In OpenCL code in activations.cl, make the type of floating point
literals to be float. Otherwise the values will be interpreted as
doubles, causing Beignet to have type conversion issues.
fixed problem in concat layer by disabling memory re-use in layers with multiple inputs
trying to fix the tests when Halide is used to run deep nets
another attempt to fix Halide tests
see if the Halide tests will pass with concat layer fusion turned off
trying to fix failures in halide tests; another try
one more experiment to make halide_concat & halide_enet tests pass
continue attempts to fix halide tests
moving on
uncomment parallel concat layer
seemingly fixed failures in Halide tests and re-enabled concat layer fusion; thanks to dkurt for the patch
Main purpose of this namespace is to avoid using of incompatible
binaries that will cause applications crashes.
This additional namespace will not impact "Source code API".
This change allows to maintain ABI checks (with easy filtering out).
* another round of dnn optimization:
* increased malloc alignment across OpenCV from 16 to 64 bytes to make it AVX2 and even AVX-512 friendly
* improved SIMD optimization of pooling layer, optimized average pooling
* cleaned up convolution layer implementation
* made activation layer "attacheable" to all other layers, including fully connected and addition layer.
* fixed bug in the fusion algorithm: "LayerData::consumers" should not be cleared, because it desctibes the topology.
* greatly optimized permutation layer, which improved SSD performance
* parallelized element-wise binary/ternary/... ops (sum, prod, max)
* also, added missing copyrights to many of the layer implementation files
* temporarily disabled (again) the check for intermediate blobs consistency; fixed warnings from various builders