opencv/opencv_contrib
1
0
Fork 0
mirror of https://github.com/opencv/opencv_contrib.git synced 2025-10-23 18:09:25 +08:00
Code Issues Releases Wiki Activity

Added im2row, tiny optimiziations

Browse Source
This commit is contained in:
arrybn
2016-12-23 10:56:01 +03:00
parent 6891d7788f
commit 310a096b46
6 changed files with 223 additions and 48 deletions
Download Patch File Download Diff File Expand all files Collapse all files

94
modules/dnn/samples/torch_enet.cpp
View File

@@ -20,13 +20,14 @@ const String keys =
"https://www.dropbox.com/sh/dywzk3gyb12hpe5/AAD5YkUa8XgMpHs2gCRgmCVCa }"
"{model m || path to Torch .net model file (model_best.net) }"
"{image i || path to image file }"
"{i_blob | .0 | input blob name) }"
"{o_blob || output blob name) }"
"{c_names c || path to file with classnames for channels (categories.txt) }"
"{c_names c || path to file with classnames for channels (optional, categories.txt) }"
"{result r || path to save output blob (optional, binary format, NCHW order) }"
"{show s || whether to show all output channels or not}"
;
std::vector<String> readClassNames(const char *filename);
static void colorizeSegmentation(Blob &score, Mat &segm,
Mat &legend, vector<String> &classNames);
int main(int argc, char **argv)
{
@@ -40,8 +41,6 @@ int main(int argc, char **argv)
String modelFile = parser.get<String>("model");
String imageFile = parser.get<String>("image");
String inBlobName = parser.get<String>("i_blob");
String outBlobName = parser.get<String>("o_blob");
if (!parser.check())
{
@@ -78,7 +77,7 @@ int main(int argc, char **argv)
//! [Initialize network]
//! [Prepare blob]
Mat img = imread(imageFile);
Mat img = imread(imageFile), input;
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
@@ -91,15 +90,15 @@ int main(int argc, char **argv)
resize(img, img, inputImgSize); //Resize image to input size
if(img.channels() == 3)
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
cv::cvtColor(img, input, cv::COLOR_BGR2RGB);
img.convertTo(img, CV_32F, 1/255.0);
input.convertTo(input, CV_32F, 1/255.0);
dnn::Blob inputBlob = dnn::Blob::fromImages(img); //Convert Mat to dnn::Blob image batch
dnn::Blob inputBlob = dnn::Blob::fromImages(input); //Convert Mat to dnn::Blob image batch
//! [Prepare blob]
//! [Set input blob]
net.setBlob(inBlobName, inputBlob); //set the network input
net.setBlob("", inputBlob); //set the network input
//! [Set input blob]
cv::TickMeter tm;
@@ -112,7 +111,8 @@ int main(int argc, char **argv)
tm.stop();
//! [Gather output]
dnn::Blob prob = net.getBlob(outBlobName); //gather output of "prob" layer
dnn::Blob prob = net.getBlob(net.getLayerNames().back()); //gather output of "prob" layer
Mat& result = prob.matRef();
@@ -129,6 +129,8 @@ int main(int argc, char **argv)
std::cout << "Output blob shape " << shape << std::endl;
std::cout << "Inference time, ms: " << tm.getTimeMilli() << std::endl;
if (parser.has("show"))
{
std::vector<String> classNames;
if(!classNamesFile.empty()) {
classNames = readClassNames(classNamesFile.c_str());
@@ -136,17 +138,17 @@ int main(int argc, char **argv)
classNames = std::vector<String>(classNames.begin() + classNames.size() - prob.channels(),
classNames.end());
}
Mat segm, legend;
colorizeSegmentation(prob, segm, legend, classNames);
for(int i_c = 0; i_c < prob.channels(); i_c++) {
ostringstream convert;
convert << "Channel #" << i_c;
Mat show;
addWeighted(img, 0.2, segm, 0.8, 0.0, show);
if(classNames.size() == prob.channels())
convert << ": " << classNames[i_c];
imshow(convert.str().c_str(), prob.getPlane(0, i_c));
}
imshow("Result", show);
if(classNames.size())
imshow("Legend", legend);
waitKey();
}
return 0;
} //main
@@ -174,3 +176,57 @@ std::vector<String> readClassNames(const char *filename)
fp.close();
return classNames;
}
static void colorizeSegmentation(Blob &score, Mat &segm, Mat &legend, vector<String> &classNames)
{
const int rows = score.rows();
const int cols = score.cols();
const int chns = score.channels();
vector<Vec3i> colors;
RNG rng(12345678);
cv::Mat maxCl(rows, cols, CV_8UC1);
cv::Mat maxVal(rows, cols, CV_32FC1);
for (int ch = 0; ch < chns; ch++)
{
colors.push_back(Vec3i(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256)));
for (int row = 0; row < rows; row++)
{
const float *ptrScore = score.ptrf(0, ch, row);
uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
float *ptrMaxVal = maxVal.ptr<float>(row);
for (int col = 0; col < cols; col++)
{
if (ptrScore[col] > ptrMaxVal[col])
{
ptrMaxVal[col] = ptrScore[col];
ptrMaxCl[col] = ch;
}
}
}
}
segm.create(rows, cols, CV_8UC3);
for (int row = 0; row < rows; row++)
{
const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
cv::Vec3b *ptrSegm = segm.ptr<cv::Vec3b>(row);
for (int col = 0; col < cols; col++)
{
ptrSegm[col] = colors[ptrMaxCl[col]];
}
}
if (classNames.size() == colors.size())
{
int blockHeight = 30;
legend.create(blockHeight*classNames.size(), 200, CV_8UC3);
for(int i = 0; i < classNames.size(); i++)
{
cv::Mat block = legend.rowRange(i*blockHeight, (i+1)*blockHeight);
block = colors[i];
putText(block, classNames[i], Point(0, blockHeight/2), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
}
}
}

48
modules/dnn/src/layers/convolution_layer.cpp
View File

@@ -58,8 +58,7 @@ BaseConvolutionLayerImpl::BaseConvolutionLayerImpl():
inpH(0), inpW(0), inpCn(0),
outH(0), outW(0), outCn(0),
inpGroupCn(0), outGroupCn(0),
ksize(0), colBlobCols(0),
bias(false), tryUseOpenCL(false)
ksize(0), bias(false), tryUseOpenCL(false)
{
#if HAVE_CBLAS
if (getBlasThreads() != cv::getThreadNum())
@@ -111,7 +110,7 @@ void BaseConvolutionLayerImpl::allocate(const std::vector<Blob*> &inputs, std::v
if (!is1x1())
{
colBlob.create(Shape(ksize, colBlobCols), input.type(), allocFlags);
colRowBlob.create(colRowBlobShape, input.type(), allocFlags);
}
}
@@ -152,7 +151,7 @@ void ConvolutionLayerImpl::computeInpOutShape(const Blob &input)
inpGroupCn = inpCn / group;
ksize = inpGroupCn * kernel.height * kernel.width;
colBlobCols = outH * outW;
colRowBlobShape = BlobShape(outH * outW, ksize);
}
template<typename XMat>
@@ -174,7 +173,8 @@ void ConvolutionLayerImpl::forward_(std::vector<Blob*> &inputs, std::vector<Blob
for (int g = 0; g < group; g++)
{
XMat colMat, curInp = slice(inpMat, n, _Range(g * inpGroupCn, inpGroupCn));
im2col(curInp, colMat);
im2row(curInp, colMat);
_Range kerRange(g * outGroupCn, outGroupCn);
XMat kerMat = weightsMat.rowRange(kerRange);
@@ -182,7 +182,7 @@ void ConvolutionLayerImpl::forward_(std::vector<Blob*> &inputs, std::vector<Blob
_Range outRange((g + n * group) * outGroupCn, outGroupCn);
XMat dstMat = outMat.rowRange(outRange);
dnn::gemm(kerMat, colMat, 1, dstMat, 0);
dnn::gemm(kerMat, colMat, 1, dstMat, 0, GEMM_2_T);
if (bias)
{
@@ -209,8 +209,8 @@ void ConvolutionLayerImpl::im2col(const UMat &srcImg, UMat &dstCol)
return;
}
#ifdef HAVE_OPENCL
CV_Assert(im2col_ocl(srcImg, inpGroupCn, inpH, inpW, kernel.height, kernel.width, pad.height, pad.width, stride.height, stride.width, dilation.height, dilation.width, this->colBlob.umatRef()));
dstCol = this->colBlob.umatRefConst();
CV_Assert(im2col_ocl(srcImg, inpGroupCn, inpH, inpW, kernel.height, kernel.width, pad.height, pad.width, stride.height, stride.width, dilation.height, dilation.width, this->colRowBlob.umatRef()));
dstCol = this->colRowBlob.umatRefConst();
#else
CV_Error(Error::StsInternal, "");
dstCol = srcImg; //supress warning
@@ -225,7 +225,7 @@ void ConvolutionLayerImpl::im2col(const Mat &srcImg, Mat &dstCol)
return;
}
Mat &colMat = colBlob.matRef();
Mat &colMat = colRowBlob.matRef();
if (srcImg.type() == CV_32F)
im2col_CpuPBody<float>::run(srcImg.ptr<float>(), inpGroupCn, inpH, inpW, kernel.height,
kernel.width, pad.height, pad.width, stride.height, stride.width,
@@ -238,6 +238,32 @@ void ConvolutionLayerImpl::im2col(const Mat &srcImg, Mat &dstCol)
dstCol = colMat;
}
void ConvolutionLayerImpl::im2row(const Mat &srcImg, Mat &dstRow)
{
if (is1x1())
{
dstRow = reshaped(srcImg, Shape(ksize, outH*outW)).t();
return;
}
Mat &colMat = colRowBlob.matRef();
if (srcImg.type() == CV_32F)
im2row_CpuPBody<float>::run(srcImg.ptr<float>(), inpGroupCn, inpH, inpW, kernel.height,
kernel.width, pad.height, pad.width, stride.height, stride.width,
dilation.height, dilation.width, outW, outH, colMat.ptr<float>());
if (srcImg.type() == CV_64F)
im2row_CpuPBody<double>::run(srcImg.ptr<double>(), inpGroupCn, inpH, inpW, kernel.height,
kernel.width, pad.height, pad.width, stride.height, stride.width,
dilation.height, dilation.width, outW, outH, colMat.ptr<double>());
dstRow = colMat;
}
void ConvolutionLayerImpl::im2row(const UMat &srcImg, UMat &dstCol)
{
CV_Error(cv::Error::StsNotImplemented, "");
}
//Deconvolution
void DeConvolutionLayerImpl::computeInpOutShape(const Blob &inpBlob)
@@ -264,7 +290,7 @@ void DeConvolutionLayerImpl::computeInpOutShape(const Blob &inpBlob)
CV_Assert(inpCn % group == 0 && outCn % group == 0);
CV_Assert(blobs[0].channels() == outCn && blobs[0].num() == inpCn / group);
colBlobCols = inpH * inpW;
colRowBlobShape = BlobShape(ksize, inpH * inpW);
}
void DeConvolutionLayerImpl::forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs)
@@ -292,7 +318,7 @@ void DeConvolutionLayerImpl::forward_(std::vector<Blob *> &inputs, std::vector<B
for (int g = 0; g < group; g++)
{
XMat dstMat = decnBlob.rowRange(_Range((g + n * group) * outGroupCn, outGroupCn));
XMat &colMat = (is1x1()) ? dstMat : colBlob.getRef<XMat>();
XMat &colMat = (is1x1()) ? dstMat : colRowBlob.getRef<XMat>();
XMat convMat = convBlob.rowRange(_Range((g + n * group) * inpGroupCn, inpGroupCn));
XMat wghtMat = weightsMat.rowRange(_Range(g * inpGroupCn, inpGroupCn));

6
modules/dnn/src/layers/convolution_layer.hpp
View File

@@ -65,12 +65,12 @@ protected:
int outH, outW, outCn;
int inpGroupCn, outGroupCn;
int ksize;
int colBlobCols;
BlobShape colRowBlobShape;
bool bias;
bool tryUseOpenCL, useOpenCL;
Blob colBlob, biasOnesBlob;
Blob colRowBlob, biasOnesBlob;
};
@@ -86,7 +86,9 @@ protected:
template<typename XMat>
void forward_(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
void im2col(const Mat &srcImg, Mat &dstCol);
void im2row(const Mat &srcImg, Mat &dstRow);
void im2col(const UMat &srcImg, UMat &dstCol);
void im2row(const UMat &srcImg, UMat &dstCol);
};
class DeConvolutionLayerImpl : public BaseConvolutionLayerImpl

6
modules/dnn/src/layers/elementwise_layers.hpp
View File

@@ -287,7 +287,9 @@ struct PowerFunctor
{
typedef PowerLayer Layer;
double power, scale, shift;
const double power;
const double scale;
const double shift;
PowerFunctor(double power_, double scale_ = 1, double shift_ = 0)
: power(power_), scale(scale_), shift(shift_) {}
@@ -295,7 +297,7 @@ struct PowerFunctor
template<typename TFloat>
inline TFloat operator()(TFloat x) const
{
return pow((TFloat)shift + (TFloat)scale * x, (TFloat)power);
return power == 1.0 ? (TFloat)shift + (TFloat)scale * x : pow((TFloat)shift + (TFloat)scale * x, (TFloat)power);
}
#ifdef HAVE_OPENCL

97
modules/dnn/src/layers/op_im2col.hpp
View File

@@ -114,6 +114,92 @@ public:
}
};
template <typename Dtype>
class im2row_CpuPBody : public cv::ParallelLoopBody
{
const Dtype* data_im;
int channels, height, width;
int kernel_h, kernel_w;
int pad_h, pad_w;
int stride_h, stride_w;
int dilation_h, dilation_w;
Dtype* data_col;
int height_col, width_col, channels_col;
im2row_CpuPBody() {}
public:
static void run(const Dtype* data_im,
int channels, int height, int width,
int kernel_h, int kernel_w,
int pad_h, int pad_w,
int stride_h, int stride_w,
int dilation_h, int dilation_w,
int height_col, int width_col,
Dtype* data_col)
{
im2row_CpuPBody<Dtype> t;
t.data_im = data_im;
t.data_col = data_col;
t.channels = channels; t.height = height; t.width = width;
t.kernel_h = kernel_h; t.kernel_w = kernel_w;
t.pad_h = pad_h; t.pad_w = pad_w;
t.stride_h = stride_h; t.stride_w = stride_w;
t.dilation_h = dilation_h; t.dilation_w = dilation_w;
t.height_col = height_col;
t.width_col = width_col;
t.channels_col = channels * kernel_h * kernel_w;
cv::parallel_for_(Range(0, t.height_col*t.width_col), t, 16);
}
virtual void operator ()(const Range &r) const
{
int dh = dilation_h, dw = dilation_w;
Dtype* data_col_ = data_col;
const Dtype* data_im_ = data_im;
for (int row = r.start; row < r.end; ++row)
{
int out_c = row % width_col;
int out_r = row / width_col;
int out_row_offset = row*kernel_h*kernel_w*channels;
int start_in_r = out_r * stride_h - pad_h;
int start_in_c = out_c * stride_w - pad_w;
int start_k_r = std::max(0, cvCeil(-start_in_r/(float)dilation_h));
int end_k_r = std::min(kernel_h, cvCeil((height - start_in_r)/(float)dilation_h));
int start_k_c = std::max(0, cvCeil(-start_in_c/(float)dilation_w));
int end_k_c = std::min(kernel_w, cvCeil((width - start_in_c)/(float)dilation_w));
for(int i_c = 0; i_c < channels; i_c++)
{
int channels_offset = i_c * width * height;
int out_ch_offset = i_c*kernel_h*kernel_w;
int in_r = start_in_r + start_k_r*dilation_h;
for(int k_r = start_k_r; k_r < end_k_r; k_r++, in_r += dh)
{
int row_offset = in_r*width;
int out_col_offset = k_r*kernel_w;
int in_c = start_in_c + start_k_c*dilation_w;
for(int k_c = start_k_c; k_c < end_k_c; k_c++, in_c += dw)
{
int in_index = channels_offset + row_offset + in_c;
int out_index = out_row_offset + out_ch_offset + out_col_offset + k_c;
data_col_[out_index] = data_im_[in_index];
}
}
}
}
}
};
template <typename Dtype>
class col2im_CpuPBody : public cv::ParallelLoopBody
{
@@ -154,6 +240,10 @@ public:
virtual void operator ()(const Range &r) const
{
const Dtype* data_col_ = data_col;
Dtype* data_im_ = data_im;
int coeff_h_col = (1 - stride_h * kernel_w * height_col) * width_col;
int coeff_w_col = (1 - stride_w * height_col * width_col);
for (int index = r.start; index < r.end; index++)
{
Dtype val = 0;
@@ -170,14 +260,13 @@ public:
// equivalent implementation
int offset =
(c * kernel_h * kernel_w + h * kernel_w + w) * height_col * width_col;
int coeff_h_col = (1 - stride_h * kernel_w * height_col) * width_col;
int coeff_w_col = (1 - stride_w * height_col * width_col);
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
val += data_col_[offset + h_col * coeff_h_col + w_col * coeff_w_col];
}
}
data_im[index] = val;
data_im_[index] = val;
}
}
};

2
modules/dnn/src/torch/torch_importer.cpp
View File

@@ -197,7 +197,7 @@ struct TorchImporter : public ::cv::dnn::Importer
if (typeStr == "Double")
return CV_64F;
else if (typeStr == "Float")
else if (typeStr == "Float" || typeStr == "Cuda")
return CV_32F;
else if (typeStr == "Byte")
return CV_8U;