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An option to be indepedent of OpenCV #167
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@beru |
relocate stb_image files
@nyanp |
@@ -54,7 +56,7 @@ OPTION(BUILD_DOCS "Set to ON to build documentation" OFF) | |||
# Find Dependencies | |||
# ---------------------------------------------------------------------------- | |||
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IF(BUILD_EXAMPLES) | |||
IF(USE_OPENCV) |
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@beru
In this PR CMake requires opencv even if we don't compile the mnist example (This is why AppVeyor failed to build). We need opencv only when BUILD_EXAMPLS && USE_OPENCV. Could you fix it?
…omment) indentation adjustments
Thank you for pointing out the problem. I updated CMakeLists.txt file. |
@beru |
@beru nice contribution! Take a look how I solved this for NNPACK: https://github.com/edgarriba/tiny-cnn/tree/feat/generic-computational-graph-device-abstraction/cmake/Modules Extra link: Writing find modules https://cmake.org/Wiki/CMake:How_To_Find_Libraries |
@nyanp This need some fixes for new branch. |
relocate stb_image files
…omment) indentation adjustments
relocate stb_image files
relocate stb_image files
* v0.0.1 -> v.0.1.0 - Now we can handle non-sequential model as ```network<graph>``` #108 #153 - Catch up the latest format of caffe's proto #162 - Improve the default behaviour of re-init weight #136 - Add more tests and documents #73 - Remove dependency of OpenCV in MNIST example * Create DeviceAbstraction.puml Create test.md Update test.md Update and rename test.md to device-abstraction-uml.md Create device-abstraction-uml.puml Update readme.md reorganize cmake reorganize cmake remove test warnings remove mock files Update device-abstraction-uml.puml * add tests&comments * cleanup codes currently we don't need hand-written ctor/dtors in dropout * Remove OpenCV dependency closes #2 * fix comments * update build instruction file * support CNN_USE_OPENCV build config * fix build configuration file * change the location of stb_image header files * follows the comment #167 (comment) relocate stb_image files * Update layer.h * Implement on deconvolution and unpooling layer. * core module skeletons * Added test case in separate cpp file to force link errors if there are duplicate symbols when including tiny_cnn.h in multiple .cpp files. * Fix linker error due to duplicate symbols. * Update README.md - Update layer catalogue - Upgrade example codes to v0.1.0 * update first convolutional layer abstraction version with NNPACK support base methods for new API update find_package NNPACK update tiny_backend fix segfault by adding move constructor to conv layer refactor nnp_backend update UML with new design fix required libs in cmake, set float_t as float and set activation vector as input in nnpack fix padding and add assertion after nnp_convolution_inference fix CMake warnings and reorganize modules fix data race fix broken tests Reorganize CMake modules add epsilon for broken tests fix broken tests. float_t was missing in some layers. fix clang errors
@edgarriba Talking about using CMake find_package command feature for stb_image. |
relocate stb_image files
* Remove OpenCV dependency closes #2 * update build instruction file * change the location of stb_image header files * follows the comment #167 (comment) relocate stb_image files * Implement on deconvolution and unpooling layer. * Create DeviceAbstraction.puml Create test.md Update test.md Update and rename test.md to device-abstraction-uml.md Create device-abstraction-uml.puml Update readme.md reorganize cmake reorganize cmake remove test warnings remove mock files Update device-abstraction-uml.puml * switch std::bind to lambdas * first commit to split into small backends split tiny convolutional kernels split NNPACK kernel and reorganize backend files split maxpool kernels simplify conv2d_back signature split fully connected layer kernels reorganize folders check NNPACK prerequisites add coloured nn_error, nn_warning and nn_info uncomment tiny backend
relocate stb_image files
…omment) indentation adjustments
An option to be indepedent of OpenCV
* v0.0.1 -> v.0.1.0 - Now we can handle non-sequential model as ```network<graph>``` tiny-dnn#108 tiny-dnn#153 - Catch up the latest format of caffe's proto tiny-dnn#162 - Improve the default behaviour of re-init weight tiny-dnn#136 - Add more tests and documents tiny-dnn#73 - Remove dependency of OpenCV in MNIST example * Create DeviceAbstraction.puml Create test.md Update test.md Update and rename test.md to device-abstraction-uml.md Create device-abstraction-uml.puml Update readme.md reorganize cmake reorganize cmake remove test warnings remove mock files Update device-abstraction-uml.puml * add tests&comments * cleanup codes currently we don't need hand-written ctor/dtors in dropout * Remove OpenCV dependency closes #2 * fix comments * update build instruction file * support CNN_USE_OPENCV build config * fix build configuration file * change the location of stb_image header files * follows the comment tiny-dnn#167 (comment) relocate stb_image files * Update layer.h * Implement on deconvolution and unpooling layer. * core module skeletons * Added test case in separate cpp file to force link errors if there are duplicate symbols when including tiny_cnn.h in multiple .cpp files. * Fix linker error due to duplicate symbols. * Update README.md - Update layer catalogue - Upgrade example codes to v0.1.0 * update first convolutional layer abstraction version with NNPACK support base methods for new API update find_package NNPACK update tiny_backend fix segfault by adding move constructor to conv layer refactor nnp_backend update UML with new design fix required libs in cmake, set float_t as float and set activation vector as input in nnpack fix padding and add assertion after nnp_convolution_inference fix CMake warnings and reorganize modules fix data race fix broken tests Reorganize CMake modules add epsilon for broken tests fix broken tests. float_t was missing in some layers. fix clang errors
* Remove OpenCV dependency closes #2 * update build instruction file * change the location of stb_image header files * follows the comment tiny-dnn#167 (comment) relocate stb_image files * Implement on deconvolution and unpooling layer. * Create DeviceAbstraction.puml Create test.md Update test.md Update and rename test.md to device-abstraction-uml.md Create device-abstraction-uml.puml Update readme.md reorganize cmake reorganize cmake remove test warnings remove mock files Update device-abstraction-uml.puml * switch std::bind to lambdas * first commit to split into small backends split tiny convolutional kernels split NNPACK kernel and reorganize backend files split maxpool kernels simplify conv2d_back signature split fully connected layer kernels reorganize folders check NNPACK prerequisites add coloured nn_error, nn_warning and nn_info uncomment tiny backend
It's hard to deny that OpenCV is a de-facto standard library in computer vision.
But it's rather huge for my PC-9801 computer.