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refactored DNN (#1102)

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* the first commit in the merged dnn: convert some public API from Blob's to Mat's

* temporarily or permantently removed OpenCL optimizations, which are not always stable nor usually very efficient; we'll likely use Halide instead

* got rid of Blob and BlobShape completely; use cv::Mat and std::vector<int> instead

* fixed a few compile errors

* got rid of separate .hpp files with layer declarations; instead, put everything into the respective .cpp files

* normalized all the layers' constructors; we concentrate on loading deep networks layers from files instead of constructing them from scratch, so we retained only SomeLayer::SomeLayer(const LayerParams& params); constructors

* fixed sample compilation

* suppress doxygen warnings

* trying to fix python bindings generation for DNN module

* temporarily disable python bindings while we refactor the module

* fix win32/win64 compile errors; remove trailing whitespaces

* fix win32/win64 compile errors; remove trailing whitespaces
This commit is contained in:
Vadim Pisarevsky
2017-04-25 09:33:51 +03:00
committed by GitHub
parent 4317e27d6b
commit 3f5b465501
89 changed files with 3744 additions and 7261 deletions
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43
modules/dnn/samples/torch_enet.cpp
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@@ -27,12 +27,12 @@ const String keys =
;
std::vector<String> readClassNames(const char *filename);
static void colorizeSegmentation(Blob &score, Mat &segm,
static void colorizeSegmentation(const Mat &score, Mat &segm,
Mat &legend, vector<String> &classNames);
int main(int argc, char **argv)
{
cv::CommandLineParser parser(argc, argv, keys);
CommandLineParser parser(argc, argv, keys);
if (parser.has("help"))
{
@@ -78,31 +78,27 @@ int main(int argc, char **argv)
//! [Initialize network]
//! [Prepare blob]
Mat img = imread(imageFile), input;
Mat img = imread(imageFile, 1);
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
exit(-1);
}
cv::Size inputImgSize = cv::Size(512, 512);
Size inputImgSize(512, 512);
if (inputImgSize != img.size())
resize(img, img, inputImgSize); //Resize image to input size
if(img.channels() == 3)
cv::cvtColor(img, input, cv::COLOR_BGR2RGB);
input.convertTo(input, CV_32F, 1/255.0);
dnn::Blob inputBlob = dnn::Blob::fromImages(input); //Convert Mat to dnn::Blob image batch
Mat inputBlob = blobFromImage(img, 1./255, true); //Convert Mat to image batch
//! [Prepare blob]
//! [Set input blob]
net.setBlob("", inputBlob); //set the network input
//! [Set input blob]
cv::TickMeter tm;
TickMeter tm;
tm.start();
//! [Make forward pass]
@@ -119,11 +115,7 @@ int main(int argc, char **argv)
oBlob = parser.get<String>("o_blob");
}
dnn::Blob prob = net.getBlob(oBlob); //gather output of "prob" layer
Mat& result = prob.matRef();
BlobShape shape = prob.shape();
Mat result = net.getBlob(oBlob); //gather output of "prob" layer
if (!resultFile.empty()) {
CV_Assert(result.isContinuous());
@@ -133,20 +125,21 @@ int main(int argc, char **argv)
fout.close();
}
std::cout << "Output blob shape " << shape << std::endl;
std::cout << "Output blob: " << result.size[0] << " x " << result.size[1] << " x " << result.size[2] << " x " << result.size[3] << "\n";
std::cout << "Inference time, ms: " << tm.getTimeMilli() << std::endl;
if (parser.has("show"))
{
size_t nclasses = result.size[1];
std::vector<String> classNames;
if(!classNamesFile.empty()) {
classNames = readClassNames(classNamesFile.c_str());
if (classNames.size() > prob.channels())
classNames = std::vector<String>(classNames.begin() + classNames.size() - prob.channels(),
if (classNames.size() > nclasses)
classNames = std::vector<String>(classNames.begin() + classNames.size() - nclasses,
classNames.end());
}
Mat segm, legend;
colorizeSegmentation(prob, segm, legend, classNames);
colorizeSegmentation(result, segm, legend, classNames);
Mat show;
addWeighted(img, 0.2, segm, 0.8, 0.0, show);
@@ -184,11 +177,11 @@ std::vector<String> readClassNames(const char *filename)
return classNames;
}
static void colorizeSegmentation(Blob &score, Mat &segm, Mat &legend, vector<String> &classNames)
static void colorizeSegmentation(const Mat &score, Mat &segm, Mat &legend, vector<String> &classNames)
{
const int rows = score.rows();
const int cols = score.cols();
const int chns = score.channels();
const int rows = score.size[2];
const int cols = score.size[3];
const int chns = score.size[1];
vector<Vec3i> colors;
RNG rng(12345678);
@@ -200,7 +193,7 @@ static void colorizeSegmentation(Blob &score, Mat &segm, Mat &legend, vector<Str
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);
const float *ptrScore = score.ptr<float>(0, ch, row);
uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
float *ptrMaxVal = maxVal.ptr<float>(row);
for (int col = 0; col < cols; col++)