNode>& node,
const std::vector<Mat*> &inputs,
const std::vector<Mat> &outputs,
int targetId) const
{
#ifdef HAVE_HALIDE
if (targetId != DNN_TARGET_CPU)
{
Layer::applyHalideScheduler(node, inputs, outputs, targetId);
return;
}
Halide::Var x("x"), y("y"), c("c"), n("n"), tile("tile"),
xi("xi"), yi("yi"), ci("ci"), xo("xo"), yo("yo"), co("co");
Halide::Func& top = node.dynamicCast<HalideBackendNode>()->funcs.back();
int outW, outH, outC, outN;
getCanonicalSize(outputs[0].size, &outW, &outH, &outC, &outN);
if (outW < 8 || outH < 8)
{
if (outC > 8)
top.split(c, co, ci, 8)
.fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile)
.parallel(tile)
.vectorize(ci);
else
{
top.fuse(y, c, tile).fuse(n, tile, tile)
.parallel(tile);
if (outW > 1)
top.vectorize(x);
}
}
else
{
if (outC > 8)
top.split(x, xo, xi, 8).split(y, yo, yi, 8).split(c, co, ci, 8)
.fuse(xo, yo, tile).fuse(co, tile, tile).fuse(n, tile, tile)
.parallel(tile)
.vectorize(xi);
else
top.split(x, xo, xi, 8).split(y, yo, yi, 8)
.fuse(xo, yo, tile).fuse(c, tile, tile).fuse(n, tile, tile)
.parallel(tile)
.vectorize(xi);
}
#endif // HAVE_HALIDE
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
CV_Assert(inputs.size() != 0);
Size in(inputs[0][3], inputs[0][2]), out;
if (globalPooling)
{
out.height = 1;
out.width = 1;
}
else if (padMode.empty())
{
//Yeah, something strange Caffe scheme-)
out.height = static_cast<int>(ceil(static_cast<float>(in.height + 2 * pad.height -
kernel.height) / stride.height)) + 1;
out.width = static_cast<int>(ceil(static_cast<float>(in.width + 2 * pad.width -
kernel.width) / stride.width)) + 1;
if (pad.height || pad.width)
{
// If we have padding, ensure that the last pooling starts strictly
// inside the image (instead of at the padding); otherwise clip the last.
if ((out.height - 1) * stride.height >= in.height + pad.height)
--out.height;
if ((out.width - 1) * stride.width >= in.width + pad.width)
--out.width;
CV_Assert((out.height - 1) * stride.height < in.height + pad.height);
CV_Assert((out.width - 1) * stride.width < in.width + pad.width);
}
}
else
{
getConvPoolOutParams(in, kernel, stride,
padMode, out);
}
outputs.resize(type == MAX ? 2 * inputs.size() : inputs.size());
for (size_t i = 0; i < inputs.size(); i++)
{
size_t index = type == MAX ? 2*i : i;
int dims[] = {inputs[i][0], inputs[i][1], out.height, out.width};
outputs[index] = shape(dims);
if (type == MAX)
outputs[index + 1] = shape(dims);
}
return false;
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
{
(void)inputs; // suppress unused variable warning
long flops = 0;
for(int i = 0; i < outputs.size(); i++)
{
if (type == MAX)
{
if (i%2 == 0)
flops += total(outputs[i])*kernel.area();
}
else
{
flops += total(outputs[i])*(kernel.area() + 1);
}
}
return flops;
}
};
Ptr<PoolingLayer> PoolingLayer::create(const LayerParams& params)
{
return Ptr<PoolingLayer>(new PoolingLayerImpl(params));
}
}
}