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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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81
modules/dnn/misc/python/pyopencv_dnn.hpp
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@@ -1,66 +1,5 @@
#ifdef HAVE_OPENCV_DNN
typedef dnn::DictValue LayerId;
typedef std::vector<cv::dnn::Blob> vector_Blob;
template<>
bool pyopencv_to(PyObject *o, dnn::Blob &blob, const char *name);
template<> struct pyopencvVecConverter<dnn::Blob>
{
static bool to(PyObject* obj, std::vector<dnn::Blob>& value, const ArgInfo info)
{
if (PyArray_Check(obj))
{
value.resize(1);
return pyopencv_to(obj, value[0], info.name);
}
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<dnn::Blob>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<>
bool pyopencv_to(PyObject *o, std::vector<dnn::Blob> &blobs, const char *name) //required for Layer::blobs RW
{
return pyopencvVecConverter<dnn::Blob>::to(o, blobs, ArgInfo(name, false));
}
template<>
bool pyopencv_to(PyObject *o, dnn::Blob &blob, const char *name)
{
Mat &dst = blob.matRef();
if (!pyopencv_to(o, dst, name))
return false;
if (PyArray_Check(o)) //try fix channels
{
PyArrayObject* oarr = (PyArrayObject*) o;
if (PyArray_NDIM(oarr) == dst.dims)
return true;
int ndims = PyArray_NDIM(oarr);
std::vector<int> shape(ndims);
const npy_intp* _sizes = PyArray_DIMS(oarr);
for (int i = 0; i < ndims; i++)
shape[i] = (int)_sizes[i];
dst = dst.reshape(1, ndims, &shape[0]);
}
return true;
}
template<>
PyObject *pyopencv_from(const dnn::Blob &blob)
{
return pyopencv_from(blob.matRefConst());
}
template<>
bool pyopencv_to(PyObject *o, dnn::DictValue &dv, const char *name)
@@ -87,22 +26,4 @@ bool pyopencv_to(PyObject *o, dnn::DictValue &dv, const char *name)
return false;
}
template<>
bool pyopencv_to(PyObject *o, dnn::BlobShape &shape, const char *name)
{
std::vector<int> data;
if (!pyopencv_to_generic_vec(o, data, ArgInfo(name, false)))
return false;
shape = data.size() ? dnn::BlobShape((int)data.size(), &data[0]) : dnn::BlobShape::empty();
return true;
}
template<>
PyObject *pyopencv_from(const dnn::BlobShape &shape)
{
std::vector<int> data(shape.ptr(), shape.ptr() + shape.dims());
return pyopencv_from_generic_vec(data);
}
#endif
#endif