COMPMID-556: Cherry-picked minor fixes from Github
- Added --api 21 to documentation
- Removed include of a runtime header in Core
- cherry-picked 2 small fixes from Github
commit 869d424d6fd5df7b15a858f2c5f853536f7a0aca
Author: giorgio-arena <arena.cpp@gmail.com>
Date: Mon Oct 23 16:58:59 2017 +0100
Update 00_introduction.dox
commit f054c210e493111061a458b887f7c4edaca06a9f
Author: Forrest Iandola <fiandola@gmail.com>
Date: Mon Oct 16 00:44:24 2017 -0700
fix comment
typo
kernerl's --> kernel's
Change-Id: I0a3893148a9565acbfd18d340da41845ce3ad44f
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/93460
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
diff --git a/docs/00_introduction.dox b/docs/00_introduction.dox
index 2df12ed..72a0ede 100644
--- a/docs/00_introduction.dox
+++ b/docs/00_introduction.dox
@@ -514,14 +514,16 @@
i.e. to cross compile the "graph_lenet" example for Linux 32bit:
- arm-linux-gnueabihf-g++ examples/graph_lenet.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute_graph -larm_compute -larm_compute_core -lOpenCL -o graph_lenet -DARM_COMPUTE_CL
+ arm-linux-gnueabihf-g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute_graph -larm_compute -larm_compute_core -lOpenCL -o graph_lenet -DARM_COMPUTE_CL
i.e. to cross compile the "graph_lenet" example for Linux 64bit:
- aarch64-linux-gnu-g++ examples/graph_lenet.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -L. -larm_compute_graph -larm_compute -larm_compute_core -lOpenCL -o graph_lenet -DARM_COMPUTE_CL
+ aarch64-linux-gnu-g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 -L. -larm_compute_graph -larm_compute -larm_compute_core -lOpenCL -o graph_lenet -DARM_COMPUTE_CL
(notice the only difference with the 32 bit command is that we don't need the -mfpu option and the compiler's name is different)
+@note If compiling using static libraries, this order must be followed when linking: arm_compute_graph_static, arm_compute, arm_compute_core
+
To compile natively (i.e directly on an ARM device) for NEON for Linux 32bit:
g++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -larm_compute -larm_compute_core -o neon_convolution
@@ -541,14 +543,16 @@
i.e. to cross compile the "graph_lenet" example for Linux 32bit:
- g++ examples/graph_lenet.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute_graph -larm_compute -larm_compute_core -lOpenCL -o graph_lenet -DARM_COMPUTE_CL
+ g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute_graph -larm_compute -larm_compute_core -lOpenCL -o graph_lenet -DARM_COMPUTE_CL
i.e. to cross compile the "graph_lenet" example for Linux 64bit:
- g++ examples/graph_lenet.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 L. -larm_compute_graph -larm_compute -larm_compute_core -lOpenCL -o graph_lenet -DARM_COMPUTE_CL
+ g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 L. -larm_compute_graph -larm_compute -larm_compute_core -lOpenCL -o graph_lenet -DARM_COMPUTE_CL
(notice the only difference with the 32 bit command is that we don't need the -mfpu option)
+@note If compiling using static libraries, this order must be followed when linking: arm_compute_graph_static, arm_compute, arm_compute_core
+
@note These two commands assume libarm_compute.so is available in your library path, if not add the path to it using -L
To run the built executable simply run:
@@ -574,8 +578,8 @@
- Generate the 32 and/or 64 toolchains by running the following commands:
- $NDK/build/tools/make_standalone_toolchain.py --arch arm64 --install-dir $MY_TOOLCHAINS/aarch64-linux-android-4.9 --stl gnustl
- $NDK/build/tools/make_standalone_toolchain.py --arch arm --install-dir $MY_TOOLCHAINS/arm-linux-androideabi-4.9 --stl gnustl
+ $NDK/build/tools/make_standalone_toolchain.py --arch arm64 --install-dir $MY_TOOLCHAINS/aarch64-linux-android-4.9 --stl gnustl --api 21
+ $NDK/build/tools/make_standalone_toolchain.py --arch arm --install-dir $MY_TOOLCHAINS/arm-linux-androideabi-4.9 --stl gnustl --api 21
@attention Due to some NDK issues make sure you use g++ & gnustl for aarch64 and clang++ & gnustl for armv7
diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
index 8642a19..60a3a1b 100644
--- a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
@@ -1082,7 +1082,7 @@
the third thread [16,24] and the fourth thread [25,31].
The algorithm outer loop iterates over Z, P, Y, X where P is the depth/3rd dimension of each kernel. This order is not arbitrary, the main benefit of this
- is that we setup the neon registers containing the kernerl's values only once and then compute each XY using the preloaded registers as opposed as doing this for every XY value.
+ is that we setup the neon registers containing the kernel's values only once and then compute each XY using the preloaded registers as opposed as doing this for every XY value.
The algorithm does not require allocating any additional memory amd computes the results directly in-place in two stages:
1) Convolve plane 0 with kernel 0 and initialize the corresponding output plane with these values.
diff --git a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
index 2c90d9a..c622d4f 100644
--- a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
+++ b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
@@ -30,7 +30,6 @@
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"
-#include "arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h"
#include <arm_neon.h>
#include <climits>