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Merge pull request #12060 from alalek:dnn_debug_layers

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Alexander Alekhin 2018-07-26 15:14:32 +00:00
commit 9137e2d635
2 changed files with 141 additions and 8 deletions
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26
modules/dnn/include/opencv2/dnn/shape_utils.hpp
View File

@ -44,7 +44,9 @@
#include <opencv2/core.hpp> #include <opencv2/core.hpp>
#include <opencv2/core/types_c.h> #include <opencv2/core/types_c.h>
#include <iostream>
#include <ostream> #include <ostream>
#include <sstream>
namespace cv { namespace cv {
namespace dnn { namespace dnn {
@ -178,13 +180,25 @@ static inline MatShape concat(const MatShape& a, const MatShape& b)
return c; return c;
} }
inline void print(const MatShape& shape, const String& name = "") static inline std::string toString(const MatShape& shape, const String& name = "")
{ {
printf("%s: [", name.c_str()); std::ostringstream ss;
size_t i, n = shape.size(); if (!name.empty())
for( i = 0; i < n; i++ ) ss << name << ' ';
printf(" %d", shape[i]); ss << '[';
printf(" ]\n"); for(size_t i = 0, n = shape.size(); i < n; ++i)
ss << ' ' << shape[i];
ss << " ]";
return ss.str();
}
static inline void print(const MatShape& shape, const String& name = "")
{
std::cout << toString(shape, name) << std::endl;
}
static inline std::ostream& operator<<(std::ostream &out, const MatShape& shape)
{
out << toString(shape);
return out;
} }
inline int clamp(int ax, int dims) inline int clamp(int ax, int dims)

123
modules/dnn/src/dnn.cpp
View File

@ -74,6 +74,10 @@ static int PARAM_DNN_BACKEND_DEFAULT = (int)utils::getConfigurationParameterSize
#endif #endif
); );
// Additional checks (slowdowns execution!)
static bool DNN_CHECK_NAN_INF = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF", false);
static bool DNN_CHECK_NAN_INF_DUMP = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_DUMP", false);
static bool DNN_CHECK_NAN_INF_RAISE_ERROR = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_RAISE_ERROR", false);
using std::vector; using std::vector;
using std::map; using std::map;
@ -2053,10 +2057,75 @@ struct Net::Impl
{ {
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)) if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{ {
std::vector<UMat> umat_inputBlobs = OpenCLBackendWrapper::getUMatVector(ld.inputBlobsWrappers);
std::vector<UMat> umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); std::vector<UMat> umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
layer->forward(OpenCLBackendWrapper::getUMatVector(ld.inputBlobsWrappers), std::vector<UMat> umat_internalBlobs = OpenCLBackendWrapper::getUMatVector(ld.internalBlobsWrappers);
layer->forward(umat_inputBlobs,
umat_outputBlobs, umat_outputBlobs,
OpenCLBackendWrapper::getUMatVector(ld.internalBlobsWrappers)); umat_internalBlobs);
if (DNN_CHECK_NAN_INF)
{
bool fail = false;
for (size_t i = 0; i < umat_outputBlobs.size(); ++i)
{
UMat& u = umat_outputBlobs[i];
Mat m;
if (u.depth() == CV_16S) // FP16
convertFp16(u, m);
else
m = u.getMat(ACCESS_READ);
if (!checkRange(m))
{
std::cerr << "WARNING: NaN detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl;
std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl;
fail = true;
}
else if (!checkRange(m, true, NULL, -1e6, 1e6))
{
std::cerr << "WARNING: Inf detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl;
std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl;
fail = true;
}
}
if (fail)
{
for (size_t i = 0; i < umat_inputBlobs.size(); ++i)
{
UMat& u = umat_inputBlobs[i];
Mat m;
if (u.depth() == CV_16S) // FP16
convertFp16(u, m);
else
m = u.getMat(ACCESS_READ);
std::cout << "INPUT " << i << " " << cv::typeToString(u.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
for (size_t i = 0; i < umat_outputBlobs.size(); ++i)
{
UMat& u = umat_outputBlobs[i];
Mat m;
if (u.depth() == CV_16S) // FP16
convertFp16(u, m);
else
m = u.getMat(ACCESS_READ);
std::cout << "OUTPUT " << i << " " << cv::typeToString(u.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
for (size_t i = 0; i < umat_internalBlobs.size(); ++i)
{
UMat& u = umat_internalBlobs[i];
Mat m;
if (u.depth() == CV_16S) // FP16
convertFp16(u, m);
else
m = u.getMat(ACCESS_READ);
std::cout << "INTERNAL " << i << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << cv::typeToString(u.type()) << " " << m.reshape(1, 1) << std::endl;
}
if (DNN_CHECK_NAN_INF_RAISE_ERROR)
CV_Assert(!fail);
}
}
OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umat_outputBlobs); OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umat_outputBlobs);
} }
else else
@ -2069,6 +2138,56 @@ struct Net::Impl
layer->forward(ld.inputBlobs, ld.outputBlobs, ld.internals); layer->forward(ld.inputBlobs, ld.outputBlobs, ld.internals);
if (DNN_CHECK_NAN_INF)
{
bool fail = false;
for (size_t i = 0; i < ld.outputBlobs.size(); ++i)
{
const Mat& m = ld.outputBlobs[i];
if (!checkRange(m))
{
std::cerr << "WARNING: NaN detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl;
std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl;
fail = true;
}
else if (!checkRange(m, true, NULL, -1e6, 1e6))
{
std::cerr << "WARNING: Inf detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl;
std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl;
fail = true;
}
}
if (fail)
{
for (size_t i = 0; i < ld.inputBlobs.size(); ++i)
{
const Mat* pM = ld.inputBlobs[i];
if (!pM)
{
std::cout << "INPUT " << i << " is NULL" << std::endl;
continue;
}
const Mat& m = *pM;
std::cout << "INPUT " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
for (size_t i = 0; i < ld.outputBlobs.size(); ++i)
{
const Mat& m = ld.outputBlobs[i];
std::cout << "OUTPUT " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
for (size_t i = 0; i < ld.internals.size(); ++i)
{
const Mat& m = ld.internals[i];
std::cout << "INTERNAL " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
if (DNN_CHECK_NAN_INF_RAISE_ERROR)
CV_Assert(!fail);
}
}
for (int i = 0, n = ld.outputBlobsWrappers.size(); i < n; ++i) for (int i = 0, n = ld.outputBlobsWrappers.size(); i < n; ++i)
{ {
if (!ld.outputBlobsWrappers[i].empty()) if (!ld.outputBlobsWrappers[i].empty())