support eltwise sum with different number of input channels in CUDA backend
* add shortcut primitive
* add offsets in shortcut kernel
* skip tests involving more than two inputs
* remove redundant modulus operation
* support multiple inputs
* remove whole file indentation
* skip acc in0 trunc test if weighted
* use shortcut iff channels are unequal
Enable cuda4dnn on hardware without support for __half
* Enable cuda4dnn on hardware without support for half (ie. compute capability < 5.3)
Update CMakeLists.txt
Lowered minimum CC to 3.0
* UPD: added ifdef on new copy kernel
* added fp16 support detection at runtime
* Clarified #if condition on atomicAdd definition
* More explicit CMake error message
Fix implicit conversion from array to scalar in python bindings
* Fix wrong conversion behavior for primitive types
- Introduce ArgTypeInfo namedtuple instead of plain tuple.
If strict conversion parameter for type is set to true, it is
handled like object argument in PyArg_ParseTupleAndKeywords and
converted to concrete type with the appropriate pyopencv_to function
call.
- Remove deadcode and unused variables.
- Fix implicit conversion from numpy array with 1 element to scalar
- Fix narrowing conversion to size_t type.
* Fix wrong conversion behavior for primitive types
- Introduce ArgTypeInfo namedtuple instead of plain tuple.
If strict conversion parameter for type is set to true, it is
handled like object argument in PyArg_ParseTupleAndKeywords and
converted to concrete type with the appropriate pyopencv_to function
call.
- Remove deadcode and unused variables.
- Fix implicit conversion from numpy array with 1 element to scalar
- Fix narrowing conversion to size_t type.·
- Enable tests with wrong conversion behavior
- Restrict passing None as value
- Restrict bool to integer/floating types conversion
* Add PyIntType support for Python 2
* Remove possible narrowing conversion of size_t
* Bindings conversion update
- Remove unused macro
- Add better conversion for types to numpy types descriptors
- Add argument name to fail messages
- NoneType treated as a valid argument. Better handling will be added
as a standalone patch
* Add descriptor specialization for size_t
* Add check for signed to unsigned integer conversion safety
- If signed integer is positive it can be safely converted
to unsigned
- Add check for plain python 2 objects
- Add check for numpy scalars
- Add simple type_traits implementation for better code style
* Resolve type "overflow" false negative in safe casting check
- Move type_traits to separate header
* Add copyright message to type_traits.hpp
* Limit conversion scope for integral numpy types
- Made canBeSafelyCasted specialized only for size_t, so
type_traits header became unused and was removed.
- Added clarification about descriptor pointer
Add lightweight IE hardware targets checks
nGraph: Concat with paddings
Enable more nGraph tests
Restore FP32->FP16 for GPU plugin of IE
try to fix buildbot
Use lightweight IE targets check only starts from R4
- some of `icvCvt_BGR*` functions have R with B channels
swapped what leads to the wrong conversion
- renames misleading `rgb` variable name to `bgr`
- swap back the conversion coefficients, `cB` should be the first
Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>
