From fa9dfc97258ee097d68b801c12c84845595355a0 Mon Sep 17 00:00:00 2001 From: Jeongseok Lee Date: Sun, 25 Feb 2018 19:09:23 -0800 Subject: [PATCH 01/36] Add in-source build script to examples --- CMakeLists.txt | 68 +++++-------------- cmake/DARTMacros.cmake | 6 +- examples/addDeleteSkels/CMakeLists.txt | 5 ++ examples/addDeleteSkels/InSourceBuild.cmake | 16 +++++ examples/atlasSimbicon/CMakeLists.txt | 5 ++ examples/atlasSimbicon/InSourceBuild.cmake | 16 +++++ examples/bipedStand/CMakeLists.txt | 5 ++ examples/bipedStand/InSourceBuild.cmake | 16 +++++ examples/hardcodedDesign/CMakeLists.txt | 5 ++ examples/hardcodedDesign/InSourceBuild.cmake | 16 +++++ examples/hybridDynamics/CMakeLists.txt | 5 ++ examples/hybridDynamics/InSourceBuild.cmake | 16 +++++ examples/jointConstraints/CMakeLists.txt | 5 ++ examples/jointConstraints/InSourceBuild.cmake | 16 +++++ examples/mixedChain/CMakeLists.txt | 5 ++ examples/mixedChain/InSourceBuild.cmake | 16 +++++ .../operationalSpaceControl/CMakeLists.txt | 5 ++ .../InSourceBuild.cmake | 16 +++++ examples/osgExamples/CMakeLists.txt | 1 + .../osgExamples/osgAtlasPuppet/CMakeLists.txt | 5 ++ .../osgAtlasPuppet/InSourceBuild.cmake | 16 +++++ .../osgAtlasSimbicon/CMakeLists.txt | 5 ++ .../osgAtlasSimbicon/InSourceBuild.cmake | 16 +++++ .../osgExamples/osgDragAndDrop/CMakeLists.txt | 5 ++ .../osgDragAndDrop/InSourceBuild.cmake | 16 +++++ examples/osgExamples/osgEmpty/CMakeLists.txt | 5 ++ .../osgExamples/osgEmpty/InSourceBuild.cmake | 16 +++++ .../osgExamples/osgHuboPuppet/CMakeLists.txt | 5 ++ .../osgHuboPuppet/InSourceBuild.cmake | 16 +++++ examples/osgExamples/osgImGui/CMakeLists.txt | 5 ++ .../osgExamples/osgImGui/InSourceBuild.cmake | 16 +++++ .../osgOperationalSpaceControl/CMakeLists.txt | 5 ++ .../InSourceBuild.cmake | 16 +++++ .../osgExamples/osgSoftBodies/CMakeLists.txt | 5 ++ .../osgSoftBodies/InSourceBuild.cmake | 16 +++++ .../osgExamples/osgTinkertoy/CMakeLists.txt | 5 ++ .../osgTinkertoy/InSourceBuild.cmake | 16 +++++ .../osgExamples/osgWamIkFast/CMakeLists.txt | 5 ++ .../osgWamIkFast/InSourceBuild.cmake | 25 +++++++ .../osgWamIkFast/ikfast/CMakeLists.txt | 5 ++ examples/rigidChain/CMakeLists.txt | 5 ++ examples/rigidChain/InSourceBuild.cmake | 16 +++++ examples/rigidCubes/CMakeLists.txt | 5 ++ examples/rigidCubes/InSourceBuild.cmake | 12 ++++ examples/rigidLoop/CMakeLists.txt | 5 ++ examples/rigidLoop/InSourceBuild.cmake | 16 +++++ examples/rigidShapes/CMakeLists.txt | 5 ++ examples/rigidShapes/InSourceBuild.cmake | 16 +++++ examples/simpleFrames/CMakeLists.txt | 5 ++ examples/simpleFrames/InSourceBuild.cmake | 16 +++++ examples/softBodies/CMakeLists.txt | 5 ++ examples/softBodies/InSourceBuild.cmake | 16 +++++ examples/speedTest/CMakeLists.txt | 5 ++ examples/speedTest/InSourceBuild.cmake | 16 +++++ examples/vehicle/CMakeLists.txt | 5 ++ examples/vehicle/InSourceBuild.cmake | 16 +++++ 56 files changed, 577 insertions(+), 54 deletions(-) create mode 100644 examples/addDeleteSkels/InSourceBuild.cmake create mode 100644 examples/atlasSimbicon/InSourceBuild.cmake create mode 100644 examples/bipedStand/InSourceBuild.cmake create mode 100644 examples/hardcodedDesign/InSourceBuild.cmake create mode 100644 examples/hybridDynamics/InSourceBuild.cmake create mode 100644 examples/jointConstraints/InSourceBuild.cmake create mode 100644 examples/mixedChain/InSourceBuild.cmake create mode 100644 examples/operationalSpaceControl/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgAtlasPuppet/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgAtlasSimbicon/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgDragAndDrop/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgEmpty/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgHuboPuppet/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgImGui/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgOperationalSpaceControl/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgSoftBodies/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgTinkertoy/InSourceBuild.cmake create mode 100644 examples/osgExamples/osgWamIkFast/InSourceBuild.cmake create mode 100644 examples/rigidChain/InSourceBuild.cmake create mode 100644 examples/rigidCubes/InSourceBuild.cmake create mode 100644 examples/rigidLoop/InSourceBuild.cmake create mode 100644 examples/rigidShapes/InSourceBuild.cmake create mode 100644 examples/simpleFrames/InSourceBuild.cmake create mode 100644 examples/softBodies/InSourceBuild.cmake create mode 100644 examples/speedTest/InSourceBuild.cmake create mode 100644 examples/vehicle/InSourceBuild.cmake diff --git a/CMakeLists.txt b/CMakeLists.txt index 7b4c38eb25d59..01f882b0c617e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -265,65 +265,31 @@ endif(DART_VERBOSE) add_subdirectory(dart) +set(DART_IN_SOURCE_BUILD True) + if(TARGET dart) # Add a "tests" target to build unit tests. enable_testing() add_subdirectory(unittests EXCLUDE_FROM_ALL) - if (TARGET dart-utils-urdf AND TARGET dart-gui) + # Add example subdirectories and an "examples" target. + add_subdirectory(examples) + get_property(examples GLOBAL PROPERTY DART_EXAMPLES) + add_custom_target(examples DEPENDS ${examples}) - # Add example targets - dart_add_example(examples/addDeleteSkels dart dart-utils-urdf dart-gui) - dart_add_example(examples/atlasSimbicon dart dart-utils-urdf dart-gui) - dart_add_example(examples/bipedStand dart dart-utils-urdf dart-gui) - dart_add_example(examples/hardcodedDesign dart dart-utils-urdf dart-gui) - dart_add_example(examples/hybridDynamics dart dart-utils-urdf dart-gui) - dart_add_example(examples/jointConstraints dart dart-utils-urdf dart-gui) - dart_add_example(examples/mixedChain dart dart-utils-urdf dart-gui) - dart_add_example(examples/operationalSpaceControl dart dart-utils-urdf dart-gui) - dart_add_example(examples/rigidChain dart dart-utils-urdf dart-gui) - dart_add_example(examples/rigidCubes dart dart-utils-urdf dart-gui) - dart_add_example(examples/rigidLoop dart dart-utils-urdf dart-gui) - dart_add_example(examples/rigidShapes dart dart-utils-urdf dart-gui) - dart_add_example(examples/simpleFrames dart dart-utils-urdf dart-gui) - dart_add_example(examples/softBodies dart dart-utils-urdf dart-gui) - dart_add_example(examples/speedTest dart dart-utils-urdf dart-gui) - dart_add_example(examples/vehicle dart dart-utils-urdf dart-gui) - - if(TARGET dart-gui-osg) - dart_add_example(examples/osgExamples/osgAtlasPuppet dart dart-utils-urdf dart-gui-osg) - dart_add_example(examples/osgExamples/osgAtlasSimbicon dart dart-utils-urdf dart-gui-osg) - dart_add_example(examples/osgExamples/osgDragAndDrop dart dart-utils-urdf dart-gui-osg) - dart_add_example(examples/osgExamples/osgEmpty dart dart-utils-urdf dart-gui-osg) - dart_add_example(examples/osgExamples/osgHuboPuppet dart dart-utils-urdf dart-gui-osg) - dart_add_example(examples/osgExamples/osgImGui dart dart-utils-urdf dart-gui-osg) - dart_add_example(examples/osgExamples/osgOperationalSpaceControl dart dart-utils-urdf dart-gui-osg) - dart_add_example(examples/osgExamples/osgSoftBodies dart dart-utils-urdf dart-gui-osg) - dart_add_example(examples/osgExamples/osgTinkertoy dart dart-utils-urdf dart-gui-osg) - - add_library(example_wamIk examples/osgExamples/osgWamIkFast/ikfast/ikfast71.Transform6D.4_6_9_10_11_12_f8.cpp) - target_link_libraries(example_wamIk PUBLIC dart) - target_compile_definitions(example_wamIk PUBLIC IKFAST_NO_MAIN IKFAST_CLIBRARY) - target_compile_options(example_wamIk PRIVATE -w) - - dart_add_example(examples/osgExamples/osgWamIkFast dart dart-utils-urdf dart-gui-osg example_wamIk) - endif() - - # Add an "examples" target to build examples. - get_property(examples GLOBAL PROPERTY DART_EXAMPLES) - add_custom_target(examples DEPENDS ${examples}) + if(DART_VERBOSE) + message(STATUS "") + message(STATUS "[ Examples ]") + foreach(example ${examples}) + message(STATUS "Adding example: ${example}") + endforeach(example ${examples}) + else(DART_VERBOSE) + list(LENGTH examples examples_length) + message(STATUS "Adding ${examples_length} examples") + endif(DART_VERBOSE) - if(DART_VERBOSE) - message(STATUS "") - message(STATUS "[ Examples ]") - foreach(example ${examples}) - message(STATUS "Adding example: ${example}") - endforeach(example ${examples}) - else(DART_VERBOSE) - list(LENGTH examples examples_length) - message(STATUS "Adding ${examples_length} examples") - endif(DART_VERBOSE) + if (TARGET dart-utils-urdf AND TARGET dart-gui) # Add tutorial targets if(TARGET dart-collision-bullet) diff --git a/cmake/DARTMacros.cmake b/cmake/DARTMacros.cmake index 88c8cb670a395..7d98037dfd4ce 100644 --- a/cmake/DARTMacros.cmake +++ b/cmake/DARTMacros.cmake @@ -142,9 +142,9 @@ function(dart_add_custom_target rel_dir property_name) endfunction() #=============================================================================== -function(dart_add_example rel_dir) - dart_add_custom_target(${rel_dir} DART_EXAMPLES ${ARGN}) -endfunction() +function(dart_add_example) + dart_property_add(DART_EXAMPLES ${ARGN}) +endfunction(dart_add_example) #=============================================================================== function(dart_add_tutorial rel_dir) diff --git a/examples/addDeleteSkels/CMakeLists.txt b/examples/addDeleteSkels/CMakeLists.txt index 9c53854355776..48e8c49d529b3 100644 --- a/examples/addDeleteSkels/CMakeLists.txt +++ b/examples/addDeleteSkels/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(addDeleteSkels) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/addDeleteSkels/InSourceBuild.cmake b/examples/addDeleteSkels/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/addDeleteSkels/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/atlasSimbicon/CMakeLists.txt b/examples/atlasSimbicon/CMakeLists.txt index 0a010c0f647db..416647e30165c 100644 --- a/examples/atlasSimbicon/CMakeLists.txt +++ b/examples/atlasSimbicon/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(atlasSimbicon) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/atlasSimbicon/InSourceBuild.cmake b/examples/atlasSimbicon/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/atlasSimbicon/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/bipedStand/CMakeLists.txt b/examples/bipedStand/CMakeLists.txt index 5f49e7faa7a4a..7b415bb7c4e3e 100644 --- a/examples/bipedStand/CMakeLists.txt +++ b/examples/bipedStand/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(bipedStand) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/bipedStand/InSourceBuild.cmake b/examples/bipedStand/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/bipedStand/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/hardcodedDesign/CMakeLists.txt b/examples/hardcodedDesign/CMakeLists.txt index 392af90711d3e..be0d714c0d42d 100644 --- a/examples/hardcodedDesign/CMakeLists.txt +++ b/examples/hardcodedDesign/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(hardcodedDesign) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/hardcodedDesign/InSourceBuild.cmake b/examples/hardcodedDesign/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/hardcodedDesign/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/hybridDynamics/CMakeLists.txt b/examples/hybridDynamics/CMakeLists.txt index 96fa7a97faf45..24c0dfcd062e0 100644 --- a/examples/hybridDynamics/CMakeLists.txt +++ b/examples/hybridDynamics/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(hybridDynamics) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/hybridDynamics/InSourceBuild.cmake b/examples/hybridDynamics/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/hybridDynamics/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/jointConstraints/CMakeLists.txt b/examples/jointConstraints/CMakeLists.txt index f777352f66f1c..454afee397133 100644 --- a/examples/jointConstraints/CMakeLists.txt +++ b/examples/jointConstraints/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(jointConstraints) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/jointConstraints/InSourceBuild.cmake b/examples/jointConstraints/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/jointConstraints/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/mixedChain/CMakeLists.txt b/examples/mixedChain/CMakeLists.txt index e646856b560ec..3e56c1c10cca5 100644 --- a/examples/mixedChain/CMakeLists.txt +++ b/examples/mixedChain/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(mixedChain) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/mixedChain/InSourceBuild.cmake b/examples/mixedChain/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/mixedChain/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/operationalSpaceControl/CMakeLists.txt b/examples/operationalSpaceControl/CMakeLists.txt index 54a5323d4434c..d27847aa6edf6 100644 --- a/examples/operationalSpaceControl/CMakeLists.txt +++ b/examples/operationalSpaceControl/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(operationalSpaceControl) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/operationalSpaceControl/InSourceBuild.cmake b/examples/operationalSpaceControl/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/operationalSpaceControl/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/CMakeLists.txt b/examples/osgExamples/CMakeLists.txt index 33f15cdc9feda..dca1592571903 100644 --- a/examples/osgExamples/CMakeLists.txt +++ b/examples/osgExamples/CMakeLists.txt @@ -7,3 +7,4 @@ add_subdirectory(osgImGui) add_subdirectory(osgOperationalSpaceControl) add_subdirectory(osgSoftBodies) add_subdirectory(osgTinkertoy) +add_subdirectory(osgWamIkFast) diff --git a/examples/osgExamples/osgAtlasPuppet/CMakeLists.txt b/examples/osgExamples/osgAtlasPuppet/CMakeLists.txt index 035a49c33a246..ca5edc96eacad 100644 --- a/examples/osgExamples/osgAtlasPuppet/CMakeLists.txt +++ b/examples/osgExamples/osgAtlasPuppet/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgAtlasPuppet) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgAtlasPuppet/InSourceBuild.cmake b/examples/osgExamples/osgAtlasPuppet/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgAtlasPuppet/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgAtlasSimbicon/CMakeLists.txt b/examples/osgExamples/osgAtlasSimbicon/CMakeLists.txt index 055b61a6d924b..e2d1e3f9aef6b 100644 --- a/examples/osgExamples/osgAtlasSimbicon/CMakeLists.txt +++ b/examples/osgExamples/osgAtlasSimbicon/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgAtlasSimbicon) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgAtlasSimbicon/InSourceBuild.cmake b/examples/osgExamples/osgAtlasSimbicon/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgAtlasSimbicon/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgDragAndDrop/CMakeLists.txt b/examples/osgExamples/osgDragAndDrop/CMakeLists.txt index cd8eb79409019..a89bfe5ede43b 100644 --- a/examples/osgExamples/osgDragAndDrop/CMakeLists.txt +++ b/examples/osgExamples/osgDragAndDrop/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgDragAndDrop) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgDragAndDrop/InSourceBuild.cmake b/examples/osgExamples/osgDragAndDrop/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgDragAndDrop/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgEmpty/CMakeLists.txt b/examples/osgExamples/osgEmpty/CMakeLists.txt index 3b9e5532080d2..43919da1d1f11 100644 --- a/examples/osgExamples/osgEmpty/CMakeLists.txt +++ b/examples/osgExamples/osgEmpty/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgEmpty) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgEmpty/InSourceBuild.cmake b/examples/osgExamples/osgEmpty/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgEmpty/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgHuboPuppet/CMakeLists.txt b/examples/osgExamples/osgHuboPuppet/CMakeLists.txt index 70f3c297edf46..cceef8373c4f1 100644 --- a/examples/osgExamples/osgHuboPuppet/CMakeLists.txt +++ b/examples/osgExamples/osgHuboPuppet/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgHubuPuppet) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgHuboPuppet/InSourceBuild.cmake b/examples/osgExamples/osgHuboPuppet/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgHuboPuppet/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgImGui/CMakeLists.txt b/examples/osgExamples/osgImGui/CMakeLists.txt index 68d7e800dcf23..bc6c9e4e9ba3a 100644 --- a/examples/osgExamples/osgImGui/CMakeLists.txt +++ b/examples/osgExamples/osgImGui/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgImGui) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgImGui/InSourceBuild.cmake b/examples/osgExamples/osgImGui/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgImGui/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgOperationalSpaceControl/CMakeLists.txt b/examples/osgExamples/osgOperationalSpaceControl/CMakeLists.txt index 9662e1882284a..826fad8f8dad7 100644 --- a/examples/osgExamples/osgOperationalSpaceControl/CMakeLists.txt +++ b/examples/osgExamples/osgOperationalSpaceControl/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgOperationalSpaceControl) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgOperationalSpaceControl/InSourceBuild.cmake b/examples/osgExamples/osgOperationalSpaceControl/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgOperationalSpaceControl/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgSoftBodies/CMakeLists.txt b/examples/osgExamples/osgSoftBodies/CMakeLists.txt index 6697046b7710f..71653e63e53e4 100644 --- a/examples/osgExamples/osgSoftBodies/CMakeLists.txt +++ b/examples/osgExamples/osgSoftBodies/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgSoftBodies) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgSoftBodies/InSourceBuild.cmake b/examples/osgExamples/osgSoftBodies/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgSoftBodies/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgTinkertoy/CMakeLists.txt b/examples/osgExamples/osgTinkertoy/CMakeLists.txt index d86d6d1da497e..e204761279c48 100644 --- a/examples/osgExamples/osgTinkertoy/CMakeLists.txt +++ b/examples/osgExamples/osgTinkertoy/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgTinkertoy) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui-osg CONFIG) diff --git a/examples/osgExamples/osgTinkertoy/InSourceBuild.cmake b/examples/osgExamples/osgTinkertoy/InSourceBuild.cmake new file mode 100644 index 0000000000000..3fe66937e5e8d --- /dev/null +++ b/examples/osgExamples/osgTinkertoy/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui-osg) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgWamIkFast/CMakeLists.txt b/examples/osgExamples/osgWamIkFast/CMakeLists.txt index 887a31d9209e4..7cd38beca1620 100644 --- a/examples/osgExamples/osgWamIkFast/CMakeLists.txt +++ b/examples/osgExamples/osgWamIkFast/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(osgWamIkfast) find_package(DART 6.1.1 REQUIRED COMPONENTS utils-urdf gui-osg external-ikfast) diff --git a/examples/osgExamples/osgWamIkFast/InSourceBuild.cmake b/examples/osgExamples/osgWamIkFast/InSourceBuild.cmake new file mode 100644 index 0000000000000..f5cc650dbcd45 --- /dev/null +++ b/examples/osgExamples/osgWamIkFast/InSourceBuild.cmake @@ -0,0 +1,25 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui-osg) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_library(example_wamIk + "${CMAKE_CURRENT_SOURCE_DIR}/ikfast/ikfast71.Transform6D.4_6_9_10_11_12_f8.cpp" +) +target_link_libraries(example_wamIk PUBLIC dart) +target_compile_definitions(example_wamIk PUBLIC IKFAST_NO_MAIN IKFAST_CLIBRARY) +target_compile_options(example_wamIk PRIVATE -w) + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} + dart dart-utils-urdf dart-gui-osg example_wamIk +) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/osgExamples/osgWamIkFast/ikfast/CMakeLists.txt b/examples/osgExamples/osgWamIkFast/ikfast/CMakeLists.txt index 0271ac35a2ac4..57c35e486af09 100644 --- a/examples/osgExamples/osgWamIkFast/ikfast/CMakeLists.txt +++ b/examples/osgExamples/osgWamIkFast/ikfast/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(wamIkfast) find_package(DART 6.1.1 REQUIRED COMPONENTS external-ikfast) diff --git a/examples/rigidChain/CMakeLists.txt b/examples/rigidChain/CMakeLists.txt index 15cc03df630b8..8dc6ea7c9159c 100644 --- a/examples/rigidChain/CMakeLists.txt +++ b/examples/rigidChain/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(rigidChain) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/rigidChain/InSourceBuild.cmake b/examples/rigidChain/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/rigidChain/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/rigidCubes/CMakeLists.txt b/examples/rigidCubes/CMakeLists.txt index b466cdef135c0..fb53533cd6a22 100644 --- a/examples/rigidCubes/CMakeLists.txt +++ b/examples/rigidCubes/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(rigidCubes) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/rigidCubes/InSourceBuild.cmake b/examples/rigidCubes/InSourceBuild.cmake new file mode 100644 index 0000000000000..bdb6122954400 --- /dev/null +++ b/examples/rigidCubes/InSourceBuild.cmake @@ -0,0 +1,12 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example_old(${example_name}) diff --git a/examples/rigidLoop/CMakeLists.txt b/examples/rigidLoop/CMakeLists.txt index 33f292a3c00ef..460c5a17b09b1 100644 --- a/examples/rigidLoop/CMakeLists.txt +++ b/examples/rigidLoop/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(rigidLoop) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/rigidLoop/InSourceBuild.cmake b/examples/rigidLoop/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/rigidLoop/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/rigidShapes/CMakeLists.txt b/examples/rigidShapes/CMakeLists.txt index 15cbe16e0beb1..86223aa07ef3e 100644 --- a/examples/rigidShapes/CMakeLists.txt +++ b/examples/rigidShapes/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(rigidShapes) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/rigidShapes/InSourceBuild.cmake b/examples/rigidShapes/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/rigidShapes/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/simpleFrames/CMakeLists.txt b/examples/simpleFrames/CMakeLists.txt index 969965de2993f..c12f106524181 100644 --- a/examples/simpleFrames/CMakeLists.txt +++ b/examples/simpleFrames/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(simpleFrames) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/simpleFrames/InSourceBuild.cmake b/examples/simpleFrames/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/simpleFrames/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/softBodies/CMakeLists.txt b/examples/softBodies/CMakeLists.txt index e1799ffcf5b19..2e1f631f84a44 100644 --- a/examples/softBodies/CMakeLists.txt +++ b/examples/softBodies/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(softBodies) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/softBodies/InSourceBuild.cmake b/examples/softBodies/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/softBodies/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/speedTest/CMakeLists.txt b/examples/speedTest/CMakeLists.txt index be549155253f2..3d9cbe30661b9 100644 --- a/examples/speedTest/CMakeLists.txt +++ b/examples/speedTest/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(speedTest) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/speedTest/InSourceBuild.cmake b/examples/speedTest/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/speedTest/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) diff --git a/examples/vehicle/CMakeLists.txt b/examples/vehicle/CMakeLists.txt index 753c131cbca51..60cdaf1badf77 100644 --- a/examples/vehicle/CMakeLists.txt +++ b/examples/vehicle/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(vehicle) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/examples/vehicle/InSourceBuild.cmake b/examples/vehicle/InSourceBuild.cmake new file mode 100644 index 0000000000000..ace5b6b5a4233 --- /dev/null +++ b/examples/vehicle/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(example_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${example_name} ${srcs}) +target_link_libraries(${example_name} dart dart-utils-urdf dart-gui) +set_target_properties(${example_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_example(${example_name}) From b9f7e71938a5b476d5fbea305e275583c72c1502 Mon Sep 17 00:00:00 2001 From: Jeongseok Lee Date: Sun, 25 Feb 2018 19:25:10 -0800 Subject: [PATCH 02/36] Add in-source build script to tutorials --- CMakeLists.txt | 47 ++++++------------- cmake/DARTMacros.cmake | 4 +- .../tutorialBiped-Finished/CMakeLists.txt | 5 ++ .../InSourceBuild.cmake | 18 +++++++ tutorials/tutorialBiped/CMakeLists.txt | 5 ++ tutorials/tutorialBiped/InSourceBuild.cmake | 18 +++++++ .../CMakeLists.txt | 5 ++ .../InSourceBuild.cmake | 16 +++++++ tutorials/tutorialCollisions/CMakeLists.txt | 5 ++ .../tutorialCollisions/InSourceBuild.cmake | 16 +++++++ .../tutorialDominoes-Finished/CMakeLists.txt | 5 ++ .../InSourceBuild.cmake | 16 +++++++ tutorials/tutorialDominoes/CMakeLists.txt | 5 ++ .../tutorialDominoes/InSourceBuild.cmake | 16 +++++++ .../CMakeLists.txt | 5 ++ .../InSourceBuild.cmake | 16 +++++++ .../tutorialMultiPendulum/CMakeLists.txt | 5 ++ .../tutorialMultiPendulum/InSourceBuild.cmake | 16 +++++++ 18 files changed, 188 insertions(+), 35 deletions(-) create mode 100644 tutorials/tutorialBiped-Finished/InSourceBuild.cmake create mode 100644 tutorials/tutorialBiped/InSourceBuild.cmake create mode 100644 tutorials/tutorialCollisions-Finished/InSourceBuild.cmake create mode 100644 tutorials/tutorialCollisions/InSourceBuild.cmake create mode 100644 tutorials/tutorialDominoes-Finished/InSourceBuild.cmake create mode 100644 tutorials/tutorialDominoes/InSourceBuild.cmake create mode 100644 tutorials/tutorialMultiPendulum-Finished/InSourceBuild.cmake create mode 100644 tutorials/tutorialMultiPendulum/InSourceBuild.cmake diff --git a/CMakeLists.txt b/CMakeLists.txt index 01f882b0c617e..f00a88f17badb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -289,40 +289,21 @@ if(TARGET dart) message(STATUS "Adding ${examples_length} examples") endif(DART_VERBOSE) - if (TARGET dart-utils-urdf AND TARGET dart-gui) - - # Add tutorial targets - if(TARGET dart-collision-bullet) - dart_add_tutorial(tutorials/tutorialBiped dart-utils-urdf dart-gui dart-collision-bullet) - dart_add_tutorial(tutorials/tutorialBiped-Finished dart-utils-urdf dart-gui dart-collision-bullet) - else() - dart_add_tutorial(tutorials/tutorialBiped dart-utils-urdf dart-gui) - dart_add_tutorial(tutorials/tutorialBiped-Finished dart-utils-urdf dart-gui) - endif() - - dart_add_tutorial(tutorials/tutorialCollisions dart-utils-urdf dart-gui) - dart_add_tutorial(tutorials/tutorialCollisions-Finished dart-utils-urdf dart-gui) - dart_add_tutorial(tutorials/tutorialDominoes dart-utils-urdf dart-gui) - dart_add_tutorial(tutorials/tutorialDominoes-Finished dart-utils-urdf dart-gui) - dart_add_tutorial(tutorials/tutorialMultiPendulum dart-utils-urdf dart-gui) - dart_add_tutorial(tutorials/tutorialMultiPendulum-Finished dart-utils-urdf dart-gui) - - # Add a "tutorials" target to build tutorials. - get_property(tutorials GLOBAL PROPERTY DART_TUTORIALS) - add_custom_target(tutorials DEPENDS ${tutorials}) - - if(DART_VERBOSE) - message(STATUS "") - message(STATUS "[ Tutorials ]") - foreach(tutorial ${tutorials}) - message(STATUS "Adding tutorial: ${tutorial}") - endforeach(tutorial ${tutorials}) - else(DART_VERBOSE) - list(LENGTH tutorials tutorials_length) - message(STATUS "Adding ${tutorials_length} tutorials") - endif(DART_VERBOSE) + # Add a "tutorials" target to build tutorials. + add_subdirectory(tutorials) + get_property(tutorials GLOBAL PROPERTY DART_TUTORIALS) + add_custom_target(tutorials DEPENDS ${tutorials}) - endif() + if(DART_VERBOSE) + message(STATUS "") + message(STATUS "[ Tutorials ]") + foreach(tutorial ${tutorials}) + message(STATUS "Adding tutorial: ${tutorial}") + endforeach(tutorial ${tutorials}) + else(DART_VERBOSE) + list(LENGTH tutorials tutorials_length) + message(STATUS "Adding ${tutorials_length} tutorials") + endif(DART_VERBOSE) endif() diff --git a/cmake/DARTMacros.cmake b/cmake/DARTMacros.cmake index 7d98037dfd4ce..0594e96e019be 100644 --- a/cmake/DARTMacros.cmake +++ b/cmake/DARTMacros.cmake @@ -147,8 +147,8 @@ function(dart_add_example) endfunction(dart_add_example) #=============================================================================== -function(dart_add_tutorial rel_dir) - dart_add_custom_target(${rel_dir} DART_TUTORIALS ${ARGN}) +function(dart_add_tutorial) + dart_property_add(DART_TUTORIALS ${ARGN}) endfunction(dart_add_tutorial) #=============================================================================== diff --git a/tutorials/tutorialBiped-Finished/CMakeLists.txt b/tutorials/tutorialBiped-Finished/CMakeLists.txt index 3e96ec0e621a3..9b5b0a099a0c9 100644 --- a/tutorials/tutorialBiped-Finished/CMakeLists.txt +++ b/tutorials/tutorialBiped-Finished/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(tutorialBiped-Finished) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/tutorials/tutorialBiped-Finished/InSourceBuild.cmake b/tutorials/tutorialBiped-Finished/InSourceBuild.cmake new file mode 100644 index 0000000000000..f88d28847356a --- /dev/null +++ b/tutorials/tutorialBiped-Finished/InSourceBuild.cmake @@ -0,0 +1,18 @@ +get_filename_component(tutorial_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-collision-bullet + OR NOT TARGET dart-utils-urdf + OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${tutorial_name} ${srcs}) +target_link_libraries(${tutorial_name} dart dart-utils-urdf dart-gui) +set_target_properties(${tutorial_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_tutorial(${tutorial_name}) diff --git a/tutorials/tutorialBiped/CMakeLists.txt b/tutorials/tutorialBiped/CMakeLists.txt index 2674a027da17c..397590a25f0f2 100644 --- a/tutorials/tutorialBiped/CMakeLists.txt +++ b/tutorials/tutorialBiped/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(tutorialBiped) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/tutorials/tutorialBiped/InSourceBuild.cmake b/tutorials/tutorialBiped/InSourceBuild.cmake new file mode 100644 index 0000000000000..f88d28847356a --- /dev/null +++ b/tutorials/tutorialBiped/InSourceBuild.cmake @@ -0,0 +1,18 @@ +get_filename_component(tutorial_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-collision-bullet + OR NOT TARGET dart-utils-urdf + OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${tutorial_name} ${srcs}) +target_link_libraries(${tutorial_name} dart dart-utils-urdf dart-gui) +set_target_properties(${tutorial_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_tutorial(${tutorial_name}) diff --git a/tutorials/tutorialCollisions-Finished/CMakeLists.txt b/tutorials/tutorialCollisions-Finished/CMakeLists.txt index 11f4ff2d342fb..c83fe236c3832 100644 --- a/tutorials/tutorialCollisions-Finished/CMakeLists.txt +++ b/tutorials/tutorialCollisions-Finished/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(tutorialCollisions-Finished) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/tutorials/tutorialCollisions-Finished/InSourceBuild.cmake b/tutorials/tutorialCollisions-Finished/InSourceBuild.cmake new file mode 100644 index 0000000000000..0f0fb26c0d0f4 --- /dev/null +++ b/tutorials/tutorialCollisions-Finished/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(tutorial_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${tutorial_name} ${srcs}) +target_link_libraries(${tutorial_name} dart dart-utils-urdf dart-gui) +set_target_properties(${tutorial_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_tutorial(${tutorial_name}) diff --git a/tutorials/tutorialCollisions/CMakeLists.txt b/tutorials/tutorialCollisions/CMakeLists.txt index 9c0c7b8f2a1ea..dd85282b611cf 100644 --- a/tutorials/tutorialCollisions/CMakeLists.txt +++ b/tutorials/tutorialCollisions/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(tutorialCollisions) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/tutorials/tutorialCollisions/InSourceBuild.cmake b/tutorials/tutorialCollisions/InSourceBuild.cmake new file mode 100644 index 0000000000000..0f0fb26c0d0f4 --- /dev/null +++ b/tutorials/tutorialCollisions/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(tutorial_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${tutorial_name} ${srcs}) +target_link_libraries(${tutorial_name} dart dart-utils-urdf dart-gui) +set_target_properties(${tutorial_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_tutorial(${tutorial_name}) diff --git a/tutorials/tutorialDominoes-Finished/CMakeLists.txt b/tutorials/tutorialDominoes-Finished/CMakeLists.txt index a4c3380a140cd..22344b29c8570 100644 --- a/tutorials/tutorialDominoes-Finished/CMakeLists.txt +++ b/tutorials/tutorialDominoes-Finished/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(tutorialDominoes-Finished) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/tutorials/tutorialDominoes-Finished/InSourceBuild.cmake b/tutorials/tutorialDominoes-Finished/InSourceBuild.cmake new file mode 100644 index 0000000000000..0f0fb26c0d0f4 --- /dev/null +++ b/tutorials/tutorialDominoes-Finished/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(tutorial_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${tutorial_name} ${srcs}) +target_link_libraries(${tutorial_name} dart dart-utils-urdf dart-gui) +set_target_properties(${tutorial_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_tutorial(${tutorial_name}) diff --git a/tutorials/tutorialDominoes/CMakeLists.txt b/tutorials/tutorialDominoes/CMakeLists.txt index bac6086ab84c4..55b960ec67e55 100644 --- a/tutorials/tutorialDominoes/CMakeLists.txt +++ b/tutorials/tutorialDominoes/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(tutorialDominoes) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/tutorials/tutorialDominoes/InSourceBuild.cmake b/tutorials/tutorialDominoes/InSourceBuild.cmake new file mode 100644 index 0000000000000..0f0fb26c0d0f4 --- /dev/null +++ b/tutorials/tutorialDominoes/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(tutorial_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${tutorial_name} ${srcs}) +target_link_libraries(${tutorial_name} dart dart-utils-urdf dart-gui) +set_target_properties(${tutorial_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_tutorial(${tutorial_name}) diff --git a/tutorials/tutorialMultiPendulum-Finished/CMakeLists.txt b/tutorials/tutorialMultiPendulum-Finished/CMakeLists.txt index 4819b3c54908c..2ee29e72b208b 100644 --- a/tutorials/tutorialMultiPendulum-Finished/CMakeLists.txt +++ b/tutorials/tutorialMultiPendulum-Finished/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(tutorialMultiPendulum-Finished) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/tutorials/tutorialMultiPendulum-Finished/InSourceBuild.cmake b/tutorials/tutorialMultiPendulum-Finished/InSourceBuild.cmake new file mode 100644 index 0000000000000..0f0fb26c0d0f4 --- /dev/null +++ b/tutorials/tutorialMultiPendulum-Finished/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(tutorial_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${tutorial_name} ${srcs}) +target_link_libraries(${tutorial_name} dart dart-utils-urdf dart-gui) +set_target_properties(${tutorial_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_tutorial(${tutorial_name}) diff --git a/tutorials/tutorialMultiPendulum/CMakeLists.txt b/tutorials/tutorialMultiPendulum/CMakeLists.txt index eb8a68d8c0848..3e5f05054d35f 100644 --- a/tutorials/tutorialMultiPendulum/CMakeLists.txt +++ b/tutorials/tutorialMultiPendulum/CMakeLists.txt @@ -1,5 +1,10 @@ cmake_minimum_required(VERSION 2.8.12) +if(DART_IN_SOURCE_BUILD) + include(${CMAKE_CURRENT_SOURCE_DIR}/InSourceBuild.cmake) + return() +endif() + project(tutorialMultiPendulum) find_package(DART 6.2.0 REQUIRED COMPONENTS utils-urdf gui CONFIG) diff --git a/tutorials/tutorialMultiPendulum/InSourceBuild.cmake b/tutorials/tutorialMultiPendulum/InSourceBuild.cmake new file mode 100644 index 0000000000000..0f0fb26c0d0f4 --- /dev/null +++ b/tutorials/tutorialMultiPendulum/InSourceBuild.cmake @@ -0,0 +1,16 @@ +get_filename_component(tutorial_name ${CMAKE_CURRENT_LIST_DIR} NAME) + +if(NOT TARGET dart-utils-urdf OR NOT TARGET dart-gui) + return() +endif() + +file(GLOB srcs "*.cpp" "*.hpp") + +add_executable(${tutorial_name} ${srcs}) +target_link_libraries(${tutorial_name} dart dart-utils-urdf dart-gui) +set_target_properties(${tutorial_name} + PROPERTIES + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" +) + +dart_add_tutorial(${tutorial_name}) From be77d75263dd949c141c865a1bccc567d6eff6f8 Mon Sep 17 00:00:00 2001 From: Yifeng Jiang Date: Fri, 29 Sep 2017 17:22:57 -0400 Subject: [PATCH 03/36] Add human meshes and skel files --- data/skel/kima/abdomen.dae | 18933 +++++++++ data/skel/kima/head.dae | 34634 ++++++++++++++++ data/skel/kima/kima_human_edited.skel | 932 + data/skel/kima/kima_human_left_arm.skel | 349 + data/skel/kima/kima_human_left_leg.skel | 327 + data/skel/kima/l-clavicle.dae | 20111 +++++++++ data/skel/kima/l-foot.dae | 28208 +++++++++++++ data/skel/kima/pelvis.dae | 35919 ++++++++++++++++ data/skel/kima/r-clavicle.dae | 20090 +++++++++ data/skel/kima/thorax.dae | 47337 ++++++++++++++++++++++ 10 files changed, 206840 insertions(+) create mode 100644 data/skel/kima/abdomen.dae create mode 100644 data/skel/kima/head.dae create mode 100644 data/skel/kima/kima_human_edited.skel create mode 100644 data/skel/kima/kima_human_left_arm.skel create mode 100644 data/skel/kima/kima_human_left_leg.skel create mode 100644 data/skel/kima/l-clavicle.dae create mode 100644 data/skel/kima/l-foot.dae create mode 100644 data/skel/kima/pelvis.dae create mode 100644 data/skel/kima/r-clavicle.dae create mode 100644 data/skel/kima/thorax.dae diff --git a/data/skel/kima/abdomen.dae b/data/skel/kima/abdomen.dae new file mode 100644 index 0000000000000..b18c56b2c4013 --- /dev/null +++ b/data/skel/kima/abdomen.dae @@ -0,0 +1,18933 @@ + + + FBX COLLADA 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3707 31893 3761 31894 3783 31895 3707 31896 3783 31897 3733 31898 3733 31899 3783 31900 3803 31901 3733 31902 3803 31903 3753 31904 3753 31905 3803 31906 3833 31907 3753 31908 3833 31909 3795 31910 3795 31911 3833 31912 3877 31913 3795 31914 3877 31915 3845 31916 3845 31917 3877 31918 3917 31919 3845 31920 3917 31921 3889 31922 3889 31923 3917 31924 3975 31925 3889 31926 3975 31927 3959 31928 3959 31929 3975 31930 4034 31931

 + + + + + + 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.0000001.0000000.000000 0.000000 0.001000 -0.139654 0.000000 0.001000 0.000000 -0.336607 -0.001000 0.000000 0.000000 0.142969 0.000000 0.000000 0.000000 1.0000001.000000 + 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.0000001.0000000.001000 0.000000 0.000000 -0.044205 0.000000 0.001000 0.000000 -0.360883 0.000000 0.000000 0.001000 -0.048487 0.000000 0.000000 0.000000 1.0000001.000000 + 24.0000000.0416678.333333 + + + + + + From 69ed7db1320fcab3a1fef78d48077baac3579cd3 Mon Sep 17 00:00:00 2001 From: Yifeng Jiang Date: Fri, 29 Sep 2017 17:39:37 -0400 Subject: [PATCH 04/36] update skel files --- data/skel/kima/kima_human_edited.skel | 1 + data/skel/kima/kima_human_left_arm.skel | 34 +++---------------------- 2 files changed, 5 insertions(+), 30 deletions(-) diff --git a/data/skel/kima/kima_human_edited.skel b/data/skel/kima/kima_human_edited.skel index c0ecfd65edf93..19e0d239df818 100644 --- a/data/skel/kima/kima_human_edited.skel +++ b/data/skel/kima/kima_human_edited.skel @@ -588,6 +588,7 @@ pelvis l-upperleg zyx + -1.570796 diff --git a/data/skel/kima/kima_human_left_arm.skel b/data/skel/kima/kima_human_left_arm.skel index 1b38c827272f9..58353d7cdacb3 100644 --- a/data/skel/kima/kima_human_left_arm.skel +++ b/data/skel/kima/kima_human_left_arm.skel @@ -256,7 +256,7 @@ 0.0 0.0 0.0 0.0 0.0 1.570796 l-clavicle l-upperarm - zyx + zyx 0.1 @@ -269,6 +269,7 @@ + -2.09 1.0 @@ -276,37 +277,10 @@ 0.1 - 0.0 -3 0.0 + 0.0 0.0 0.0 0 0.0 0.0 - - 0.0 0.0 0.0 0.0 0.0 0.0 l-upperarm @@ -321,7 +295,7 @@ 0.1 - 0.3 + 0.0 0 K{bx$R6Oh|=d?;|+b+Z9ir)S?yN)>hqr_y`ss3E@F^GHJ1_1R33O zVw^YJWNKe9jpK^bhX(;%DnPGU~KRtNV>2Kc*SkgEK-MyggO z;ipwiAhTD22+S{|Vjl*{!G+HBQ(_0n590Jj&X+RtVJPY~?%{RqC3y7W9*DYs#`4kV 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z2z*-}G_H7x19datvB?R<((7PhRYqdUr_)na?r7-nj-=gf2E92>tm%L*ZQ?ZkvmSZ4-iy=%s11z40IPmQLc$StxD88cNYY0@LB3#rUs97TR=9U#F#e}LxGJpO>s8Bj*3YrRd9v@pRY_= zx;)_IGideZHh!NIE^~EnWZddG4$Mv$q8uk;Jb}!!Ff_UWIhrYmloCfDlQK|gpB zXqXkqR*tw(a~gs>mp4P+01vWtT|iUXCG%>oWn7DP;l#>Tnb_L{XZ~^&7I&$`k{6!n zw9kNk>TAJOIag8aS_zl^IDXqUhG}d#FMOY60b|?8VeP0nXmCDBaaELTmLwk@^7Bwt zuM(~OwLDZDvy5i;)8du5T;M--)p&Yu5{ObR;=_&y*l73#C9!Kr;WZCfZTOzlgrs1i-~b7< znoOq;l|t9biWoZOLxSg@g7)WnrgR8Z7RxP0{(N>8$6r$GXAnc z4AChjFLerJeV(hC&`LjyNEn{m=M*>?;`z;aJ;smAG=&9+#5i0`J=%gn9=0PWv`*3fr<}w}*F7VBkjY(2YJ3-@cD)cH^F|R{ZLH4|k4n zRXiPaR^&ed=Uu1MUe3~1x%mUDeN@0U?f3w4jy(G7q$#w-_am&?rcM8jS77Vx5nFrn zA^5+8VD;!JXr*G%M~OC}3*b!#!eN0Az5sM7GrKDfkJWM_Mn;()m-W%F;L zuB5R-^sp6w%RdUwM@BHwWfmK3t3u1PTJJ8fV#^D{vU_ou+3XwwbfW_F2?-I?Hr7Mx{ROS8L_)Y73jlXx}jI}3eQbUz_aT{ zZ2qYy@UEf;t&^It)KLb9{9i-6YaIqFwSi>O8SpZn%*NYSq1j$-s$?!<=SwMFV#`vXHtdRD?lskMH_jwYlXgG@JM4xbIOA#!O zaAa?OuxE4At1;T#lq$QQgxx1bAjIJ%K26|=^I1dSwWSMA2Fzufyn677|67>%T^j@h zPN!eu3qW|W1k$vI;B&kjJGZzO%Lh6k&88m{Zui4ZbzNFrT#oYJ_MnyWDF53W; literal 0 HcmV?d00001 From b048fb3f288a6b2b74e4394bca0208a46a3d0301 Mon Sep 17 00:00:00 2001 From: Yifeng Jiang Date: Mon, 2 Oct 2017 12:18:42 -0400 Subject: [PATCH 06/36] add human arm & leg joint position constraints --- .../HumanArmJointLimitConstraint.cpp | 413 ++++++++++++++++ .../HumanArmJointLimitConstraint.hpp | 191 ++++++++ .../HumanLegJointLimitConstraint.cpp | 447 ++++++++++++++++++ .../HumanLegJointLimitConstraint.hpp | 193 ++++++++ dart/constraint/SmartPointer.hpp | 2 + 5 files changed, 1246 insertions(+) create mode 100644 dart/constraint/HumanArmJointLimitConstraint.cpp create mode 100644 dart/constraint/HumanArmJointLimitConstraint.hpp create mode 100644 dart/constraint/HumanLegJointLimitConstraint.cpp create mode 100644 dart/constraint/HumanLegJointLimitConstraint.hpp diff --git a/dart/constraint/HumanArmJointLimitConstraint.cpp b/dart/constraint/HumanArmJointLimitConstraint.cpp new file mode 100644 index 0000000000000..4cc234aeb2376 --- /dev/null +++ b/dart/constraint/HumanArmJointLimitConstraint.cpp @@ -0,0 +1,413 @@ +/* + * Copyright (c) 2014-2016, Humanoid Lab, Georgia Tech Research Corporation + * Copyright (c) 2014-2017, Graphics Lab, Georgia Tech Research Corporation + * Copyright (c) 2016-2017, Personal Robotics Lab, Carnegie Mellon University + * All rights reserved. + * + * This file is provided under the following "BSD-style" License: + * Redistribution and use in source and binary forms, with or + * without modification, are permitted provided that the following + * conditions are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND + * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, + * INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF + * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF + * USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED + * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#include "dart/constraint/HumanArmJointLimitConstraint.hpp" + +#include + +#include "dart/external/odelcpsolver/lcp.h" + +#include "dart/common/Console.hpp" +#include "dart/dynamics/BodyNode.hpp" +#include "dart/dynamics/Joint.hpp" +#include "dart/dynamics/Skeleton.hpp" + +#define DART_ERROR_ALLOWANCE 0.0 +#define DART_ERP 0.01 +#define DART_MAX_ERV 1e+1 +#define DART_CFM 1e-9 + +using namespace tiny_dnn; +using namespace tiny_dnn::activation; +using namespace tiny_dnn::layers; + +namespace dart { +namespace constraint { + +double HumanArmJointLimitConstraint::mErrorAllowance = DART_ERROR_ALLOWANCE; +double HumanArmJointLimitConstraint::mErrorReductionParameter = DART_ERP; +double HumanArmJointLimitConstraint::mMaxErrorReductionVelocity = DART_MAX_ERV; +double HumanArmJointLimitConstraint::mConstraintForceMixing = DART_CFM; + +//============================================================================== +HumanArmJointLimitConstraint::HumanArmJointLimitConstraint( + dynamics::Joint* _shldjoint, dynamics::Joint* _elbowjoint, bool _isMirror) + : ConstraintBase(), + mShldJoint(_shldjoint), + mElbowJoint(_elbowjoint), + mUArmNode(_shldjoint->getChildBodyNode()), + mLArmNode(_elbowjoint->getChildBodyNode()), + mIsMirror(_isMirror), + mAppliedImpulseIndex(0) +{ + assert(mShldJoint); + assert(mElbowJoint); + assert(mUArmNode); + assert(mLArmNode); + + assert(mShldJoint->getDof() == 3); + assert(mELbowJoint->getDof() == 1); + assert(mUArmNode->getSkeleton() == mLArmNode->getSkeleton()); + assert(mElbowJoint->getParentBodyNode() == mUArmNode); + + mLifeTime = 0; + mActive = false; + + // load neural net weights from external file + mNet.load(DART_DATA_PATH"/neuralnets/net-larm"); +} + +//============================================================================== +HumanArmJointLimitConstraint::~HumanArmJointLimitConstraint() +{ +} + +//============================================================================== +void HumanArmJointLimitConstraint::setErrorAllowance(double _allowance) +{ + // Clamp error reduction parameter if it is out of the range + if (_allowance < 0.0) + { + dtwarn << "Error reduction parameter[" << _allowance + << "] is lower than 0.0. " + << "It is set to 0.0." << std::endl; + mErrorAllowance = 0.0; + } + + mErrorAllowance = _allowance; +} + +//============================================================================== +double HumanArmJointLimitConstraint::getErrorAllowance() +{ + return mErrorAllowance; +} + +//============================================================================== +void HumanArmJointLimitConstraint::setErrorReductionParameter(double _erp) +{ + // Clamp error reduction parameter if it is out of the range [0, 1] + if (_erp < 0.0) + { + dtwarn << "Error reduction parameter[" << _erp << "] is lower than 0.0. " + << "It is set to 0.0." << std::endl; + mErrorReductionParameter = 0.0; + } + if (_erp > 1.0) + { + dtwarn << "Error reduction parameter[" << _erp << "] is greater than 1.0. " + << "It is set to 1.0." << std::endl; + mErrorReductionParameter = 1.0; + } + + mErrorReductionParameter = _erp; +} + +//============================================================================== +double HumanArmJointLimitConstraint::getErrorReductionParameter() +{ + return mErrorReductionParameter; +} + +//============================================================================== +void HumanArmJointLimitConstraint::setMaxErrorReductionVelocity(double _erv) +{ + // Clamp maximum error reduction velocity if it is out of the range + if (_erv < 0.0) + { + dtwarn << "Maximum error reduction velocity[" << _erv + << "] is lower than 0.0. " + << "It is set to 0.0." << std::endl; + mMaxErrorReductionVelocity = 0.0; + } + + mMaxErrorReductionVelocity = _erv; +} + +//============================================================================== +double HumanArmJointLimitConstraint::getMaxErrorReductionVelocity() +{ + return mMaxErrorReductionVelocity; +} + +//============================================================================== +void HumanArmJointLimitConstraint::setConstraintForceMixing(double _cfm) +{ + // Clamp constraint force mixing parameter if it is out of the range + if (_cfm < 1e-9) + { + dtwarn << "Constraint force mixing parameter[" << _cfm + << "] is lower than 1e-9. " << "It is set to 1e-9." << std::endl; + mConstraintForceMixing = 1e-9; + } + if (_cfm > 1.0) + { + dtwarn << "Constraint force mixing parameter[" << _cfm + << "] is greater than 1.0. " << "It is set to 1.0." << std::endl; + mConstraintForceMixing = 1.0; + } + + mConstraintForceMixing = _cfm; +} + +//============================================================================== +double HumanArmJointLimitConstraint::getConstraintForceMixing() +{ + return mConstraintForceMixing; +} + +//============================================================================== +void HumanArmJointLimitConstraint::update() +{ + double qz = mShldJoint->getPosition(0); + double qx = mShldJoint->getPosition(1); + double qy = mShldJoint->getPosition(2); + double qe = mElbowJoint->getPosition(0); + + double qz_d = mShldJoint->getVelocity(0); + double qx_d = mShldJoint->getVelocity(1); + double qy_d = mShldJoint->getVelocity(2); + double qe_d = mElbowJoint->getVelocity(0); + + // if isMirror (right-arm), set up a mirrored euler joint for shoulder + // i.e. pass the mirrored config to NN + if (mIsMirror) + { + qz = -qz; qy = -qy; + } + + double qsin[6] = {cos(qz), sin(qz), cos(qx), sin(qx), cos(qy + 2*M_PI/3), cos(qe)}; + vec_t input; + input.assign(qsin, qsin+6); + vec_t pred_vec = mNet.predict(input); + double C = *(pred_vec.begin()); + + mViolation = C - 0.5; + + // if not active, no variable matters, no need to update + mActive = false; + mDim = 0; + + // active: mViolation <= 0 (C(q)-0.5<=0) + if (mViolation <= 0.0) + { + if (mActive) + { + ++mLifeTime; + } + else + { + mActive = true; + mLifeTime = 0; + } + + // do back-propogation to obtain gradient + layer* l; + vec_t out_grad = {1}; + vec_t in_grad; + for (int n = mNet.layer_size()-1; n >= 0; n--) + { + // implement chain rule layer by layer + l = mNet[n]; + if (l->layer_type() == "fully-connected") + { + auto Wb = l->weights(); + vec_t W = *(Wb[0]); + in_grad.assign(W.size()/out_grad.size(), 0); + for (size_t c = 0; c < in_grad.size(); c++) { + in_grad[c] = vectorize::dot(&out_grad[0], &W[c * out_grad.size()], out_grad.size()); + } + } + else + { + // this is activation layer + std::vector out_t; + l->output(out_t); + vec_t out_v = (*(out_t[0]))[0]; + in_grad.assign(out_grad.begin(), out_grad.end()); + // first arg (x) is used only to infer the size of input, which should be the same as output y + (dynamic_cast(l))->backward_activation(out_v, out_v, in_grad, out_grad); + } + out_grad.assign(in_grad.begin(), in_grad.end()); + in_grad.clear(); + } + + mJacobian[0] = out_grad[0] * (-sin(qz)) + out_grad[1] * (cos(qz)); + mJacobian[1] = out_grad[2] * (-sin(qx)) + out_grad[3] * (cos(qx)); + mJacobian[2] = out_grad[4] * (-sin(qy + 2*M_PI/3)); + mJacobian[3] = out_grad[5] * (-sin(qe)); + + // note that we also need to take the mirror of the NN gradient for right-arm + if (mIsMirror) + { + mJacobian[0] = -mJacobian[0]; + mJacobian[2] = -mJacobian[2]; + } + + // TODO: Normalize grad seems unnecessary? + + Eigen::Vector4d q_d; + q_d << qz_d, qx_d, qy_d, qe_d; + mNegativeVel = -mJacobian.dot(q_d); + + mLowerBound = 0.0; + mUpperBound = dInfinity; + mDim = 1; + } +} + +//============================================================================== +void HumanArmJointLimitConstraint::getInformation(ConstraintInfo* _lcp) +{ + // if non-active, should not call getInfo() + assert(isActive()); + + // assume caller will allocate enough space for _lcp variables + assert(_lcp->w[0] == 0.0); + assert(_lcp->findex[0] == -1); + + double bouncingVel = -mViolation - mErrorAllowance; + if (bouncingVel < 0.0) + { + bouncingVel = 0.0; + } + bouncingVel *= _lcp->invTimeStep * mErrorReductionParameter; + if (bouncingVel > mMaxErrorReductionVelocity) + bouncingVel = mMaxErrorReductionVelocity; + + _lcp->b[0] = mNegativeVel + bouncingVel; + _lcp->lo[0] = mLowerBound; + _lcp->hi[0] = mUpperBound; + + if (mLifeTime) + _lcp->x[0] = mOldX; + else + _lcp->x[0] = 0.0; +} + +//============================================================================== +void HumanArmJointLimitConstraint::applyUnitImpulse(std::size_t _index) +{ + // the dim of constraint = 1, valid _index can only be 0 + assert(_index < mDim && "Invalid Index."); + assert(isActive()); + + const dynamics::SkeletonPtr& skeleton = mShldJoint->getSkeleton(); + skeleton->clearConstraintImpulses(); + + for (std::size_t i = 0; i < 3; i++) + { + mShldJoint->setConstraintImpulse(i, mJacobian[i]); + } + mElbowJoint->setConstraintImpulse(0, mJacobian[3]); + + // Use the body which is placed later in the list of body nodes in this skeleton + skeleton->updateBiasImpulse(mLArmNode); + skeleton->updateVelocityChange(); + + for (std::size_t i = 0; i < 3; i++) + { + mShldJoint->setConstraintImpulse(i, 0.0); + } + mElbowJoint->setConstraintImpulse(0, 0.0); + + mAppliedImpulseIndex = _index; // which is 0 +} + +//============================================================================== +void HumanArmJointLimitConstraint::getVelocityChange(double* _delVel, bool _withCfm) +{ + assert(_delVel != nullptr && "Null pointer is not allowed."); + _delVel[0] = 0.0; + + if (mShldJoint->getSkeleton()->isImpulseApplied()) + { + Eigen::Vector4d delq_d; + for (std::size_t i = 0; i < 3; i++) + { + delq_d[i] = mShldJoint->getVelocityChange(i); + } + delq_d[3] = mElbowJoint->getVelocityChange(0); + + _delVel[0] = mJacobian.dot(delq_d); + } + + if (_withCfm) + { + _delVel[mAppliedImpulseIndex] += _delVel[mAppliedImpulseIndex] + * mConstraintForceMixing; + } +} + +//============================================================================== +void HumanArmJointLimitConstraint::excite() +{ + mShldJoint->getSkeleton()->setImpulseApplied(true); +} + +//============================================================================== +void HumanArmJointLimitConstraint::unexcite() +{ + mShldJoint->getSkeleton()->setImpulseApplied(false); +} + +//============================================================================== +void HumanArmJointLimitConstraint::applyImpulse(double* _lambda) +{ + assert(isActive()); + + // the dim of constraint = 1 + auto con_force = _lambda[0]; + mOldX = con_force; + + for (std::size_t i = 0; i < 3; i++) + { + mShldJoint->setConstraintImpulse(i, + mShldJoint->getConstraintImpulse(i) + mJacobian[i] * con_force); + } + mElbowJoint->setConstraintImpulse(0, + mElbowJoint->getConstraintImpulse(0) + mJacobian[3] * con_force); +} + +//============================================================================== +dynamics::SkeletonPtr HumanArmJointLimitConstraint::getRootSkeleton() const +{ + return mShldJoint->getSkeleton()->mUnionRootSkeleton.lock(); +} + +//============================================================================== +bool HumanArmJointLimitConstraint::isActive() const +{ + return mActive; +} + +} // namespace constraint +} // namespace dart diff --git a/dart/constraint/HumanArmJointLimitConstraint.hpp b/dart/constraint/HumanArmJointLimitConstraint.hpp new file mode 100644 index 0000000000000..5888dfb32c82f --- /dev/null +++ b/dart/constraint/HumanArmJointLimitConstraint.hpp @@ -0,0 +1,191 @@ +/* + * Copyright (c) 2014-2016, Humanoid Lab, Georgia Tech Research Corporation + * Copyright (c) 2014-2017, Graphics Lab, Georgia Tech Research Corporation + * Copyright (c) 2016-2017, Personal Robotics Lab, Carnegie Mellon University + * All rights reserved. + * + * This file is provided under the following "BSD-style" License: + * Redistribution and use in source and binary forms, with or + * without modification, are permitted provided that the following + * conditions are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND + * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, + * INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF + * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF + * USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED + * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef DART_CONSTRAINT_HUMANARMJOINTLIMITCONSTRAINT_HPP_ +#define DART_CONSTRAINT_HUMANARMJOINTLIMITCONSTRAINT_HPP_ + +#include "dart/constraint/ConstraintBase.hpp" +#include "dart/math/MathTypes.hpp" +#include "tiny_dnn/tiny_dnn.h" + +namespace dart { + +namespace dynamics { +class BodyNode; +class Joint; +} // namespace dynamics + +namespace constraint { + +/// HumanArmJointLimitConstraint handles joint position limits on human arm, +/// representing range of motion of shoulder and elbow joints. +class HumanArmJointLimitConstraint : public ConstraintBase +{ +public: + /// Constructor + explicit HumanArmJointLimitConstraint( + dynamics::Joint* _shldjoint, dynamics::Joint* _elbowjoint, bool _isMirror); + + /// Destructor + virtual ~HumanArmJointLimitConstraint(); + + //---------------------------------------------------------------------------- + // Property settings + //---------------------------------------------------------------------------- + + /// Set global error reduction parameter + static void setErrorAllowance(double _allowance); + + /// Get global error reduction parameter + static double getErrorAllowance(); + + /// Set global error reduction parameter + static void setErrorReductionParameter(double _erp); + + /// Get global error reduction parameter + static double getErrorReductionParameter(); + + /// Set global error reduction parameter + static void setMaxErrorReductionVelocity(double _erv); + + /// Get global error reduction parameter + static double getMaxErrorReductionVelocity(); + + /// Set global constraint force mixing parameter + static void setConstraintForceMixing(double _cfm); + + /// Get global constraint force mixing parameter + static double getConstraintForceMixing(); + + //---------------------------------------------------------------------------- + // Friendship + //---------------------------------------------------------------------------- + + friend class ConstraintSolver; + friend class ConstrainedGroup; + +protected: + //---------------------------------------------------------------------------- + // Constraint virtual functions + //---------------------------------------------------------------------------- + + // Documentation inherited + void update() override; + + // Documentation inherited + void getInformation(ConstraintInfo* _lcp) override; + + // Documentation inherited + void applyUnitImpulse(std::size_t _index) override; + + // Documentation inherited + void getVelocityChange(double* _delVel, bool _withCfm) override; + + // Documentation inherited + void excite() override; + + // Documentation inherited + void unexcite() override; + + // Documentation inherited + void applyImpulse(double* _lambda) override; + + // Documentation inherited + dynamics::SkeletonPtr getRootSkeleton() const override; + + // Documentation inherited + bool isActive() const override; + +private: + /// arm joints involved + dynamics::Joint* mShldJoint; + dynamics::Joint* mElbowJoint; + + /// arm body nodes involved + dynamics::BodyNode* mUArmNode; + dynamics::BodyNode* mLArmNode; + + /// A workaround to have a de facto left-handed euler joint + /// for right shoulder, so it could share same limits with left shoulder. + /// left-arm set to false, right-arm set to true. + bool mIsMirror; + + /// the neural network for calculating limits + tiny_dnn::network mNet; + + /// Gradient of the neural net function + Eigen::Vector4d mJacobian; + + /// Index of applied impulse + std::size_t mAppliedImpulseIndex; + + /// + std::size_t mLifeTime; + + /// + double mViolation; + + /// + double mNegativeVel; + + /// + double mOldX; + + /// + double mUpperBound; + + /// + double mLowerBound; + + /// + bool mActive; + + /// Global constraint error allowance + static double mErrorAllowance; + + /// Global constraint error redection parameter in the range of [0, 1]. The + /// default is 0.01. + static double mErrorReductionParameter; + + /// Maximum error reduction velocity + static double mMaxErrorReductionVelocity; + + /// Global constraint force mixing parameter in the range of [1e-9, 1]. The + /// default is 1e-5 + /// \sa http://www.ode.org/ode-latest-userguide.html#sec_3_8_0 + static double mConstraintForceMixing; +}; + +} // namespace constraint +} // namespace dart + +#endif // DART_CONSTRAINT_HUMANARMJOINTLIMITCONSTRAINT_HPP_ + diff --git a/dart/constraint/HumanLegJointLimitConstraint.cpp b/dart/constraint/HumanLegJointLimitConstraint.cpp new file mode 100644 index 0000000000000..faeae379832ee --- /dev/null +++ b/dart/constraint/HumanLegJointLimitConstraint.cpp @@ -0,0 +1,447 @@ +/* + * Copyright (c) 2014-2016, Humanoid Lab, Georgia Tech Research Corporation + * Copyright (c) 2014-2017, Graphics Lab, Georgia Tech Research Corporation + * Copyright (c) 2016-2017, Personal Robotics Lab, Carnegie Mellon University + * All rights reserved. + * + * This file is provided under the following "BSD-style" License: + * Redistribution and use in source and binary forms, with or + * without modification, are permitted provided that the following + * conditions are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND + * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, + * INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF + * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF + * USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED + * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#include "dart/constraint/HumanLegJointLimitConstraint.hpp" + +#include + +#include "dart/external/odelcpsolver/lcp.h" + +#include "dart/common/Console.hpp" +#include "dart/dynamics/BodyNode.hpp" +#include "dart/dynamics/Joint.hpp" +#include "dart/dynamics/Skeleton.hpp" + +#define DART_ERROR_ALLOWANCE 0.0 +#define DART_ERP 0.01 +#define DART_MAX_ERV 1e+1 +#define DART_CFM 1e-9 + +using namespace tiny_dnn; +using namespace tiny_dnn::activation; +using namespace tiny_dnn::layers; + +namespace dart { +namespace constraint { + +double HumanLegJointLimitConstraint::mErrorAllowance = DART_ERROR_ALLOWANCE; +double HumanLegJointLimitConstraint::mErrorReductionParameter = DART_ERP; +double HumanLegJointLimitConstraint::mMaxErrorReductionVelocity = DART_MAX_ERV; +double HumanLegJointLimitConstraint::mConstraintForceMixing = DART_CFM; + +//============================================================================== +HumanLegJointLimitConstraint::HumanLegJointLimitConstraint( + dynamics::Joint* _hipjoint, dynamics::Joint* _kneejoint, dynamics::Joint* _anklejoint, bool _isMirror) + : ConstraintBase(), + mHipJoint(_hipjoint), + mKneeJoint(_kneejoint), + mAnkleJoint(_anklejoint), + mThighNode(_hipjoint->getChildBodyNode()), + mLowerLegNode(_kneejoint->getChildBodyNode()), + mFootNode(_anklejoint->getChildBodyNode()), + mIsMirror(_isMirror), + mAppliedImpulseIndex(0) +{ + assert(mHipJoint); + assert(mKneeJoint); + assert(mAnkleJoint); + assert(mThighNode); + assert(mLowerLegNode); + assert(mFootNode); + + assert(mHipJoint->getDof() == 3); + assert(mKneeJoint->getDof() == 1); + assert(mAnkleJoint->getDof() == 2); + + assert(mThighNode->getSkeleton() == mLowerLegNode->getSkeleton()); + assert(mLowerLegNode->getSkeleton() == mFootNode->getSkeleton()); + assert(mKneeJoint->getParentBodyNode() == mThighNode); + assert(mAnkleJoint->getParentBodyNode() == mLowerLegNode); + + mLifeTime = 0; + mActive = false; + + // load neural net weights from external file + mNet.load(DART_DATA_PATH"/neuralnets/net-lleg"); +} + + + +//============================================================================== +HumanLegJointLimitConstraint::~HumanLegJointLimitConstraint() +{ +} + +//============================================================================== +void HumanLegJointLimitConstraint::setErrorAllowance(double _allowance) +{ + // Clamp error reduction parameter if it is out of the range + if (_allowance < 0.0) + { + dtwarn << "Error reduction parameter[" << _allowance + << "] is lower than 0.0. " + << "It is set to 0.0." << std::endl; + mErrorAllowance = 0.0; + } + + mErrorAllowance = _allowance; +} + +//============================================================================== +double HumanLegJointLimitConstraint::getErrorAllowance() +{ + return mErrorAllowance; +} + +//============================================================================== +void HumanLegJointLimitConstraint::setErrorReductionParameter(double _erp) +{ + // Clamp error reduction parameter if it is out of the range [0, 1] + if (_erp < 0.0) + { + dtwarn << "Error reduction parameter[" << _erp << "] is lower than 0.0. " + << "It is set to 0.0." << std::endl; + mErrorReductionParameter = 0.0; + } + if (_erp > 1.0) + { + dtwarn << "Error reduction parameter[" << _erp << "] is greater than 1.0. " + << "It is set to 1.0." << std::endl; + mErrorReductionParameter = 1.0; + } + + mErrorReductionParameter = _erp; +} + +//============================================================================== +double HumanLegJointLimitConstraint::getErrorReductionParameter() +{ + return mErrorReductionParameter; +} + +//============================================================================== +void HumanLegJointLimitConstraint::setMaxErrorReductionVelocity(double _erv) +{ + // Clamp maximum error reduction velocity if it is out of the range + if (_erv < 0.0) + { + dtwarn << "Maximum error reduction velocity[" << _erv + << "] is lower than 0.0. " + << "It is set to 0.0." << std::endl; + mMaxErrorReductionVelocity = 0.0; + } + + mMaxErrorReductionVelocity = _erv; +} + +//============================================================================== +double HumanLegJointLimitConstraint::getMaxErrorReductionVelocity() +{ + return mMaxErrorReductionVelocity; +} + +//============================================================================== +void HumanLegJointLimitConstraint::setConstraintForceMixing(double _cfm) +{ + // Clamp constraint force mixing parameter if it is out of the range + if (_cfm < 1e-9) + { + dtwarn << "Constraint force mixing parameter[" << _cfm + << "] is lower than 1e-9. " << "It is set to 1e-9." << std::endl; + mConstraintForceMixing = 1e-9; + } + if (_cfm > 1.0) + { + dtwarn << "Constraint force mixing parameter[" << _cfm + << "] is greater than 1.0. " << "It is set to 1.0." << std::endl; + mConstraintForceMixing = 1.0; + } + + mConstraintForceMixing = _cfm; +} + +//============================================================================== +double HumanLegJointLimitConstraint::getConstraintForceMixing() +{ + return mConstraintForceMixing; +} + +//============================================================================== +void HumanLegJointLimitConstraint::update() +{ + double qz = mHipJoint->getPosition(0); + double qx = mHipJoint->getPosition(1); + double qy = mHipJoint->getPosition(2); + double qe = mKneeJoint->getPosition(0); + double hx = mAnkleJoint->getPosition(0); + double hy = mAnkleJoint->getPosition(1); + + double qz_d = mHipJoint->getVelocity(0); + double qx_d = mHipJoint->getVelocity(1); + double qy_d = mHipJoint->getVelocity(2); + double qe_d = mKneeJoint->getVelocity(0); + double hx_d = mAnkleJoint->getVelocity(0); + double hy_d = mAnkleJoint->getVelocity(1); + + // if isMirror (right-lrg), set up a mirrored euler joint for hip + // i.e. pass the mirrored config to NN + if (mIsMirror) + { + qz = -qz; qy = -qy; + } + + double qsin[8] = {cos(qz), sin(qz), cos(qx), sin(qx), + cos(qy + M_PI_2), cos(qe), cos(hx + M_PI_2), cos(hy + M_PI_2)}; + vec_t input; + input.assign(qsin, qsin+8); + vec_t pred_vec = mNet.predict(input); + double C = *(pred_vec.begin()); + + mViolation = C - 0.5; + + // if not active, no variable matters, no need to update + mActive = false; + mDim = 0; + + // active: mViolation <= 0 (C(q)-0.5<=0) + if (mViolation <= 0.0) + { + if (mActive) + { + ++mLifeTime; + } + else + { + mActive = true; + mLifeTime = 0; + } + + // do back-propogation to obtain gradient + layer* l; + vec_t out_grad = {1}; + vec_t in_grad; + for (int n = mNet.layer_size()-1; n >= 0; n--) + { + // implement chain rule layer by layer + l = mNet[n]; + if (l->layer_type() == "fully-connected") + { + auto Wb = l->weights(); + vec_t W = *(Wb[0]); + in_grad.assign(W.size()/out_grad.size(), 0); + for (size_t c = 0; c < in_grad.size(); c++) { + in_grad[c] = vectorize::dot(&out_grad[0], &W[c * out_grad.size()], out_grad.size()); + } + } + else + { + // this is activation layer + std::vector out_t; + l->output(out_t); + vec_t out_v = (*(out_t[0]))[0]; + + in_grad.assign(out_grad.begin(), out_grad.end()); + // first arg (x) is used only to infer the size of input, which should be the same as output y + (dynamic_cast(l))->backward_activation(out_v, out_v, in_grad, out_grad); + } + out_grad.assign(in_grad.begin(), in_grad.end()); + in_grad.clear(); + } + + mJacobian[0] = out_grad[0] * (-sin(qz)) + out_grad[1] * (cos(qz)); + mJacobian[1] = out_grad[2] * (-sin(qx)) + out_grad[3] * (cos(qx)); + mJacobian[2] = out_grad[4] * (-sin(qy + M_PI_2)); + mJacobian[3] = out_grad[5] * (-sin(qe)); + mJacobian[4] = out_grad[6] * (-sin(hx + M_PI_2)); + mJacobian[5] = out_grad[7] * (-sin(hy + M_PI_2)); + + // note that we also need to take the mirror of the NN gradient for right-leg + if (mIsMirror) + { + mJacobian[0] = -mJacobian[0]; + mJacobian[2] = -mJacobian[2]; + } + + // TODO: Normalize grad seems unnecessary? + + Eigen::Vector6d q_d; + q_d << qz_d, qx_d, qy_d, qe_d, hx_d, hy_d; + mNegativeVel = -mJacobian.dot(q_d); + + mLowerBound = 0.0; + mUpperBound = dInfinity; + mDim = 1; + } +} + +//============================================================================== +void HumanLegJointLimitConstraint::getInformation(ConstraintInfo* _lcp) +{ + // if non-active, should not call getInfo() + assert(isActive()); + + // assume caller will allocate enough space for _lcp variables + assert(_lcp->w[0] == 0.0); + assert(_lcp->findex[0] == -1); + + double bouncingVel = -mViolation - mErrorAllowance; + if (bouncingVel < 0.0) + { + bouncingVel = 0.0; + } + bouncingVel *= _lcp->invTimeStep * mErrorReductionParameter; + if (bouncingVel > mMaxErrorReductionVelocity) + bouncingVel = mMaxErrorReductionVelocity; + + _lcp->b[0] = mNegativeVel + bouncingVel; + _lcp->lo[0] = mLowerBound; + _lcp->hi[0] = mUpperBound; + + if (mLifeTime) + _lcp->x[0] = mOldX; + else + _lcp->x[0] = 0.0; +} + +//============================================================================== +void HumanLegJointLimitConstraint::applyUnitImpulse(std::size_t _index) +{ + // the dim of constraint = 1, valid _index can only be 0 + assert(_index < mDim && "Invalid Index."); + assert(isActive()); + + const dynamics::SkeletonPtr& skeleton = mHipJoint->getSkeleton(); + skeleton->clearConstraintImpulses(); + + for (std::size_t i = 0; i < 3; i++) + { + mHipJoint->setConstraintImpulse(i, mJacobian[i]); + } + mKneeJoint->setConstraintImpulse(0, mJacobian[3]); + for (std::size_t i = 0; i < 2; i++) + { + mAnkleJoint->setConstraintImpulse(i, mJacobian[4+i]); + } + + // Use the body which is placed later in the list of body nodes in this skeleton + skeleton->updateBiasImpulse(mFootNode); + skeleton->updateVelocityChange(); + + for (std::size_t i = 0; i < 3; i++) + { + mHipJoint->setConstraintImpulse(i, 0.0); + } + mKneeJoint->setConstraintImpulse(0, 0.0); + for (std::size_t i = 0; i < 2; i++) + { + mAnkleJoint->setConstraintImpulse(i, 0.0); + } + + mAppliedImpulseIndex = _index; // which is 0 +} + +//============================================================================== +void HumanLegJointLimitConstraint::getVelocityChange(double* _delVel, bool _withCfm) +{ + assert(_delVel != nullptr && "Null pointer is not allowed."); + _delVel[0] = 0.0; + + if (mHipJoint->getSkeleton()->isImpulseApplied()) + { + Eigen::Vector6d delq_d; + for (std::size_t i = 0; i < 3; i++) + { + delq_d[i] = mHipJoint->getVelocityChange(i); + } + delq_d[3] = mKneeJoint->getVelocityChange(0); + for (std::size_t i = 0; i < 2; i++) + { + delq_d[4+i] = mAnkleJoint->getVelocityChange(i); + } + _delVel[0] = mJacobian.dot(delq_d); + } + + if (_withCfm) + { + _delVel[mAppliedImpulseIndex] += _delVel[mAppliedImpulseIndex] + * mConstraintForceMixing; + } +} + +//============================================================================== +void HumanLegJointLimitConstraint::excite() +{ + mHipJoint->getSkeleton()->setImpulseApplied(true); +} + +//============================================================================== +void HumanLegJointLimitConstraint::unexcite() +{ + mHipJoint->getSkeleton()->setImpulseApplied(false); +} + +//============================================================================== +void HumanLegJointLimitConstraint::applyImpulse(double* _lambda) +{ + assert(isActive()); + + // the dim of constraint = 1 + auto con_force = _lambda[0]; + mOldX = con_force; + + for (std::size_t i = 0; i < 3; i++) + { + mHipJoint->setConstraintImpulse(i, + mHipJoint->getConstraintImpulse(i) + mJacobian[i] * con_force); + } + mKneeJoint->setConstraintImpulse(0, + mKneeJoint->getConstraintImpulse(0) + mJacobian[3] * con_force); + for (std::size_t i = 0; i < 2; i++) + { + mAnkleJoint->setConstraintImpulse(i, + mAnkleJoint->getConstraintImpulse(i) + mJacobian[4+i] * con_force); + } +} + +//============================================================================== +dynamics::SkeletonPtr HumanLegJointLimitConstraint::getRootSkeleton() const +{ + return mHipJoint->getSkeleton()->mUnionRootSkeleton.lock(); +} + +//============================================================================== +bool HumanLegJointLimitConstraint::isActive() const +{ + return mActive; +} + +} // namespace constraint +} // namespace dart diff --git a/dart/constraint/HumanLegJointLimitConstraint.hpp b/dart/constraint/HumanLegJointLimitConstraint.hpp new file mode 100644 index 0000000000000..433e0ecc2f799 --- /dev/null +++ b/dart/constraint/HumanLegJointLimitConstraint.hpp @@ -0,0 +1,193 @@ +/* + * Copyright (c) 2014-2016, Humanoid Lab, Georgia Tech Research Corporation + * Copyright (c) 2014-2017, Graphics Lab, Georgia Tech Research Corporation + * Copyright (c) 2016-2017, Personal Robotics Lab, Carnegie Mellon University + * All rights reserved. + * + * This file is provided under the following "BSD-style" License: + * Redistribution and use in source and binary forms, with or + * without modification, are permitted provided that the following + * conditions are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND + * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, + * INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF + * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF + * USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED + * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef DART_CONSTRAINT_HUMANLEGJOINTLIMITCONSTRAINT_HPP_ +#define DART_CONSTRAINT_HUMANLEGJOINTLIMITCONSTRAINT_HPP_ + +#include "dart/constraint/ConstraintBase.hpp" +#include "dart/math/MathTypes.hpp" +#include "tiny_dnn/tiny_dnn.h" + +namespace dart { + +namespace dynamics { +class BodyNode; +class Joint; +} // namespace dynamics + +namespace constraint { + +/// HumanLegJointLimitConstraint handles joint position limits on human leg, +/// representing range of motion of hip, knee and ankle joints. +class HumanLegJointLimitConstraint : public ConstraintBase +{ +public: + /// Constructor + explicit HumanLegJointLimitConstraint( + dynamics::Joint* _hipjoint, dynamics::Joint* _kneejoint, dynamics::Joint* _anklejoint, bool _isMirror); + + /// Destructor + virtual ~HumanLegJointLimitConstraint(); + + //---------------------------------------------------------------------------- + // Property settings + //---------------------------------------------------------------------------- + + /// Set global error reduction parameter + static void setErrorAllowance(double _allowance); + + /// Get global error reduction parameter + static double getErrorAllowance(); + + /// Set global error reduction parameter + static void setErrorReductionParameter(double _erp); + + /// Get global error reduction parameter + static double getErrorReductionParameter(); + + /// Set global error reduction parameter + static void setMaxErrorReductionVelocity(double _erv); + + /// Get global error reduction parameter + static double getMaxErrorReductionVelocity(); + + /// Set global constraint force mixing parameter + static void setConstraintForceMixing(double _cfm); + + /// Get global constraint force mixing parameter + static double getConstraintForceMixing(); + + //---------------------------------------------------------------------------- + // Friendship + //---------------------------------------------------------------------------- + + friend class ConstraintSolver; + friend class ConstrainedGroup; + +protected: + //---------------------------------------------------------------------------- + // Constraint virtual functions + //---------------------------------------------------------------------------- + + // Documentation inherited + void update() override; + + // Documentation inherited + void getInformation(ConstraintInfo* _lcp) override; + + // Documentation inherited + void applyUnitImpulse(std::size_t _index) override; + + // Documentation inherited + void getVelocityChange(double* _delVel, bool _withCfm) override; + + // Documentation inherited + void excite() override; + + // Documentation inherited + void unexcite() override; + + // Documentation inherited + void applyImpulse(double* _lambda) override; + + // Documentation inherited + dynamics::SkeletonPtr getRootSkeleton() const override; + + // Documentation inherited + bool isActive() const override; + +private: + /// leg joints involved + dynamics::Joint* mHipJoint; + dynamics::Joint* mKneeJoint; + dynamics::Joint* mAnkleJoint; + + /// leg body nodes involved + dynamics::BodyNode* mThighNode; + dynamics::BodyNode* mLowerLegNode; + dynamics::BodyNode* mFootNode; + + /// A workaround to have a de facto left-handed euler joint + /// for right hip, so it could share same limits with left hip. + /// left-leg set to false, right-leg set to true. + bool mIsMirror; + + /// the neural network for calculating limits + tiny_dnn::network mNet; + + /// Gradient of the neural net function + Eigen::Vector6d mJacobian; + + /// Index of applied impulse + std::size_t mAppliedImpulseIndex; + + /// + std::size_t mLifeTime; + + /// + double mViolation; + + /// + double mNegativeVel; + + /// + double mOldX; + + /// + double mUpperBound; + + /// + double mLowerBound; + + /// + bool mActive; + + /// Global constraint error allowance + static double mErrorAllowance; + + /// Global constraint error redection parameter in the range of [0, 1]. The + /// default is 0.01. + static double mErrorReductionParameter; + + /// Maximum error reduction velocity + static double mMaxErrorReductionVelocity; + + /// Global constraint force mixing parameter in the range of [1e-9, 1]. The + /// default is 1e-5 + /// \sa http://www.ode.org/ode-latest-userguide.html#sec_3_8_0 + static double mConstraintForceMixing; +}; + +} // namespace constraint +} // namespace dart + +#endif // DART_CONSTRAINT_HUMANLEGJOINTLIMITCONSTRAINT_HPP_ + diff --git a/dart/constraint/SmartPointer.hpp b/dart/constraint/SmartPointer.hpp index 7fc7647f91715..b01e6308c393f 100644 --- a/dart/constraint/SmartPointer.hpp +++ b/dart/constraint/SmartPointer.hpp @@ -51,6 +51,8 @@ DART_COMMON_MAKE_SHARED_WEAK(LCPSolver) DART_COMMON_MAKE_SHARED_WEAK(BallJointConstraint) DART_COMMON_MAKE_SHARED_WEAK(WeldJointConstraint) +DART_COMMON_MAKE_SHARED_WEAK(HumanArmJointLimitConstraint) +DART_COMMON_MAKE_SHARED_WEAK(HumanLegJointLimitConstraint) DART_COMMON_MAKE_SHARED_WEAK(BalanceConstraint) From c47ac0f2168ead0af2ce960a1d9b5cbfa6dcf62b Mon Sep 17 00:00:00 2001 From: Yifeng Jiang Date: Mon, 2 Oct 2017 17:51:47 -0400 Subject: [PATCH 07/36] add external headers tiny-dnn --- tiny_dnn/activations/activation_layer.h | 127 ++ tiny_dnn/activations/elu_layer.h | 47 + tiny_dnn/activations/leaky_relu_layer.h | 99 ++ tiny_dnn/activations/relu_layer.h | 48 + tiny_dnn/activations/selu_layer.h | 111 ++ tiny_dnn/activations/sigmoid_layer.h | 47 + tiny_dnn/activations/softmax_layer.h | 64 + tiny_dnn/activations/softplus_layer.h | 113 ++ tiny_dnn/activations/softsign_layer.h | 49 + tiny_dnn/activations/tanh_layer.h | 47 + tiny_dnn/activations/tanh_p1m2_layer.h | 49 + tiny_dnn/config.h | 94 ++ tiny_dnn/core/backend.h | 172 ++ tiny_dnn/core/backend_avx.h | 261 +++ tiny_dnn/core/backend_tiny.h | 399 +++++ tiny_dnn/core/framework/device.fwd.h | 114 ++ tiny_dnn/core/framework/device.h | 93 ++ tiny_dnn/core/framework/op_kernel.h | 145 ++ tiny_dnn/core/framework/program.h | 73 + tiny_dnn/core/framework/program_manager.h | 159 ++ tiny_dnn/core/framework/tensor.h | 269 +++ tiny_dnn/core/framework/tensor_utils.h | 275 ++++ .../core/kernels/avx_deconv2d_back_kernel.h | 30 + tiny_dnn/core/kernels/avx_deconv2d_kernel.h | 29 + tiny_dnn/core/kernels/avx_kernel_common.h | 442 +++++ tiny_dnn/core/kernels/conv2d_grad_op.h | 53 + tiny_dnn/core/kernels/conv2d_grad_op_avx.h | 901 +++++++++++ tiny_dnn/core/kernels/conv2d_op.h | 54 + tiny_dnn/core/kernels/conv2d_op_avx.h | 471 ++++++ tiny_dnn/core/kernels/conv2d_op_internal.h | 182 +++ tiny_dnn/core/kernels/conv2d_op_libdnn.h | 266 +++ tiny_dnn/core/kernels/conv2d_op_nnpack.h | 87 + tiny_dnn/core/kernels/conv2d_op_opencl.h | 140 ++ .../core/kernels/fully_connected_grad_op.h | 55 + tiny_dnn/core/kernels/fully_connected_op.h | 57 + .../core/kernels/fully_connected_op_avx.h | 244 +++ .../kernels/fully_connected_op_internal.h | 74 + .../core/kernels/fully_connected_op_nnpack.h | 61 + .../core/kernels/global_avepool_grad_op.h | 45 + tiny_dnn/core/kernels/global_avepool_op.h | 45 + tiny_dnn/core/kernels/global_avepool_op_avx.h | 155 ++ .../core/kernels/global_avepool_op_internal.h | 52 + tiny_dnn/core/kernels/maxpool_grad_op.h | 49 + tiny_dnn/core/kernels/maxpool_op.h | 64 + tiny_dnn/core/kernels/maxpool_op_avx.h | 35 + tiny_dnn/core/kernels/maxpool_op_internal.h | 60 + tiny_dnn/core/kernels/maxpool_op_nnpack.h | 61 + tiny_dnn/core/kernels/nnp_deconv2d_kernel.h | 31 + .../core/kernels/recurrent_cell_grad_op.h | 55 + tiny_dnn/core/kernels/recurrent_cell_op.h | 54 + .../core/kernels/recurrent_cell_op_internal.h | 164 ++ .../core/kernels/tiny_deconv2d_back_kernel.h | 105 ++ tiny_dnn/core/kernels/tiny_deconv2d_kernel.h | 68 + .../core/kernels/tiny_quantization_kernel.h | 258 +++ .../kernels/tiny_quantized_conv2d_kernel.h | 472 ++++++ .../kernels/tiny_quantized_deconv2d_kernel.h | 465 ++++++ .../tiny_quantized_fully_connected_kernel.h | 376 +++++ .../kernels/tiny_quantized_matmul_kernel.h | 137 ++ tiny_dnn/core/params/conv_params.h | 169 ++ tiny_dnn/core/params/deconv_params.h | 33 + tiny_dnn/core/params/fully_params.h | 28 + tiny_dnn/core/params/global_avepool_params.h | 25 + tiny_dnn/core/params/maxpool_params.h | 46 + tiny_dnn/core/params/params.h | 32 + tiny_dnn/core/params/recurrent_cell_params.h | 31 + tiny_dnn/core/session.h | 39 + tiny_dnn/io/caffe/CPPLINT.cfg | 1 + tiny_dnn/io/caffe/caffe.proto | 1379 ++++++++++++++++ tiny_dnn/io/caffe/layer_factory.h | 179 ++ tiny_dnn/io/caffe/layer_factory_impl.h | 1112 +++++++++++++ tiny_dnn/io/cifar10_parser.h | 89 + tiny_dnn/io/display.h | 111 ++ tiny_dnn/io/layer_factory.h | 39 + tiny_dnn/io/mnist_parser.h | 159 ++ tiny_dnn/layers/arithmetic_layer.h | 78 + tiny_dnn/layers/average_pooling_layer.h | 286 ++++ tiny_dnn/layers/average_unpooling_layer.h | 236 +++ tiny_dnn/layers/batch_normalization_layer.h | 258 +++ tiny_dnn/layers/concat_layer.h | 111 ++ tiny_dnn/layers/convolutional_layer.h | 520 ++++++ tiny_dnn/layers/deconvolutional_layer.h | 500 ++++++ tiny_dnn/layers/dropout_layer.h | 147 ++ tiny_dnn/layers/fully_connected_layer.h | 134 ++ .../layers/global_average_pooling_layer.h | 139 ++ tiny_dnn/layers/input_layer.h | 52 + tiny_dnn/layers/layer.h | 996 ++++++++++++ tiny_dnn/layers/layers.h | 30 + tiny_dnn/layers/linear_layer.h | 87 + tiny_dnn/layers/lrn_layer.h | 167 ++ tiny_dnn/layers/max_pooling_layer.h | 256 +++ tiny_dnn/layers/max_unpooling_layer.h | 183 +++ tiny_dnn/layers/partial_connected_layer.h | 149 ++ tiny_dnn/layers/power_layer.h | 110 ++ .../layers/quantized_convolutional_layer.h | 497 ++++++ .../layers/quantized_deconvolutional_layer.h | 497 ++++++ .../layers/quantized_fully_connected_layer.h | 115 ++ tiny_dnn/layers/recurrent_cell_layer.h | 178 ++ tiny_dnn/layers/slice_layer.h | 223 +++ tiny_dnn/lossfunctions/loss_function.h | 220 +++ tiny_dnn/models/alexnet.h | 40 + tiny_dnn/network.h | 1152 +++++++++++++ tiny_dnn/node.h | 262 +++ tiny_dnn/nodes.h | 618 +++++++ tiny_dnn/optimizers/optimizer.h | 247 +++ tiny_dnn/tiny_dnn.h | 168 ++ tiny_dnn/util/aligned_allocator.h | 128 ++ tiny_dnn/util/colored_print.h | 78 + tiny_dnn/util/deform.h | 20 + tiny_dnn/util/deserialization_helper.h | 155 ++ tiny_dnn/util/gradient_check.h | 134 ++ tiny_dnn/util/graph_visualizer.h | 119 ++ tiny_dnn/util/image.h | 464 ++++++ tiny_dnn/util/macro.h | 22 + tiny_dnn/util/math_functions.h | 98 ++ tiny_dnn/util/nn_error.h | 66 + tiny_dnn/util/parallel_for.h | 206 +++ tiny_dnn/util/product.h | 789 +++++++++ tiny_dnn/util/random.h | 81 + tiny_dnn/util/serialization_functions.h | 903 +++++++++++ tiny_dnn/util/serialization_helper.h | 125 ++ tiny_dnn/util/serialization_layer_list.h | 52 + tiny_dnn/util/target_cost.h | 82 + tiny_dnn/util/util.h | 442 +++++ tiny_dnn/util/weight_init.h | 111 ++ tiny_dnn/xtensor/xadapt.hpp | 212 +++ tiny_dnn/xtensor/xarray.hpp | 523 ++++++ tiny_dnn/xtensor/xassign.hpp | 263 +++ tiny_dnn/xtensor/xaxis_iterator.hpp | 195 +++ tiny_dnn/xtensor/xbroadcast.hpp | 337 ++++ tiny_dnn/xtensor/xbuffer_adaptor.hpp | 592 +++++++ tiny_dnn/xtensor/xbuilder.hpp | 866 ++++++++++ tiny_dnn/xtensor/xcomplex.hpp | 244 +++ tiny_dnn/xtensor/xcontainer.hpp | 869 ++++++++++ tiny_dnn/xtensor/xcsv.hpp | 166 ++ tiny_dnn/xtensor/xeval.hpp | 57 + tiny_dnn/xtensor/xexception.hpp | 170 ++ tiny_dnn/xtensor/xexpression.hpp | 218 +++ tiny_dnn/xtensor/xfunction.hpp | 830 ++++++++++ tiny_dnn/xtensor/xfunctorview.hpp | 1221 ++++++++++++++ tiny_dnn/xtensor/xgenerator.hpp | 298 ++++ tiny_dnn/xtensor/xindexview.hpp | 645 ++++++++ tiny_dnn/xtensor/xinfo.hpp | 113 ++ tiny_dnn/xtensor/xio.hpp | 673 ++++++++ tiny_dnn/xtensor/xiterable.hpp | 1032 ++++++++++++ tiny_dnn/xtensor/xiterator.hpp | 727 +++++++++ tiny_dnn/xtensor/xlayout.hpp | 91 ++ tiny_dnn/xtensor/xmath.hpp | 1436 +++++++++++++++++ tiny_dnn/xtensor/xmissing.hpp | 639 ++++++++ tiny_dnn/xtensor/xnoalias.hpp | 100 ++ tiny_dnn/xtensor/xoffsetview.hpp | 39 + tiny_dnn/xtensor/xoperation.hpp | 522 ++++++ tiny_dnn/xtensor/xoptional.hpp | 969 +++++++++++ tiny_dnn/xtensor/xrandom.hpp | 229 +++ tiny_dnn/xtensor/xreducer.hpp | 623 +++++++ tiny_dnn/xtensor/xscalar.hpp | 610 +++++++ tiny_dnn/xtensor/xsemantic.hpp | 563 +++++++ tiny_dnn/xtensor/xslice.hpp | 526 ++++++ tiny_dnn/xtensor/xstorage.hpp | 536 ++++++ tiny_dnn/xtensor/xstridedview.hpp | 901 +++++++++++ tiny_dnn/xtensor/xstrides.hpp | 243 +++ tiny_dnn/xtensor/xtensor.hpp | 455 ++++++ tiny_dnn/xtensor/xtensor_config.hpp | 38 + tiny_dnn/xtensor/xtensor_forward.hpp | 127 ++ tiny_dnn/xtensor/xutils.hpp | 996 ++++++++++++ tiny_dnn/xtensor/xvectorize.hpp | 95 ++ tiny_dnn/xtensor/xview.hpp | 945 +++++++++++ tiny_dnn/xtensor/xview_utils.hpp | 274 ++++ 167 files changed, 46163 insertions(+) create mode 100755 tiny_dnn/activations/activation_layer.h create mode 100755 tiny_dnn/activations/elu_layer.h create mode 100755 tiny_dnn/activations/leaky_relu_layer.h create mode 100755 tiny_dnn/activations/relu_layer.h create mode 100755 tiny_dnn/activations/selu_layer.h create mode 100755 tiny_dnn/activations/sigmoid_layer.h create mode 100755 tiny_dnn/activations/softmax_layer.h create mode 100755 tiny_dnn/activations/softplus_layer.h create mode 100755 tiny_dnn/activations/softsign_layer.h create mode 100755 tiny_dnn/activations/tanh_layer.h create mode 100755 tiny_dnn/activations/tanh_p1m2_layer.h create mode 100755 tiny_dnn/config.h create mode 100755 tiny_dnn/core/backend.h create mode 100755 tiny_dnn/core/backend_avx.h create mode 100755 tiny_dnn/core/backend_tiny.h create mode 100755 tiny_dnn/core/framework/device.fwd.h create mode 100755 tiny_dnn/core/framework/device.h create mode 100755 tiny_dnn/core/framework/op_kernel.h create mode 100755 tiny_dnn/core/framework/program.h create mode 100755 tiny_dnn/core/framework/program_manager.h create mode 100755 tiny_dnn/core/framework/tensor.h create mode 100755 tiny_dnn/core/framework/tensor_utils.h create mode 100755 tiny_dnn/core/kernels/avx_deconv2d_back_kernel.h create mode 100755 tiny_dnn/core/kernels/avx_deconv2d_kernel.h create mode 100755 tiny_dnn/core/kernels/avx_kernel_common.h create mode 100755 tiny_dnn/core/kernels/conv2d_grad_op.h create mode 100755 tiny_dnn/core/kernels/conv2d_grad_op_avx.h create mode 100755 tiny_dnn/core/kernels/conv2d_op.h create mode 100755 tiny_dnn/core/kernels/conv2d_op_avx.h create mode 100755 tiny_dnn/core/kernels/conv2d_op_internal.h create mode 100755 tiny_dnn/core/kernels/conv2d_op_libdnn.h create mode 100755 tiny_dnn/core/kernels/conv2d_op_nnpack.h create mode 100755 tiny_dnn/core/kernels/conv2d_op_opencl.h create mode 100755 tiny_dnn/core/kernels/fully_connected_grad_op.h create mode 100755 tiny_dnn/core/kernels/fully_connected_op.h create mode 100755 tiny_dnn/core/kernels/fully_connected_op_avx.h create mode 100755 tiny_dnn/core/kernels/fully_connected_op_internal.h create mode 100755 tiny_dnn/core/kernels/fully_connected_op_nnpack.h create mode 100755 tiny_dnn/core/kernels/global_avepool_grad_op.h create mode 100755 tiny_dnn/core/kernels/global_avepool_op.h create mode 100755 tiny_dnn/core/kernels/global_avepool_op_avx.h create mode 100755 tiny_dnn/core/kernels/global_avepool_op_internal.h create mode 100755 tiny_dnn/core/kernels/maxpool_grad_op.h create mode 100755 tiny_dnn/core/kernels/maxpool_op.h create mode 100755 tiny_dnn/core/kernels/maxpool_op_avx.h create mode 100755 tiny_dnn/core/kernels/maxpool_op_internal.h create mode 100755 tiny_dnn/core/kernels/maxpool_op_nnpack.h create mode 100755 tiny_dnn/core/kernels/nnp_deconv2d_kernel.h create mode 100755 tiny_dnn/core/kernels/recurrent_cell_grad_op.h create mode 100755 tiny_dnn/core/kernels/recurrent_cell_op.h create mode 100755 tiny_dnn/core/kernels/recurrent_cell_op_internal.h create mode 100755 tiny_dnn/core/kernels/tiny_deconv2d_back_kernel.h create mode 100755 tiny_dnn/core/kernels/tiny_deconv2d_kernel.h create mode 100755 tiny_dnn/core/kernels/tiny_quantization_kernel.h create mode 100755 tiny_dnn/core/kernels/tiny_quantized_conv2d_kernel.h create mode 100755 tiny_dnn/core/kernels/tiny_quantized_deconv2d_kernel.h create mode 100755 tiny_dnn/core/kernels/tiny_quantized_fully_connected_kernel.h create mode 100755 tiny_dnn/core/kernels/tiny_quantized_matmul_kernel.h create mode 100755 tiny_dnn/core/params/conv_params.h create mode 100755 tiny_dnn/core/params/deconv_params.h create mode 100755 tiny_dnn/core/params/fully_params.h create mode 100755 tiny_dnn/core/params/global_avepool_params.h create mode 100755 tiny_dnn/core/params/maxpool_params.h create mode 100755 tiny_dnn/core/params/params.h create mode 100755 tiny_dnn/core/params/recurrent_cell_params.h create mode 100755 tiny_dnn/core/session.h create mode 100755 tiny_dnn/io/caffe/CPPLINT.cfg create mode 100755 tiny_dnn/io/caffe/caffe.proto create mode 100755 tiny_dnn/io/caffe/layer_factory.h create mode 100755 tiny_dnn/io/caffe/layer_factory_impl.h create mode 100755 tiny_dnn/io/cifar10_parser.h create mode 100755 tiny_dnn/io/display.h create mode 100755 tiny_dnn/io/layer_factory.h create mode 100755 tiny_dnn/io/mnist_parser.h create mode 100755 tiny_dnn/layers/arithmetic_layer.h create mode 100755 tiny_dnn/layers/average_pooling_layer.h create mode 100755 tiny_dnn/layers/average_unpooling_layer.h create mode 100755 tiny_dnn/layers/batch_normalization_layer.h create mode 100755 tiny_dnn/layers/concat_layer.h create mode 100755 tiny_dnn/layers/convolutional_layer.h create mode 100755 tiny_dnn/layers/deconvolutional_layer.h create mode 100755 tiny_dnn/layers/dropout_layer.h create mode 100755 tiny_dnn/layers/fully_connected_layer.h create mode 100755 tiny_dnn/layers/global_average_pooling_layer.h create mode 100755 tiny_dnn/layers/input_layer.h create mode 100755 tiny_dnn/layers/layer.h create mode 100755 tiny_dnn/layers/layers.h create mode 100755 tiny_dnn/layers/linear_layer.h create mode 100755 tiny_dnn/layers/lrn_layer.h create mode 100755 tiny_dnn/layers/max_pooling_layer.h create mode 100755 tiny_dnn/layers/max_unpooling_layer.h create mode 100755 tiny_dnn/layers/partial_connected_layer.h create mode 100755 tiny_dnn/layers/power_layer.h create mode 100755 tiny_dnn/layers/quantized_convolutional_layer.h create mode 100755 tiny_dnn/layers/quantized_deconvolutional_layer.h create mode 100755 tiny_dnn/layers/quantized_fully_connected_layer.h create mode 100755 tiny_dnn/layers/recurrent_cell_layer.h create mode 100755 tiny_dnn/layers/slice_layer.h create mode 100755 tiny_dnn/lossfunctions/loss_function.h create mode 100755 tiny_dnn/models/alexnet.h create mode 100755 tiny_dnn/network.h create mode 100755 tiny_dnn/node.h create mode 100755 tiny_dnn/nodes.h create mode 100755 tiny_dnn/optimizers/optimizer.h create mode 100755 tiny_dnn/tiny_dnn.h create mode 100755 tiny_dnn/util/aligned_allocator.h create mode 100755 tiny_dnn/util/colored_print.h create mode 100755 tiny_dnn/util/deform.h create mode 100755 tiny_dnn/util/deserialization_helper.h create mode 100755 tiny_dnn/util/gradient_check.h create mode 100755 tiny_dnn/util/graph_visualizer.h create mode 100755 tiny_dnn/util/image.h create mode 100755 tiny_dnn/util/macro.h create mode 100755 tiny_dnn/util/math_functions.h create mode 100755 tiny_dnn/util/nn_error.h create mode 100755 tiny_dnn/util/parallel_for.h create mode 100755 tiny_dnn/util/product.h create mode 100755 tiny_dnn/util/random.h create mode 100755 tiny_dnn/util/serialization_functions.h create mode 100755 tiny_dnn/util/serialization_helper.h create mode 100755 tiny_dnn/util/serialization_layer_list.h create mode 100755 tiny_dnn/util/target_cost.h create mode 100755 tiny_dnn/util/util.h create mode 100755 tiny_dnn/util/weight_init.h create mode 100755 tiny_dnn/xtensor/xadapt.hpp create mode 100755 tiny_dnn/xtensor/xarray.hpp create mode 100755 tiny_dnn/xtensor/xassign.hpp create mode 100755 tiny_dnn/xtensor/xaxis_iterator.hpp create mode 100755 tiny_dnn/xtensor/xbroadcast.hpp create mode 100755 tiny_dnn/xtensor/xbuffer_adaptor.hpp create mode 100755 tiny_dnn/xtensor/xbuilder.hpp create mode 100755 tiny_dnn/xtensor/xcomplex.hpp create mode 100755 tiny_dnn/xtensor/xcontainer.hpp create mode 100755 tiny_dnn/xtensor/xcsv.hpp create mode 100755 tiny_dnn/xtensor/xeval.hpp create mode 100755 tiny_dnn/xtensor/xexception.hpp create mode 100755 tiny_dnn/xtensor/xexpression.hpp create mode 100755 tiny_dnn/xtensor/xfunction.hpp create mode 100755 tiny_dnn/xtensor/xfunctorview.hpp create mode 100755 tiny_dnn/xtensor/xgenerator.hpp create mode 100755 tiny_dnn/xtensor/xindexview.hpp create mode 100755 tiny_dnn/xtensor/xinfo.hpp create mode 100755 tiny_dnn/xtensor/xio.hpp create mode 100755 tiny_dnn/xtensor/xiterable.hpp create mode 100755 tiny_dnn/xtensor/xiterator.hpp create mode 100755 tiny_dnn/xtensor/xlayout.hpp create mode 100755 tiny_dnn/xtensor/xmath.hpp create mode 100755 tiny_dnn/xtensor/xmissing.hpp create mode 100755 tiny_dnn/xtensor/xnoalias.hpp create mode 100755 tiny_dnn/xtensor/xoffsetview.hpp create mode 100755 tiny_dnn/xtensor/xoperation.hpp create mode 100755 tiny_dnn/xtensor/xoptional.hpp create mode 100755 tiny_dnn/xtensor/xrandom.hpp create mode 100755 tiny_dnn/xtensor/xreducer.hpp create mode 100755 tiny_dnn/xtensor/xscalar.hpp create mode 100755 tiny_dnn/xtensor/xsemantic.hpp create mode 100755 tiny_dnn/xtensor/xslice.hpp create mode 100755 tiny_dnn/xtensor/xstorage.hpp create mode 100755 tiny_dnn/xtensor/xstridedview.hpp create mode 100755 tiny_dnn/xtensor/xstrides.hpp create mode 100755 tiny_dnn/xtensor/xtensor.hpp create mode 100755 tiny_dnn/xtensor/xtensor_config.hpp create mode 100755 tiny_dnn/xtensor/xtensor_forward.hpp create mode 100755 tiny_dnn/xtensor/xutils.hpp create mode 100755 tiny_dnn/xtensor/xvectorize.hpp create mode 100755 tiny_dnn/xtensor/xview.hpp create mode 100755 tiny_dnn/xtensor/xview_utils.hpp diff --git a/tiny_dnn/activations/activation_layer.h b/tiny_dnn/activations/activation_layer.h new file mode 100755 index 0000000000000..8498f2d93261d --- /dev/null +++ b/tiny_dnn/activations/activation_layer.h @@ -0,0 +1,127 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +class activation_layer : public layer { + public: + /** + * Construct an activation layer which will take shape when connected to some + * layer. Connection happens like ( layer1 << act_layer1 ) and shape of this + * layer is inferred at that time. + */ + activation_layer() : activation_layer(shape3d(0, 0, 0)) {} + + /** + * Construct a flat activation layer with specified number of neurons. + * This constructor is suitable for adding an activation layer after + * flat layers such as fully connected layers. + * + * @param in_dim [in] number of elements of the input + */ + explicit activation_layer(size_t in_dim) + : activation_layer(shape3d(in_dim, 1, 1)) {} + + /** + * Construct an activation layer with specified width, height and channels. + * This constructor is suitable for adding an activation layer after spatial + * layers such as convolution / pooling layers. + * + * @param in_width [in] number of input elements along width + * @param in_height [in] number of input elements along height + * @param in_channels [in] number of channels (input elements along depth) + */ + activation_layer(size_t in_width, size_t in_height, size_t in_channels) + : activation_layer(shape3d(in_width, in_height, in_channels)) {} + + /** + * Construct an activation layer with specified input shape. + * + * @param in_shape [in] shape of input tensor + */ + explicit activation_layer(const shape3d &in_shape) + : layer({vector_type::data}, {vector_type::data}), in_shape_(in_shape) {} + + /** + * Construct an activation layer given the previous layer. + * @param prev_layer previous layer + */ + explicit activation_layer(const layer &prev_layer) + : layer({vector_type::data}, {vector_type::data}), + in_shape_(prev_layer.out_shape()[0]) {} + + std::vector in_shape() const override { return {in_shape_}; } + + std::vector out_shape() const override { return {in_shape_}; } + + void set_in_shape(const shape3d &in_shape) override { + this->in_shape_ = in_shape; + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + const tensor_t &x = *in_data[0]; + tensor_t &y = *out_data[0]; + for_i(x.size(), [&](size_t i) { forward_activation(x[i], y[i]); }); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + tensor_t &dx = *in_grad[0]; + const tensor_t &dy = *out_grad[0]; + const tensor_t &x = *in_data[0]; + const tensor_t &y = *out_data[0]; + for_i(x.size(), + [&](size_t i) { backward_activation(x[i], y[i], dx[i], dy[i]); }); + } + + std::string layer_type() const override = 0; + + /** + * Populate vec_t of elements 'y' according to activation y = f(x). + * Child classes must override this method, apply activation function + * element wise over a vec_t of elements. + * + * @param x input vector + * @param y output vector (values to be assigned based on input) + **/ + virtual void forward_activation(const vec_t &x, vec_t &y) = 0; + + /** + * Populate vec_t of elements 'dx' according to gradient of activation. + * + * @param x input vector of current layer (same as forward_activation) + * @param y output vector of current layer (same as forward_activation) + * @param dx gradient of input vectors (i-th element correspond with x[i]) + * @param dy gradient of output vectors (i-th element correspond with y[i]) + **/ + virtual void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) = 0; + + /** + * Target value range for learning. + */ + virtual std::pair scale() const = 0; + + private: + shape3d in_shape_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/elu_layer.h b/tiny_dnn/activations/elu_layer.h new file mode 100755 index 0000000000000..00b0a35695cf4 --- /dev/null +++ b/tiny_dnn/activations/elu_layer.h @@ -0,0 +1,47 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class elu_layer : public activation_layer { + public: + using activation_layer::activation_layer; + + std::string layer_type() const override { return "elu-activation"; } + + void forward_activation(const vec_t &x, vec_t &y) override { + for (size_t j = 0; j < x.size(); j++) { + y[j] = x[j] < float_t(0) ? (std::exp(x[j]) - float_t(1)) : x[j]; + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + for (size_t j = 0; j < x.size(); j++) { + // dx = dy * (gradient of elu) + dx[j] = dy[j] * (y[j] > float_t(0) ? float_t(1) : (float_t(1) + y[j])); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0.1), float_t(0.9)); + } + + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/leaky_relu_layer.h b/tiny_dnn/activations/leaky_relu_layer.h new file mode 100755 index 0000000000000..7c54629f0c54c --- /dev/null +++ b/tiny_dnn/activations/leaky_relu_layer.h @@ -0,0 +1,99 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class leaky_relu_layer : public activation_layer { + public: + // using activation_layer::activation_layer; + /** + * Construct a leaky ReLU which will take shape when connected to some + * layer. Connection happens like ( layer1 << act_layer1 ) and shape of this + * layer is inferred at that time. + */ + explicit leaky_relu_layer(const float_t epsilon = 0.01) + : leaky_relu_layer(shape3d(0, 0, 0), epsilon) {} + + /** + * Construct a flat leaky ReLU with specified number of neurons. + * This constructor is suitable for adding an activation layer after + * flat layers such as fully connected layers. + * + * @param in_dim [in] number of elements of the input + */ + explicit leaky_relu_layer(size_t in_dim, const float_t epsilon = 0.01) + : leaky_relu_layer(shape3d(in_dim, 1, 1), epsilon) {} + + /** + * Construct a leaky ReLU with specified width, height and channels. + * This constructor is suitable for adding an activation layer after spatial + * layers such as convolution / pooling layers. + * + * @param in_width [in] number of input elements along width + * @param in_height [in] number of input elements along height + * @param in_channels [in] number of channels (input elements along depth) + */ + leaky_relu_layer(size_t in_width, + size_t in_height, + size_t in_channels, + const float_t epsilon = 0.01) + : leaky_relu_layer(shape3d(in_width, in_height, in_channels), epsilon) {} + + /** + * Construct a leaky ReLU layer with specified input shape. + * + * @param in_shape [in] shape of input tensor + */ + explicit leaky_relu_layer(const shape3d &in_shape, + const float_t epsilon = 0.01) + : activation_layer(in_shape), epsilon_(epsilon) {} + + /** + * Construct a leaky ReLU layer given the previous layer. + * @param prev_layer previous layer + */ + explicit leaky_relu_layer(const layer &prev_layer, + const float_t epsilon = 0.01) + : activation_layer(prev_layer), epsilon_(epsilon) {} + + std::string layer_type() const override { return "leaky-relu-activation"; } + + float_t epsilon_value() const { return epsilon_; } + + void forward_activation(const vec_t &x, vec_t &y) override { + for (size_t j = 0; j < x.size(); j++) { + y[j] = x[j] > float_t(0) ? x[j] : epsilon_ * x[j]; + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + for (size_t j = 0; j < x.size(); j++) { + // dx = dy * (gradient of leaky relu) + dx[j] = dy[j] * (y[j] > float_t(0) ? float_t(1) : epsilon_); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0.1), float_t(0.9)); + } + + float_t epsilon_; + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/relu_layer.h b/tiny_dnn/activations/relu_layer.h new file mode 100755 index 0000000000000..6f8a2fea9c544 --- /dev/null +++ b/tiny_dnn/activations/relu_layer.h @@ -0,0 +1,48 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class relu_layer : public activation_layer { + public: + using activation_layer::activation_layer; + + std::string layer_type() const override { return "relu-activation"; } + + void forward_activation(const vec_t &x, vec_t &y) override { + for (size_t j = 0; j < x.size(); j++) { + y[j] = std::max(float_t(0), x[j]); + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + for (size_t j = 0; j < x.size(); j++) { + // dx = dy * (gradient of relu) + dx[j] = dy[j] * (y[j] > float_t(0) ? float_t(1) : float_t(0)); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0.1), float_t(0.9)); + } + + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/selu_layer.h b/tiny_dnn/activations/selu_layer.h new file mode 100755 index 0000000000000..1adcc6a46882c --- /dev/null +++ b/tiny_dnn/activations/selu_layer.h @@ -0,0 +1,111 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class selu_layer : public activation_layer { + public: + /** + * Construct a selu which will take shape when connected to some + * layer. Connection happens like ( layer1 << act_layer1 ) and shape of this + * layer is inferred at that time. + */ + + explicit selu_layer(const float_t lambda = 1.05070, + const float_t alpha = 1.67326) + : selu_layer(shape3d(0, 0, 0), lambda, alpha) {} + + /** + * Construct a flat selu with specified number of neurons. + * This constructor is suitable for adding an activation layer after + * flat layers such as fully connected layers. + * + * @param in_dim [in] number of elements of the input + */ + + selu_layer(size_t in_dim, + const float_t lambda = 1.05070, + const float_t alpha = 1.67326) + : selu_layer(shape3d(in_dim, 1, 1), lambda, alpha) {} + + /** + * Construct a selu with specified width, height and channels. + * This constructor is suitable for adding an activation layer after spatial + * layers such as convolution / pooling layers. + * + * @param in_width [in] number of input elements along width + * @param in_height [in] number of input elements along height + * @param in_channels [in] number of channels (input elements along depth) + */ + + selu_layer(size_t in_width, + size_t in_height, + size_t in_channels, + const float_t lambda = 1.05070, + const float_t alpha = 1.67326) + : selu_layer(shape3d(in_width, in_height, in_channels), lambda, alpha) {} + + /** + * Construct a selu layer with specified input shape. + * + * @param in_shape [in] shape of input tensor + */ + + selu_layer(const shape3d &in_shape, + const float_t lambda = 1.05070, + const float_t alpha = 1.67326) + : activation_layer(in_shape), lambda_(lambda), alpha_(alpha) {} + + selu_layer(const layer &prev_layer, + const float_t lambda = 1.05070, + const float_t alpha = 1.67326) + : activation_layer(prev_layer), lambda_(lambda), alpha_(alpha) {} + + std::string layer_type() const override { return "selu-activation"; } + + float_t lambda_value() { return lambda_; } + + float_t alpha_value() { return alpha_; } + + void forward_activation(const vec_t &x, vec_t &y) override { + for (size_t j = 0; j < x.size(); j++) { + y[j] = + lambda_ * + (x[j] > float_t(0) ? x[j] : alpha_ * (std::exp(x[j]) - float_t(1))); + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + // dx = dy * (gradient of selu) + for (size_t j = 0; j < x.size(); j++) { + dx[j] = + dy[j] * lambda_ * (x[j] > float_t(0) ? 1.0 : alpha_ * std::exp(x[j])); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0.1), float_t(0.9)); + } + + float_t lambda_; + float_t alpha_; + + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/sigmoid_layer.h b/tiny_dnn/activations/sigmoid_layer.h new file mode 100755 index 0000000000000..551ddef65f7b3 --- /dev/null +++ b/tiny_dnn/activations/sigmoid_layer.h @@ -0,0 +1,47 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class sigmoid_layer : public activation_layer { + public: + using activation_layer::activation_layer; + + std::string layer_type() const override { return "sigmoid-activation"; } + + void forward_activation(const vec_t &x, vec_t &y) override { + for (size_t j = 0; j < x.size(); j++) { + y[j] = float_t(1) / (float_t(1) + std::exp(-x[j])); + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + for (size_t j = 0; j < x.size(); j++) { + // dx = dy * (gradient of sigmoid) + dx[j] = dy[j] * y[j] * (float_t(1) - y[j]); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0.1), float_t(0.9)); + } + + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/softmax_layer.h b/tiny_dnn/activations/softmax_layer.h new file mode 100755 index 0000000000000..528fa9a8dac1d --- /dev/null +++ b/tiny_dnn/activations/softmax_layer.h @@ -0,0 +1,64 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class softmax_layer : public activation_layer { + public: + using activation_layer::activation_layer; + + std::string layer_type() const override { return "softmax-activation"; } + + void forward_activation(const vec_t &x, vec_t &y) override { + const float_t alpha = *std::max_element(x.begin(), x.end()); + float_t denominator(0); + for (size_t j = 0; j < x.size(); j++) { + y[j] = std::exp(x[j] - alpha); + denominator += y[j]; + } + for (size_t j = 0; j < x.size(); j++) { + y[j] /= denominator; + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + const size_t len = dy.size(); + +// auxilliary vector to store element wise softmax gradients of all elements + +#if HAS_CXX11_THREAD_LOCAL + thread_local +#endif + vec_t df(len, 0); + for (size_t j = 0; j < x.size(); j++) { + for (size_t k = 0; k < x.size(); k++) { + df[k] = (k == j) ? y[j] * (float_t(1) - y[j]) : -y[k] * y[j]; + } + // dx = dy * (gradient of softmax) + dx[j] = vectorize::dot(&dy[0], &df[0], len); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0), float_t(1)); + } + + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/softplus_layer.h b/tiny_dnn/activations/softplus_layer.h new file mode 100755 index 0000000000000..0229acf3ca91c --- /dev/null +++ b/tiny_dnn/activations/softplus_layer.h @@ -0,0 +1,113 @@ +/* + Copyright (c) 2017, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class softplus_layer : public activation_layer { + public: + // using activation_layer::activation_layer; + /** + * Construct a softplus which will take shape when connected to some + * layer. Connection happens like ( layer1 << act_layer1 ) and shape of this + * layer is inferred at that time. + */ + explicit softplus_layer(const float_t beta = 1.0, + const float_t threshold = 20.0) + : softplus_layer(shape3d(0, 0, 0), beta, threshold) {} + + /** + * Construct a flat softplus with specified number of neurons. + * This constructor is suitable for adding an activation layer after + * flat layers such as fully connected layers. + * + * @param in_dim [in] number of elements of the input + */ + softplus_layer(size_t in_dim, + const float_t beta = 1.0, + const float_t threshold = 20.0) + : softplus_layer(shape3d(in_dim, 1, 1), beta, threshold) {} + + /** + * Construct a softplus with specified width, height and channels. + * This constructor is suitable for adding an activation layer after spatial + * layers such as convolution / pooling layers. + * + * @param in_width [in] number of input elements along width + * @param in_height [in] number of input elements along height + * @param in_channels [in] number of channels (input elements along depth) + */ + softplus_layer(size_t in_width, + size_t in_height, + size_t in_channels, + const float_t beta = 1.0, + const float_t threshold = 20.0) + : softplus_layer( + shape3d(in_width, in_height, in_channels), beta, threshold) {} + + /** + * Construct a softplus layer with specified input shape. + * + * @param in_shape [in] shape of input tensor + */ + softplus_layer(const shape3d &in_shape, + const float_t beta = 1.0, + const float_t threshold = 20.0) + : activation_layer(in_shape), beta_(beta), threshold_(threshold) {} + + /** + * Construct a softplus layer given the previous layer. + * @param prev_layer previous layer + */ + softplus_layer(const layer &prev_layer, + const float_t beta = 1.0, + const float_t threshold = 20.0) + : activation_layer(prev_layer), beta_(beta), threshold_(threshold) {} + + std::string layer_type() const override { return "softplus-activation"; } + + float_t beta_value() const { return beta_; } + + float_t threshold_value() const { return threshold_; } + + void forward_activation(const vec_t &x, vec_t &y) override { + for (size_t j = 0; j < x.size(); j++) { + float_t betain = beta_ * x[j]; + y[j] = (betain > threshold_) ? x[j] + : (1 / beta_) * std::log1p(std::exp(betain)); + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + for (size_t j = 0; j < x.size(); j++) { + float_t betaout = beta_ * y[j]; + float_t exp_bo = std::exp(betaout); + // dx = dy * (gradient of softplus) + dx[j] = (betaout > threshold_) ? dy[j] : dy[j] * (exp_bo - 1) / exp_bo; + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0.1), float_t(0.9)); + } + + float_t beta_; + float_t threshold_; + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/softsign_layer.h b/tiny_dnn/activations/softsign_layer.h new file mode 100755 index 0000000000000..f316f1e0ca525 --- /dev/null +++ b/tiny_dnn/activations/softsign_layer.h @@ -0,0 +1,49 @@ +/* + Copyright (c) 2017, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class softsign_layer : public activation_layer { + public: + using activation_layer::activation_layer; + + std::string layer_type() const override { return "softsign-activation"; } + + void forward_activation(const vec_t &x, vec_t &y) override { + for (size_t j = 0; j < x.size(); j++) { + y[j] = x[j] / (1.0 + std::abs(x[j])); + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + CNN_UNREFERENCED_PARAMETER(y); + for (size_t j = 0; j < x.size(); j++) { + // dx = dy * (gradient of softsign) + auto d = 1.0 + std::abs(x[j]); + dx[j] = dy[j] / (d * d); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0.1), float_t(0.9)); + } + + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/tanh_layer.h b/tiny_dnn/activations/tanh_layer.h new file mode 100755 index 0000000000000..6cec802b7bbf0 --- /dev/null +++ b/tiny_dnn/activations/tanh_layer.h @@ -0,0 +1,47 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class tanh_layer : public activation_layer { + public: + using activation_layer::activation_layer; + + std::string layer_type() const override { return "tanh-activation"; } + + void forward_activation(const vec_t &x, vec_t &y) override { + for (size_t j = 0; j < x.size(); j++) { + y[j] = std::tanh(x[j]); + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + for (size_t j = 0; j < x.size(); j++) { + // dx = dy * (gradient of tanh) + dx[j] = dy[j] * (float_t(1) - sqr(y[j])); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(-0.8), float_t(0.8)); + } + + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/activations/tanh_p1m2_layer.h b/tiny_dnn/activations/tanh_p1m2_layer.h new file mode 100755 index 0000000000000..62047279a4ec2 --- /dev/null +++ b/tiny_dnn/activations/tanh_p1m2_layer.h @@ -0,0 +1,49 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class tanh_p1m2_layer : public activation_layer { + public: + using activation_layer::activation_layer; + + std::string layer_type() const override { return "tanh-scaled-activation"; } + + void forward_activation(const vec_t &x, vec_t &y) override { + float_t ep; + for (size_t j = 0; j < x.size(); j++) { + ep = std::exp(x[j]); + y[j] = ep / (ep + (float_t(1) / ep)); + } + } + + void backward_activation(const vec_t &x, + const vec_t &y, + vec_t &dx, + const vec_t &dy) override { + for (size_t j = 0; j < x.size(); j++) { + // dx = dy * (gradient of tanh-scaled) + dx[j] = dy[j] * (2 * y[j] * (float_t(1) - y[j])); + } + } + + std::pair scale() const override { + return std::make_pair(float_t(0.1), float_t(0.9)); + } + + friend struct serialization_buddy; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/config.h b/tiny_dnn/config.h new file mode 100755 index 0000000000000..cb3c3304e48d0 --- /dev/null +++ b/tiny_dnn/config.h @@ -0,0 +1,94 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +/** + * define if you want to use intel TBB library + */ +// #define CNN_USE_TBB + +/** + * define to enable avx vectorization + */ +// #define CNN_USE_AVX + +/** + * define to enable sse2 vectorization + */ +// #define CNN_USE_SSE + +/** + * define to enable OMP parallelization + */ +// #define CNN_USE_OMP + +/** + * define to enable Grand Central Dispatch parallelization + */ +// #define CNN_USE_GCD + +/** + * define to use exceptions + */ +#define CNN_USE_EXCEPTIONS + +/** + * comment out if you want tiny-dnn to be quiet + */ +#define CNN_USE_STDOUT + +// #define CNN_SINGLE_THREAD + +/** + * disable serialization/deserialization function + * You can uncomment this to speedup compilation & linking time, + * if you don't use network::save / network::load functions. + **/ +// #define CNN_NO_SERIALIZATION + +/** + * Enable Image API support. + * Currently we use stb by default. + **/ +// #define DNN_USE_IMAGE_API + +/** + * Enable Gemmlowp support. + **/ +#ifdef USE_GEMMLOWP +#if !defined(_MSC_VER) && !defined(_WIN32) && !defined(WIN32) +#define CNN_USE_GEMMLOWP // gemmlowp doesn't support MSVC/mingw +#endif +#endif // USE_GEMMLOWP + +/** + * number of task in batch-gradient-descent. + * @todo automatic optimization + */ +#ifdef CNN_USE_OMP +#define CNN_TASK_SIZE 100 +#else +#define CNN_TASK_SIZE 8 +#endif + +namespace tiny_dnn { + +/** + * calculation data type + * you can change it to float, or user defined class (fixed point,etc) + **/ +#ifdef CNN_USE_DOUBLE +typedef double float_t; +#else +typedef float float_t; +#endif + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/backend.h b/tiny_dnn/core/backend.h new file mode 100755 index 0000000000000..6d50df7e2d50e --- /dev/null +++ b/tiny_dnn/core/backend.h @@ -0,0 +1,172 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/core/params/conv_params.h" +#include "tiny_dnn/core/params/deconv_params.h" +#include "tiny_dnn/core/params/fully_params.h" +#include "tiny_dnn/core/params/global_avepool_params.h" +#include "tiny_dnn/core/params/maxpool_params.h" +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/node.h" + +#ifdef CNN_USE_NNPACK +#include +#endif + +namespace tiny_dnn { +namespace core { + +// TODO(edgar): remove this +class context; + +enum class backend_t { internal, nnpack, libdnn, avx, opencl }; + +inline std::ostream &operator<<(std::ostream &os, backend_t type) { + switch (type) { + case backend_t::internal: os << "Internal"; break; + case backend_t::nnpack: os << "NNPACK"; break; + case backend_t::libdnn: os << "LibDNN"; break; + case backend_t::avx: os << "AVX"; break; + case backend_t::opencl: os << "OpenCL"; break; + default: throw nn_error("Not supported ostream enum."); break; + } + return os; +} + +inline backend_t default_engine() { +#ifdef CNN_USE_AVX +#if defined(__AVX__) || defined(__AVX2__) + return backend_t::avx; +#else +#error "your compiler does not support AVX" +#endif +#else + return backend_t::internal; +#endif +} + +#ifdef CNN_USE_NNPACK +// Singleton to keep a global state whether NNPACK is initialized. +// Before using the API an initialization is required. For this reason +// we need to get an instance of the object in order to avoid a throw error. +// +// Usage: +// NNPackInitializer::getInstance().initialize(); +// +class NNPackInitializer { + public: + // We create a static instance of the object in case + // that it wasn't created before and we return it. + static NNPackInitializer &getInstance() { + static NNPackInitializer instance; + return instance; + } + + // Tries to initialize NNPACK. + // Calls an internal method to initialize in case that it's not, + // otherwise it returns a void. + // Throws an error if we do not succed with initialization. + void initialize() { + if (initialized_) return; // alredy initialized, do nothig. + + // calls internal method to initialize + nnp_status init_status = nnp_initialize(); + if (init_status != nnp_status_success) { + throw nn_error("Cannot initialize NNPACK."); + } + + // succeded with initialization. We set the global + // state to avoid exception errors in addition to + // reuse code. + initialized_ = true; + } + + private: + /** Flag to store whether NNPACK is initialized */ + bool initialized_ = false; +}; + +// TODO(you): create an interface to let users choose the algorithm +inline nnp_convolution_algorithm nnp_algorithm() { + return nnp_convolution_algorithm_auto; +} + +// TODO(you): create an interface to let users choose the transform strategy +inline nnp_convolution_transform_strategy nnp_kts() { + // some algorithm accept tuple based only + return nnp_convolution_transform_strategy_tuple_based; +} +#endif + +class backend { + public: + // context holds solution-dependent parameters + // context should be able to hold any types of structures (like boost::any) + explicit backend(context *ctx_ = nullptr) { + CNN_UNREFERENCED_PARAMETER(ctx_); + } + + // core math functions + + virtual void conv2d_q(const std::vector &in_data, + std::vector &out_data) = 0; + + virtual void conv2d_eq(const std::vector &in_data, + std::vector &out_data) = 0; + + virtual void conv2d_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) = 0; + + virtual void deconv2d(const std::vector &in_data, + std::vector &out_data) = 0; + + virtual void deconv2d_q(const std::vector &in_data, + std::vector &out_data) = 0; + + virtual void deconv2d_eq(const std::vector &in_data, + std::vector &out_data) = 0; + + virtual void deconv2d(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) = 0; + + virtual void deconv2d_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) = 0; + + virtual void fully_q(const std::vector &in_data, + std::vector &out_data) = 0; + + virtual void fully_eq(const std::vector &in_data, + std::vector &out_data) = 0; + + virtual void fully_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) = 0; + + context *get_context() const { return ctx_; } + + void set_layer(layer *layer) { layer_ = layer; } + + virtual backend_t type() const = 0; + + protected: + context *ctx_; + layer *layer_; +}; + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/backend_avx.h b/tiny_dnn/core/backend_avx.h new file mode 100755 index 0000000000000..4ec2139d302f4 --- /dev/null +++ b/tiny_dnn/core/backend_avx.h @@ -0,0 +1,261 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/core/backend.h" +#include "tiny_dnn/core/kernels/avx_deconv2d_back_kernel.h" +#include "tiny_dnn/core/kernels/avx_deconv2d_kernel.h" + +namespace tiny_dnn { +namespace core { + +class avx_backend : public backend { + public: + // context holds solution-dependent parameters + // context should be able to hold any types of structures (like boost::any) + + // convolution + avx_backend(conv_params *params, + std::function f1, + std::function f2, + conv_layer_worker_specific_storage *ptr) + : +#if 0 + params_c_(params), + conv_layer_worker_storage_(ptr), +#endif + copy_and_pad_input(f1), + copy_and_unpad_delta(f2) { + CNN_UNREFERENCED_PARAMETER(params); + CNN_UNREFERENCED_PARAMETER(ptr); + } + + // quantized convolution + avx_backend( + conv_params *params, + std::function f1, + std::function f2, + std::function f3, + conv_layer_worker_specific_storage *ptr) + : +#if 0 + params_c_(params), + conv_layer_worker_storage_(ptr), +#endif + copy_and_pad_input(f1), + copy_and_unpad_delta(f2) { + CNN_UNREFERENCED_PARAMETER(params); + CNN_UNREFERENCED_PARAMETER(ptr); + } + + // deconvolution + avx_backend(deconv_params *params, + std::function f1, + std::function f2, + deconv_layer_worker_specific_storage *ptr) + : params_d_(params), + deconv_layer_worker_storage_(ptr), + copy_and_unpad_output(f1), + copy_and_pad_delta(f2) {} + + // quantized deconvolution + avx_backend( + deconv_params *params, + std::function f1, + std::function f2, + std::function f3, + deconv_layer_worker_specific_storage *ptr) + : params_d_(params), + deconv_layer_worker_storage_(ptr), + copy_and_unpad_output(f1), + copy_and_pad_delta(f2), + backward_activation(f3) {} + +// maxpooling +#if 0 + avx_backend(std::vector> *out2in, + std::vector *in2out, + max_pooling_layer_worker_specific_storage *ptr) + : max_pooling_layer_worker_storage_(ptr), + out2in_(out2in), + in2out_(in2out) {} +#endif + + // fully_connected + explicit avx_backend(fully_params *params) +#if 0 + : params_f_(params) +#endif + { + CNN_UNREFERENCED_PARAMETER(params); + } + + // quantized fully_connected + avx_backend( + fully_params *params, + std::function f) + : +#if 0 + params_f_(params), +#endif + backward_activation(f) { + CNN_UNREFERENCED_PARAMETER(params); + } + + // core math functions + + void conv2d_q(const std::vector &in_data, + std::vector &out_data) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + throw nn_error("conv2d_q not implemented yet."); + } + + void conv2d_eq(const std::vector &in_data, + std::vector &out_data) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + throw nn_error("conv2d_eq not implemented yet."); + } + + void conv2d_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(out_grad); + CNN_UNREFERENCED_PARAMETER(in_grad); + throw nn_error("conv2d_q not implemented yet."); + } + + void deconv2d(const std::vector &in_data, + std::vector &out_data) override { + (*deconv_layer_worker_storage_).prev_out_ = in_data[0]; + const vec_t &W = (*in_data[1])[0]; + const vec_t &bias = (*in_data[2])[0]; + tensor_t &out = *out_data[0]; + const tensor_t &in = *in_data[0]; // input + + fill_tensor(out, float_t{0}); + + kernels::avx_deconv2d_kernel(*params_d_, in, W, bias, out, + layer_->parallelize()); + + copy_and_unpad_output(out); + out = *(*deconv_layer_worker_storage_).curr_out_unpadded_; + } + + void deconv2d_q(const std::vector &in_data, + std::vector &out_data) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + throw nn_error("not implemented yet."); + } + + void deconv2d_eq(const std::vector &in_data, + std::vector &out_data) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + throw nn_error("not implemented yet."); + } + + void deconv2d(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(out_data); + deconv_layer_worker_specific_storage &cws = (*deconv_layer_worker_storage_); + if (params_d_->pad_type == padding::same) + copy_and_pad_delta(cws.curr_delta_padded, *in_grad[0]); + + const tensor_t &prev_out = *(cws.prev_out_); + const vec_t &W = (*in_data[1])[0]; + tensor_t &dW = *in_grad[1]; + tensor_t &db = *in_grad[2]; + tensor_t &curr_delta = (params_d_->pad_type == padding::same) + ? cws.curr_delta_padded + : *out_grad[0]; + tensor_t *prev_delta = in_grad[0]; + + assert(W.size() == params_d_->weight.size()); + assert(dW[0].size() == params_d_->weight.size()); + assert(curr_delta[0].size() == layer_->out_shape()[0].size()); + + fill_tensor(*prev_delta, float_t{0}); + + kernels::avx_deconv2d_back_kernel(*params_d_, prev_out, W, dW, db, + curr_delta, prev_delta); + } + + void deconv2d_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(out_grad); + CNN_UNREFERENCED_PARAMETER(in_grad); + throw nn_error("not implemented yet."); + } + + void fully_q(const std::vector &in_data, + std::vector &out_data) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + throw nn_error("not implemented yet."); + } + + void fully_eq(const std::vector &in_data, + std::vector &out_data) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + throw nn_error("not implemented yet."); + } + + void fully_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(out_grad); + CNN_UNREFERENCED_PARAMETER(in_grad); + throw nn_error("not implemented yet."); + } + + backend_t type() const override { return backend_t::avx; } + + private: + deconv_params *params_d_; + deconv_layer_worker_specific_storage *deconv_layer_worker_storage_; +#if 0 + // Pointers to the convolution parameters + conv_params *params_c_; + fully_params *params_f_; + + // Pointers to the workers + conv_layer_worker_specific_storage *conv_layer_worker_storage_; + max_pooling_layer_worker_specific_storage *max_pooling_layer_worker_storage_; + std::vector> *out2in_; + std::vector *in2out_; +#endif + + /* Pointers to parent class functions */ + std::function copy_and_pad_input; + std::function copy_and_unpad_output; + std::function copy_and_unpad_delta; + std::function copy_and_pad_delta; + std::function + backward_activation; +}; + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/backend_tiny.h b/tiny_dnn/core/backend_tiny.h new file mode 100755 index 0000000000000..3494a1245ad4c --- /dev/null +++ b/tiny_dnn/core/backend_tiny.h @@ -0,0 +1,399 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/config.h" +#include "tiny_dnn/core/backend.h" + +#include "tiny_dnn/core/kernels/tiny_deconv2d_back_kernel.h" +#include "tiny_dnn/core/kernels/tiny_deconv2d_kernel.h" +#include "tiny_dnn/core/kernels/tiny_quantized_conv2d_kernel.h" +#include "tiny_dnn/core/kernels/tiny_quantized_deconv2d_kernel.h" + +#ifdef CNN_USE_GEMMLOWP +#include "tiny_dnn/core/kernels/tiny_quantized_fully_connected_kernel.h" +#endif // CNN_USE_GEMMLOWP + +namespace tiny_dnn { +namespace core { + +class tiny_backend : public backend { + public: + // context holds solution-dependent parameters + // context should be able to hold any types of structures (like boost::any) + + // convolution + tiny_backend(conv_params *params, + std::function f1, + std::function f2, + conv_layer_worker_specific_storage *ptr) + : params_c_(params), + conv_layer_worker_storage_(ptr), + copy_and_pad_input(f1), + copy_and_unpad_delta(f2) {} + + // quantized convolution + tiny_backend( + conv_params *params, + std::function f1, + std::function f2, + std::function f3, + conv_layer_worker_specific_storage *ptr) + : params_c_(params), + conv_layer_worker_storage_(ptr), + copy_and_pad_input(f1), + copy_and_unpad_delta(f2) {} + + // deconvolution + tiny_backend(deconv_params *params, + std::function f1, + std::function f2, + deconv_layer_worker_specific_storage *ptr) + : params_d_(params), + deconv_layer_worker_storage_(ptr), + copy_and_unpad_output(f1), + copy_and_pad_delta(f2) {} + + // quantized deconvolution + tiny_backend( + deconv_params *params, + std::function f1, + std::function f2, + std::function f3, + deconv_layer_worker_specific_storage *ptr) + : params_d_(params), + deconv_layer_worker_storage_(ptr), + copy_and_unpad_output(f1), + copy_and_pad_delta(f2), + backward_activation(f3) {} + + // fully_connected + explicit tiny_backend(fully_params *params) +#if 0 + : params_f_(params) +#endif + { + CNN_UNREFERENCED_PARAMETER(params); + } + + // quantized fully_connected + tiny_backend( + fully_params *params, + std::function f) + : /*params_f_(params),*/ backward_activation(f) { + CNN_UNREFERENCED_PARAMETER(params); + } + + // core math functions + + // quantized convolution + void conv2d_q(const std::vector &in_data, + std::vector &out_data) override { + copy_and_pad_input(*in_data[0]); + const vec_t &W = (*in_data[1])[0]; + const vec_t &bias = (*in_data[2])[0]; + tensor_t &out = *out_data[0]; + const std::vector &in = + (*conv_layer_worker_storage_).prev_out_padded_; // input // NOLINT + + fill_tensor(out, float_t{0}); + + for (size_t i = 0; i < in.size(); i++) { + kernels::tiny_quantized_conv2d_kernel(*params_c_, *in[i], W, bias, out[i], + layer_->parallelize()); + } + } + + // efficient quantization without abundant quantization/dequantization + void conv2d_eq(const std::vector &in_data, + std::vector &out_data) override { + copy_and_pad_input(*in_data[0]); + const vec_t &W = (*in_data[1])[0]; + const vec_t &bias = (*in_data[2])[0]; + const tensor_t &in_r = *in_data[3]; + const vec_t &W_r = (*in_data[4])[0]; + const vec_t &b_r = (*in_data[5])[0]; + tensor_t &out = *out_data[0]; + tensor_t &out_r = *out_data[1]; + + const std::vector &in = + (*conv_layer_worker_storage_).prev_out_padded_; // input // NOLINT + + fill_tensor(out, float_t{0}); + for (size_t i = 0; i < in.size(); i++) { + kernels::tiny_quantized_conv2d_kernel(*params_c_, *in[i], W, bias, + in_r[i], W_r, b_r, out[i], out_r[i], + layer_->parallelize()); + } + } + + void conv2d_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + conv_layer_worker_specific_storage &cws = (*conv_layer_worker_storage_); + + std::vector &prev_out = cws.prev_out_padded_; + const vec_t &W = (*in_data[1])[0]; + tensor_t &dW = *in_grad[1]; + tensor_t &db = *in_grad[2]; + tensor_t &curr_delta = *out_grad[0]; + tensor_t *prev_delta = (params_c_->pad_type == padding::same) + ? &cws.prev_delta_padded_ + : in_grad[0]; + + assert(W.size() == params_c_->weight.size()); + assert(dW[0].size() == params_c_->weight.size()); + assert(curr_delta[0].size() == layer_->out_shape()[0].size()); + + backward_activation(*out_grad[0], *out_data[0], curr_delta); + + fill_tensor(*prev_delta, float_t{0}); + + for (size_t i = 0; i < prev_out.size(); i++) { + kernels::tiny_quantized_conv2d_back_kernel(*params_c_, *prev_out[i], W, + dW[i], db[i], curr_delta[i], + &(*prev_delta)[i]); + } + + if (params_c_->pad_type == padding::same) { + copy_and_unpad_delta(cws.prev_delta_padded_, *in_grad[0]); + } + } + + void deconv2d(const std::vector &in_data, + std::vector &out_data) override { + (*deconv_layer_worker_storage_).prev_out_ = in_data[0]; + const vec_t &W = (*in_data[1])[0]; + const vec_t &bias = (*in_data[2])[0]; + tensor_t &out = *out_data[0]; + const tensor_t &in = *in_data[0]; // input + + fill_tensor( + out, float_t{0}, + params_d_->out.size()); // deconv2d-kernel requires padded size buffer + + kernels::tiny_deconv2d_kernel(*params_d_, in, W, bias, out, + layer_->parallelize()); + + copy_and_unpad_output(out); + out = *(*deconv_layer_worker_storage_).curr_out_unpadded_; + } + + // quantized deconvolution + void deconv2d_q(const std::vector &in_data, + std::vector &out_data) override { + (*deconv_layer_worker_storage_).prev_out_ = in_data[0]; + const tensor_t &in = *in_data[0]; // input + const vec_t &W = (*in_data[1])[0]; + const vec_t &bias = (*in_data[2])[0]; + tensor_t &out = *out_data[0]; + + fill_tensor( + out, float_t{0}, + params_d_->out.size()); // deconv2d-kernel requires padded size buffer + + for (size_t i = 0; i < in.size(); i++) { + kernels::tiny_quantized_deconv2d_kernel(*params_d_, in[i], W, bias, + out[i], layer_->parallelize()); + } + + copy_and_unpad_output(out); + out = *(*deconv_layer_worker_storage_).curr_out_unpadded_; + } + + // efficient quantization without abundant quantization/dequantization + void deconv2d_eq(const std::vector &in_data, + std::vector &out_data) override { + (*deconv_layer_worker_storage_).prev_out_ = in_data[0]; + const tensor_t &in = *in_data[0]; // input + const vec_t &W = (*in_data[1])[0]; + const vec_t &bias = (*in_data[2])[0]; + const tensor_t &in_r = *in_data[3]; + const vec_t &W_r = (*in_data[4])[0]; + const vec_t &b_r = (*in_data[5])[0]; + tensor_t &out = *out_data[0]; + tensor_t &out_r = *out_data[1]; + + fill_tensor( + out, float_t{0}, + params_d_->out.size()); // deconv2d-kernel requires padded size buffer + + for (size_t i = 0; i < in.size(); i++) { + kernels::tiny_quantized_deconv2d_kernel(*params_d_, in[i], W, bias, + in_r[i], W_r, b_r, out[i], + out_r[i], layer_->parallelize()); + } + + copy_and_unpad_output(out); + out = *(*deconv_layer_worker_storage_).curr_out_unpadded_; + } + + void deconv2d(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + deconv_layer_worker_specific_storage &cws = (*deconv_layer_worker_storage_); + if (params_d_->pad_type == padding::same) + copy_and_pad_delta(cws.curr_delta_padded, *in_grad[0]); + + const tensor_t &prev_out = *(cws.prev_out_); + const vec_t &W = (*in_data[1])[0]; + tensor_t &dW = *in_grad[1]; + tensor_t &db = *in_grad[2]; + tensor_t &curr_delta = (params_d_->pad_type == padding::same) + ? cws.curr_delta_padded + : *out_grad[0]; + tensor_t *prev_delta = in_grad[0]; + + assert(W.size() == params_d_->weight.size()); + assert(dW[0].size() == params_d_->weight.size()); + assert(curr_delta[0].size() == layer_->out_shape()[0].size()); + + backward_activation(*out_grad[0], *out_data[0], curr_delta); + + fill_tensor(*prev_delta, float_t{0}); + + kernels::tiny_deconv2d_back_kernel(*params_d_, prev_out, W, dW, db, + curr_delta, prev_delta); + } + + void deconv2d_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + deconv_layer_worker_specific_storage &cws = (*deconv_layer_worker_storage_); + if (params_d_->pad_type == padding::same) + copy_and_pad_delta(cws.curr_delta_padded, *in_grad[0]); + + const tensor_t &prev_out = *(cws.prev_out_); + const vec_t &W = (*in_data[1])[0]; + tensor_t &dW = *in_grad[1]; + tensor_t &db = *in_grad[2]; + tensor_t &curr_delta = (params_d_->pad_type == padding::same) + ? cws.curr_delta_padded + : *out_grad[1]; + tensor_t *prev_delta = in_grad[0]; + + assert(W.size() == params_d_->weight.size()); + assert(dW[0].size() == params_d_->weight.size()); + assert(curr_delta[0].size() == layer_->out_shape()[0].size()); + + backward_activation(*out_grad[0], *out_data[0], curr_delta); + + fill_tensor(*prev_delta, float_t{0}); + + for (size_t i = 0; i < prev_out.size(); i++) { + kernels::tiny_quantized_deconv2d_back_kernel(*params_d_, prev_out[i], W, + dW[i], db[i], curr_delta[i], + &(*prev_delta)[i]); + } + } + + void fully_q(const std::vector &in_data, + std::vector &out_data) override { +#ifdef CNN_USE_GEMMLOWP + const tensor_t &in = *in_data[0]; + const vec_t &W = (*in_data[1])[0]; + tensor_t &out = *out_data[0]; + + for (size_t i = 0; i < in.size(); i++) { + kernels::tiny_quantized_fully_connected_kernel( + *params_f_, in[i], W, params_f_->has_bias_ ? (*in_data[2])[0] : vec_t(), + out[i], layer_->parallelize()); + } +#else + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + throw nn_not_implemented_error( + "quantized fully op requires gemmlowp " + "library. please define CNN_USE_GEMMLOWP"); +#endif + } + + void fully_eq(const std::vector &in_data, + std::vector &out_data) override { +#ifdef CNN_USE_GEMMLOWP + const tensor_t &in = *in_data[0]; + const vec_t &W = (*in_data[1])[0]; + vec_t &b = (*in_data[2])[0]; + const tensor_t &in_r = *in_data[3]; + const vec_t &W_r = (*in_data[4])[0]; + const vec_t &b_r = (*in_data[5])[0]; + tensor_t &out = *out_data[0]; + tensor_t &out_r = *out_data[1]; + + for (size_t i = 0; i < in.size(); i++) { + kernels::tiny_quantized_fully_connected_kernel( + *params_f_, in[i], W, b, in_r[i], W_r, b_r, out[i], out_r[i], + layer_->parallelize()); + } +#else + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + throw nn_not_implemented_error( + "quantized fully op requires gemmlowp " + "library. please define CNN_USE_GEMMLOWP"); +#endif + } + + void fully_q(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { +#ifdef CNN_USE_GEMMLOWP + const tensor_t &prev_out = *in_data[0]; + const vec_t &W = (*in_data[1])[0]; + tensor_t &dW = *in_grad[1]; + tensor_t &db = *in_grad[2]; + tensor_t &prev_delta = *in_grad[0]; + tensor_t &curr_delta = *out_grad[0]; + + backward_activation(*out_grad[0], *out_data[0], curr_delta); + + for (size_t i = 0; i < prev_out.size(); i++) { + kernels::tiny_quantized_fully_connected_back_kernel( + *params_f_, prev_out[i], W, dW[i], prev_delta[i], curr_delta[i], db[i], + layer_->parallelize()); + } +#else + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(out_grad); + CNN_UNREFERENCED_PARAMETER(in_grad); + throw nn_not_implemented_error( + "quantized fully op requires gemmlowp " + "library. please define CNN_USE_GEMMLOWP"); +#endif + } + + backend_t type() const override { return default_engine(); } + + private: + /* Pointer to the convolution parameters */ + conv_params *params_c_; + deconv_params *params_d_; + // fully_params *params_f_; + + /* Pointer to the workers */ + conv_layer_worker_specific_storage *conv_layer_worker_storage_; + deconv_layer_worker_specific_storage *deconv_layer_worker_storage_; + + /* Pointers to parent class functions */ + std::function copy_and_pad_input; + std::function copy_and_unpad_output; + std::function copy_and_unpad_delta; + std::function copy_and_pad_delta; + std::function + backward_activation; +}; + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/framework/device.fwd.h b/tiny_dnn/core/framework/device.fwd.h new file mode 100755 index 0000000000000..5dc99c562526b --- /dev/null +++ b/tiny_dnn/core/framework/device.fwd.h @@ -0,0 +1,114 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#if defined(USE_OPENCL) || defined(USE_CUDA) +#ifdef USE_OPENCL +#include "third_party/CLCudaAPI/clpp11.h" +#else +#include "third_party/CLCudaAPI/cupp11.h" +#endif +#endif + +namespace tiny_dnn { + +enum class device_t { NONE, CPU, GPU /*, FPGA */ }; + +inline std::ostream &operator<<(std::ostream &os, device_t type) { + switch (type) { + case device_t::NONE: os << "NONE"; break; + case device_t::CPU: os << "CPU"; break; + case device_t::GPU: os << "GPU"; break; + default: + throw nn_error("Not supported ostream enum: " + + to_string(static_cast(type))); + break; + } + return os; +} + +/* The class models a physical device */ +class Device { + public: + /* Custom CPU constructor + * + * @param type The device type. Can be only CPU. + */ + inline explicit Device(device_t type); + + /* CPU/GPU OpenCL constructor. + * Device context is initialized in constructor. + * + * @param type The device type. Can be both CPU and GPU. + * @param platform_id The platform identification number. + * @param device_id The device identification number. + */ + inline explicit Device(device_t type, + const int platform_id, + const int device_id); + + // Returns the device type + device_t type() const { return type_; } + + // Returns true if CLCudaAPI is enabled to this device + bool hasCLCudaAPI() const { return has_clcuda_api_; } + + // Returns the platform id + int platformId() const { return platform_id_; } + + // Returns the device id + int deviceId() const { return device_id_; } + +#if defined(USE_OPENCL) || defined(USE_CUDA) + // Returns the CLCudaAPI Device object + CLCudaAPI::Device device() const { return *device_; } + + // Returns the CLCudaAPI Context object + CLCudaAPI::Context context() const { return *context_; } + + // Returns the CLCudaAPI Queue object + CLCudaAPI::Queue queue() const { return *queue_; } +#endif + + bool operator==(const Device &d) const { + if (d.type() == this->type() && d.hasCLCudaAPI() == this->hasCLCudaAPI() && + d.platformId() == this->platformId() && + d.deviceId() == this->deviceId()) { + return true; + } + return false; + } + + /* Registers and create an OpenCL program per Operation type. + * + * @param l The layer to be registered + */ + inline void registerOp(layer &l); + + private: + /* The device type */ + device_t type_; + /* Boolean to check if device has OpenCL */ + bool has_clcuda_api_; + /* The platform identification number */ + int platform_id_; + /* The device identification number */ + int device_id_; +#if defined(USE_OPENCL) || defined(USE_CUDA) + /* The CLCudaAPI device */ + std::shared_ptr device_; + /* The CLCudaAPI device context */ + std::shared_ptr context_; + /* The CLCudaAPI device queue */ + std::shared_ptr queue_; +#endif +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/framework/device.h b/tiny_dnn/core/framework/device.h new file mode 100755 index 0000000000000..ce1be75637de5 --- /dev/null +++ b/tiny_dnn/core/framework/device.h @@ -0,0 +1,93 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/device.fwd.h" +#include "tiny_dnn/core/framework/program_manager.h" + +namespace tiny_dnn { + +inline Device::Device(device_t type) : type_(type), has_clcuda_api_(false) { + nn_info("Initializing Non-OpenCL device ..."); + if (type == device_t::GPU) { + throw nn_error( + "Bad GPU device initialization. " + "Please provide platform_id and device_id"); + } + nn_info("Initializing Non-OpenCL device ... OK"); +} + +inline Device::Device(device_t type, const int platform_id, const int device_id) + : type_(type), + has_clcuda_api_(true), + platform_id_(platform_id), + device_id_(device_id) { +#if defined(USE_OPENCL) || defined(USE_CUDA) + // Instantiate Platform and Device + nn_info("Initializing OpenCL platform ..."); + auto platform = CLCudaAPI::Platform(platform_id); + + // Print short pltform info + nn_info("Initializing OpenCL platform ... OK"); + nn_info("-- Running on platform " + to_string(platform_id) + ". Found " + + to_string(platform.NumDevices()) + " devices."); + + // Create and retain device object + nn_info("Initializing OpenCL device ..."); + device_.reset(new CLCudaAPI::Device(platform, device_id)); + + // Print short device info + nn_info("Initializing OpenCL device ... OK"); + nn_info("-- Running on device " + to_string(device_->Name()) + " of " + + to_string(device_->Vendor())); + nn_info("-- Device type: " + to_string(device_->Type())); + nn_info("-- Capabilities: " + to_string(device_->Capabilities())); + + // check device type + if (type == device_t::CPU && !device_->IsCPU()) { + throw nn_error("Not found a CPU device. You are on: " + + to_string(device_->Type())); + } else if (type == device_t::GPU && !device_->IsGPU()) { + throw nn_error("Not found a GPU device. You are on: " + + to_string(device_->Type())); + } + + // Create and retain device context + nn_info("Initializing OpenCL device context ..."); + + context_.reset(new CLCudaAPI::Context(*device_)); + queue_.reset(new CLCudaAPI::Queue(*context_, *device_)); + + nn_info("Initializing OpenCL device context ... OK"); +#else + nn_error("TinyDNN has not been compiled with OpenCL or CUDA support."); +#endif +} + +inline void Device::registerOp(layer &l) { + // TODO(egdar/nyanp): Should we raise an error here? + if (!hasCLCudaAPI()) { + throw nn_error("Cannot register layer: " + l.layer_type() + + ". Device has disabled OpenCL support. Please " + "specify platform and device in " + "Device constructor"); + } + + if (l.engine() != core::backend_t::opencl && + l.engine() != core::backend_t::libdnn) { + throw nn_error("Cannot register layer: " + l.layer_type() + + ". Enabled engine: " + to_string(l.engine()) + + ". OpenCL engine " + "(backend_t::opencl) should be used."); + } + + // Register the op to this device + ProgramManager::getInstance().registerOp(*this, l); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/framework/op_kernel.h b/tiny_dnn/core/framework/op_kernel.h new file mode 100755 index 0000000000000..e305e5be75add --- /dev/null +++ b/tiny_dnn/core/framework/op_kernel.h @@ -0,0 +1,145 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/core/framework/device.fwd.h" +#include "tiny_dnn/core/params/conv_params.h" + +namespace tiny_dnn { +namespace core { + +class OpKernel; // delared below + +class OpKernelConstruction { + public: + OpKernelConstruction() {} + explicit OpKernelConstruction(Device *device, Params *params) + : device_(device), params_(params) {} + + // Returns the device raw pointer + Device *device() const { return device_; } + + // Returns the device raw pointer + Params *params() const { return params_; } + + private: + Device *device_ = nullptr; + Params *params_ = nullptr; +}; + +class OpKernelContext { + public: + struct OpParams { + // the op kernel being computed. + OpKernel *op_kernel_ptr = nullptr; + + // the device on which the kernel is running. + Device *device_ptr = nullptr; + + // the layer on which kernel is runnning + layer *layer_ptr_ = nullptr; + + // the operation params + Params *params_ptr_ = nullptr; + + // parallelize operation + bool parallelize = false; + + backend_t engine = default_engine(); + }; + + OpKernelContext() + : in_data_(nullptr), + out_data_(nullptr), + out_grad_(nullptr), + in_grad_(nullptr) { + op_params_ = std::unique_ptr(new OpParams()); + } + + void set_in_out(const std::vector &in_data, + std::vector &out_data) { + in_data_ = const_cast *>(&in_data); + out_data_ = &out_data; + } + + void set_in_out(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) { + in_data_ = const_cast *>(&in_data); + out_data_ = const_cast *>(&out_data); + out_grad_ = &out_grad; + in_grad_ = &in_grad; + } + + tensor_t &input(const int idx) { return *(*in_data_)[idx]; } + const tensor_t &input(const int idx) const { return *(*in_data_)[idx]; } + + tensor_t &output(const int idx) { return *(*out_data_)[idx]; } + const tensor_t &output(const int idx) const { return *(*out_data_)[idx]; } + + tensor_t &input_grad(const int idx) { return *(*in_grad_)[idx]; } + const tensor_t &input_grad(const int idx) const { return *(*in_grad_)[idx]; } + + tensor_t &output_grad(const int idx) { return *(*out_grad_)[idx]; } + const tensor_t &output_grad(const int idx) const { + return *(*out_grad_)[idx]; + } + + void setParams(Params *params) { op_params_->params_ptr_ = params; } + + Params *params() const { return op_params_->params_ptr_; } + + void setParallelize(const bool parallelize) { + op_params_->parallelize = parallelize; + } + + bool parallelize() const { return op_params_->parallelize; } + + void setDevice(Device *device) { op_params_->device_ptr = device; } + + Device *device() const { return op_params_->device_ptr; } + + void setLayer(layer *layer) { op_params_->layer_ptr_ = layer; } + + layer *Layer() const { return op_params_->layer_ptr_; } + + backend_t engine() const { return op_params_->engine; } + + void setEngine(const backend_t engine) { op_params_->engine = engine; } + + private: + std::vector *in_data_; + std::vector *out_data_; + std::vector *out_grad_; + std::vector *in_grad_; + + std::unique_ptr op_params_; +}; + +class OpKernel { + public: + OpKernel() {} + explicit OpKernel(const OpKernelConstruction &context) + : device_(context.device()), params_(context.params()) {} + + virtual ~OpKernel() {} + + virtual void compute(OpKernelContext &context) = 0; + + protected: + Device *device_ = nullptr; + Params *params_ = nullptr; +}; + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/framework/program.h b/tiny_dnn/core/framework/program.h new file mode 100755 index 0000000000000..f404a1bf62d40 --- /dev/null +++ b/tiny_dnn/core/framework/program.h @@ -0,0 +1,73 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/core/framework/device.fwd.h" +#include "tiny_dnn/layers/layer.h" + +#if defined(USE_OPENCL) || defined(USE_CUDA) +#ifdef USE_OPENCL +#include "third_party/CLCudaAPI/clpp11.h" +#else +#include "third_party/CLCudaAPI/cupp11.h" +#endif +#endif + +namespace tiny_dnn { + +/* The class models a program to be stored in the register. + * Each instance of this class will be used as key in the register. + */ +class Program { + public: + explicit Program(const Device *device, const layer *op) + : device_(device), op_(op) {} + + // Returns the device associated to the program + const Device *device() const { return device_; } + + // Return the layer pointer + const layer *op() const { return op_; } + + bool operator==(const Program &p) const { + if (p.device() == this->device() && + p.op()->layer_type() == this->op()->layer_type()) { + return true; + } + return false; + } + + private: + const Device *device_; + const layer *op_; +}; + +/* Hash function to store Programs in the register. + */ +class ProgramHash { + public: + size_t operator()(const Program &p) const { + // check there is a device and an op assigned + // to the input program. + if (p.device() == nullptr || p.op() == nullptr) { + throw nn_error("No Op or Device in Program."); + } + + // Compute individual hash values for data members and combine + // them using XOR and bit shifting. + return (std::hash()(static_cast(p.device()->type())) ^ + std::hash()(p.device()->hasCLCudaAPI()) ^ + std::hash()(p.device()->platformId()) ^ + std::hash()(p.device()->deviceId()) ^ + std::hash()(p.op()->layer_type())); + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/framework/program_manager.h b/tiny_dnn/core/framework/program_manager.h new file mode 100755 index 0000000000000..49ef6265d9d25 --- /dev/null +++ b/tiny_dnn/core/framework/program_manager.h @@ -0,0 +1,159 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/layers/layer.h" + +#include "tiny_dnn/core/framework/device.fwd.h" +#include "tiny_dnn/core/framework/program.h" + +#if defined(USE_OPENCL) || defined(USE_CUDA) +#ifdef USE_OPENCL +#include "third_party/CLCudaAPI/clpp11.h" +#else +#include "third_party/CLCudaAPI/cupp11.h" +#endif +#endif + +namespace tiny_dnn { + +/* The class models a general manager to handle compiled OpenCL programs. + * Since we need to retain compiled programs per layer type, it's + * implemented as Singleton. + */ +class ProgramManager { + public: + /* This function is called to create an instance of the class. + * Calling the constructor publicly is not allowed. + * The constructor is private and is only called by this Instance function. + */ + static ProgramManager &getInstance() { + static ProgramManager instance; + return instance; + } + + /* Registers and compiles a kernel source code. + * + * Creates a new program based on the kernel string. + * Note that the kernel string is moved-out when constructing the + * program to save copying: it should no longer be used in the + * remainder of this function. + */ + void registerOp(const Device &device, layer &layer) { +#if defined(USE_OPENCL) || defined(USE_CUDA) + // Register device to layer + layer.setDevice(device); + layer.createOp(); + +/* + // retrieve incoming device an layer + CLCudaAPI::Device device_ = device.device(); + CLCudaAPI::Context context_ = device.context(); + + // check if previous program was build with this + // Devce and Layer. + Program key_program(&device, &layer); + + auto iter = programs_.find(key_program); + if (iter != programs_.end()) { + nn_warn("Program already registered."); + return; + } + + // Define op kernel string and instantiate program + // TODO(edgar): load from `cl_kernels` dir. + // std::ifstream cl_file("opencl_hello_world.cl"); + std::ifstream cl_file(layer.kernel_file()); + std::string program_tail{std::istreambuf_iterator(cl_file), + std::istreambuf_iterator()}; + // fixed kernel params + std::string program_head = + std::string("#define Dtype float\n") + + std::string("#define Dtype4 float4\n") + + std::string("#define int_tp int\n") + + std::string("#define CONCAT(A,B) A##_##B\n") + + std::string("#define TEMPLATE(name,type) CONCAT(name,type)\n"); + + // per layer params + program_head += layer.kernel_header(); + + std::cout << layer.kernel_header() << std::endl; + + std::string program_string = std::string{program_head} + + std::string{program_tail}; + auto program = CLCudaAPI::Program(context_, std::move(program_string)); +*/ +/* + * Builds this program and checks for any compilation errors. + * If there are any, they are printed and execution is halted. + */ +/* nn_info("Compiling the kernel ..."); + auto compiler_options = std::vector{}; + auto build_status = program.Build(device_, compiler_options); + + if (build_status != CLCudaAPI::BuildStatus::kSuccess) { + auto message = program.GetBuildInfo(device_); + //throw nn_error("Compiler error(s)/warning(s) found: " + + // to_string(message.c_str())); + nn_warn("Compiler error(s)/warning(s) found: " + + to_string(message.c_str())); + return; + } + nn_info("Compiling the kernel ... OK"); + + // Kernel compilation succeed: Register program. + programs_.insert({ key_program, program }); +*/ +#else // USE_OPENCL OR USE_CUDA + CNN_UNREFERENCED_PARAMETER(device); + CNN_UNREFERENCED_PARAMETER(layer); +#endif + } + + // Returns the number of registered programs + size_t num_programs() const { +#if defined(USE_OPENCL) || defined(USE_CUDA) + return programs_.size(); +#else + return size_t(0); +#endif + } + +// Returns a CLCudaProgram given a key Program +// based on internal device and op. +#if defined(USE_OPENCL) || defined(USE_CUDA) + CLCudaAPI::Program program(const Program &program) { + auto p = programs_.find(program); + if (p == programs_.end()) { + throw nn_error("Cannot retrieve program."); + } + return p->second; + } +#endif + + // Removes the current programs from the general state + void reset() { +#if defined(USE_OPENCL) || defined(USE_CUDA) + programs_.clear(); +#endif + } + + protected: + ProgramManager() = default; + ProgramManager(const ProgramManager &) = delete; + ProgramManager &operator=(const ProgramManager &) = delete; + +#if defined(USE_OPENCL) || defined(USE_CUDA) + /* Container holding compiled kernels */ + std::unordered_map programs_; +#endif +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/framework/tensor.h b/tiny_dnn/core/framework/tensor.h new file mode 100755 index 0000000000000..be75a8a917a73 --- /dev/null +++ b/tiny_dnn/core/framework/tensor.h @@ -0,0 +1,269 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/core/framework/device.fwd.h" + +#if defined(USE_OPENCL) || defined(USE_CUDA) +#ifdef USE_OPENCL +#include "third_party/CLCudaAPI/clpp11.h" +#else +#include "third_party/CLCudaAPI/cupp11.h" +#endif +#endif + +namespace tiny_dnn { + +/** + * A tensor of the given dimension. + * + * U is type of the data stored + * Storage is type of underlying storage + */ +template > +class Tensor { + typedef U *UPtr; + + public: + /** + * Initializes an empty tensor. + * @return + */ + Tensor() {} + + /** + * Constructor that accepts an initializer list of shape and create a + * Tensor with that shape. For example, given shape = {2,3,4,5,6}, tensor + * will be of size 2x3x4x5x6. Note: tensor isn't initialized by default + * @param shape array containing N integers, sizes of dimensions + * @return + */ + explicit Tensor(std::vector const &shape) : storage_(shape) {} + + /** + * Constructor that accepts an initializer list of shape and create a + * Tensor with that shape. For example, given shape = {2,3,4,5,6}, tensor + * will be of size 2x3x4x5x6. Note: tensor isn't initialized by default + * @param shape array containing N integers, sizes of dimensions + * @return + */ + explicit Tensor(std::initializer_list const &shape) + : storage_(shape) {} + + /** + * Constructor that accepts an initializer list of shape and create a + * Tensor with that shape and filling it with value. For example, + * given shape = {2,3,4,5,6}, value = 0 tensor will be of size 2x3x4x5x6 + * filled with zeros + * @param shape shape array containing N integers, sizes of dimensions + * @param value value to fill + */ + explicit Tensor(std::initializer_list const &shape, U value) + : storage_(shape, value) {} + + /** + * + * @param T + * @return + */ + Tensor &operator=(const Tensor &T) { + storage_ = T.storage_; + return *this; + } + +// ~Tensor() = default; + +// TODO(Randl): implement copy and move constructors +#if 0 + // TODO(Randl) :deep copy + Tensor(const Tensor&other) { + other.fromDevice(); + shape_ = other.shape_; + storage_pointer_ = other.storage_pointer_; + data_is_on_host_ = true; + data_dirty_ = true; + // device_data_ is intentionally left uninitialized. + } + // TODO(Randl): Move constructors for Tensor and TensorStorage + Tensor &operator = (const Tensor& other) {} + + Tensor(Tensor&& other) = default; // move ctor + Tensor &operator = (Tensor&&) = default; // move assign +#endif + + /** + * + * @return the tensor shape + */ + const auto shape() const { return storage_.shape(); } + + /** + * + * @return Tensor's number of dimensions + */ + const auto dim() const { return storage_.dimension(); } + + /** + * + * @return the total number of elements in Tensor + */ + const size_t size() const { return storage_.size(); } + + /** + * Access to indexes in tensor + * @param args indexes in tensor + * @return the value of a specified index in the tensor + */ + template + U &host_at(const Args... args) { + return storage_(args...); + } + + /** + * Constant access to indexes in tensor + * @param args indexes in tensor + * @return the value of a specified index in the tensor + */ + template + U host_at(const Args... args) const { + return storage_(args...); + } + + /** + * + * @return Iterator pointing to the beginning of Tensor + */ + auto host_begin() { return storage_.xbegin(); } + + const auto host_begin() const { return storage_.cxbegin(); } + + auto host_end() { return storage_.xend(); } + + const auto host_end() const { return storage_.cxend(); } + +// TODO(Randl) +/* +const auto host_flatten() const { + // fromDevice(); + return xt::broadcast(storage_, {size()}); +} + +auto host_data() { + // fromDevice(); + return xt::broadcast(storage_, {size()}); +}*/ +// TODO(ּּRandl): should we enable this again? +#if 0 + U* mutable_host_data() { + static_assert(!kConst, "Non-constant operation on constant Tensor"); + // fromDevice(); + // data_dirty_ = true; + return storage_pointer_->data(offset); + } +#endif + +#if defined(USE_OPENCL) || defined(USE_CUDA) + const void *device_data() const { + storage_ptr_->toDevice(); + return (*storage_ptr_->device_data_)(); + } + + void *mutable_device_data() { + static_assert(!kConst, "Non-constant operation on constant Tensor"); + storage_ptr_->toDevice(); + storage_ptr_->data_dirty_ = true; + return (*storage_ptr_->device_data_)(); + } +#endif + + /** + * Fill tensor with particular value + * @param value + * @return + */ + Tensor &fill(U value) { + // static_assert(!kConst, "Non-constant operation on constant Tensor"); + // data_is_on_host_ = true; + // data_dirty_ = true; + std::fill(storage_.xbegin(), storage_.xend(), value); + return *this; + } + + // TODO(Randl): checked version + /** + * Reshape tensor + * @param shape new shape + */ + void reshape(const std::vector &shape) { storage_.reshape(shape); } + + Tensor operator[](size_t index) { return Tensor(storage_[index]); } + + /** + * Returns new Tensor which has shared storage with current, but different + * shape + * + * Example: + * @code + * Tensor b({4,2}); + * a = b.subView({2,2,2}; + * @endcode + * b is Tensor of shape 4x2 and a is Tensor of shape 2x2x2. Changing a(0,0,0) + * will change b(0,0) too. + * @param new_shape + * @return + */ + /* + Tensor>> + subView(std::initializer_list const &new_shape) { + auto res = Tensor>>(xt::broadcast(storage_, storage_.shape())); + res.storage_.reshape(new_shape); + return res; + }*/ + + /** + * Returns view of current Tensor + * @tparam Values index type + */ + template + Tensor...>> subView( + std::initializer_list... lists) { + using SharedTensor = Tensor...>>; + return SharedTensor(storage_, + xt::range(*(lists.begin()), *(lists.begin() + 1))...); + } + + /* + // TODO: is needed? + bool isSubView() const { + return true; //std::is_same(Storage, xt::xview); + } +*/ + + /** + * Creates Tensor given the storage + * @tparam T + * @param storage + */ + template + explicit Tensor(T &storage, xt::xrange r1, Args... args) + : storage_(xt::view(storage, r1, args...)) {} + + template + friend inline std::ostream &operator<<(std::ostream &os, + const Tensor &tensor); + + private: + Storage storage_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/framework/tensor_utils.h b/tiny_dnn/core/framework/tensor_utils.h new file mode 100755 index 0000000000000..9a65d6cbc77b7 --- /dev/null +++ b/tiny_dnn/core/framework/tensor_utils.h @@ -0,0 +1,275 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include // std::fill, std::generate +#include // sqrt +#include +#include +#include // std::accumulate + +#include "tiny_dnn/xtensor/xio.hpp" + +#include "tiny_dnn/core/framework/tensor.h" + +namespace tiny_dnn { + +/** + * + * @tparam T + * @tparam S + * @param os + * @param tensor + * @return + */ +template +inline std::ostream &operator<<(std::ostream &os, const Tensor &tensor) { + os << tensor.storage_; + return os; +} + +// utilities for element-wise and tensor-scalar/scalar-tensor operations + +template +void binary_tensor_tensor_elementwise_operation(Tensor &dst, + const Tensor &src1, + const Tensor &src2, + F f) { + if (src1.shape() != src2.shape()) { + throw nn_error("Tensor must have same shape"); + } + + dst.reshape(src1.shape()); + + auto pdst = dst.host_begin(); + auto psrc1 = src1.host_begin(); + auto psrc2 = src2.host_begin(); + + for_i(true, dst.size(), [pdst, psrc1, psrc2, &f](size_t i) { + *std::next(pdst, i) = f(*std::next(psrc1, i), *std::next(psrc2, i)); + }); +} + +template +void unary_tensor_elementwise_operation(Tensor &dst, + const Tensor &src, + F f) { + dst.reshape(src.shape()); + + auto pdst = dst.host_begin(); + auto psrc = src.host_begin(); + + for_i(true, dst.size(), [pdst, psrc, &f](size_t i) { + *std::next(pdst, i) = f(*std::next(psrc, i)); + }); +} + +template +void binary_tensor_scalar_operation(Tensor &dst, + const Tensor &src1, + TS2 src2, + F f) { + dst.reshape(src1.shape()); + + auto pdst = dst.host_begin(); + auto psrc1 = src1.host_begin(); + + for_i(true, dst.size(), [pdst, psrc1, src2, &f](size_t i) { + *std::next(pdst, i) = f(*std::next(psrc1, i), src2); + }); +} + +template +void binary_scalar_tensor_operation(Tensor &dst, + TS1 src1, + const Tensor &src2, + F f) { + dst.reshape(src2.shape()); + + auto pdst = dst.host_begin(); + auto psrc2 = src2.host_begin(); + + for_i(true, dst.size(), [pdst, src1, psrc2, &f](size_t i) { + *std::next(pdst, i) = f(src1, *std::next(psrc2, i)); + }); +} + +// implementation of + +namespace details { + +template +auto plus(TS1 s1, TS2 s2) -> decltype(s1 + s2) { + return s1 + s2; +} + +template +auto minus(TS1 s1, TS2 s2) -> decltype(s1 - s2) { + return s1 - s2; +} + +template +auto multiplies(TS1 s1, TS2 s2) -> decltype(s1 * s2) { + return s1 * s2; +} + +template +auto divides_checked(TS1 s1, TS2 s2) -> decltype(s1 / s2) { + typedef decltype(s1 / s2) result_type; + return (s2 == result_type{}) ? std::numeric_limits::quiet_NaN() + : s1 / s2; +} + +template +auto divides_unchecked(TS1 s1, TS2 s2) -> decltype(s1 / s2) { + return s1 / s2; +} + +template +T sqrt_checked(T s1) { + return (s1 <= T{}) ? std::numeric_limits::quiet_NaN() : sqrt(s1); +} + +// do not inline - this function converts the std::exp +// overloadeds in a single templated function. +template +T exp(T s1) { + return std::exp(s1); +} + +} // namespace details + +template +void layer_add(Tensor &dst, TS1 src1, const Tensor &src2) { + binary_scalar_tensor_operation(dst, src1, src2, details::plus); +} + +template +void layer_add(Tensor &dst, const Tensor &src1, TS2 src2) { + binary_tensor_scalar_operation(dst, src1, src2, details::plus); +} + +template +void layer_add(Tensor &dst, + const Tensor &src1, + const Tensor &src2) { + binary_tensor_tensor_elementwise_operation(dst, src1, src2, + details::plus); +} + +template +void layer_sub(Tensor &dst, TS1 src1, const Tensor &src2) { + binary_scalar_tensor_operation(dst, src1, src2, details::minus); +} + +template +void layer_sub(Tensor &dst, const Tensor &src1, TS2 src2) { + binary_tensor_scalar_operation(dst, src1, src2, details::minus); +} + +template +void layer_sub(Tensor &dst, + const Tensor &src1, + const Tensor &src2) { + binary_tensor_tensor_elementwise_operation(dst, src1, src2, + details::minus); +} + +template +void layer_mul(Tensor &dst, TS1 src1, const Tensor &src2) { + binary_scalar_tensor_operation(dst, src1, src2, + details::multiplies); +} + +template +void layer_mul(Tensor &dst, const Tensor &src1, TS2 src2) { + binary_tensor_scalar_operation(dst, src1, src2, + details::multiplies); +} + +template +void layer_mul(Tensor &dst, + const Tensor &src1, + const Tensor &src2) { + binary_tensor_tensor_elementwise_operation(dst, src1, src2, + details::multiplies); +} + +template +void layer_div(Tensor &dst, TS1 src1, const Tensor &src2) { + binary_scalar_tensor_operation(dst, src1, src2, + details::divides_checked); +} + +template +void layer_div(Tensor &dst, const Tensor &src1, TS2 src2) { + if (src2 == TS2(0.0)) { + dst.reshape(src1.shape()); + dst.fill(std::numeric_limits::quiet_NaN()); + } else { + binary_tensor_scalar_operation(dst, src1, src2, + details::divides_unchecked); + } +} + +template +void layer_div(Tensor &dst, + const Tensor &src1, + const Tensor &src2) { + binary_tensor_tensor_elementwise_operation( + dst, src1, src2, details::divides_checked); +} + +template +void layer_sqrt(Tensor &dst, const Tensor &src1) { + return unary_tensor_elementwise_operation(dst, src1, + details::sqrt_checked); +} + +template +void layer_exp(Tensor &dst, const Tensor &src1) { + return unary_tensor_elementwise_operation(dst, src1, details::exp); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/avx_deconv2d_back_kernel.h b/tiny_dnn/core/kernels/avx_deconv2d_back_kernel.h new file mode 100755 index 0000000000000..c12170e0eeca1 --- /dev/null +++ b/tiny_dnn/core/kernels/avx_deconv2d_back_kernel.h @@ -0,0 +1,30 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/params/deconv_params.h" + +namespace tiny_dnn { +namespace core { +namespace kernels { + +inline void avx_deconv2d_back_kernel(const deconv_params ¶ms, + const tensor_t &prev_out, + const vec_t &W, + tensor_t &dW, + tensor_t &db, + tensor_t &curr_delta, + tensor_t *prev_delta) { + // fallback to non-avx version + tiny_deconv2d_back_kernel(params, prev_out, W, dW, db, curr_delta, + prev_delta); +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/avx_deconv2d_kernel.h b/tiny_dnn/core/kernels/avx_deconv2d_kernel.h new file mode 100755 index 0000000000000..a14b0cf58a2e2 --- /dev/null +++ b/tiny_dnn/core/kernels/avx_deconv2d_kernel.h @@ -0,0 +1,29 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/kernels/tiny_deconv2d_kernel.h" +#include "tiny_dnn/core/params/deconv_params.h" + +namespace tiny_dnn { +namespace core { +namespace kernels { + +inline void avx_deconv2d_kernel(const deconv_params ¶ms, + const tensor_t &in, + const vec_t &W, + const vec_t &bias, + tensor_t &out, + const bool layer_parallelize) { + // fallback to non-avx version + tiny_deconv2d_kernel(params, in, W, bias, out, layer_parallelize); +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/avx_kernel_common.h b/tiny_dnn/core/kernels/avx_kernel_common.h new file mode 100755 index 0000000000000..15c2871ac4a25 --- /dev/null +++ b/tiny_dnn/core/kernels/avx_kernel_common.h @@ -0,0 +1,442 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#ifndef CNN_USE_AVX +#error Advanced Vector Extensions required. +#endif + +#ifndef _mm256_set_m128 +#define _mm256_set_m128(va, vb) \ + _mm256_insertf128_ps(_mm256_castps128_ps256(vb), va, 1) +#endif + +#ifdef CNN_USE_AVX2 +inline __m256 madd256_ps(__m256 a, __m256 b, __m256 c) { + return _mm256_fmadd_ps(a, b, c); +} +inline __m128 madd128_ps(__m128 a, __m128 b, __m128 c) { + return _mm_fmadd_ps(a, b, c); +} +inline __m128 madd128_ss(__m128 a, __m128 b, __m128 c) { + return _mm_fmadd_ss(a, b, c); +} +inline __m256d madd256_pd(__m256d a, __m256d b, __m256d c) { + return _mm256_fmadd_pd(a, b, c); +} +inline __m128d madd128_pd(__m128d a, __m128d b, __m128d c) { + return _mm_fmadd_pd(a, b, c); +} +inline __m128d madd128_sd(__m128d a, __m128d b, __m128d c) { + return _mm_fmadd_sd(a, b, c); +} +#else +inline __m256 madd256_ps(__m256 a, __m256 b, __m256 c) { + return _mm256_add_ps(_mm256_mul_ps(a, b), c); +} +inline __m128 madd128_ps(__m128 a, __m128 b, __m128 c) { + return _mm_add_ps(_mm_mul_ps(a, b), c); +} +inline __m128 madd128_ss(__m128 a, __m128 b, __m128 c) { + return _mm_add_ss(_mm_mul_ss(a, b), c); +} +inline __m256d madd256_pd(__m256d a, __m256d b, __m256d c) { + return _mm256_add_pd(_mm256_mul_pd(a, b), c); +} +inline __m128d madd128_pd(__m128d a, __m128d b, __m128d c) { + return _mm_add_pd(_mm_mul_pd(a, b), c); +} +inline __m128d madd128_sd(__m128d a, __m128d b, __m128d c) { + return _mm_add_sd(_mm_mul_sd(a, b), c); +} +#endif + +// Horizontally add elements of __m256 type argument (sadly, _mm256_hadd_ps +// isn't good enough) +// http://stackoverflow.com/a/13222410/4699324 +// x = ( x7, x6, x5, x4, x3, x2, x1, x0 ) +// use _mm_cvtss_f32 if you need a float result instead of a __m128 +inline __m128 hsum256_ps(__m256 x) { + // hiQuad = ( x7, x6, x5, x4 ) + const __m128 hiQuad = _mm256_extractf128_ps(x, 1); + // loQuad = ( x3, x2, x1, x0 ) + const __m128 loQuad = _mm256_castps256_ps128(x); + // sumQuad = ( x3+x7, x2+x6, x1+x5, x0+x4 ) + const __m128 sumQuad = _mm_add_ps(loQuad, hiQuad); + // loDual = ( -, -, x1+x5, x0+x4 ) + const __m128 loDual = sumQuad; + // hiDual = ( -, -, x3+x7, x2+x6 ) + const __m128 hiDual = _mm_movehl_ps(sumQuad, sumQuad); + // sumDual = ( -, -, x1+x3 + x5+x7, x0+x2 + x4+x6 ) + const __m128 sumDual = _mm_add_ps(loDual, hiDual); + // lo = ( -, -, -, x0+x2 + x4+x6 ) + const __m128 lo = sumDual; + // hi = ( -, -, -, x1+x3 + x5+x7 ) + const __m128 hi = _mm_shuffle_ps(sumDual, sumDual, 0x1); + // sum = ( -, -, -, x0+x1+x2+x3 + x4+x5+x6+x7 ) + const __m128 sum = _mm_add_ss(lo, hi); + return sum; +} + +// Horizontally add elements of each __m256 type arguments at once +inline __m128 hsum2x256_ps(__m256 a, __m256 b) { + // (b3, b2, b1, b0, a3, a2, a1, a0) + __m256 x = _mm256_permute2f128_ps(a, b, 0x20); + // (b7, b6, b5, b4, a7, a6, a5, a4) + __m256 y = _mm256_permute2f128_ps(a, b, 0x31); + // (b3+b7, b2+b6, b1+b5, b0+b4, a3+a7, a2+a6, a1+a5, a0+a4) + x = _mm256_add_ps(x, y); + // (-, -, b3+b7, b2+b6, -, -, a3+a7, a2+a6) + y = _mm256_permute_ps(x, _MM_SHUFFLE(3, 2, 3, 2)); + // (-, -, b1+b5+b3+b7, b0+b4+b2+b6, -, -, a1+a5+a3+a7, a0+a4+a2+a6) + x = _mm256_add_ps(x, y); + // (-, -, -, b1+b5+b3+b7, -, -, -, a1+a5+a3+a7) + y = _mm256_permute_ps(x, _MM_SHUFFLE(1, 1, 1, 1)); + // (-, -, -, b1+b5+b3+b7+b0+b4+b2+b6, -, -, -, a1+a5+a3+a7+a0+a4+a2+a6) + x = _mm256_add_ps(x, y); + // (-, -, -, b1+b5+b3+b7+b0+b4+b2+b6) + __m128 upper = _mm256_extractf128_ps(x, 1); + // (-, -, -, -, -, -, b1+b5+b3+b7+b0+b4+b2+b6, a1+a5+a3+a7+a0+a4+a2+a6) + __m128 ret = _mm_unpacklo_ps(_mm256_castps256_ps128(x), upper); + return ret; +} + +// Horizontally add elements of each __m256 type arguments at once +// in a : ( a7, a6, a5, a4, a3, a2, a1, a0 ) +// in b : ( b7, b6, b5, b4, b3, b2, b1, b0 ) +// in c : ( c7, c6, c5, c4, c3, c2, c1, c0 ) +// in d : ( d7, d6, d5, d4, d3, d2, d1, d0 ) +// out : ( dsum, csum, bsum, asum ) +inline __m128 hsum4x256_ps(const __m256 &a, + const __m256 &b, + const __m256 &c, + const __m256 &d) { + __m256 t0, t1, t2, t3; + __m256 tt0, tt1; + t0 = _mm256_unpacklo_ps(a, b); // b5,a5,b4,a4, b1,a1,b0,a0 + t1 = _mm256_unpackhi_ps(a, b); // b7,a7,b6,a6, b3,a3,b2,a2 + t2 = _mm256_unpacklo_ps(c, d); // d5,c5,d4,c4, d1,c1,d0,c0 + t3 = _mm256_unpackhi_ps(c, d); // d7,c7,d6,c6, d3,c3,d2,c2 + + tt0 = _mm256_add_ps(t0, t1); // b57,a57,b46,a46, b13,a13,b02,a02 + tt1 = _mm256_add_ps(t2, t3); // d57,c57,d46,c46, d13,c13,d02,c02 + + t0 = _mm256_shuffle_ps( + tt0, tt1, _MM_SHUFFLE(1, 0, 1, 0)); // d46,c46,b46,a46, d02,c02,b02,a02 + t1 = _mm256_shuffle_ps( + tt0, tt1, _MM_SHUFFLE(3, 2, 3, 2)); // d57,c57,b57,a57, d13,c13,b13,a13 + + tt0 = + _mm256_add_ps(t0, t1); // d4567,c4567,b4567,a4567, d0123,c0123,b0123,a0123 + __m128 upper = _mm256_extractf128_ps(tt0, 1); + + return _mm_add_ps(_mm256_castps256_ps128(tt0), upper); +} + +inline __m256 hsum8x256_ps(const __m256 &a, + const __m256 &b, + const __m256 &c, + const __m256 &d, + const __m256 &e, + const __m256 &f, + const __m256 &g, + const __m256 &h) { + __m256 t0, t1, t2, t3, t4, t5, t6, t7; + __m256 tt0, tt1, tt2, tt3; + t0 = _mm256_unpacklo_ps(a, b); // b5,a5,b4,a4, b1,a1,b0,a0 + t1 = _mm256_unpackhi_ps(a, b); // b7,a7,b6,a6, b3,a3,b2,a2 + t2 = _mm256_unpacklo_ps(c, d); // d5,c5,d4,c4, d1,c1,d0,c0 + t3 = _mm256_unpackhi_ps(c, d); // d7,c7,d6,c6, d3,c3,d2,c2 + t4 = _mm256_unpacklo_ps(e, f); // f5,e5,f4,e4, f1,e1,f0,e0 + t5 = _mm256_unpackhi_ps(e, f); // f7,e7,f6,e6, f3,e3,f2,e2 + t6 = _mm256_unpacklo_ps(g, h); // h5,g5,h4,g4, h1,g1,h0,g0 + t7 = _mm256_unpackhi_ps(g, h); // h7,g7,h6,g6, h3,g3,h2,g2 + + tt0 = _mm256_add_ps(t0, t1); // b57,a57,b46,a46, b13,a13,b02,a02 + tt1 = _mm256_add_ps(t2, t3); // d57,c57,d46,c46, d13,c13,d02,c02 + tt2 = _mm256_add_ps(t4, t5); // f57,e57,f46,e46, f13,e13,f02,e02 + tt3 = _mm256_add_ps(t6, t7); // h57,g57,h46,g46, h13,g13,h02,g02 + + t0 = _mm256_shuffle_ps( + tt0, tt1, _MM_SHUFFLE(1, 0, 1, 0)); // d46,c46,b46,a46, d02,c02,b02,a02 + t1 = _mm256_shuffle_ps( + tt0, tt1, _MM_SHUFFLE(3, 2, 3, 2)); // d57,c57,b57,a57, d13,c13,b13,a13 + t2 = _mm256_shuffle_ps( + tt2, tt3, _MM_SHUFFLE(1, 0, 1, 0)); // h46,g46,f46,e46, h02,g02,f02,e02 + t3 = _mm256_shuffle_ps( + tt2, tt3, _MM_SHUFFLE(3, 2, 3, 2)); // h57,g57,f57,e57, h13,g13,f13,e13 + + tt0 = + _mm256_add_ps(t0, t1); // d4567,c4567,b4567,a4567, d0123,c0123,b0123,a0123 + tt1 = + _mm256_add_ps(t2, t3); // h4567,g4567,f4567,e4567, h0123,g0123,f0123,e0123 + + t0 = _mm256_permute2f128_ps( + tt0, tt1, 0x20); // h0123,g0123,f0123,e0123, d0123,c0123,b0123,a0123 + t1 = _mm256_permute2f128_ps( + tt0, tt1, 0x31); // h4567,g4567,f4567,e4567, d4567,c4567,b4567,a4567 + + return _mm256_add_ps(t0, t1); +} + +inline __m128d hsum256_pd(__m256d x) { + // hiDual = ( x3, x2 ) + const __m128d hiDual = _mm256_extractf128_pd(x, 1); + // loDual = ( x1, x0 ) + const __m128d loDual = _mm256_castpd256_pd128(x); + // sumQuad = ( x2+x3, x0+x1 ) + const __m128d sumDual = _mm_add_pd(loDual, hiDual); + // sum = ( 0, x0+x1+x2+x3 ); + const __m128d sum = _mm_hadd_pd(sumDual, _mm_setzero_pd()); + return sum; +} + +template +struct foobar : std::false_type {}; + +// Byte Shift YMM Register Across 128-bit Lanes + +#ifdef CNN_USE_AVX2 + +template +struct Range; + +template > +struct m256_shift_left_impl {}; + +template +struct m256_shift_left_impl> { + static __m256 doit(__m256i a) { return a; } +}; + +template +struct m256_shift_left_impl> { + static __m256 doit(__m256 a) { + __m256 mask = _mm256_permute2f128_ps(a, a, 0x08); + return _mm256_castsi256_ps(_mm256_alignr_epi8( + _mm256_castps_si256(a), _mm256_castps_si256(mask), 16 - N)); + } +}; + +template +inline __m256 leftShift(__m256 a) { + return m256_shift_left_impl::doit(a); +} + +#else // #ifdef CNN_USE_AVX2 + +// limitation : shift amount is immediate and is multiples of 4 + +template +inline __m256 leftShift(__m256 a) { + static_assert(foobar::value, "unsupported shift amount"); + return a; +} + +// http://stackoverflow.com/q/19516585 +template <> +inline __m256 leftShift<4>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x6, x5, x4, x7, x2, x1, x0, x3) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(2, 1, 0, 3)); + // t1 = (x2, x1, x0, x3, 0, 0, 0, 0) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x08); + // y = (x6, x5, x4, x3, x2, x1, x0, 0) + __m256 y = _mm256_blend_ps(t0, t1, 0x11); + return y; +} + +// http://stackoverflow.com/q/19516585 +template <> +inline __m256 leftShift<8>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x5, x4, x7, x6, x1, x0, x3, x2) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(1, 0, 3, 2)); + // t1 = (x1, x0, x3, x2, 0, 0, 0, 0) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x08); + // y = (x5, x4, x3, x2, x1, x0, 0, 0) + __m256 y = _mm256_blend_ps(t0, t1, 0x33 /* 0b00110011 */); + return y; +} + +template <> +inline __m256 leftShift<12>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x4, x7, x6, x5, x0, x3, x2, x1) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(0, 3, 2, 1)); + // t1 = (x0, x3, x2, x1, 0, 0, 0, 0) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x08); + // y = (x4, x3, x2, x1, x0, 0, 0, 0) + __m256 y = _mm256_blend_ps(t0, t1, 0x77 /* 0b01110111 */); + return y; +} + +template <> +inline __m256 leftShift<16>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // y = (x3, x2, x1, x0, 0, 0, 0, 0) + __m256 y = _mm256_permute2f128_ps(x, x, 0x08); + return y; +} + +template <> +inline __m256 leftShift<20>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x6, x5, x4, x7, x2, x1, x0, x3) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(2, 1, 0, 3)); + // t1 = (x2, x1, x0, x3, 0, 0, 0, 0) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x08); + // y = (x2, x1, x0, 0, 0, 0, 0, 0) + __m256 y = _mm256_blend_ps(t1, _mm256_setzero_ps(), 0x10 /* 0b00010000 */); + return y; +} + +template <> +inline __m256 leftShift<24>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x5, x4, x7, x6, x1, x0, x3, x2) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(1, 0, 3, 2)); + // t1 = (x1, x0, x3, x2, 0, 0, 0, 0) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x08); + // y = (x1, x0, 0, 0, 0, 0, 0, 0) + __m256 y = _mm256_blend_ps(_mm256_setzero_ps(), t1, 0xC0 /* 0b11000000 */); + return y; +} + +template <> +inline __m256 leftShift<28>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x4, x7, x6, x5, x0, x3, x2, x1) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(0, 3, 2, 1)); + // t1 = (x0, x3, x2, x1, 0, 0, 0, 0) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x08); + // y = (x0, 0, 0, 0, 0, 0, 0, 0) + __m256 y = _mm256_blend_ps(_mm256_setzero_ps(), t1, 0x80 /* 0b10000000 */); + return y; +} + +#endif // #ifdef CNN_USE_AVX2 + +template +inline __m256 rightShift(__m256 a) { + static_assert(foobar::value, "unsupported shift amount"); + return a; +} + +// http://stackoverflow.com/a/19532415/4699324 +template <> +inline __m256 rightShift<4>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x4, x7, x6, x5, x0, x3, x2, x1) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(0, 3, 2, 1)); + // t1 = (0, 0, 0, 0, x4, x7, x6, x5) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x81); + + // ( -, x7, x6, x5, -, x3, x2, x1) + // ( 0, -, -, -, x4, -, -, -) + // y = ( 0, x7, x6, x5, x4, x3, x2, x1) + __m256 y = _mm256_blend_ps(t0, t1, 0x88 /* 0b10001000 */); + return y; +} + +template <> +inline __m256 rightShift<8>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x5, x4, x7, x6, x1, x0, x3, x2) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(1, 0, 3, 2)); + // t1 = (0, 0, 0, 0, x5, x4, x7, x6) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x81); + + // ( -, -, x7, x6, -, -, x3, x2) + // ( 0, 0, -, -, x5, x4, -, -) + // y = ( 0, 0, x7, x6, x5, x4, x3, x2) + __m256 y = _mm256_blend_ps(t0, t1, 0xCC /* 0b11001100 */); + return y; +} + +template <> +inline __m256 rightShift<12>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x6, x5, x4, x7, x2, x1, x0, x3) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(2, 1, 0, 3)); + // t1 = ( 0, 0, 0, 0, x6, x5, x4, x7) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x81); + + // ( -, -, -, x7, -, -, -, x3) + // ( 0, 0, 0, -, x6, x5, x4, -) + // y = ( 0, 0, 0, x7, x6, x5, x4, x3) + __m256 y = _mm256_blend_ps(t0, t1, 0xEE /* 0b11101110 */); + return y; +} + +template <> +inline __m256 rightShift<16>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // y = ( 0, 0, 0, 0, x7, x6, x5, x4) + __m256 y = _mm256_permute2f128_ps(x, x, 0x81); + return y; +} + +template <> +inline __m256 rightShift<20>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x4, x7, x6, x5, x0, x3, x2, x1) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(0, 3, 2, 1)); + // t1 = ( 0, 0, 0, 0, x4, x7, x6, x5) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x81); + + // ( -, -, -, -, -, x7, x6, x5) + // ( 0, 0, 0, 0, 0, -, -, -) + // y = ( 0, 0, 0, 0, 0, x7, x6, x5) + __m256 y = _mm256_blend_ps(t1, _mm256_setzero_ps(), 0xF8 /* 0b11111000 */); + return y; +} + +template <> +inline __m256 rightShift<24>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x5, x4, x7, x6, x1, x0, x3, x2) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(1, 0, 3, 2)); + // t1 = ( 0, 0, 0, 0, x5, x4, x7, x6) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x81); + + // ( -, -, -, -, -, -, x7, x6) + // ( 0, 0, 0, 0, 0, 0, -, -) + // y = ( 0, 0, 0, 0, 0, 0, x7, x6) + __m256 y = _mm256_blend_ps(t1, _mm256_setzero_ps(), 0xFC /* 0b11111100 */); + return y; +} + +template <> +inline __m256 rightShift<28>(__m256 x) { + // x = (x7, x6, x5, x4, x3, x2, x1, x0) + + // t0 = (x6, x5, x4, x7, x2, x1, x0, x3) + __m256 t0 = _mm256_permute_ps(x, _MM_SHUFFLE(2, 1, 0, 3)); + // t1 = ( 0, 0, 0, 0, x6, x5, x4, x7) + __m256 t1 = _mm256_permute2f128_ps(t0, t0, 0x81); + + // ( -, -, -, -, -, -, -, x7) + // ( 0, 0, 0, 0, 0, 0, 0, -) + // y = ( 0, 0, 0, 0, 0, 0, 0, x7) + __m256 y = _mm256_blend_ps(t1, _mm256_setzero_ps(), 0xFE /* 0b11111110 */); + return y; +} diff --git a/tiny_dnn/core/kernels/conv2d_grad_op.h b/tiny_dnn/core/kernels/conv2d_grad_op.h new file mode 100755 index 0000000000000..1bae9cbcd8c90 --- /dev/null +++ b/tiny_dnn/core/kernels/conv2d_grad_op.h @@ -0,0 +1,53 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" + +#include "tiny_dnn/core/kernels/conv2d_grad_op_avx.h" +#include "tiny_dnn/core/kernels/conv2d_op_internal.h" + +namespace tiny_dnn { + +class Conv2dGradOp : public core::OpKernel { + public: + explicit Conv2dGradOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto params = OpKernel::params_->conv(); + + // incoming/outcoming data + const tensor_t &prev_out = context.input(0); + const tensor_t &W = context.input(1); + tensor_t &dW = context.input_grad(1); + tensor_t &db = context.input_grad(2); + tensor_t &prev_delta = context.input_grad(0); + tensor_t &curr_delta = context.output_grad(0); + + // initalize outputs + fill_tensor(prev_delta, float_t{0}); + + // call convolution algorithm depending + // on the selected engine type + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::internal) { + kernels::conv2d_op_internal(prev_out, W[0], dW, db, curr_delta, + prev_delta, params, context.parallelize()); + } else if (engine == core::backend_t::avx) { + kernels::conv2d_grad_op_avx(prev_out, W[0], dW, db, curr_delta, + prev_delta, params, context.parallelize()); + } else { + throw nn_error("Not supported engine: " + to_string(engine)); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/conv2d_grad_op_avx.h b/tiny_dnn/core/kernels/conv2d_grad_op_avx.h new file mode 100755 index 0000000000000..f3b24882eb8f5 --- /dev/null +++ b/tiny_dnn/core/kernels/conv2d_grad_op_avx.h @@ -0,0 +1,901 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include "tiny_dnn/core/kernels/conv2d_op_internal.h" +#include "tiny_dnn/core/params/conv_params.h" + +#ifdef CNN_USE_AVX +#include "tiny_dnn/core/kernels/avx_kernel_common.h" +#endif + +namespace tiny_dnn { +namespace kernels { + +#ifdef CNN_USE_AVX + +// float ver +template +inline void accumulate_db(const index3d &out, + const std::vector &curr_delta, + std::vector &db) { + if (out.width_ == 1 && out.height_ == 1) { + size_t nblocks = out.depth_ / 8; + for (size_t i = 0; i < nblocks; ++i) { + _mm256_storeu_ps(&db[i * 8], + _mm256_add_ps(_mm256_loadu_ps(&db[i * 8]), + _mm256_loadu_ps(&curr_delta[i * 8]))); + } + for (size_t outc = nblocks * 8; outc < out.depth_; ++outc) { + db[outc] += curr_delta[outc]; + } + } else { + auto area = out.area(); + size_t n8 = area / 64; + size_t n4 = (area % 64) / 32; + size_t n2 = (area % 32) / 16; + size_t n1 = (area % 16) / 8; + size_t remainder = area & 7; + // prepare load-mask beforehand + static const int32_t masks[] = { + -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, + }; + __m256i mask = _mm256_loadu_si256((const __m256i *)(masks + 8 - remainder)); + for (size_t outc = 0; outc < out.depth_; ++outc) { + size_t idx = out.get_index(0, 0, outc); + const float *delta = &curr_delta[idx]; + __m256 sum0 = _mm256_setzero_ps(); + __m256 sum1 = _mm256_setzero_ps(); + __m256 sum2 = _mm256_setzero_ps(); + __m256 sum3 = _mm256_setzero_ps(); + __m256 sum4 = _mm256_setzero_ps(); + __m256 sum5 = _mm256_setzero_ps(); + __m256 sum6 = _mm256_setzero_ps(); + __m256 sum7 = _mm256_setzero_ps(); + for (size_t i = 0; i < n8; ++i) { + sum0 = _mm256_add_ps(sum0, _mm256_loadu_ps(delta + i * 64 + 0)); + sum1 = _mm256_add_ps(sum1, _mm256_loadu_ps(delta + i * 64 + 8)); + sum2 = _mm256_add_ps(sum2, _mm256_loadu_ps(delta + i * 64 + 16)); + sum3 = _mm256_add_ps(sum3, _mm256_loadu_ps(delta + i * 64 + 24)); + sum4 = _mm256_add_ps(sum4, _mm256_loadu_ps(delta + i * 64 + 32)); + sum5 = _mm256_add_ps(sum5, _mm256_loadu_ps(delta + i * 64 + 40)); + sum6 = _mm256_add_ps(sum6, _mm256_loadu_ps(delta + i * 64 + 48)); + sum7 = _mm256_add_ps(sum7, _mm256_loadu_ps(delta + i * 64 + 56)); + } + delta += n8 * 64; + if (n4) { + sum0 = _mm256_add_ps(sum0, _mm256_loadu_ps(delta + 0)); + sum1 = _mm256_add_ps(sum1, _mm256_loadu_ps(delta + 8)); + sum2 = _mm256_add_ps(sum2, _mm256_loadu_ps(delta + 16)); + sum3 = _mm256_add_ps(sum3, _mm256_loadu_ps(delta + 24)); + delta += 32; + } + if (n2) { + sum4 = _mm256_add_ps(sum4, _mm256_loadu_ps(delta + 0)); + sum5 = _mm256_add_ps(sum5, _mm256_loadu_ps(delta + 8)); + delta += 16; + } + if (n1) { + sum6 = _mm256_add_ps(sum6, _mm256_loadu_ps(delta)); + delta += 8; + } + sum0 = _mm256_add_ps(sum0, sum1); + sum2 = _mm256_add_ps(sum2, sum3); + sum4 = _mm256_add_ps(sum4, sum5); + sum6 = _mm256_add_ps(sum6, sum7); + sum1 = _mm256_maskload_ps(delta, mask); + sum0 = _mm256_add_ps(sum0, sum2); + sum4 = _mm256_add_ps(sum4, sum6); + sum0 = _mm256_add_ps(sum0, sum4); + sum0 = _mm256_add_ps(sum0, sum1); + db[outc] += _mm_cvtss_f32(hsum256_ps(sum0)); + } + } +} // accumulate_db + +// float ver +template +inline void accumulate_dw(const core::conv_params ¶ms, + const std::vector &prev_out, + const std::vector &curr_delta, + std::vector &dW, + std::vector &db) { + CNN_UNREFERENCED_PARAMETER(db); + auto &in = params.in; + auto &out = params.out; + auto &in_padded = params.in_padded; + auto &tbl = params.tbl; + auto w_stride = params.w_stride; + const size_t in_padded_area = in_padded.area(); + static const __m256i imask = _mm256_setr_epi32(-1, -1, -1, -1, -1, 0, 0, 0); + + if (out.width_ == 1 && out.height_ == 1) { + const float *pprev_out = &prev_out[0]; + alignas(32) float floats[28]; + for (size_t inc = 0; inc < in.depth_; ++inc, pprev_out += in_padded_area) { + size_t in_padded_width = in_padded.width_; + _mm256_store_ps(&floats[0], + _mm256_loadu_ps(pprev_out + in_padded_width * 0)); + _mm256_storeu_ps(&floats[5], + _mm256_loadu_ps(pprev_out + in_padded_width * 1)); + _mm256_storeu_ps(&floats[10], + _mm256_loadu_ps(pprev_out + in_padded_width * 2)); + _mm256_storeu_ps(&floats[15], + _mm256_loadu_ps(pprev_out + in_padded_width * 3)); + _mm256_storeu_ps(&floats[20], _mm256_maskload_ps( + pprev_out + in_padded_width * 4, imask)); + __m256 prevos0 = _mm256_load_ps(&floats[0]); + __m256 prevos1 = _mm256_load_ps(&floats[8]); + __m256 prevos2 = _mm256_load_ps(&floats[16]); + __m128 prevos3 = _mm_load_ss(&floats[24]); + size_t widx = 25 * inc; + size_t widx_delta = 25 * in.depth_; + float *pdW = &dW[widx]; + for (size_t outc = 0; outc < out.depth_; outc++, pdW += widx_delta) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + __m256 delta = _mm256_broadcast_ss(&curr_delta[outc]); + __m256 w0 = _mm256_loadu_ps(pdW + 0); + __m256 w1 = _mm256_loadu_ps(pdW + 8); + __m256 w2 = _mm256_loadu_ps(pdW + 16); + __m128 w3 = _mm_load_ss(pdW + 24); + w0 = madd256_ps(prevos0, delta, w0); + w1 = madd256_ps(prevos1, delta, w1); + w2 = madd256_ps(prevos2, delta, w2); + w3 = madd128_ss(prevos3, _mm256_castps256_ps128(delta), w3); + _mm256_storeu_ps(pdW + 0, w0); + _mm256_storeu_ps(pdW + 8, w1); + _mm256_storeu_ps(pdW + 16, w2); + _mm_store_ss(pdW + 24, w3); + } + } + } else { + // prepare load-mask beforehand + const size_t nblocks = out.width_ >> 3; + static const int32_t masks[] = { + -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, + }; + const size_t remainder = out.width_ & 7; + __m256i mask = _mm256_loadu_si256((const __m256i *)(masks + 8 - remainder)); + auto &weight = params.weight; + size_t prevo_delta = in_padded.width_ * params.h_stride; + const size_t out_width = out.width_; + const size_t out_height = out.height_; + assert(1 < out_width); + assert(1 < out_height); + __m256 sum0, sum1, sum2, sum3, sum4; + if (w_stride > 1) { + for (size_t inc = 0; inc < in.depth_; ++inc) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + const float *delta = &curr_delta[out.get_index(0, 0, outc)]; + if (!tbl.is_connected(outc, inc)) { + continue; + } + size_t widx = weight.get_index(0, 0, in.depth_ * outc + inc); + // weight.height_ + for (size_t wy = 0; wy < 5; ++wy) { + // weight.width_ + for (size_t wx = 0; wx < 5; ++wx, ++widx) { + size_t prev_out_idx = in_padded.get_index(wx, wy, inc); + const float *prevo = &prev_out[prev_out_idx]; + float_t dst{0}; + for (size_t y = 0, prevo_idx = 0, delta_idx = 0; y < out_height; + ++y, prevo_idx += prevo_delta, delta_idx += out_width) { + for (size_t x = 0; x < out_width; ++x) { + dst += prevo[prevo_idx + x * params.w_stride] * + delta[delta_idx + x]; + } + } + dW[widx] += dst; + } // for wx + } // for wy + } // for outc + } // for inc + } else if (nblocks == 1 && remainder != 0) { + for (size_t inc = 0; inc < in.depth_; ++inc) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + const float *delta = &curr_delta[out.get_index(0, 0, outc)]; + size_t widx = weight.get_index(0, 0, in.depth_ * outc + inc); + float *pdw = &dW[widx]; + // weight.height_ + for (size_t wy = 0; wy < 5; ++wy) { + size_t prev_out_idx = in_padded.get_index(0, wy, inc); + const float *pa = &prev_out[prev_out_idx]; + const float *pb = delta; + // y = 0 + sum0 = sum1 = sum2 = sum3 = sum4 = _mm256_setzero_ps(); + for (size_t y = 0; y < out_height; ++y) { + // vectorize::dot + __m256 a0 = _mm256_loadu_ps(pa + 0); + __m256 a1 = _mm256_loadu_ps(pa + 1); + __m256 a2 = _mm256_loadu_ps(pa + 2); + __m256 a3 = _mm256_loadu_ps(pa + 3); + __m256 a4 = _mm256_loadu_ps(pa + 4); + __m256 b = _mm256_loadu_ps(pb); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + a0 = _mm256_maskload_ps(pa + 010, mask); + a1 = _mm256_maskload_ps(pa + 011, mask); + a2 = _mm256_maskload_ps(pa + 012, mask); + a3 = _mm256_maskload_ps(pa + 013, mask); + a4 = _mm256_maskload_ps(pa + 014, mask); + b = _mm256_maskload_ps(pb + 010, mask); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + pa += prevo_delta; + pb += out_width; + } + _mm_storeu_ps(pdw + wy * 5, + _mm_add_ps(_mm_loadu_ps(pdw + wy * 5), + hsum4x256_ps(sum0, sum1, sum2, sum3))); + _mm_store_ss( + pdw + wy * 5 + 4, + _mm_add_ss(_mm_load_ss(pdw + wy * 5 + 4), hsum256_ps(sum4))); + } // for wy + } // for outc + } // for inc + } else if (nblocks > 1 && remainder != 0) { + for (size_t inc = 0; inc < in.depth_; ++inc) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + const float *delta = &curr_delta[out.get_index(0, 0, outc)]; + size_t widx = weight.get_index(0, 0, in.depth_ * outc + inc); + float *pdw = &dW[widx]; + // weight.height_ + for (size_t wy = 0; wy < 5; ++wy) { + size_t prev_out_idx = in_padded.get_index(0, wy, inc); + const float *pa = &prev_out[prev_out_idx]; + const float *pb = delta; + sum0 = sum1 = sum2 = sum3 = sum4 = _mm256_setzero_ps(); + for (size_t y = 0; y < out_height; + ++y, pa += prevo_delta, pb += out_width) { + // vectorize::dot + __m256 a0 = _mm256_loadu_ps(pa + 0); + __m256 a1 = _mm256_loadu_ps(pa + 1); + __m256 a2 = _mm256_loadu_ps(pa + 2); + __m256 a3 = _mm256_loadu_ps(pa + 3); + __m256 a4 = _mm256_loadu_ps(pa + 4); + __m256 b = _mm256_loadu_ps(pb); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + a0 = _mm256_loadu_ps(pa + 010); + a1 = _mm256_loadu_ps(pa + 011); + a2 = _mm256_loadu_ps(pa + 012); + a3 = _mm256_loadu_ps(pa + 013); + a4 = _mm256_loadu_ps(pa + 014); + b = _mm256_loadu_ps(pb + 010); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + for (size_t i = 2; i < nblocks; ++i) { + a0 = _mm256_loadu_ps(pa + 8 * i + 0); + a1 = _mm256_loadu_ps(pa + 8 * i + 1); + a2 = _mm256_loadu_ps(pa + 8 * i + 2); + a3 = _mm256_loadu_ps(pa + 8 * i + 3); + a4 = _mm256_loadu_ps(pa + 8 * i + 4); + b = _mm256_loadu_ps(pb + 8 * i); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + } + a0 = _mm256_maskload_ps(pa + 8 * nblocks + 0, mask); + a1 = _mm256_maskload_ps(pa + 8 * nblocks + 1, mask); + a2 = _mm256_maskload_ps(pa + 8 * nblocks + 2, mask); + a3 = _mm256_maskload_ps(pa + 8 * nblocks + 3, mask); + a4 = _mm256_maskload_ps(pa + 8 * nblocks + 4, mask); + b = _mm256_maskload_ps(pb + 8 * nblocks, mask); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + } + _mm_storeu_ps(pdw + wy * 5, + _mm_add_ps(_mm_loadu_ps(pdw + wy * 5), + hsum4x256_ps(sum0, sum1, sum2, sum3))); + _mm_store_ss( + pdw + wy * 5 + 4, + _mm_add_ss(_mm_load_ss(pdw + wy * 5 + 4), hsum256_ps(sum4))); + } // for wy + } // for outc + } // for inc + } else if (nblocks == 0) { + assert(remainder != 0); + for (size_t inc = 0; inc < in.depth_; ++inc) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + const float *delta = &curr_delta[out.get_index(0, 0, outc)]; + size_t widx = weight.get_index(0, 0, in.depth_ * outc + inc); + float *pdw = &dW[widx]; + // weight.height_ + for (size_t wy = 0; wy < 5; ++wy) { + size_t prev_out_idx = in_padded.get_index(0, wy, inc); + const float *pa = &prev_out[prev_out_idx]; + const float *pb = delta; + // vectorize::dot + sum0 = sum1 = sum2 = sum3 = sum4 = _mm256_setzero_ps(); + for (size_t y = 0; y < out_height; ++y) { + // vectorize::dot + __m256 a0 = _mm256_maskload_ps(pa + 0, mask); + __m256 a1 = _mm256_maskload_ps(pa + 1, mask); + __m256 a2 = _mm256_maskload_ps(pa + 2, mask); + __m256 a3 = _mm256_maskload_ps(pa + 3, mask); + __m256 a4 = _mm256_maskload_ps(pa + 4, mask); + __m256 b = _mm256_maskload_ps(pb, mask); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + pa += prevo_delta; + pb += out_width; + } + _mm_storeu_ps(pdw + wy * 5, + _mm_add_ps(_mm_loadu_ps(pdw + wy * 5), + hsum4x256_ps(sum0, sum1, sum2, sum3))); + _mm_store_ss( + pdw + wy * 5 + 4, + _mm_add_ss(_mm_load_ss(pdw + wy * 5 + 4), hsum256_ps(sum4))); + } // for wy + } // for outc + } // for inc + } else if (nblocks == 1) { + assert(remainder == 0); + for (size_t inc = 0; inc < in.depth_; ++inc) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + const float *delta = &curr_delta[out.get_index(0, 0, outc)]; + size_t widx = weight.get_index(0, 0, in.depth_ * outc + inc); + float *pdw = &dW[widx]; + // weight.height_ + for (size_t wy = 0; wy < 5; ++wy) { + size_t prev_out_idx = in_padded.get_index(0, wy, inc); + const float *pa = &prev_out[prev_out_idx]; + const float *pb = delta; + // vectorize::dot + sum0 = sum1 = sum2 = sum3 = sum4 = _mm256_setzero_ps(); + for (size_t y = 0; y < out_height; ++y) { + // vectorize::dot + __m256 a0 = _mm256_loadu_ps(pa + 0); + __m256 a1 = _mm256_loadu_ps(pa + 1); + __m256 a2 = _mm256_loadu_ps(pa + 2); + __m256 a3 = _mm256_loadu_ps(pa + 3); + __m256 a4 = _mm256_loadu_ps(pa + 4); + __m256 b = _mm256_loadu_ps(pb); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + pa += prevo_delta; + pb += out_width; + } + _mm_storeu_ps(pdw + wy * 5, + _mm_add_ps(_mm_loadu_ps(pdw + wy * 5), + hsum4x256_ps(sum0, sum1, sum2, sum3))); + _mm_store_ss( + pdw + wy * 5 + 4, + _mm_add_ss(_mm_load_ss(pdw + wy * 5 + 4), hsum256_ps(sum4))); + } // for wy + } // for outc + } // for inc + } else { + assert(nblocks > 1); + assert(remainder == 0); + for (size_t inc = 0; inc < in.depth_; ++inc) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + const float *delta = &curr_delta[out.get_index(0, 0, outc)]; + size_t widx = weight.get_index(0, 0, in.depth_ * outc + inc); + float *pdw = &dW[widx]; + // weight.height_ + for (size_t wy = 0; wy < 5; ++wy) { + size_t prev_out_idx = in_padded.get_index(0, wy, inc); + const float *pa = &prev_out[prev_out_idx]; + const float *pb = delta; + // vectorize::dot + sum0 = sum1 = sum2 = sum3 = sum4 = _mm256_setzero_ps(); + for (size_t y = 0; y < out_height; ++y) { + // vectorize::dot + __m256 a0 = _mm256_loadu_ps(pa + 0); + __m256 a1 = _mm256_loadu_ps(pa + 1); + __m256 a2 = _mm256_loadu_ps(pa + 2); + __m256 a3 = _mm256_loadu_ps(pa + 3); + __m256 a4 = _mm256_loadu_ps(pa + 4); + __m256 b = _mm256_loadu_ps(pb); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + a0 = _mm256_loadu_ps(pa + 010); + a1 = _mm256_loadu_ps(pa + 011); + a2 = _mm256_loadu_ps(pa + 012); + a3 = _mm256_loadu_ps(pa + 013); + a4 = _mm256_loadu_ps(pa + 014); + b = _mm256_loadu_ps(pb + 010); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + for (size_t i = 2; i < nblocks; ++i) { + a0 = _mm256_loadu_ps(pa + 8 * i + 0); + a1 = _mm256_loadu_ps(pa + 8 * i + 1); + a2 = _mm256_loadu_ps(pa + 8 * i + 2); + a3 = _mm256_loadu_ps(pa + 8 * i + 3); + a4 = _mm256_loadu_ps(pa + 8 * i + 4); + b = _mm256_loadu_ps(pb + 8 * i); + sum0 = madd256_ps(a0, b, sum0); + sum1 = madd256_ps(a1, b, sum1); + sum2 = madd256_ps(a2, b, sum2); + sum3 = madd256_ps(a3, b, sum3); + sum4 = madd256_ps(a4, b, sum4); + } + pa += prevo_delta; + pb += out_width; + } + _mm_storeu_ps(pdw + wy * 5, + _mm_add_ps(_mm_loadu_ps(pdw + wy * 5), + hsum4x256_ps(sum0, sum1, sum2, sum3))); + _mm_store_ss( + pdw + wy * 5 + 4, + _mm_add_ss(_mm_load_ss(pdw + wy * 5 + 4), hsum256_ps(sum4))); + } // for wy + } // for outc + } // for inc + } + } // else +} // accumulate_dw + +// float ver +template +void avx_conv2d_5x5_back_kernel_one( + const core::conv_params ¶ms, + const std::vector &prev_out, + const std::vector &W, + std::vector &dW, + std::vector &db, + std::vector &curr_delta, + std::vector *prev_delta) { + auto &in = params.in; + auto &out = params.out; + auto &in_padded = params.in_padded; + auto &tbl = params.tbl; + auto w_stride = params.w_stride; + const size_t in_padded_area = in_padded.area(); + float *pdelta_dst_org = &(*prev_delta)[0]; + const size_t h_stride2 = params.h_stride * in_padded.width_; + static const __m256i imask = _mm256_setr_epi32(-1, -1, -1, -1, -1, 0, 0, 0); + static const __m256 mask = + _mm256_castsi256_ps(_mm256_setr_epi32(-1, -1, -1, -1, -1, 0, 0, 0)); + const size_t out_width = out.width_; + const size_t out_height = out.height_; + // propagate delta to previous layer + if (w_stride == 1 && out_width >= 4) { + const size_t nblocks = out_width / 4; + if (out_width % 4) { + for (size_t inc = 0; inc < in.depth_; + ++inc, pdelta_dst_org += in_padded_area) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + const float *pw = &W[25 * (in.depth_ * outc + inc)]; + const float *pdelta_src = &curr_delta[out.get_index(0, 0, outc)]; + float *pdelta_dst = pdelta_dst_org; + __m256 w0a = _mm256_and_ps(_mm256_loadu_ps(pw + 0), mask); + __m256 w1a = _mm256_and_ps(_mm256_loadu_ps(pw + 5), mask); + __m256 w2a = _mm256_and_ps(_mm256_loadu_ps(pw + 10), mask); + __m256 w3a = _mm256_and_ps(_mm256_loadu_ps(pw + 15), mask); + __m256 w4a = _mm256_maskload_ps(pw + 20, imask); + __m256 w0b = leftShift<4>(w0a); + __m256 w1b = leftShift<4>(w1a); + __m256 w2b = leftShift<4>(w2a); + __m256 w3b = leftShift<4>(w3a); + __m256 w4b = leftShift<4>(w4a); + __m256 w0c = leftShift<8>(w0a); + __m256 w1c = leftShift<8>(w1a); + __m256 w2c = leftShift<8>(w2a); + __m256 w3c = leftShift<8>(w3a); + __m256 w4c = leftShift<8>(w4a); + __m256 w0d = leftShift<12>(w0a); + __m256 w1d = leftShift<12>(w1a); + __m256 w2d = leftShift<12>(w2a); + __m256 w3d = leftShift<12>(w3a); + __m256 w4d = leftShift<12>(w4a); + for (size_t y = 0; y < out_height; + ++y, pdelta_src += out_width, pdelta_dst += h_stride2) { + float *delta_dst0 = pdelta_dst; + float *delta_dst1 = &pdelta_dst[in_padded.width_ * 1]; + float *delta_dst2 = &pdelta_dst[in_padded.width_ * 2]; + float *delta_dst3 = &pdelta_dst[in_padded.width_ * 3]; + float *delta_dst4 = &pdelta_dst[in_padded.width_ * 4]; + for (size_t n = 0; n < nblocks; ++n) { + __m256 delta_src = + _mm256_broadcast_ps((const __m128 *)(pdelta_src + n * 4)); + __m256 dst0 = _mm256_loadu_ps(delta_dst0 + 4 * n); + __m256 dst1 = _mm256_loadu_ps(delta_dst1 + 4 * n); + __m256 dst2 = _mm256_loadu_ps(delta_dst2 + 4 * n); + __m256 dst3 = _mm256_loadu_ps(delta_dst3 + 4 * n); + __m256 dst4 = _mm256_loadu_ps(delta_dst4 + 4 * n); + __m256 delta_src0 = + _mm256_permute_ps(delta_src, _MM_SHUFFLE(0, 0, 0, 0)); + __m256 delta_src1 = + _mm256_permute_ps(delta_src, _MM_SHUFFLE(1, 1, 1, 1)); + __m256 delta_src2 = + _mm256_permute_ps(delta_src, _MM_SHUFFLE(2, 2, 2, 2)); + __m256 delta_src3 = + _mm256_permute_ps(delta_src, _MM_SHUFFLE(3, 3, 3, 3)); + dst0 = madd256_ps(w0a, delta_src0, dst0); + dst1 = madd256_ps(w1a, delta_src0, dst1); + dst2 = madd256_ps(w2a, delta_src0, dst2); + dst3 = madd256_ps(w3a, delta_src0, dst3); + dst4 = madd256_ps(w4a, delta_src0, dst4); + dst0 = madd256_ps(w0b, delta_src1, dst0); + dst1 = madd256_ps(w1b, delta_src1, dst1); + dst2 = madd256_ps(w2b, delta_src1, dst2); + dst3 = madd256_ps(w3b, delta_src1, dst3); + dst4 = madd256_ps(w4b, delta_src1, dst4); + dst0 = madd256_ps(w0c, delta_src2, dst0); + dst1 = madd256_ps(w1c, delta_src2, dst1); + dst2 = madd256_ps(w2c, delta_src2, dst2); + dst3 = madd256_ps(w3c, delta_src2, dst3); + dst4 = madd256_ps(w4c, delta_src2, dst4); + dst0 = madd256_ps(w0d, delta_src3, dst0); + _mm256_storeu_ps(delta_dst0 + 4 * n, dst0); + dst1 = madd256_ps(w1d, delta_src3, dst1); + _mm256_storeu_ps(delta_dst1 + 4 * n, dst1); + dst2 = madd256_ps(w2d, delta_src3, dst2); + _mm256_storeu_ps(delta_dst2 + 4 * n, dst2); + dst3 = madd256_ps(w3d, delta_src3, dst3); + _mm256_storeu_ps(delta_dst3 + 4 * n, dst3); + dst4 = madd256_ps(w4d, delta_src3, dst4); + _mm256_storeu_ps(delta_dst4 + 4 * n, dst4); + } // for nblocks + for (size_t x = nblocks * 4; x < out_width; ++x) { + __m256 delta_src = _mm256_broadcast_ss(pdelta_src + x); + __m256 dst0 = _mm256_loadu_ps(delta_dst0 + x); + __m256 dst1 = _mm256_loadu_ps(delta_dst1 + x); + __m256 dst2 = _mm256_loadu_ps(delta_dst2 + x); + __m256 dst3 = _mm256_loadu_ps(delta_dst3 + x); + __m256 dst4 = _mm256_maskload_ps(delta_dst4 + x, imask); + dst0 = madd256_ps(w0a, delta_src, dst0); + dst1 = madd256_ps(w1a, delta_src, dst1); + dst2 = madd256_ps(w2a, delta_src, dst2); + dst3 = madd256_ps(w3a, delta_src, dst3); + dst4 = madd256_ps(w4a, delta_src, dst4); + _mm256_maskstore_ps(delta_dst0 + x, imask, dst0); + _mm256_maskstore_ps(delta_dst1 + x, imask, dst1); + _mm256_maskstore_ps(delta_dst2 + x, imask, dst2); + _mm256_maskstore_ps(delta_dst3 + x, imask, dst3); + _mm256_maskstore_ps(delta_dst4 + x, imask, dst4); + } // for x + } // for out_height + } // for out.depth_ + } // for in.depth_ + } else { + for (size_t inc = 0; inc < in.depth_; + ++inc, pdelta_dst_org += in_padded_area) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + const float *pw = &W[25 * (in.depth_ * outc + inc)]; + const float *pdelta_src = &curr_delta[out.get_index(0, 0, outc)]; + float *pdelta_dst = pdelta_dst_org; + __m256 w0a = _mm256_and_ps(_mm256_loadu_ps(pw + 0), mask); + __m256 w1a = _mm256_and_ps(_mm256_loadu_ps(pw + 5), mask); + __m256 w2a = _mm256_and_ps(_mm256_loadu_ps(pw + 10), mask); + __m256 w3a = _mm256_and_ps(_mm256_loadu_ps(pw + 15), mask); + __m256 w4a = _mm256_maskload_ps(pw + 20, imask); + __m256 w0b = leftShift<4>(w0a); + __m256 w1b = leftShift<4>(w1a); + __m256 w2b = leftShift<4>(w2a); + __m256 w3b = leftShift<4>(w3a); + __m256 w4b = leftShift<4>(w4a); + __m256 w0c = leftShift<8>(w0a); + __m256 w1c = leftShift<8>(w1a); + __m256 w2c = leftShift<8>(w2a); + __m256 w3c = leftShift<8>(w3a); + __m256 w4c = leftShift<8>(w4a); + __m256 w0d = leftShift<12>(w0a); + __m256 w1d = leftShift<12>(w1a); + __m256 w2d = leftShift<12>(w2a); + __m256 w3d = leftShift<12>(w3a); + __m256 w4d = leftShift<12>(w4a); + size_t y = 0; + do { + float *delta_dst0 = pdelta_dst; + float *delta_dst1 = &pdelta_dst[in_padded.width_ * 1]; + float *delta_dst2 = &pdelta_dst[in_padded.width_ * 2]; + float *delta_dst3 = &pdelta_dst[in_padded.width_ * 3]; + float *delta_dst4 = &pdelta_dst[in_padded.width_ * 4]; + size_t n = 0; + do { + __m256 delta_src = + _mm256_broadcast_ps((const __m128 *)(pdelta_src + n * 4)); + __m256 dst0 = _mm256_loadu_ps(delta_dst0 + 4 * n); + __m256 dst1 = _mm256_loadu_ps(delta_dst1 + 4 * n); + __m256 dst2 = _mm256_loadu_ps(delta_dst2 + 4 * n); + __m256 dst3 = _mm256_loadu_ps(delta_dst3 + 4 * n); + __m256 dst4 = _mm256_loadu_ps(delta_dst4 + 4 * n); + __m256 delta_src0 = + _mm256_permute_ps(delta_src, _MM_SHUFFLE(0, 0, 0, 0)); + __m256 delta_src1 = + _mm256_permute_ps(delta_src, _MM_SHUFFLE(1, 1, 1, 1)); + __m256 delta_src2 = + _mm256_permute_ps(delta_src, _MM_SHUFFLE(2, 2, 2, 2)); + __m256 delta_src3 = + _mm256_permute_ps(delta_src, _MM_SHUFFLE(3, 3, 3, 3)); + dst0 = madd256_ps(w0a, delta_src0, dst0); + dst1 = madd256_ps(w1a, delta_src0, dst1); + dst2 = madd256_ps(w2a, delta_src0, dst2); + dst3 = madd256_ps(w3a, delta_src0, dst3); + dst4 = madd256_ps(w4a, delta_src0, dst4); + dst0 = madd256_ps(w0b, delta_src1, dst0); + dst1 = madd256_ps(w1b, delta_src1, dst1); + dst2 = madd256_ps(w2b, delta_src1, dst2); + dst3 = madd256_ps(w3b, delta_src1, dst3); + dst4 = madd256_ps(w4b, delta_src1, dst4); + dst0 = madd256_ps(w0c, delta_src2, dst0); + dst1 = madd256_ps(w1c, delta_src2, dst1); + dst2 = madd256_ps(w2c, delta_src2, dst2); + dst3 = madd256_ps(w3c, delta_src2, dst3); + dst4 = madd256_ps(w4c, delta_src2, dst4); + dst0 = madd256_ps(w0d, delta_src3, dst0); + _mm256_storeu_ps(delta_dst0 + 4 * n, dst0); + dst1 = madd256_ps(w1d, delta_src3, dst1); + _mm256_storeu_ps(delta_dst1 + 4 * n, dst1); + dst2 = madd256_ps(w2d, delta_src3, dst2); + _mm256_storeu_ps(delta_dst2 + 4 * n, dst2); + dst3 = madd256_ps(w3d, delta_src3, dst3); + _mm256_storeu_ps(delta_dst3 + 4 * n, dst3); + dst4 = madd256_ps(w4d, delta_src3, dst4); + _mm256_storeu_ps(delta_dst4 + 4 * n, dst4); + ++n; + } while (n < nblocks); + ++y; + pdelta_src += out_width; + pdelta_dst += h_stride2; + } while (y < out_height); + } // for out.depth_ + } // for in.depth_ + } + } else if (out_height == 1 && out_width == 1) { + for (size_t inc = 0; inc < in.depth_; + ++inc, pdelta_dst_org += in_padded_area) { + __m256 sum0 = _mm256_setzero_ps(); + __m256 sum1 = _mm256_setzero_ps(); + __m256 sum2 = _mm256_setzero_ps(); + __m128 sum3 = _mm_setzero_ps(); + + size_t widx = 25 * inc; + size_t wstep = 25 * in.depth_; + __m256 delta_src; + if (tbl.is_empty()) { + for (size_t outc = 0; outc < out.depth_; ++outc, widx += wstep) { + delta_src = _mm256_broadcast_ss(&curr_delta[outc]); + const float *pw = (const float *)&W[widx]; + __m256 w0 = _mm256_loadu_ps(pw + 0); + __m256 w1 = _mm256_loadu_ps(pw + 8); + __m256 w2 = _mm256_loadu_ps(pw + 16); + __m128 w3 = _mm_load_ss(pw + 24); + sum0 = madd256_ps(w0, delta_src, sum0); + sum1 = madd256_ps(w1, delta_src, sum1); + sum2 = madd256_ps(w2, delta_src, sum2); + sum3 = madd128_ss(w3, _mm256_castps256_ps128(delta_src), sum3); + } + } else { + for (size_t outc = 0; outc < out.depth_; ++outc, widx += wstep) { + if (!tbl.is_connected(outc, inc)) { + continue; + } + delta_src = _mm256_broadcast_ss(&curr_delta[outc]); + const float *pw = (const float *)&W[widx]; + __m256 w0 = _mm256_loadu_ps(pw + 0); + __m256 w1 = _mm256_loadu_ps(pw + 8); + __m256 w2 = _mm256_loadu_ps(pw + 16); + __m128 w3 = _mm_load_ss(pw + 24); + sum0 = madd256_ps(w0, delta_src, sum0); + sum1 = madd256_ps(w1, delta_src, sum1); + sum2 = madd256_ps(w2, delta_src, sum2); + sum3 = madd128_ss(w3, _mm256_castps256_ps128(delta_src), sum3); + } + } + float *delta_dst0 = pdelta_dst_org; + float *delta_dst1 = &pdelta_dst_org[in_padded.width_ * 1]; + float *delta_dst2 = &pdelta_dst_org[in_padded.width_ * 2]; + float *delta_dst3 = &pdelta_dst_org[in_padded.width_ * 3]; + float *delta_dst4 = &pdelta_dst_org[in_padded.width_ * 4]; + __m256 dst0 = _mm256_loadu_ps(delta_dst0); + __m256 dst1 = _mm256_loadu_ps(delta_dst1); + __m256 dst2 = _mm256_loadu_ps(delta_dst2); + __m256 dst3 = _mm256_loadu_ps(delta_dst3); + __m256 dst4 = _mm256_maskload_ps(delta_dst4, imask); + + // *FROM + // 1110 0000 + // 3222 2211 + // 4444 3333 + // ---- ---4 + // + // *TO + // ---0 0000 + // ---1 1111 + // ---2 2222 + // ---3 3333 + // ---4 4444 + __m256 new_sum0 = + _mm256_blend_ps(_mm256_setzero_ps(), sum0, 0x1F /* 0b00011111 */); + __m256 new_sum1 = + _mm256_blend_ps(_mm256_setzero_ps(), + _mm256_or_ps(rightShift<20>(sum0), leftShift<12>(sum1)), + 0x1F /* 0b00011111 */); + __m256 new_sum2 = _mm256_blend_ps( + _mm256_setzero_ps(), rightShift<8>(sum1), 0x1F /* 0b00011111 */); + __m256 new_sum3 = + _mm256_blend_ps(_mm256_setzero_ps(), + _mm256_or_ps(rightShift<28>(sum1), leftShift<4>(sum2)), + 0x1F /* 0b00011111 */); + __m256 new_sum4 = + _mm256_blend_ps(_mm256_setzero_ps(), + _mm256_set_m128(sum3, _mm256_extractf128_ps(sum2, 1)), + 0x1F /* 0b00011111 */); + dst0 = _mm256_add_ps(dst0, new_sum0); + dst1 = _mm256_add_ps(dst1, new_sum1); + dst2 = _mm256_add_ps(dst2, new_sum2); + dst3 = _mm256_add_ps(dst3, new_sum3); + dst4 = _mm256_add_ps(dst4, new_sum4); + + _mm256_maskstore_ps(delta_dst0, imask, dst0); + _mm256_maskstore_ps(delta_dst1, imask, dst1); + _mm256_maskstore_ps(delta_dst2, imask, dst2); + _mm256_maskstore_ps(delta_dst3, imask, dst3); + _mm256_maskstore_ps(delta_dst4, imask, dst4); + } // for + } else { + for (size_t inc = 0; inc < in.depth_; + ++inc, pdelta_dst_org += in_padded_area) { + for (size_t outc = 0; outc < out.depth_; ++outc) { + if (!tbl.is_connected(outc, inc)) continue; + + const float *pw = &W[25 * (in.depth_ * outc + inc)]; + const float *pdelta_src = &curr_delta[out.get_index(0, 0, outc)]; + float *pdelta_dst = pdelta_dst_org; + __m256 w0a = _mm256_maskload_ps(pw + 0, imask); + __m256 w1a = _mm256_maskload_ps(pw + 5, imask); + __m256 w2a = _mm256_maskload_ps(pw + 10, imask); + __m256 w3a = _mm256_maskload_ps(pw + 15, imask); + __m256 w4a = _mm256_maskload_ps(pw + 20, imask); + for (size_t y = 0; y < out_height; + ++y, pdelta_src += out_width, pdelta_dst += h_stride2) { + float *delta_dst0 = pdelta_dst; + float *delta_dst1 = &pdelta_dst[in_padded.width_ * 1]; + float *delta_dst2 = &pdelta_dst[in_padded.width_ * 2]; + float *delta_dst3 = &pdelta_dst[in_padded.width_ * 3]; + float *delta_dst4 = &pdelta_dst[in_padded.width_ * 4]; + for (size_t x = 0; x < out_width; ++x) { + __m256 delta_src = _mm256_broadcast_ss(pdelta_src + x); + __m256 dst0 = _mm256_loadu_ps(delta_dst0); + __m256 dst1 = _mm256_loadu_ps(delta_dst1); + __m256 dst2 = _mm256_loadu_ps(delta_dst2); + __m256 dst3 = _mm256_loadu_ps(delta_dst3); + __m256 dst4 = _mm256_maskload_ps(delta_dst4, imask); + dst0 = madd256_ps(w0a, delta_src, dst0); + dst1 = madd256_ps(w1a, delta_src, dst1); + dst2 = madd256_ps(w2a, delta_src, dst2); + dst3 = madd256_ps(w3a, delta_src, dst3); + dst4 = madd256_ps(w4a, delta_src, dst4); + _mm256_storeu_ps(delta_dst0, dst0); + _mm256_storeu_ps(delta_dst1, dst1); + _mm256_storeu_ps(delta_dst2, dst2); + _mm256_storeu_ps(delta_dst3, dst3); + _mm256_maskstore_ps(delta_dst4, imask, dst4); + delta_dst0 += w_stride; + delta_dst1 += w_stride; + delta_dst2 += w_stride; + delta_dst3 += w_stride; + delta_dst4 += w_stride; + } // for x + } // for y + } // for outc + } // for inc + } + + accumulate_dw(params, prev_out, curr_delta, dW, db); + + if (params.has_bias) { + accumulate_db(out, curr_delta, db); + } +} // avx_conv2d_5x5_back_kernel float ver + +// double ver +template +void avx_conv2d_5x5_back_kernel( + const core::conv_params ¶ms, + const std::vector> &prev_out, + const std::vector &W, + std::vector> &dW, + std::vector> &db, + std::vector> &curr_delta, + std::vector> &prev_delta, + bool layer_parallelize) { + // backward-pass fallbacks to tiny-backend when float_t is double + conv2d_op_internal(prev_out, W, dW, db, curr_delta, prev_delta, params, + layer_parallelize); +} + +// float ver +template +void avx_conv2d_5x5_back_kernel( + const core::conv_params ¶ms, + const std::vector> &prev_out, + const std::vector &W, + std::vector> &dW, + std::vector> &db, + std::vector> &curr_delta, + std::vector> &prev_delta, + bool layer_parallelize) { + for_i(layer_parallelize, prev_out.size(), [&](size_t sample) { + avx_conv2d_5x5_back_kernel_one(params, prev_out[sample], W, dW[sample], + db[sample], curr_delta[sample], + &prev_delta[sample]); + }); +} + +#endif // CNN_USE_AVX + +inline void conv2d_grad_op_avx(const tensor_t &prev_out, + const vec_t &W, + tensor_t &dW, + tensor_t &db, + tensor_t &curr_delta, + tensor_t &prev_delta, + const core::conv_params ¶ms, + const bool layer_parallelize) { +#ifdef CNN_USE_AVX + if (params.weight.height_ == 5 && params.weight.width_ == 5) { + avx_conv2d_5x5_back_kernel(params, prev_out, W, dW, db, curr_delta, + prev_delta, layer_parallelize); + return; + } +#endif + + conv2d_op_internal(prev_out, W, dW, db, curr_delta, prev_delta, params, + layer_parallelize); +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/conv2d_op.h b/tiny_dnn/core/kernels/conv2d_op.h new file mode 100755 index 0000000000000..aaddedacf97c8 --- /dev/null +++ b/tiny_dnn/core/kernels/conv2d_op.h @@ -0,0 +1,54 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" + +#include "tiny_dnn/core/kernels/conv2d_op_avx.h" +#include "tiny_dnn/core/kernels/conv2d_op_internal.h" +#include "tiny_dnn/core/kernels/conv2d_op_nnpack.h" + +namespace tiny_dnn { + +class Conv2dOp : public core::OpKernel { + public: + explicit Conv2dOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto params = OpKernel::params_->conv(); + + // incomimg/outcoming data + const tensor_t &in_data = context.input(0); + const tensor_t &W = context.input(1); + const tensor_t &bias = context.input(2); + tensor_t &out_data = context.output(0); + + // initialize outputs + fill_tensor(out_data, float_t{0}); + + // call convolution algorithm depending + // on the selected engine type + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::internal) { + kernels::conv2d_op_internal(in_data, W[0], bias[0], out_data, params, + context.parallelize()); + } else if (engine == core::backend_t::nnpack) { + kernels::conv2d_op_nnpack(in_data, W[0], bias[0], out_data, params); + } else if (engine == core::backend_t::avx) { + kernels::conv2d_op_avx(in_data, W[0], bias[0], out_data, params, + context.parallelize()); + } else { + throw nn_error("Not supported engine: " + to_string(engine)); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/conv2d_op_avx.h b/tiny_dnn/core/kernels/conv2d_op_avx.h new file mode 100755 index 0000000000000..3a6025a31ff55 --- /dev/null +++ b/tiny_dnn/core/kernels/conv2d_op_avx.h @@ -0,0 +1,471 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include "tiny_dnn/core/kernels/conv2d_op_internal.h" +#include "tiny_dnn/core/params/conv_params.h" + +#ifdef CNN_USE_AVX +#include "tiny_dnn/core/kernels/avx_kernel_common.h" +#endif + +namespace tiny_dnn { +namespace kernels { + +#ifdef CNN_USE_AVX + +// float ver +template +void avx_conv2d_5x5_kernel(const core::conv_params ¶ms, + const std::vector &in, + const std::vector &W, + const std::vector &bias, + std::vector &a, + const bool layer_parallelize) { + CNN_UNREFERENCED_PARAMETER(layer_parallelize); + assert(params.weight.height_ == 5 && params.weight.width_ == 5); + + auto &out = params.out; + auto &in_padded = params.in_padded; + auto &tbl = params.tbl; + auto w_stride = params.w_stride; + + const size_t out_area = out.area(); + size_t oidx = 0; + float bias_scale = params.has_bias ? 1.0f : 0.0f; + const size_t stride = params.h_stride * in_padded.width_; + const size_t inarea = in_padded.area(); + + static const __m256i imask = _mm256_setr_epi32(-1, -1, -1, -1, -1, 0, 0, 0); + // static const __m256 mask = _mm256_castsi256_ps(_mm256_setr_epi32(-1, -1, + // -1, -1, -1, 0, 0, 0)); + + const __m128 y_bias_scale = _mm_set_ss(bias_scale); + if (out.height_ == 1 && out.width_ == 1) { + const float *pw = (const float *)&W[0]; + for (size_t o = 0; o < out.depth_; ++o) { + __m256 sum0 = _mm256_setzero_ps(); + __m256 sum1 = _mm256_setzero_ps(); + __m256 sum2 = _mm256_setzero_ps(); + __m128 sum3 = _mm_setzero_ps(); + const float *pi = (const float *)&in[0]; + for (size_t inc = 0; inc < params.in.depth_; + ++inc, pw += 25, pi += inarea) { + if (!tbl.is_connected(o, inc)) { + continue; + } + __m256 w0 = _mm256_loadu_ps(pw + 0); + __m256 w1 = _mm256_loadu_ps(pw + 8); + __m256 w2 = _mm256_loadu_ps(pw + 16); + __m256 i0 = _mm256_loadu_ps(pi + 0); + __m256 i1 = _mm256_loadu_ps(pi + 8); + __m256 i2 = _mm256_loadu_ps(pi + 16); + __m128 w3 = _mm_load_ss(pw + 24); + __m128 i3 = _mm_load_ss(pi + 24); + __m256 tmp0 = _mm256_mul_ps(w0, i0); + __m256 tmp1 = _mm256_mul_ps(w1, i1); + __m256 tmp2 = _mm256_mul_ps(w2, i2); + __m128 tmp3 = _mm_mul_ps(w3, i3); + sum0 = _mm256_add_ps(tmp0, sum0); + sum1 = _mm256_add_ps(tmp1, sum1); + sum2 = _mm256_add_ps(tmp2, sum2); + sum3 = _mm_add_ps(tmp3, sum3); + } + __m256 sum = _mm256_add_ps(_mm256_add_ps(sum0, sum1), sum2); + __m128 b = _mm_load_ss(&bias[o]); + __m128 hsum = hsum256_ps(sum); + b = madd128_ss(b, y_bias_scale, sum3); + _mm_store_ss(&a[o], _mm_add_ss(hsum, b)); + } + } else { + const size_t nblocks = out.width_ / 4; + for (size_t o = 0; o < out.depth_; ++o, oidx += out_area) { + float *pa = &a[oidx]; + // init to bias value + float b = bias[o] * bias_scale; + { + size_t headSize = 0; + __m256 b2 = _mm256_set1_ps(b); + if (oidx & 7) { + headSize = 8 - (oidx & 7); + assert(headSize < out_area); + for (size_t i = 0; i < headSize; ++i) { + _mm_store_ss(&pa[i], _mm256_castps256_ps128(b2)); + } + } + size_t cnt = (out_area - headSize) / 16; + float *pa2 = pa + headSize; + for (size_t i = 0; i < cnt; ++i) { + _mm256_store_ps(&pa2[i * 16 + 0], b2); + _mm256_store_ps(&pa2[i * 16 + 8], b2); + } + for (size_t i = headSize + cnt * 16; i < out_area; ++i) { + pa[i] = b; + } + } + for (size_t inc = 0; inc < params.in.depth_; ++inc) { + if (!tbl.is_connected(o, inc)) continue; + + const float *pw = (const float *)&W[25 * (params.in.depth_ * o + inc)]; + const float *pi = (const float *)&in[in_padded.get_index(0, 0, inc)]; + + __m256 w0a = _mm256_maskload_ps(pw + 0, imask); + __m256 w1a = _mm256_maskload_ps(pw + 5, imask); + __m256 w2a = _mm256_maskload_ps(pw + 10, imask); + __m256 w3a = _mm256_maskload_ps(pw + 15, imask); + __m256 w4a = _mm256_maskload_ps(pw + 20, imask); + __m256 w0b = leftShift<4>(w0a); + __m256 w1b = leftShift<4>(w1a); + __m256 w2b = leftShift<4>(w2a); + __m256 w3b = leftShift<4>(w3a); + __m256 w4b = leftShift<4>(w4a); + __m256 w0c = leftShift<8>(w0a); + __m256 w1c = leftShift<8>(w1a); + __m256 w2c = leftShift<8>(w2a); + __m256 w3c = leftShift<8>(w3a); + __m256 w4c = leftShift<8>(w4a); + __m256 w0d = leftShift<12>(w0a); + __m256 w1d = leftShift<12>(w1a); + __m256 w2d = leftShift<12>(w2a); + __m256 w3d = leftShift<12>(w3a); + __m256 w4d = leftShift<12>(w4a); + float *ppa = pa; + if (w_stride == 1) { + if (nblocks) { + for (size_t y = 0; y < out.height_; ++y, ppa += out.width_) { + const float *pi0 = (pi + y * stride); + const float *pi1 = pi0 + 1 * in_padded.width_; + const float *pi2 = pi0 + 2 * in_padded.width_; + const float *pi3 = pi0 + 3 * in_padded.width_; + const float *pi4 = pi0 + 4 * in_padded.width_; + __m256 dst0, dst1, dst2, dst3; + __m256 i0 = _mm256_loadu_ps(pi0); + __m256 i1 = _mm256_loadu_ps(pi1); + __m256 i2 = _mm256_loadu_ps(pi2); + __m256 i3 = _mm256_loadu_ps(pi3); + __m256 i4 = _mm256_loadu_ps(pi4); + dst0 = _mm256_mul_ps(w0a, i0); + dst1 = _mm256_mul_ps(w0b, i0); + dst2 = _mm256_mul_ps(w0c, i0); + dst3 = _mm256_mul_ps(w0d, i0); + dst0 = madd256_ps(w1a, i1, dst0); + dst1 = madd256_ps(w1b, i1, dst1); + dst2 = madd256_ps(w1c, i1, dst2); + dst3 = madd256_ps(w1d, i1, dst3); + dst0 = madd256_ps(w2a, i2, dst0); + dst1 = madd256_ps(w2b, i2, dst1); + dst2 = madd256_ps(w2c, i2, dst2); + dst3 = madd256_ps(w2d, i2, dst3); + dst0 = madd256_ps(w3a, i3, dst0); + dst1 = madd256_ps(w3b, i3, dst1); + dst2 = madd256_ps(w3c, i3, dst2); + dst3 = madd256_ps(w3d, i3, dst3); + dst0 = madd256_ps(w4a, i4, dst0); + dst1 = madd256_ps(w4b, i4, dst1); + dst2 = madd256_ps(w4c, i4, dst2); + dst3 = madd256_ps(w4d, i4, dst3); + __m128 sum = _mm_loadu_ps(ppa); + __m128 hsum0123 = hsum4x256_ps(dst0, dst1, dst2, dst3); + sum = _mm_add_ps(sum, hsum0123); + _mm_storeu_ps(ppa, sum); + for (size_t i = 1; i < nblocks; ++i) { + i0 = _mm256_loadu_ps(pi0 + i * 4); + i1 = _mm256_loadu_ps(pi1 + i * 4); + i2 = _mm256_loadu_ps(pi2 + i * 4); + i3 = _mm256_loadu_ps(pi3 + i * 4); + i4 = _mm256_loadu_ps(pi4 + i * 4); + dst0 = _mm256_mul_ps(w0a, i0); + dst1 = _mm256_mul_ps(w0b, i0); + dst2 = _mm256_mul_ps(w0c, i0); + dst3 = _mm256_mul_ps(w0d, i0); + dst0 = madd256_ps(w1a, i1, dst0); + dst1 = madd256_ps(w1b, i1, dst1); + dst2 = madd256_ps(w1c, i1, dst2); + dst3 = madd256_ps(w1d, i1, dst3); + dst0 = madd256_ps(w2a, i2, dst0); + dst1 = madd256_ps(w2b, i2, dst1); + dst2 = madd256_ps(w2c, i2, dst2); + dst3 = madd256_ps(w2d, i2, dst3); + dst0 = madd256_ps(w3a, i3, dst0); + dst1 = madd256_ps(w3b, i3, dst1); + dst2 = madd256_ps(w3c, i3, dst2); + dst3 = madd256_ps(w3d, i3, dst3); + dst0 = madd256_ps(w4a, i4, dst0); + dst1 = madd256_ps(w4b, i4, dst1); + dst2 = madd256_ps(w4c, i4, dst2); + dst3 = madd256_ps(w4d, i4, dst3); + sum = _mm_loadu_ps(ppa + i * 4); + hsum0123 = hsum4x256_ps(dst0, dst1, dst2, dst3); + sum = _mm_add_ps(sum, hsum0123); + _mm_storeu_ps(ppa + i * 4, sum); + } + for (size_t x = nblocks * 4; x < out.width_; ++x) { + sum = _mm_load_ss(&ppa[x]); + i0 = _mm256_loadu_ps(pi0 + x); + i1 = _mm256_loadu_ps(pi1 + x); + i2 = _mm256_loadu_ps(pi2 + x); + i3 = _mm256_loadu_ps(pi3 + x); + i4 = _mm256_maskload_ps(pi4 + x, imask); + __m256 sum0 = _mm256_mul_ps(w0a, i0); + __m256 sum1 = _mm256_mul_ps(w1a, i1); + sum0 = madd256_ps(w2a, i2, sum0); + sum1 = madd256_ps(w3a, i3, sum1); + sum0 = madd256_ps(w4a, i4, sum0); + sum0 = _mm256_add_ps(sum0, sum1); + _mm_store_ss(&ppa[x], _mm_add_ss(sum, hsum256_ps(sum0))); + } // x loop + } // y loop + } else { // if (nblocks) { + for (size_t y = 0; y < out.height_; ++y, ppa += out.width_) { + const float *pi0 = (pi + y * stride); + const float *pi1 = pi0 + 1 * in_padded.width_; + const float *pi2 = pi0 + 2 * in_padded.width_; + const float *pi3 = pi0 + 3 * in_padded.width_; + const float *pi4 = pi0 + 4 * in_padded.width_; + for (size_t x = 0; x < out.width_; ++x) { + __m128 sum = _mm_load_ss(&ppa[x]); + __m256 i0 = _mm256_loadu_ps(pi0 + x); + __m256 i1 = _mm256_loadu_ps(pi1 + x); + __m256 i2 = _mm256_loadu_ps(pi2 + x); + __m256 i3 = _mm256_loadu_ps(pi3 + x); + __m256 i4 = _mm256_maskload_ps(pi4 + x, imask); + __m256 sum0 = _mm256_mul_ps(w0a, i0); + __m256 sum1 = _mm256_mul_ps(w1a, i1); + sum0 = madd256_ps(w2a, i2, sum0); + sum1 = madd256_ps(w3a, i3, sum1); + sum0 = madd256_ps(w4a, i4, sum0); + sum0 = _mm256_add_ps(sum0, sum1); + _mm_store_ss(&ppa[x], _mm_add_ss(sum, hsum256_ps(sum0))); + } // x loop + } // y loop + } + } else { // if (w_stride == 1) { + for (size_t y = 0; y < out.height_; ++y, ppa += out.width_) { + const float *pi0 = (pi + y * stride); + const float *pi1 = pi0 + 1 * in_padded.width_; + const float *pi2 = pi0 + 2 * in_padded.width_; + const float *pi3 = pi0 + 3 * in_padded.width_; + const float *pi4 = pi0 + 4 * in_padded.width_; + for (size_t x = 0; x < out.width_; ++x) { + __m128 sum = _mm_load_ss(&ppa[x]); + __m256 i0 = _mm256_loadu_ps(pi0); + __m256 i1 = _mm256_loadu_ps(pi1); + __m256 i2 = _mm256_loadu_ps(pi2); + __m256 i3 = _mm256_loadu_ps(pi3); + __m256 i4 = _mm256_maskload_ps(pi4, imask); + __m256 sum0 = _mm256_mul_ps(w0a, i0); + __m256 sum1 = _mm256_mul_ps(w1a, i1); + sum0 = madd256_ps(w2a, i2, sum0); + sum1 = madd256_ps(w3a, i3, sum1); + sum0 = madd256_ps(w4a, i4, sum0); + sum0 = _mm256_add_ps(sum0, sum1); + _mm_store_ss(&ppa[x], _mm_add_ss(sum, hsum256_ps(sum0))); + pi0 += w_stride; + pi1 += w_stride; + pi2 += w_stride; + pi3 += w_stride; + pi4 += w_stride; + } // x loop + } // y loop + } + } // in depth loop + } // out depth loop + } // else +} // avx_conv2d_5x5_kernel float ver + +// double ver +template +void avx_conv2d_5x5_kernel(const core::conv_params ¶ms, + const std::vector &in, + const std::vector &W, + const std::vector &bias, + std::vector &a, + const bool layer_parallelize) { + assert(params.weight.height_ == 5 && params.weight.width_ == 5); + + auto &out = params.out; + auto &in_padded = params.in_padded; + auto &tbl = params.tbl; + auto w_stride = params.w_stride; + + const size_t out_area = out.area(); + double bias_scale = params.has_bias ? 1.0 : 0.0; + const __m128d y_bias_scale = _mm_set_sd(bias_scale); + size_t oidx = 0; + + const size_t in_stride = params.h_stride * in_padded.width_; + const size_t in_padded_area = in_padded.area(); + + if (out.height_ == 1 && out.width_ == 1) { + const double *pw = &W[0]; + for (size_t o = 0; o < out.depth_; ++o) { + __m256d sum0 = _mm256_setzero_pd(); + __m256d sum1 = _mm256_setzero_pd(); + __m256d sum2 = _mm256_setzero_pd(); + __m256d sum3 = _mm256_setzero_pd(); + __m256d sum4 = _mm256_setzero_pd(); + __m256d sum5 = _mm256_setzero_pd(); + __m128d sum6 = _mm_setzero_pd(); + size_t inidx = 0; + for (size_t inc = 0; inc < params.in.depth_; + ++inc, pw += 25, inidx += in_padded_area) { + if (!tbl.is_connected(o, inc)) { + continue; + } + __m256d w0 = _mm256_loadu_pd(pw + 0); + __m256d w1 = _mm256_loadu_pd(pw + 4); + __m256d w2 = _mm256_loadu_pd(pw + 8); + __m256d w3 = _mm256_loadu_pd(pw + 12); + __m256d w4 = _mm256_loadu_pd(pw + 16); + __m256d w5 = _mm256_loadu_pd(pw + 20); + __m128d w6 = _mm_load_sd(pw + 24); + const double *pi = (const double *)&in[inidx]; + __m256d i0 = _mm256_loadu_pd(pi + 0); + __m256d i1 = _mm256_loadu_pd(pi + 4); + __m256d i2 = _mm256_loadu_pd(pi + 8); + __m256d i3 = _mm256_loadu_pd(pi + 12); + __m256d i4 = _mm256_loadu_pd(pi + 16); + __m256d i5 = _mm256_loadu_pd(pi + 20); + __m128d i6 = _mm_load_sd(pi + 24); + __m256d tmp0 = _mm256_mul_pd(w0, i0); + __m256d tmp1 = _mm256_mul_pd(w1, i1); + __m256d tmp2 = _mm256_mul_pd(w2, i2); + __m256d tmp3 = _mm256_mul_pd(w3, i3); + __m256d tmp4 = _mm256_mul_pd(w4, i4); + __m256d tmp5 = _mm256_mul_pd(w5, i5); + __m128d tmp6 = _mm_mul_pd(w6, i6); + sum0 = _mm256_add_pd(tmp0, sum0); + sum1 = _mm256_add_pd(tmp1, sum1); + sum2 = _mm256_add_pd(tmp2, sum2); + sum3 = _mm256_add_pd(tmp3, sum3); + sum4 = _mm256_add_pd(tmp4, sum4); + sum5 = _mm256_add_pd(tmp5, sum5); + sum6 = _mm_add_pd(tmp6, sum6); + } + sum0 = _mm256_add_pd(sum0, sum1); + sum2 = _mm256_add_pd(sum2, sum3); + sum4 = _mm256_add_pd(sum4, sum5); + sum0 = _mm256_add_pd(sum0, sum2); + __m256d sum = _mm256_add_pd(sum0, sum4); + __m128d b = _mm_load_sd(&bias[o]); + __m128d hsum = hsum256_pd(sum); + b = madd128_sd(b, y_bias_scale, sum6); + _mm_store_sd(&a[o], _mm_add_sd(hsum, b)); + } + } else { + for (size_t o = 0; o < out.depth_; ++o, oidx += out_area) { + double *pa = &a[oidx]; + double b = bias[o] * bias_scale; + { + size_t headSize = 0; + __m256d b2 = _mm256_set1_pd(b); + if (oidx & 3) { + headSize = 4 - (oidx & 3); + assert(headSize < out_area); + for (size_t i = 0; i < headSize; ++i) { + _mm_store_sd(&pa[i], _mm256_castpd256_pd128(b2)); + } + } + size_t cnt = (out_area - headSize) / 8; + double *pa2 = pa + headSize; + for (size_t i = 0; i < cnt; ++i) { + _mm256_store_pd(&pa2[i * 8 + 0], b2); + _mm256_store_pd(&pa2[i * 8 + 4], b2); + } + for (size_t i = headSize + cnt * 8; i < out_area; ++i) { + _mm_store_sd(&pa[i], _mm256_castpd256_pd128(b2)); + } + } + + for (size_t inc = 0; inc < params.in.depth_; ++inc) { + if (!tbl.is_connected(o, inc)) continue; + + const double *pw = + (const double *)&W[25 * (params.in.depth_ * o + inc)]; + const double *pi = &in[in_padded.get_index(0, 0, inc)]; + + __m256d w0a = _mm256_loadu_pd(pw + 0); + __m128d w0b = _mm_load_sd(pw + 4); + __m256d w1a = _mm256_loadu_pd(pw + 5); + __m128d w1b = _mm_load_sd(pw + 9); + __m256d w2a = _mm256_loadu_pd(pw + 10); + __m128d w2b = _mm_load_sd(pw + 14); + __m256d w3a = _mm256_loadu_pd(pw + 15); + __m128d w3b = _mm_load_sd(pw + 19); + __m256d w4a = _mm256_loadu_pd(pw + 20); + __m128d w4b = _mm_load_sd(pw + 24); + + double *ppa = pa; + for (size_t y = 0; y < out.height_; + ++y, pi += in_stride, ppa += out.width_) { + const double *pi0 = pi + 0 * in_padded.width_; + const double *pi1 = pi + 1 * in_padded.width_; + const double *pi2 = pi + 2 * in_padded.width_; + const double *pi3 = pi + 3 * in_padded.width_; + const double *pi4 = pi + 4 * in_padded.width_; + for (size_t x = 0; x < out.width_; ++x) { + __m128d sum = _mm_load_sd(&ppa[x]); + __m256d i0a = _mm256_loadu_pd(pi0); + __m128d i0b = _mm_load_sd(pi0 + 4); + __m256d i1a = _mm256_loadu_pd(pi1); + __m128d i1b = _mm_load_sd(pi1 + 4); + __m256d i2a = _mm256_loadu_pd(pi2); + __m128d i2b = _mm_load_sd(pi2 + 4); + __m256d i3a = _mm256_loadu_pd(pi3); + __m128d i3b = _mm_load_sd(pi3 + 4); + __m256d i4a = _mm256_loadu_pd(pi4); + __m128d i4b = _mm_load_sd(pi4 + 4); + __m256d sum_a = _mm256_mul_pd(w0a, i0a); + __m128d sum_b = _mm_mul_sd(w0b, i0b); + sum_a = madd256_pd(w1a, i1a, sum_a); + sum_b = madd128_pd(w1b, i1b, sum_b); + sum_a = madd256_pd(w2a, i2a, sum_a); + sum_b = madd128_pd(w2b, i2b, sum_b); + sum_a = madd256_pd(w3a, i3a, sum_a); + sum_b = madd128_pd(w3b, i3b, sum_b); + sum_a = madd256_pd(w4a, i4a, sum_a); + sum_b = madd128_pd(w4b, i4b, sum_b); + __m128d sum_c = hsum256_pd(sum_a); + sum = _mm_add_sd(sum, sum_b); + _mm_store_sd(&ppa[x], _mm_add_sd(sum, sum_c)); + pi0 += w_stride; + pi1 += w_stride; + pi2 += w_stride; + pi3 += w_stride; + pi4 += w_stride; + } // x loop + } // y loop + } // in depth loop + } // out depth loop + } // else +} // avx_conv2d_5x5_kernel double ver + +#endif // CNN_USE_AVX + +inline void conv2d_op_avx(const tensor_t &in_data, + const vec_t &W, + const vec_t &bias, + tensor_t &out_data, + const core::conv_params ¶ms, + const bool layer_parallelize) { +#ifdef CNN_USE_AVX + if (params.weight.height_ == 5 && params.weight.width_ == 5) { + // @todo consider better parallelization + for_i(layer_parallelize, in_data.size(), [&](size_t i) { + avx_conv2d_5x5_kernel(params, in_data[i], W, bias, out_data[i], + layer_parallelize); + }); + return; + } +#endif + conv2d_op_internal(in_data, W, bias, out_data, params, layer_parallelize); +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/conv2d_op_internal.h b/tiny_dnn/core/kernels/conv2d_op_internal.h new file mode 100755 index 0000000000000..c0cfe7bdd0776 --- /dev/null +++ b/tiny_dnn/core/kernels/conv2d_op_internal.h @@ -0,0 +1,182 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +namespace tiny_dnn { +namespace kernels { + +inline void conv2d_op_internal(const tensor_t &in_data, + const vec_t &W, + const vec_t &bias, + tensor_t &out_data, + const core::conv_params ¶ms, + const bool parallelize) { + for_(parallelize, 0u, in_data.size(), + [&](const blocked_range &r) { + size_t out_area = params.out.area(); + size_t iw = params.in_padded.width_; + size_t id = params.in.depth_; + size_t ow = params.out.width_; + size_t oh = params.out.height_; + size_t od = params.out.depth_; + size_t kw = params.weight.width_; + size_t kh = params.weight.height_; + size_t elem_stride = params.w_stride; + size_t line_stride = iw * params.h_stride; + for (size_t sample = r.begin(); sample < r.end(); sample++) { + const vec_t &in = in_data[sample]; + vec_t &a = out_data[sample]; + for (size_t o = 0; o < od; o++) { + float_t *pa = &a[params.out.get_index(0, 0, o)]; + for (size_t inc = 0; inc < id; inc++) { + if (!params.tbl.is_connected(o, inc)) continue; + size_t idx; + idx = params.weight.get_index(0, 0, id * o + inc); + const float_t *pw = &W[idx]; + idx = params.in_padded.get_index(0, 0, inc); + const float_t *pin = &in[idx]; + float_t *pout = pa; + for (size_t y = 0; y < oh; y++) { + const float_t *pin_line = pin; + for (size_t x = 0; x < ow; x++) { + const float_t *pin_element = pin_line; + const float_t *pw_element = pw; + float_t sum{0}; + // should be optimized for small kernel(3x3,5x5) + for (size_t wy = 0; wy < kh; wy++) { // NOLINT + for (size_t wx = 0; wx < kw; wx++) { // NOLINT + sum += pw_element[wx] * pin_element[wx]; + } + pw_element += kw; + pin_element += iw; + } + pout[x] += sum; + pin_line += elem_stride; + } + pout += ow; + pin += line_stride; + } + } + if (params.has_bias) { + vectorize::add(bias[o], out_area, pa); + } + } + } + }, + 0u); +} + +/******************************************************************/ + +template +void conv2d_op_internal(const tensor_t &prev_out, + const vec_t &W, + tensor_t &dW, + tensor_t &db, + tensor_t &curr_delta, + tensor_t &prev_delta, + const core::conv_params ¶ms, + const bool parallelize) { + typedef typename vec_t::value_type float_t; + + for_i(parallelize, prev_out.size(), [&](size_t sample) { + // propagate delta to previous layer + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + if (!params.tbl.is_connected(outc, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * outc + inc; + idx = params.weight.get_index(0, 0, idx); + const float_t *pw = &W[idx]; + + idx = params.out.get_index(0, 0, outc); + const float_t *pdelta_src = &curr_delta[sample][idx]; + + idx = params.in_padded.get_index(0, 0, inc); + // float_t* pdelta_dst = &(*prev_delta)[sample][idx]; + float_t *pdelta_dst = &prev_delta[sample][idx]; + + for (size_t y = 0; y < params.out.height_; y++) { + for (size_t x = 0; x < params.out.width_; x++) { + const float_t *ppw = pw; + + idx = y * params.out.width_ + x; + const float_t ppdelta_src = pdelta_src[idx]; + + float_t *ppdelta_dst = + pdelta_dst + y * params.h_stride * params.in_padded.width_ + + x * params.w_stride; + + for (size_t wy = 0; wy < params.weight.height_; wy++) { // NOLINT + for (size_t wx = 0; wx < params.weight.width_; wx++) { // NOLINT + idx = wy * params.in_padded.width_ + wx; + ppdelta_dst[idx] += *ppw++ * ppdelta_src; + } + } + } + } + } + } + + // accumulate dw + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + if (!params.tbl.is_connected(outc, inc)) continue; + + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + float_t dst{0}; + + size_t idx = 0; + idx = params.in_padded.get_index(wx, wy, inc); + const float_t *prevo = &prev_out[sample][idx]; + + idx = params.out.get_index(0, 0, outc); + const float_t *delta = &curr_delta[sample][idx]; + + if (params.w_stride > 1) { + for (size_t y = 0; y < params.out.height_; y++) { + size_t prevo_idx = + y * params.in_padded.width_ * params.h_stride; + size_t delta_idx = y * params.out.width_; + + for (size_t x = 0; x < params.out.width_; x++) { + dst += prevo[prevo_idx + x * params.w_stride] * + delta[delta_idx + x]; + } + } + } else { + for (size_t y = 0; y < params.out.height_; y++) { + dst += vectorize::dot( + prevo + y * params.in_padded.width_ * params.h_stride, + delta + y * params.out.width_, params.out.width_); + } + } + + idx = params.in.depth_ * outc + inc; + dW[sample][params.weight.get_index(wx, wy, idx)] += dst; + } + } + } + } + + // accumulate db + if (params.has_bias) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + size_t idx = params.out.get_index(0, 0, outc); + const float_t *delta = &curr_delta[sample][idx]; + const float_t *deltaa = delta + params.out.width_ * params.out.height_; + db[sample][outc] += std::accumulate(delta, deltaa, float_t{0}); + } + } + }); +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/conv2d_op_libdnn.h b/tiny_dnn/core/kernels/conv2d_op_libdnn.h new file mode 100755 index 0000000000000..011927b25bd8f --- /dev/null +++ b/tiny_dnn/core/kernels/conv2d_op_libdnn.h @@ -0,0 +1,266 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/core/framework/op_kernel.h" + +#ifdef CNN_USE_LIBDNN +#include "libdnn.hpp" +#endif + +namespace tiny_dnn { + +class Conv2dLibDNNForwardOp : public core::OpKernel { + public: + explicit Conv2dLibDNNForwardOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) +#ifdef CNN_USE_LIBDNN + , + initialized_(false) +#endif + { + // TODO(edgar): remove this if statement when refactor + // the init_backend() routine at layer level. + if (OpKernel::device_ != nullptr) { + auto params = OpKernel::params_->conv(); + init_libdnn(OpKernel::device_, params); + } + } + + void compute(core::OpKernelContext &context) override { +#ifdef CNN_USE_LIBDNN + // incoming/outcoming datm + const tensor_t &in_data = context.input(0); + const tensor_t &W = context.input(1); + const tensor_t &bias = context.input(2); + tensor_t &out_data = context.output(1); + + // retrieve the convolutional parameters and pad input + // Conv2d::setParams(context.params()); + + // initialize outputs + fill_tensor(out_data, float_t{0}); + + // retrive device context and queue + + CLCudaAPI::Context ctx = OpKernel::device_->context(); + CLCudaAPI::Queue queue = OpKernel::device_->queue(); + + for (size_t i = 0; i < in_data.size(); ++i) { + // allocate data to GPU + + auto dev_in = CLCudaAPI::Buffer(ctx, queue, in_data[i].begin(), + in_data[i].end()); + + auto dev_W = + CLCudaAPI::Buffer(ctx, queue, W[0].begin(), W[0].end()); + + auto dev_bias = + CLCudaAPI::Buffer(ctx, queue, bias[0].begin(), bias[0].end()); + + auto dev_out = CLCudaAPI::Buffer(ctx, queue, out_data[i].begin(), + out_data[i].end()); + + // cast data types and call libdnn + + // TODO(edgar): set a global variable with batch size or + // embedd this inside the next gen Tensor class. + const int batch_size = 1; + + const float_t *input_ptr = double_cast(dev_in()); + const float_t *weights_ptr = double_cast(dev_W()); + const float_t *bias_ptr = double_cast(dev_bias()); + + float_t *output_ptr = mutable_double_cast(dev_out()); + + // first time, tune the kernel + + // TODO(edgar/naibaf): enable when second generation + // kernel are available + + if (!initialized_) { + /*kernel_->Tune(const_cast(output_ptr), nullptr, + const_cast(weights_ptr), nullptr, + const_cast(bias_ptr), nullptr, + const_cast(input_ptr), nullptr, + batch_size); + initialized_ = true;*/ + } + + // call libdnn forward + + kernel_->Forward(input_ptr, weights_ptr, bias_ptr, output_ptr, + batch_size); + + // Upload data GPU -> CPU + /*std::vector dev_W_shadow(W.size(), 0); + dev_W.Read(queue, W.size(), dev_W_shadow); + + // FOR DEBUG ONLY + nn_warn("W kernel"); + for (size_t j = 0; j < W.size(); ++j) { + std::cout << dev_W_shadow[j] << " "; + } + std::cout << std::endl; + + // Upload data GPU -> CPU + std::vector dev_in_shadow(in_data_padded[i].size(), 0); + dev_in.Read(queue, in_data_padded[i].size(), dev_in_shadow); + + // FOR DEBUG ONLY + nn_warn("input kernel"); + for (size_t j = 0; j < in_data_padded[i].size(); ++j) { + std::cout << dev_in_shadow[j] << " "; + } + std::cout << std::endl;*/ + + // Upload data GPU -> CPU + // TODO(edgar): trigger this only when is needed + std::vector out(out_data[i].size(), 0); + dev_out.Read(queue, out_data[i].size(), out); + + /* + // FOR DEBUG ONLY + nn_warn("output kernel"); + for (size_t j = 0; j < out.size(); ++j) { + std::cout << out[j] << " "; + } + std::cout << std::endl; + */ + + // copy data to be activated + std::copy(std::begin(out), std::end(out), std::begin(out_data[i])); + } + +#else + CNN_UNREFERENCED_PARAMETER(context); + throw nn_error("TinyDNN was not compiled with LibDNN support."); +#endif + } + + private: +#ifdef CNN_USE_LIBDNN + float_t *mutable_double_cast(const cl_mem cl_mem_gpu) { + return static_cast(reinterpret_cast(cl_mem_gpu)); + } + + const float_t *double_cast(const cl_mem cl_mem_gpu) { + return reinterpret_cast( + reinterpret_cast(cl_mem_gpu)); + } +#endif + + void init_libdnn(const Device *device, const core::conv_params ¶ms) { +#ifdef CNN_USE_LIBDNN + assert(device != nullptr); + + // Context needs to be initialized with one device and queue + greentea::device::setupViennaCLContext( + device->deviceId(), device->context()(), device->device()(), + device->queue()()); + + dev_ptr_ = std::make_shared( + device->deviceId(), device->deviceId(), /* list_id, */ +// TODO(edgar): refactor this since it's possible +// to have OpenCL and CUDA. +#if defined(USE_OPENCL) + greentea::Backend::BACKEND_OpenCL); +#elif defined(USE_CUDA) + greentea::Backend::BACKEND_CUDA); +#else + greentea::Backend::BACKEND_CPU); +#endif + + // Initialize device pointer in libdnn + dev_ptr_->Init(); + + // Setup libdnn params + greentea::LibDNNConfig config; + + config.dev_ptr = dev_ptr_.get(); + + // NCHW shape setups + + const float_t dy = params.in_padded.height_ - params.in.height_; + const float_t dx = params.in_padded.width_ - params.in.width_; + + std::vector in_shape = {1, params.in.depth_, params.in.height_, + params.in.width_}; + + std::vector out_shape = {1, params.out.depth_, params.out.height_, + params.out.width_}; + + std::vector kernel = {params.weight.height_, params.weight.width_}; + + std::vector pad = {dy / 2, dx / 2}; + + std::vector stride = {params.h_stride, params.w_stride}; + + std::vector dilation = {1, 1}; + + config.in_shape = in_shape; + config.out_shape = out_shape; + config.pad = pad; + config.kernel = kernel; + config.stride = stride; + config.dilation = dilation; + config.group = 1; + + config.bias_term = params.has_bias; + + // Disables some optimizations but may give more stable results + config.fast_unsafe_math = false; + // Disables backward pass of weights during kernel.Backward(); + config.weights_backward = false; + // Disables backward pass for bias during kernel.Backward(); + config.bias_backward = false; + + // (Disabling bias and weight backward pass only propagates the data + // gradient (error)) + + if (std::is_same::value || + dev_ptr_->CheckCapability("cl_khr_int64_base_atomics")) { + config.wgalgo = greentea::LIBDNN_CONVOLUTION_WG_ALGO_ATOMIC; + config.bwalgo = greentea::LIBDNN_CONVOLUTION_BW_ALGO_COL2IM_ATOMIC; + } else { + config.wgalgo = greentea::LIBDNN_CONVOLUTION_WG_ALGO_DIRECT; + config.bwalgo = greentea::LIBDNN_CONVOLUTION_BW_ALGO_IM2COL; + } + + // generate sources and compile kernel + kernel_.reset(new greentea::LibDNNConv(config)); +#else + CNN_UNREFERENCED_PARAMETER(device); + CNN_UNREFERENCED_PARAMETER(params); +#endif + } + + private: +#ifdef CNN_USE_LIBDNN + std::shared_ptr dev_ptr_; + std::shared_ptr> kernel_; + bool initialized_; +#endif +}; + +class Conv2dLibDNNBackwardOp : public core::OpKernel { + public: + explicit Conv2dLibDNNBackwardOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + CNN_UNREFERENCED_PARAMETER(context); + throw nn_error("Not implemented yet."); + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/conv2d_op_nnpack.h b/tiny_dnn/core/kernels/conv2d_op_nnpack.h new file mode 100755 index 0000000000000..0c9621143844c --- /dev/null +++ b/tiny_dnn/core/kernels/conv2d_op_nnpack.h @@ -0,0 +1,87 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/backend.h" +#include "tiny_dnn/core/params/conv_params.h" + +namespace tiny_dnn { +namespace kernels { + +inline void conv2d_op_nnpack(const tensor_t &in_data, + const vec_t &W, + const vec_t &bias, + tensor_t &out_data, + const core::conv_params ¶ms) { +#ifdef CNN_USE_NNPACK + // call singleton to initialize NNPACK + core::NNPackInitializer::getInstance().initialize(); + + const auto algorithm = core::nnp_algorithm(); + const auto kernel_transform_strategy = core::nnp_kts(); + + const size_t input_channels = params.in.depth_; + const size_t output_channels = params.out.depth_; + + // input data passed by convolution layer has been padded already + // set input_size to padded size + const nnp_size input_size = {params.in_padded.width_, + params.in_padded.height_}; + + const nnp_size kernel_size = {params.weight.width_, params.weight.height_}; + + // input padded ,so no need to do padding + const float_t dx{0.0}; // params.in_padded.width_ - params.in.width_; + const float_t dy{0.0}; // params.in_padded.height_ - params.in.height_; + + // we'll assume that padding is symmetric + + const nnp_padding padding = { + dy / 2), // top + dx / 2), // right + dy / 2), // bottom + dx / 2) // left + }; + + const nnp_size stride = {params.w_stride, params.h_stride}; + + const float *input_ptr = in_data[0].data(); + const float *kernel_ptr = W.data(); + const float *bias_ptr = bias.data(); + + float *output_ptr = out_data[0].data(); + + // TODO(edgarriba): embed it into a class + const size_t num_mkl_threads = 1; + pthreadpool_t threadpool = pthreadpool_create(num_mkl_threads); + + nnp_profile *profile = nullptr; + + nnp_status status = nnp_convolution_inference( + algorithm, kernel_transform_strategy, input_channels, output_channels, + input_size, padding, kernel_size, stride, input_ptr, kernel_ptr, bias_ptr, + output_ptr, threadpool, profile); + + if (status != nnp_status_success) { + throw nn_error("Could not succeed with nnp_convolution_inference"); + } + + // TODO(edgarriba): embed it into a class + pthreadpool_destroy(threadpool); +#else + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(W); + CNN_UNREFERENCED_PARAMETER(bias); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(params); + throw nn_error("TinyDNN has not been compiled with NNPACK support."); +#endif +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/conv2d_op_opencl.h b/tiny_dnn/core/kernels/conv2d_op_opencl.h new file mode 100755 index 0000000000000..fde5e6480da91 --- /dev/null +++ b/tiny_dnn/core/kernels/conv2d_op_opencl.h @@ -0,0 +1,140 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/core/framework/op_kernel.h" + +namespace tiny_dnn { + +class Conv2dOpenCLForwardOp : public core::OpKernel { + public: + explicit Conv2dOpenCLForwardOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { +#if defined(USE_OPENCL) || defined(USE_CUDA) + auto params = OpKernel::params_->conv(); + + // incoming/outcoming data + const tensor_t &in_data = context.input(0); + const tensor_t &W = context.input(1); + const tensor_t &bias = context.input(2); + tensor_t &out_data = context.output(1); + + // initialize outputs + fill_tensor(out_data, float_t{0}); + + // retrieve program from register + CLCudaAPI::Program program = ProgramManager::getInstance().program( + Program(context.device(), context.Layer())); + nn_warn("Got Program"); + + // Creates the kernel from the compiled program and sets the three + // arguments. + // Note that the indices of the arguments have to be set according to + // their + // order in the kernel. + auto kernel = CLCudaAPI::Kernel(program, "CFMulti"); + nn_warn("Got Kernel"); + + tiny_dnn::Device *device = context.device(); + CLCudaAPI::Context ctx = context.device()->context(); + CLCudaAPI::Queue queue = context.device()->queue(); + + // TODO(edgar): check if we really need that + for (size_t i = 0; i < in_data.size(); ++i) { + // Creates device buffers and copies the host data to these + // device buffers. + + auto dev_in = CLCudaAPI::Buffer(ctx, queue, in_data[i].begin(), + in_data[i].end()); + + auto dev_W = + CLCudaAPI::Buffer(ctx, queue, W[0].begin(), W[0].end()); + + auto dev_bias = + CLCudaAPI::Buffer(ctx, queue, bias[0].begin(), bias[0].end()); + + auto dev_out = CLCudaAPI::Buffer(ctx, queue, out_data[i].begin(), + out_data[i].end()); + + kernel.SetArgument(0, dev_in); // image_data + kernel.SetArgument(1, 0); // image_offset + kernel.SetArgument(2, dev_W); // kernel_data + kernel.SetArgument(3, 0); // kernel_offset + kernel.SetArgument(4, dev_bias); // bias + kernel.SetArgument(5, 0); // bias_offset + kernel.SetArgument(6, dev_out); // convolved_image + kernel.SetArgument(7, 0); // convolved_image_offset + + kernel.SetArgument(8, static_cast(params.in.width_)); // WIDTH + kernel.SetArgument(9, + static_cast(params.in.height_)); // HEIGHT + kernel.SetArgument( + 10, + static_cast(params.out.width_)); // OUTPUT_W + kernel.SetArgument( + 11, static_cast(params.out.height_)); // OUTPUT_H + + // We make sure that work group size is multiple of 16 + size_t res = device->device().MaxWorkGroupSize() % 16; + size_t size = device->device().MaxWorkGroupSize() - res; + + auto global = std::vector{size}; + auto local = std::vector{16}; + + // Creates a new CLCudaAPI event to be able to time kernels + auto event = CLCudaAPI::Event(); + + // Enqueues the kernel and waits for the result. + // Note that launching the kernel is always a-synchronous and thus + // requires finishing the queue in order to complete the operation. + nn_info("## Running the kernel ..."); + + kernel.Launch(queue, global, local, event.pointer()); + queue.Finish(event); + + nn_info(" > Took " + to_string(event.GetElapsedTime()) + " ms"); + + // Upload data GPU -> CPU + std::vector out(out_data[i].size(), 0); + dev_out.Read(queue, out_data[i].size(), out); + + // FOR DEBUG ONLY + nn_warn("output kernel"); + for (size_t j = 0; j < out.size(); ++j) { + std::cout << out[j] << " "; + } + std::cout << std::endl; + + // copy back + std::copy(std::begin(out), std::end(out), + std::back_inserter(out_data[i])); + } +#else + CNN_UNREFERENCED_PARAMETER(context); + throw nn_error("Not compiled with OpenCL"); +#endif + } +}; + +class Conv2dOpenCLBackwardOp : public core::OpKernel { + public: + explicit Conv2dOpenCLBackwardOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + CNN_UNREFERENCED_PARAMETER(context); + nn_error("Not implemented yet."); + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/fully_connected_grad_op.h b/tiny_dnn/core/kernels/fully_connected_grad_op.h new file mode 100755 index 0000000000000..76ea1266b3b14 --- /dev/null +++ b/tiny_dnn/core/kernels/fully_connected_grad_op.h @@ -0,0 +1,55 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" + +#include "tiny_dnn/core/kernels/fully_connected_op_avx.h" +#include "tiny_dnn/core/kernels/fully_connected_op_internal.h" + +namespace tiny_dnn { + +class FullyConnectedGradOp : public core::OpKernel { + public: + explicit FullyConnectedGradOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto params = OpKernel::params_->fully(); + + // incoming/outcoming data + const tensor_t &prev_out = context.input(0); + const tensor_t &W = context.input(1); + tensor_t &dW = context.input_grad(1); + tensor_t *db = params.has_bias_ ? &context.input_grad(2) : nullptr; + tensor_t &prev_delta = context.input_grad(0); + tensor_t &curr_delta = context.output_grad(0); + tensor_t dummy; // need lvalue for non-const reference + + // initialize outputs + fill_tensor(prev_delta, float_t{0}); + + // call the algorithm depending on the selected engine type + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::internal) { + kernels::fully_connected_op_internal( + prev_out, W[0], dW, params.has_bias_ ? *db : dummy, curr_delta, + prev_delta, params, context.parallelize()); + } else if (engine == core::backend_t::avx) { + kernels::fully_connected_op_avx( + prev_out, W[0], dW, params.has_bias_ ? *db : dummy, curr_delta, + prev_delta, params, context.parallelize()); + } else { + throw nn_error("Not supported engine: " + to_string(engine)); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/fully_connected_op.h b/tiny_dnn/core/kernels/fully_connected_op.h new file mode 100755 index 0000000000000..3cc7feb0fbd93 --- /dev/null +++ b/tiny_dnn/core/kernels/fully_connected_op.h @@ -0,0 +1,57 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" + +#include "tiny_dnn/core/kernels/fully_connected_op_avx.h" +#include "tiny_dnn/core/kernels/fully_connected_op_internal.h" +#include "tiny_dnn/core/kernels/fully_connected_op_nnpack.h" + +namespace tiny_dnn { + +class FullyConnectedOp : public core::OpKernel { + public: + explicit FullyConnectedOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto params = OpKernel::params_->fully(); + + // incomimg/outcoming data + const tensor_t &in_data = context.input(0); + const tensor_t &W = context.input(1); + const tensor_t *bias = params.has_bias_ ? &context.input(2) : nullptr; + tensor_t &out_data = context.output(0); + + // initialize outputs + fill_tensor(out_data, float_t{0}); + + // call the algorithm depending on the selected engine type + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::internal) { + kernels::fully_connected_op_internal( + in_data, W[0], params.has_bias_ ? (*bias)[0] : vec_t(), out_data, + params, context.parallelize()); + } else if (engine == core::backend_t::nnpack) { + kernels::fully_connected_op_nnpack( + in_data, W[0], params.has_bias_ ? (*bias)[0] : vec_t(), out_data, + params, context.parallelize()); + } else if (engine == core::backend_t::avx) { + kernels::fully_connected_op_avx(in_data, W[0], + params.has_bias_ ? (*bias)[0] : vec_t(), + out_data, params, context.parallelize()); + } else { + throw nn_error("Not supported engine: " + to_string(engine)); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/fully_connected_op_avx.h b/tiny_dnn/core/kernels/fully_connected_op_avx.h new file mode 100755 index 0000000000000..78270e43cf345 --- /dev/null +++ b/tiny_dnn/core/kernels/fully_connected_op_avx.h @@ -0,0 +1,244 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/core/kernels/fully_connected_op_internal.h" + +namespace tiny_dnn { +namespace kernels { + +#ifdef CNN_USE_AVX + +template +inline void avx_fully_connected_forward_kernel( + const std::vector> &in_data, + const std::vector &W, + const std::vector &bias, + std::vector> &out_data, + const core::fully_params ¶ms, + const bool layer_parallelize) { + if (params.has_bias_) { + size_t nblocks = params.out_size_ / 8; + size_t nremains = params.out_size_ & 7; + if (nremains) { + int32_t mask_src[] = { + -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, + }; + __m256i imask = + _mm256_loadu_si256((__m256i const *)(mask_src + 8 - nremains)); + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const auto &in = in_data[sample]; + auto &out = out_data[sample]; + { + for (size_t i = 0; i < nblocks; ++i) { + __m256 b = _mm256_loadu_ps(&bias[8 * i]); + _mm256_storeu_ps(&out[8 * i], b); + } + auto b = _mm256_maskload_ps(&bias[8 * nblocks], imask); + _mm256_maskstore_ps(&out[8 * nblocks], imask, b); + } + for (size_t c = 0; c < params.in_size_; c++) { + auto in_val = _mm256_set1_ps(in[c]); + const float *pW = &W[c * params.out_size_]; + for (size_t i = 0; i < nblocks / 2; ++i) { + __m256 sum0 = _mm256_loadu_ps(&out[16 * i]); + __m256 sum1 = _mm256_loadu_ps(&out[16 * i + 8]); + __m256 w0 = _mm256_loadu_ps(pW + 16 * i); + __m256 w1 = _mm256_loadu_ps(pW + 16 * i + 8); + sum0 = madd256_ps(w0, in_val, sum0); + sum1 = madd256_ps(w1, in_val, sum1); + _mm256_storeu_ps(&out[16 * i], sum0); + _mm256_storeu_ps(&out[16 * i + 8], sum1); + } + if (nblocks & 1) { + __m256 sum0 = _mm256_loadu_ps(&out[nblocks / 2 * 16]); + __m256 w0 = _mm256_loadu_ps(pW + nblocks / 2 * 16); + sum0 = madd256_ps(w0, in_val, sum0); + _mm256_storeu_ps(&out[nblocks / 2 * 16], sum0); + } + __m256 sum = _mm256_maskload_ps(&out[8 * nblocks], imask); + __m256 w = _mm256_maskload_ps(pW + 8 * nblocks, imask); + sum = madd256_ps(w, in_val, sum); + _mm256_maskstore_ps(&out[8 * nblocks], imask, sum); + } + }); + } else { + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const auto &in = in_data[sample]; + auto &out = out_data[sample]; + for (size_t i = 0; i < nblocks; ++i) { + __m256 b = _mm256_loadu_ps(&bias[8 * i]); + _mm256_storeu_ps(&out[8 * i], b); + } + for (size_t c = 0; c < params.in_size_; c++) { + auto in_val = _mm256_set1_ps(in[c]); + const float *pW = &W[c * params.out_size_]; + for (size_t i = 0; i < nblocks; ++i) { + __m256 sum = _mm256_loadu_ps(&out[8 * i]); + __m256 w = _mm256_loadu_ps(pW + 8 * i); + sum = madd256_ps(w, in_val, sum); + _mm256_storeu_ps(&out[8 * i], sum); + } + } + }); + } + } else { + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const auto &in = in_data[sample]; + auto &out = out_data[sample]; + for (size_t i = 0; i < params.out_size_; i++) { + float sum = 0.0f; + for (size_t c = 0; c < params.in_size_; c++) { + sum += W[c * params.out_size_ + i] * in[c]; + } + out[i] = sum; + } + }); + } +} + +template +inline void avx_fully_connected_forward_kernel( + const std::vector> &in_data, + const std::vector &W, + const std::vector &bias, + std::vector> &out_data, + const core::fully_params ¶ms, + const bool layer_parallelize) { + // fallback to tiny-backend when float_t is double + fully_connected_op_internal(in_data, W, bias, out_data, params, + layer_parallelize); +} + +template +inline void avx_fully_connected_back_kernel( + const std::vector> &prev_out, + const std::vector &W, + std::vector> &dW, + std::vector> &db, + std::vector> &curr_delta, + std::vector> &prev_delta, + const core::fully_params ¶ms, + const bool layer_parallelize) { + if (params.has_bias_) { + for (size_t sample = 0; sample < prev_out.size(); sample++) { + auto &prev_delta2 = prev_delta[sample]; + auto &curr_delta2 = curr_delta[sample]; + auto &prev_out2 = prev_out[sample]; + auto &dW2 = dW[sample]; + auto &db2 = db[sample]; + for (size_t c = 0; c < params.in_size_; c++) { + // propagate delta to previous layer + // prev_delta[c] += current_delta[r] * W_[c * out_size_ + r] + prev_delta2[c] += vectorize::dot( + &curr_delta2[0], &W[c * params.out_size_], params.out_size_); + } + for_(layer_parallelize, 0u, params.out_size_, + [&](const blocked_range &r) { + // accumulate weight-step using delta + // dW[c * out_size + i] += current_delta[i] * prev_out[c] + size_t len = r.end() - r.begin(); + for (size_t c = 0; c < params.in_size_; c++) { + vectorize::muladd(&curr_delta2[r.begin()], prev_out2[c], len, + &dW2[c * params.out_size_ + r.begin()]); + } + // vec_t& db = *in_grad[2]; + vectorize::reduce(&curr_delta2[r.begin()], len, &db2[r.begin()]); + }); + } + } else { + for (size_t sample = 0; sample < prev_out.size(); sample++) { + auto &prev_delta2 = prev_delta[sample]; + auto &curr_delta2 = curr_delta[sample]; + auto &prev_out2 = prev_out[sample]; + auto &dW2 = dW[sample]; + for (size_t c = 0; c < params.in_size_; c++) { + // propagate delta to previous layer + // prev_delta[c] += current_delta[r] * W_[c * out_size_ + r] + prev_delta2[c] += vectorize::dot( + &curr_delta2[0], &W[c * params.out_size_], params.out_size_); + } + for_(layer_parallelize, 0u, params.out_size_, + [&](const blocked_range &r) { + // accumulate weight-step using delta + // dW[c * out_size + i] += current_delta[i] * prev_out[c] + size_t len = r.end() - r.begin(); + for (size_t c = 0; c < params.in_size_; c++) { + vectorize::muladd(&curr_delta2[r.begin()], prev_out2[c], len, + &dW2[c * params.out_size_ + r.begin()]); + } + }); + } + } +} + +template +inline void avx_fully_connected_back_kernel( + const std::vector> &prev_out, + const std::vector &W, + std::vector> &dW, + std::vector> &db, + std::vector> &curr_delta, + std::vector> &prev_delta, + const core::fully_params ¶ms, + const bool layer_parallelize) { + // fallback to tiny-backend when float_t is double + fully_connected_op_internal(prev_out, W, dW, db, curr_delta, prev_delta, + params, layer_parallelize); +} + +#endif // CNN_USE_AVX + +inline void fully_connected_op_avx(const tensor_t &in_data, + const vec_t &W, + const vec_t &bias, + tensor_t &out_data, + const core::fully_params ¶ms, + const bool layer_parallelize) { +#ifdef CNN_USE_AVX + avx_fully_connected_forward_kernel(in_data, W, bias, out_data, params, + layer_parallelize); +#else + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(W); + CNN_UNREFERENCED_PARAMETER(bias); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(params); + CNN_UNREFERENCED_PARAMETER(layer_parallelize); + throw nn_error("TinyDNN has not been compiled with AVX support."); +#endif +} + +inline void fully_connected_op_avx(const tensor_t &prev_out, + const vec_t &W, + tensor_t &dW, + tensor_t &db, + tensor_t &curr_delta, + tensor_t &prev_delta, + const core::fully_params ¶ms, + const bool layer_parallelize) { +#ifdef CNN_USE_AVX + avx_fully_connected_back_kernel(prev_out, W, dW, db, curr_delta, prev_delta, + params, layer_parallelize); +#else + CNN_UNREFERENCED_PARAMETER(prev_out); + CNN_UNREFERENCED_PARAMETER(W); + CNN_UNREFERENCED_PARAMETER(dW); + CNN_UNREFERENCED_PARAMETER(db); + CNN_UNREFERENCED_PARAMETER(curr_delta); + CNN_UNREFERENCED_PARAMETER(prev_delta); + CNN_UNREFERENCED_PARAMETER(params); + CNN_UNREFERENCED_PARAMETER(layer_parallelize); + throw nn_error("TinyDNN has not been compiled with AVX support."); +#endif +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/fully_connected_op_internal.h b/tiny_dnn/core/kernels/fully_connected_op_internal.h new file mode 100755 index 0000000000000..2a27de12a5386 --- /dev/null +++ b/tiny_dnn/core/kernels/fully_connected_op_internal.h @@ -0,0 +1,74 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/params/fully_params.h" + +namespace tiny_dnn { +namespace kernels { + +inline void fully_connected_op_internal(const tensor_t &in_data, + const vec_t &W, + const vec_t &bias, + tensor_t &out_data, + const core::fully_params ¶ms, + const bool layer_parallelize) { + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const vec_t &in = in_data[sample]; + vec_t &out = out_data[sample]; + + for (size_t i = 0; i < params.out_size_; i++) { + out[i] = float_t{0}; + for (size_t c = 0; c < params.in_size_; c++) { + out[i] += W[c * params.out_size_ + i] * in[c]; + } + + if (params.has_bias_) { + out[i] += bias[i]; + } + } + }); +} + +inline void fully_connected_op_internal(const tensor_t &prev_out, + const vec_t &W, + tensor_t &dW, + tensor_t &db, + tensor_t &curr_delta, + tensor_t &prev_delta, + const core::fully_params ¶ms, + const bool layer_parallelize) { + for (size_t sample = 0; sample < prev_out.size(); sample++) { + for (size_t c = 0; c < params.in_size_; c++) { + // propagate delta to previous layer + // prev_delta[c] += current_delta[r] * W_[c * out_size_ + r] + prev_delta[sample][c] += vectorize::dot( + &curr_delta[sample][0], &W[c * params.out_size_], params.out_size_); + } + + for_(layer_parallelize, 0, params.out_size_, [&](const blocked_range &r) { + // accumulate weight-step using delta + // dW[c * out_size + i] += current_delta[i] * prev_out[c] + for (size_t c = 0; c < params.in_size_; c++) { + vectorize::muladd(&curr_delta[sample][r.begin()], prev_out[sample][c], + r.end() - r.begin(), + &dW[sample][c * params.out_size_ + r.begin()]); + } + + if (params.has_bias_) { + // vec_t& db = *in_grad[2]; + for (size_t i = r.begin(); i < r.end(); i++) { + db[sample][i] += curr_delta[sample][i]; + } + } + }); + } +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/fully_connected_op_nnpack.h b/tiny_dnn/core/kernels/fully_connected_op_nnpack.h new file mode 100755 index 0000000000000..cb6a37c7815fe --- /dev/null +++ b/tiny_dnn/core/kernels/fully_connected_op_nnpack.h @@ -0,0 +1,61 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/backend.h" +#include "tiny_dnn/core/params/fully_params.h" + +namespace tiny_dnn { +namespace kernels { + +inline void fully_connected_op_nnpack(const tensor_t &in_data, + const vec_t &W, + const vec_t &bias, + tensor_t &out_data, + const core::fully_params ¶ms, + const bool layer_parallelize) { +#ifdef CNN_USE_NNPACK + // call singleton to initialize NNPACK + NNPackInitializer::getInstance().initialize(); + + const float *kernel_ptr = W.data(); + const float *input_ptr = in_data[0].data(); + float *output_ptr = out_data[0].data(); + + // TODO(edgarriba): embed it into a class + const size_t num_mkl_threads = 1; + pthreadpool_t threadpool = pthreadpool_create(num_mkl_threads); + + const auto status = + nnp_fully_connected_inference(params.in_size_, params.out_size_, input_ptr, + kernel_ptr, output_ptr, threadpool); + + if (status != nnp_status_success) { + throw nn_error("Could not succeed with nnp_max_pooling_output"); + } + + // TODO(edgarriba): embed it into a class + pthreadpool_destroy(threadpool); + + if (params.has_bias_) { + for_i(layer_parallelize, params.out_size_, + [&](size_t i) { output_ptr[i] += bias[i]; }); + } +#else + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(W); + CNN_UNREFERENCED_PARAMETER(bias); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(params); + CNN_UNREFERENCED_PARAMETER(layer_parallelize); + throw nn_error("TinyDNN has not been compiled with NNPACK support."); +#endif +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/global_avepool_grad_op.h b/tiny_dnn/core/kernels/global_avepool_grad_op.h new file mode 100755 index 0000000000000..5cdef260b7392 --- /dev/null +++ b/tiny_dnn/core/kernels/global_avepool_grad_op.h @@ -0,0 +1,45 @@ +/* + Copyright (c) 2017, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" +#include "tiny_dnn/core/kernels/global_avepool_op_avx.h" +#include "tiny_dnn/core/kernels/global_avepool_op_internal.h" + +namespace tiny_dnn { + +class GlobalAvePoolGradOp : public core::OpKernel { + public: + explicit GlobalAvePoolGradOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto ¶ms = OpKernel::params_->global_avepool(); + + // incoming/outcoming data + tensor_t &prev_delta = context.input_grad(0); + tensor_t &curr_delta = context.output_grad(0); + + // initialize outputs + fill_tensor(prev_delta, float_t{0}); + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::avx) { +#ifdef CNN_USE_AVX + kernels::global_avepool_grad_op_avx(prev_delta, curr_delta, params, + context.parallelize()); +#endif + } else { + kernels::global_avepool_grad_op_internal(prev_delta, curr_delta, params, + context.parallelize()); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/global_avepool_op.h b/tiny_dnn/core/kernels/global_avepool_op.h new file mode 100755 index 0000000000000..cdee50095b8e0 --- /dev/null +++ b/tiny_dnn/core/kernels/global_avepool_op.h @@ -0,0 +1,45 @@ +/* + Copyright (c) 2017, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" +#include "tiny_dnn/core/kernels/global_avepool_op_avx.h" +#include "tiny_dnn/core/kernels/global_avepool_op_internal.h" + +namespace tiny_dnn { + +class GlobalAvePoolOp : public core::OpKernel { + public: + explicit GlobalAvePoolOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto ¶ms = OpKernel::params_->global_avepool(); + + // incomimg / outcoming data + const tensor_t &in_data = context.input(0); + tensor_t &out_data = context.output(0); + + // initialize outputs + fill_tensor(out_data, float_t{0}); + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::avx) { +#ifdef CNN_USE_AVX + kernels::global_avepool_op_avx(in_data, out_data, params, + context.parallelize()); +#endif + } else { + kernels::global_avepool_op_internal(in_data, out_data, params, + context.parallelize()); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/global_avepool_op_avx.h b/tiny_dnn/core/kernels/global_avepool_op_avx.h new file mode 100755 index 0000000000000..cf6b528b34b18 --- /dev/null +++ b/tiny_dnn/core/kernels/global_avepool_op_avx.h @@ -0,0 +1,155 @@ +/* + Copyright (c) 2017, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +namespace tiny_dnn { +namespace kernels { + +#ifdef CNN_USE_AVX +#ifdef CNN_USE_DOUBLE + +// double version +inline void global_avepool_op_avx(const tensor_t &in_data, + tensor_t &out_data, + const core::global_avepool_params ¶ms, + const bool layer_parallelize) { + const size_t pool_area = params.in.width_ * params.in.height_; + const size_t nblocks_per_channel = pool_area / 4; + const size_t nremains_per_channel = pool_area & 3; + + static const int64_t mask_src[] = {-1, -1, -1, -1, 0, 0, 0, 0}; + __m256i imask = + _mm256_loadu_si256((__m256i const *)(mask_src + 4 - nremains_per_channel)); + __m128d one_m = _mm_cvtsi64_sd(_mm_setzero_pd(), 1); + __m128d pool_area_m = _mm_cvtsi64_sd(_mm_setzero_pd(), pool_area); + __m128d pool_area_inv_m = _mm_div_sd(one_m, pool_area_m); + + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const vec_t &in = in_data[sample]; + vec_t &out = out_data[sample]; + for (size_t i = 0; i < params.in.depth_; i++) { + const double *pin = &in[i * pool_area]; + __m256d sum0 = + vectorize::accumulate(pin, nblocks_per_channel); + sum0 = _mm256_add_pd( + sum0, + _mm256_maskload_pd(pin + pool_area - nremains_per_channel, imask)); + _mm_store_sd(&out[i], _mm_mul_sd(hsum256_pd(sum0), pool_area_inv_m)); + } + }); +} + +// double version +inline void global_avepool_grad_op_avx( + tensor_t &prev_delta, + const tensor_t &curr_delta, + const core::global_avepool_params ¶ms, + const bool layer_parallelize) { + const size_t pool_area = params.in.width_ * params.in.height_; + const double pool_area_inv = 1.0 / pool_area; + const size_t nblocks_per_channel = pool_area / 4; + const size_t nremains_per_channel = pool_area & 3; + + int64_t mask_src[] = {-1, -1, -1, -1, 0, 0, 0, 0}; + __m256i imask = + _mm256_loadu_si256((__m256i const *)(mask_src + 4 - nremains_per_channel)); + __m256d pool_area_inv_m = _mm256_set1_pd(pool_area_inv); + + for_i(layer_parallelize, prev_delta.size(), [&](size_t sample) { + vec_t &prev = prev_delta[sample]; + const vec_t &curr = curr_delta[sample]; + + for (size_t i = 0; i < params.in.depth_; i++) { + size_t j = 0; + __m256d prev_m = _mm256_broadcast_sd(&curr[i]); + prev_m = _mm256_mul_pd(prev_m, pool_area_inv_m); + + while (j < nblocks_per_channel) { + _mm256_storeu_pd(&prev[i * pool_area + 4 * j], prev_m); + j++; + } + _mm256_maskstore_pd(&prev[i * pool_area + 4 * j], imask, prev_m); + } + }); +} + +#else // CNN_USE_DOUBLE + +// float version +inline void global_avepool_op_avx(const tensor_t &in_data, + tensor_t &out_data, + const core::global_avepool_params ¶ms, + const bool layer_parallelize) { + const size_t pool_area = params.in.width_ * params.in.height_; + const size_t nblocks_per_channel = pool_area / 8; + const size_t nremains_per_channel = pool_area & 7; + + static const int32_t mask_src[] = { + -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, + }; + __m256i imask = + _mm256_loadu_si256((__m256i const *)(mask_src + 8 - nremains_per_channel)); + __m128 one_m = _mm_cvtsi32_ss(_mm_setzero_ps(), 1); + __m128 pool_area_m = + _mm_cvtsi32_ss(_mm_setzero_ps(), static_cast(pool_area)); + __m128 pool_area_inv_m = _mm_div_ss(one_m, pool_area_m); + + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const vec_t &in = in_data[sample]; + vec_t &out = out_data[sample]; + for (size_t i = 0; i < params.in.depth_; i++) { + const float *pin = &in[i * pool_area]; + __m256 sum0 = + vectorize::accumulate(pin, nblocks_per_channel); + sum0 = _mm256_add_ps( + sum0, + _mm256_maskload_ps(pin + pool_area - nremains_per_channel, imask)); + _mm_store_ss(&out[i], _mm_mul_ss(hsum256_ps(sum0), pool_area_inv_m)); + } + }); +} + +// float version +inline void global_avepool_grad_op_avx( + tensor_t &prev_delta, + const tensor_t &curr_delta, + const core::global_avepool_params ¶ms, + const bool layer_parallelize) { + const size_t pool_area = params.in.width_ * params.in.height_; + const float pool_area_inv = 1.0f / static_cast(pool_area); + const size_t nblocks_per_channel = pool_area / 8; + const size_t nremains_per_channel = pool_area & 7; + + int32_t mask_src[] = { + -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, + }; + __m256i imask = + _mm256_loadu_si256((__m256i const *)(mask_src + 8 - nremains_per_channel)); + + for_i(layer_parallelize, prev_delta.size(), [&](size_t sample) { + vec_t &prev = prev_delta[sample]; + const vec_t &curr = curr_delta[sample]; + + for (size_t i = 0; i < params.in.depth_; i++) { + size_t j = 0; + const __m256 prev_m = _mm256_set1_ps(curr[i] * pool_area_inv); + + while (j < nblocks_per_channel) { + _mm256_storeu_ps(&prev[i * pool_area + 8 * j], prev_m); + j++; + } + _mm256_maskstore_ps(&prev[i * pool_area + 8 * j], imask, prev_m); + } + }); +} + +#endif // CNN_USE_DOUBLE +#endif // CNN_USE_AVX + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/global_avepool_op_internal.h b/tiny_dnn/core/kernels/global_avepool_op_internal.h new file mode 100755 index 0000000000000..95323443883f2 --- /dev/null +++ b/tiny_dnn/core/kernels/global_avepool_op_internal.h @@ -0,0 +1,52 @@ +/* + Copyright (c) 2017, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +namespace tiny_dnn { +namespace kernels { + +inline void global_avepool_op_internal( + const tensor_t &in_data, + tensor_t &out_data, + const core::global_avepool_params ¶ms, + const bool layer_parallelize) { + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const vec_t &in = in_data[sample]; + vec_t &out = out_data[sample]; + + const size_t pool_area = params.in.width_ * params.in.height_; + for (size_t i = 0; i < params.in.depth_; i++) { + for (size_t j = 0; j < pool_area; j++) { + out[i] += in[i * pool_area + j]; + } + out[i] /= pool_area; + } + }); +} + +inline void global_avepool_grad_op_internal( + tensor_t &prev_delta, + const tensor_t &curr_delta, + const core::global_avepool_params ¶ms, + const bool layer_parallelize) { + for_i(layer_parallelize, prev_delta.size(), [&](size_t sample) { + vec_t &prev = prev_delta[sample]; + const vec_t &curr = curr_delta[sample]; + + const size_t pool_area = params.in.width_ * params.in.height_; + for (size_t i = 0; i < params.in.depth_; i++) { + const float_t pi = curr[i] / pool_area; + for (size_t j = 0; j < pool_area; j++) { + prev[i * pool_area + j] = pi; + } + } + }); +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/maxpool_grad_op.h b/tiny_dnn/core/kernels/maxpool_grad_op.h new file mode 100755 index 0000000000000..ab0fcacca95e3 --- /dev/null +++ b/tiny_dnn/core/kernels/maxpool_grad_op.h @@ -0,0 +1,49 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" + +#include "tiny_dnn/core/kernels/maxpool_op_avx.h" +#include "tiny_dnn/core/kernels/maxpool_op_internal.h" + +namespace tiny_dnn { + +class MaxPoolGradOp : public core::OpKernel { + public: + explicit MaxPoolGradOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto ¶ms = OpKernel::params_->maxpool(); + + // incoming/outcoming data + tensor_t &prev_delta = context.input_grad(0); + tensor_t &curr_delta = context.output_grad(0); + + // initialize outputs + fill_tensor(prev_delta, float_t{0}); + + // call the algorithm depending on the selected engine type + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::internal) { + kernels::maxpool_grad_op_internal(prev_delta, curr_delta, + params.out2inmax, params.in2out, + context.parallelize()); + } else if (engine == core::backend_t::avx) { + kernels::maxpool_grad_op_avx(prev_delta, curr_delta, params.out2inmax, + params.in2out, context.parallelize()); + } else { + throw nn_error("Not supported engine: " + to_string(engine)); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/maxpool_op.h b/tiny_dnn/core/kernels/maxpool_op.h new file mode 100755 index 0000000000000..f886d326b29b7 --- /dev/null +++ b/tiny_dnn/core/kernels/maxpool_op.h @@ -0,0 +1,64 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" + +#include "tiny_dnn/core/kernels/maxpool_op_avx.h" +#include "tiny_dnn/core/kernels/maxpool_op_internal.h" +#include "tiny_dnn/core/kernels/maxpool_op_nnpack.h" + +namespace tiny_dnn { + +class MaxPoolOp : public core::OpKernel { + public: + explicit MaxPoolOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto ¶ms = OpKernel::params_->maxpool(); + + // incomimg/outcoming data + const tensor_t &in_data = context.input(0); + tensor_t &out_data = context.output(0); + + // initialize outputs + fill_tensor(out_data, float_t{0}); + + // call convolution algorithm depending + // on the selected engine type + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::internal) { + kernels::maxpool_op_internal(in_data, out_data, params.out2inmax, + params.out2in, context.parallelize()); + } else if (engine == core::backend_t::nnpack) { + // NNPACK supports stride != 2 or pool_size !=2 + // there's optimization over stride=2 and pool_size=2 + /* + if (params.stride_x != 2 || params.stride_y != 2) { + throw nn_error("NNPACK Max-Pool requires a stride == 2."); + } + + if (params.pool_size_x != 2 || params.pool_size_y != 2) { + throw nn_error("NNPACK Max-Pool requires a pool size == 2."); + } + + */ + kernels::maxpool_op_nnpack(in_data, out_data, params); + } else if (engine == core::backend_t::avx) { + kernels::maxpool_op_avx(in_data, out_data, params.out2inmax, + params.out2in, context.parallelize()); + } else { + throw nn_error("Not supported engine: " + to_string(engine)); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/maxpool_op_avx.h b/tiny_dnn/core/kernels/maxpool_op_avx.h new file mode 100755 index 0000000000000..796d1c4949b45 --- /dev/null +++ b/tiny_dnn/core/kernels/maxpool_op_avx.h @@ -0,0 +1,35 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/core/kernels/maxpool_op_internal.h" + +namespace tiny_dnn { +namespace kernels { + +inline void maxpool_op_avx(const tensor_t &in_data, + tensor_t &out_data, + std::vector> &max_idx, + const std::vector> &out2in, + const bool layer_parallelize) { + maxpool_op_internal(in_data, out_data, max_idx, out2in, layer_parallelize); +} + +inline void maxpool_grad_op_avx(tensor_t &prev_delta, + const tensor_t &curr_delta, + std::vector> &max_idx, + const std::vector &in2out, + const bool layer_parallelize) { + maxpool_grad_op_internal(prev_delta, curr_delta, max_idx, in2out, + layer_parallelize); +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/maxpool_op_internal.h b/tiny_dnn/core/kernels/maxpool_op_internal.h new file mode 100755 index 0000000000000..f1f417eb36758 --- /dev/null +++ b/tiny_dnn/core/kernels/maxpool_op_internal.h @@ -0,0 +1,60 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +namespace tiny_dnn { +namespace kernels { + +inline void maxpool_op_internal(const tensor_t &in_data, + tensor_t &out_data, + std::vector> &max_idx, + const std::vector> &out2in, + const bool layer_parallelize) { + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const vec_t &in = in_data[sample]; + vec_t &out = out_data[sample]; + std::vector &max = max_idx[sample]; + + for (size_t i = 0; i < out2in.size(); i++) { + const auto &in_index = out2in[i]; + float_t max_value = std::numeric_limits::lowest(); + size_t idx = 0; + for (auto j : in_index) { + if (in[j] > max_value) { + max_value = in[j]; + idx = j; + } + } + max[i] = idx; + out[i] = max_value; + } + }); +} + +inline void maxpool_grad_op_internal(tensor_t &prev_delta, + const tensor_t &curr_delta, + std::vector> &max_idx, + const std::vector &in2out, + const bool layer_parallelize) { + for_i(layer_parallelize, prev_delta.size(), [&](size_t sample) { + vec_t &prev = prev_delta[sample]; + const vec_t &curr = curr_delta[sample]; + const std::vector &max = max_idx[sample]; + + for (size_t i = 0; i < in2out.size(); i++) { + size_t outi = in2out[i]; + prev[i] = (max[outi] == i) ? curr[outi] : float_t{0}; + } + }); +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/maxpool_op_nnpack.h b/tiny_dnn/core/kernels/maxpool_op_nnpack.h new file mode 100755 index 0000000000000..316d5fd254e9a --- /dev/null +++ b/tiny_dnn/core/kernels/maxpool_op_nnpack.h @@ -0,0 +1,61 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/backend.h" +#include "tiny_dnn/core/params/maxpool_params.h" + +namespace tiny_dnn { +namespace kernels { + +inline void maxpool_op_nnpack(const tensor_t &in_data, + tensor_t &out_data, + const core::maxpool_params ¶ms) { +#ifdef CNN_USE_NNPACK + // call singleton to initialize NNPACK + NNPackInitializer::getInstance().initialize(); + + const size_t input_channels = params.in.depth_; + + const nnp_size input_size = {params.in.width_, params.in.height_}; + + const nnp_padding input_padding = {0, 0, 0, 0}; + + const nnp_size pooling_size = {params.pool_size_x, params.pool_size_y}; + + const nnp_size pooling_stride = {params.stride_x, params.stride_y}; + + const float *input_ptr = in_data[0].data(); + float *output_ptr = out_data[0].data(); + + // TODO(edgarriba): embed it into a class + const size_t num_mkl_threads = 1; + pthreadpool_t threadpool = pthreadpool_create(num_mkl_threads); + + const size_t batch_size = 1; + + const auto status = nnp_max_pooling_output( + batch_size, input_channels, input_size, input_padding, pooling_size, + pooling_stride, input_ptr, output_ptr, threadpool); + + if (status != nnp_status_success) { + throw nn_error("Could not succeed with nnp_max_pooling_output"); + } + + // TODO(edgarriba): embed it into a class + pthreadpool_destroy(threadpool); +#else + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(params); + throw nn_error("TinyDNN has not been compiled with NNPACK support."); +#endif +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/nnp_deconv2d_kernel.h b/tiny_dnn/core/kernels/nnp_deconv2d_kernel.h new file mode 100755 index 0000000000000..796dd7827ce83 --- /dev/null +++ b/tiny_dnn/core/kernels/nnp_deconv2d_kernel.h @@ -0,0 +1,31 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/core/params/deconv_params.h" + +#ifdef CNN_USE_NNPACK +#include + +namespace tiny_dnn { +namespace core { +namespace kernels { + +inline void nnp_deconv2d_kernel(const conv_params ¶ms, + const std::vector &in, + const vec_t &W, + const vec_t &bias, + tensor_t &a) {} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn + +#endif diff --git a/tiny_dnn/core/kernels/recurrent_cell_grad_op.h b/tiny_dnn/core/kernels/recurrent_cell_grad_op.h new file mode 100755 index 0000000000000..230e553c47077 --- /dev/null +++ b/tiny_dnn/core/kernels/recurrent_cell_grad_op.h @@ -0,0 +1,55 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" + +#include "tiny_dnn/core/kernels/recurrent_cell_op_internal.h" + +namespace tiny_dnn { + +class RecurrentCellGradOp : public core::OpKernel { + public: + explicit RecurrentCellGradOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto params = OpKernel::params_->recurrent_cell(); + // incoming/outcoming data + const tensor_t &prev_out = context.input(0); + const tensor_t &h = context.input(1); + const tensor_t &U = context.input(2); + const tensor_t &W = context.input(3); + const tensor_t &V = context.input(4); + tensor_t &dU = context.input_grad(2); + tensor_t &dW = context.input_grad(3); + tensor_t &dV = context.input_grad(4); + tensor_t *db = params.has_bias_ ? &context.input_grad(5) : nullptr; + tensor_t *dc = params.has_bias_ ? &context.input_grad(6) : nullptr; + tensor_t &prev_output_delta = context.input_grad(0); + tensor_t &prev_state_delta = context.input_grad(1); + tensor_t &curr_output_delta = context.output_grad(0); + tensor_t &curr_state_delta = context.output_grad(1); + const tensor_t &out_state = context.output(1); + tensor_t dummy; // need lvalue for non-const reference + + // initialize outputs + fill_tensor(prev_output_delta, float_t{0}); + fill_tensor(prev_state_delta, float_t{0}); + + // call the algorithm depending on the selected engine type + + kernels::recurrent_cell_op_internal( + prev_out, h, U[0], W[0], V[0], dU, dW, dV, params.has_bias_ ? *db : dummy, + params.has_bias_ ? *dc : dummy, curr_output_delta, curr_state_delta, + prev_output_delta, prev_state_delta, out_state, params, + context.parallelize()); + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/recurrent_cell_op.h b/tiny_dnn/core/kernels/recurrent_cell_op.h new file mode 100755 index 0000000000000..7cbeb416759a9 --- /dev/null +++ b/tiny_dnn/core/kernels/recurrent_cell_op.h @@ -0,0 +1,54 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/framework/op_kernel.h" +#include "tiny_dnn/core/kernels/recurrent_cell_op_internal.h" + +namespace tiny_dnn { + +class RecurrentCellOp : public core::OpKernel { + public: + explicit RecurrentCellOp(const core::OpKernelConstruction &context) + : core::OpKernel(context) {} + + void compute(core::OpKernelContext &context) override { + auto params = OpKernel::params_->recurrent_cell(); + + // incomimg/outcoming data + const tensor_t &in_data = context.input(0); + const tensor_t &prev_h = context.input(1); + const tensor_t &U = context.input(2); + const tensor_t &W = context.input(3); + const tensor_t &V = context.input(4); + const tensor_t *bias = params.has_bias_ ? &context.input(5) : nullptr; + const tensor_t *c = params.has_bias_ ? &context.input(6) : nullptr; + tensor_t &out_data = context.output(0); + tensor_t &next_h = context.output(1); + + // initialize outputs + fill_tensor(out_data, float_t{0}); + fill_tensor(next_h, float_t{0}); + + // call the algorithm depending on the selected engine type + + const core::backend_t engine = context.engine(); + + if (engine == core::backend_t::internal || engine == core::backend_t::avx) { + kernels::recurrent_cell_op_internal( + in_data, prev_h, U[0], W[0], V[0], + params.has_bias_ ? (*bias)[0] : vec_t(), + params.has_bias_ ? (*c)[0] : vec_t(), out_data, next_h, params, + context.parallelize()); + } else { + throw nn_error("Not supported engine: " + to_string(engine)); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/recurrent_cell_op_internal.h b/tiny_dnn/core/kernels/recurrent_cell_op_internal.h new file mode 100755 index 0000000000000..08d1cc4f7f2f0 --- /dev/null +++ b/tiny_dnn/core/kernels/recurrent_cell_op_internal.h @@ -0,0 +1,164 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/params/recurrent_cell_params.h" + +namespace tiny_dnn { +namespace kernels { + +inline void recurrent_cell_op_internal( + const tensor_t &in_data, + const tensor_t &prev_h, + const vec_t &U, + const vec_t &W, + const vec_t &V, + const vec_t &bias, + const vec_t &c, + tensor_t &out_data, + tensor_t &out_h, + const core::recurrent_cell_params ¶ms, + const bool layer_parallelize) { + for_i(layer_parallelize, in_data.size(), [&](size_t sample) { + const vec_t &in = in_data[sample]; + const vec_t &prev_state = prev_h[sample]; + vec_t &out = out_data[sample]; + vec_t &next_state = out_h[sample]; + + for (size_t o = 0; o < params.out_size_; o++) { + float_t next_state_ = 0; + + // W * h(t-1) + for (size_t o_2 = 0; o_2 < params.out_size_; o_2++) { + next_state_ += W[o_2 * params.out_size_ + o] * prev_state[o_2]; + } + + // U*x(t) + for (size_t i = 0; i < params.in_size_; i++) { + next_state_ += U[i * params.out_size_ + o] * in[i]; + } + + if (params.has_bias_) { + next_state_ += bias[o]; + } + next_state[o] = next_state_; + } + + params.activation_->forward_activation(next_state, next_state); + + // V matrix is out_size_ x out_size_ + for (size_t o = 0; o < params.out_size_; o++) { + float_t out_ = 0; + for (size_t o_2 = 0; o_2 < params.out_size_; o_2++) { + out_ += V[o_2 * params.out_size_ + o] * next_state[o_2]; + } + + if (params.has_bias_) { + out_ += c[o]; + } + out[o] = out_; + } + }); +} + +inline void recurrent_cell_op_internal( + const tensor_t &prev_out, + const tensor_t &prev_h, + const vec_t &U, + const vec_t &W, + const vec_t &V, + tensor_t &dU, + tensor_t &dW, + tensor_t &dV, + tensor_t &db, + tensor_t &dc, + const tensor_t &curr_output_delta, + tensor_t &curr_state_delta, + tensor_t &prev_output_delta, + tensor_t &prev_state_delta, + const tensor_t &out_h, + const core::recurrent_cell_params ¶ms, + const bool layer_parallelize) { + for (size_t sample = 0; sample < prev_out.size(); sample++) { + const vec_t &prev_out_ = prev_out[sample]; + const vec_t &prev_h_ = prev_h[sample]; + vec_t &dU_ = dU[sample]; + vec_t &dW_ = dW[sample]; + vec_t &dV_ = dV[sample]; + vec_t &db_ = db[sample]; + vec_t &dc_ = dc[sample]; + const vec_t &curr_output_delta_ = curr_output_delta[sample]; + vec_t &curr_state_delta_ = curr_state_delta[sample]; + vec_t &prev_output_delta_ = prev_output_delta[sample]; + vec_t &prev_state_delta_ = prev_state_delta[sample]; + const vec_t &out_h_ = out_h[sample]; + + // from output to h + for (size_t o = 0; o < params.out_size_; o++) { + // propagate delta from output to h. + curr_state_delta_[o] += vectorize::dot( + &curr_output_delta_[0], &V[o * params.out_size_], params.out_size_); + } + + // h'(t) + params.activation_->backward_activation(prev_h_, out_h_, curr_state_delta_, + curr_state_delta_); + + // \delta h(t) -W-> h(t-1) + for (size_t o = 0; o < params.out_size_; o++) { + prev_state_delta_[o] += vectorize::dot( + &curr_state_delta_[0], &W[o * params.out_size_], params.out_size_); + } + + // \delta h(t) -U-> \delta x(t) + for (size_t i = 0; i < params.in_size_; i++) { + prev_output_delta_[i] += vectorize::dot( + &curr_state_delta_[0], &U[i * params.out_size_], params.out_size_); + } + + for_(layer_parallelize, 0, size_t(params.out_size_), + [&](const blocked_range &r) { + // accumulate weight-step using delta + // dW[c * out_size + i] += current_delta[i] * prev_out[o] + const size_t begin = r.begin(); + const size_t end = r.end(); + const size_t stride = end - begin; + for (size_t o = 0; o < params.out_size_; o++) { + vectorize::muladd(&curr_output_delta_[begin], out_h_[o], stride, + &dV_[o * params.out_size_ + begin]); + } + + if (params.has_bias_) { + // vec_t& dc; + for (size_t o = begin; o < end; o++) { + dc_[o] += curr_output_delta_[o]; + } + } + + for (size_t o = 0; o < params.out_size_; o++) { + vectorize::muladd(&curr_state_delta_[begin], prev_h_[o], stride, + &dW_[o * params.out_size_ + begin]); + } + + for (size_t i = 0; i < params.in_size_; i++) { + vectorize::muladd(&curr_state_delta[sample][begin], prev_out_[i], + stride, &dU_[i * params.out_size_ + begin]); + } + + if (params.has_bias_) { + // vec_t& db; + for (size_t o = begin; o < end; o++) { + db_[o] += curr_state_delta_[o]; + } + } + }); + } +} + +} // namespace kernels +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/tiny_deconv2d_back_kernel.h b/tiny_dnn/core/kernels/tiny_deconv2d_back_kernel.h new file mode 100755 index 0000000000000..404fac59e4219 --- /dev/null +++ b/tiny_dnn/core/kernels/tiny_deconv2d_back_kernel.h @@ -0,0 +1,105 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/params/deconv_params.h" + +namespace tiny_dnn { +namespace core { +namespace kernels { + +inline void tiny_deconv2d_back_kernel(const deconv_params ¶ms, + const tensor_t &prev_out, + const vec_t &W, + tensor_t &dW, + tensor_t &db, + tensor_t &curr_delta, + tensor_t *prev_delta) { + // propagate delta to previous layer + for_i(prev_out.size(), [&](size_t sample) { + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + if (!params.tbl.is_connected(outc, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * outc + inc; + idx = params.weight.get_index(0, 0, idx); + const float_t *pw = &W[idx]; + + idx = params.out_unpadded.get_index(0, 0, outc); + const float_t *pdelta_src = &curr_delta[sample][idx]; + + idx = params.in.get_index(0, 0, inc); + float_t *pdelta_dst = &(*prev_delta)[sample][idx]; + + for (size_t y = 0; y < params.in.height_; y++) { + for (size_t x = 0; x < params.in.width_; x++) { + const float_t *ppw = pw; + + float_t *ppdelta_dst = pdelta_dst + y * params.in.width_ + x; + float_t sum{0}; + + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + idx = (y * params.h_stride + wy) * params.out.width_ + + (x * params.w_stride + wx); + sum += ppw[wy * params.weight.width_ + wx] * pdelta_src[idx]; + } + } + *ppdelta_dst += sum; + } + } + } + } + + // accumulate dw + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + if (!params.tbl.is_connected(outc, inc)) continue; + + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + float_t dst{0}; + + size_t idx = 0; + idx = params.in.get_index(0, 0, inc); + const float_t *prevo = &prev_out[sample][idx]; + + idx = params.out.get_index(wx, wy, outc); + const float_t *delta = &curr_delta[sample][idx]; + + for (size_t y = 0; y < params.in.height_; y++) { + dst += + vectorize::dot(prevo + y * params.in.width_, + delta + y * params.out.width_, params.in.width_); + } + + idx = params.in.depth_ * outc + inc; + dW[sample][params.weight.get_index(wx, wy, idx)] += dst; + } + } + } + } + + // accumulate db + if (params.has_bias) { + // vec_t& db = *in_grad[2]; + + for (size_t outc = 0; outc < params.out.depth_; outc++) { + size_t idx = params.out.get_index(0, 0, outc); + const float_t *delta = &curr_delta[sample][idx]; + const float_t *deltaa = delta + params.out.width_ * params.out.height_; + db[sample][outc] += std::accumulate(delta, deltaa, float_t{0}); + } + } + }); +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/tiny_deconv2d_kernel.h b/tiny_dnn/core/kernels/tiny_deconv2d_kernel.h new file mode 100755 index 0000000000000..99978645febb0 --- /dev/null +++ b/tiny_dnn/core/kernels/tiny_deconv2d_kernel.h @@ -0,0 +1,68 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/params/deconv_params.h" + +namespace tiny_dnn { +namespace core { +namespace kernels { + +inline void tiny_deconv2d_kernel(const deconv_params ¶ms, + const tensor_t &in, + const vec_t &W, + const vec_t &bias, + tensor_t &out, + const bool layer_parallelize) { + for_i(layer_parallelize, in.size(), [&](size_t sample) { + for (size_t o = 0; o < params.out.depth_; o++) { + for (size_t inc = 0; inc < params.in.depth_; inc++) { + if (!params.tbl.is_connected(o, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * o + inc; + idx = params.weight.get_index(0, 0, idx); + assert(idx < W.size()); + const float_t *pw = &W[idx]; + + idx = params.in.get_index(0, 0, inc); + assert(sample < in.size() && idx <= in[sample].size()); + const float_t *pi = &in[sample][idx]; + + idx = params.out.get_index(0, 0, o); + assert(sample < out.size() && idx <= out[sample].size()); + float_t *pout = &out[sample][idx]; + + for (size_t y = 0; y < params.in.height_; y++) { + for (size_t x = 0; x < params.in.width_; x++) { + const float_t *ppw = pw; + const float_t *ppi = pi + y * params.in.width_ + x; + // should be optimized for small kernel(3x3,5x5) + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + pout[(y * params.h_stride + wy) * params.out.width_ + + (x * params.w_stride + wx)] += + ppw[wy * params.weight.width_ + wx] * (*ppi); + } + } + } + } + } + + if (params.has_bias) { + float_t *pout = &out[sample][params.out.get_index(0, 0, o)]; + float_t *pout2 = pout + params.out.width_ * params.out.height_; + std::for_each(pout, pout2, [&](float_t &f) { f += bias[o]; }); + } + } + }); +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/tiny_quantization_kernel.h b/tiny_dnn/core/kernels/tiny_quantization_kernel.h new file mode 100755 index 0000000000000..97fcbc01099c4 --- /dev/null +++ b/tiny_dnn/core/kernels/tiny_quantization_kernel.h @@ -0,0 +1,258 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +namespace tiny_dnn { +namespace core { +namespace kernels { + +template +T highest() { + return (std::numeric_limits::max)(); +} + +template +T lowest() { + return std::numeric_limits::is_integer + ? (std::numeric_limits::min)() + : (-(std::numeric_limits::max)()); +} + +// We have to be able to detect and handle overflows in int32, so this function +// uses doubles and int64's to make sure we have enough room. +template +int64_t float_to_quantized_unclamped(float_t input, + float_t range_min, + float_t range_max) { + if (range_min == range_max) { + return 0; + } + const int number_of_bits = sizeof(T) * 8; + const int64_t number_of_steps = static_cast(1) << number_of_bits; + const double range_adjust = (number_of_steps / (number_of_steps - 1.0)); + const double range = ((range_max - range_min) * range_adjust); + const double range_scale = (number_of_steps / range); + int64_t quantized = static_cast(round(input * range_scale) - + round(range_min * range_scale)); + const int64_t lowest_quantized = static_cast(lowest()); + quantized += lowest_quantized; + return quantized; +} + +inline int32_t int64_to_int32(int64_t src) { + assert(src <= std::numeric_limits::max() && + src >= std::numeric_limits::min()); + return static_cast(src); +} + +// This converts the float into the final quantized type, clamping/saturating +// any over or underflows. +template +T float_to_quantized(float_t input, float_t range_min, float_t range_max) { + int64_t quantized = + float_to_quantized_unclamped(input, range_min, range_max); + const int64_t lowest_quantized = static_cast(lowest()); + const int64_t highest_quantized = static_cast(highest()); + quantized = std::max(quantized, lowest_quantized); + quantized = std::min(quantized, highest_quantized); + return static_cast(static_cast(quantized)); +} + +template +float quantized_to_float(T input, float_t range_min, float_t range_max) { + if (range_min == range_max) { + return range_min; + } + const int number_of_bits = sizeof(T) * 8; + const int64_t number_of_steps = static_cast(1) << number_of_bits; + const double range_adjust = (number_of_steps / (number_of_steps - 1.0)); + const double range = ((range_max - range_min) * range_adjust); + const double range_scale = (range / number_of_steps); + const int64_t lowest_quantized = static_cast(lowest()); + const double offset_input = static_cast(input) - lowest_quantized; + const double result = range_min + (offset_input * range_scale); + return static_cast(result); +} + +template +float float_for_one_quantized_level(float_t range_min, float_t range_max) { + const int64_t highest_ = static_cast(highest()); + const int64_t lowest_ = static_cast(lowest()); + const float float_for_one_quantized_level = + (range_max - range_min) / (highest_ - lowest_); + return float_for_one_quantized_level; +} + +template +void quantization_range_for_multiplication(float_t min_a, + float_t max_a, + float_t min_b, + float_t max_b, + float_t *min_c, + float_t *max_c) { + const float_t a_float_for_one_quant_level = + float_for_one_quantized_level(min_a, max_a); + const float_t b_float_for_one_quant_level = + float_for_one_quantized_level(min_b, max_b); + + const int64_t c_highest = static_cast(highest()); + const int64_t c_lowest = static_cast(lowest()); + const float c_float_for_one_quant_level = + a_float_for_one_quant_level * b_float_for_one_quant_level; + + *min_c = c_float_for_one_quant_level * c_lowest; + *max_c = c_float_for_one_quant_level * c_highest; +} + +template +inline T2 requantize_in_new_range(T1 input, + float_t min_input, + float_t max_input, + float_t min_new, + float_t max_new) { + const float_t input_float = + quantized_to_float(input, min_input, max_input); + return float_to_quantized(input_float, min_new, max_new); +} + +template +inline void requantize_many_in_new_range(T1 *input, + size_t count, + float_t min_input, + float_t max_input, + float_t min_output, + float_t max_output, + T2 *output) { + for (size_t index = 0; index < count; ++index) { + const float_t input_float = + quantized_to_float(input[index], min_input, max_input); + output[index] = float_to_quantized(input_float, min_output, max_output); + } +} + +// Because converting 32-bit accumulated results down to eight bit is a common +// case, we have a specialized code path to handle it as efficiently as +// possible using only fixed-point math for the inner loop. +template <> +inline void requantize_many_in_new_range(int32_t *input, + size_t count, + float_t min_input, + float_t max_input, + float_t min_output, + float_t max_output, + uint8_t *output) { + // Initially we calculate all the constants we need once, before we go into + // the inner loop. + const int fp_shift = 16; + const float input_range = max_input - min_input; + const float output_range = max_output - min_output; + const float recip_output_range = (255.0f / output_range); + const int64_t recip_output_range_fp = + static_cast(recip_output_range * (1 << fp_shift)); + const int64_t range_scale_fp = + static_cast(255.0f * (1 << fp_shift) * input_range / output_range); + const int64_t input_offset_fp = static_cast( + (min_input * recip_output_range_fp) + (range_scale_fp >> 1)); + const int64_t output_offset_fp = + static_cast(round((min_output * 255.0f) / output_range)); + const int64_t rounding_delta = 1 << (fp_shift - 1); + // Inside this loop we just do minimal adds, multiplies, and shifts, in a + // way + // that could be easily adapted for a SIMD implementation. It should also be + // possible to perform all the calculations in 32-bit rather than 64, but + // that's not been implemented yet. + for (size_t index = 0; index < count; ++index) { + const int64_t input_value = static_cast(input[index]); + const int64_t fp_value = + ((input_value * range_scale_fp) >> 32) + input_offset_fp; + const int64_t round_intermediate = + ((fp_value >= 0) ? (fp_value + rounding_delta) + : (fp_value - rounding_delta)) >> + fp_shift; + int64_t quantized_int64 = (round_intermediate - output_offset_fp); + quantized_int64 = std::max(quantized_int64, 0LL); + quantized_int64 = std::min(quantized_int64, 255LL); + output[index] = static_cast(static_cast(quantized_int64)); + } +} + +// REQUIRES: 'result->NumElements() == input.NumElements()' +template +void float_tensor_to_quantized_in_place(const vec_t &input, + float_t min, + float_t max, + std::vector *result) { + const size_t data_size = input.size(); + for (size_t i = 0; i < data_size; ++i) { + (*result)[i] = float_to_quantized(input[i], min, max); + } +} + +template +std::vector float_tensor_to_quantized(const vec_t &input, + float_t min, + float_t max) { + std::vector result(input.size(), static_cast(0)); + float_tensor_to_quantized_in_place(input, min, max, &result); + return result; +} + +// REQUIRES: 'result->NumElements() == input.NumElements()' +template +void quantized_tensor_to_float_in_place(const std::vector &input, + float_t min, + float_t max, + vec_t *result) { + const size_t data_size = input.size(); + for (size_t i = 0; i < data_size; ++i) { + (*result)[i] = quantized_to_float(input[i], min, max); + } +} + +template +vec_t quantized_tensor_to_float(const std::vector &input, + float_t min, + float_t max) { + vec_t result(input.size(), static_cast(0)); + quantized_tensor_to_float_in_place(input, min, max, &result); + return result; +} + +template +void quantize_down_and_shrink_range(std::vector &input, + float_t min_input, + float_t max_input, + float_t *min_new, + float_t *max_new, + std::vector *output) { + const int32_t input_lowest_quantized = static_cast(lowest()); + const int32_t input_highest_quantized = static_cast(highest()); + T1 actual_min_quantized = input_highest_quantized; + T1 actual_max_quantized = input_lowest_quantized; + for (size_t i = 0; i < input.size(); ++i) { + const T1 value = input[i]; + actual_min_quantized = std::min(actual_min_quantized, value); + actual_max_quantized = std::max(actual_max_quantized, value); + } + // We want to make sure that the minimum is no larger than zero, so that the + // convolution operation can run efficiently. + *min_new = std::min( + 0.0f, quantized_to_float(actual_min_quantized, min_input, max_input)); + *max_new = quantized_to_float(actual_max_quantized, min_input, max_input); + requantize_many_in_new_range(&input[0], input.size(), + min_input, max_input, *min_new, + *max_new, &(*output)[0]); +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/tiny_quantized_conv2d_kernel.h b/tiny_dnn/core/kernels/tiny_quantized_conv2d_kernel.h new file mode 100755 index 0000000000000..f861d5f5c0d50 --- /dev/null +++ b/tiny_dnn/core/kernels/tiny_quantized_conv2d_kernel.h @@ -0,0 +1,472 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/core/kernels/tiny_quantization_kernel.h" +#include "tiny_dnn/core/params/conv_params.h" + +namespace tiny_dnn { +namespace core { +namespace kernels { + +inline void tiny_quantized_conv2d_kernel(const conv_params ¶ms, + const vec_t &in, + const vec_t &W, + const vec_t &bias, + vec_t &a, + const bool layer_parallelize) { + // image quantization + float_t min_input(in[0]); + float_t max_input(in[0]); + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t ins = 0; + ins < params.in_padded.height_ * params.in_padded.height_; ins++) { + size_t idx = params.in_padded.get_index(0, 0, inc); + min_input = std::min(min_input, (&in[idx])[ins]); + max_input = std::max(max_input, (&in[idx])[ins]); + } + } + std::vector in_quantized = + float_tensor_to_quantized(in, min_input, max_input); + // filter quantization + float_t min_filter(W[0]); + float_t max_filter(W[0]); + for (size_t inc = 0; inc < params.in_padded.depth_; inc++) { + for (size_t ins = 0; ins < params.weight.height_ * params.weight.height_; + ins++) { + size_t idx = params.in_padded.get_index(0, 0, inc); + min_filter = std::min(min_filter, (&W[idx])[ins]); + max_filter = std::max(max_filter, (&W[idx])[ins]); + } + } + if (min_filter == max_filter) { + max_filter = W[0] + 1e-3f; + min_filter = W[0] - 1e-3f; + } + std::vector W_quantized = + float_tensor_to_quantized(W, min_filter, max_filter); + // bias quantization + float_t min_bias(0); + float_t max_bias(0); + std::vector bias_quantized; + if (params.has_bias) { + for (size_t inc = 0; inc < params.out.depth_; inc++) { + min_bias = std::min(min_bias, bias[inc]); + max_bias = std::max(max_bias, bias[inc]); + } + if (min_bias == max_bias) { + max_bias = bias[0] + 1e-3f; + min_bias = bias[0] - 1e-3f; + } + bias_quantized = + float_tensor_to_quantized(bias, min_bias, max_bias); + } + // output range + float_t min_output_value; + float_t max_output_value; + quantization_range_for_multiplication( + min_input, max_input, min_filter, max_filter, &min_output_value, + &max_output_value); + + std::vector a_quantized(a.size(), static_cast(0)); + + // calculating offset + const int32_t offset_input = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_input, max_input)); + const int32_t offset_filter = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_filter, max_filter)); + const int32_t zero_in_total_space = int64_to_int32( + float_to_quantized(0.0f, min_output_value, max_output_value)); + + for_i(layer_parallelize, params.out.depth_, [&](size_t o) { + for (size_t inc = 0; inc < params.in.depth_; inc++) { + if (!params.tbl.is_connected(o, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * o + inc; + idx = params.weight.get_index(0, 0, idx); + const uint8_t *pw = &W_quantized[idx]; + + idx = params.in_padded.get_index(0, 0, inc); + const uint8_t *pi = &in_quantized[idx]; + + idx = params.out.get_index(0, 0, o); + int32_t *pa_quantized = &a_quantized[idx]; + + for (size_t y = 0; y < params.out.height_; y++) { + for (size_t x = 0; x < params.out.width_; x++) { + const uint8_t *ppw = pw; + const uint8_t *ppi = pi + + params.in_padded.width_ * (y * params.h_stride) + + x * params.w_stride; + int32_t sum = 0; + + // should be optimized for small kernel(3x3,5x5) + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + idx = wy * params.in_padded.width_ + wx; + sum += (static_cast(*ppw++) - offset_filter) * + (static_cast(ppi[idx]) - offset_input); + } + } + pa_quantized[y * params.out.width_ + x] += sum; + } + } + } + if (params.has_bias) { + int32_t *pa_quantized = &a_quantized[params.out.get_index(0, 0, o)]; + int32_t *paa_quantized = + pa_quantized + params.out.width_ * params.out.height_; + std::for_each(pa_quantized, paa_quantized, [&](int32_t &f) { + f += (bias_quantized[o] - zero_in_total_space); + }); + } + }); + + float_t min_output_requantized; + float_t max_output_requantized; + std::vector a_requantized(a_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + a_quantized, min_output_value, max_output_value, &min_output_requantized, + &max_output_requantized, &a_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + a = quantized_tensor_to_float(a_requantized, min_output_requantized, + max_output_requantized); +} + +inline void tiny_quantized_conv2d_back_kernel(const conv_params ¶ms, + const vec_t &prev_out, + const vec_t &W, + vec_t &dW, + vec_t &db, + vec_t &curr_delta, + vec_t *prev_delta) { + // previous output quantization + float_t min_prev_out(prev_out[0]); + float_t max_prev_out(prev_out[0]); + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t ins = 0; + ins < params.in_padded.height_ * params.in_padded.height_; ins++) { + size_t idx = params.in_padded.get_index(0, 0, inc); + min_prev_out = std::min(min_prev_out, (&prev_out[idx])[ins]); + max_prev_out = std::max(min_prev_out, (&prev_out[idx])[ins]); + } + } + std::vector prev_out_quantized = + float_tensor_to_quantized(prev_out, min_prev_out, max_prev_out); + + // filter quantization + float_t min_filter(W[0]); + float_t max_filter(W[0]); + for (size_t inc = 0; inc < params.in_padded.depth_; inc++) { + for (size_t ins = 0; ins < params.weight.height_ * params.weight.height_; + ins++) { + size_t idx = params.in_padded.get_index(0, 0, inc); + min_filter = std::min(min_filter, (&W[idx])[ins]); + max_filter = std::max(max_filter, (&W[idx])[ins]); + } + } + if (min_filter == max_filter) { + max_filter = W[0] + 1e-3f; + min_filter = W[0] - 1e-3f; + } + std::vector W_quantized = + float_tensor_to_quantized(W, min_filter, max_filter); + + // current delta quantization + float_t min_curr_delta(curr_delta[0]); + float_t max_curr_delta(curr_delta[0]); + for (size_t inc = 0; inc < params.out.depth_; inc++) { + for (size_t ins = 0; ins < params.out.height_ * params.out.height_; ins++) { + size_t idx = params.out.get_index(0, 0, inc); + min_curr_delta = std::min(min_curr_delta, (&curr_delta[idx])[ins]); + max_curr_delta = std::max(max_curr_delta, (&curr_delta[idx])[ins]); + } + } + std::vector curr_delta_quantized = + float_tensor_to_quantized(curr_delta, min_curr_delta, + max_curr_delta); + + // output range for previous delta + float_t min_prev_delta_value; + float_t max_prev_delta_value; + quantization_range_for_multiplication( + min_curr_delta, max_curr_delta, min_filter, max_filter, + &min_prev_delta_value, &max_prev_delta_value); + + std::vector prev_delta_quantized(prev_delta->size(), + static_cast(0)); + + // output range for dW + float_t min_dW_value; + float_t max_dW_value; + quantization_range_for_multiplication( + min_curr_delta, max_curr_delta, min_prev_out, max_prev_out, &min_dW_value, + &max_dW_value); + + std::vector dW_quantized(dW.size(), static_cast(0)); + + // calculating offset + const int32_t offset_prev_out = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_prev_out, max_prev_out)); + const int32_t offset_filter = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_filter, max_filter)); + const int32_t offset_curr_delta = + int64_to_int32(float_to_quantized_unclamped(0.0f, min_curr_delta, + max_curr_delta)); + // const int32_t zero_in_prev_delta = + // float_to_quantized(0.0f, min_prev_delta_value, + // max_prev_delta_value); + + // propagate delta to previous layer + for_i(params.in.depth_, [&](size_t inc) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + if (!params.tbl.is_connected(outc, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * outc + inc; + idx = params.weight.get_index(0, 0, idx); + const uint8_t *pw = &W_quantized[idx]; + + idx = params.out.get_index(0, 0, outc); + const uint8_t *pdelta_src = &curr_delta_quantized[idx]; + + idx = params.in_padded.get_index(0, 0, inc); + int32_t *pdelta_quantized_dst = &(prev_delta_quantized)[idx]; + + for (size_t y = 0; y < params.out.height_; y++) { + for (size_t x = 0; x < params.out.width_; x++) { + const uint8_t *ppw = pw; + + idx = y * params.out.width_ + x; + const uint8_t ppdelta_src = pdelta_src[idx]; + + int32_t *ppdelta_quantized_dst = + pdelta_quantized_dst + + y * params.h_stride * params.in_padded.width_ + x * params.w_stride; + + for (size_t wy = 0; wy < params.weight.height_; wy++) { // NOLINT + for (size_t wx = 0; wx < params.weight.width_; wx++) { // NOLINT + idx = wy * params.in_padded.width_ + wx; + ppdelta_quantized_dst[idx] += + (static_cast(*ppw++) - offset_filter) * + (static_cast(ppdelta_src) - offset_curr_delta); + } + } + } + } + } + }); + + float_t min_prev_delta_requantized; + float_t max_prev_delta_requantized; + std::vector prev_delta_requantized(prev_delta_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + prev_delta_quantized, min_prev_delta_value, max_prev_delta_value, + &min_prev_delta_requantized, &max_prev_delta_requantized, + &prev_delta_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + vec_t prev_delta_vec = quantized_tensor_to_float( + prev_delta_requantized, min_prev_delta_requantized, + max_prev_delta_requantized); + prev_delta = &prev_delta_vec; + + // Accumulate dw + for_i(params.in.depth_, [&](size_t inc) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + if (!params.tbl.is_connected(outc, inc)) continue; + + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + int32_t dst = int32_t(0); + + size_t idx = 0; + idx = params.in_padded.get_index(wx, wy, inc); + const uint8_t *prevo = &prev_out_quantized[idx]; + + idx = params.out.get_index(0, 0, outc); + const uint8_t *delta = &curr_delta_quantized[idx]; + + for (size_t y = 0; y < params.out.height_; y++) { + for (size_t x = 0; x < params.out.width_; x++) { + dst += + (static_cast( + *(prevo + y * params.in_padded.width_ + x)) - + offset_prev_out) * + (static_cast(*(delta + y * params.out.width_ + x)) - + offset_curr_delta); + } + } + + idx = params.in.depth_ * outc + inc; + dW_quantized[params.weight.get_index(wx, wy, idx)] += dst; + } + } + } + }); + + float_t min_dW_requantized; + float_t max_dW_requantized; + std::vector dW_requantized(dW_quantized.size(), + static_cast(0)); + + // requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + dW_quantized, min_dW_value, max_dW_value, &min_dW_requantized, + &max_dW_requantized, &dW_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + dW = quantized_tensor_to_float(dW_requantized, min_dW_requantized, + max_dW_requantized); + + // Accumulate db + if (params.has_bias) { + // vec_t& db = *in_grad[2]; + + for (size_t outc = 0; outc < params.out.depth_; outc++) { + size_t idx = params.out.get_index(0, 0, outc); + const float_t *delta = &curr_delta[idx]; + const float_t *deltaa = delta + params.out.width_ * params.out.height_; + db[outc] += std::accumulate(delta, deltaa, float_t{0}); + } + } +} + +inline void tiny_quantized_conv2d_kernel(const conv_params ¶ms, + const vec_t &in, + const vec_t &W, + const vec_t &bias, + const vec_t &in_r, + const vec_t &W_r, + const vec_t &b_r, + vec_t &a, + vec_t &a_r, + const bool layer_parallelize) { + // filter range + float_t min_filter(W_r[0]); + float_t max_filter(W_r[1]); + if (W_r[0] == W_r[1]) { + max_filter = W_r[1] + 1e-3f; + min_filter = W_r[0] - 1e-3f; + } + // bias range + float_t min_bias(b_r[0]); + float_t max_bias(b_r[1]); + if (params.has_bias) { + if (min_bias == max_bias) { + max_bias = b_r[1] + 1e-3f; + min_bias = b_r[0] - 1e-3f; + } + } + // output range + float_t min_output_value; + float_t max_output_value; + quantization_range_for_multiplication( + in_r[0], in_r[1], min_filter, max_filter, &min_output_value, + &max_output_value); + // data type restore + std::vector in_quantized, W_quantized, bias_quantized; + for (size_t i = 0; i < in.size(); i++) { + in_quantized.push_back(static_cast(in[i])); + } + for (size_t i = 0; i < W.size(); i++) { + W_quantized.push_back(static_cast(W[i])); + } + for (size_t i = 0; i < bias.size(); i++) { + bias_quantized.push_back(static_cast(bias[i])); + } + + std::vector a_quantized(a.size(), static_cast(0)); + + // calculating offset + const int32_t offset_input = int64_to_int32( + float_to_quantized_unclamped(0.0f, in_r[0], in_r[1])); + const int32_t offset_filter = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_filter, max_filter)); + const int32_t zero_in_total_space = int64_to_int32( + float_to_quantized(0.0f, min_output_value, max_output_value)); + + for_i(layer_parallelize, params.out.depth_, [&](size_t o) { + for (size_t inc = 0; inc < params.in.depth_; inc++) { + if (!params.tbl.is_connected(o, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * o + inc; + idx = params.weight.get_index(0, 0, idx); + const uint8_t *pw = &W_quantized[idx]; + + idx = params.in_padded.get_index(0, 0, inc); + const uint8_t *pi = &in_quantized[idx]; + + idx = params.out.get_index(0, 0, o); + int32_t *pa_quantized = &a_quantized[idx]; + + for (size_t y = 0; y < params.out.height_; y++) { + for (size_t x = 0; x < params.out.width_; x++) { + const uint8_t *ppw = pw; + const uint8_t *ppi = pi + + params.in_padded.width_ * (y * params.h_stride) + + x * params.w_stride; + int32_t sum = 0; + + // should be optimized for small kernel(3x3,5x5) + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + idx = wy * params.in_padded.width_ + wx; + sum += (static_cast(*ppw++) - offset_filter) * + (static_cast(ppi[idx]) - offset_input); + } + } + pa_quantized[y * params.out.width_ + x] += sum; + } + } + } + if (params.has_bias) { + int32_t *pa_quantized = &a_quantized[params.out.get_index(0, 0, o)]; + int32_t *paa_quantized = + pa_quantized + params.out.width_ * params.out.height_; + std::for_each(pa_quantized, paa_quantized, [&](int32_t &f) { + f += static_cast((bias[o] - zero_in_total_space)); + }); + } + }); + + float_t min_output_requantized; + float_t max_output_requantized; + std::vector a_requantized(a_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + a_quantized, min_output_value, max_output_value, &min_output_requantized, + &max_output_requantized, &a_requantized); + // store directly in float datatype + for (size_t i = 0; i < a_requantized.size(); i++) { + a[i] = static_cast(a_requantized[i]); + } + a_r[0] = min_output_requantized; + a_r[1] = max_output_requantized; +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/tiny_quantized_deconv2d_kernel.h b/tiny_dnn/core/kernels/tiny_quantized_deconv2d_kernel.h new file mode 100755 index 0000000000000..931dfa476d48b --- /dev/null +++ b/tiny_dnn/core/kernels/tiny_quantized_deconv2d_kernel.h @@ -0,0 +1,465 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/core/kernels/tiny_quantization_kernel.h" +#include "tiny_dnn/core/params/deconv_params.h" + +namespace tiny_dnn { +namespace core { +namespace kernels { + +inline void tiny_quantized_deconv2d_kernel(const deconv_params ¶ms, + const vec_t &in, + const vec_t &W, + const vec_t &bias, + vec_t &out, + const bool layer_parallelize) { + // image quantization + float_t min_input(in[0]); + float_t max_input(in[0]); + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t ins = 0; ins < params.in.height_ * params.in.height_; ins++) { + size_t idx = params.in.get_index(0, 0, inc); + min_input = std::min(min_input, (&in[idx])[ins]); + max_input = std::max(max_input, (&in[idx])[ins]); + } + } + std::vector in_quantized = + float_tensor_to_quantized(in, min_input, max_input); + // filter quantization + float_t min_filter(W[0]); + float_t max_filter(W[0]); + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t ins = 0; ins < params.weight.height_ * params.weight.height_; + ins++) { + size_t idx = params.in.get_index(0, 0, inc); + min_filter = std::min(min_filter, (&W[idx])[ins]); + max_filter = std::max(max_filter, (&W[idx])[ins]); + } + } + if (min_filter == max_filter) { + max_filter = W[0] + 1e-3f; + min_filter = W[0] - 1e-3f; + } + std::vector W_quantized = + float_tensor_to_quantized(W, min_filter, max_filter); + // bias quantization + float_t min_bias(0); + float_t max_bias(0); + std::vector bias_quantized; + if (params.has_bias) { + for (size_t inc = 0; inc < params.out.depth_; inc++) { + min_bias = std::min(min_bias, bias[inc]); + max_bias = std::max(max_bias, bias[inc]); + } + if (min_bias == max_bias) { + max_bias = bias[0] + 1e-3f; + min_bias = bias[0] - 1e-3f; + } + bias_quantized = + float_tensor_to_quantized(bias, min_bias, max_bias); + } + + // output range + float_t min_output_value; + float_t max_output_value; + quantization_range_for_multiplication( + min_input, max_input, min_filter, max_filter, &min_output_value, + &max_output_value); + + std::vector out_quantized(out.size(), static_cast(0)); + + // calculating offset + const int32_t offset_input = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_input, max_input)); + const int32_t offset_filter = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_filter, max_filter)); + const int32_t zero_in_total_space = int64_to_int32( + float_to_quantized(0.0f, min_output_value, max_output_value)); + + for_i(layer_parallelize, params.out.depth_, [&](size_t o) { + for (size_t inc = 0; inc < params.in.depth_; inc++) { + if (!params.tbl.is_connected(o, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * o + inc; + idx = params.weight.get_index(0, 0, idx); + const uint8_t *pw = &W_quantized[idx]; + + idx = params.in.get_index(0, 0, inc); + const uint8_t *pi = &in_quantized[idx]; + + idx = params.out.get_index(0, 0, o); + int32_t *pout_quantized = &out_quantized[idx]; + + for (size_t y = 0; y < params.in.height_; y++) { + for (size_t x = 0; x < params.in.width_; x++) { + const uint8_t *ppw = pw; + const uint8_t *ppi = pi + y * params.in.width_ + x; + // should be optimized for small kernel(3x3,5x5) + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + pout_quantized[(y * params.h_stride + wy) * params.out.width_ + + (x * params.w_stride + wx)] += + static_cast(ppw[wy * params.weight.width_ + wx] - + offset_filter) * + static_cast(*ppi - offset_input); + } + } + } + } + } + if (params.has_bias) { + int32_t *pout_quantized = &out_quantized[params.out.get_index(0, 0, o)]; + int32_t *ppout_quantized = + pout_quantized + params.out.width_ * params.out.height_; + std::for_each(pout_quantized, ppout_quantized, [&](int32_t &f) { + f += (bias_quantized[o] - zero_in_total_space); + }); + } + }); + + float_t min_output_requantized; + float_t max_output_requantized; + std::vector out_requantized(out_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + out_quantized, min_output_value, max_output_value, &min_output_requantized, + &max_output_requantized, &out_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + out = quantized_tensor_to_float( + out_requantized, min_output_requantized, max_output_requantized); +} + +inline void tiny_quantized_deconv2d_back_kernel(const deconv_params ¶ms, + const vec_t &prev_out, + const vec_t &W, + vec_t &dW, + vec_t &db, + vec_t &curr_delta, + vec_t *prev_delta) { + // previous output quantization + float_t min_prev_out(prev_out[0]); + float_t max_prev_out(prev_out[0]); + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t ins = 0; ins < params.in.height_ * params.in.height_; ins++) { + size_t idx = params.in.get_index(0, 0, inc); + min_prev_out = std::min(min_prev_out, (&prev_out[idx])[ins]); + max_prev_out = std::max(min_prev_out, (&prev_out[idx])[ins]); + } + } + std::vector prev_out_quantized = + float_tensor_to_quantized(prev_out, min_prev_out, max_prev_out); + + // filter quantization + float_t min_filter(W[0]); + float_t max_filter(W[0]); + for (size_t inc = 0; inc < params.in.depth_; inc++) { + for (size_t ins = 0; ins < params.weight.height_ * params.weight.height_; + ins++) { + size_t idx = params.in.get_index(0, 0, inc); + min_filter = std::min(min_filter, (&W[idx])[ins]); + max_filter = std::max(max_filter, (&W[idx])[ins]); + } + } + if (min_filter == max_filter) { + max_filter = W[0] + 1e-3f; + min_filter = W[0] - 1e-3f; + } + std::vector W_quantized = + float_tensor_to_quantized(W, min_filter, max_filter); + + // current delta quantization + float_t min_curr_delta(curr_delta[0]); + float_t max_curr_delta(curr_delta[0]); + for (size_t inc = 0; inc < params.out.depth_; inc++) { + for (size_t ins = 0; ins < params.out.height_ * params.out.height_; ins++) { + size_t idx = params.out.get_index(0, 0, inc); + min_curr_delta = std::min(min_curr_delta, (&curr_delta[idx])[ins]); + max_curr_delta = std::max(max_curr_delta, (&curr_delta[idx])[ins]); + } + } + std::vector curr_delta_quantized = + float_tensor_to_quantized(curr_delta, min_curr_delta, + max_curr_delta); + + // output range for previous delta + float_t min_prev_delta_value; + float_t max_prev_delta_value; + quantization_range_for_multiplication( + min_curr_delta, max_curr_delta, min_filter, max_filter, + &min_prev_delta_value, &max_prev_delta_value); + + std::vector prev_delta_quantized(prev_delta->size(), + static_cast(0)); + + // output range for dW + float_t min_dW_value; + float_t max_dW_value; + quantization_range_for_multiplication( + min_curr_delta, max_curr_delta, min_prev_out, max_prev_out, &min_dW_value, + &max_dW_value); + + std::vector dW_quantized(dW.size(), static_cast(0)); + + // calculating offset + // TODO(wangdiya): do we need to check overflows? + const int32_t offset_prev_out = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_prev_out, max_prev_out)); + const int32_t offset_filter = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_filter, max_filter)); + const int32_t offset_curr_delta = + int64_to_int32(float_to_quantized_unclamped(0.0f, min_curr_delta, + max_curr_delta)); + // const int32_t zero_in_prev_delta = + // float_to_quantized(0.0f, min_prev_delta_value, + // max_prev_delta_value); + + // propagate delta to previous layer + for_i(params.in.depth_, [&](size_t inc) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + if (!params.tbl.is_connected(outc, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * outc + inc; + idx = params.weight.get_index(0, 0, idx); + const uint8_t *pw = &W_quantized[idx]; + + idx = params.out_unpadded.get_index(0, 0, outc); + const uint8_t *pdelta_src = &curr_delta_quantized[idx]; + + idx = params.in.get_index(0, 0, inc); + int32_t *pdelta_quantized_dst = &(prev_delta_quantized)[idx]; + + for (size_t y = 0; y < params.in.height_; y++) { + for (size_t x = 0; x < params.in.width_; x++) { + const uint8_t *ppw = pw; + int32_t *ppdelta_quantized_dst = + pdelta_quantized_dst + y * params.in.width_ + x; + int32_t sum = int32_t(0); + + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + sum += + static_cast(ppw[wy * params.weight.width_ + wx] - + offset_filter) * + static_cast( + pdelta_src[(y * params.h_stride + wy) * params.out.width_ + + (x * params.w_stride + wx)] - + offset_curr_delta); + } + } + *ppdelta_quantized_dst += sum; + } + } + } + }); + + float_t min_prev_delta_requantized; + float_t max_prev_delta_requantized; + std::vector prev_delta_requantized(prev_delta_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + prev_delta_quantized, min_prev_delta_value, max_prev_delta_value, + &min_prev_delta_requantized, &max_prev_delta_requantized, + &prev_delta_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + vec_t prev_delta_vec = quantized_tensor_to_float( + prev_delta_requantized, min_prev_delta_requantized, + max_prev_delta_requantized); + prev_delta = &prev_delta_vec; + + // Accumulate dw + for_i(params.in.depth_, [&](size_t inc) { + for (size_t outc = 0; outc < params.out.depth_; outc++) { + if (!params.tbl.is_connected(outc, inc)) continue; + + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + int32_t dst = int32_t(0); + + size_t idx = 0; + idx = params.in.get_index(0, 0, inc); + const uint8_t *prevo = &prev_out_quantized[idx]; + + idx = params.out.get_index(wx, wy, outc); + const uint8_t *delta = &curr_delta_quantized[idx]; + + for (size_t y = 0; y < params.in.height_; y++) { + for (size_t x = 0; x < params.in.width_; x++) { + dst += + (static_cast(*(prevo + y * params.in.width_ + x)) - + offset_prev_out) * + (static_cast(*(delta + y * params.out.width_ + x)) - + offset_curr_delta); + } + } + + idx = params.in.depth_ * outc + inc; + dW_quantized[params.weight.get_index(wx, wy, idx)] += dst; + } + } + } + }); + + float_t min_dW_requantized; + float_t max_dW_requantized; + std::vector dW_requantized(dW_quantized.size(), + static_cast(0)); + + // requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + dW_quantized, min_dW_value, max_dW_value, &min_dW_requantized, + &max_dW_requantized, &dW_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + dW = quantized_tensor_to_float(dW_requantized, min_dW_requantized, + max_dW_requantized); + + // Accumulate db + if (params.has_bias) { + // vec_t& db = *in_grad[2]; + + for (size_t outc = 0; outc < params.out.depth_; outc++) { + size_t idx = params.out.get_index(0, 0, outc); + const float_t *delta = &curr_delta[idx]; + const float_t *deltaa = delta + params.out.width_ * params.out.height_; + db[outc] += std::accumulate(delta, deltaa, float_t{0}); + } + } +} + +inline void tiny_quantized_deconv2d_kernel(const deconv_params ¶ms, + const vec_t &in, + const vec_t &W, + const vec_t &bias, + const vec_t &in_r, + const vec_t &W_r, + const vec_t &b_r, + vec_t &out, + vec_t &out_r, + const bool layer_parallelize) { + // filter range + float_t min_filter(W_r[0]); + float_t max_filter(W_r[1]); + if (W_r[0] == W_r[1]) { + max_filter = W_r[1] + 1e-3f; + min_filter = W_r[0] - 1e-3f; + } + // bias range + float_t min_bias(b_r[0]); + float_t max_bias(b_r[1]); + if (params.has_bias) { + if (min_bias == max_bias) { + max_bias = b_r[1] + 1e-3f; + min_bias = b_r[0] - 1e-3f; + } + } + // output range + float_t min_output_value; + float_t max_output_value; + quantization_range_for_multiplication( + in_r[0], in_r[1], min_filter, max_filter, &min_output_value, + &max_output_value); + // data type restore + std::vector in_quantized, W_quantized, bias_quantized; + for (size_t i = 0; i < in.size(); i++) { + in_quantized.push_back(static_cast(in[i])); + } + for (size_t i = 0; i < W.size(); i++) { + W_quantized.push_back(static_cast(W[i])); + } + for (size_t i = 0; i < bias.size(); i++) { + bias_quantized.push_back(static_cast(bias[i])); + } + + std::vector out_quantized(out.size(), static_cast(0)); + + // calculating offset + const int32_t offset_input = int64_to_int32( + float_to_quantized_unclamped(0.0f, in_r[0], in_r[1])); + const int32_t offset_filter = int64_to_int32( + float_to_quantized_unclamped(0.0f, min_filter, max_filter)); + const int32_t zero_in_total_space = int64_to_int32( + float_to_quantized(0.0f, min_output_value, max_output_value)); + + for_i(layer_parallelize, params.out.depth_, [&](size_t o) { + for (size_t inc = 0; inc < params.in.depth_; inc++) { + if (!params.tbl.is_connected(o, inc)) continue; + + size_t idx = 0; + idx = params.in.depth_ * o + inc; + idx = params.weight.get_index(0, 0, idx); + const uint8_t *pw = &W_quantized[idx]; + + idx = params.in.get_index(0, 0, inc); + const uint8_t *pi = &in_quantized[idx]; + + idx = params.out.get_index(0, 0, o); + int32_t *pout_quantized = &out_quantized[idx]; + + for (size_t y = 0; y < params.in.height_; y++) { + for (size_t x = 0; x < params.in.width_; x++) { + const uint8_t *ppw = pw; + const uint8_t *ppi = pi + y * params.in.width_ + x; + // should be optimized for small kernel(3x3,5x5) + for (size_t wy = 0; wy < params.weight.height_; wy++) { + for (size_t wx = 0; wx < params.weight.width_; wx++) { + pout_quantized[(y * params.h_stride + wy) * params.out.width_ + + (x * params.w_stride + wx)] += + static_cast(ppw[wy * params.weight.width_ + wx] - + offset_filter) * + static_cast(*ppi - offset_input); + } + } + } + } + } + if (params.has_bias) { + int32_t *pout_quantized = &out_quantized[params.out.get_index(0, 0, o)]; + int32_t *poutout_quantized = + pout_quantized + params.out.width_ * params.out.height_; + std::for_each(pout_quantized, poutout_quantized, [&](int32_t &f) { + f += static_cast((bias[o] - zero_in_total_space)); + }); + } + }); + + float_t min_output_requantized; + float_t max_output_requantized; + std::vector out_requantized(out_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + out_quantized, min_output_value, max_output_value, &min_output_requantized, + &max_output_requantized, &out_requantized); + // store directly in float datatype + for (size_t i = 0; i < out_requantized.size(); i++) { + out[i] = static_cast(out_requantized[i]); + } + out_r[0] = min_output_requantized; + out_r[1] = max_output_requantized; +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/kernels/tiny_quantized_fully_connected_kernel.h b/tiny_dnn/core/kernels/tiny_quantized_fully_connected_kernel.h new file mode 100755 index 0000000000000..d0dd70d13cd1f --- /dev/null +++ b/tiny_dnn/core/kernels/tiny_quantized_fully_connected_kernel.h @@ -0,0 +1,376 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#ifdef CNN_USE_GEMMLOWP +#include "tiny_dnn/core/kernels/tiny_quantization_kernel.h" +#include "tiny_dnn/core/kernels/tiny_quantized_matmul_kernel.h" +#include "tiny_dnn/core/params/fully_params.h" + +namespace tiny_dnn { +namespace core { +namespace kernels { + +inline void tiny_quantized_fully_connected_kernel( + const fully_params ¶ms, + const vec_t &in, + const vec_t &W, + const vec_t &b, + vec_t &out, + const bool layer_parallelize) { + // input quantization + float_t min_input(in[0]); + float_t max_input(in[0]); + for (size_t c = 0; c < params.in_size_; c++) { + min_input = std::min(min_input, in[c]); + max_input = std::max(max_input, in[c]); + } + std::vector in_quantized = + float_tensor_to_quantized(in, min_input, max_input); + // filter quantization + float_t min_filter(W[0]); + float_t max_filter(W[0]); + for (size_t c = 0; c < W.size(); c++) { + min_filter = std::min(min_filter, W[c]); + max_filter = std::max(max_filter, W[c]); + } + if (min_filter == max_filter) { + max_filter = W[0] + 1e-3f; + min_filter = W[0] - 1e-3f; + } + std::vector W_quantized = + float_tensor_to_quantized(W, min_filter, max_filter); + // output range + float_t min_output_value; + float_t max_output_value; + quantization_range_for_multiplication( + min_input, max_input, min_filter, max_filter, &min_output_value, + &max_output_value); + // bias quantization + float_t min_bias(0); + float_t max_bias(0); + std::vector bias_quantized; + if (params.has_bias_) { + for (size_t inc = 0; inc < b.size(); inc++) { + min_bias = std::min(min_bias, b[inc]); + max_bias = std::max(max_bias, b[inc]); + } + if (min_bias == max_bias) { + max_bias = b[0] + 1e-3f; + min_bias = b[0] - 1e-3f; + } + bias_quantized = float_tensor_to_quantized(b, min_bias, max_bias); + } + min_output_value += min_bias; + max_output_value += max_bias; + + std::vector out_quantized(out.size(), static_cast(0)); + + // calculating offset + const int32_t offset_input = + float_to_quantized_unclamped(0.0f, min_input, max_input); + const int32_t offset_filter = + float_to_quantized_unclamped(0.0f, min_filter, max_filter); + const int32_t zero_in_total_space = + float_to_quantized(0.0f, min_output_value, max_output_value); + + const int32_t offset_output = 0; + const int32_t mult_output = 1; + const int32_t shift_output = 0; + + bool use_gemm = false; + if (use_gemm) { + std::vector shape{params.in_size_, 1, params.out_size_, + params.in_size_}; + tiny_quantized_matmul(in_quantized, W_quantized, out_quantized, shape, + offset_input, offset_filter, offset_output, + mult_output, shift_output); + if (params.has_bias_) { + for_i(layer_parallelize, params.out_size_, + [&](size_t i) { out[i] += b[i]; }); + } + } else { + for_i(layer_parallelize, params.out_size_, [&](size_t i) { + for (size_t c = 0; c < params.in_size_; c++) { + out_quantized[i] += + static_cast(W_quantized[c * params.out_size_ + i] - + offset_filter) * + static_cast(in_quantized[c] - offset_input); + } + if (params.has_bias_) { + out_quantized[i] += (bias_quantized[i] - zero_in_total_space); + } + }); + } + + float_t min_output_requantized; + float_t max_output_requantized; + std::vector out_requantized(out_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + out_quantized, min_output_value, max_output_value, &min_output_requantized, + &max_output_requantized, &out_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + out = quantized_tensor_to_float( + out_requantized, min_output_requantized, max_output_requantized); +} + +inline void tiny_quantized_fully_connected_back_kernel( + const fully_params ¶ms, + const vec_t &prev_out, + const vec_t &W, + vec_t &dW, + vec_t &prev_delta, + vec_t &curr_delta, + vec_t &db, + const bool layer_parallelize) { + // previous output quantization + float_t min_prev_out(prev_out[0]); + float_t max_prev_out(prev_out[0]); + for (size_t inc = 0; inc < prev_out.size(); inc++) { + min_prev_out = std::min(min_prev_out, prev_out[inc]); + max_prev_out = std::max(min_prev_out, prev_out[inc]); + } + std::vector prev_out_quantized = + float_tensor_to_quantized(prev_out, min_prev_out, max_prev_out); + + // filter quantization + float_t min_filter(W[0]); + float_t max_filter(W[0]); + for (size_t c = 0; c < W.size(); c++) { + min_filter = std::min(min_filter, W[c]); + max_filter = std::max(max_filter, W[c]); + } + if (min_filter == max_filter) { + max_filter = W[0] + 1e-3f; + min_filter = W[0] - 1e-3f; + } + std::vector W_quantized = + float_tensor_to_quantized(W, min_filter, max_filter); + + // current delta quantization + float_t min_curr_delta(curr_delta[0]); + float_t max_curr_delta(curr_delta[0]); + for (size_t inc = 0; inc < curr_delta.size(); inc++) { + min_curr_delta = std::min(min_curr_delta, curr_delta[inc]); + max_curr_delta = std::max(max_curr_delta, curr_delta[inc]); + } + std::vector curr_delta_quantized = + float_tensor_to_quantized(curr_delta, min_curr_delta, + max_curr_delta); + + // output range for previous delta + float_t min_prev_delta_value; + float_t max_prev_delta_value; + quantization_range_for_multiplication( + min_curr_delta, max_curr_delta, min_filter, max_filter, + &min_prev_delta_value, &max_prev_delta_value); + + std::vector prev_delta_quantized(prev_delta.size(), + static_cast(0)); + + // output range for dW + float_t min_dW_value; + float_t max_dW_value; + quantization_range_for_multiplication( + min_curr_delta, max_curr_delta, min_prev_out, max_prev_out, &min_dW_value, + &max_dW_value); + + std::vector dW_quantized(dW.size(), static_cast(0)); + + // calculating offset + const int32_t offset_prev_out = + float_to_quantized_unclamped(0.0f, min_prev_out, max_prev_out); + const int32_t offset_filter = + float_to_quantized_unclamped(0.0f, min_filter, max_filter); + const int32_t offset_curr_delta = + float_to_quantized_unclamped(0.0f, min_curr_delta, max_curr_delta); + // const int32_t zero_in_prev_delta = + // float_to_quantized(0.0f, min_prev_delta_value, + // max_prev_delta_value); + + for (size_t c = 0; c < params.in_size_; c++) { + // propagate delta to previous layer + // prev_delta[c] += current_delta[r] * W_[c * out_size_ + r] + for (size_t io = 0; io < params.out_size_; io++) { + prev_delta_quantized[c] += + (static_cast(curr_delta_quantized[io]) - offset_curr_delta) * + (static_cast(W_quantized[c * params.out_size_ + io]) - + offset_filter); + } + } + + float_t min_prev_delta_requantized; + float_t max_prev_delta_requantized; + std::vector prev_delta_requantized(prev_delta_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + prev_delta_quantized, min_prev_delta_value, max_prev_delta_value, + &min_prev_delta_requantized, &max_prev_delta_requantized, + &prev_delta_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + prev_delta = quantized_tensor_to_float(prev_delta_requantized, + min_prev_delta_requantized, + max_prev_delta_requantized); + + for_(layer_parallelize, 0, size_t(params.out_size_), + [&](const blocked_range &r) { + // accumulate weight-step using delta + // dW[c * out_size + i] += current_delta[i] * prev_out[c] + for (size_t c = 0; c < params.in_size_; c++) { + for (size_t io = 0; io < params.out_size_; io++) { + dW_quantized[c * params.out_size_ + io] += + (static_cast(curr_delta_quantized[io]) - + offset_curr_delta) * + (static_cast(prev_out_quantized[c]) - offset_prev_out); + } + } + + if (params.has_bias_) { + // vec_t& db = *in_grad[2]; + for (size_t i = r.begin(); i < r.end(); i++) { + db[i] += curr_delta[i]; + } + } + }); + + float_t min_dW_requantized; + float_t max_dW_requantized; + std::vector dW_requantized(dW_quantized.size(), + static_cast(0)); + + // requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + dW_quantized, min_dW_value, max_dW_value, &min_dW_requantized, + &max_dW_requantized, &dW_requantized); + + // dequantize to flaot, this could be removed within concatenated quantized + // network + dW = quantized_tensor_to_float(dW_requantized, min_dW_requantized, + max_dW_requantized); +} + +inline void tiny_quantized_fully_connected_kernel( + const fully_params ¶ms, + const vec_t &in, + const vec_t &W, + const vec_t &b, + const vec_t &in_r, + const vec_t &W_r, + const vec_t &b_r, + vec_t &out, + vec_t &out_r, + const bool layer_parallelize) { + // filter range + float_t min_filter(W_r[0]); + float_t max_filter(W_r[1]); + if (min_filter == max_filter) { + max_filter = W_r[1] + 1e-3f; + min_filter = W_r[0] - 1e-3f; + } + // bias range + float_t min_bias(b_r[0]); + float_t max_bias(b_r[1]); + if (params.has_bias_) { + if (min_bias == max_bias) { + max_bias = b_r[1] + 1e-3f; + min_bias = b_r[0] - 1e-3f; + } + } + // output range + float_t min_output_value; + float_t max_output_value; + quantization_range_for_multiplication( + in_r[0], in_r[1], min_filter, max_filter, &min_output_value, + &max_output_value); + // data type restore + std::vector in_quantized, W_quantized, bias_quantized; + for (size_t i = 0; i < in.size(); i++) { + in_quantized.push_back(static_cast(in[i])); + } + for (size_t i = 0; i < W.size(); i++) { + W_quantized.push_back(static_cast(W[i])); + } + for (size_t i = 0; i < b.size(); i++) { + bias_quantized.push_back(static_cast(b[i])); + } + min_output_value += min_bias; + max_output_value += max_bias; + + std::vector out_quantized(out.size(), static_cast(0)); + + // calculating offset + const int32_t offset_input = + float_to_quantized_unclamped(0.0f, in_r[0], in_r[1]); + const int32_t offset_filter = + float_to_quantized_unclamped(0.0f, min_filter, max_filter); + const int32_t zero_in_total_space = + float_to_quantized(0.0f, min_output_value, max_output_value); + + const int32_t offset_output = 0; + const int32_t mult_output = 1; + const int32_t shift_output = 0; + + bool use_gemm = false; + if (use_gemm) { + std::vector shape{params.in_size_, 1, params.out_size_, + params.in_size_}; + tiny_quantized_matmul(in_quantized, W_quantized, out_quantized, shape, + offset_input, offset_filter, offset_output, + mult_output, shift_output); + if (params.has_bias_) { + for_i(layer_parallelize, params.out_size_, + [&](size_t i) { out[i] += b[i]; }); + } + } else { + for_i(layer_parallelize, params.out_size_, [&](size_t i) { + for (size_t c = 0; c < params.in_size_; c++) { + out_quantized[i] += + static_cast(W_quantized[c * params.out_size_ + i] - + offset_filter) * + static_cast(in_quantized[c] - offset_input); + } + if (params.has_bias_) { + out_quantized[i] += (bias_quantized[i] - zero_in_total_space); + } + }); + } + + float_t min_output_requantized; + float_t max_output_requantized; + std::vector out_requantized(out_quantized.size(), + static_cast(0)); + + // Requantize from 32bits to 8 bits for next layer + quantize_down_and_shrink_range( + out_quantized, min_output_value, max_output_value, &min_output_requantized, + &max_output_requantized, &out_requantized); + // store directly in float datatype + for (size_t i = 0; i < out_requantized.size(); i++) { + out[i] = static_cast(out_requantized[i]); + } + out_r[0] = min_output_requantized; + out_r[1] = max_output_requantized; +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn + +#endif // CNN_USE_GEMMLOWP diff --git a/tiny_dnn/core/kernels/tiny_quantized_matmul_kernel.h b/tiny_dnn/core/kernels/tiny_quantized_matmul_kernel.h new file mode 100755 index 0000000000000..a6023daae03c1 --- /dev/null +++ b/tiny_dnn/core/kernels/tiny_quantized_matmul_kernel.h @@ -0,0 +1,137 @@ +/* + +Copyright 2015 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +============================================================================== + +*/ +#pragma once + +#include +#include + +// Implements a quantized eight-bit version of the matmul operation. + +#include "third_party/gemmlowp/public/gemmlowp.h" +#include "tiny_dnn/core/kernels/tiny_quantization_kernel.h" + +namespace tiny_dnn { +namespace core { +namespace kernels { + +template +void gemmlowp_multiply(const uint8_t *a_data, + const uint8_t *b_data, + int32_t *c_data, + int m, + int n, + int k, + int offset_a, + int offset_b, + int lda, + int ldb, + int ldc) { + const uint8_t *a_data_as_uint8 = a_data; + const uint8_t *b_data_as_uint8 = b_data; + int32_t *c_data_as_int32 = c_data; + static const gemmlowp::MapOrder ResultOrder = + !TransposeC ? gemmlowp::MapOrder::RowMajor : gemmlowp::MapOrder::ColMajor; + static const gemmlowp::MapOrder LhsOrder = + !TransposeA ? gemmlowp::MapOrder::RowMajor : gemmlowp::MapOrder::ColMajor; + static const gemmlowp::MapOrder RhsOrder = + !TransposeB ? gemmlowp::MapOrder::RowMajor : gemmlowp::MapOrder::ColMajor; + gemmlowp::MatrixMap lhs(a_data_as_uint8, m, k, + lda); + gemmlowp::MatrixMap rhs(b_data_as_uint8, k, n, + ldb); + gemmlowp::MatrixMap result(c_data_as_int32, m, n, + ldc); + const std::tuple<> empty_pipeline = {}; + gemmlowp::GemmContext context; + gemmlowp::GemmWithOutputPipeline( + &context, lhs, rhs, &result, -offset_a, -offset_b, empty_pipeline); +} + +template +void tiny_quantized_matmul(const std::vector &a, + const std::vector &b, + std::vector &c, + const std::vector shape_all, + const int32_t offset_a, + const int32_t offset_b, + const int32_t offset_c, + const int32_t mult_c, + const int32_t shift_c) { + // Make sure that we have valid quantization ranges for the input buffers. + // If the difference between the min and max is negative or zero, it makes + // it hard to do meaningful intermediate operations on the values. + + int transpose_a_ = 0; + int transpose_b_ = 1; + int a_dim_remaining = 1 - transpose_a_; + int b_dim_remaining = 1 - transpose_b_; + + const T1 *a_data = &a[0]; + const T2 *b_data = &b[0]; + Toutput *c_data = &c[0]; + + const bool transpose_c = false; + const size_t m = shape_all[a_dim_remaining]; + const size_t n = shape_all[2 + b_dim_remaining]; + const size_t k = shape_all[transpose_a_]; + const size_t lda = shape_all[1]; + const size_t ldb = shape_all[3]; + const size_t ldc = n; + + // The gemmlowp optimized library only works for a particular set of data + // types, so check if we meet those requirements and + // fall back to a slower reference implementation if not. + if (std::is_same() && std::is_same() && + std::is_same() && (offset_c == 0) && (mult_c == 1) && + (shift_c == 0) && (transpose_c == false)) { + if (transpose_a_) { + if (transpose_b_) { + gemmlowp_multiply(a_data, b_data, c_data, m, n, k, + offset_a, offset_b, lda, ldb, ldc); + } else { + gemmlowp_multiply( + a_data, b_data, c_data, m, n, k, offset_a, offset_b, lda, ldb, ldc); + } + } else { + if (transpose_b_) { + gemmlowp_multiply( + a_data, b_data, c_data, m, n, k, offset_a, offset_b, lda, ldb, ldc); + } else { + gemmlowp_multiply( + a_data, b_data, c_data, m, n, k, offset_a, offset_b, lda, ldb, ldc); + } + } + } else { +#if 0 + ReferenceGemm( + transpose_a_, transpose_b_, transpose_c, m, n, k, a_data, offset_a, + lda, b_data, offset_b, ldb, c_data, shift_c, offset_c, mult_c, ldc); +#endif + } + + float min_c_value; + float max_c_value; + quantization_range_for_multiplication( + min_a, max_a, min_b, max_b, &min_c_value, &max_c_value); +} + +} // namespace kernels +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/params/conv_params.h b/tiny_dnn/core/params/conv_params.h new file mode 100755 index 0000000000000..82150fd3b8f39 --- /dev/null +++ b/tiny_dnn/core/params/conv_params.h @@ -0,0 +1,169 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/core/params/params.h" + +namespace tiny_dnn { +namespace core { + +struct conv_layer_worker_specific_storage { + std::vector prev_out_padded_; + std::vector prev_out_buf_; + std::vector prev_delta_padded_; +}; + +struct connection_table { + connection_table() : rows_(0), cols_(0) {} + connection_table(const bool *ar, size_t rows, size_t cols) + : connected_(rows * cols), rows_(rows), cols_(cols) { + std::copy(ar, ar + rows * cols, connected_.begin()); + } + connection_table(size_t ngroups, size_t rows, size_t cols) + : connected_(rows * cols, false), rows_(rows), cols_(cols) { + if (rows % ngroups || cols % ngroups) { + throw nn_error("invalid group size"); + } + + size_t row_group = rows / ngroups; + size_t col_group = cols / ngroups; + + size_t idx = 0; + + for (size_t g = 0; g < ngroups; g++) { + for (size_t r = 0; r < row_group; r++) { + for (size_t c = 0; c < col_group; c++) { + idx = (r + g * row_group) * cols_ + c + g * col_group; + connected_[idx] = true; + } + } + } + } + + bool is_connected(size_t x, size_t y) const { + return is_empty() ? true : connected_[y * cols_ + x]; + } + + bool is_empty() const { return rows_ == 0 && cols_ == 0; } + + std::deque connected_; + size_t rows_; + size_t cols_; +}; + +class conv_params : public Params { + public: + connection_table tbl; + index3d in; + index3d in_padded; + index3d out; + index3d weight; + bool has_bias; + padding pad_type; + size_t w_stride; + size_t h_stride; + + friend std::ostream &operator<<(std::ostream &o, + const core::conv_params ¶m) { + o << "in: " << param.in << "\n"; + o << "out: " << param.out << "\n"; + o << "in_padded: " << param.in_padded << "\n"; + o << "weight: " << param.weight << "\n"; + o << "has_bias: " << param.has_bias << "\n"; + o << "w_stride: " << param.w_stride << "\n"; + o << "h_stride: " << param.h_stride << "\n"; + return o; + } +}; + +inline conv_params &Params::conv() { + return *(static_cast(this)); +} + +class Conv2dPadding { + public: + Conv2dPadding() {} + explicit Conv2dPadding(const conv_params ¶ms) : params_(params) {} + + /* Applies padding to an input tensor given the convolution parameters + * + * @param in The input tensor + * @param out The output tensor with padding applied + */ + void copy_and_pad_input(const tensor_t &in, tensor_t &out) { + if (params_.pad_type == padding::valid) { + return; + } + + tensor_t buf(in.size()); + + for_i(true, buf.size(), [&](size_t sample) { + // alloc temporary buffer. + buf[sample].resize(params_.in_padded.size()); + + // make padded version in order to avoid corner-case in fprop/bprop + for (size_t c = 0; c < params_.in.depth_; c++) { + float_t *pimg = &buf[sample][params_.in_padded.get_index( + params_.weight.width_ / 2, params_.weight.height_ / 2, c)]; + const float_t *pin = &in[sample][params_.in.get_index(0, 0, c)]; + + for (size_t y = 0; y < params_.in.height_; y++) { + std::copy(pin, pin + params_.in.width_, pimg); + pin += params_.in.width_; + pimg += params_.in_padded.width_; + } + } + }); + + // shrink buffer to output + out = buf; + } + + /* Applies unpadding to an input tensor given the convolution parameters + * + * @param in The input tensor + * @param out The output tensor with unpadding applied + */ + void copy_and_unpad_delta(const tensor_t &delta, tensor_t &delta_unpadded) { + if (params_.pad_type == padding::valid) { + return; + } + + tensor_t buf(delta.size()); + + for_i(true, buf.size(), [&](size_t sample) { + // alloc temporary buffer. + buf[sample].resize(params_.in.size()); + + for (size_t c = 0; c < params_.in.depth_; c++) { + const float_t *pin = &delta[sample][params_.in_padded.get_index( + params_.weight.width_ / 2, params_.weight.height_ / 2, c)]; + float_t *pdst = &buf[sample][params_.in.get_index(0, 0, c)]; + + for (size_t y = 0; y < params_.in.height_; y++) { + std::copy(pin, pin + params_.in.width_, pdst); + pdst += params_.in.width_; + pin += params_.in_padded.width_; + } + } + }); + + // shrink buffer to output + delta_unpadded = buf; + } + + private: + conv_params params_; +}; + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/params/deconv_params.h b/tiny_dnn/core/params/deconv_params.h new file mode 100755 index 0000000000000..d9d6d544b8ec5 --- /dev/null +++ b/tiny_dnn/core/params/deconv_params.h @@ -0,0 +1,33 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +namespace tiny_dnn { +namespace core { + +struct deconv_layer_worker_specific_storage { + const tensor_t *prev_out_; + const tensor_t *curr_out_unpadded_; + tensor_t curr_out_buf_; + tensor_t curr_delta_padded; +}; + +struct deconv_params { + connection_table tbl; + index3d in; + index3d out; + index3d out_unpadded; + index3d weight; + bool has_bias; + padding pad_type; + size_t w_stride; + size_t h_stride; +}; + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/params/fully_params.h b/tiny_dnn/core/params/fully_params.h new file mode 100755 index 0000000000000..45abdf458b0d3 --- /dev/null +++ b/tiny_dnn/core/params/fully_params.h @@ -0,0 +1,28 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/core/params/params.h" + +namespace tiny_dnn { +namespace core { + +class fully_params : public Params { + public: + size_t in_size_; + size_t out_size_; + bool has_bias_; +}; + +// TODO(nyanp): can we do better here? +inline fully_params &Params::fully() { + return *(static_cast(this)); +} + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/params/global_avepool_params.h b/tiny_dnn/core/params/global_avepool_params.h new file mode 100755 index 0000000000000..5f022ebf5061d --- /dev/null +++ b/tiny_dnn/core/params/global_avepool_params.h @@ -0,0 +1,25 @@ +/* + Copyright (c) 2017, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once +#include "tiny_dnn/core/params/params.h" + +namespace tiny_dnn { +namespace core { + +class global_avepool_params : public Params { + public: + shape3d in; + shape3d out; +}; + +inline global_avepool_params &Params::global_avepool() { + return *(static_cast(this)); +} + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/params/maxpool_params.h b/tiny_dnn/core/params/maxpool_params.h new file mode 100755 index 0000000000000..51ff770b20493 --- /dev/null +++ b/tiny_dnn/core/params/maxpool_params.h @@ -0,0 +1,46 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/core/params/params.h" + +namespace tiny_dnn { +namespace core { + +class maxpool_params : public Params { + public: + index3d in; + index3d out; + size_t pool_size_x; + size_t pool_size_y; + size_t stride_x; + size_t stride_y; + padding pad_type; + + /* mapping out => max_index(in) (1:1) */ + std::vector> out2inmax; + /* mapping out => in (1:N) */ + std::vector> out2in; + /* mapping in => out (N:1) */ + std::vector in2out; +}; + +struct max_pooling_layer_worker_specific_storage { + /* mapping out => max_index(in) (1:1) */ + std::vector> out2inmax_; +}; + +// TODO(nyanp): can we do better here? +inline maxpool_params &Params::maxpool() { + return *(static_cast(this)); +} + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/params/params.h b/tiny_dnn/core/params/params.h new file mode 100755 index 0000000000000..37afc1af6c6b3 --- /dev/null +++ b/tiny_dnn/core/params/params.h @@ -0,0 +1,32 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +namespace tiny_dnn { +namespace core { + +class conv_params; +class fully_params; +class maxpool_params; +class global_avepool_params; +class recurrent_cell_params; + +/* Base class to model operation parameters */ +class Params { + public: + Params() {} + + conv_params &conv(); + fully_params &fully(); + maxpool_params &maxpool(); + global_avepool_params &global_avepool(); + recurrent_cell_params &recurrent_cell(); +}; + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/params/recurrent_cell_params.h b/tiny_dnn/core/params/recurrent_cell_params.h new file mode 100755 index 0000000000000..9fe310741a6fb --- /dev/null +++ b/tiny_dnn/core/params/recurrent_cell_params.h @@ -0,0 +1,31 @@ +/* + Copyright (c) 2016, Taiga Nomi, Edgar Riba + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/core/params/params.h" + +namespace tiny_dnn { +namespace core { + +class recurrent_cell_params : public Params { + public: + size_t in_size_; + size_t out_size_; + std::shared_ptr activation_{}; + bool has_bias_; +}; + +inline recurrent_cell_params &Params::recurrent_cell() { + return *(static_cast(this)); +} + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/core/session.h b/tiny_dnn/core/session.h new file mode 100755 index 0000000000000..91baa447e0268 --- /dev/null +++ b/tiny_dnn/core/session.h @@ -0,0 +1,39 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/core/device.h" + +namespace tiny_dnn { +namespace core { + +class session { + public: + explicit session(const std::string name) : name_(name) {} + + std::string get_name() const { return name_; } + size_t get_num_devices() const { return devices_.size(); } + + // will call construct graph + // should we here specify the devices to use? + void schedule_session(/* network& net */); + + // will call forward or backward methods + void run_session(/* data */); + + private: + std::string name_; + std::vector> devices_; +}; + +} // namespace core +} // namespace tiny_dnn diff --git a/tiny_dnn/io/caffe/CPPLINT.cfg b/tiny_dnn/io/caffe/CPPLINT.cfg new file mode 100755 index 0000000000000..c7502bdcbfef7 --- /dev/null +++ b/tiny_dnn/io/caffe/CPPLINT.cfg @@ -0,0 +1 @@ +exclude_files=caffe.pb.h diff --git a/tiny_dnn/io/caffe/caffe.proto b/tiny_dnn/io/caffe/caffe.proto new file mode 100755 index 0000000000000..1581071863102 --- /dev/null +++ b/tiny_dnn/io/caffe/caffe.proto @@ -0,0 +1,1379 @@ +syntax = "proto2"; + +package caffe; + +// Specifies the shape (dimensions) of a Blob. +message BlobShape { + repeated int64 dim = 1 [packed = true]; +} + +message BlobProto { + optional BlobShape shape = 7; + repeated float data = 5 [packed = true]; + repeated float diff = 6 [packed = true]; + repeated double double_data = 8 [packed = true]; + repeated double double_diff = 9 [packed = true]; + + // 4D dimensions -- deprecated. Use "shape" instead. + optional int32 num = 1 [default = 0]; + optional int32 channels = 2 [default = 0]; + optional int32 height = 3 [default = 0]; + optional int32 width = 4 [default = 0]; +} + +// The BlobProtoVector is simply a way to pass multiple blobproto instances +// around. +message BlobProtoVector { + repeated BlobProto blobs = 1; +} + +message Datum { + optional int32 channels = 1; + optional int32 height = 2; + optional int32 width = 3; + // the actual image data, in bytes + optional bytes data = 4; + optional int32 label = 5; + // Optionally, the datum could also hold float data. + repeated float float_data = 6; + // If true data contains an encoded image that need to be decoded + optional bool encoded = 7 [default = false]; +} + +message FillerParameter { + // The filler type. + optional string type = 1 [default = 'constant']; + optional float value = 2 [default = 0]; // the value in constant filler + optional float min = 3 [default = 0]; // the min value in uniform filler + optional float max = 4 [default = 1]; // the max value in uniform filler + optional float mean = 5 [default = 0]; // the mean value in Gaussian filler + optional float std = 6 [default = 1]; // the std value in Gaussian filler + // The expected number of non-zero output weights for a given input in + // Gaussian filler -- the default -1 means don't perform sparsification. + optional int32 sparse = 7 [default = -1]; + // Normalize the filler variance by fan_in, fan_out, or their average. + // Applies to 'xavier' and 'msra' fillers. + enum VarianceNorm { + FAN_IN = 0; + FAN_OUT = 1; + AVERAGE = 2; + } + optional VarianceNorm variance_norm = 8 [default = FAN_IN]; +} + +message NetParameter { + optional string name = 1; // consider giving the network a name + // DEPRECATED. See InputParameter. The input blobs to the network. + repeated string input = 3; + // DEPRECATED. See InputParameter. The shape of the input blobs. + repeated BlobShape input_shape = 8; + + // 4D input dimensions -- deprecated. Use "input_shape" instead. + // If specified, for each input blob there should be four + // values specifying the num, channels, height and width of the input blob. + // Thus, there should be a total of (4 * #input) numbers. + repeated int32 input_dim = 4; + + // Whether the network will force every layer to carry out backward operation. + // If set False, then whether to carry out backward is determined + // automatically according to the net structure and learning rates. + optional bool force_backward = 5 [default = false]; + // The current "state" of the network, including the phase, level, and stage. + // Some layers may be included/excluded depending on this state and the states + // specified in the layers' include and exclude fields. + optional NetState state = 6; + + // Print debugging information about results while running Net::Forward, + // Net::Backward, and Net::Update. + optional bool debug_info = 7 [default = false]; + + // The layers that make up the net. Each of their configurations, including + // connectivity and behavior, is specified as a LayerParameter. + repeated LayerParameter layer = 100; // ID 100 so layers are printed last. + + // DEPRECATED: use 'layer' instead. + repeated V1LayerParameter layers = 2; +} + +// NOTE +// Update the next available ID when you add a new SolverParameter field. +// +// SolverParameter next available ID: 41 (last added: type) +message SolverParameter { + ////////////////////////////////////////////////////////////////////////////// + // Specifying the train and test networks + // + // Exactly one train net must be specified using one of the following fields: + // train_net_param, train_net, net_param, net + // One or more test nets may be specified using any of the following fields: + // test_net_param, test_net, net_param, net + // If more than one test net field is specified (e.g., both net and + // test_net are specified), they will be evaluated in the field order given + // above: (1) test_net_param, (2) test_net, (3) net_param/net. + // A test_iter must be specified for each test_net. + // A test_level and/or a test_stage may also be specified for each test_net. + ////////////////////////////////////////////////////////////////////////////// + + // Proto filename for the train net, possibly combined with one or more + // test nets. + optional string net = 24; + // Inline train net param, possibly combined with one or more test nets. + optional NetParameter net_param = 25; + + optional string train_net = 1; // Proto filename for the train net. + repeated string test_net = 2; // Proto filenames for the test nets. + optional NetParameter train_net_param = 21; // Inline train net params. + repeated NetParameter test_net_param = 22; // Inline test net params. + + // The states for the train/test nets. Must be unspecified or + // specified once per net. + // + // By default, all states will have solver = true; + // train_state will have phase = TRAIN, + // and all test_state's will have phase = TEST. + // Other defaults are set according to the NetState defaults. + optional NetState train_state = 26; + repeated NetState test_state = 27; + + // The number of iterations for each test net. + repeated int32 test_iter = 3; + + // The number of iterations between two testing phases. + optional int32 test_interval = 4 [default = 0]; + optional bool test_compute_loss = 19 [default = false]; + // If true, run an initial test pass before the first iteration, + // ensuring memory availability and printing the starting value of the loss. + optional bool test_initialization = 32 [default = true]; + optional float base_lr = 5; // The base learning rate + // the number of iterations between displaying info. If display = 0, no info + // will be displayed. + optional int32 display = 6; + // Display the loss averaged over the last average_loss iterations + optional int32 average_loss = 33 [default = 1]; + optional int32 max_iter = 7; // the maximum number of iterations + // accumulate gradients over `iter_size` x `batch_size` instances + optional int32 iter_size = 36 [default = 1]; + + // The learning rate decay policy. The currently implemented learning rate + // policies are as follows: + // - fixed: always return base_lr. + // - step: return base_lr * gamma ^ (floor(iter / step)) + // - exp: return base_lr * gamma ^ iter + // - inv: return base_lr * (1 + gamma * iter) ^ (- power) + // - multistep: similar to step but it allows non uniform steps defined by + // stepvalue + // - poly: the effective learning rate follows a polynomial decay, to be + // zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power) + // - sigmoid: the effective learning rate follows a sigmod decay + // return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize)))) + // + // where base_lr, max_iter, gamma, step, stepvalue and power are defined + // in the solver parameter protocol buffer, and iter is the current iteration. + optional string lr_policy = 8; + optional float gamma = 9; // The parameter to compute the learning rate. + optional float power = 10; // The parameter to compute the learning rate. + optional float momentum = 11; // The momentum value. + optional float weight_decay = 12; // The weight decay. + // regularization types supported: L1 and L2 + // controlled by weight_decay + optional string regularization_type = 29 [default = "L2"]; + // the stepsize for learning rate policy "step" + optional int32 stepsize = 13; + // the stepsize for learning rate policy "multistep" + repeated int32 stepvalue = 34; + + // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm, + // whenever their actual L2 norm is larger. + optional float clip_gradients = 35 [default = -1]; + + optional int32 snapshot = 14 [default = 0]; // The snapshot interval + optional string snapshot_prefix = 15; // The prefix for the snapshot. + // whether to snapshot diff in the results or not. Snapshotting diff will help + // debugging but the final protocol buffer size will be much larger. + optional bool snapshot_diff = 16 [default = false]; + enum SnapshotFormat { + HDF5 = 0; + BINARYPROTO = 1; + } + optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO]; + // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default. + enum SolverMode { + CPU = 0; + GPU = 1; + } + optional SolverMode solver_mode = 17 [default = GPU]; + // the device_id will that be used in GPU mode. Use device_id = 0 in default. + optional int32 device_id = 18 [default = 0]; + // If non-negative, the seed with which the Solver will initialize the Caffe + // random number generator -- useful for reproducible results. Otherwise, + // (and by default) initialize using a seed derived from the system clock. + optional int64 random_seed = 20 [default = -1]; + + // type of the solver + optional string type = 40 [default = "SGD"]; + + // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam + optional float delta = 31 [default = 1e-8]; + // parameters for the Adam solver + optional float momentum2 = 39 [default = 0.999]; + + // RMSProp decay value + // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t) + optional float rms_decay = 38; + + // If true, print information about the state of the net that may help with + // debugging learning problems. + optional bool debug_info = 23 [default = false]; + + // If false, don't save a snapshot after training finishes. + optional bool snapshot_after_train = 28 [default = true]; + + // DEPRECATED: old solver enum types, use string instead + enum SolverType { + SGD = 0; + NESTEROV = 1; + ADAGRAD = 2; + RMSPROP = 3; + ADADELTA = 4; + ADAM = 5; + } + // DEPRECATED: use type instead of solver_type + optional SolverType solver_type = 30 [default = SGD]; +} + +// A message that stores the solver snapshots +message SolverState { + optional int32 iter = 1; // The current iteration + optional string learned_net = 2; // The file that stores the learned net. + repeated BlobProto history = 3; // The history for sgd solvers + optional int32 current_step = 4 [default = 0]; // The current step for learning rate +} + +enum Phase { + TRAIN = 0; + TEST = 1; +} + +message NetState { + optional Phase phase = 1 [default = TEST]; + optional int32 level = 2 [default = 0]; + repeated string stage = 3; +} + +message NetStateRule { + // Set phase to require the NetState have a particular phase (TRAIN or TEST) + // to meet this rule. + optional Phase phase = 1; + + // Set the minimum and/or maximum levels in which the layer should be used. + // Leave undefined to meet the rule regardless of level. + optional int32 min_level = 2; + optional int32 max_level = 3; + + // Customizable sets of stages to include or exclude. + // The net must have ALL of the specified stages and NONE of the specified + // "not_stage"s to meet the rule. + // (Use multiple NetStateRules to specify conjunctions of stages.) + repeated string stage = 4; + repeated string not_stage = 5; +} + +// Specifies training parameters (multipliers on global learning constants, +// and the name and other settings used for weight sharing). +message ParamSpec { + // The names of the parameter blobs -- useful for sharing parameters among + // layers, but never required otherwise. To share a parameter between two + // layers, give it a (non-empty) name. + optional string name = 1; + + // Whether to require shared weights to have the same shape, or just the same + // count -- defaults to STRICT if unspecified. + optional DimCheckMode share_mode = 2; + enum DimCheckMode { + // STRICT (default) requires that num, channels, height, width each match. + STRICT = 0; + // PERMISSIVE requires only the count (num*channels*height*width) to match. + PERMISSIVE = 1; + } + + // The multiplier on the global learning rate for this parameter. + optional float lr_mult = 3 [default = 1.0]; + + // The multiplier on the global weight decay for this parameter. + optional float decay_mult = 4 [default = 1.0]; +} + +// NOTE +// Update the next available ID when you add a new LayerParameter field. +// +// LayerParameter next available layer-specific ID: 146 (last added: parameter_param) +message LayerParameter { + optional string name = 1; // the layer name + optional string type = 2; // the layer type + repeated string bottom = 3; // the name of each bottom blob + repeated string top = 4; // the name of each top blob + + // The train / test phase for computation. + optional Phase phase = 10; + + // The amount of weight to assign each top blob in the objective. + // Each layer assigns a default value, usually of either 0 or 1, + // to each top blob. + repeated float loss_weight = 5; + + // Specifies training parameters (multipliers on global learning constants, + // and the name and other settings used for weight sharing). + repeated ParamSpec param = 6; + + // The blobs containing the numeric parameters of the layer. + repeated BlobProto blobs = 7; + + // Specifies whether to backpropagate to each bottom. If unspecified, + // Caffe will automatically infer whether each input needs backpropagation + // to compute parameter gradients. If set to true for some inputs, + // backpropagation to those inputs is forced; if set false for some inputs, + // backpropagation to those inputs is skipped. + // + // The size must be either 0 or equal to the number of bottoms. + repeated bool propagate_down = 11; + + // Rules controlling whether and when a layer is included in the network, + // based on the current NetState. You may specify a non-zero number of rules + // to include OR exclude, but not both. If no include or exclude rules are + // specified, the layer is always included. If the current NetState meets + // ANY (i.e., one or more) of the specified rules, the layer is + // included/excluded. + repeated NetStateRule include = 8; + repeated NetStateRule exclude = 9; + + // Parameters for data pre-processing. + optional TransformationParameter transform_param = 100; + + // Parameters shared by loss layers. + optional LossParameter loss_param = 101; + + // Layer type-specific parameters. + // + // Note: certain layers may have more than one computational engine + // for their implementation. These layers include an Engine type and + // engine parameter for selecting the implementation. + // The default for the engine is set by the ENGINE switch at compile-time. + optional AccuracyParameter accuracy_param = 102; + optional ArgMaxParameter argmax_param = 103; + optional BatchNormParameter batch_norm_param = 139; + optional BiasParameter bias_param = 141; + optional ConcatParameter concat_param = 104; + optional ContrastiveLossParameter contrastive_loss_param = 105; + optional ConvolutionParameter convolution_param = 106; + optional CropParameter crop_param = 144; + optional DataParameter data_param = 107; + optional DropoutParameter dropout_param = 108; + optional DummyDataParameter dummy_data_param = 109; + optional EltwiseParameter eltwise_param = 110; + optional ELUParameter elu_param = 140; + optional EmbedParameter embed_param = 137; + optional ExpParameter exp_param = 111; + optional FlattenParameter flatten_param = 135; + optional HDF5DataParameter hdf5_data_param = 112; + optional HDF5OutputParameter hdf5_output_param = 113; + optional HingeLossParameter hinge_loss_param = 114; + optional ImageDataParameter image_data_param = 115; + optional InfogainLossParameter infogain_loss_param = 116; + optional InnerProductParameter inner_product_param = 117; + optional InputParameter input_param = 143; + optional LogParameter log_param = 134; + optional LRNParameter lrn_param = 118; + optional MemoryDataParameter memory_data_param = 119; + optional MVNParameter mvn_param = 120; + optional ParameterParameter parameter_param = 145; + optional PoolingParameter pooling_param = 121; + optional PowerParameter power_param = 122; + optional PReLUParameter prelu_param = 131; + optional PythonParameter python_param = 130; + optional ReductionParameter reduction_param = 136; + optional ReLUParameter relu_param = 123; + optional ReshapeParameter reshape_param = 133; + optional ScaleParameter scale_param = 142; + optional SigmoidParameter sigmoid_param = 124; + optional SoftmaxParameter softmax_param = 125; + optional SPPParameter spp_param = 132; + optional SliceParameter slice_param = 126; + optional TanHParameter tanh_param = 127; + optional ThresholdParameter threshold_param = 128; + optional TileParameter tile_param = 138; + optional WindowDataParameter window_data_param = 129; +} + +// Message that stores parameters used to apply transformation +// to the data layer's data +message TransformationParameter { + // For data pre-processing, we can do simple scaling and subtracting the + // data mean, if provided. Note that the mean subtraction is always carried + // out before scaling. + optional float scale = 1 [default = 1]; + // Specify if we want to randomly mirror data. + optional bool mirror = 2 [default = false]; + // Specify if we would like to randomly crop an image. + optional uint32 crop_size = 3 [default = 0]; + // mean_file and mean_value cannot be specified at the same time + optional string mean_file = 4; + // if specified can be repeated once (would substract it from all the channels) + // or can be repeated the same number of times as channels + // (would subtract them from the corresponding channel) + repeated float mean_value = 5; + // Force the decoded image to have 3 color channels. + optional bool force_color = 6 [default = false]; + // Force the decoded image to have 1 color channels. + optional bool force_gray = 7 [default = false]; +} + +// Message that stores parameters shared by loss layers +message LossParameter { + // If specified, ignore instances with the given label. + optional int32 ignore_label = 1; + // How to normalize the loss for loss layers that aggregate across batches, + // spatial dimensions, or other dimensions. Currently only implemented in + // SoftmaxWithLoss layer. + enum NormalizationMode { + // Divide by the number of examples in the batch times spatial dimensions. + // Outputs that receive the ignore label will NOT be ignored in computing + // the normalization factor. + FULL = 0; + // Divide by the total number of output locations that do not take the + // ignore_label. If ignore_label is not set, this behaves like FULL. + VALID = 1; + // Divide by the batch size. + BATCH_SIZE = 2; + // Do not normalize the loss. + NONE = 3; + } + optional NormalizationMode normalization = 3 [default = VALID]; + // Deprecated. Ignored if normalization is specified. If normalization + // is not specified, then setting this to false will be equivalent to + // normalization = BATCH_SIZE to be consistent with previous behavior. + optional bool normalize = 2; +} + +// Messages that store parameters used by individual layer types follow, in +// alphabetical order. + +message AccuracyParameter { + // When computing accuracy, count as correct by comparing the true label to + // the top k scoring classes. By default, only compare to the top scoring + // class (i.e. argmax). + optional uint32 top_k = 1 [default = 1]; + + // The "label" axis of the prediction blob, whose argmax corresponds to the + // predicted label -- may be negative to index from the end (e.g., -1 for the + // last axis). For example, if axis == 1 and the predictions are + // (N x C x H x W), the label blob is expected to contain N*H*W ground truth + // labels with integer values in {0, 1, ..., C-1}. + optional int32 axis = 2 [default = 1]; + + // If specified, ignore instances with the given label. + optional int32 ignore_label = 3; +} + +message ArgMaxParameter { + // If true produce pairs (argmax, maxval) + optional bool out_max_val = 1 [default = false]; + optional uint32 top_k = 2 [default = 1]; + // The axis along which to maximise -- may be negative to index from the + // end (e.g., -1 for the last axis). + // By default ArgMaxLayer maximizes over the flattened trailing dimensions + // for each index of the first / num dimension. + optional int32 axis = 3; +} + +message ConcatParameter { + // The axis along which to concatenate -- may be negative to index from the + // end (e.g., -1 for the last axis). Other axes must have the + // same dimension for all the bottom blobs. + // By default, ConcatLayer concatenates blobs along the "channels" axis (1). + optional int32 axis = 2 [default = 1]; + + // DEPRECATED: alias for "axis" -- does not support negative indexing. + optional uint32 concat_dim = 1 [default = 1]; +} + +message BatchNormParameter { + // If false, accumulate global mean/variance values via a moving average. If + // true, use those accumulated values instead of computing mean/variance + // across the batch. + optional bool use_global_stats = 1; + // How much does the moving average decay each iteration? + optional float moving_average_fraction = 2 [default = .999]; + // Small value to add to the variance estimate so that we don't divide by + // zero. + optional float eps = 3 [default = 1e-5]; +} + +message BiasParameter { + // The first axis of bottom[0] (the first input Blob) along which to apply + // bottom[1] (the second input Blob). May be negative to index from the end + // (e.g., -1 for the last axis). + // + // For example, if bottom[0] is 4D with shape 100x3x40x60, the output + // top[0] will have the same shape, and bottom[1] may have any of the + // following shapes (for the given value of axis): + // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60 + // (axis == 1 == -3) 3; 3x40; 3x40x60 + // (axis == 2 == -2) 40; 40x60 + // (axis == 3 == -1) 60 + // Furthermore, bottom[1] may have the empty shape (regardless of the value of + // "axis") -- a scalar bias. + optional int32 axis = 1 [default = 1]; + + // (num_axes is ignored unless just one bottom is given and the bias is + // a learned parameter of the layer. Otherwise, num_axes is determined by the + // number of axes by the second bottom.) + // The number of axes of the input (bottom[0]) covered by the bias + // parameter, or -1 to cover all axes of bottom[0] starting from `axis`. + // Set num_axes := 0, to add a zero-axis Blob: a scalar. + optional int32 num_axes = 2 [default = 1]; + + // (filler is ignored unless just one bottom is given and the bias is + // a learned parameter of the layer.) + // The initialization for the learned bias parameter. + // Default is the zero (0) initialization, resulting in the BiasLayer + // initially performing the identity operation. + optional FillerParameter filler = 3; +} + +message ContrastiveLossParameter { + // margin for dissimilar pair + optional float margin = 1 [default = 1.0]; + // The first implementation of this cost did not exactly match the cost of + // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2. + // legacy_version = false (the default) uses (margin - d)^2 as proposed in the + // Hadsell paper. New models should probably use this version. + // legacy_version = true uses (margin - d^2). This is kept to support / + // reproduce existing models and results + optional bool legacy_version = 2 [default = false]; +} + +message ConvolutionParameter { + optional uint32 num_output = 1; // The number of outputs for the layer + optional bool bias_term = 2 [default = true]; // whether to have bias terms + + // Pad, kernel size, and stride are all given as a single value for equal + // dimensions in all spatial dimensions, or once per spatial dimension. + repeated uint32 pad = 3; // The padding size; defaults to 0 + repeated uint32 kernel_size = 4; // The kernel size + repeated uint32 stride = 6; // The stride; defaults to 1 + // Factor used to dilate the kernel, (implicitly) zero-filling the resulting + // holes. (Kernel dilation is sometimes referred to by its use in the + // algorithme à trous from Holschneider et al. 1987.) + repeated uint32 dilation = 18; // The dilation; defaults to 1 + + // For 2D convolution only, the *_h and *_w versions may also be used to + // specify both spatial dimensions. + optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only) + optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only) + optional uint32 kernel_h = 11; // The kernel height (2D only) + optional uint32 kernel_w = 12; // The kernel width (2D only) + optional uint32 stride_h = 13; // The stride height (2D only) + optional uint32 stride_w = 14; // The stride width (2D only) + + optional uint32 group = 5 [default = 1]; // The group size for group conv + + optional FillerParameter weight_filler = 7; // The filler for the weight + optional FillerParameter bias_filler = 8; // The filler for the bias + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 15 [default = DEFAULT]; + + // The axis to interpret as "channels" when performing convolution. + // Preceding dimensions are treated as independent inputs; + // succeeding dimensions are treated as "spatial". + // With (N, C, H, W) inputs, and axis == 1 (the default), we perform + // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for + // groups g>1) filters across the spatial axes (H, W) of the input. + // With (N, C, D, H, W) inputs, and axis == 1, we perform + // N independent 3D convolutions, sliding (C/g)-channels + // filters across the spatial axes (D, H, W) of the input. + optional int32 axis = 16 [default = 1]; + + // Whether to force use of the general ND convolution, even if a specific + // implementation for blobs of the appropriate number of spatial dimensions + // is available. (Currently, there is only a 2D-specific convolution + // implementation; for input blobs with num_axes != 2, this option is + // ignored and the ND implementation will be used.) + optional bool force_nd_im2col = 17 [default = false]; +} + +message CropParameter { + // To crop, elements of the first bottom are selected to fit the dimensions + // of the second, reference bottom. The crop is configured by + // - the crop `axis` to pick the dimensions for cropping + // - the crop `offset` to set the shift for all/each dimension + // to align the cropped bottom with the reference bottom. + // All dimensions up to but excluding `axis` are preserved, while + // the dimensions including and trailing `axis` are cropped. + // If only one `offset` is set, then all dimensions are offset by this amount. + // Otherwise, the number of offsets must equal the number of cropped axes to + // shift the crop in each dimension accordingly. + // Note: standard dimensions are N,C,H,W so the default is a spatial crop, + // and `axis` may be negative to index from the end (e.g., -1 for the last + // axis). + optional int32 axis = 1 [default = 2]; + repeated uint32 offset = 2; +} + +message DataParameter { + enum DB { + LEVELDB = 0; + LMDB = 1; + } + // Specify the data source. + optional string source = 1; + // Specify the batch size. + optional uint32 batch_size = 4; + // The rand_skip variable is for the data layer to skip a few data points + // to avoid all asynchronous sgd clients to start at the same point. The skip + // point would be set as rand_skip * rand(0,1). Note that rand_skip should not + // be larger than the number of keys in the database. + // DEPRECATED. Each solver accesses a different subset of the database. + optional uint32 rand_skip = 7 [default = 0]; + optional DB backend = 8 [default = LEVELDB]; + // DEPRECATED. See TransformationParameter. For data pre-processing, we can do + // simple scaling and subtracting the data mean, if provided. Note that the + // mean subtraction is always carried out before scaling. + optional float scale = 2 [default = 1]; + optional string mean_file = 3; + // DEPRECATED. See TransformationParameter. Specify if we would like to randomly + // crop an image. + optional uint32 crop_size = 5 [default = 0]; + // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror + // data. + optional bool mirror = 6 [default = false]; + // Force the encoded image to have 3 color channels + optional bool force_encoded_color = 9 [default = false]; + // Prefetch queue (Number of batches to prefetch to host memory, increase if + // data access bandwidth varies). + optional uint32 prefetch = 10 [default = 4]; +} + +message DropoutParameter { + optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio +} + +// DummyDataLayer fills any number of arbitrarily shaped blobs with random +// (or constant) data generated by "Fillers" (see "message FillerParameter"). +message DummyDataParameter { + // This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N + // shape fields, and 0, 1 or N data_fillers. + // + // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used. + // If 1 data_filler is specified, it is applied to all top blobs. If N are + // specified, the ith is applied to the ith top blob. + repeated FillerParameter data_filler = 1; + repeated BlobShape shape = 6; + + // 4D dimensions -- deprecated. Use "shape" instead. + repeated uint32 num = 2; + repeated uint32 channels = 3; + repeated uint32 height = 4; + repeated uint32 width = 5; +} + +message EltwiseParameter { + enum EltwiseOp { + PROD = 0; + SUM = 1; + MAX = 2; + } + optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation + repeated float coeff = 2; // blob-wise coefficient for SUM operation + + // Whether to use an asymptotically slower (for >2 inputs) but stabler method + // of computing the gradient for the PROD operation. (No effect for SUM op.) + optional bool stable_prod_grad = 3 [default = true]; +} + +// Message that stores parameters used by ELULayer +message ELUParameter { + // Described in: + // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate + // Deep Network Learning by Exponential Linear Units (ELUs). arXiv + optional float alpha = 1 [default = 1]; +} + +// Message that stores parameters used by EmbedLayer +message EmbedParameter { + optional uint32 num_output = 1; // The number of outputs for the layer + // The input is given as integers to be interpreted as one-hot + // vector indices with dimension num_input. Hence num_input should be + // 1 greater than the maximum possible input value. + optional uint32 input_dim = 2; + + optional bool bias_term = 3 [default = true]; // Whether to use a bias term + optional FillerParameter weight_filler = 4; // The filler for the weight + optional FillerParameter bias_filler = 5; // The filler for the bias + +} + +// Message that stores parameters used by ExpLayer +message ExpParameter { + // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0. + // Or if base is set to the default (-1), base is set to e, + // so y = exp(shift + scale * x). + optional float base = 1 [default = -1.0]; + optional float scale = 2 [default = 1.0]; + optional float shift = 3 [default = 0.0]; +} + +/// Message that stores parameters used by FlattenLayer +message FlattenParameter { + // The first axis to flatten: all preceding axes are retained in the output. + // May be negative to index from the end (e.g., -1 for the last axis). + optional int32 axis = 1 [default = 1]; + + // The last axis to flatten: all following axes are retained in the output. + // May be negative to index from the end (e.g., the default -1 for the last + // axis). + optional int32 end_axis = 2 [default = -1]; +} + +// Message that stores parameters used by HDF5DataLayer +message HDF5DataParameter { + // Specify the data source. + optional string source = 1; + // Specify the batch size. + optional uint32 batch_size = 2; + + // Specify whether to shuffle the data. + // If shuffle == true, the ordering of the HDF5 files is shuffled, + // and the ordering of data within any given HDF5 file is shuffled, + // but data between different files are not interleaved; all of a file's + // data are output (in a random order) before moving onto another file. + optional bool shuffle = 3 [default = false]; +} + +message HDF5OutputParameter { + optional string file_name = 1; +} + +message HingeLossParameter { + enum Norm { + L1 = 1; + L2 = 2; + } + // Specify the Norm to use L1 or L2 + optional Norm norm = 1 [default = L1]; +} + +message ImageDataParameter { + // Specify the data source. + optional string source = 1; + // Specify the batch size. + optional uint32 batch_size = 4 [default = 1]; + // The rand_skip variable is for the data layer to skip a few data points + // to avoid all asynchronous sgd clients to start at the same point. The skip + // point would be set as rand_skip * rand(0,1). Note that rand_skip should not + // be larger than the number of keys in the database. + optional uint32 rand_skip = 7 [default = 0]; + // Whether or not ImageLayer should shuffle the list of files at every epoch. + optional bool shuffle = 8 [default = false]; + // It will also resize images if new_height or new_width are not zero. + optional uint32 new_height = 9 [default = 0]; + optional uint32 new_width = 10 [default = 0]; + // Specify if the images are color or gray + optional bool is_color = 11 [default = true]; + // DEPRECATED. See TransformationParameter. For data pre-processing, we can do + // simple scaling and subtracting the data mean, if provided. Note that the + // mean subtraction is always carried out before scaling. + optional float scale = 2 [default = 1]; + optional string mean_file = 3; + // DEPRECATED. See TransformationParameter. Specify if we would like to randomly + // crop an image. + optional uint32 crop_size = 5 [default = 0]; + // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror + // data. + optional bool mirror = 6 [default = false]; + optional string root_folder = 12 [default = ""]; +} + +message InfogainLossParameter { + // Specify the infogain matrix source. + optional string source = 1; +} + +message InnerProductParameter { + optional uint32 num_output = 1; // The number of outputs for the layer + optional bool bias_term = 2 [default = true]; // whether to have bias terms + optional FillerParameter weight_filler = 3; // The filler for the weight + optional FillerParameter bias_filler = 4; // The filler for the bias + + // The first axis to be lumped into a single inner product computation; + // all preceding axes are retained in the output. + // May be negative to index from the end (e.g., -1 for the last axis). + optional int32 axis = 5 [default = 1]; + // Specify whether to transpose the weight matrix or not. + // If transpose == true, any operations will be performed on the transpose + // of the weight matrix. The weight matrix itself is not going to be transposed + // but rather the transfer flag of operations will be toggled accordingly. + optional bool transpose = 6 [default = false]; +} + +message InputParameter { + // This layer produces N >= 1 top blob(s) to be assigned manually. + // Define N shapes to set a shape for each top. + // Define 1 shape to set the same shape for every top. + // Define no shape to defer to reshaping manually. + repeated BlobShape shape = 1; +} + +// Message that stores parameters used by LogLayer +message LogParameter { + // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0. + // Or if base is set to the default (-1), base is set to e, + // so y = ln(shift + scale * x) = log_e(shift + scale * x) + optional float base = 1 [default = -1.0]; + optional float scale = 2 [default = 1.0]; + optional float shift = 3 [default = 0.0]; +} + +// Message that stores parameters used by LRNLayer +message LRNParameter { + optional uint32 local_size = 1 [default = 5]; + optional float alpha = 2 [default = 1.]; + optional float beta = 3 [default = 0.75]; + enum NormRegion { + ACROSS_CHANNELS = 0; + WITHIN_CHANNEL = 1; + } + optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS]; + optional float k = 5 [default = 1.]; + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 6 [default = DEFAULT]; +} + +message MemoryDataParameter { + optional uint32 batch_size = 1; + optional uint32 channels = 2; + optional uint32 height = 3; + optional uint32 width = 4; +} + +message MVNParameter { + // This parameter can be set to false to normalize mean only + optional bool normalize_variance = 1 [default = true]; + + // This parameter can be set to true to perform DNN-like MVN + optional bool across_channels = 2 [default = false]; + + // Epsilon for not dividing by zero while normalizing variance + optional float eps = 3 [default = 1e-9]; +} + +message ParameterParameter { + optional BlobShape shape = 1; +} + +message PoolingParameter { + enum PoolMethod { + MAX = 0; + AVE = 1; + STOCHASTIC = 2; + } + optional PoolMethod pool = 1 [default = MAX]; // The pooling method + // Pad, kernel size, and stride are all given as a single value for equal + // dimensions in height and width or as Y, X pairs. + optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X) + optional uint32 pad_h = 9 [default = 0]; // The padding height + optional uint32 pad_w = 10 [default = 0]; // The padding width + optional uint32 kernel_size = 2; // The kernel size (square) + optional uint32 kernel_h = 5; // The kernel height + optional uint32 kernel_w = 6; // The kernel width + optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X) + optional uint32 stride_h = 7; // The stride height + optional uint32 stride_w = 8; // The stride width + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 11 [default = DEFAULT]; + // If global_pooling then it will pool over the size of the bottom by doing + // kernel_h = bottom->height and kernel_w = bottom->width + optional bool global_pooling = 12 [default = false]; +} + +message PowerParameter { + // PowerLayer computes outputs y = (shift + scale * x) ^ power. + optional float power = 1 [default = 1.0]; + optional float scale = 2 [default = 1.0]; + optional float shift = 3 [default = 0.0]; +} + +message PythonParameter { + optional string module = 1; + optional string layer = 2; + // This value is set to the attribute `param_str` of the `PythonLayer` object + // in Python before calling the `setup()` method. This could be a number, + // string, dictionary in Python dict format, JSON, etc. You may parse this + // string in `setup` method and use it in `forward` and `backward`. + optional string param_str = 3 [default = '']; + // Whether this PythonLayer is shared among worker solvers during data parallelism. + // If true, each worker solver sequentially run forward from this layer. + // This value should be set true if you are using it as a data layer. + optional bool share_in_parallel = 4 [default = false]; +} + +// Message that stores parameters used by ReductionLayer +message ReductionParameter { + enum ReductionOp { + SUM = 1; + ASUM = 2; + SUMSQ = 3; + MEAN = 4; + } + + optional ReductionOp operation = 1 [default = SUM]; // reduction operation + + // The first axis to reduce to a scalar -- may be negative to index from the + // end (e.g., -1 for the last axis). + // (Currently, only reduction along ALL "tail" axes is supported; reduction + // of axis M through N, where N < num_axes - 1, is unsupported.) + // Suppose we have an n-axis bottom Blob with shape: + // (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)). + // If axis == m, the output Blob will have shape + // (d0, d1, d2, ..., d(m-1)), + // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1)) + // times, each including (dm * d(m+1) * ... * d(n-1)) individual data. + // If axis == 0 (the default), the output Blob always has the empty shape + // (count 1), performing reduction across the entire input -- + // often useful for creating new loss functions. + optional int32 axis = 2 [default = 0]; + + optional float coeff = 3 [default = 1.0]; // coefficient for output +} + +// Message that stores parameters used by ReLULayer +message ReLUParameter { + // Allow non-zero slope for negative inputs to speed up optimization + // Described in: + // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities + // improve neural network acoustic models. In ICML Workshop on Deep Learning + // for Audio, Speech, and Language Processing. + optional float negative_slope = 1 [default = 0]; + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 2 [default = DEFAULT]; +} + +message ReshapeParameter { + // Specify the output dimensions. If some of the dimensions are set to 0, + // the corresponding dimension from the bottom layer is used (unchanged). + // Exactly one dimension may be set to -1, in which case its value is + // inferred from the count of the bottom blob and the remaining dimensions. + // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8: + // + // layer { + // type: "Reshape" bottom: "input" top: "output" + // reshape_param { ... } + // } + // + // If "input" is 2D with shape 2 x 8, then the following reshape_param + // specifications are all equivalent, producing a 3D blob "output" with shape + // 2 x 2 x 4: + // + // reshape_param { shape { dim: 2 dim: 2 dim: 4 } } + // reshape_param { shape { dim: 0 dim: 2 dim: 4 } } + // reshape_param { shape { dim: 0 dim: 2 dim: -1 } } + // reshape_param { shape { dim: 0 dim:-1 dim: 4 } } + // + optional BlobShape shape = 1; + + // axis and num_axes control the portion of the bottom blob's shape that are + // replaced by (included in) the reshape. By default (axis == 0 and + // num_axes == -1), the entire bottom blob shape is included in the reshape, + // and hence the shape field must specify the entire output shape. + // + // axis may be non-zero to retain some portion of the beginning of the input + // shape (and may be negative to index from the end; e.g., -1 to begin the + // reshape after the last axis, including nothing in the reshape, + // -2 to include only the last axis, etc.). + // + // For example, suppose "input" is a 2D blob with shape 2 x 8. + // Then the following ReshapeLayer specifications are all equivalent, + // producing a blob "output" with shape 2 x 2 x 4: + // + // reshape_param { shape { dim: 2 dim: 2 dim: 4 } } + // reshape_param { shape { dim: 2 dim: 4 } axis: 1 } + // reshape_param { shape { dim: 2 dim: 4 } axis: -3 } + // + // num_axes specifies the extent of the reshape. + // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on + // input axes in the range [axis, axis+num_axes]. + // num_axes may also be -1, the default, to include all remaining axes + // (starting from axis). + // + // For example, suppose "input" is a 2D blob with shape 2 x 8. + // Then the following ReshapeLayer specifications are equivalent, + // producing a blob "output" with shape 1 x 2 x 8. + // + // reshape_param { shape { dim: 1 dim: 2 dim: 8 } } + // reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 } + // reshape_param { shape { dim: 1 } num_axes: 0 } + // + // On the other hand, these would produce output blob shape 2 x 1 x 8: + // + // reshape_param { shape { dim: 2 dim: 1 dim: 8 } } + // reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 } + // + optional int32 axis = 2 [default = 0]; + optional int32 num_axes = 3 [default = -1]; +} + +message ScaleParameter { + // The first axis of bottom[0] (the first input Blob) along which to apply + // bottom[1] (the second input Blob). May be negative to index from the end + // (e.g., -1 for the last axis). + // + // For example, if bottom[0] is 4D with shape 100x3x40x60, the output + // top[0] will have the same shape, and bottom[1] may have any of the + // following shapes (for the given value of axis): + // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60 + // (axis == 1 == -3) 3; 3x40; 3x40x60 + // (axis == 2 == -2) 40; 40x60 + // (axis == 3 == -1) 60 + // Furthermore, bottom[1] may have the empty shape (regardless of the value of + // "axis") -- a scalar multiplier. + optional int32 axis = 1 [default = 1]; + + // (num_axes is ignored unless just one bottom is given and the scale is + // a learned parameter of the layer. Otherwise, num_axes is determined by the + // number of axes by the second bottom.) + // The number of axes of the input (bottom[0]) covered by the scale + // parameter, or -1 to cover all axes of bottom[0] starting from `axis`. + // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar. + optional int32 num_axes = 2 [default = 1]; + + // (filler is ignored unless just one bottom is given and the scale is + // a learned parameter of the layer.) + // The initialization for the learned scale parameter. + // Default is the unit (1) initialization, resulting in the ScaleLayer + // initially performing the identity operation. + optional FillerParameter filler = 3; + + // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but + // may be more efficient). Initialized with bias_filler (defaults to 0). + optional bool bias_term = 4 [default = false]; + optional FillerParameter bias_filler = 5; +} + +message SigmoidParameter { + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 1 [default = DEFAULT]; +} + +message SliceParameter { + // The axis along which to slice -- may be negative to index from the end + // (e.g., -1 for the last axis). + // By default, SliceLayer concatenates blobs along the "channels" axis (1). + optional int32 axis = 3 [default = 1]; + repeated uint32 slice_point = 2; + + // DEPRECATED: alias for "axis" -- does not support negative indexing. + optional uint32 slice_dim = 1 [default = 1]; +} + +// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer +message SoftmaxParameter { + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 1 [default = DEFAULT]; + + // The axis along which to perform the softmax -- may be negative to index + // from the end (e.g., -1 for the last axis). + // Any other axes will be evaluated as independent softmaxes. + optional int32 axis = 2 [default = 1]; +} + +message TanHParameter { + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 1 [default = DEFAULT]; +} + +// Message that stores parameters used by TileLayer +message TileParameter { + // The index of the axis to tile. + optional int32 axis = 1 [default = 1]; + + // The number of copies (tiles) of the blob to output. + optional int32 tiles = 2; +} + +// Message that stores parameters used by ThresholdLayer +message ThresholdParameter { + optional float threshold = 1 [default = 0]; // Strictly positive values +} + +message WindowDataParameter { + // Specify the data source. + optional string source = 1; + // For data pre-processing, we can do simple scaling and subtracting the + // data mean, if provided. Note that the mean subtraction is always carried + // out before scaling. + optional float scale = 2 [default = 1]; + optional string mean_file = 3; + // Specify the batch size. + optional uint32 batch_size = 4; + // Specify if we would like to randomly crop an image. + optional uint32 crop_size = 5 [default = 0]; + // Specify if we want to randomly mirror data. + optional bool mirror = 6 [default = false]; + // Foreground (object) overlap threshold + optional float fg_threshold = 7 [default = 0.5]; + // Background (non-object) overlap threshold + optional float bg_threshold = 8 [default = 0.5]; + // Fraction of batch that should be foreground objects + optional float fg_fraction = 9 [default = 0.25]; + // Amount of contextual padding to add around a window + // (used only by the window_data_layer) + optional uint32 context_pad = 10 [default = 0]; + // Mode for cropping out a detection window + // warp: cropped window is warped to a fixed size and aspect ratio + // square: the tightest square around the window is cropped + optional string crop_mode = 11 [default = "warp"]; + // cache_images: will load all images in memory for faster access + optional bool cache_images = 12 [default = false]; + // append root_folder to locate images + optional string root_folder = 13 [default = ""]; +} + +message SPPParameter { + enum PoolMethod { + MAX = 0; + AVE = 1; + STOCHASTIC = 2; + } + optional uint32 pyramid_height = 1; + optional PoolMethod pool = 2 [default = MAX]; // The pooling method + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 6 [default = DEFAULT]; +} + +// DEPRECATED: use LayerParameter. +message V1LayerParameter { + repeated string bottom = 2; + repeated string top = 3; + optional string name = 4; + repeated NetStateRule include = 32; + repeated NetStateRule exclude = 33; + enum LayerType { + NONE = 0; + ABSVAL = 35; + ACCURACY = 1; + ARGMAX = 30; + BNLL = 2; + CONCAT = 3; + CONTRASTIVE_LOSS = 37; + CONVOLUTION = 4; + DATA = 5; + DECONVOLUTION = 39; + DROPOUT = 6; + DUMMY_DATA = 32; + EUCLIDEAN_LOSS = 7; + ELTWISE = 25; + EXP = 38; + FLATTEN = 8; + HDF5_DATA = 9; + HDF5_OUTPUT = 10; + HINGE_LOSS = 28; + IM2COL = 11; + IMAGE_DATA = 12; + INFOGAIN_LOSS = 13; + INNER_PRODUCT = 14; + LRN = 15; + MEMORY_DATA = 29; + MULTINOMIAL_LOGISTIC_LOSS = 16; + MVN = 34; + POOLING = 17; + POWER = 26; + RELU = 18; + SIGMOID = 19; + SIGMOID_CROSS_ENTROPY_LOSS = 27; + SILENCE = 36; + SOFTMAX = 20; + SOFTMAX_LOSS = 21; + SPLIT = 22; + SLICE = 33; + TANH = 23; + WINDOW_DATA = 24; + THRESHOLD = 31; + } + optional LayerType type = 5; + repeated BlobProto blobs = 6; + repeated string param = 1001; + repeated DimCheckMode blob_share_mode = 1002; + enum DimCheckMode { + STRICT = 0; + PERMISSIVE = 1; + } + repeated float blobs_lr = 7; + repeated float weight_decay = 8; + repeated float loss_weight = 35; + optional AccuracyParameter accuracy_param = 27; + optional ArgMaxParameter argmax_param = 23; + optional ConcatParameter concat_param = 9; + optional ContrastiveLossParameter contrastive_loss_param = 40; + optional ConvolutionParameter convolution_param = 10; + optional DataParameter data_param = 11; + optional DropoutParameter dropout_param = 12; + optional DummyDataParameter dummy_data_param = 26; + optional EltwiseParameter eltwise_param = 24; + optional ExpParameter exp_param = 41; + optional HDF5DataParameter hdf5_data_param = 13; + optional HDF5OutputParameter hdf5_output_param = 14; + optional HingeLossParameter hinge_loss_param = 29; + optional ImageDataParameter image_data_param = 15; + optional InfogainLossParameter infogain_loss_param = 16; + optional InnerProductParameter inner_product_param = 17; + optional LRNParameter lrn_param = 18; + optional MemoryDataParameter memory_data_param = 22; + optional MVNParameter mvn_param = 34; + optional PoolingParameter pooling_param = 19; + optional PowerParameter power_param = 21; + optional ReLUParameter relu_param = 30; + optional SigmoidParameter sigmoid_param = 38; + optional SoftmaxParameter softmax_param = 39; + optional SliceParameter slice_param = 31; + optional TanHParameter tanh_param = 37; + optional ThresholdParameter threshold_param = 25; + optional WindowDataParameter window_data_param = 20; + optional TransformationParameter transform_param = 36; + optional LossParameter loss_param = 42; + optional V0LayerParameter layer = 1; +} + +// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters +// in Caffe. We keep this message type around for legacy support. +message V0LayerParameter { + optional string name = 1; // the layer name + optional string type = 2; // the string to specify the layer type + + // Parameters to specify layers with inner products. + optional uint32 num_output = 3; // The number of outputs for the layer + optional bool biasterm = 4 [default = true]; // whether to have bias terms + optional FillerParameter weight_filler = 5; // The filler for the weight + optional FillerParameter bias_filler = 6; // The filler for the bias + + optional uint32 pad = 7 [default = 0]; // The padding size + optional uint32 kernelsize = 8; // The kernel size + optional uint32 group = 9 [default = 1]; // The group size for group conv + optional uint32 stride = 10 [default = 1]; // The stride + enum PoolMethod { + MAX = 0; + AVE = 1; + STOCHASTIC = 2; + } + optional PoolMethod pool = 11 [default = MAX]; // The pooling method + optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio + + optional uint32 local_size = 13 [default = 5]; // for local response norm + optional float alpha = 14 [default = 1.]; // for local response norm + optional float beta = 15 [default = 0.75]; // for local response norm + optional float k = 22 [default = 1.]; + + // For data layers, specify the data source + optional string source = 16; + // For data pre-processing, we can do simple scaling and subtracting the + // data mean, if provided. Note that the mean subtraction is always carried + // out before scaling. + optional float scale = 17 [default = 1]; + optional string meanfile = 18; + // For data layers, specify the batch size. + optional uint32 batchsize = 19; + // For data layers, specify if we would like to randomly crop an image. + optional uint32 cropsize = 20 [default = 0]; + // For data layers, specify if we want to randomly mirror data. + optional bool mirror = 21 [default = false]; + + // The blobs containing the numeric parameters of the layer + repeated BlobProto blobs = 50; + // The ratio that is multiplied on the global learning rate. If you want to + // set the learning ratio for one blob, you need to set it for all blobs. + repeated float blobs_lr = 51; + // The weight decay that is multiplied on the global weight decay. + repeated float weight_decay = 52; + + // The rand_skip variable is for the data layer to skip a few data points + // to avoid all asynchronous sgd clients to start at the same point. The skip + // point would be set as rand_skip * rand(0,1). Note that rand_skip should not + // be larger than the number of keys in the database. + optional uint32 rand_skip = 53 [default = 0]; + + // Fields related to detection (det_*) + // foreground (object) overlap threshold + optional float det_fg_threshold = 54 [default = 0.5]; + // background (non-object) overlap threshold + optional float det_bg_threshold = 55 [default = 0.5]; + // Fraction of batch that should be foreground objects + optional float det_fg_fraction = 56 [default = 0.25]; + + // optional bool OBSOLETE_can_clobber = 57 [default = true]; + + // Amount of contextual padding to add around a window + // (used only by the window_data_layer) + optional uint32 det_context_pad = 58 [default = 0]; + + // Mode for cropping out a detection window + // warp: cropped window is warped to a fixed size and aspect ratio + // square: the tightest square around the window is cropped + optional string det_crop_mode = 59 [default = "warp"]; + + // For ReshapeLayer, one needs to specify the new dimensions. + optional int32 new_num = 60 [default = 0]; + optional int32 new_channels = 61 [default = 0]; + optional int32 new_height = 62 [default = 0]; + optional int32 new_width = 63 [default = 0]; + + // Whether or not ImageLayer should shuffle the list of files at every epoch. + // It will also resize images if new_height or new_width are not zero. + optional bool shuffle_images = 64 [default = false]; + + // For ConcatLayer, one needs to specify the dimension for concatenation, and + // the other dimensions must be the same for all the bottom blobs. + // By default it will concatenate blobs along the channels dimension. + optional uint32 concat_dim = 65 [default = 1]; + + optional HDF5OutputParameter hdf5_output_param = 1001; +} + +message PReLUParameter { + // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers: + // Surpassing Human-Level Performance on ImageNet Classification, 2015. + + // Initial value of a_i. Default is a_i=0.25 for all i. + optional FillerParameter filler = 1; + // Whether or not slope paramters are shared across channels. + optional bool channel_shared = 2 [default = false]; +} diff --git a/tiny_dnn/io/caffe/layer_factory.h b/tiny_dnn/io/caffe/layer_factory.h new file mode 100755 index 0000000000000..3a38fde187a91 --- /dev/null +++ b/tiny_dnn/io/caffe/layer_factory.h @@ -0,0 +1,179 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include +#include + +#include "tiny_dnn/io/caffe/caffe.pb.h" + +#include "tiny_dnn/io/caffe/layer_factory_impl.h" +#include "tiny_dnn/lossfunctions/loss_function.h" +#include "tiny_dnn/network.h" +#include "tiny_dnn/optimizers/optimizer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * create whole network and load weights from caffe's netparameter + * + * @param layer [in] netparameter of caffemodel + * @param data_shape [in] size of input data (width x height x channels) + */ +inline std::shared_ptr> create_net_from_caffe_net( + const caffe::NetParameter &layer, const shape3d &data_shape) { + detail::caffe_layer_vector src_net(layer); + shape_t shape; + + if (data_shape.size() > 0) { + shape = data_shape; + } else { + if (layer.input_shape_size() > 0) { + // input_shape is deprecated in Caffe + // blob dimensions are ordered by number N x channel K x height H x + // width + // W + int depth = static_cast(layer.input_shape(0).dim(1)); + int height = static_cast(layer.input_shape(0).dim(2)); + int width = static_cast(layer.input_shape(0).dim(3)); + shape = shape3d(width, height, depth); + } else if (src_net[0].has_input_param()) { + // blob dimensions are ordered by number N x channel K x height H x + // width + // W + int depth = static_cast(src_net[0].input_param().shape(0).dim(1)); + int height = static_cast(src_net[0].input_param().shape(0).dim(2)); + int width = static_cast(src_net[0].input_param().shape(0).dim(3)); + shape = shape3d(width, height, depth); + } else { + throw nn_error( + "input_shape not found in caffemodel. must specify input " + "shape explicitly"); + } + } + + auto dst_net = std::make_shared>(layer.name()); + + for (size_t i = 0; i < src_net.size(); i++) { + auto type = src_net[i].type(); + + if (detail::layer_skipped(type)) { + continue; + } + + if (!detail::layer_supported(type)) { + throw nn_error("error: tiny-dnn does not support this layer type:" + + type); + } + + shape_t shape_next = shape; + auto layer = detail::create(src_net[i], shape, &shape_next); + + nn_info("convert " + type + " => " + typeid(*layer).name()); + nn_info("shape:" + to_string(shape_next)); + + *dst_net << layer; + shape = shape_next; + } + + return dst_net; +} + +/** + * create whole network and load weights from caffe's netparameter + * + * @param layer [in] netparameter of caffemodel + * @param data_shape [in] size of input data (width x height x channels) + */ +inline std::shared_ptr> create_net_from_caffe_protobinary( + const std::string &caffebinarymodel, const shape3d &data_shape) { + caffe::NetParameter np; + + detail::read_proto_from_binary(caffebinarymodel, &np); + return create_net_from_caffe_net(np, data_shape); +} + +/** + * create whole network and load weights from caffe's netparameter + * + * @param layer [in] netparameter of caffe prototxt + */ +inline std::shared_ptr> create_net_from_caffe_prototxt( + const std::string &caffeprototxt, const shape3d &shape = shape3d()) { + caffe::NetParameter np; + + detail::read_proto_from_text(caffeprototxt, &np); + return create_net_from_caffe_net(np, shape); +} + +/** + * reload network weights from caffe's netparameter + * this must be called after the network layers are constructed + * + * @param layer [in] caffe's netparameter + * @param net [out] tiny-dnn's network + */ +template +inline void reload_weight_from_caffe_net(const caffe::NetParameter &layer, + network *net) { + detail::caffe_layer_vector src_net(layer); + + size_t tiny_layer_idx = 0; + + for (size_t caffe_layer_idx = 0; caffe_layer_idx < src_net.size(); + caffe_layer_idx++) { + auto type = src_net[caffe_layer_idx].type(); + + if (detail::layer_skipped(type) || !detail::layer_has_weights(type)) { + continue; + } + + if (!detail::layer_supported(type)) { + throw nn_error("error: tiny-dnn does not support this layer type:" + + type); + } + + while (tiny_layer_idx < net->depth() && + !detail::layer_match(type, (*net)[tiny_layer_idx]->layer_type())) { + tiny_layer_idx++; + } + + if (tiny_layer_idx >= net->depth()) { + throw nn_error( + "error: tiny-dnn does not support loading weights " + "for this layer type: " + + type); + } + + // load weight + detail::load(src_net[caffe_layer_idx], (*net)[tiny_layer_idx++]); + } +} + +/** + * reload network weights from caffe's netparameter + * this must be called after the network layers are constructed + * + * @param caffebinary [in] caffe's trained model file(binary format) + * @param net [out] tiny-dnn's network + */ +template +inline void reload_weight_from_caffe_protobinary(const std::string &caffebinary, + network *net) { + caffe::NetParameter np; + + detail::read_proto_from_binary(caffebinary, &np); + reload_weight_from_caffe_net(np, net); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/io/caffe/layer_factory_impl.h b/tiny_dnn/io/caffe/layer_factory_impl.h new file mode 100755 index 0000000000000..dcc134aea5ce9 --- /dev/null +++ b/tiny_dnn/io/caffe/layer_factory_impl.h @@ -0,0 +1,1112 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/io/caffe/caffe.pb.h" + +#include "tiny_dnn/layers/average_pooling_layer.h" +#include "tiny_dnn/layers/convolutional_layer.h" +#include "tiny_dnn/layers/deconvolutional_layer.h" +#include "tiny_dnn/layers/dropout_layer.h" +#include "tiny_dnn/layers/fully_connected_layer.h" +#include "tiny_dnn/layers/linear_layer.h" +#include "tiny_dnn/layers/lrn_layer.h" +#include "tiny_dnn/layers/max_pooling_layer.h" + +typedef tiny_dnn::shape3d shape_t; + +#if defined(_MSC_VER) || defined(WIN32) +#define _NOMINMAX +#include +#include +#define CNN_OPEN_BINARY(filename) open(filename, _O_RDONLY | _O_BINARY) +#define CNN_OPEN_TXT(filename) open(filename, _O_RDONLY) +#pragma warning(push) +#pragma warning(disable : 4996) +#else +#include +#include +#include +#include +#define CNN_OPEN_BINARY(filename) open(filename, O_RDONLY) +#define CNN_OPEN_TXT(filename) open(filename, O_RDONLY) +#endif + +namespace tiny_dnn { +namespace detail { + +inline void read_proto_from_text(const std::string &prototxt, + google::protobuf::Message *message) { + int fd = CNN_OPEN_TXT(prototxt.c_str()); + if (fd == -1) { + throw nn_error("file not found: " + prototxt); + } + + google::protobuf::io::FileInputStream input(fd); + input.SetCloseOnDelete(true); + + if (!google::protobuf::TextFormat::Parse(&input, message)) { + throw nn_error("failed to parse"); + } +} + +inline void read_proto_from_binary(const std::string &protobinary, + google::protobuf::Message *message) { + int fd = CNN_OPEN_BINARY(protobinary.c_str()); + google::protobuf::io::FileInputStream rawstr(fd); + google::protobuf::io::CodedInputStream codedstr(&rawstr); + + rawstr.SetCloseOnDelete(true); + codedstr.SetTotalBytesLimit(std::numeric_limits::max(), + std::numeric_limits::max() / 2); + + if (!message->ParseFromCodedStream(&codedstr)) { + throw nn_error("failed to parse"); + } +} + +inline std::shared_ptr create_filler( + const std::string &filler) { + if (filler == "xavier") { + return std::make_shared(); + } else if (filler == "constant") { + return std::make_shared(); + } else if (filler == "gaussian") { + return std::make_shared(); + } else { + throw nn_error("unsupported filler type"); + } +} + +template +inline bool get_kernel_size_2d(const param &p, layer_size_t *kernel) { + if (p.has_kernel_w() && p.has_kernel_h()) { + if (p.kernel_w() != p.kernel_h()) { + throw nn_error("unsupported kernel shape"); + } + *kernel = p.kernel_w(); + return true; + } + return false; +} + +template +inline bool get_kernel_size_2d(const param &p, + layer_size_t *kernel_w, + layer_size_t *kernel_h) { + if (p.has_kernel_w() && p.has_kernel_h()) { + *kernel_w = p.kernel_w(); + *kernel_h = p.kernel_h(); + return true; + } + return false; +} + +inline layer_size_t get_kernel_size_2d(const caffe::ConvolutionParameter &p) { + layer_size_t window_size; + if (!get_kernel_size_2d(p, &window_size)) { + if (p.kernel_size_size() > 1) { + throw nn_error("unsupported kernel shape"); + } + window_size = p.kernel_size(0); + } + return window_size; +} + +inline std::shared_ptr create_max_pool(layer_size_t pool_size_w, + layer_size_t pool_size_h, + layer_size_t stride_w, + layer_size_t stride_h, + padding pad_type, + const shape_t &bottom_shape, + shape_t *top_shape) { + using max_pool = max_pooling_layer; + auto mp = std::make_shared( + bottom_shape.width_, bottom_shape.height_, bottom_shape.depth_, pool_size_w, + pool_size_h, stride_w, stride_h, pad_type); + + *top_shape = mp->out_shape()[0]; + mp->init_weight(); + + return mp; +} + +inline std::shared_ptr create_ave_pool(layer_size_t pool_size_w, + layer_size_t pool_size_h, + layer_size_t stride_w, + layer_size_t stride_h, + padding pad_type, + const shape_t &bottom_shape, + shape_t *top_shape) { + using ave_pool = average_pooling_layer; + auto ap = std::make_shared( + bottom_shape.width_, bottom_shape.height_, bottom_shape.depth_, pool_size_w, + pool_size_h, stride_w, stride_h, pad_type); + + // tiny-dnn has trainable parameter in average-pooling layer + float_t weight = 1.0 / (pool_size_w * pool_size_h); + + vec_t &w = *ap->weights()[0]; + vec_t &b = *ap->weights()[1]; + + vectorize::fill(&w[0], w.size(), weight); + vectorize::fill(&b[0], b.size(), float_t{0}); + + *top_shape = ap->out_shape()[0]; + ap->init_weight(); + ap->set_trainable(false); + + return ap; +} + +inline std::shared_ptr create_softmax(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *) { + auto sm = std::make_shared(bottom_shape.size()); + sm->init_weight(); + return sm; +} + +inline std::shared_ptr create_sigmoid(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *) { + auto ce = std::make_shared(bottom_shape.size()); + return ce; +} + +inline std::shared_ptr create_tanh(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *) { + auto tanh = std::make_shared(bottom_shape.size()); + return tanh; +} + +inline std::shared_ptr create_power(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *) { + auto power = std::make_shared( + bottom_shape, layer.power_param().power(), layer.power_param().scale()); + return power; +} + +inline std::shared_ptr create_pooling(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *top_shape) { + if (!layer.has_pooling_param()) { + throw nn_error("pool param missing"); + } + + auto pool_param = layer.pooling_param(); + + layer_size_t h_stride = 0; + layer_size_t w_stride = 0; + layer_size_t pool_size_w = 0; + layer_size_t pool_size_h = 0; + layer_size_t h_pad = 0; + layer_size_t w_pad = 0; + padding pad_type = padding::valid; + + if (!get_kernel_size_2d(pool_param, &pool_size_w, &pool_size_h)) { + pool_size_w = pool_size_h = pool_param.kernel_size(); + } + + if (pool_param.has_stride() || pool_param.has_stride_h()) { + h_stride = + pool_param.has_stride() ? pool_param.stride() : pool_param.stride_h(); + } + + if (pool_param.has_stride() || pool_param.has_stride_w()) { + w_stride = + pool_param.has_stride() ? pool_param.stride() : pool_param.stride_w(); + } + + if (pool_param.has_pad() || pool_param.has_pad_w()) { + w_pad = pool_param.has_pad() ? pool_param.pad() : pool_param.pad_w(); + } + + if (pool_param.has_pad() || pool_param.has_pad_h()) { + h_pad = pool_param.has_pad() ? pool_param.pad() : pool_param.pad_h(); + } + + if (w_pad != 0) { + if (w_pad == (pool_size_w - 1) / 2) { + pad_type = padding::same; + } else { + throw nn_error("unsupported padding type"); + } + // NB: w_pad == pool_size_w - 1 could lead to pad_type = padding::full, + // if such a type existed + } + + if (h_pad != 0) { + if (h_pad == (pool_size_h - 1) / 2) { + pad_type = padding::same; + } else { + throw nn_error("unsupported padding type"); + } + // NB: h_pad == pool_size_h - 1 could lead to pad_type = padding::full, + // if such a type existed + } + + if (pool_param.has_pool()) { + auto type = pool_param.pool(); + + switch (type) { + case caffe::PoolingParameter_PoolMethod_MAX: + return create_max_pool(pool_size_w, pool_size_h, w_stride, h_stride, + pad_type, bottom_shape, top_shape); + case caffe::PoolingParameter_PoolMethod_AVE: + return create_ave_pool(pool_size_w, pool_size_h, w_stride, h_stride, + pad_type, bottom_shape, top_shape); + default: throw nn_error("unsupported layer type"); + } + } + + // default: max-pool + return create_max_pool(pool_size_w, pool_size_h, w_stride, h_stride, pad_type, + bottom_shape, top_shape); +} + +inline std::shared_ptr create_relu(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *) { + auto relu = std::make_shared(bottom_shape.size()); + return relu; +} + +inline std::shared_ptr create_elu(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *) { + auto elu = std::make_shared(bottom_shape.size()); + return elu; +} + +inline std::shared_ptr create_batchnorm( + const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *top_shape) { + using bn_layer = batch_normalization_layer; + + *top_shape = bottom_shape; + + float_t eps = 1e-5f; + float_t momentum = 0.999f; + + if (layer.has_batch_norm_param()) { + auto bn_param = layer.batch_norm_param(); + + if (bn_param.has_eps()) { + eps = bn_param.eps(); + } + if (bn_param.has_moving_average_fraction()) { + momentum = bn_param.moving_average_fraction(); + } + } + + auto bn = std::make_shared(bottom_shape.area(), bottom_shape.depth_, + eps, momentum, net_phase::test); + + // weight + if (layer.blobs_size() > 0) { + auto global_stats = layer.blobs(); + if (global_stats.size() != 3) { + throw std::runtime_error("unexpected bn stored statistics"); + } + + float_t scale_factor = + global_stats.Get(2).data(0) == 0 ? 0 : 1 / global_stats.Get(2).data(0); + vec_t mean(bottom_shape.depth_); + vec_t variance(bottom_shape.depth_); + + for (size_t i = 0; i < mean.size(); i++) { + mean[i] = global_stats.Get(0).data(i) * scale_factor; + variance[i] = global_stats.Get(1).data(i) * scale_factor; + } + bn->set_mean(mean); + bn->set_variance(variance); + } + + return bn; +} + +inline void load_weights_fullyconnected(const caffe::LayerParameter &src, + layer *dst) { + auto weights = src.blobs(0); + int curr = 0; + + const auto dst_out_size = dst->out_size(); + const auto dst_in_size = dst->in_size(); + + if (dst_out_size * dst_in_size != static_cast(weights.data_size())) { + throw nn_error(std::string("layer size mismatch!") + "caffe(" + src.name() + + "):" + to_string(weights.data_size()) + "\n" + "tiny-dnn(" + + dst->layer_type() + "):" + to_string(dst->weights().size())); + } + + vec_t &w = *dst->weights()[0]; + vec_t &b = *dst->weights()[1]; + + // fill weights + for (size_t o = 0; o < dst_out_size; o++) { + for (size_t i = 0; i < dst_in_size; i++) { + // TODO(karandesai): how to access to weights? + // dst->weight()[i * dst->out_size() + o] = weights.data(curr++); // + // transpose + w[i * dst_out_size + o] = weights.data(curr++); // transpose + } + } + + // fill bias + if (src.inner_product_param().bias_term()) { + auto biases = src.blobs(1); + for (size_t o = 0; o < dst_out_size; o++) { + // TODO(karandesai): how to access to biases? + // dst->bias()[o] = biases.data(o); + b[o] = biases.data(o); + } + } +} + +inline std::shared_ptr create_fullyconnected( + const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *top_shape) { + using fc_layer = fully_connected_layer; + + if (!layer.has_inner_product_param()) { + throw nn_error("inner-product param missing"); + } + + layer_size_t dim_input = 0, dim_output = 0; + bool has_bias = true; + + auto ip_param = layer.inner_product_param(); + has_bias = ip_param.bias_term(); + + dim_output = ip_param.num_output(); + dim_input = bottom_shape.size(); + + auto ip = std::make_shared(dim_input, dim_output, has_bias); + + // filler + if (ip_param.has_weight_filler()) { + ip->weight_init(create_filler(ip_param.weight_filler().type())); + } + + if (ip_param.has_bias_filler()) { + ip->bias_init(create_filler(ip_param.bias_filler().type())); + } + + // weight + if (layer.blobs_size() > 0) { + load_weights_fullyconnected(layer, ip.get()); + } + + // TODO(karan): check if it works + *top_shape = ip->out_shape()[0]; + return ip; +} + +inline void load_weights_conv(const caffe::LayerParameter &src, layer *dst) { + // fill weight + auto weights = src.blobs(0); + + // TODO(karan): check if it works + // int out_channels = dst->out_shape().depth_; + // int in_channels = dst->in_shape().depth_; + int out_channels = dst->out_data_shape()[0].depth_; + int in_channels = dst->in_data_shape()[0].depth_; + + core::connection_table table; + auto conv_param = src.convolution_param(); + int dst_idx = 0; + int src_idx = 0; + int window_size = get_kernel_size_2d(conv_param); + + if (conv_param.has_group()) { + table = + core::connection_table(conv_param.group(), in_channels, out_channels); + } + + vec_t &w = *dst->weights()[0]; + vec_t &b = *dst->weights()[1]; + + // fill weights + for (int o = 0; o < out_channels; o++) { + for (int i = 0; i < in_channels; i++) { + if (!table.is_connected(o, i)) { + dst_idx += window_size * window_size; + continue; + } + for (int x = 0; x < window_size * window_size; x++) { + // dst->weight()[dst_idx++] = weights.data(src_idx++); + w[dst_idx++] = weights.data(src_idx++); + } + } + } + + // fill bias + if (conv_param.bias_term()) { + auto biases = src.blobs(1); + for (int o = 0; o < out_channels; o++) { + // dst->bias()[o] = biases.data(o); + b[o] = biases.data(o); + } + } +} + +inline void load_weights_batchnorm(const caffe::LayerParameter &src, + layer *dst) { + if (dst->layer_type() != "batch-norm") + throw nn_error("batch-norm layer expected"); + + if (src.blobs_size() > 0) { + auto global_stats = src.blobs(); + if (global_stats.size() != 3) { + throw nn_error("unexpected format for batch-norm statistics"); + } + + float_t scale_factor = + global_stats.Get(2).data(0) == 0 ? 0 : 1 / global_stats.Get(2).data(0); + + int in_channels = dst->in_shape().at(0).depth_; + vec_t mean(in_channels); + vec_t variance(in_channels); + + for (size_t i = 0; i < mean.size(); i++) { + mean[i] = global_stats.Get(0).data(i) * scale_factor; + variance[i] = global_stats.Get(1).data(i) * scale_factor; + } + auto bnl = dynamic_cast(dst); + bnl->set_mean(mean); + bnl->set_variance(variance); + + } else { + throw nn_error("batch-norm layer missing blobs"); + } + + return; +} + +inline void load_weights_pool(const caffe::LayerParameter &src, layer *dst) { + auto pool_param = src.pooling_param(); + + if (dst->weights().size()) { + layer_size_t pool_size = 0; + + if (!get_kernel_size_2d(pool_param, &pool_size)) { + pool_size = pool_param.kernel_size(); + } + + // tiny-dnn has trainable parameter in average-pooling layer + float_t weight = 1.0 / sqr(pool_size); + + // TODO(karan) + /*if (!dst->weight().empty()) { + std::fill(dst->weight().begin(), dst->weight().end(), weight); + } + if (!dst->bias().empty()) { + std::fill(dst->bias().begin(), dst->bias().end(), float_t{0}); + dst->init_bias(); + }*/ + + vec_t &w = *dst->weights()[0]; + vec_t &b = *dst->weights()[1]; + + if (!w.empty()) { + vectorize::fill(&w[0], w.size(), weight); + } + if (!b.empty()) { + vectorize::fill(&b[0], b.size(), float_t{0}); + // dst->init_bias(); + } + } +} + +inline std::shared_ptr create_lrn(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *top_shape) { + if (!layer.has_lrn_param()) { + throw nn_error("lrn param missing"); + } + + auto lrn_param = layer.lrn_param(); + layer_size_t local_size = 5; + float_t alpha = 1; + float_t beta = 5; + norm_region region = norm_region::across_channels; + + if (lrn_param.has_local_size()) local_size = lrn_param.local_size(); + if (lrn_param.has_alpha()) alpha = lrn_param.alpha(); + if (lrn_param.has_beta()) beta = lrn_param.beta(); + if (lrn_param.has_norm_region()) { + if (lrn_param.norm_region() == + caffe::LRNParameter_NormRegion_WITHIN_CHANNEL) // NOLINT + region = norm_region::within_channels; + } + + auto lrn = std::make_shared( + bottom_shape.width_, bottom_shape.height_, local_size, bottom_shape.depth_, + alpha, beta, region); + return lrn; +} + +inline std::shared_ptr create_dropout(const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *top_shape) { + if (!layer.has_dropout_param()) { + throw nn_error("dropout param missing"); + } + + float_t dropout_rate = float_t(0.5); + + if (layer.dropout_param().has_dropout_ratio()) { + dropout_rate = layer.dropout_param().dropout_ratio(); + } + + auto dropout = std::make_shared(bottom_shape.size(), + dropout_rate, net_phase::test); + return dropout; +} + +inline std::shared_ptr create_convlayer( + const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *top_shape) { + using conv_layer = convolutional_layer; + + if (!layer.has_convolution_param()) { + throw nn_error("convolution param missing"); + } + + // layer parameters + layer_size_t in_width = 0, in_height = 0, window_size = 0; + layer_size_t in_channels = 0, out_channels = 0; + layer_size_t w_stride = 1, h_stride = 1; + bool has_bias = true; + padding pad_type = padding::valid; + core::connection_table table; + + auto conv_param = layer.convolution_param(); + + // shape + out_channels = conv_param.num_output(); + in_channels = bottom_shape.depth_; + in_width = bottom_shape.width_; + in_height = bottom_shape.height_; + has_bias = conv_param.bias_term(); + window_size = get_kernel_size_2d(conv_param); + + // padding + if (conv_param.pad_size() == 1 || + (conv_param.has_pad_w() && conv_param.has_pad_h())) { + uint32_t pad_w = + conv_param.pad_size() == 1 ? conv_param.pad(0) : conv_param.pad_w(); + + uint32_t pad_h = + conv_param.pad_size() == 1 ? conv_param.pad(0) : conv_param.pad_h(); + + if (pad_w != pad_h) { + throw nn_error("conv:not supported padding size"); + } + + // 0 ... valid, (window_size-1)/2 ... same + if (pad_w == (window_size - 1) / 2) { + pad_type = padding::same; + } else if (pad_w == 0) { + pad_type = padding::valid; + } else { + throw nn_error("conv:not supported padding size"); + } + } + + // stride + if (conv_param.stride_size() == 1 || conv_param.has_stride_h()) { + h_stride = conv_param.stride_size() == 1 ? conv_param.stride(0) + : conv_param.stride_h(); + } + + if (conv_param.stride_size() == 1 || conv_param.has_stride_w()) { + w_stride = conv_param.stride_size() == 1 ? conv_param.stride(0) + : conv_param.stride_w(); + } + + // group + if (conv_param.has_group()) { + table = + core::connection_table(conv_param.group(), in_channels, out_channels); + } + + auto conv = std::make_shared( + in_width, in_height, window_size, in_channels, out_channels, table, + pad_type, has_bias, w_stride, h_stride); + // filler + if (conv_param.has_weight_filler()) { + conv->weight_init(create_filler(conv_param.weight_filler().type())); + } + + if (conv_param.has_bias_filler()) { + conv->bias_init(create_filler(conv_param.bias_filler().type())); + } + + // set weight (optional) + if (layer.blobs_size() > 0) { // blobs(0)...weight, blobs(1)...bias + load_weights_conv(layer, conv.get()); + } + + *top_shape = conv->out_shape()[0]; + return conv; +} + +inline std::shared_ptr create_deconvlayer( + const caffe::LayerParameter &layer, + const shape_t &bottom_shape, + shape_t *top_shape) { + using deconv_layer = deconvolutional_layer; + + if (!layer.has_convolution_param()) { + throw nn_error("deconvolution param missing"); + } + + // layer parameters + layer_size_t in_width = 0, in_height = 0, window_size = 0; + layer_size_t in_channels = 0, out_channels = 0; + layer_size_t w_stride = 1, h_stride = 1; + bool has_bias = true; + padding pad_type = padding::valid; + core::connection_table table; + + auto deconv_param = layer.convolution_param(); + + // shape + out_channels = deconv_param.num_output(); + in_channels = bottom_shape.depth_; + in_width = bottom_shape.width_; + in_height = bottom_shape.height_; + has_bias = deconv_param.bias_term(); + window_size = get_kernel_size_2d(deconv_param); + + // unpadding + if (deconv_param.pad_size() == 1 || + (deconv_param.has_pad_w() && deconv_param.has_pad_h())) { + uint32_t unpad_w = + deconv_param.pad_size() == 1 ? deconv_param.pad(0) : deconv_param.pad_w(); + + uint32_t unpad_h = + deconv_param.pad_size() == 1 ? deconv_param.pad(0) : deconv_param.pad_h(); + + if (unpad_w != unpad_h) { + throw nn_error("deconv:not supported unpadding size"); + } + + // 0 ... valid, (window_size-1)/2 ... same + if (unpad_w == (window_size - 1) / 2) { + pad_type = padding::same; + } else if (unpad_w == 0) { + pad_type = padding::valid; + } else { + throw nn_error("deconv:not supported unpadding size"); + } + } + + // stride + if (deconv_param.stride_size() == 1 || deconv_param.has_stride_h()) { + h_stride = deconv_param.stride_size() == 1 ? deconv_param.stride(0) + : deconv_param.stride_h(); + } + + if (deconv_param.stride_size() == 1 || deconv_param.has_stride_w()) { + w_stride = deconv_param.stride_size() == 1 ? deconv_param.stride(0) + : deconv_param.stride_w(); + } + + // group + if (deconv_param.has_group()) { + table = + core::connection_table(deconv_param.group(), in_channels, out_channels); + } + + auto deconv = std::make_shared( + in_width, in_height, window_size, in_channels, out_channels, table, + pad_type, has_bias, w_stride, h_stride); + // filler + if (deconv_param.has_weight_filler()) { + deconv->weight_init(create_filler(deconv_param.weight_filler().type())); + } + + if (deconv_param.has_bias_filler()) { + deconv->bias_init(create_filler(deconv_param.bias_filler().type())); + } + + // set weight (optional) + if (layer.blobs_size() > 0) { // blobs(0)...weight, blobs(1)...bias + load_weights_conv(layer, deconv.get()); + } + *top_shape = deconv->out_shape()[0]; + return deconv; +} + +inline bool layer_skipped(const std::string &type) { + if (type == "Data" || type == "EuclideanLoss" || type == "Input" || + type == "HDF5Data" || type == "Split" || type == "Accuracy") + return true; + return false; +} + +inline bool layer_has_weights(const std::string &type) { + static const char *activations[] = {"SoftmaxWithLoss", + "SigmoidCrossEntropyLoss", + "LRN", + "Dropout", + "ReLU", + "ELU", + "Sigmoid", + "TanH", + "Softmax"}; + for (unsigned int i = 0; i < sizeof(activations) / sizeof(activations[0]); + i++) { + if (activations[i] == type) return false; + } + return true; +} + +inline bool layer_supported(const std::string &type) { + static const char *supported[] = {"InnerProduct", + "Convolution", + "Deconvolution", + "Pooling", + "LRN", + "Dropout", + "SoftmaxWithLoss", + "SigmoidCrossEntropyLoss", + "ReLU", + "ELU", + "Sigmoid", + "TanH", + "Softmax", + "BatchNorm", + "Power"}; + + for (size_t i = 0; i < sizeof(supported) / sizeof(supported[0]); i++) { + if (supported[i] == type) return true; + } + return false; +} + +inline bool layer_match(const std::string &caffetype, + const std::string &tiny_dnn_type) { + const char *conversions[][2] = {{"InnerProduct", "fully-connected"}, + {"Convolution", "conv"}, + {"Deconvolution", "deconv"}, + {"Pooling", "ave-pool"}, + {"Pooling", "max-pool"}, + {"BatchNorm", "batch-norm"}}; + + for (size_t i = 0; i < sizeof(conversions) / sizeof(conversions[0]); i++) { + if (conversions[i][0] == caffetype && conversions[i][1] == tiny_dnn_type) + return true; + } + return false; +} + +inline std::shared_ptr create(const caffe::LayerParameter &layer, + const shape_t &in_shape, + shape_t *out_shape) { + const std::string layer_type = layer.type(); + + if (layer_type == "Convolution") { + return detail::create_convlayer(layer, in_shape, out_shape); + } + + if (layer_type == "Deconvolution") { + return detail::create_deconvlayer(layer, in_shape, out_shape); + } + + if (layer_type == "InnerProduct") { + return detail::create_fullyconnected(layer, in_shape, out_shape); + } + + if (layer_type == "Pooling") { + return detail::create_pooling(layer, in_shape, out_shape); + } + + if (layer_type == "BatchNorm") { + return detail::create_batchnorm(layer, in_shape, out_shape); + } + + if (layer_type == "LRN") { + return detail::create_lrn(layer, in_shape, out_shape); + } + + if (layer_type == "Dropout") { + return detail::create_dropout(layer, in_shape, out_shape); + } + + if (layer_type == "SoftmaxWithLoss" || layer_type == "Softmax") { + return detail::create_softmax(layer, in_shape, out_shape); + } + + if (layer_type == "SigmoidCrossEntropyLoss" || layer_type == "Sigmoid") { + return detail::create_sigmoid(layer, in_shape, out_shape); + } + + if (layer_type == "ReLU") { + return detail::create_relu(layer, in_shape, out_shape); + } + + if (layer_type == "ELU") { + return detail::create_elu(layer, in_shape, out_shape); + } + + if (layer_type == "TanH") { + return detail::create_tanh(layer, in_shape, out_shape); + } + + if (layer_type == "Power") { + return detail::create_power(layer, in_shape, out_shape); + } + + throw nn_error("layer parser not found"); +} + +inline void load(const caffe::LayerParameter &src, layer *dst) { + typedef std::function + factoryimpl; // NOLINT + std::unordered_map factory_registry; + + factory_registry["Convolution"] = detail::load_weights_conv; + factory_registry["Deconvolution"] = detail::load_weights_conv; + factory_registry["InnerProduct"] = detail::load_weights_fullyconnected; + factory_registry["Pooling"] = detail::load_weights_pool; + factory_registry["BatchNorm"] = detail::load_weights_batchnorm; + + if (factory_registry.find(src.type()) == factory_registry.end()) { + throw nn_error("layer parser not found"); + } + + return factory_registry[src.type()](src, dst); +} + +struct layer_node { + const caffe::LayerParameter *layer; + const layer_node *next; // top-side + const layer_node *prev; // bottom-side + + layer_node() : layer(0), next(0), prev(0) {} + explicit layer_node(const caffe::LayerParameter *l) + : layer(l), next(0), prev(0) {} +}; + +// parse caffe net and interpret as single layer vector +class caffe_layer_vector { + public: + explicit caffe_layer_vector(const caffe::NetParameter &net_orig) + : net(net_orig) { + if (net.layers_size() > 0) { + upgradev1net(net_orig, &net); + } + + nodes.reserve(net.layer_size()); + + for (int i = 0; i < net.layer_size(); i++) { + auto &l = net.layer(i); + + if (layer_table.find(l.name()) != layer_table.end()) continue; + + nodes.emplace_back(&l); + layer_table[l.name()] = &nodes.back(); + } + + for (size_t i = 0; i < nodes.size(); i++) { + auto &l = nodes[i]; + + if (l.layer->bottom_size() > 0 && blob_table[l.layer->bottom(0)]) { + auto &bottom = blob_table[l.layer->bottom(0)]; + l.prev = bottom; + layer_table[bottom->layer->name()]->next = &l; + } + + if (l.layer->top_size() > 0) { + blob_table[l.layer->top(0)] = &l; + } + } + + auto root = std::find_if(nodes.begin(), nodes.end(), + [](const layer_node &n) { return n.prev == 0; }); + + if (root == nodes.end()) { + throw nn_error("root layer not found"); + } + + root_node = &*root; + const layer_node *current = &*root; + + while (current) { + node_list.push_back(current->layer); + current = current->next; + } + } + + size_t size() const { return node_list.size(); } + + const caffe::LayerParameter &operator[](size_t index) const { + return *(node_list[index]); + } + + private: + void upgradev1net(const caffe::NetParameter &old, + caffe::NetParameter *dst) const { + dst->CopyFrom(old); + dst->clear_layers(); + dst->clear_layer(); + + for (int i = 0; i < old.layers_size(); i++) { + upgradev1layer(old.layers(i), dst->add_layer()); + } + } + + const char *v1type2name(caffe::V1LayerParameter_LayerType type) const { + switch (type) { + case caffe::V1LayerParameter_LayerType_NONE: return ""; + case caffe::V1LayerParameter_LayerType_ABSVAL: return "AbsVal"; + case caffe::V1LayerParameter_LayerType_ACCURACY: return "Accuracy"; + case caffe::V1LayerParameter_LayerType_ARGMAX: return "ArgMax"; + case caffe::V1LayerParameter_LayerType_BNLL: return "BNLL"; + case caffe::V1LayerParameter_LayerType_CONCAT: return "Concat"; + case caffe::V1LayerParameter_LayerType_CONTRASTIVE_LOSS: + return "ContrastiveLoss"; + case caffe::V1LayerParameter_LayerType_CONVOLUTION: return "Convolution"; + case caffe::V1LayerParameter_LayerType_DECONVOLUTION: + return "Deconvolution"; + case caffe::V1LayerParameter_LayerType_DATA: return "Data"; + case caffe::V1LayerParameter_LayerType_DROPOUT: return "Dropout"; + case caffe::V1LayerParameter_LayerType_DUMMY_DATA: return "DummyData"; + case caffe::V1LayerParameter_LayerType_EUCLIDEAN_LOSS: + return "EuclideanLoss"; + case caffe::V1LayerParameter_LayerType_ELTWISE: return "Eltwise"; + case caffe::V1LayerParameter_LayerType_EXP: return "Exp"; + case caffe::V1LayerParameter_LayerType_FLATTEN: return "Flatten"; + case caffe::V1LayerParameter_LayerType_HDF5_DATA: return "HDF5Data"; + case caffe::V1LayerParameter_LayerType_HDF5_OUTPUT: return "HDF5Output"; + case caffe::V1LayerParameter_LayerType_HINGE_LOSS: return "HingeLoss"; + case caffe::V1LayerParameter_LayerType_IM2COL: return "Im2col"; + case caffe::V1LayerParameter_LayerType_IMAGE_DATA: return "ImageData"; + case caffe::V1LayerParameter_LayerType_INFOGAIN_LOSS: + return "InfogainLoss"; + case caffe::V1LayerParameter_LayerType_INNER_PRODUCT: + return "InnerProduct"; + case caffe::V1LayerParameter_LayerType_LRN: return "LRN"; + case caffe::V1LayerParameter_LayerType_MEMORY_DATA: return "MemoryData"; + case caffe::V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS: + return "MultinomialLogisticLoss"; + case caffe::V1LayerParameter_LayerType_MVN: return "MVN"; + case caffe::V1LayerParameter_LayerType_POOLING: return "Pooling"; + case caffe::V1LayerParameter_LayerType_POWER: return "Power"; + case caffe::V1LayerParameter_LayerType_RELU: return "ReLU"; + case caffe::V1LayerParameter_LayerType_SIGMOID: return "Sigmoid"; + case caffe::V1LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS: + return "SigmoidCrossEntropyLoss"; + case caffe::V1LayerParameter_LayerType_SILENCE: return "Silence"; + case caffe::V1LayerParameter_LayerType_SOFTMAX: return "Softmax"; + case caffe::V1LayerParameter_LayerType_SOFTMAX_LOSS: + return "SoftmaxWithLoss"; + case caffe::V1LayerParameter_LayerType_SPLIT: return "Split"; + case caffe::V1LayerParameter_LayerType_SLICE: return "Slice"; + case caffe::V1LayerParameter_LayerType_TANH: return "TanH"; + case caffe::V1LayerParameter_LayerType_WINDOW_DATA: return "WindowData"; + case caffe::V1LayerParameter_LayerType_THRESHOLD: return "Threshold"; + default: throw nn_error("unknown v1 layer-type"); + } + } + + void upgradev1layer(const caffe::V1LayerParameter &old, + caffe::LayerParameter *dst) const { + dst->Clear(); + + for (int i = 0; i < old.bottom_size(); i++) { + dst->add_bottom(old.bottom(i)); + } + + for (int i = 0; i < old.top_size(); i++) { + dst->add_top(old.top(i)); + } + + if (old.has_name()) dst->set_name(old.name()); + if (old.has_type()) dst->set_type(v1type2name(old.type())); + + for (int i = 0; i < old.blobs_size(); i++) { + dst->add_blobs()->CopyFrom(old.blobs(i)); + } + + for (int i = 0; i < old.param_size(); i++) { + while (dst->param_size() <= i) dst->add_param(); + dst->mutable_param(i)->set_name(old.param(i)); + } + +#define COPY_PARAM(name) \ + if (old.has_##name##_param()) { \ + dst->mutable_##name##_param()->CopyFrom(old.name##_param()); \ + } + + COPY_PARAM(accuracy); + COPY_PARAM(argmax); + COPY_PARAM(concat); + COPY_PARAM(contrastive_loss); + COPY_PARAM(convolution); + COPY_PARAM(data); + COPY_PARAM(dropout); + COPY_PARAM(dummy_data); + COPY_PARAM(eltwise); + COPY_PARAM(exp); + COPY_PARAM(hdf5_data); + COPY_PARAM(hdf5_output); + COPY_PARAM(hinge_loss); + COPY_PARAM(image_data); + COPY_PARAM(infogain_loss); + COPY_PARAM(inner_product); + COPY_PARAM(lrn); + COPY_PARAM(memory_data); + COPY_PARAM(mvn); + COPY_PARAM(pooling); + COPY_PARAM(power); + COPY_PARAM(relu); + COPY_PARAM(sigmoid); + COPY_PARAM(softmax); + COPY_PARAM(slice); + COPY_PARAM(tanh); + COPY_PARAM(threshold); + COPY_PARAM(window_data); + COPY_PARAM(transform); + COPY_PARAM(loss); +#undef COPY_PARAM + } + + caffe::NetParameter net; + layer_node *root_node; + /* layer name -> layer */ + std::map layer_table; + /* blob name -> bottom holder */ + std::map blob_table; + std::vector nodes; + std::vector node_list; +}; + +} // namespace detail +} // namespace tiny_dnn + +#ifdef _MSC_VER +#pragma warning(pop) +#endif diff --git a/tiny_dnn/io/cifar10_parser.h b/tiny_dnn/io/cifar10_parser.h new file mode 100755 index 0000000000000..862e93cba627b --- /dev/null +++ b/tiny_dnn/io/cifar10_parser.h @@ -0,0 +1,89 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include + +#include "tiny_dnn/util/util.h" + +#define CIFAR10_IMAGE_DEPTH (3) +#define CIFAR10_IMAGE_WIDTH (32) +#define CIFAR10_IMAGE_HEIGHT (32) +#define CIFAR10_IMAGE_AREA (CIFAR10_IMAGE_WIDTH * CIFAR10_IMAGE_HEIGHT) +#define CIFAR10_IMAGE_SIZE (CIFAR10_IMAGE_AREA * CIFAR10_IMAGE_DEPTH) + +namespace tiny_dnn { + +/** + * parse CIFAR-10 database format images + * + * @param filename [in] filename of database(binary version) + * @param train_images [out] parsed images + * @param train_labels [out] parsed labels + * @param scale_min [in] min-value of output + * @param scale_max [in] max-value of output + * @param x_padding [in] adding border width (left,right) + * @param y_padding [in] adding border width (top,bottom) + **/ +inline void parse_cifar10(const std::string &filename, + std::vector *train_images, + std::vector *train_labels, + float_t scale_min, + float_t scale_max, + int x_padding, + int y_padding) { + if (x_padding < 0 || y_padding < 0) + throw nn_error("padding size must not be negative"); + if (scale_min >= scale_max) + throw nn_error("scale_max must be greater than scale_min"); + + std::ifstream ifs(filename.c_str(), std::ios::in | std::ios::binary); + if (ifs.fail() || ifs.bad()) + throw nn_error("failed to open file:" + filename); + + uint8_t label; + std::vector buf(CIFAR10_IMAGE_SIZE); + + while (ifs.read(reinterpret_cast(&label), 1)) { + vec_t img; + + if (!ifs.read(reinterpret_cast(&buf[0]), CIFAR10_IMAGE_SIZE)) break; + + if (x_padding || y_padding) { + int w = CIFAR10_IMAGE_WIDTH + 2 * x_padding; + int h = CIFAR10_IMAGE_HEIGHT + 2 * y_padding; + + img.resize(w * h * CIFAR10_IMAGE_DEPTH, scale_min); + + for (int c = 0; c < CIFAR10_IMAGE_DEPTH; c++) { + for (int y = 0; y < CIFAR10_IMAGE_HEIGHT; y++) { + for (int x = 0; x < CIFAR10_IMAGE_WIDTH; x++) { + img[c * w * h + (y + y_padding) * w + x + x_padding] = + scale_min + + (scale_max - scale_min) * + buf[c * CIFAR10_IMAGE_AREA + y * CIFAR10_IMAGE_WIDTH + x] / 255; + } + } + } + } else { + std::transform(buf.begin(), buf.end(), std::back_inserter(img), + [=](unsigned char c) { + return scale_min + (scale_max - scale_min) * c / 255; + }); + } + + train_images->push_back(img); + train_labels->push_back(label); + } +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/io/display.h b/tiny_dnn/io/display.h new file mode 100755 index 0000000000000..4d0d5c3eb18b6 --- /dev/null +++ b/tiny_dnn/io/display.h @@ -0,0 +1,111 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ + +// addapted from boost progress.hpp, made c++11 only // + +#pragma once + +#include // for ostream, cout, etc +#include // for string + +namespace tiny_dnn { + +class timer { + public: + timer() : t1(std::chrono::high_resolution_clock::now()) {} + float_t elapsed() { + return std::chrono::duration_cast>( + std::chrono::high_resolution_clock::now() - t1) + .count(); + } + void restart() { t1 = std::chrono::high_resolution_clock::now(); } + void start() { t1 = std::chrono::high_resolution_clock::now(); } + void stop() { t2 = std::chrono::high_resolution_clock::now(); } + float_t total() { + stop(); + return std::chrono::duration_cast>(t2 - t1) + .count(); + } + ~timer() {} + + private: + std::chrono::high_resolution_clock::time_point t1, t2; +}; + +// progress_display --------------------------------------------------------// + +// progress_display displays an appropriate indication of +// progress at an appropriate place in an appropriate form. + +class progress_display { + public: + explicit progress_display(size_t expected_count_, + std::ostream &os = std::cout, + const std::string &s1 = "\n", // leading strings + const std::string &s2 = "", + const std::string &s3 = "") + // os is hint; implementation may ignore, particularly in embedded systems + : m_os(os), m_s1(s1), m_s2(s2), m_s3(s3) { + restart(expected_count_); + } + + void restart(size_t expected_count_) { + // Effects: display appropriate scale + // Postconditions: count()==0, expected_count()==expected_count_ + _count = _next_tic_count = _tic = 0; + _expected_count = expected_count_; + + m_os << m_s1 << "0% 10 20 30 40 50 60 70 80 90 100%\n" + << m_s2 << "|----|----|----|----|----|----|----|----|----|----|" + << std::endl // endl implies flush, which ensures display + << m_s3; + if (!_expected_count) _expected_count = 1; // prevent divide by zero + } // restart + + size_t operator+=(size_t increment) { + // Effects: Display appropriate progress tic if needed. + // Postconditions: count()== original count() + increment + // Returns: count(). + if ((_count += increment) >= _next_tic_count) { + display_tic(); + } + return _count; + } + + size_t operator++() { return operator+=(1); } + size_t count() const { return _count; } + size_t expected_count() const { return _expected_count; } + + private: + std::ostream &m_os; // may not be present in all imps + const std::string m_s1; // string is more general, safer than + const std::string m_s2; // const char *, and efficiency or size are + const std::string m_s3; // not issues + + size_t _count, _expected_count, _next_tic_count; + size_t _tic; + void display_tic() { + // use of floating point ensures that both large and small counts + // work correctly. static_cast<>() is also used several places + // to suppress spurious compiler warnings. + size_t tics_needed = static_cast( + (static_cast(_count) / _expected_count) * 50.0); + do { + m_os << '*' << std::flush; + } while (++_tic < tics_needed); + _next_tic_count = static_cast((_tic / 50.0) * _expected_count); + if (_count == _expected_count) { + if (_tic < 51) m_os << '*'; + m_os << std::endl; + } + } // display_tic + + progress_display &operator=(const progress_display &) = delete; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/io/layer_factory.h b/tiny_dnn/io/layer_factory.h new file mode 100755 index 0000000000000..d3d55f9f2a91b --- /dev/null +++ b/tiny_dnn/io/layer_factory.h @@ -0,0 +1,39 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/layers/fully_connected_layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * create multi-layer perceptron + */ +template +network make_mlp(Iter first, Iter last) { + typedef network net_t; + net_t n; + + Iter next = first + 1; + for (; next != last; ++first, ++next) + n << fully_connected_layer(*first, *next) << activation(*next); + return n; +} + +/** + * create multi-layer perceptron + */ +template +network make_mlp(const std::vector &units) { + return make_mlp(units.begin(), units.end()); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/io/mnist_parser.h b/tiny_dnn/io/mnist_parser.h new file mode 100755 index 0000000000000..719bf741dcd38 --- /dev/null +++ b/tiny_dnn/io/mnist_parser.h @@ -0,0 +1,159 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include + +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { +namespace detail { + +struct mnist_header { + uint32_t magic_number; + uint32_t num_items; + uint32_t num_rows; + uint32_t num_cols; +}; + +inline void parse_mnist_header(std::ifstream &ifs, mnist_header &header) { + ifs.read(reinterpret_cast(&header.magic_number), 4); + ifs.read(reinterpret_cast(&header.num_items), 4); + ifs.read(reinterpret_cast(&header.num_rows), 4); + ifs.read(reinterpret_cast(&header.num_cols), 4); + + if (is_little_endian()) { + reverse_endian(&header.magic_number); + reverse_endian(&header.num_items); + reverse_endian(&header.num_rows); + reverse_endian(&header.num_cols); + } + + if (header.magic_number != 0x00000803 || header.num_items <= 0) + throw nn_error("MNIST label-file format error"); + if (ifs.fail() || ifs.bad()) throw nn_error("file error"); +} + +inline void parse_mnist_image(std::ifstream &ifs, + const mnist_header &header, + float_t scale_min, + float_t scale_max, + int x_padding, + int y_padding, + vec_t &dst) { + const int width = header.num_cols + 2 * x_padding; + const int height = header.num_rows + 2 * y_padding; + + std::vector image_vec(header.num_rows * header.num_cols); + + ifs.read(reinterpret_cast(&image_vec[0]), + header.num_rows * header.num_cols); + + dst.resize(width * height, scale_min); + + for (uint32_t y = 0; y < header.num_rows; y++) + for (uint32_t x = 0; x < header.num_cols; x++) + dst[width * (y + y_padding) + x + x_padding] = + (image_vec[y * header.num_cols + x] / float_t(255)) * + (scale_max - scale_min) + + scale_min; +} + +} // namespace detail + +/** + * parse MNIST database format labels with rescaling/resizing + * http://yann.lecun.com/exdb/mnist/ + * + * @param label_file [in] filename of database (i.e.train-labels-idx1-ubyte) + * @param labels [out] parsed label data + **/ +inline void parse_mnist_labels(const std::string &label_file, + std::vector *labels) { + std::ifstream ifs(label_file.c_str(), std::ios::in | std::ios::binary); + + if (ifs.bad() || ifs.fail()) + throw nn_error("failed to open file:" + label_file); + + uint32_t magic_number, num_items; + + ifs.read(reinterpret_cast(&magic_number), 4); + ifs.read(reinterpret_cast(&num_items), 4); + + if (is_little_endian()) { // MNIST data is big-endian format + reverse_endian(&magic_number); + reverse_endian(&num_items); + } + + if (magic_number != 0x00000801 || num_items <= 0) + throw nn_error("MNIST label-file format error"); + + labels->resize(num_items); + for (uint32_t i = 0; i < num_items; i++) { + uint8_t label; + ifs.read(reinterpret_cast(&label), 1); + (*labels)[i] = static_cast(label); + } +} + +/** + * parse MNIST database format images with rescaling/resizing + * http://yann.lecun.com/exdb/mnist/ + * - if original image size is WxH, output size is + *(W+2*x_padding)x(H+2*y_padding) + * - extra padding pixels are filled with scale_min + * + * @param image_file [in] filename of database (i.e.train-images-idx3-ubyte) + * @param images [out] parsed image data + * @param scale_min [in] min-value of output + * @param scale_max [in] max-value of output + * @param x_padding [in] adding border width (left,right) + * @param y_padding [in] adding border width (top,bottom) + * + * [example] + * scale_min=-1.0, scale_max=1.0, x_padding=1, y_padding=0 + * + * [input] [output] + * 64 64 64 -1.0 -0.5 -0.5 -0.5 -1.0 + * 128 128 128 -1.0 0.0 0.0 0.0 -1.0 + * 255 255 255 -1.0 1.0 1.0 1.0 -1.0 + * + **/ +inline void parse_mnist_images(const std::string &image_file, + std::vector *images, + float_t scale_min, + float_t scale_max, + int x_padding, + int y_padding) { + if (x_padding < 0 || y_padding < 0) + throw nn_error("padding size must not be negative"); + if (scale_min >= scale_max) + throw nn_error("scale_max must be greater than scale_min"); + + std::ifstream ifs(image_file.c_str(), std::ios::in | std::ios::binary); + + if (ifs.bad() || ifs.fail()) + throw nn_error("failed to open file:" + image_file); + + detail::mnist_header header; + + detail::parse_mnist_header(ifs, header); + + images->resize(header.num_items); + for (uint32_t i = 0; i < header.num_items; i++) { + vec_t image; + detail::parse_mnist_image(ifs, header, scale_min, scale_max, x_padding, + y_padding, image); + (*images)[i] = image; + } +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/arithmetic_layer.h b/tiny_dnn/layers/arithmetic_layer.h new file mode 100755 index 0000000000000..8f810a3ed5233 --- /dev/null +++ b/tiny_dnn/layers/arithmetic_layer.h @@ -0,0 +1,78 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * element-wise add N vectors ```y_i = x0_i + x1_i + ... + xnum_i``` + **/ +class elementwise_add_layer : public layer { + public: + /** + * @param num_args [in] number of inputs + * @param dim [in] number of elements for each input + */ + elementwise_add_layer(size_t num_args, size_t dim) + : layer(std::vector(num_args, vector_type::data), + {vector_type::data}), + num_args_(num_args), + dim_(dim) {} + + std::string layer_type() const override { return "elementwise-add"; } + + std::vector in_shape() const override { + return std::vector(num_args_, shape3d(dim_, 1, 1)); + } + + std::vector out_shape() const override { + return {shape3d(dim_, 1, 1)}; + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + const tensor_t &in1 = *in_data[0]; + tensor_t &out = *out_data[0]; + + out = in1; + + // @todo parallelize + for (size_t sample = 0; sample < in1.size(); ++sample) { + for (size_t i = 1; i < num_args_; i++) { + std::transform((*in_data[i])[sample].begin(), + (*in_data[i])[sample].end(), out[sample].begin(), + out[sample].begin(), + [](float_t x, float_t y) { return x + y; }); + } + } + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + for (size_t i = 0; i < num_args_; i++) *in_grad[i] = *out_grad[0]; + } + + friend struct serialization_buddy; + + private: + size_t num_args_; + size_t dim_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/average_pooling_layer.h b/tiny_dnn/layers/average_pooling_layer.h new file mode 100755 index 0000000000000..769fa95fd2c22 --- /dev/null +++ b/tiny_dnn/layers/average_pooling_layer.h @@ -0,0 +1,286 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include + +#include "tiny_dnn/layers/partial_connected_layer.h" +#include "tiny_dnn/util/util.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif // DNN_USE_IMAGE_API + +namespace tiny_dnn { + +// forward_propagation +inline void tiny_average_pooling_kernel( + bool parallelize, + const std::vector &in_data, + std::vector &out_data, + const shape3d &out_dim, + float_t scale_factor, + std::vector &out2wi) { + for_i(parallelize, in_data[0]->size(), [&](size_t sample) { + const vec_t &in = (*in_data[0])[sample]; + const vec_t &W = (*in_data[1])[0]; + const vec_t &b = (*in_data[2])[0]; + vec_t &out = (*out_data[0])[sample]; + + auto oarea = out_dim.area(); + size_t idx = 0; + for (size_t d = 0; d < out_dim.depth_; ++d) { + float_t weight = W[d] * scale_factor; + float_t bias = b[d]; + for (size_t i = 0; i < oarea; ++i, ++idx) { + const auto &connections = out2wi[idx]; + float_t value{0}; + for (auto connection : connections) value += in[connection.second]; + value *= weight; + value += bias; + out[idx] = value; + } + } + + assert(out.size() == out2wi.size()); + }); +} + +// back_propagation +inline void tiny_average_pooling_back_kernel( + bool parallelize, + const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad, + const shape3d &in_dim, + float_t scale_factor, + std::vector &weight2io, + std::vector &in2wo, + std::vector> &bias2out) { + CNN_UNREFERENCED_PARAMETER(out_data); + for_i(parallelize, in_data[0]->size(), [&](size_t sample) { + const vec_t &prev_out = (*in_data[0])[sample]; + const vec_t &W = (*in_data[1])[0]; + vec_t &dW = (*in_grad[1])[sample]; + vec_t &db = (*in_grad[2])[sample]; + vec_t &prev_delta = (*in_grad[0])[sample]; + vec_t &curr_delta = (*out_grad[0])[sample]; + + auto inarea = in_dim.area(); + size_t idx = 0; + for (size_t i = 0; i < in_dim.depth_; ++i) { + float_t weight = W[i] * scale_factor; + for (size_t j = 0; j < inarea; ++j, ++idx) { + prev_delta[idx] = weight * curr_delta[in2wo[idx][0].second]; + } + } + + for (size_t i = 0; i < weight2io.size(); ++i) { + const auto &connections = weight2io[i]; + float_t diff{0}; + + for (auto connection : connections) + diff += prev_out[connection.first] * curr_delta[connection.second]; + + dW[i] += diff * scale_factor; + } + + for (size_t i = 0; i < bias2out.size(); i++) { + const std::vector &outs = bias2out[i]; + float_t diff{0}; + + for (auto o : outs) diff += curr_delta[o]; + + db[i] += diff; + } + }); +} + +/** + * average pooling with trainable weights + **/ +class average_pooling_layer : public partial_connected_layer { + public: + using Base = partial_connected_layer; + + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param pool_size [in] factor by which to downscale + **/ + average_pooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t pool_size) + : average_pooling_layer(in_width, + in_height, + in_channels, + pool_size, + (in_height == 1 ? 1 : pool_size)) {} + + average_pooling_layer(const shape3d &in_shape, + size_t pool_size, + size_t stride) + : average_pooling_layer( + in_shape.width_, in_shape.width_, in_shape.depth_, pool_size, stride) {} + + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param pool_size [in] factor by which to downscale + * @param stride [in] interval at which to apply the filters to the + *input + **/ + average_pooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t pool_size, + size_t stride) + : average_pooling_layer(in_width, + in_height, + in_channels, + pool_size, + (in_height == 1 ? 1 : pool_size), + stride, + stride, + padding::valid) {} + + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param pool_size_x [in] factor by which to downscale + * @param pool_size_y [in] factor by which to downscale + * @param stride_x [in] interval at which to apply the filters to the + *input + * @param stride_y [in] interval at which to apply the filters to the + *input + * @param pad_type [in] padding mode(same/valid) + **/ + average_pooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t pool_size_x, + size_t pool_size_y, + size_t stride_x, + size_t stride_y, + padding pad_type = padding::valid) + : Base(in_width * in_height * in_channels, + conv_out_length(in_width, pool_size_x, stride_x, pad_type) * + conv_out_length(in_height, pool_size_y, stride_y, pad_type) * + in_channels, + in_channels, + in_channels, + float_t(1) / (pool_size_x * pool_size_y)), + stride_x_(stride_x), + stride_y_(stride_y), + pool_size_x_(pool_size_x), + pool_size_y_(pool_size_y), + pad_type_(pad_type), + in_(in_width, in_height, in_channels), + out_(conv_out_length(in_width, pool_size_x, stride_x, pad_type), + conv_out_length(in_height, pool_size_y, stride_y, pad_type), + in_channels), + w_(pool_size_x, pool_size_y, in_channels) { + if ((in_width % pool_size_x) || (in_height % pool_size_y)) { + pooling_size_mismatch(in_width, in_height, pool_size_x, pool_size_y); + } + + init_connection(pool_size_x, pool_size_y); + } + + std::vector> in_shape() const override { + return {in_, w_, index3d(1, 1, out_.depth_)}; + } + + std::vector> out_shape() const override { return {out_}; } + + std::string layer_type() const override { return "ave-pool"; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + tiny_average_pooling_kernel(parallelize_, in_data, out_data, out_, + Base::scale_factor_, Base::out2wi_); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + tiny_average_pooling_back_kernel( + parallelize_, in_data, out_data, out_grad, in_grad, in_, + Base::scale_factor_, Base::weight2io_, Base::in2wo_, Base::bias2out_); + } + + std::pair pool_size() const { + return std::make_pair(pool_size_x_, pool_size_y_); + } + + friend struct serialization_buddy; + + private: + size_t stride_x_; + size_t stride_y_; + size_t pool_size_x_; + size_t pool_size_y_; + padding pad_type_; + shape3d in_; + shape3d out_; + shape3d w_; + + static size_t pool_out_dim(size_t in_size, + size_t pooling_size, + size_t stride) { + return static_cast( + std::ceil((static_cast(in_size) - pooling_size) / stride) + 1); + } + + void init_connection(size_t pooling_size_x, size_t pooling_size_y) { + for (size_t c = 0; c < in_.depth_; ++c) { + for (size_t y = 0; y < in_.height_ - pooling_size_y + 1; y += stride_y_) { + for (size_t x = 0; x < in_.width_ - pooling_size_x + 1; + x += stride_x_) { + connect_kernel(pooling_size_x, pooling_size_y, x, y, c); + } + } + } + + for (size_t c = 0; c < in_.depth_; ++c) { + for (size_t y = 0; y < out_.height_; ++y) { + for (size_t x = 0; x < out_.width_; ++x) { + this->connect_bias(c, out_.get_index(x, y, c)); + } + } + } + } + + void connect_kernel(size_t pooling_size_x, + size_t pooling_size_y, + size_t x, + size_t y, + size_t inc) { + size_t dymax = std::min(pooling_size_y, in_.height_ - y); + size_t dxmax = std::min(pooling_size_x, in_.width_ - x); + size_t dstx = x / stride_x_; + size_t dsty = y / stride_y_; + size_t outidx = out_.get_index(dstx, dsty, inc); + for (size_t dy = 0; dy < dymax; ++dy) { + for (size_t dx = 0; dx < dxmax; ++dx) { + this->connect_weight(in_.get_index(x + dx, y + dy, inc), outidx, inc); + } + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/average_unpooling_layer.h b/tiny_dnn/layers/average_unpooling_layer.h new file mode 100755 index 0000000000000..23302d53d9dab --- /dev/null +++ b/tiny_dnn/layers/average_unpooling_layer.h @@ -0,0 +1,236 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/layers/partial_connected_layer.h" +#include "tiny_dnn/util/util.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif // DNN_USE_IMAGE_API + +namespace tiny_dnn { + +// forward_propagation +inline void tiny_average_unpooling_kernel( + bool parallelize, + const std::vector &in_data, + std::vector &out_data, + const shape3d &out_dim, + float_t scale_factor, + std::vector &out2wi) { + CNN_UNREFERENCED_PARAMETER(scale_factor); + for_i(parallelize, in_data[0]->size(), [&](size_t sample) { + const vec_t &in = (*in_data[0])[sample]; + const vec_t &W = (*in_data[1])[0]; + const vec_t &b = (*in_data[2])[0]; + vec_t &out = (*out_data[0])[sample]; + + auto oarea = out_dim.area(); + size_t idx = 0; + for (size_t d = 0; d < out_dim.depth_; ++d) { + float_t weight = W[d]; // * scale_factor; + float_t bias = b[d]; + for (size_t i = 0; i < oarea; ++i, ++idx) { + const auto &connections = out2wi[idx]; + float_t value{0}; + for (auto connection : connections) value += in[connection.second]; + value *= weight; + value += bias; + out[idx] = value; + } + } + + assert(out.size() == out2wi.size()); + }); +} + +// back_propagation +inline void tiny_average_unpooling_back_kernel( + bool parallelize, + const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad, + const shape3d &in_dim, + float_t scale_factor, + std::vector &weight2io, + std::vector &in2wo, + std::vector> &bias2out) { + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(scale_factor); + for_i(parallelize, in_data[0]->size(), [&](size_t sample) { + const vec_t &prev_out = (*in_data[0])[sample]; + const vec_t &W = (*in_data[1])[0]; + vec_t &dW = (*in_grad[1])[sample]; + vec_t &db = (*in_grad[2])[sample]; + vec_t &prev_delta = (*in_grad[0])[sample]; + vec_t &curr_delta = (*out_grad[0])[sample]; + + auto inarea = in_dim.area(); + size_t idx = 0; + for (size_t i = 0; i < in_dim.depth_; ++i) { + float_t weight = W[i]; // * scale_factor; + for (size_t j = 0; j < inarea; ++j, ++idx) { + prev_delta[idx] = weight * curr_delta[in2wo[idx][0].second]; + } + } + + for (size_t i = 0; i < weight2io.size(); ++i) { + const auto &connections = weight2io[i]; + float_t diff{0}; + + for (auto connection : connections) + diff += prev_out[connection.first] * curr_delta[connection.second]; + + dW[i] += diff; // * scale_factor; + } + + for (size_t i = 0; i < bias2out.size(); i++) { + const std::vector &outs = bias2out[i]; + float_t diff{0}; + + for (auto o : outs) diff += curr_delta[o]; + + db[i] += diff; + } + }); +} + +/** + * average pooling with trainable weights + **/ +class average_unpooling_layer : public partial_connected_layer { + public: + using Base = partial_connected_layer; + + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param pooling_size [in] factor by which to upscale + **/ + average_unpooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t pooling_size) + : Base(in_width * in_height * in_channels, + in_width * in_height * in_channels * sqr(pooling_size), + in_channels, + in_channels, + float_t(1) * sqr(pooling_size)), + stride_(pooling_size), + in_(in_width, in_height, in_channels), + out_(in_width * pooling_size, in_height * pooling_size, in_channels), + w_(pooling_size, (in_height == 1 ? 1 : pooling_size), in_channels) { + init_connection(pooling_size); + } + + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param pooling_size [in] factor by which to upscale + * @param stride [in] interval at which to apply the filters to the + *input + **/ + average_unpooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t pooling_size, + size_t stride) + : Base(in_width * in_height * in_channels, + unpool_out_dim(in_width, pooling_size, stride) * + unpool_out_dim(in_height, pooling_size, stride) * in_channels, + in_channels, + in_channels, + float_t(1) * sqr(pooling_size)), + stride_(stride), + in_(in_width, in_height, in_channels), + out_(unpool_out_dim(in_width, pooling_size, stride), + unpool_out_dim(in_height, pooling_size, stride), + in_channels), + w_(pooling_size, (in_height == 1 ? 1 : pooling_size), in_channels) { + init_connection(pooling_size); + } + + std::vector> in_shape() const override { + return {in_, w_, index3d(1, 1, out_.depth_)}; + } + + std::vector> out_shape() const override { return {out_}; } + + std::string layer_type() const override { return "ave-unpool"; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + tiny_average_unpooling_kernel(parallelize_, in_data, out_data, out_, + Base::scale_factor_, Base::out2wi_); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + tiny_average_unpooling_back_kernel( + parallelize_, in_data, out_data, out_grad, in_grad, in_, + Base::scale_factor_, Base::weight2io_, Base::in2wo_, Base::bias2out_); + } + + friend struct serialization_buddy; + + private: + size_t stride_; + shape3d in_; + shape3d out_; + shape3d w_; + + static size_t unpool_out_dim(size_t in_size, + size_t pooling_size, + size_t stride) { + return static_cast((in_size - 1) * stride + pooling_size); + } + + void init_connection(size_t pooling_size) { + for (size_t c = 0; c < in_.depth_; ++c) { + for (size_t y = 0; y < in_.height_; ++y) { + for (size_t x = 0; x < in_.width_; ++x) { + connect_kernel(pooling_size, x, y, c); + } + } + } + + for (size_t c = 0; c < in_.depth_; ++c) { + for (size_t y = 0; y < out_.height_; ++y) { + for (size_t x = 0; x < out_.width_; ++x) { + this->connect_bias(c, out_.get_index(x, y, c)); + } + } + } + } + + void connect_kernel(size_t pooling_size, size_t x, size_t y, size_t inc) { + size_t dymax = std::min(pooling_size, out_.height_ - y); + size_t dxmax = std::min(pooling_size, out_.width_ - x); + size_t dstx = x * stride_; + size_t dsty = y * stride_; + size_t inidx = in_.get_index(x, y, inc); + for (size_t dy = 0; dy < dymax; ++dy) { + for (size_t dx = 0; dx < dxmax; ++dx) { + this->connect_weight(inidx, out_.get_index(dstx + dx, dsty + dy, inc), + inc); + } + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/batch_normalization_layer.h b/tiny_dnn/layers/batch_normalization_layer.h new file mode 100755 index 0000000000000..63375cfdb40e4 --- /dev/null +++ b/tiny_dnn/layers/batch_normalization_layer.h @@ -0,0 +1,258 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/math_functions.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * Batch Normalization + * + * Normalize the activations of the previous layer at each batch + **/ +class batch_normalization_layer : public layer { + public: + typedef layer Base; + + /** + * @param prev_layer [in] previous layer to be connected with this layer + * @param epsilon [in] small positive value to avoid zero-division + * @param momentum [in] momentum in the computation of the exponential + *average of the mean/stddev of the data + * @param phase [in] specify the current context (train/test) + **/ + batch_normalization_layer(const layer &prev_layer, + float_t epsilon = 1e-5, + float_t momentum = 0.999, + net_phase phase = net_phase::train) + : Base({vector_type::data}, {vector_type::data}), + in_channels_(prev_layer.out_shape()[0].depth_), + in_spatial_size_(prev_layer.out_shape()[0].area()), + phase_(phase), + momentum_(momentum), + eps_(epsilon), + update_immidiately_(false) { + init(); + } + + /** + * @param in_spatial_size [in] spatial size (WxH) of the input data + * @param in_channels [in] channels of the input data + * @param epsilon [in] small positive value to avoid zero-division + * @param momentum [in] momentum in the computation of the exponential + *average of the mean/stddev of the data + * @param phase [in] specify the current context (train/test) + **/ + batch_normalization_layer(size_t in_spatial_size, + size_t in_channels, + float_t epsilon = 1e-5, + float_t momentum = 0.999, + net_phase phase = net_phase::train) + : Base({vector_type::data}, {vector_type::data}), + in_channels_(in_channels), + in_spatial_size_(in_spatial_size), + phase_(phase), + momentum_(momentum), + eps_(epsilon), + update_immidiately_(false) { + init(); + } + + virtual ~batch_normalization_layer() {} + + ///< number of incoming connections for each output unit + size_t fan_in_size() const override { return 1; } + + ///< number of outgoing connections for each input unit + size_t fan_out_size() const override { return 1; } + + std::vector> in_shape() const override { + return {index3d(in_spatial_size_, 1, in_channels_)}; + } + + std::vector> out_shape() const override { + return {index3d(in_spatial_size_, 1, in_channels_)}; + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + tensor_t &prev_delta = *in_grad[0]; + tensor_t &curr_delta = *out_grad[0]; + const tensor_t &curr_out = *out_data[0]; + const size_t num_samples = curr_out.size(); + + CNN_UNREFERENCED_PARAMETER(in_data); + + tensor_t delta_dot_y = curr_out; + vec_t mean_delta_dot_y, mean_delta, mean_Y; + + for (size_t i = 0; i < num_samples; i++) { + for (size_t j = 0; j < curr_out[0].size(); j++) { + delta_dot_y[i][j] *= curr_delta[i][j]; + } + } + + moments(delta_dot_y, in_spatial_size_, in_channels_, mean_delta_dot_y); + moments(curr_delta, in_spatial_size_, in_channels_, mean_delta); + // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then + // + // dE(Y)/dX = + // (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y) + // ./ sqrt(var(X) + eps) + // + for_i(num_samples, [&](size_t i) { + for (size_t j = 0; j < in_channels_; j++) { + for (size_t k = 0; k < in_spatial_size_; k++) { + size_t index = j * in_spatial_size_ + k; + + prev_delta[i][index] = curr_delta[i][index] - mean_delta[j] - + mean_delta_dot_y[j] * curr_out[i][index]; + + // stddev_ is calculated in the forward pass + prev_delta[i][index] /= stddev_[j]; + } + } + }); + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + vec_t &mean = (phase_ == net_phase::train) ? mean_current_ : mean_; + vec_t &variance = + (phase_ == net_phase::train) ? variance_current_ : variance_; + tensor_t &in = *in_data[0]; + tensor_t &out = *out_data[0]; + + if (phase_ == net_phase::train) { + // calculate mean/variance from this batch in train phase + moments(*in_data[0], in_spatial_size_, in_channels_, mean, variance); + } + + // y = (x - mean) ./ sqrt(variance + eps) + calc_stddev(variance); + + for_i(in_data[0]->size(), [&](size_t i) { + const float_t *inptr = &in[i][0]; + float_t *outptr = &out[i][0]; + + for (size_t j = 0; j < in_channels_; j++) { + float_t m = mean[j]; + + for (size_t k = 0; k < in_spatial_size_; k++) { + *outptr++ = (*inptr++ - m) / stddev_[j]; + } + } + }); + + if (phase_ == net_phase::train && update_immidiately_) { + mean_ = mean_current_; + variance_ = variance_current_; + } + } + + void set_context(net_phase ctx) override { phase_ = ctx; } + + std::string layer_type() const override { return "batch-norm"; } + + void post_update() override { + for (size_t i = 0; i < mean_.size(); i++) { + mean_[i] = momentum_ * mean_[i] + (1 - momentum_) * mean_current_[i]; + variance_[i] = + momentum_ * variance_[i] + (1 - momentum_) * variance_current_[i]; + } + } + + void save( + std::ostream &os, + const int precision = std::numeric_limits::digits10 + 2 + /*by default, we want there to be enough precision*/) const override { + Base::save(os, precision); + for (auto m : mean_) os << m << " "; + for (auto v : variance_) os << v << " "; + } + + void load(std::istream &is, + const int precision = std::numeric_limits::digits10 + 2 + /*by default, we want there to be enough precision*/) override { + Base::load(is, precision); + for (auto &m : mean_) is >> m; + for (auto &v : variance_) is >> v; + } + + void load(const std::vector &src, int &idx) override { + Base::load(src, idx); + for (auto &m : mean_) m = src[idx++]; + for (auto &v : variance_) v = src[idx++]; + } + + void update_immidiately(bool update) { update_immidiately_ = update; } + + void set_stddev(const vec_t &stddev) { stddev_ = stddev; } + + void set_mean(const vec_t &mean) { mean_ = mean; } + + void set_variance(const vec_t &variance) { + variance_ = variance; + calc_stddev(variance); + } + + float_t epsilon() const { return eps_; } + + float_t momentum() const { return momentum_; } + + friend struct serialization_buddy; + + private: + void calc_stddev(const vec_t &variance) { + for (size_t i = 0; i < in_channels_; i++) { + stddev_[i] = sqrt(variance[i] + eps_); + } + } + + void init() { + mean_current_.resize(in_channels_); + mean_.resize(in_channels_); + variance_current_.resize(in_channels_); + variance_.resize(in_channels_); + tmp_mean_.resize(in_channels_); + stddev_.resize(in_channels_); + } + + size_t in_channels_; + size_t in_spatial_size_; + + net_phase phase_; + float_t momentum_; + float_t eps_; + + // mean/variance for this mini-batch + vec_t mean_current_; + vec_t variance_current_; + + vec_t tmp_mean_; + + // moving average of mean/variance + vec_t mean_; + vec_t variance_; + vec_t stddev_; + + // for test + bool update_immidiately_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/concat_layer.h b/tiny_dnn/layers/concat_layer.h new file mode 100755 index 0000000000000..ab97d4e9c187a --- /dev/null +++ b/tiny_dnn/layers/concat_layer.h @@ -0,0 +1,111 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * concat N layers along depth + * + * @code + * // in: [3,1,1],[3,1,1] out: [3,1,2] (in W,H,K order) + * concat_layer l1(2,3); + * + * // in: [3,2,2],[3,2,5] out: [3,2,7] (in W,H,K order) + * concat_layer l2({shape3d(3,2,2),shape3d(3,2,5)}); + * @endcode + **/ +class concat_layer : public layer { + public: + /** + * @param in_shapes [in] shapes of input tensors + */ + explicit concat_layer(const std::vector &in_shapes) + : layer(std::vector(in_shapes.size(), vector_type::data), + {vector_type::data}), + in_shapes_(in_shapes) { + set_outshape(); + } + + /** + * @param num_args [in] number of input tensors + * @param ndim [in] number of elements for each input + */ + concat_layer(size_t num_args, size_t ndim) + : layer(std::vector(num_args, vector_type::data), + {vector_type::data}), + in_shapes_(std::vector(num_args, shape3d(ndim, 1, 1))) { + set_outshape(); + } + + void set_outshape() { + out_shape_ = in_shapes_.front(); + for (size_t i = 1; i < in_shapes_.size(); i++) { + if (in_shapes_[i].area() != out_shape_.area()) + throw nn_error("each input shapes to concat must have same WxH size"); + out_shape_.depth_ += in_shapes_[i].depth_; + } + } + + std::string layer_type() const override { return "concat"; } + + std::vector in_shape() const override { return in_shapes_; } + + std::vector out_shape() const override { return {out_shape_}; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + const size_t num_samples = (*out_data[0]).size(); + + for_i(num_samples, [&](size_t s) { + float_t *outs = &(*out_data[0])[s][0]; + + for (size_t i = 0; i < in_shapes_.size(); i++) { + const float_t *ins = &(*in_data[i])[s][0]; + size_t dim = in_shapes_[i].size(); + outs = std::copy(ins, ins + dim, outs); + } + }); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + + size_t num_samples = (*out_grad[0]).size(); + + for_i(num_samples, [&](size_t s) { + const float_t *outs = &(*out_grad[0])[s][0]; + + for (size_t i = 0; i < in_shapes_.size(); i++) { + size_t dim = in_shapes_[i].size(); + float_t *ins = &(*in_grad[i])[s][0]; + std::copy(outs, outs + dim, ins); + outs += dim; + } + }); + } + + friend struct serialization_buddy; + + private: + std::vector in_shapes_; + shape3d out_shape_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/convolutional_layer.h b/tiny_dnn/layers/convolutional_layer.h new file mode 100755 index 0000000000000..aba35ff8bebd0 --- /dev/null +++ b/tiny_dnn/layers/convolutional_layer.h @@ -0,0 +1,520 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include + +#include "tiny_dnn/core/kernels/conv2d_grad_op.h" +#include "tiny_dnn/core/kernels/conv2d_op.h" +#include "tiny_dnn/core/kernels/conv2d_op_libdnn.h" +#include "tiny_dnn/core/kernels/conv2d_op_opencl.h" + +#include "tiny_dnn/util/util.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif // DNN_USE_IMAGE_API + +namespace tiny_dnn { + +/** + * 2D convolution layer + * + * take input as two-dimensional *image* and applying filtering operation. + **/ +class convolutional_layer : public layer { + public: + /** + * constructing convolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_size [in] window(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param padding [in] rounding strategy + * - valid: use valid pixels of input only. + *```output-size = (in-width - window_width + 1) * + *(in-height - window_height + 1) * out_channels``` + * - same: add zero-padding to keep same + *width/height. + *```output-size = in-width * in-height * + *out_channels``` + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to apply + *the filters to the input + * @param h_stride [in] specify the vertical interval at which to apply + *the + *filters to the input + * @param backend_type [in] specify backend engine you use + **/ + convolutional_layer(size_t in_width, + size_t in_height, + size_t window_size, + size_t in_channels, + size_t out_channels, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::default_engine()) + : convolutional_layer(in_width, + in_height, + window_size, + window_size, + in_channels, + out_channels, + core::connection_table(), + pad_type, + has_bias, + w_stride, + h_stride, + backend_type) {} + + /** + * constructing convolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_width [in] window_width(kernel) size of convolution + * @param window_height [in] window_height(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param padding [in] rounding strategy + * - valid: use valid pixels of input only. + *```output-size = (in-width - window_width + 1) * + *(in-height - window_height + 1) * out_channels``` + * - same: add zero-padding to keep same + *width/height. ```output-size = in-width * in-height + ** out_channels``` + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply + *the filters to the input + * @param h_stride [in] specify the vertical interval at which to apply + *the filters to the input + * @param backend_type [in] specify backend engine you use + **/ + convolutional_layer(size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t in_channels, + size_t out_channels, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::default_engine()) + : convolutional_layer(in_width, + in_height, + window_width, + window_height, + in_channels, + out_channels, + core::connection_table(), + pad_type, + has_bias, + w_stride, + h_stride, + backend_type) {} + + /** + * constructing convolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_size [in] window(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param connection_table [in] definition of connections between in-channels + *and out-channels + * @param pad_type [in] rounding strategy + * - valid: use valid pixels of input only. + *```output-size = (in-width - window_width + 1) + ** (in-height - window_height + 1) * out_channels``` + * - same: add zero-padding to keep same + *width/height. ```output-size = in-width * + *in-height * out_channels``` + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply the filters to the input + * @param h_stride [in] specify the vertical interval at which to + *apply + *the filters to the input + * @param backend_type [in] specify backend engine you use + **/ + convolutional_layer(size_t in_width, + size_t in_height, + size_t window_size, + size_t in_channels, + size_t out_channels, + const core::connection_table &connection_table, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::default_engine()) + : convolutional_layer(in_width, + in_height, + window_size, + window_size, + in_channels, + out_channels, + connection_table, + pad_type, + has_bias, + w_stride, + h_stride, + backend_type) {} + + /** + * constructing convolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_width [in] window_width(kernel) size of convolution + * @param window_height [in] window_height(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param connection_table [in] definition of connections between in-channels + *and out-channels + * @param pad_type [in] rounding strategy + * - valid: use valid pixels of input only. + *```output-size = (in-width - window_width + 1) + ** (in-height - window_height + 1) * out_channels``` + * - same: add zero-padding to keep same + *width/height. ```output-size = in-width * + *in-height * out_channels``` + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply the filters to the input + * @param h_stride [in] specify the vertical interval at which to + *apply + *the filters to the input + * @param backend_type [in] specify backend engine you use + **/ + convolutional_layer(size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t in_channels, + size_t out_channels, + const core::connection_table &connection_table, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::default_engine()) + : layer(std_input_order(has_bias), {vector_type::data}) { + conv_set_params(shape3d(in_width, in_height, in_channels), window_width, + window_height, out_channels, pad_type, has_bias, w_stride, + h_stride, connection_table); + init_backend(backend_type); + layer::set_backend_type(backend_type); + } + + // move constructor + convolutional_layer(convolutional_layer &&other) // NOLINT + : layer(std::move(other)), + params_(std::move(other.params_)), + padding_op_(std::move(other.padding_op_)), + kernel_fwd_(std::move(other.kernel_fwd_)), + kernel_back_(std::move(other.kernel_back_)), + cws_(std::move(other.cws_)) { + init_backend(std::move(other.engine())); + } + + ///< number of incoming connections for each output unit + size_t fan_in_size() const override { + return params_.weight.width_ * params_.weight.height_ * params_.in.depth_; + } + + ///< number of outgoing connections for each input unit + size_t fan_out_size() const override { + return (params_.weight.width_ / params_.w_stride) * + (params_.weight.height_ / params_.h_stride) * params_.out.depth_; + } + + /** + * @param in_data input vectors of this layer (data, weight, bias) + * @param out_data output vectors + **/ + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + // apply padding to the input tensor + padding_op_.copy_and_pad_input(*in_data[0], cws_.prev_out_padded_); + + fwd_in_data_.resize(in_data.size()); + std::copy(in_data.begin(), in_data.end(), fwd_in_data_.begin()); + fwd_in_data_[0] = in_data_padded(in_data); + + // forward convolutional op context + fwd_ctx_.set_in_out(fwd_in_data_, out_data); + fwd_ctx_.setParallelize(layer::parallelize()); + fwd_ctx_.setEngine(layer::engine()); + + // launch convolutional kernel + kernel_fwd_->compute(fwd_ctx_); + } + + /** + * return delta of previous layer (delta=\frac{dE}{da}, a=wx in + *fully-connected layer) + * @param in_data input vectors (same vectors as forward_propagation) + * @param out_data output vectors (same vectors as forward_propagation) + * @param out_grad gradient of output vectors (i-th vector correspond + *with + *out_data[i]) + * @param in_grad gradient of input vectors (i-th vector correspond + *with + *in_data[i]) + **/ + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + bwd_in_data_.resize(in_data.size()); + std::copy(in_data.begin(), in_data.end(), bwd_in_data_.begin()); + bwd_in_data_[0] = in_data_padded(in_data); + + bwd_in_grad_.resize(in_grad.size()); + std::copy(in_grad.begin(), in_grad.end(), bwd_in_grad_.begin()); + if (params_.pad_type == padding::same) { + bwd_in_grad_[0] = &cws_.prev_delta_padded_; + } + + bwd_ctx_.set_in_out(bwd_in_data_, out_data, out_grad, bwd_in_grad_); + bwd_ctx_.setParams(¶ms_); + bwd_ctx_.setParallelize(layer::parallelize()); + bwd_ctx_.setEngine(layer::engine()); + + // launch convolutional kernel + kernel_back_->compute(bwd_ctx_); + + // unpad deltas + padding_op_.copy_and_unpad_delta(cws_.prev_delta_padded_, *in_grad[0]); + } + + void set_sample_count(size_t sample_count) override { + layer::set_sample_count(sample_count); + cws_.prev_delta_padded_.resize(sample_count, + vec_t(params_.in_padded.size(), float_t(0))); + } + + std::vector> in_shape() const override { + if (params_.has_bias) { + return {params_.in, params_.weight, + index3d(1, 1, params_.out.depth_)}; + } else { + return {params_.in, params_.weight}; + } + } + + std::vector> out_shape() const override { + return {params_.out}; + } + + std::string layer_type() const override { return std::string("conv"); } + + // TODO(edgar): check this + std::string kernel_file() const override { + return std::string( + "../tiny_cnn/core/kernels/cl_kernels/conv_layer_spatial.cl"); + } + + // TODO(edgar): is it really needed? + std::string kernel_header() const override { + std::stringstream ss; + ss << "#define MULTI\n"; + ss << "#define KERNEL_H " << params_.weight.height_ << "\n"; + ss << "#define KERNEL_W " << params_.weight.width_ << "\n"; + ss << "#define CHANNELS " << params_.weight.depth_ << "\n"; + ss << "#define STRIDE_H " << params_.h_stride << "\n"; + ss << "#define STRIDE_W " << params_.w_stride << "\n"; + ss << "#define APPLY_BIAS " << params_.has_bias << "\n"; + ss << "#define OUTPUT_Z " << params_.out.depth_ << "\n"; + // TODO(edgar): REVISE THIS + ss << "#define ZPAR " << params_.out.depth_ << "\n"; + return ss.str(); + } + +#ifdef DNN_USE_IMAGE_API + image<> weight_to_image() const { + image<> img; + const size_t border_width = 1; + const auto pitch = params_.weight.width_ + border_width; + const auto width = params_.out.depth_ * pitch + border_width; + const auto height = params_.in.depth_ * pitch + border_width; + const image<>::intensity_t bg_color = 255; + const vec_t &W = *this->weights()[0]; + + img.resize(width, height); + img.fill(bg_color); + + auto minmax = std::minmax_element(W.begin(), W.end()); + + for (size_t r = 0; r < params_.in.depth_; ++r) { + for (size_t c = 0; c < params_.out.depth_; ++c) { + if (!params_.tbl.is_connected(c, r)) continue; + + const auto top = r * pitch + border_width; + const auto left = c * pitch + border_width; + + size_t idx = 0; + + for (size_t y = 0; y < params_.weight.height_; ++y) { + for (size_t x = 0; x < params_.weight.width_; ++x) { + idx = c * params_.in.depth_ + r; + idx = params_.weight.get_index(x, y, idx); + const float_t w = W[idx]; + + img.at(left + x, top + y) = static_cast::intensity_t>( + rescale(w, *minmax.first, *minmax.second, 0, 255)); + } + } + } + } + return img; + } +#endif // DNN_USE_IMAGE_API + + friend struct serialization_buddy; + + private: + tensor_t *in_data_padded(const std::vector &in) { + return (params_.pad_type == padding::valid) ? in[0] + : &cws_.prev_out_padded_; + } + + void conv_set_params( + const shape3d &in, + size_t w_width, + size_t w_height, + size_t outc, + padding ptype, + bool has_bias, + size_t w_stride, + size_t h_stride, + const core::connection_table &tbl = core::connection_table()) { + params_.in = in; + params_.in_padded = + shape3d(in_length(in.width_, w_width, ptype), + in_length(in.height_, w_height, ptype), in.depth_); + params_.out = + shape3d(conv_out_length(in.width_, w_width, w_stride, ptype), + conv_out_length(in.height_, w_height, h_stride, ptype), outc); + params_.weight = shape3d(w_width, w_height, in.depth_ * outc); + params_.has_bias = has_bias; + params_.pad_type = ptype; + params_.w_stride = w_stride; + params_.h_stride = h_stride; + params_.tbl = tbl; + + // init padding buffer + if (params_.pad_type == padding::same) { + cws_.prev_delta_padded_.resize( + 1, vec_t(params_.in_padded.size(), float_t(0))); + } + + // set parameters to padding operation + padding_op_ = core::Conv2dPadding(params_); + } + + size_t in_length(size_t in_length, + size_t window_size, + padding pad_type) const { + return pad_type == padding::same ? (in_length + window_size - 1) + : in_length; + } + + static size_t conv_out_dim(size_t in_width, + size_t in_height, + size_t window_size, + size_t w_stride, + size_t h_stride, + padding pad_type) { + return conv_out_length(in_width, window_size, w_stride, pad_type) * + conv_out_length(in_height, window_size, h_stride, pad_type); + } + + size_t conv_out_dim(size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t w_stride, + size_t h_stride, + padding pad_type) const { + return conv_out_length(in_width, window_width, w_stride, pad_type) * + conv_out_length(in_height, window_height, h_stride, pad_type); + } + + void createOp() override { init_backend(layer::engine()); } + + void init_backend(const core::backend_t backend_type) { + core::OpKernelConstruction ctx = + core::OpKernelConstruction(layer::device(), ¶ms_); + + if (backend_type == core::backend_t::internal || + backend_type == core::backend_t::nnpack || + backend_type == core::backend_t::avx) { + kernel_fwd_.reset(new Conv2dOp(ctx)); + kernel_back_.reset(new Conv2dGradOp(ctx)); + return; + } else if (backend_type == core::backend_t::opencl) { + throw nn_error("Not implemented engine: " + to_string(backend_type)); + /*kernel_fwd_.reset(new Conv2dOpenCLForwardOp(ctx)); + kernel_back_.reset(new Conv2dOpenCLBackwardOp(ctx)); + return;*/ + } else if (backend_type == core::backend_t::libdnn) { + if (layer::device() == nullptr) return; + kernel_fwd_.reset(new Conv2dLibDNNForwardOp(ctx)); + kernel_back_.reset(new Conv2dLibDNNBackwardOp(ctx)); + return; + } else { + throw nn_error("Not supported engine: " + to_string(backend_type)); + } + } + + private: + /* The convolution parameters */ + core::conv_params params_; + + /* Padding operation */ + core::Conv2dPadding padding_op_; + + /* forward op context */ + core::OpKernelContext fwd_ctx_; + + /* backward op context */ + core::OpKernelContext bwd_ctx_; + + /* Forward and backward ops */ + std::shared_ptr kernel_fwd_; + std::shared_ptr kernel_back_; + + std::vector fwd_in_data_; + std::vector bwd_in_data_; + std::vector bwd_in_grad_; + + /* Buffer to store padded data */ + struct conv_layer_worker_specific_storage { + tensor_t prev_out_padded_; + tensor_t prev_delta_padded_; + } cws_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/deconvolutional_layer.h b/tiny_dnn/layers/deconvolutional_layer.h new file mode 100755 index 0000000000000..975d8cd2d6b85 --- /dev/null +++ b/tiny_dnn/layers/deconvolutional_layer.h @@ -0,0 +1,500 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/core/backend_tiny.h" +#ifdef CNN_USE_AVX +#include "tiny_dnn/core/backend_avx.h" +#endif // CNN_USE_AVX + +#include "tiny_dnn/util/util.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif // DNN_USE_IMAGE_API + +namespace tiny_dnn { + +/** + * 2D deconvolution layer + * + * take input as two-dimensional *image* and applying filtering operation. + **/ +class deconvolutional_layer : public layer { + public: + /** + * constructing deconvolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_size [in] window(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param padding [in] rounding strategy + * valid: use valid pixels of input only. + *output-size + *= (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same width/height. + *output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to apply + *the filters to the input + * @param h_stride [in] specify the vertical interval at which to apply + *the + *filters to the input + **/ + deconvolutional_layer(size_t in_width, + size_t in_height, + size_t window_size, + size_t in_channels, + size_t out_channels, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::default_engine()) + : layer(std_input_order(has_bias), {vector_type::data}) { + deconv_set_params(shape3d(in_width, in_height, in_channels), window_size, + window_size, out_channels, pad_type, has_bias, w_stride, + h_stride); + init_backend(backend_type); + } + + /** + * constructing deconvolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_width [in] window_width(kernel) size of convolution + * @param window_height [in] window_height(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param padding [in] rounding strategy + * valid: use valid pixels of input only. + *output-size + *= (in-width - window_width + 1) * + *(in-height - window_height + 1) * out_channels + * same: add zero-padding to keep same width/height. + *output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to apply + *the filters to the input + * @param h_stride [in] specify the vertical interval at which to apply + *the + *filters to the input + **/ + deconvolutional_layer(size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t in_channels, + size_t out_channels, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::default_engine()) + : layer(std_input_order(has_bias), {vector_type::data}) { + deconv_set_params(shape3d(in_width, in_height, in_channels), window_width, + window_height, out_channels, pad_type, has_bias, w_stride, + h_stride); + init_backend(backend_type); + } + + /** + * constructing deconvolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_size [in] window(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param connection_table [in] definition of connections between in-channels + *and out-channels + * @param pad_type [in] rounding strategy + * valid: use valid pixels of input only. + *output-size = (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same + *width/height. output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply the filters to the input + * @param h_stride [in] specify the vertical interval at which to + *apply + *the filters to the input + **/ + deconvolutional_layer(size_t in_width, + size_t in_height, + size_t window_size, + size_t in_channels, + size_t out_channels, + const core::connection_table &connection_table, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::default_engine()) + : layer(std_input_order(has_bias), {vector_type::data}) { + deconv_set_params(shape3d(in_width, in_height, in_channels), window_size, + window_size, out_channels, pad_type, has_bias, w_stride, + h_stride, connection_table); + init_backend(backend_type); + } + + /** + * constructing deconvolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_width [in] window_width(kernel) size of convolution + * @param window_height [in] window_height(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param connection_table [in] definition of connections between in-channels + *and out-channels + * @param pad_type [in] rounding strategy + * valid: use valid pixels of input only. + *output-size = (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same + *width/height. output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply the filters to the input + * @param h_stride [in] specify the vertical interval at which to + *apply + *the filters to the input + **/ + deconvolutional_layer(size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t in_channels, + size_t out_channels, + const core::connection_table &connection_table, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::default_engine()) + : layer(std_input_order(has_bias), {vector_type::data}) { + deconv_set_params(shape3d(in_width, in_height, in_channels), window_width, + window_height, out_channels, pad_type, has_bias, w_stride, + h_stride, connection_table); + init_backend(backend_type); + } + + // move constructor + deconvolutional_layer(deconvolutional_layer &&other) + : layer(std::move(other)), + params_(std::move(other.params_)), + backend_type_(std::move(other.backend_type_)), + deconv_layer_worker_storage_( + std::move(other.deconv_layer_worker_storage_)) { + init_backend(std::move(layer::backend_type())); + } + + ///< number of incoming connections for each output unit + size_t fan_in_size() const override { + return params_.weight.width_ * params_.weight.height_ * params_.in.depth_; + } + + ///< number of outgoing connections for each input unit + size_t fan_out_size() const override { + return (params_.weight.width_ * params_.w_stride) * + (params_.weight.height_ * params_.h_stride) * params_.out.depth_; + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + // launch deconvolutional kernel + layer::backend_->deconv2d(in_data, out_data); + } + + /** + * return delta of previous layer (delta=\frac{dE}{da}, a=wx in + *fully-connected layer) + * @param worker_index id of current worker-task + * @param in_data input vectors (same vectors as forward_propagation) + * @param out_data output vectors (same vectors as forward_propagation) + * @param out_grad gradient of output vectors (i-th vector correspond + *with + *out_data[i]) + * @param in_grad gradient of input vectors (i-th vector correspond + *with + *in_data[i]) + **/ + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + layer::backend_->deconv2d(in_data, out_data, out_grad, in_grad); + } + + std::vector> in_shape() const override { + if (params_.has_bias) { + return {params_.in, params_.weight, + index3d(1, 1, params_.out.depth_)}; + } else { + return {params_.in, params_.weight}; + } + } + + std::vector> out_shape() const override { + return {params_.out_unpadded}; + } + + std::string layer_type() const override { return "deconv"; } + +#ifdef DNN_USE_IMAGE_API + image<> weight_to_image() const { + image<> img; + const size_t border_width = 1; + const auto pitch = params_.weight.width_ + border_width; + const auto width = params_.out.depth_ * pitch + border_width; + const auto height = params_.in.depth_ * pitch + border_width; + const image<>::intensity_t bg_color = 255; + const vec_t &W = *this->weights()[0]; + + img.resize(width, height); + img.fill(bg_color); + + auto minmax = std::minmax_element(W.begin(), W.end()); + + for (size_t r = 0; r < params_.in.depth_; ++r) { + for (size_t c = 0; c < params_.out.depth_; ++c) { + if (!params_.tbl.is_connected(c, r)) continue; + + const auto top = r * pitch + border_width; + const auto left = c * pitch + border_width; + + size_t idx = 0; + + for (size_t y = 0; y < params_.weight.height_; ++y) { + for (size_t x = 0; x < params_.weight.width_; ++x) { + idx = params_.weight.get_index(x, y, c * params_.in.depth_ + r); + const float_t w = W[idx]; + + img.at(left + x, top + y) = static_cast::intensity_t>( + rescale(w, *minmax.first, *minmax.second, 0, 255)); + } + } + } + } + return img; + } +#endif // DNN_USE_IMAGE_API + + friend struct serialization_buddy; + + private: + void init_backend(const core::backend_t backend_type) { + std::shared_ptr backend = nullptr; + + // allocate new backend + if (backend_type == core::backend_t::internal) { + backend = std::make_shared( + ¶ms_, + [this](const tensor_t &in) { return copy_and_unpad_output(in); }, + [this](const tensor_t &delta, tensor_t &dst) { + return copy_and_pad_delta(delta, dst); + }, + &deconv_layer_worker_storage_); +#ifdef CNN_USE_AVX + } else if (backend_type == core::backend_t::avx) { + backend = std::make_shared( + ¶ms_, + [this](const tensor_t &in) { return copy_and_unpad_output(in); }, + [this](const tensor_t &delta, tensor_t &dst) { + return copy_and_pad_delta(delta, dst); + }, + &deconv_layer_worker_storage_); +#endif + } else { + throw nn_error("Not supported backend type."); + } + + if (backend) { + layer::set_backend(backend); + layer::backend_->set_layer(this); + } else { + throw nn_error("Could not allocate the backend."); + } + } + + void deconv_set_params( + const shape3d &in, + size_t w_width, + size_t w_height, + size_t outc, + padding ptype, + bool has_bias, + size_t w_stride, + size_t h_stride, + const core::connection_table &tbl = core::connection_table()) { + params_.in = in; + params_.out = + shape3d(deconv_out_length(in.width_, w_width, w_stride), + deconv_out_length(in.height_, w_height, h_stride), outc); + params_.out_unpadded = shape3d( + deconv_out_unpadded_length(in.width_, w_width, w_stride, ptype), + deconv_out_unpadded_length(in.height_, w_height, h_stride, ptype), outc); + params_.weight = shape3d(w_width, w_height, in.depth_ * outc); + params_.has_bias = has_bias; + params_.pad_type = ptype; + params_.w_stride = w_stride; + params_.h_stride = h_stride; + params_.tbl = tbl; + } + + void init_workers(size_t sample_count) { + core::deconv_layer_worker_specific_storage &dws = + deconv_layer_worker_storage_; + + if (params_.pad_type == padding::same) { + dws.curr_out_buf_.resize(sample_count, + vec_t(params_.out_unpadded.size(), float_t{0})); + dws.curr_delta_padded.resize(sample_count, + vec_t(params_.out.size(), float_t{0})); + } else { + dws.curr_out_buf_.clear(); + } + } + + size_t in_length(size_t in_length, + size_t window_size, + padding pad_type) const { + CNN_UNREFERENCED_PARAMETER(window_size); + CNN_UNREFERENCED_PARAMETER(pad_type); + return in_length; + } + + static size_t deconv_out_length(size_t in_length, + size_t window_size, + size_t stride) { + return (size_t)ceil((float_t)(in_length)*stride + window_size - 1); + } + + static size_t deconv_out_unpadded_length(size_t in_length, + size_t window_size, + size_t stride, + padding pad_type) { + return pad_type == padding::same + ? (size_t)ceil((float_t)in_length * stride) + : (size_t)ceil((float_t)(in_length)*stride + window_size - 1); + } + + static size_t deconv_out_dim(size_t in_width, + size_t in_height, + size_t window_size, + size_t w_stride, + size_t h_stride, + padding pad_type) { + return deconv_out_unpadded_length(in_width, window_size, w_stride, + pad_type) * + deconv_out_unpadded_length(in_height, window_size, h_stride, + pad_type); + } + + size_t deconv_out_dim(size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t w_stride, + size_t h_stride, + padding pad_type) const { + return deconv_out_unpadded_length(in_width, window_width, w_stride, + pad_type) * + deconv_out_unpadded_length(in_height, window_height, h_stride, + pad_type); + } + + void copy_and_pad_delta(const tensor_t &delta, tensor_t &delta_padded) { + if (params_.pad_type == padding::valid) { + delta_padded = delta; + } else { + for (size_t sample = 0; sample < delta.size(); sample++) { + vec_t &dst = delta_padded[sample]; + const vec_t &src = delta[sample]; + + for (size_t c = 0; c < params_.in.depth_; c++) { + float_t *pdst = &dst[params_.in.get_index(0, 0, c)]; + const float_t *pin = &src[params_.in.get_index(0, 0, c)]; + + for (size_t y = 0; y < params_.in.height_; + y++, pdst += params_.in.width_, pin += params_.in.width_) { + std::copy(pin, pin + params_.in.width_, pdst); + } + } + } + } + } + + void copy_and_unpad_output(const tensor_t &out) { + core::deconv_layer_worker_specific_storage &dws = + deconv_layer_worker_storage_; + + dws.curr_out_buf_ = + tensor_t(out.size(), vec_t(params_.out_unpadded.size(), 0)); + tensor_t *dst_tensor = &dws.curr_out_buf_; + + if (params_.pad_type == padding::valid) { + dws.curr_out_unpadded_ = &out; + } else { + // make unpadded version in order to restore scale in fprop/bprop + for (size_t sample = 0; sample < out.size(); sample++) { + size_t idx = 0; + vec_t &dst = (*dst_tensor)[sample]; + size_t wieght_w_half = params_.weight.width_ / 2; + size_t wieght_h_half = params_.weight.height_ / 2; + + for (size_t c = 0; c < params_.out_unpadded.depth_; c++) { + float_t *pimg = &dst[params_.out_unpadded.get_index(0, 0, c)]; + idx = params_.out.get_index(wieght_w_half, wieght_h_half, c); + const float_t *pout = &out[sample][idx]; + + for (size_t y = wieght_h_half; + y < params_.out_unpadded.height_ + wieght_h_half; + y++, pout += params_.out.width_, + pimg += params_.out_unpadded.width_) { + std::copy(pout, pout + params_.out_unpadded.width_, pimg); + } + } + } + + dws.curr_out_unpadded_ = &dws.curr_out_buf_; + } + } + + /* The convolution parameters */ + core::deconv_params params_; + + /* The type of backend */ + core::backend_t backend_type_; + + core::deconv_layer_worker_specific_storage deconv_layer_worker_storage_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/dropout_layer.h b/tiny_dnn/layers/dropout_layer.h new file mode 100755 index 0000000000000..38b488b099c3f --- /dev/null +++ b/tiny_dnn/layers/dropout_layer.h @@ -0,0 +1,147 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * applies dropout to the input + **/ +class dropout_layer : public layer { + public: + /** + * @param in_dim [in] number of elements of the input + * @param dropout_rate [in] (0-1) fraction of the input units to be dropped + * @param phase [in] initial state of the dropout + **/ + dropout_layer(size_t in_dim, + float_t dropout_rate, + net_phase phase = net_phase::train) + : layer({vector_type::data}, {vector_type::data}), + phase_(phase), + dropout_rate_(dropout_rate), + scale_(float_t(1) / (float_t(1) - dropout_rate_)), + in_size_(in_dim) { + mask_.resize(1, std::vector(in_dim)); + clear_mask(); + } + + dropout_layer(const dropout_layer &obj) = default; + virtual ~dropout_layer() {} + + dropout_layer(dropout_layer &&obj) = default; + dropout_layer &operator=(const dropout_layer &obj) = default; + dropout_layer &operator=(dropout_layer &&obj) = default; + + void set_dropout_rate(float_t rate) { + dropout_rate_ = rate; + scale_ = float_t(1) / (float_t(1) - dropout_rate_); + } + + float_t dropout_rate() const { return dropout_rate_; } + + ///< number of incoming connections for each output unit + size_t fan_in_size() const override { return 1; } + + ///< number of outgoing connections for each input unit + size_t fan_out_size() const override { return 1; } + + std::vector> in_shape() const override { + return {index3d(in_size_, 1, 1)}; + } + + std::vector> out_shape() const override { + return {index3d(in_size_, 1, 1)}; + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + tensor_t &prev_delta = *in_grad[0]; + const tensor_t &curr_delta = *out_grad[0]; + + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + + for_i(prev_delta.size(), [&](size_t sample) { + // assert(prev_delta[sample].size() == curr_delta[sample].size()); + // assert(mask_[sample].size() == prev_delta[sample].size()); + size_t sz = prev_delta[sample].size(); + for (size_t i = 0; i < sz; ++i) { + prev_delta[sample][i] = mask_[sample][i] * curr_delta[sample][i]; + } + }); + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + const tensor_t &in = *in_data[0]; + tensor_t &out = *out_data[0]; + + const size_t sample_count = in.size(); + + if (mask_.size() < sample_count) { + mask_.resize(sample_count, mask_[0]); + } + + for_i(sample_count, [&](size_t sample) { + std::vector &mask = mask_[sample]; + + const vec_t &in_vec = in[sample]; + vec_t &out_vec = out[sample]; + + if (phase_ == net_phase::train) { + for (size_t i = 0; i < in_vec.size(); i++) + mask[i] = bernoulli(dropout_rate_); + + for (size_t i = 0; i < in_vec.size(); i++) + out_vec[i] = mask[i] * scale_ * in_vec[i]; + } else { + for (size_t i = 0, end = in_vec.size(); i < end; i++) + out_vec[i] = in_vec[i]; + } + }); + } + + /** + * set dropout-context (training-phase or test-phase) + **/ + void set_context(net_phase ctx) override { phase_ = ctx; } + + std::string layer_type() const override { return "dropout"; } + + // currently used by tests only + const std::vector &get_mask(size_t sample_index) const { + return mask_[sample_index]; + } + + void clear_mask() { + for (auto &sample : mask_) { + std::fill(sample.begin(), sample.end(), 0); + } + } + + friend struct serialization_buddy; + + private: + net_phase phase_; + float_t dropout_rate_; + float_t scale_; + size_t in_size_; + std::vector> mask_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/fully_connected_layer.h b/tiny_dnn/layers/fully_connected_layer.h new file mode 100755 index 0000000000000..6ccae9af76312 --- /dev/null +++ b/tiny_dnn/layers/fully_connected_layer.h @@ -0,0 +1,134 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" + +#include "tiny_dnn/core/kernels/fully_connected_grad_op.h" +#include "tiny_dnn/core/kernels/fully_connected_op.h" + +namespace tiny_dnn { + +/** + * compute fully-connected(matmul) operation + **/ +class fully_connected_layer : public layer { + public: + /** + * @param in_dim [in] number of elements of the input + * @param out_dim [in] number of elements of the output + * @param has_bias [in] whether to include additional bias to the layer + **/ + fully_connected_layer(size_t in_dim, + size_t out_dim, + bool has_bias = true, + core::backend_t backend_type = core::default_engine()) + : layer(std_input_order(has_bias), {vector_type::data}) { + set_params(in_dim, out_dim, has_bias); + init_backend(backend_type); + layer::set_backend_type(backend_type); + } + + // move constructor + fully_connected_layer(fully_connected_layer &&other) + : layer(std::move(other)), + params_(std::move(other.params_)), + kernel_fwd_(std::move(other.kernel_fwd_)), + kernel_back_(std::move(other.kernel_back_)) { + init_backend(std::move(other.engine())); + } + + size_t fan_in_size() const override { return params_.in_size_; } + + size_t fan_out_size() const override { return params_.out_size_; } + + std::vector> in_shape() const override { + if (params_.has_bias_) { + return {index3d(params_.in_size_, 1, 1), + index3d(params_.in_size_, params_.out_size_, 1), + index3d(params_.out_size_, 1, 1)}; + } else { + return {index3d(params_.in_size_, 1, 1), + index3d(params_.in_size_, params_.out_size_, 1)}; + } + } + + std::vector> out_shape() const override { + return {index3d(params_.out_size_, 1, 1)}; + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + // forward fully connected op context + fwd_ctx_.set_in_out(in_data, out_data); + fwd_ctx_.setParallelize(layer::parallelize()); + fwd_ctx_.setEngine(layer::engine()); + + // launch fully connected kernel + kernel_fwd_->compute(fwd_ctx_); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + // backward fully connected op context + bwd_ctx_.set_in_out(in_data, out_data, out_grad, in_grad); + bwd_ctx_.setParallelize(layer::parallelize()); + bwd_ctx_.setEngine(layer::engine()); + + // launch fully connected kernel + kernel_back_->compute(bwd_ctx_); + } + + std::string layer_type() const override { return "fully-connected"; } + + friend struct serialization_buddy; + + protected: + void set_params(const size_t in_size, const size_t out_size, bool has_bias) { + params_.in_size_ = in_size; + params_.out_size_ = out_size; + params_.has_bias_ = has_bias; + } + + void init_backend(core::backend_t backend_type) { + core::OpKernelConstruction ctx = + core::OpKernelConstruction(layer::device(), ¶ms_); + + if (backend_type == core::backend_t::internal || + backend_type == core::backend_t::avx || + backend_type == core::backend_t::nnpack) { + kernel_fwd_.reset(new FullyConnectedOp(ctx)); + kernel_back_.reset(new FullyConnectedGradOp(ctx)); + } else { + throw nn_error("Not supported engine: " + to_string(backend_type)); + } + } + + private: + /* The layer parameters */ + core::fully_params params_; + + /* forward op context */ + core::OpKernelContext fwd_ctx_; + + /* backward op context */ + core::OpKernelContext bwd_ctx_; + + /* Forward and backward ops */ + std::shared_ptr kernel_fwd_; + std::shared_ptr kernel_back_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/global_average_pooling_layer.h b/tiny_dnn/layers/global_average_pooling_layer.h new file mode 100755 index 0000000000000..c014343f19e2e --- /dev/null +++ b/tiny_dnn/layers/global_average_pooling_layer.h @@ -0,0 +1,139 @@ +/* + Copyright (c) 2015, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +#include "tiny_dnn/core/kernels/global_avepool_grad_op.h" +#include "tiny_dnn/core/kernels/global_avepool_op.h" + +namespace tiny_dnn { + +/** + * applies channel-wise global average pooling to spatial data. + **/ +class global_average_pooling_layer : public layer { + public: + global_average_pooling_layer( + const shape3d &in_shape, + core::backend_t backend_type = core::default_engine()) + : global_average_pooling_layer( + in_shape.width_, in_shape.height_, in_shape.depth_, backend_type) {} + + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels (depth) + **/ + global_average_pooling_layer( + size_t in_width, + size_t in_height, + size_t in_channels, + core::backend_t backend_type = core::default_engine()) + : layer({vector_type::data}, {vector_type::data}) { + set_global_avepool_params(shape3d(in_width, in_height, in_channels), + shape3d(in_channels, 1, 1)); + + init_backend(backend_type); + } + + // move constructor + global_average_pooling_layer(global_average_pooling_layer &&other) // NOLINT + : layer(std::move(other)), params_(std::move(other.params_)) { + init_backend(std::move(layer::engine())); + } + + size_t fan_in_size() const override { + return params_.in.width_ * params_.in.height_; + } + + size_t fan_out_size() const override { return 1; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + fwd_ctx_.set_in_out(in_data, out_data); + fwd_ctx_.setParallelize(layer::parallelize()); + fwd_ctx_.setEngine(layer::engine()); + + kernel_fwd_->compute(fwd_ctx_); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + bwd_ctx_.set_in_out(in_data, out_data, out_grad, in_grad); + bwd_ctx_.setParallelize(layer::parallelize()); + bwd_ctx_.setEngine(layer::engine()); + + kernel_back_->compute(bwd_ctx_); + } + + std::vector> in_shape() const override { + return {params_.in}; + } + + std::vector> out_shape() const override { + return {params_.out}; + } + + std::string layer_type() const override { + return std::string("global-ave-pool"); + } + + std::pair pool_size() const { + return std::make_pair(params_.in.width_, params_.in.height_); + } + + friend struct serialization_buddy; + + private: + core::global_avepool_params params_; + + /* forward op context */ + core::OpKernelContext fwd_ctx_; + + /* backward op context */ + core::OpKernelContext bwd_ctx_; + + /* Forward and backward ops */ + std::shared_ptr kernel_fwd_; + std::shared_ptr kernel_back_; + + void init_backend(core::backend_t backend_type) { + core::OpKernelConstruction ctx = + core::OpKernelConstruction(layer::device(), ¶ms_); + + layer::set_backend_type(backend_type); + if (backend_type == core::backend_t::avx) { +#ifndef CNN_USE_AVX + nn_warn( + "tiny-dnn has not been compiled with AVX support, " + "fallback to internal backend for global avepool layer.\n"); + layer::set_backend_type(core::backend_t::internal); +#endif + } + kernel_fwd_.reset(new GlobalAvePoolOp(ctx)); + kernel_back_.reset(new GlobalAvePoolGradOp(ctx)); + return; + } + + void set_global_avepool_params(const shape3d &in, const shape3d &out) { + params_.in = in; + params_.out = out; + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/input_layer.h b/tiny_dnn/layers/input_layer.h new file mode 100755 index 0000000000000..75c53b2b64ee3 --- /dev/null +++ b/tiny_dnn/layers/input_layer.h @@ -0,0 +1,52 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +class input_layer : public layer { + public: + explicit input_layer(const shape3d &shape) + : layer({vector_type::data}, {vector_type::data}), shape_(shape) {} + + explicit input_layer(size_t in_dim) + : layer({vector_type::data}, {vector_type::data}), + shape_(shape3d(in_dim, 1, 1)) {} + + std::vector in_shape() const override { return {shape_}; } + std::vector out_shape() const override { return {shape_}; } + std::string layer_type() const override { return "input"; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + *out_data[0] = *in_data[0]; + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + // do nothing + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(out_grad); + CNN_UNREFERENCED_PARAMETER(in_grad); + } + + friend struct serialization_buddy; + + private: + shape3d shape_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/layer.h b/tiny_dnn/layers/layer.h new file mode 100755 index 0000000000000..0508d2084b98e --- /dev/null +++ b/tiny_dnn/layers/layer.h @@ -0,0 +1,996 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/core/backend.h" +#include "tiny_dnn/core/framework/device.fwd.h" +#include "tiny_dnn/node.h" + +#include "tiny_dnn/util/parallel_for.h" +#include "tiny_dnn/util/product.h" +#include "tiny_dnn/util/util.h" +#include "tiny_dnn/util/weight_init.h" + +#include "tiny_dnn/optimizers/optimizer.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif + +namespace tiny_dnn { + +/** + * base class of all kind of NN layers + * + * sub-class should override these methods: + * - forward_propagation ... body of forward-pass calculation + * - back_propagation ... body of backward-pass calculation + * - in_shape ... specify input data shapes + * - out_shape ... specify output data shapes + * - layer_type ... name of layer + **/ +class layer : public node { + public: + friend void connection_mismatch(const layer &from, const layer &to); + + virtual ~layer() = default; + + /** + * @brief Defaul layer constructor that instantiates a N-input, M-output + *layer + * + * @param in_type[N] type of input vector (data, weight, bias...) + * @param out_type[M] type of output vector + * + **/ + layer(const std::vector &in_type, + const std::vector &out_type) + : node(in_type.size(), out_type.size()), + initialized_(false), + parallelize_(true), + in_channels_(in_type.size()), + out_channels_(out_type.size()), + in_type_(in_type), + out_type_(out_type) { + weight_init_ = std::make_shared(); + bias_init_ = std::make_shared(); + trainable_ = true; + } + + layer(const layer &) = default; + layer &operator=(const layer &) = default; + + layer(layer &&) = default; + layer &operator=(layer &&) = default; + + void set_parallelize(bool parallelize) { parallelize_ = parallelize; } + + void set_backend(std::shared_ptr backend) { + backend_ = backend; + } + + void set_backend_type(core::backend_t backend_type) { + backend_type_ = backend_type; + } + + ///////////////////////////////////////////////////////////////////////// + // getter + + bool parallelize() const { return parallelize_; } + + // TODO(edgar): Deprecated: use the below method + core::backend_t backend_type() const { return backend_->type(); } + + core::backend_t engine() const { return backend_type_; } + + virtual std::string kernel_file() const { + return std::string("empty_kernel_str"); + } + + virtual std::string kernel_header() const { return std::string(); } + + virtual void createOp() {} + + void setDevice(const Device &device) { + device_ptr_ = const_cast(&device); + } + + Device *device() const { return device_ptr_; } + + std::shared_ptr backend() { return backend_; } + + ///< number of incoming edges in this layer + size_t in_channels() const { return in_channels_; } + + ///< number of outgoing edges in this layer + size_t out_channels() const { return out_channels_; } + + size_t in_data_size() const { + return sumif(in_shape(), + [&](size_t i) { // NOLINT + return in_type_[i] == vector_type::data; + }, + [](const shape3d &s) { return s.size(); }); + } + + size_t out_data_size() const { + return sumif(out_shape(), + [&](size_t i) { // NOLINT + return out_type_[i] == vector_type::data; + }, + [](const shape3d &s) { return s.size(); }); + } + + std::vector in_data_shape() { + return filter(in_shape(), [&](size_t i) { // NOLINT + return in_type_[i] == vector_type::data; + }); + } + + std::vector out_data_shape() { + return filter(out_shape(), [&](size_t i) { // NOLINT + return out_type_[i] == vector_type::data; + }); + } + + ///! @deprecated use in_data_size() instead + size_t in_size() const { return in_data_size(); } + + ///! @deprecated use out_data_size() instead + size_t out_size() const { return out_data_size(); } + + std::vector weights() const { + std::vector v; + for (size_t i = 0; i < in_channels_; i++) { + if (is_trainable_weight(in_type_[i])) { + v.push_back(get_weight_data(i)); + } + } + return v; + } + + std::vector weights() { + std::vector v; + for (size_t i = 0; i < in_channels_; i++) { + if (is_trainable_weight(in_type_[i])) { + v.push_back(get_weight_data(i)); + } + } + return v; + } + + std::vector weights_grads() { + std::vector v; + for (size_t i = 0; i < in_channels_; i++) { + if (is_trainable_weight(in_type_[i])) { + v.push_back(ith_in_node(i)->get_gradient()); + } + } + return v; + } + + std::vector inputs() { + std::vector nodes(in_channels_); + for (size_t i = 0; i < in_channels_; i++) { + nodes[i] = ith_in_node(i); + } + return nodes; + } + + std::vector outputs() { + std::vector nodes(out_channels_); + for (size_t i = 0; i < out_channels_; i++) { + nodes[i] = ith_out_node(i); + } + return nodes; + } + + std::vector outputs() const { + std::vector nodes(out_channels_); + for (size_t i = 0; i < out_channels_; i++) { + nodes[i] = const_cast(this)->ith_out_node(i); + } + return nodes; + } + + void set_out_grads(const std::vector *grad, size_t cnt) { + CNN_UNREFERENCED_PARAMETER(cnt); + size_t n = 0; + for (size_t i = 0; i < out_channels_; i++) { + if (out_type_[i] != vector_type::data) continue; + tensor_t &dst_grad = *ith_out_node(i)->get_gradient(); + assert(n < cnt); + const auto &src_grad = grad[n++]; + size_t sz = src_grad.size(); + dst_grad.resize(sz); + for (size_t j = 0; j < sz; ++j) { + assert(dst_grad[j].size() == src_grad[j]->size()); + dst_grad[j] = *src_grad[j]; + } + } + } + + void set_in_data(const std::vector *data, size_t cnt) { + CNN_UNREFERENCED_PARAMETER(cnt); + size_t n = 0; + for (size_t i = 0; i < in_channels_; i++) { + if (in_type_[i] != vector_type::data) continue; + tensor_t &dst_data = *ith_in_node(i)->get_data(); + size_t in_size = ith_in_node(i)->shape().size(); + assert(n < cnt); + const auto &src_data = data[n++]; + size_t sz = src_data.size(); + dst_data.resize(sz); + + CNN_UNREFERENCED_PARAMETER(in_size); + + for (size_t j = 0; j < sz; ++j) { + assert( + src_data[j]->size() == + in_size); // checking if training data is consistent with layer shape + dst_data[j] = *src_data[j]; + } + } + } + + void output(std::vector &out) const { + out.clear(); + for (size_t i = 0; i < out_channels_; i++) { + if (out_type_[i] == vector_type::data) { + out.push_back(ith_out_node(i)->get_data()); + } + } + } + + std::vector in_types() const { return in_type_; } + + std::vector out_types() const { return out_type_; } + + void set_trainable(bool trainable) { trainable_ = trainable; } + + bool trainable() const { return trainable_; } + + /** + * return output value range + * used only for calculating target value from label-id in final(output) + *layer + * override properly if the layer is intended to be used as output layer + **/ + virtual std::pair out_value_range() const { + return {float_t{0.0}, float_t{1.0}}; + } + + /** + * array of input shapes (width x height x depth) + **/ + virtual std::vector in_shape() const = 0; + + /** + * set input shape of a layer (only used internally while shape inferring) + */ + virtual void set_in_shape(const shape3d &in_shape) { + CNN_UNREFERENCED_PARAMETER(in_shape); + throw nn_error( + "Can't set shape. Shape inferring not applicable for this " + "layer (yet)."); + } + + /** + * array of output shapes (width x height x depth) + **/ + virtual std::vector out_shape() const = 0; + + /** + * name of layer, should be unique for each concrete class + **/ + virtual std::string layer_type() const = 0; + + /** + * number of incoming connections for each output unit + * used only for weight/bias initialization methods which require fan-in + *size + *(e.g. xavier) + * override if the layer has trainable weights, and scale of initialization + *is + *important + **/ + virtual size_t fan_in_size() const { return in_shape()[0].width_; } + // override to allow initialization of multiple size weight matrices. + virtual size_t fan_in_size(size_t) const { + return fan_in_size(); // fallback to single weight matrix. + } + + /** + * number of outgoing connections for each input unit + * used only for weight/bias initialization methods which require fan-out + *size + *(e.g. xavier) + * override if the layer has trainable weights, and scale of initialization + *is + *important + **/ + virtual size_t fan_out_size() const { return out_shape()[0].width_; } + // override to allow initialization of multiple size weight vectors. + virtual size_t fan_out_size(size_t) const { + return fan_out_size(); // fallback to single weight matrix + } + + ///////////////////////////////////////////////////////////////////////// + // setter + template + layer &weight_init(const WeightInit &f) { + weight_init_ = std::make_shared(f); + return *this; + } + + template + layer &bias_init(const BiasInit &f) { + bias_init_ = std::make_shared(f); + return *this; + } + + template + layer &weight_init(std::shared_ptr f) { + weight_init_ = f; + return *this; + } + + template + layer &bias_init(std::shared_ptr f) { + bias_init_ = f; + return *this; + } + + virtual void save( + std::ostream &os, + const int precision = std::numeric_limits::digits10 + 2 + /*by default, we want there to be enough precision*/) const { + /* + if (is_exploded()) { + throw nn_error("failed to save weights because of infinite weight"); + }*/ + os << std::setprecision(precision); + auto all_weights = weights(); + for (auto &weight : all_weights) { + for (auto w : *weight) os << w << " "; + } + } + + virtual void load( + std::istream &is, + const int precision = std::numeric_limits::digits10 + 2 + /*by default, we want there to be enough precision*/) { // NOLINT + is >> std::setprecision(precision); + auto all_weights = weights(); + for (auto &weight : all_weights) { + for (auto &w : *weight) is >> w; + } + initialized_ = true; + } + + virtual void load(const std::vector &src, int &idx) { // NOLINT + auto all_weights = weights(); + for (auto &weight : all_weights) { + for (auto &w : *weight) w = src[idx++]; + } + initialized_ = true; + } + +///////////////////////////////////////////////////////////////////////// +// visualize + +///< visualize latest output of this layer +///< default implementation interpret output as 1d-vector, +///< so "visual" layer(like convolutional layer) should override this for better +/// visualization. +#ifdef DNN_USE_IMAGE_API + virtual image<> output_to_image(size_t channel = 0) const { + const vec_t *output = &(*(outputs()[channel]->get_data()))[0]; + return vec2image(*output, out_shape()[channel]); + } +#endif + + ///////////////////////////////////////////////////////////////////////// + // fprop/bprop + + /** + * @param in_data input vectors of this layer (data, weight, bias) + * @param out_data output vectors + **/ + virtual void forward_propagation(const std::vector &in_data, + std::vector &out_data) = 0; + + /** + * return delta of previous layer (delta=\frac{dE}{da}, a=wx in + *fully-connected layer) + * @param in_data input vectors (same vectors as forward_propagation) + * @param out_data output vectors (same vectors as forward_propagation) + * @param out_grad gradient of output vectors (i-th vector correspond with + *out_data[i]) + * @param in_grad gradient of input vectors (i-th vector correspond with + *in_data[i]) + **/ + virtual void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) = 0; + + /** + * return delta2 of previous layer (delta2=\frac{d^2E}{da^2}, diagonal of + *hessian matrix) + * it is never called if optimizer is hessian-free + **/ + // virtual void back_propagation_2nd(const std::vector& delta_in) = + // 0; + + // called afrer updating weight + virtual void post_update() {} + + /** + * notify changing context (train <=> test) + **/ + virtual void set_context(net_phase ctx) { CNN_UNREFERENCED_PARAMETER(ctx); } + + /* @brief Performs layer forward operation given an input tensor and + * returns the computed data in tensor form. + * + * @param input Vector of `tensor_t` with incoming data. + * + * Internally, it first allocates data without resetting the weights, + * forwards the input data to the computational graph, inside the + * forward() method the data from the computational embedded to container + * to finally be forwarded to the computational operation kernels. + * + * TODO: Probably there's an overhead of moving from/to the computational + * graph. Will be this overhead reduced once we have the Tensor + * class integrated? + */ + void forward(const std::vector &input, + std::vector &out) { // for test + // allocate data in the computational graph without + // resetting the weights. + setup(false); + + std::vector> input2; + input2.resize(input.size()); + for (size_t i = 0; i < input.size(); ++i) { + input2[i].resize(input[i].size()); + for (size_t j = 0; j < input[i].size(); ++j) { + input2[i][j] = &input[i][j]; + } + } + + // the incoming data is forwarded to the computational graph. + set_in_data(&input2[0], input2.size()); + // pick up the data from the computational graph and perform + // computation. + forward(); + // retrieve computed data and return values in form of 4D tensor. + output(out); + } + + std::vector backward( + const std::vector &out_grads) { // for test + setup(false); + + std::vector> grads2; + grads2.resize(out_grads.size()); + for (size_t i = 0; i < out_grads.size(); ++i) { + grads2[i].resize(out_grads[i].size()); + for (size_t j = 0; j < out_grads[i].size(); ++j) { + grads2[i][j] = &out_grads[i][j]; + } + } + + set_out_grads(&grads2[0], grads2.size()); + backward(); + return map_(inputs(), + [](edgeptr_t e) { return *e->get_gradient(); }); + } + + /* @brief The purpose of this method is to forward the data from the + * computational graph to the layer interface. + * + * This is one of the out of two core (forward/backward) methods that + * retrieves the data allocated in the heap by the computational graph + * and constructs the containers to handle the computation by batches. + * Additionally, the sample count a.k.a number of batches is set. + * + * Note: in_data and out_data attempt to contain tensors. However, they + * are not real tensors since tensor_t have three dimensions instead of + * four. For this reason they are embedded in to std::vector. Also note + * that when std::vector it's constructed we cannot assure + * that data is contiguous. + * + * After Tensor class integration we should be able to avoid to have + * in_data and out_data in vectors since Tensor class itself can handle + * batches storage in one single vector with contiguous data. + * + */ + void forward() { + // the computational graph + fwd_in_data_.resize(in_channels_); + fwd_out_data_.resize(out_channels_); + + // Organize input/output vectors from storage (computational graph). + // Internally ith_in_node() will create a connection/edge in the + // computational graph and will allocate memory in case that it's not + // done yet. + for (size_t i = 0; i < in_channels_; i++) { + fwd_in_data_[i] = ith_in_node(i)->get_data(); + } + + // resize outs and stuff to have room for every input sample in + // the batch + set_sample_count(fwd_in_data_[0]->size()); + + // Internally ith_out_node() will create a connection/edge to the + // computational graph and will allocate memory in case that it's not + // done yet. In addition, gradient vector are initialized to default + // values. + for (size_t i = 0; i < out_channels_; i++) { + fwd_out_data_[i] = ith_out_node(i)->get_data(); + ith_out_node(i)->clear_grads(); + } + + // call the forward computation kernel/routine + forward_propagation(fwd_in_data_, fwd_out_data_); + } + + void backward() { + bwd_in_data_.resize(in_channels_); + bwd_in_grad_.resize(in_channels_); + bwd_out_data_.resize(out_channels_); + bwd_out_grad_.resize(out_channels_); + + // organize input/output vectors from storage + for (size_t i = 0; i < in_channels_; i++) { + const auto &nd = ith_in_node(i); + bwd_in_data_[i] = nd->get_data(); + bwd_in_grad_[i] = nd->get_gradient(); + } + for (size_t i = 0; i < out_channels_; i++) { + const auto &nd = ith_out_node(i); + bwd_out_data_[i] = nd->get_data(); + bwd_out_grad_[i] = nd->get_gradient(); + } + back_propagation(bwd_in_data_, bwd_out_data_, bwd_out_grad_, bwd_in_grad_); + } + + /* @brief Allocates data in the computational graph and reset weights if + * it's needed or the data is not already initialized. + * + * @param reset_weight Boolean value to force to reset the weights. + * Weights will be automatically reset if the are not initialized. + * + */ + void setup(bool reset_weight) { + // The input shape (width x height x depth) must be equal to the number + // of input channels a.k.a the number of incoming vectors or 'edges' in + // the computational nomenclature. Same is applied to output shape and + // numbers of output edges. + if (in_shape().size() != in_channels_ || + out_shape().size() != out_channels_) { + throw nn_error("Connection mismatch at setup layer"); + } + + // An 'edge' is created in the computational graph from the current + // layer/node to each output node and allocates the needed memory. + // The number of output nodes is determined by the layer interface. + // In order to handle graph based networks, which a layer/node might + // have multiple input/output connections, we need to check that the + // connection edge does not already exists if we don't want duplicated + // memory allocation. + for (size_t i = 0; i < out_channels_; i++) { + if (!next_[i]) { + // connection edge doesn't exist, so we proceed to allocate the + // necessary memory. + next_[i] = std::make_shared(this, out_shape()[i], out_type_[i]); + } + } + + // reset the weights if necessary, or in case that the data is + // still not initialized. + if (reset_weight || !initialized_) { + init_weight(); + } + } + + /* @brief Initializes the vectors containing the trainable data. + * + * In case that a layer/node is set to be not trainable, it does + * nothing and returns a void. Otherwise, for each input connection + * and depending of the data nature (weight or bias) calls their + * pertinent initialization function and fill the vectors with the + * data generated by the mentioned functions. + * + */ + void init_weight() { + // layer/node is not trainable, do nothing and mark the layer/node + // as initialized. + if (!trainable_) { + initialized_ = true; + return; + } + + // Fill vector values with data generated by the initialization + // function. The pointer to the data is obtained from the + // computational graph and the methods fan_in_size() and fan_out_size() + // return the number of incoming/outcoming connections for each + // input/output unit. + for (size_t i = 0; i < in_channels_; i++) { + switch (in_type_[i]) { + // fill vectors of weight type + case vector_type::weight: + weight_init_->fill(get_weight_data(i), fan_in_size(i), + fan_out_size(i)); + break; + // fill vector of bias type + case vector_type::bias: + bias_init_->fill(get_weight_data(i), fan_in_size(i), fan_out_size(i)); + break; + default: break; + } + } + // in case we succeed with data initialization, we mark the + // layer/node as initialized. + initialized_ = true; + } + + void clear_grads() { + for (size_t i = 0; i < in_type_.size(); i++) { + ith_in_node(i)->clear_grads(); + } + } + + void update_weight(optimizer *o) { + auto &diff = weights_diff_; + for (size_t i = 0; i < in_type_.size(); i++) { + if (trainable() && is_trainable_weight(in_type_[i])) { + vec_t &target = *get_weight_data(i); + ith_in_node(i)->merge_grads(&diff); + // parallelize only when target size is big enough to mitigate + // thread spawning overhead. + bool parallelize = (target.size() >= 512); + o->update(diff, target, parallelize); + } + } + clear_grads(); + post_update(); + } + + bool has_same_weights(const layer &rhs, float_t eps) const { + auto w1 = weights(); + auto w2 = rhs.weights(); + if (w1.size() != w2.size()) return false; + + for (size_t i = 0; i < w1.size(); i++) { + if (w1[i]->size() != w2[i]->size()) return false; + + for (size_t j = 0; j < w1[i]->size(); j++) { + if (std::abs(w1[i]->at(j) - w2[i]->at(j)) > eps) return false; + } + } + return true; + } + + virtual void set_sample_count(size_t sample_count) { + // increase the size if necessary - but do not decrease + auto resize = [sample_count](tensor_t *tensor) { + tensor->resize(sample_count, (*tensor)[0]); + }; + + for (size_t i = 0; i < in_channels_; i++) { + if (!is_trainable_weight(in_type_[i])) { + resize(ith_in_node(i)->get_data()); + } + resize(ith_in_node(i)->get_gradient()); + } + + for (size_t i = 0; i < out_channels_; i++) { + if (!is_trainable_weight(out_type_[i])) { + resize(ith_out_node(i)->get_data()); + } + resize(ith_out_node(i)->get_gradient()); + } + } + + /** + * generate layer from cereal's Archive + **/ + template + static std::shared_ptr load_layer(InputArchive &ia); + + template + static void save_layer(OutputArchive &oa, const layer &l); + + template + void serialize_prolog(Archive &ar); + + protected: + /** Flag indication whether the layer/node is initialized */ + bool initialized_; + /** Flag indicating whether the layer/node operations ara paralellized */ + bool parallelize_; + /** The number of input vectors/edges */ + size_t in_channels_; + /** The number of output vectors/edges */ + size_t out_channels_; + /** Vector containing the type of data for inputs */ + std::vector in_type_; + /** Vector containing the type of data for outputs */ + std::vector out_type_; + /** The current backend type for operations */ + core::backend_t backend_type_; + /** The backend instance (deprecated) */ + std::shared_ptr backend_; + /** Pointer to the device on which the layer/node will run */ + Device *device_ptr_ = nullptr; + /** Used in update_weight method. Kept as a member variable to reduce + * frequent + * memory allocation */ + vec_t weights_diff_; + + template + inline void for_i(T size, Func f, size_t grainsize = 100) { + tiny_dnn::for_i(parallelize_, size, f, grainsize); + } + + friend struct serialization_buddy; + + private: + /** Flag indicating whether the layer/node parameters are trainable */ + bool trainable_; + /** Pointer to the function for weights initialization */ + std::shared_ptr weight_init_; + /** Pointer to the function for biases initialization */ + std::shared_ptr bias_init_; + + std::vector fwd_in_data_; + std::vector fwd_out_data_; + std::vector bwd_in_data_; + std::vector bwd_in_grad_; + std::vector bwd_out_data_; + std::vector bwd_out_grad_; + + /* @brief Allocates the necessary edge memory in a specific + * incoming connection. + * + * @param i The position to store the previous edge. + * + * Graphical explanation: + * + * nullptr -- |edge| -- prev(i) ---- |layer| + * nullptr -- prev(i+1) -´ + */ + void alloc_input(size_t i) const { + // the created incoming edge won't have a previous connection, + // for this reason first parameter is a nullptr. + prev_[i] = std::make_shared(nullptr, in_shape()[i], in_type_[i]); + } + + /* @brief Allocates the necessary edge memory in a specific + * outcoming connection. + * + * @param i The position to store the next edge. + * + * Graphical explanation: + * + * |layer| -- next(i) ---- |edge| + * `- next(i+1) -- nullptr + */ + void alloc_output(size_t i) const { + // the created outcoming will have the current layer as the + // previous node. + next_[i] = std::make_shared(const_cast(this), out_shape()[i], + out_type_[i]); + } + + /* @brief Creates an edge between the current node and one incoming + * or previous node. + * + * @param i The position to store the previous edge. + * + * The method checks if the edge already exists, otherwise we create it + * and the necessary memory it's allocated. The method returns the pointer + * to the previous edge. + */ + edgeptr_t ith_in_node(size_t i) { + // in case that the edge doesn't exist, we create it + if (!prev_[i]) alloc_input(i); + return prev()[i]; + } + + /* @brief Creates an edge between the current node and one outcoming + * or next node. + * + * @param i The position to store the next edge. + * + * The method checks if the edge already exists, otherwise we create it + * and the necessary memory it's allocated. The method returns the pointer + * to the next edge. + */ + edgeptr_t ith_out_node(size_t i) { + // in case that the edge doesn't exist, we create it + if (!next_[i]) alloc_output(i); + return next()[i]; + } + edgeptr_t ith_out_node(size_t i) const { return next()[i]; } + + /* @brief Retrieves weight vector from incoming edge + * @param i The position of incoming edge. + * + * Returns the mutable pointer to the edge raw data. + */ + vec_t *get_weight_data(size_t i) { + assert(is_trainable_weight(in_type_[i])); + return &(*(ith_in_node(i)->get_data()))[0]; + } + + /* @brief Retrieves weight vector from incoming edge + * @param i The position of incoming edge. + * + * Returns the non mutable pointer to the edge raw data. + */ + const vec_t *get_weight_data(size_t i) const { + assert(is_trainable_weight(in_type_[i])); + return &(*(const_cast(this)->ith_in_node(i)->get_data()))[0]; + } +}; + +inline void connect(layer *head, + layer *tail, + size_t head_index = 0, + size_t tail_index = 0) { + auto out_shape = head->out_shape()[head_index]; + auto in_shape = tail->in_shape()[tail_index]; + + head->setup(false); + + // todo (karandesai) enable shape inferring for all layers + // currently only possible for activation layers. + if (in_shape.size() == 0) { + tail->set_in_shape(out_shape); + in_shape = out_shape; + } + + if (out_shape.size() != in_shape.size()) { + connection_mismatch(*head, *tail); + } + + if (!head->next_[head_index]) { + throw nn_error("output edge must not be null"); + } + + tail->prev_[tail_index] = head->next_[head_index]; + tail->prev_[tail_index]->add_next_node(tail); +} + +inline layer &operator<<(layer &lhs, layer &rhs) { + connect(&lhs, &rhs); + return rhs; +} + +template +std::basic_ostream &operator<<( + std::basic_ostream &os, const layer &v) { + v.save(os); + return os; +} + +template +std::basic_istream &operator>>( + std::basic_istream &os, layer &v) { + v.load(os); + return os; +} + +// error message functions + +inline void connection_mismatch(const layer &from, const layer &to) { + std::ostringstream os; + + os << std::endl; + os << "output size of Nth layer must be equal to input of (N+1)th layer\n"; + + os << "layerN: " << std::setw(12) << from.layer_type() + << " in:" << from.in_data_size() << "(" << from.in_shape() << "), " + << "out:" << from.out_data_size() << "(" << from.out_shape() << ")\n"; + + os << "layerN+1: " << std::setw(12) << to.layer_type() + << " in:" << to.in_data_size() << "(" << to.in_shape() << "), " + << "out:" << to.out_data_size() << "(" << to.out_shape() << ")\n"; + + os << from.out_data_size() << " != " << to.in_data_size() << std::endl; + std::string detail_info = os.str(); + + throw nn_error("layer dimension mismatch!" + detail_info); +} + +inline void data_mismatch(const layer &layer, const vec_t &data) { + std::ostringstream os; + + os << std::endl; + os << "data dimension: " << data.size() << "\n"; + os << "network dimension: " << layer.in_data_size() << "(" + << layer.layer_type() << ":" << layer.in_shape() << ")\n"; + + std::string detail_info = os.str(); + + throw nn_error("input dimension mismatch!" + detail_info); +} + +inline void pooling_size_mismatch(size_t in_width, + size_t in_height, + size_t pooling_size_x, + size_t pooling_size_y) { + std::ostringstream os; + + os << std::endl; + os << "WxH:" << in_width << "x" << in_height << std::endl; + os << "pooling-size:" << pooling_size_x << "x" << pooling_size_y << std::endl; + + std::string detail_info = os.str(); + + throw nn_error("width/height not multiple of pooling size" + detail_info); +} + +template +void graph_traverse(layer *root_node, T &&node_callback, U &&edge_callback) { + std::unordered_set visited; + std::queue S; + + S.push(root_node); + + while (!S.empty()) { + layer *curr = S.front(); + S.pop(); + visited.insert(curr); + + node_callback(*curr); + + auto edges = curr->next(); + for (auto e : edges) { + if (e != nullptr) edge_callback(*e); + } + + auto prev = curr->prev_nodes(); + for (auto p : prev) { + // TODO(nyanp): refactoring + // which type of refactoring do you have in mind for that? + layer *l = dynamic_cast(p); + if (visited.find(l) == visited.end()) { + S.push(l); + } + } + + auto next = curr->next_nodes(); + for (auto n : next) { + // TODO(nyanp): refactoring + // which type of refactoring do you have in mind for that? + layer *l = dynamic_cast(n); + if (visited.find(l) == visited.end()) { + S.push(l); + } + } + } +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/layers.h b/tiny_dnn/layers/layers.h new file mode 100755 index 0000000000000..cf7ecd45a4210 --- /dev/null +++ b/tiny_dnn/layers/layers.h @@ -0,0 +1,30 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once +#include "tiny_dnn/layers/arithmetic_layer.h" +#include "tiny_dnn/layers/average_pooling_layer.h" +#include "tiny_dnn/layers/average_unpooling_layer.h" +#include "tiny_dnn/layers/batch_normalization_layer.h" +#include "tiny_dnn/layers/concat_layer.h" +#include "tiny_dnn/layers/convolutional_layer.h" +#include "tiny_dnn/layers/deconvolutional_layer.h" +#include "tiny_dnn/layers/dropout_layer.h" +#include "tiny_dnn/layers/fully_connected_layer.h" +#include "tiny_dnn/layers/global_average_pooling_layer.h" +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/layers/linear_layer.h" +#include "tiny_dnn/layers/lrn_layer.h" +#include "tiny_dnn/layers/max_pooling_layer.h" +#include "tiny_dnn/layers/max_unpooling_layer.h" +#include "tiny_dnn/layers/partial_connected_layer.h" +#include "tiny_dnn/layers/power_layer.h" +#include "tiny_dnn/layers/quantized_convolutional_layer.h" +#include "tiny_dnn/layers/quantized_deconvolutional_layer.h" +#include "tiny_dnn/layers/quantized_fully_connected_layer.h" +#include "tiny_dnn/layers/recurrent_cell_layer.h" +#include "tiny_dnn/layers/slice_layer.h" diff --git a/tiny_dnn/layers/linear_layer.h b/tiny_dnn/layers/linear_layer.h new file mode 100755 index 0000000000000..f58847e5d981a --- /dev/null +++ b/tiny_dnn/layers/linear_layer.h @@ -0,0 +1,87 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * element-wise operation: ```f(x) = h(scale*x+bias)``` + */ +class linear_layer : public layer { + public: + /** + * @param dim [in] number of elements + * @param scale [in] factor by which to multiply + * @param bias [in] bias term + **/ + explicit linear_layer(size_t dim, + float_t scale = float_t{1}, + float_t bias = float_t{0}) + : layer({vector_type::data}, {vector_type::data}), + dim_(dim), + scale_(scale), + bias_(bias) {} + + std::vector in_shape() const override { + return {shape3d(dim_, 1, 1)}; + } + + std::vector out_shape() const override { + return {shape3d(dim_, 1, 1)}; + } + + std::string layer_type() const override { return "linear"; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + const tensor_t &in = *in_data[0]; + tensor_t &out = *out_data[0]; + + // do nothing + CNN_UNREFERENCED_PARAMETER(out); + + // @todo revise the parallelism strategy + for_i(dim_, [&](size_t i) { + for (size_t sample = 0, sample_count = in.size(); sample < sample_count; + ++sample) + out[sample][i] = scale_ * in[sample][i] + bias_; + }); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + tensor_t &prev_delta = *in_grad[0]; + tensor_t &curr_delta = *out_grad[0]; + + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + + // @todo revise parallelism strategy + for (size_t sample = 0; sample < prev_delta.size(); ++sample) { + for_i(dim_, [&](size_t i) { + prev_delta[sample][i] = curr_delta[sample][i] * scale_; + }); + } + } + + friend struct serialization_buddy; + + protected: + size_t dim_; + float_t scale_, bias_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/lrn_layer.h b/tiny_dnn/layers/lrn_layer.h new file mode 100755 index 0000000000000..3416914223df8 --- /dev/null +++ b/tiny_dnn/layers/lrn_layer.h @@ -0,0 +1,167 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +enum class norm_region { across_channels, within_channels }; + +/** + * local response normalization + */ +class lrn_layer : public layer { + public: + lrn_layer(const shape3d &in_shape, + size_t local_size, + float_t alpha = 1.0, + float_t beta = 5.0, + norm_region region = norm_region::across_channels) + : layer({vector_type::data}, {vector_type::data}), + in_shape_(in_shape), + size_(local_size), + alpha_(alpha), + beta_(beta), + region_(region), + in_square_(in_shape_.area()) {} + + /** + * @param layer [in] the previous layer connected to this + * @param local_size [in] the number of channels(depths) to sum over + * @param in_channels [in] the number of channels of input data + * @param alpha [in] the scaling parameter (same to caffe's LRN) + * @param beta [in] the scaling parameter (same to caffe's LRN) + **/ + lrn_layer(layer *prev, + size_t local_size, + float_t alpha = 1.0, + float_t beta = 5.0, + norm_region region = norm_region::across_channels) + : lrn_layer(prev->out_data_shape()[0], local_size, alpha, beta, region) {} + + /** + * @param in_width [in] the width of input data + * @param in_height [in] the height of input data + * @param local_size [in] the number of channels(depths) to sum over + * @param in_channels [in] the number of channels of input data + * @param alpha [in] the scaling parameter (same to caffe's LRN) + * @param beta [in] the scaling parameter (same to caffe's LRN) + **/ + lrn_layer(size_t in_width, + size_t in_height, + size_t local_size, + size_t in_channels, + float_t alpha = 1.0, + float_t beta = 5.0, + norm_region region = norm_region::across_channels) + : lrn_layer(shape3d{in_width, in_height, in_channels}, + local_size, + alpha, + beta, + region) {} + + size_t fan_in_size() const override { return size_; } + + size_t fan_out_size() const override { return size_; } + + std::vector in_shape() const override { return {in_shape_}; } + + std::vector out_shape() const override { return {in_shape_}; } + + std::string layer_type() const override { return "lrn"; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + // @todo revise the parallelism strategy + for (size_t sample = 0, sample_count = in_data[0]->size(); + sample < sample_count; ++sample) { + vec_t &in = (*in_data[0])[sample]; + vec_t &out = (*out_data[0])[sample]; + + if (region_ == norm_region::across_channels) { + forward_across(in, out); + } else { + forward_within(in, out); + } + } + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + CNN_UNREFERENCED_PARAMETER(out_grad); + CNN_UNREFERENCED_PARAMETER(in_grad); + throw nn_error("not implemented"); + } + + friend struct serialization_buddy; + + private: + void forward_across(const vec_t &in, vec_t &out) { + vectorize::fill(&in_square_[0], in_square_.size(), float_t{0}); + + for (size_t i = 0; i < size_ / 2; i++) { + size_t idx = in_shape_.get_index(0, 0, i); + add_square_sum(&in[idx], in_shape_.area(), &in_square_[0]); + } + + size_t head = size_ / 2; + long tail = static_cast(head) - static_cast(size_); // NOLINT + size_t channels = in_shape_.depth_; + const size_t wxh = in_shape_.area(); + const float_t alpha_div_size = alpha_ / size_; + + for (size_t i = 0; i < channels; i++, head++, tail++) { + if (head < channels) + add_square_sum(&in[in_shape_.get_index(0, 0, head)], wxh, + &in_square_[0]); + + if (tail >= 0) + sub_square_sum(&in[in_shape_.get_index(0, 0, tail)], wxh, + &in_square_[0]); + + float_t *dst = &out[in_shape_.get_index(0, 0, i)]; + const float_t *src = &in[in_shape_.get_index(0, 0, i)]; + for (size_t j = 0; j < wxh; j++) + dst[j] = src[j] * + std::pow(float_t(1) + alpha_div_size * in_square_[j], -beta_); + } + } + + void forward_within(const vec_t &in, vec_t &out) { + CNN_UNREFERENCED_PARAMETER(in); + CNN_UNREFERENCED_PARAMETER(out); + throw nn_error("not implemented"); + } + + void add_square_sum(const float_t *src, size_t size, float_t *dst) { + for (size_t i = 0; i < size; i++) dst[i] += src[i] * src[i]; + } + + void sub_square_sum(const float_t *src, size_t size, float_t *dst) { + for (size_t i = 0; i < size; i++) dst[i] -= src[i] * src[i]; + } + + shape3d in_shape_; + + size_t size_; + float_t alpha_, beta_; + norm_region region_; + + vec_t in_square_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/max_pooling_layer.h b/tiny_dnn/layers/max_pooling_layer.h new file mode 100755 index 0000000000000..108c78955d405 --- /dev/null +++ b/tiny_dnn/layers/max_pooling_layer.h @@ -0,0 +1,256 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include + +#include "tiny_dnn/core/kernels/maxpool_grad_op.h" +#include "tiny_dnn/core/kernels/maxpool_op.h" + +#include "tiny_dnn/util/util.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif + +namespace tiny_dnn { + +/** + * applies max-pooling operaton to the spatial data + **/ +class max_pooling_layer : public layer { + public: + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param pooling_size [in] factor by which to downscale + **/ + max_pooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t pooling_size, + core::backend_t backend_type = core::default_engine()) + : max_pooling_layer(in_width, + in_height, + in_channels, + pooling_size, + (in_height == 1 ? 1 : pooling_size), + backend_type) {} + + max_pooling_layer(const shape3d &in_shape, + size_t pooling_size, + size_t stride, + core::backend_t backend_type = core::default_engine()) + : max_pooling_layer(in_shape.width_, + in_shape.height_, + in_shape.depth_, + pooling_size, + stride, + backend_type) {} + + max_pooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t pooling_size, + size_t stride, + core::backend_t backend_type = core::default_engine()) + : max_pooling_layer(in_width, + in_height, + in_channels, + pooling_size, + (in_height == 1 ? 1 : pooling_size), + stride, + stride, + padding::valid, + backend_type) {} + + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param pooling_size [in] factor by which to downscale + * @param stride [in] interval at which to apply the filters to the + *input + **/ + max_pooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t pooling_size_x, + size_t pooling_size_y, + size_t stride_x, + size_t stride_y, + padding pad_type = padding::valid, + core::backend_t backend_type = core::default_engine()) + : layer({vector_type::data}, {vector_type::data}) { + set_maxpool_params( + shape3d(in_width, in_height, in_channels), + shape3d(conv_out_length(in_width, pooling_size_x, stride_x, pad_type), + conv_out_length(in_height, pooling_size_y, stride_y, pad_type), + in_channels), + pooling_size_x, pooling_size_y, stride_x, stride_y, pad_type); + + init_connection(); + init_backend(backend_type); + layer::set_backend_type(backend_type); + } + + // move constructor + max_pooling_layer(max_pooling_layer &&other) // NOLINT + : layer(std::move(other)), params_(std::move(other.params_)) { + init_connection(); + init_backend(std::move(layer::engine())); + } + + size_t fan_in_size() const override { return params_.out2in[0].size(); } + + size_t fan_out_size() const override { return 1; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + // forward convolutional op context + fwd_ctx_.set_in_out(in_data, out_data); + fwd_ctx_.setParallelize(layer::parallelize()); + fwd_ctx_.setEngine(layer::engine()); + + // launch convolutional kernel + kernel_fwd_->compute(fwd_ctx_); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + // backward convolutional op context + bwd_ctx_.set_in_out(in_data, out_data, out_grad, in_grad); + bwd_ctx_.setParallelize(layer::parallelize()); + bwd_ctx_.setEngine(layer::engine()); + + // launch convolutional kernel + kernel_back_->compute(bwd_ctx_); + } + + std::vector> in_shape() const override { + return {params_.in}; + } + + std::vector> out_shape() const override { + return {params_.out}; + } + + std::string layer_type() const override { return std::string("max-pool"); } + + std::string kernel_file() const override { + return std::string("../tiny_cnn/core/kernels/cl_kernels/pooling.cl"); + } + + std::pair pool_size() const { + return std::make_pair(params_.pool_size_x, params_.pool_size_y); + } + + void set_sample_count(size_t sample_count) override { + layer::set_sample_count(sample_count); + params_.out2inmax.resize(sample_count, + std::vector(params_.out.size())); + } + + friend struct serialization_buddy; + + private: + /* The Max Poling operation params */ + core::maxpool_params params_; + + /* forward op context */ + core::OpKernelContext fwd_ctx_; + + /* backward op context */ + core::OpKernelContext bwd_ctx_; + + /* Forward and backward ops */ + std::shared_ptr kernel_fwd_; + std::shared_ptr kernel_back_; + + void connect_kernel(size_t pooling_size_x, + size_t pooling_size_y, + size_t outx, + size_t outy, + size_t c) { + size_t dxmax = + std::min(pooling_size_x, params_.in.width_ - outx * params_.stride_x); + + size_t dymax = + std::min(pooling_size_y, params_.in.height_ - outy * params_.stride_y); + + for (size_t dy = 0; dy < dymax; dy++) { + for (size_t dx = 0; dx < dxmax; dx++) { + size_t in_index = params_.in.get_index(outx * params_.stride_x + dx, + outy * params_.stride_y + dy, c); + size_t out_index = params_.out.get_index(outx, outy, c); + + if (in_index >= params_.in2out.size()) { + throw nn_error("index overflow"); + } + if (out_index >= params_.out2in.size()) { + throw nn_error("index overflow"); + } + params_.in2out[in_index] = out_index; + params_.out2in[out_index].push_back(in_index); + } + } + } + + void init_connection() { + params_.in2out.resize(params_.in.size()); + params_.out2in.resize(params_.out.size()); + + for (size_t c = 0; c < params_.in.depth_; ++c) { + for (size_t y = 0; y < params_.out.height_; ++y) { + for (size_t x = 0; x < params_.out.width_; ++x) { + connect_kernel(params_.pool_size_x, params_.pool_size_y, x, y, c); + } + } + } + } + + void init_backend(core::backend_t backend_type) { + core::OpKernelConstruction ctx = + core::OpKernelConstruction(layer::device(), ¶ms_); + + if (backend_type == core::backend_t::internal || + backend_type == core::backend_t::nnpack || + backend_type == core::backend_t::avx) { + kernel_fwd_.reset(new MaxPoolOp(ctx)); + kernel_back_.reset(new MaxPoolGradOp(ctx)); + return; + } else { + throw nn_error("Not supported engine: " + to_string(backend_type)); + } + } + + void set_maxpool_params(const shape3d &in, + const shape3d &out, + size_t pooling_size_x, + size_t pooling_size_y, + size_t stride_x, + size_t stride_y, + padding pad_type) { + params_.in = in; + params_.out = out; + params_.pool_size_x = pooling_size_x; + params_.pool_size_y = pooling_size_y; + params_.stride_x = stride_x; + params_.stride_y = stride_y; + params_.pad_type = pad_type; + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/max_unpooling_layer.h b/tiny_dnn/layers/max_unpooling_layer.h new file mode 100755 index 0000000000000..e28d1ceb98ead --- /dev/null +++ b/tiny_dnn/layers/max_unpooling_layer.h @@ -0,0 +1,183 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/util/util.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif + +namespace tiny_dnn { + +/** + * applies max-pooing operaton to the spatial data + **/ +class max_unpooling_layer : public layer { + public: + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param unpooling_size [in] factor by which to upscale + **/ + max_unpooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t unpooling_size) + : max_unpooling_layer(in_width, + in_height, + in_channels, + unpooling_size, + (in_height == 1 ? 1 : unpooling_size)) {} + + max_unpooling_layer(const shape3d &in_size, + size_t unpooling_size, + size_t stride) + : max_unpooling_layer(in_size.width_, + in_size.height_, + in_size.depth_, + unpooling_size, + (in_size.height_ == 1 ? 1 : unpooling_size)) { + CNN_UNREFERENCED_PARAMETER(stride); + } + + /** + * @param in_width [in] width of input image + * @param in_height [in] height of input image + * @param in_channels [in] the number of input image channels(depth) + * @param unpooling_size [in] factor by which to upscale + * @param stride [in] interval at which to apply the filters to the + *input + **/ + max_unpooling_layer(size_t in_width, + size_t in_height, + size_t in_channels, + size_t unpooling_size, + size_t stride) + : layer({vector_type::data}, {vector_type::data}), + unpool_size_(unpooling_size), + stride_(stride), + in_(in_width, in_height, in_channels), + out_(unpool_out_dim(in_width, unpooling_size, stride), + unpool_out_dim(in_height, unpooling_size, stride), + in_channels) { + worker_storage_.in2outmax_.resize(out_.size()); + init_connection(); + } + + size_t fan_in_size() const override { return 1; } + + size_t fan_out_size() const override { return in2out_[0].size(); } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + const tensor_t &in = *in_data[0]; + tensor_t &out = *out_data[0]; + + for (size_t sample = 0; sample < in_data[0]->size(); sample++) { + const vec_t &in_vec = in[sample]; + vec_t &out_vec = out[sample]; + + std::vector &max_idx = worker_storage_.in2outmax_; + + for_i(in2out_.size(), [&](size_t i) { + const auto &in_index = out2in_[i]; + out_vec[i] = (max_idx[in_index] == i) ? in_vec[in_index] : float_t{0}; + }); + } + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(out_data); + tensor_t &prev_delta = *in_grad[0]; + tensor_t &curr_delta = *out_grad[0]; + + for (size_t sample = 0; sample < in_data[0]->size(); sample++) { + vec_t &prev_delta_vec = prev_delta[sample]; + vec_t &curr_delta_vec = curr_delta[sample]; + + std::vector &max_idx = worker_storage_.in2outmax_; + + for_(parallelize_, 0, in2out_.size(), [&](const blocked_range &r) { + for (size_t i = r.begin(); i != r.end(); i++) { + size_t outi = out2in_[i]; + prev_delta_vec[i] = + (max_idx[outi] == i) ? curr_delta_vec[outi] : float_t{0}; + } + }); + } + } + + std::vector> in_shape() const override { return {in_}; } + std::vector> out_shape() const override { return {out_}; } + std::string layer_type() const override { return "max-unpool"; } + size_t unpool_size() const { return unpool_size_; } + + friend struct serialization_buddy; + + private: + size_t unpool_size_; + size_t stride_; + std::vector out2in_; // mapping out => in (N:1) + std::vector> in2out_; // mapping in => out (1:N) + + struct worker_specific_storage { + std::vector in2outmax_; // mapping max_index(out) => in (1:1) + }; + + worker_specific_storage worker_storage_; + + index3d in_; + index3d out_; + + static size_t unpool_out_dim(size_t in_size, + size_t unpooling_size, + size_t stride) { + return static_cast(in_size) * stride + unpooling_size - 1; + } + + void connect_kernel(size_t unpooling_size, size_t inx, size_t iny, size_t c) { + const size_t dxmax = std::min(unpooling_size, inx * stride_ - out_.width_); + const size_t dymax = std::min(unpooling_size, iny * stride_ - out_.height_); + + for (size_t dy = 0; dy < dymax; dy++) { + for (size_t dx = 0; dx < dxmax; dx++) { + size_t out_index = + out_.get_index(inx * stride_ + dx, iny * stride_ + dy, c); + size_t in_index = in_.get_index(inx, iny, c); + + if (in_index >= in2out_.size()) throw nn_error("index overflow"); + if (out_index >= out2in_.size()) throw nn_error("index overflow"); + out2in_[out_index] = in_index; + in2out_[in_index].push_back(out_index); + } + } + } + + void init_connection() { + in2out_.resize(in_.size()); + out2in_.resize(out_.size()); + + worker_storage_.in2outmax_.resize(in_.size()); + + for (size_t c = 0; c < in_.depth_; ++c) + for (size_t y = 0; y < in_.height_; ++y) + for (size_t x = 0; x < in_.width_; ++x) + connect_kernel(unpool_size_, x, y, c); + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/partial_connected_layer.h b/tiny_dnn/layers/partial_connected_layer.h new file mode 100755 index 0000000000000..c9b8899caecb8 --- /dev/null +++ b/tiny_dnn/layers/partial_connected_layer.h @@ -0,0 +1,149 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +class partial_connected_layer : public layer { + public: + typedef std::vector> io_connections; + typedef std::vector> wi_connections; + typedef std::vector> wo_connections; + + partial_connected_layer(size_t in_dim, + size_t out_dim, + size_t weight_dim, + size_t bias_dim, + float_t scale_factor = float_t{1}) + : layer(std_input_order(bias_dim > 0), {vector_type::data}), + weight2io_(weight_dim), + out2wi_(out_dim), + in2wo_(in_dim), + bias2out_(bias_dim), + out2bias_(out_dim), + scale_factor_(scale_factor) {} + + size_t param_size() const { + size_t total_param = 0; + for (auto w : weight2io_) + if (w.size() > 0) total_param++; + for (auto b : bias2out_) + if (b.size() > 0) total_param++; + return total_param; + } + + size_t fan_in_size() const override { return max_size(out2wi_); } + + size_t fan_out_size() const override { return max_size(in2wo_); } + + void connect_weight(size_t input_index, + size_t output_index, + size_t weight_index) { + weight2io_[weight_index].emplace_back(input_index, output_index); + out2wi_[output_index].emplace_back(weight_index, input_index); + in2wo_[input_index].emplace_back(weight_index, output_index); + } + + void connect_bias(size_t bias_index, size_t output_index) { + out2bias_[output_index] = bias_index; + bias2out_[bias_index].push_back(output_index); + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + const tensor_t &in = *in_data[0]; + const vec_t &W = (*in_data[1])[0]; + const vec_t &b = (*in_data[2])[0]; + tensor_t &out = *out_data[0]; + + // @todo revise the parallelism strategy + for (size_t sample = 0, sample_count = in.size(); sample < sample_count; + ++sample) { + vec_t &out_sample = out[sample]; + + for_i(out2wi_.size(), [&](size_t i) { + const wi_connections &connections = out2wi_[i]; + + float_t &out_element = out_sample[i]; + + out_element = float_t{0}; + + for (auto connection : connections) + out_element += W[connection.first] * in[sample][connection.second]; + + out_element *= scale_factor_; + out_element += b[out2bias_[i]]; + }); + } + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(out_data); + const tensor_t &prev_out = *in_data[0]; + const vec_t &W = (*in_data[1])[0]; + vec_t &dW = (*in_grad[1])[0]; + vec_t &db = (*in_grad[2])[0]; + tensor_t &prev_delta = *in_grad[0]; + tensor_t &curr_delta = *out_grad[0]; + + // @todo revise the parallelism strategy + for (size_t sample = 0, sample_count = prev_out.size(); + sample < sample_count; ++sample) { + for_i(in2wo_.size(), [&](size_t i) { + const wo_connections &connections = in2wo_[i]; + float_t delta{0}; + + for (auto connection : connections) + delta += W[connection.first] * curr_delta[sample][connection.second]; + + prev_delta[sample][i] = delta * scale_factor_; + }); + + for_i(weight2io_.size(), [&](size_t i) { + const io_connections &connections = weight2io_[i]; + float_t diff{0}; + + for (auto connection : connections) + diff += prev_out[sample][connection.first] * + curr_delta[sample][connection.second]; + + dW[i] += diff * scale_factor_; + }); + + for (size_t i = 0; i < bias2out_.size(); i++) { + const std::vector &outs = bias2out_[i]; + float_t diff{0}; + + for (auto o : outs) diff += curr_delta[sample][o]; + + db[i] += diff; + } + } + } + + friend struct serialization_buddy; + + protected: + std::vector weight2io_; // weight_id -> [(in_id, out_id)] + std::vector out2wi_; // out_id -> [(weight_id, in_id)] + std::vector in2wo_; // in_id -> [(weight_id, out_id)] + std::vector> bias2out_; + std::vector out2bias_; + float_t scale_factor_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/power_layer.h b/tiny_dnn/layers/power_layer.h new file mode 100755 index 0000000000000..78575c25231ef --- /dev/null +++ b/tiny_dnn/layers/power_layer.h @@ -0,0 +1,110 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * element-wise pow: ```y = scale*x^factor``` + **/ +class power_layer : public layer { + public: + typedef layer Base; + + /** + * @param in_shape [in] shape of input tensor + * @param factor [in] floating-point number that specifies a power + * @param scale [in] scale factor for additional multiply + */ + power_layer(const shape3d &in_shape, + float_t factor, + float_t scale = float_t{1.0}) + : layer({vector_type::data}, {vector_type::data}), + in_shape_(in_shape), + factor_(factor), + scale_(scale) {} + + /** + * @param prev_layer [in] previous layer to be connected + * @param factor [in] floating-point number that specifies a power + * @param scale [in] scale factor for additional multiply + */ + power_layer(const layer &prev_layer, + float_t factor, + float_t scale = float_t{1.0}) + : layer({vector_type::data}, {vector_type::data}), + in_shape_(prev_layer.out_shape()[0]), + factor_(factor), + scale_(scale) {} + + std::string layer_type() const override { return "power"; } + + std::vector in_shape() const override { return {in_shape_}; } + + std::vector out_shape() const override { return {in_shape_}; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + const tensor_t &x = *in_data[0]; + tensor_t &y = *out_data[0]; + + for (size_t i = 0; i < x.size(); i++) { + std::transform(x[i].begin(), x[i].end(), y[i].begin(), + [=](float_t x) { return scale_ * std::pow(x, factor_); }); + } + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + tensor_t &dx = *in_grad[0]; + const tensor_t &dy = *out_grad[0]; + const tensor_t &x = *in_data[0]; + const tensor_t &y = *out_data[0]; + + for (size_t i = 0; i < x.size(); i++) { + for (size_t j = 0; j < x[i].size(); j++) { + // f(x) = (scale*x)^factor + // -> + // dx = dy * df(x) + // = dy * scale * factor * (scale * x)^(factor - 1) + // = dy * scale * factor * (scale * x)^factor * (scale * + // x)^(-1) + // = dy * factor * y / x + if (std::abs(x[i][j]) > 1e-10) { + dx[i][j] = dy[i][j] * factor_ * y[i][j] / x[i][j]; + } else { + dx[i][j] = dy[i][j] * scale_ * factor_ * + std::pow(scale_ * x[i][j], factor_ - 1.0); + } + } + } + } + + float_t factor() const { return factor_; } + + float_t scale() const { return scale_; } + + friend struct serialization_buddy; + + private: + shape3d in_shape_; + float_t factor_; + float_t scale_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/quantized_convolutional_layer.h b/tiny_dnn/layers/quantized_convolutional_layer.h new file mode 100755 index 0000000000000..a50a64388f7c7 --- /dev/null +++ b/tiny_dnn/layers/quantized_convolutional_layer.h @@ -0,0 +1,497 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include + +#include "tiny_dnn/core/backend_tiny.h" +#ifdef CNN_USE_AVX +#include "tiny_dnn/core/backend_avx.h" +#endif + +#include "tiny_dnn/util/util.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif + +namespace tiny_dnn { + +/** + * 2D convolution layer + * + * take input as two-dimensional *image* and applying filtering operation. + **/ +class quantized_convolutional_layer : public layer { + public: + /** + * constructing convolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_size [in] window(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param padding [in] rounding strategy + * valid: use valid pixels of input only. + *output-size + *= (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same width/height. + *output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to apply + *the filters to the input + * @param h_stride [in] specify the vertical interval at which to apply + *the + *filters to the input + **/ + quantized_convolutional_layer( + size_t in_width, + size_t in_height, + size_t window_size, + size_t in_channels, + size_t out_channels, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + conv_set_params(shape3d(in_width, in_height, in_channels), window_size, + window_size, out_channels, pad_type, has_bias, w_stride, + h_stride); + init_backend(backend_type); + } + + /** + * constructing convolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_width [in] window_width(kernel) size of convolution + * @param window_height [in] window_height(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param padding [in] rounding strategy + * valid: use valid pixels of input only. + *output-size + *= (in-width - window_width + 1) * + *(in-height - window_height + 1) * out_channels + * same: add zero-padding to keep same width/height. + *output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to apply + *the filters to the input + * @param h_stride [in] specify the vertical interval at which to apply + *the + *filters to the input + **/ + quantized_convolutional_layer( + size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t in_channels, + size_t out_channels, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + conv_set_params(shape3d(in_width, in_height, in_channels), window_width, + window_height, out_channels, pad_type, has_bias, w_stride, + h_stride); + init_backend(backend_type); + } + + /** + * constructing convolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_size [in] window(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param connection_table [in] definition of connections between in-channels + *and out-channels + * @param pad_type [in] rounding strategy + * valid: use valid pixels of input only. + *output-size = (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same + *width/height. output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply the filters to the input + * @param h_stride [in] specify the vertical interval at which to + *apply + *the filters to the input + **/ + quantized_convolutional_layer( + size_t in_width, + size_t in_height, + size_t window_size, + size_t in_channels, + size_t out_channels, + const core::connection_table &connection_table, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + conv_set_params(shape3d(in_width, in_height, in_channels), window_size, + window_size, out_channels, pad_type, has_bias, w_stride, + h_stride, connection_table); + init_backend(backend_type); + } + + /** + * constructing convolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_width [in] window_width(kernel) size of convolution + * @param window_height [in] window_height(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param connection_table [in] definition of connections between in-channels + *and out-channels + * @param pad_type [in] rounding strategy + * valid: use valid pixels of input only. + *output-size = (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same + *width/height. output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply the filters to the input + * @param h_stride [in] specify the vertical interval at which to + *apply + *the filters to the input + **/ + quantized_convolutional_layer( + size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t in_channels, + size_t out_channels, + const core::connection_table &connection_table, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + conv_set_params(shape3d(in_width, in_height, in_channels), window_width, + window_height, out_channels, pad_type, has_bias, w_stride, + h_stride, connection_table); + init_backend(backend_type); + } + + // move constructor + quantized_convolutional_layer( + quantized_convolutional_layer &&other) // NOLINT + : layer(std::move(other)), + params_(std::move(other.params_)), + cws_(std::move(other.cws_)) { + init_backend(core::backend_t::internal); + } + + ///< number of incoming connections for each output unit + size_t fan_in_size() const override { + return params_.weight.width_ * params_.weight.height_ * params_.in.depth_; + } + + ///< number of outgoing connections for each input unit + size_t fan_out_size() const override { + return (params_.weight.width_ / params_.w_stride) * + (params_.weight.height_ / params_.h_stride) * params_.out.depth_; + } + + /** + * @param in_data input vectors of this layer (data, weight, bias) + * @param out_data output vectors + **/ + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + // launch convolutional kernel + if (in_data.size() == 3) { + layer::backend_->conv2d_q(in_data, out_data); + + } else if (in_data.size() == 6) { + layer::backend_->conv2d_eq(in_data, out_data); + } + } + + /** + * return delta of previous layer (delta=\frac{dE}{da}, a=wx in + *fully-connected layer) + * @param in_data input vectors (same vectors as forward_propagation) + * @param out_data output vectors (same vectors as forward_propagation) + * @param out_grad gradient of output vectors (i-th vector correspond + *with + *out_data[i]) + * @param in_grad gradient of input vectors (i-th vector correspond + *with + *in_data[i]) + **/ + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + layer::backend_->conv2d_q(in_data, out_data, out_grad, in_grad); + } + + std::vector> in_shape() const override { + if (params_.has_bias) { + return {params_.in, params_.weight, + index3d(1, 1, params_.out.depth_)}; + } else { + return {params_.in, params_.weight}; + } + } + + std::vector> out_shape() const override { + return {params_.out}; + } + + std::string layer_type() const override { return "q_conv"; } + +#ifdef DNN_USE_IMAGE_API + image<> weight_to_image() const { + image<> img; + const size_t border_width = 1; + const auto pitch = params_.weight.width_ + border_width; + const auto width = params_.out.depth_ * pitch + border_width; + const auto height = params_.in.depth_ * pitch + border_width; + const image<>::intensity_t bg_color = 255; + const vec_t &W = *this->weights()[0]; + + img.resize(width, height); + img.fill(bg_color); + + auto minmax = std::minmax_element(W.begin(), W.end()); + + for (size_t r = 0; r < params_.in.depth_; ++r) { + for (size_t c = 0; c < params_.out.depth_; ++c) { + if (!params_.tbl.is_connected(c, r)) continue; + + const auto top = r * pitch + border_width; + const auto left = c * pitch + border_width; + + size_t idx = 0; + + for (size_t y = 0; y < params_.weight.height_; ++y) { + for (size_t x = 0; x < params_.weight.width_; ++x) { + idx = c * params_.in.depth_ + r; + idx = params_.weight.get_index(x, y, idx); + const float_t w = W[idx]; + + img.at(left + x, top + y) = static_cast::intensity_t>( + rescale(w, *minmax.first, *minmax.second, 0, 255)); + } + } + } + } + return img; + } +#endif // DNN_USE_IMAGE_API + + friend struct serialization_buddy; + + private: + void conv_set_params( + const shape3d &in, + size_t w_width, + size_t w_height, + size_t outc, + padding ptype, + bool has_bias, + size_t w_stride, + size_t h_stride, + const core::connection_table &tbl = core::connection_table()) { + params_.in = in; + params_.in_padded = + shape3d(in_length(in.width_, w_width, ptype), + in_length(in.height_, w_height, ptype), in.depth_); + params_.out = + shape3d(conv_out_length(in.width_, w_width, w_stride, ptype), + conv_out_length(in.height_, w_height, h_stride, ptype), outc); + params_.weight = shape3d(w_width, w_height, in.depth_ * outc); + params_.has_bias = has_bias; + params_.pad_type = ptype; + params_.w_stride = w_stride; + params_.h_stride = h_stride; + params_.tbl = tbl; + } + + void init() { + if (params_.pad_type == padding::same) { + cws_.prev_out_buf_.resize(1, vec_t(params_.in_padded.size(), float_t{0})); + cws_.prev_delta_padded_.resize( + 1, vec_t(params_.in_padded.size(), float_t{0})); + } else { + cws_.prev_out_buf_.clear(); + } + } + + size_t in_length(size_t in_length, + size_t window_size, + padding pad_type) const { + return pad_type == padding::same ? (in_length + window_size - 1) + : in_length; + } + + static size_t conv_out_length(size_t in_length, + size_t window_size, + size_t stride, + padding pad_type) { + float_t tmp; + if (pad_type == padding::same) { + tmp = static_cast(in_length) / stride; + } else if (pad_type == padding::valid) { + tmp = static_cast(in_length - window_size + 1) / stride; + } else { + throw nn_error("Not recognized pad_type."); + } + return static_cast(ceil(tmp)); + } + + static size_t conv_out_dim(size_t in_width, + size_t in_height, + size_t window_size, + size_t w_stride, + size_t h_stride, + padding pad_type) { + return conv_out_length(in_width, window_size, w_stride, pad_type) * + conv_out_length(in_height, window_size, h_stride, pad_type); + } + + size_t conv_out_dim(size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t w_stride, + size_t h_stride, + padding pad_type) const { + return conv_out_length(in_width, window_width, w_stride, pad_type) * + conv_out_length(in_height, window_height, h_stride, pad_type); + } + + void copy_and_pad_input(const tensor_t &in) { + core::conv_layer_worker_specific_storage &cws = cws_; + + const size_t sample_count = in.size(); + + cws.prev_out_padded_.resize(sample_count); + + if (params_.pad_type == padding::same) { + cws.prev_out_buf_.resize(sample_count, cws.prev_out_buf_[0]); + cws.prev_delta_padded_.resize(sample_count, cws.prev_delta_padded_[0]); + } + + for (size_t sample = 0; sample < sample_count; ++sample) { + if (params_.pad_type == padding::valid) { + cws.prev_out_padded_[sample] = &(in[sample]); + } else { + vec_t *dst = &cws.prev_out_buf_[sample]; + + // make padded version in order to avoid corner-case in + // fprop/bprop + for (size_t c = 0; c < params_.in.depth_; c++) { + float_t *pimg = &(*dst)[params_.in_padded.get_index( + params_.weight.width_ / 2, params_.weight.height_ / 2, c)]; + const float_t *pin = &in[sample][params_.in.get_index(0, 0, c)]; + + for (size_t y = 0; y < params_.in.height_; y++, + pin += params_.in.width_, + pimg += params_.in_padded.width_) { + std::copy(pin, pin + params_.in.width_, pimg); + } + } + + cws.prev_out_padded_[sample] = &(cws.prev_out_buf_[sample]); + } + } + } + + void copy_and_unpad_delta(const tensor_t &delta, tensor_t &delta_unpadded) { + if (params_.pad_type == padding::valid) { + delta_unpadded = delta; + } else { + for (size_t sample = 0; sample < delta.size(); sample++) { + size_t idx = 0; + const vec_t &src = delta[sample]; + vec_t &dst = delta_unpadded[sample]; + + for (size_t c = 0; c < params_.in.depth_; c++) { + float_t *pdst = &dst[params_.in.get_index(0, 0, c)]; + idx = params_.in_padded.get_index(params_.weight.width_ / 2, + params_.weight.height_ / 2, c); + const float_t *pin = &src[idx]; + + for (size_t y = 0; y < params_.in.height_; y++) { + std::copy(pin, pin + params_.in.width_, pdst); + pdst += params_.in.width_; + pin += params_.in_padded.width_; + } + } + } + } + } + + void init_backend(const core::backend_t backend_type) { + std::shared_ptr backend = nullptr; + + // allocate new backend + if (backend_type == core::backend_t::internal) { + backend = std::make_shared( + ¶ms_, [this](const tensor_t &in) { return copy_and_pad_input(in); }, + [this](const tensor_t &delta, tensor_t &dst) { + return copy_and_unpad_delta(delta, dst); + }, + &cws_); + } else { + throw nn_error("Not supported backend type."); + } + + if (backend) { + layer::set_backend(backend); + layer::backend_->set_layer(this); + } else { + throw nn_error("Could not allocate the backend."); + } + } + + /* The convolution parameters */ + core::conv_params params_; + + /* The type of backend */ + // backend_t backend_type_; + + /* Workers buffers */ + core::conv_layer_worker_specific_storage cws_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/quantized_deconvolutional_layer.h b/tiny_dnn/layers/quantized_deconvolutional_layer.h new file mode 100755 index 0000000000000..7af934f9364e4 --- /dev/null +++ b/tiny_dnn/layers/quantized_deconvolutional_layer.h @@ -0,0 +1,497 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/core/backend_tiny.h" +#ifdef CNN_USE_AVX +#include "tiny_dnn/core/backend_avx.h" +#endif + +#include "tiny_dnn/util/util.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif + +namespace tiny_dnn { + +/** + * 2D deconvolution layer + * + * take input as two-dimensional *image* and applying filtering operation. + **/ +class quantized_deconvolutional_layer : public layer { + public: + /** + * constructing deconvolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_size [in] window(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param padding [in] rounding strategy + * valid: use valid pixels of input only. + *output-size + *= (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same width/height. + *output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to apply + *the filters to the input + * @param h_stride [in] specify the vertical interval at which to apply + *the + *filters to the input + **/ + quantized_deconvolutional_layer( + size_t in_width, + size_t in_height, + size_t window_size, + size_t in_channels, + size_t out_channels, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + deconv_set_params(shape3d(in_width, in_height, in_channels), window_size, + window_size, out_channels, pad_type, has_bias, w_stride, + h_stride); + init_backend(backend_type); + } + + /** + * constructing deconvolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_width [in] window_width(kernel) size of convolution + * @param window_height [in] window_height(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param padding [in] rounding strategy + * valid: use valid pixels of input only. + *output-size + *= (in-width - window_width + 1) * + *(in-height - window_height + 1) * out_channels + * same: add zero-padding to keep same width/height. + *output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to apply + *the filters to the input + * @param h_stride [in] specify the vertical interval at which to apply + *the + *filters to the input + **/ + quantized_deconvolutional_layer( + size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t in_channels, + size_t out_channels, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + deconv_set_params(shape3d(in_width, in_height, in_channels), window_width, + window_height, out_channels, pad_type, has_bias, w_stride, + h_stride); + init_backend(backend_type); + } + + /** + * constructing deconvolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_size [in] window(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param connection_table [in] definition of connections between in-channels + *and out-channels + * @param pad_type [in] rounding strategy + * valid: use valid pixels of input only. + *output-size = (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same + *width/height. output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply the filters to the input + * @param h_stride [in] specify the vertical interval at which to + *apply + *the filters to the input + **/ + quantized_deconvolutional_layer( + size_t in_width, + size_t in_height, + size_t window_size, + size_t in_channels, + size_t out_channels, + const core::connection_table &connection_table, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + deconv_set_params(shape3d(in_width, in_height, in_channels), window_size, + window_size, out_channels, pad_type, has_bias, w_stride, + h_stride, connection_table); + init_backend(backend_type); + } + + /** + * constructing deconvolutional layer + * + * @param in_width [in] input image width + * @param in_height [in] input image height + * @param window_width [in] window_width(kernel) size of convolution + * @param window_height [in] window_height(kernel) size of convolution + * @param in_channels [in] input image channels (grayscale=1, rgb=3) + * @param out_channels [in] output image channels + * @param connection_table [in] definition of connections between in-channels + *and out-channels + * @param pad_type [in] rounding strategy + * valid: use valid pixels of input only. + *output-size = (in-width - window_size + 1) * + *(in-height - window_size + 1) * out_channels + * same: add zero-padding to keep same + *width/height. output-size = in-width * in-height * + *out_channels + * @param has_bias [in] whether to add a bias vector to the filter + *outputs + * @param w_stride [in] specify the horizontal interval at which to + *apply the filters to the input + * @param h_stride [in] specify the vertical interval at which to + *apply + *the filters to the input + **/ + quantized_deconvolutional_layer( + size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t in_channels, + size_t out_channels, + const core::connection_table &connection_table, + padding pad_type = padding::valid, + bool has_bias = true, + size_t w_stride = 1, + size_t h_stride = 1, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + deconv_set_params(shape3d(in_width, in_height, in_channels), window_width, + window_height, out_channels, pad_type, has_bias, w_stride, + h_stride, connection_table); + init_backend(backend_type); + } + + // move constructor + quantized_deconvolutional_layer(quantized_deconvolutional_layer &&other) + : layer(std::move(other)), + params_(std::move(other.params_)), + backend_type_(std::move(other.backend_type_)), + deconv_layer_worker_storage_( + std::move(other.deconv_layer_worker_storage_)) { + init_backend(std::move(layer::engine())); + } + + ///< number of incoming connections for each output unit + size_t fan_in_size() const override { + return params_.weight.width_ * params_.weight.height_ * params_.in.depth_; + } + + ///< number of outgoing connections for each input unit + size_t fan_out_size() const override { + return (params_.weight.width_ * params_.w_stride) * + (params_.weight.height_ * params_.h_stride) * params_.out.depth_; + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + // launch deconvolutional kernel + if (in_data.size() == 3) { + layer::backend_->deconv2d_q(in_data, out_data); + + } else if (in_data.size() == 6) { + layer::backend_->deconv2d_eq(in_data, out_data); + } + } + + /** + * return delta of previous layer (delta=\frac{dE}{da}, a=wx in + *fully-connected layer) + * @param in_data input vectors (same vectors as forward_propagation) + * @param out_data output vectors (same vectors as forward_propagation) + * @param out_grad gradient of output vectors (i-th vector correspond + *with + *out_data[i]) + * @param in_grad gradient of input vectors (i-th vector correspond + *with + *in_data[i]) + **/ + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + layer::backend_->deconv2d_q(in_data, out_data, out_grad, in_grad); + } + + std::vector> in_shape() const override { + if (params_.has_bias) { + return {params_.in, params_.weight, + index3d(1, 1, params_.out.depth_)}; + } else { + return {params_.in, params_.weight}; + } + } + + std::vector> out_shape() const override { + return {params_.out_unpadded}; + } + + std::string layer_type() const override { return "q_deconv"; } + +#ifdef DNN_USE_IMAGE_API + image<> weightto_image() const { + image<> img; + const size_t border_width = 1; + const auto pitch = params_.weight.width_ + border_width; + const auto width = params_.out.depth_ * pitch + border_width; + const auto height = params_.in.depth_ * pitch + border_width; + const image<>::intensity_t bg_color = 255; + const vec_t &W = *this->weights()[0]; + + img.resize(width, height); + img.fill(bg_color); + + auto minmax = std::minmax_element(W.begin(), W.end()); + + for (size_t r = 0; r < params_.in.depth_; ++r) { + for (size_t c = 0; c < params_.out.depth_; ++c) { + if (!params_.tbl.is_connected(c, r)) continue; + + const auto top = r * pitch + border_width; + const auto left = c * pitch + border_width; + + size_t idx = 0; + + for (size_t y = 0; y < params_.weight.height_; ++y) { + for (size_t x = 0; x < params_.weight.width_; ++x) { + idx = params_.weight.get_index(x, y, c * params_.in.depth_ + r); + const float_t w = W[idx]; + + img.at(left + x, top + y) = static_cast::intensity_t>( + rescale(w, *minmax.first, *minmax.second, 0, 255)); + } + } + } + } + return img; + } +#endif // DNN_USE_IMAGE_API + + friend struct serialization_buddy; + + private: + void init_backend(const core::backend_t backend_type) { + std::shared_ptr backend = nullptr; + + // allocate new backend + if (backend_type == core::backend_t::internal) { + backend = std::make_shared( + ¶ms_, + [this](const tensor_t &in) { return copy_and_unpad_output(in); }, + [this](const tensor_t &delta, tensor_t &dst) { + return copy_and_pad_delta(delta, dst); + }, + &deconv_layer_worker_storage_); + } else { + throw nn_error("Not supported backend type."); + } + + if (backend) { + layer::set_backend(backend); + layer::backend_->set_layer(this); + } else { + throw nn_error("Could not allocate the backend."); + } + } + + void deconv_set_params( + const shape3d &in, + size_t w_width, + size_t w_height, + size_t outc, + padding ptype, + bool has_bias, + size_t w_stride, + size_t h_stride, + const core::connection_table &tbl = core::connection_table()) { + params_.in = in; + params_.out = + shape3d(deconv_out_length(in.width_, w_width, w_stride), + deconv_out_length(in.height_, w_height, h_stride), outc); + params_.out_unpadded = shape3d( + deconv_out_unpadded_length(in.width_, w_width, w_stride, ptype), + deconv_out_unpadded_length(in.height_, w_height, h_stride, ptype), outc); + params_.weight = shape3d(w_width, w_height, in.depth_ * outc); + params_.has_bias = has_bias; + params_.pad_type = ptype; + params_.w_stride = w_stride; + params_.h_stride = h_stride; + params_.tbl = tbl; + } + + void init() { + core::deconv_layer_worker_specific_storage &dws = + deconv_layer_worker_storage_; + + if (params_.pad_type == padding::same) { + dws.curr_out_buf_.resize(1, vec_t(params_.in.size(), float_t{0})); + dws.curr_delta_padded.resize(1, vec_t(params_.out.size(), float_t{0})); + } else { + dws.curr_out_buf_.clear(); + } + } + + size_t in_length(size_t in_length, + size_t window_size, + padding pad_type) const { + CNN_UNREFERENCED_PARAMETER(window_size); + CNN_UNREFERENCED_PARAMETER(pad_type); + return in_length; + } + + static size_t deconv_out_length(size_t in_length, + size_t window_size, + size_t stride) { + return (size_t)ceil((float_t)(in_length)*stride + window_size - 1); + } + + static size_t deconv_out_unpadded_length(size_t in_length, + size_t window_size, + size_t stride, + padding pad_type) { + return pad_type == padding::same + ? (size_t)ceil((float_t)in_length * stride) + : (size_t)ceil((float_t)(in_length)*stride + window_size - 1); + } + + static size_t deconv_out_dim(size_t in_width, + size_t in_height, + size_t window_size, + size_t w_stride, + size_t h_stride, + padding pad_type) { + return deconv_out_unpadded_length(in_width, window_size, w_stride, + pad_type) * + deconv_out_unpadded_length(in_height, window_size, h_stride, + pad_type); + } + + size_t deconv_out_dim(size_t in_width, + size_t in_height, + size_t window_width, + size_t window_height, + size_t w_stride, + size_t h_stride, + padding pad_type) const { + return deconv_out_unpadded_length(in_width, window_width, w_stride, + pad_type) * + deconv_out_unpadded_length(in_height, window_height, h_stride, + pad_type); + } + + void copy_and_pad_delta(const tensor_t &delta, tensor_t &delta_padded) { + if (params_.pad_type == padding::valid) { + delta_padded = delta; + } else { + for (size_t sample = 0; sample < delta.size(); sample++) { + vec_t &dst = delta_padded[sample]; + const vec_t &src = delta[sample]; + + for (size_t c = 0; c < params_.in.depth_; c++) { + float_t *pdst = &dst[params_.in.get_index(0, 0, c)]; + const float_t *pin = &src[params_.in.get_index(0, 0, c)]; + + for (size_t y = 0; y < params_.in.height_; + y++, pdst += params_.in.width_, pin += params_.in.width_) { + std::copy(pin, pin + params_.in.width_, pdst); + } + } + } + } + } + + void copy_and_unpad_output(const tensor_t &out) { + core::deconv_layer_worker_specific_storage &dws = + deconv_layer_worker_storage_; + + dws.curr_out_buf_ = + tensor_t(out.size(), vec_t(params_.out_unpadded.size(), 0)); + tensor_t *dst_tensor = &dws.curr_out_buf_; + + if (params_.pad_type == padding::valid) { + dws.curr_out_unpadded_ = &out; + } else { + // make unpadded version in order to restore scale in fprop/bprop + for (size_t sample = 0; sample < out.size(); sample++) { + size_t idx = 0; + vec_t &dst = (*dst_tensor)[sample]; + + for (size_t c = 0; c < params_.out_unpadded.depth_; c++) { + float_t *pimg = &dst[params_.out_unpadded.get_index(0, 0, c)]; + idx = params_.out.get_index(params_.weight.width_ / 2, + params_.weight.height_ / 2, c); + + const float_t *pout = &out[sample][idx]; + + for (size_t y = params_.weight.height_ / 2; + y < + params_.out_unpadded.height_ + floor(params_.weight.height_ / 2); + y++, pout += params_.out.width_, + pimg += params_.out_unpadded.width_) { + std::copy(pout, pout + params_.out_unpadded.width_, pimg); + } + } + dws.curr_out_unpadded_ = &dws.curr_out_buf_; + } + } + } + + /* The convolution parameters */ + core::deconv_params params_; + + /* The type of backend */ + core::backend_t backend_type_; + + /* Workers buffers */ + core::deconv_layer_worker_specific_storage deconv_layer_worker_storage_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/quantized_fully_connected_layer.h b/tiny_dnn/layers/quantized_fully_connected_layer.h new file mode 100755 index 0000000000000..3b41add913ee8 --- /dev/null +++ b/tiny_dnn/layers/quantized_fully_connected_layer.h @@ -0,0 +1,115 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/product.h" + +namespace tiny_dnn { + +/** + * compute fully-connected(matmul) operation + **/ +class quantized_fully_connected_layer : public layer { + public: + /** + * @param in_dim [in] number of elements of the input + * @param out_dim [in] number of elements of the output + * @param has_bias [in] whether to include additional bias to the layer + **/ + quantized_fully_connected_layer( + size_t in_dim, + size_t out_dim, + bool has_bias = true, + core::backend_t backend_type = core::backend_t::internal) + : layer(std_input_order(has_bias), {vector_type::data}) { + set_params(in_dim, out_dim, has_bias); + init_backend(backend_type); + } + + // move constructor + quantized_fully_connected_layer(quantized_fully_connected_layer &&other) + : layer(std::move(other)), params_(std::move(other.params_)) { + init_backend(core::backend_t::internal); + } + + size_t fan_in_size() const override { return params_.in_size_; } + + size_t fan_out_size() const override { return params_.out_size_; } + + std::vector> in_shape() const override { + if (params_.has_bias_) { + return {index3d(params_.in_size_, 1, 1), + index3d(params_.in_size_, params_.out_size_, 1), + index3d(params_.out_size_, 1, 1)}; + } else { + return {index3d(params_.in_size_, 1, 1), + index3d(params_.in_size_, params_.out_size_, 1)}; + } + } + + std::vector> out_shape() const override { + return {index3d(params_.out_size_, 1, 1)}; + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + if (in_data.size() == 2 || in_data.size() == 3) { + layer::backend_->fully_q(in_data, out_data); + + } else if (in_data.size() == 4 || in_data.size() == 6) { + layer::backend_->fully_eq(in_data, out_data); + } + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + layer::backend_->fully_q(in_data, out_data, out_grad, in_grad); + } + + std::string layer_type() const override { return "q_fully-connected"; } + + friend struct serialization_buddy; + + protected: + core::fully_params params_; + + void set_params(const size_t in_size, const size_t out_size, bool has_bias) { + params_.in_size_ = in_size; + params_.out_size_ = out_size; + params_.has_bias_ = has_bias; + } + + void init_backend(core::backend_t backend_type) { + std::shared_ptr backend = nullptr; + + // allocate new backend + if (backend_type == core::backend_t::internal) { + backend = std::make_shared(¶ms_); + } else { + throw nn_error("Not supported backend type."); + } + + if (backend) { + layer::set_backend(backend); + layer::set_backend_type(backend_type); + layer::backend_->set_layer(this); + } else { + throw nn_error("Could not allocate the backend."); + } + } +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/recurrent_cell_layer.h b/tiny_dnn/layers/recurrent_cell_layer.h new file mode 100755 index 0000000000000..4f2ddce9fe51a --- /dev/null +++ b/tiny_dnn/layers/recurrent_cell_layer.h @@ -0,0 +1,178 @@ +/* + Copyright (c) 2013, Taiga Nomi + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include + +#include "tiny_dnn/activations/activation_layer.h" +#include "tiny_dnn/activations/tanh_layer.h" +#include "tiny_dnn/core/kernels/recurrent_cell_grad_op.h" +#include "tiny_dnn/core/kernels/recurrent_cell_op.h" +#include "tiny_dnn/layers/layer.h" + +namespace tiny_dnn { + +/** + * RNN layer. + * + * y(t-1) y(t) > h(t) = tanh(b + W*h(t-1) + U*x(t)) + * ^ ^ > y(t) = c + V*h(t) + * |V+c | V+c + * h(t-1) -> h(t) + * ^ +b W ^ +b + * |U |U + * x(t-1) x(t) + * + **/ +class recurrent_cell_layer : public layer { + public: + using layer::parallelize_; + + inline std::vector recurrent_order(bool has_bias) { + if (has_bias) { + return {vector_type::data, // input vector + vector_type::aux, // input state (h(t-1)) + vector_type::weight, // input weights (U) + vector_type::weight, // transition weights (W) + vector_type::weight, // output weights (V) + vector_type::bias, // transition bias + vector_type::bias}; + } else { + return {vector_type::data, // input vector + vector_type::aux, // input state (h(t-1)) + vector_type::weight, // input weights (U) + vector_type::weight, // transition weights (W) + vector_type::weight}; // output weights (V) + } + } + + /** + * @param in_dim [in] number of elements of the input + * @param out_dim [in] number of elements of the output + * @param has_bias [in] whether to include additional bias to the layer + * @param activation [in] activation function to be used internally + **/ + recurrent_cell_layer(size_t in_dim, + size_t out_dim, + bool has_bias = true, + activation_layer *activation = new tanh_layer, + core::backend_t backend_type = core::default_engine()) + : layer(recurrent_order(has_bias), // output bias + {vector_type::data, // output vector + vector_type::aux}) { + set_params(in_dim, out_dim, has_bias, activation); + init_backend(backend_type); + layer::set_backend_type(backend_type); + } + + // move constructor + recurrent_cell_layer(recurrent_cell_layer &&other) + : layer(std::move(other)), + params_(std::move(other.params_)), + kernel_fwd_(std::move(other.kernel_fwd_)), + kernel_back_(std::move(other.kernel_back_)) { + init_backend(std::move(other.engine())); + } + + size_t fan_in_size(size_t i) const override { return in_shape()[i].width_; } + + size_t fan_out_size(size_t i) const override { return in_shape()[i].height_; } + + std::vector> in_shape() const override { + if (params_.has_bias_) { + return {index3d(params_.in_size_, 1, 1), // x + index3d(params_.out_size_, 1, 1), // h(t-1) + index3d(params_.in_size_, params_.out_size_, 1), // U + index3d(params_.out_size_, params_.out_size_, 1), // W + index3d(params_.out_size_, params_.out_size_, 1), // V + index3d(params_.out_size_, 1, 1), // b + index3d(params_.out_size_, 1, 1)}; // c + } else { + return {index3d(params_.in_size_, 1, 1), // x + index3d(params_.out_size_, 1, 1), // h(t-1) + index3d(params_.in_size_, params_.out_size_, 1), // U + index3d(params_.out_size_, params_.out_size_, 1), // W + index3d(params_.out_size_, params_.out_size_, 1)}; // V + } + } + + std::vector> out_shape() const override { + return {index3d(params_.out_size_, 1, 1), + index3d(params_.out_size_, 1, 1)}; // h(t) + } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + // forward fully connected op context + fwd_ctx_.set_in_out(in_data, out_data); + fwd_ctx_.setParallelize(layer::parallelize()); + fwd_ctx_.setEngine(layer::engine()); + + // launch recurrent kernel + kernel_fwd_->compute(fwd_ctx_); + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + // backward fully connected op context + bwd_ctx_.set_in_out(in_data, out_data, out_grad, in_grad); + bwd_ctx_.setParallelize(layer::parallelize()); + bwd_ctx_.setEngine(layer::engine()); + + // launch recurrent kernel + kernel_back_->compute(bwd_ctx_); + } + + void set_activation(std::shared_ptr activation) { + params_.activation_ = activation; + } + + std::string layer_type() const override { return "recurrent-cell"; } + + friend struct serialization_buddy; + + protected: + void set_params(const size_t in_size, + const size_t out_size, + bool has_bias, + activation_layer *activation) { + params_.in_size_ = in_size; + params_.out_size_ = out_size; + params_.has_bias_ = has_bias; + params_.activation_ = std::shared_ptr(activation); + } + + void init_backend(core::backend_t backend_type) { + CNN_UNREFERENCED_PARAMETER(backend_type); + core::OpKernelConstruction ctx = + core::OpKernelConstruction(layer::device(), ¶ms_); + kernel_fwd_.reset(new RecurrentCellOp(ctx)); + kernel_back_.reset(new RecurrentCellGradOp(ctx)); + } + + private: + /* The layer parameters */ + core::recurrent_cell_params params_; + + /* forward op context */ + core::OpKernelContext fwd_ctx_; + + /* backward op context */ + core::OpKernelContext bwd_ctx_; + + /* Forward and backward ops */ + std::shared_ptr kernel_fwd_; + std::shared_ptr kernel_back_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/layers/slice_layer.h b/tiny_dnn/layers/slice_layer.h new file mode 100755 index 0000000000000..507e99bc3e33e --- /dev/null +++ b/tiny_dnn/layers/slice_layer.h @@ -0,0 +1,223 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +enum class slice_type { slice_samples, slice_channels }; + +/** + * slice an input data into multiple outputs along a given slice dimension. + **/ +class slice_layer : public layer { + public: + typedef layer Base; + + /** + * @param in_shape [in] size (width * height * channels) of input data + * @param slice_type [in] target axis of slicing + * @param num_outputs [in] number of output layers + * + * example1: + * input: NxKxWxH = 4x3x2x2 (N:batch-size, K:channels, W:width, + *H:height) + * slice_type: slice_samples + * num_outputs: 3 + * + * output[0]: 1x3x2x2 + * output[1]: 1x3x2x2 + * output[2]: 2x3x2x2 (mod data is assigned to the last output) + * + * example2: + * input: NxKxWxH = 4x6x2x2 + * slice_type: slice_channels + * num_outputs: 3 + * + * output[0]: 4x2x2x2 + * output[1]: 4x2x2x2 + * output[2]: 4x2x2x2 + **/ + slice_layer(const shape3d &in_shape, + slice_type slice_type, + size_t num_outputs) + : layer(std::vector(1, vector_type::data), + std::vector(num_outputs, vector_type::data)), + in_shape_(in_shape), + slice_type_(slice_type), + num_outputs_(num_outputs) { + set_shape(); + } + + slice_layer(const layer &prev_layer, + slice_type slice_type, + size_t num_outputs) + : layer(std::vector(1, vector_type::data), + std::vector(num_outputs, vector_type::data)), + in_shape_(prev_layer.out_shape()[0]), + slice_type_(slice_type), + num_outputs_(num_outputs) { + set_shape(); + } + + std::string layer_type() const override { return "slice"; } + + std::vector in_shape() const override { return {in_shape_}; } + + std::vector out_shape() const override { return out_shapes_; } + + void forward_propagation(const std::vector &in_data, + std::vector &out_data) override { + switch (slice_type_) { + case slice_type::slice_samples: + slice_data_forward(*in_data[0], out_data); + break; + case slice_type::slice_channels: + slice_channels_forward(*in_data[0], out_data); + break; + default: throw nn_not_implemented_error(); + } + } + + void back_propagation(const std::vector &in_data, + const std::vector &out_data, + std::vector &out_grad, + std::vector &in_grad) override { + CNN_UNREFERENCED_PARAMETER(in_data); + CNN_UNREFERENCED_PARAMETER(out_data); + + switch (slice_type_) { + case slice_type::slice_samples: + slice_data_backward(out_grad, *in_grad[0]); + break; + case slice_type::slice_channels: + slice_channels_backward(out_grad, *in_grad[0]); + break; + default: throw nn_not_implemented_error(); + } + } + + slice_type get_slice_type() const { return slice_type_; } + + friend struct serialization_buddy; + + private: + void slice_data_forward(const tensor_t &in_data, + std::vector &out_data) { + const vec_t *in = &in_data[0]; + + for (size_t i = 0; i < num_outputs_; i++) { + tensor_t &out = *out_data[i]; + + std::copy(in, in + slice_size_[i], &out[0]); + + in += slice_size_[i]; + } + } + + void slice_data_backward(std::vector &out_grad, + tensor_t &in_grad) { + vec_t *in = &in_grad[0]; + + for (size_t i = 0; i < num_outputs_; i++) { + tensor_t &out = *out_grad[i]; + + std::copy(&out[0], &out[0] + slice_size_[i], in); + + in += slice_size_[i]; + } + } + + void slice_channels_forward(const tensor_t &in_data, + std::vector &out_data) { + size_t channel_idx = 0; + const size_t num_samples = in_data.size(); + const size_t spatial_dim = in_shape_.area(); + + for (size_t i = 0; i < num_outputs_; i++) { + for (size_t s = 0; s < num_samples; s++) { + float_t *out = &(*out_data[i])[s][0]; + const float_t *in = &in_data[s][0] + channel_idx * spatial_dim; + + std::copy(in, in + slice_size_[i] * spatial_dim, out); + } + channel_idx += slice_size_[i]; + } + } + + void slice_channels_backward(std::vector &out_grad, + tensor_t &in_grad) { + size_t channel_idx = 0; + const size_t num_samples = in_grad.size(); + const size_t spatial_dim = in_shape_.area(); + + for (size_t i = 0; i < num_outputs_; i++) { + for (size_t s = 0; s < num_samples; s++) { + const float_t *out = &(*out_grad[i])[s][0]; + float_t *in = &in_grad[s][0] + channel_idx * spatial_dim; + + std::copy(out, out + slice_size_[i] * spatial_dim, in); + } + channel_idx += slice_size_[i]; + } + } + + void set_sample_count(size_t sample_count) override { + if (slice_type_ == slice_type::slice_samples) { + if (num_outputs_ == 0) + throw nn_error("num_outputs must be positive integer"); + + size_t sample_per_out = sample_count / num_outputs_; + + slice_size_.resize(num_outputs_, sample_per_out); + slice_size_.back() = sample_count - (sample_per_out * (num_outputs_ - 1)); + } + Base::set_sample_count(sample_count); + } + + void set_shape() { + switch (slice_type_) { + case slice_type::slice_samples: set_shape_data(); break; + case slice_type::slice_channels: set_shape_channels(); break; + default: throw nn_not_implemented_error(); + } + } + + void set_shape_data() { out_shapes_.resize(num_outputs_, in_shape_); } + + void set_shape_channels() { + size_t channel_per_out = in_shape_.depth_ / num_outputs_; + + out_shapes_.resize(num_outputs_); + for (size_t i = 0; i < num_outputs_; i++) { + size_t ch = channel_per_out; + + if (i == num_outputs_ - 1) { + assert(in_shape_.depth_ >= i * channel_per_out); + ch = in_shape_.depth_ - i * channel_per_out; + } + + slice_size_.push_back(ch); + out_shapes_[i] = shape3d(in_shape_.width_, in_shape_.height_, ch); + } + } + + shape3d in_shape_; + slice_type slice_type_; + size_t num_outputs_; + std::vector out_shapes_; + std::vector slice_size_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/lossfunctions/loss_function.h b/tiny_dnn/lossfunctions/loss_function.h new file mode 100755 index 0000000000000..204ded9519cef --- /dev/null +++ b/tiny_dnn/lossfunctions/loss_function.h @@ -0,0 +1,220 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +// mean-squared-error loss function for regression +class mse { + public: + static float_t f(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + float_t d{0.0}; + + for (size_t i = 0; i < y.size(); ++i) d += (y[i] - t[i]) * (y[i] - t[i]); + + return d / static_cast(y.size()); + } + + static vec_t df(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + vec_t d(t.size()); + float_t factor = float_t(2) / static_cast(t.size()); + + for (size_t i = 0; i < y.size(); ++i) d[i] = factor * (y[i] - t[i]); + + return d; + } +}; + +// absolute loss function for regression +class absolute { + public: + static float_t f(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + float_t d{0}; + + for (size_t i = 0; i < y.size(); ++i) d += std::abs(y[i] - t[i]); + + return d / static_cast(y.size()); + } + + static vec_t df(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + vec_t d(t.size()); + float_t factor = float_t(1) / static_cast(t.size()); + + for (size_t i = 0; i < y.size(); ++i) { + float_t sign = y[i] - t[i]; + if (sign < float_t{0.f}) + d[i] = -factor; + else if (sign > float_t{0.f}) + d[i] = factor; + else + d[i] = {0}; + } + + return d; + } +}; + +// absolute loss with epsilon range for regression +// epsilon range [-eps, eps] with eps = 1./fraction +template +class absolute_eps { + public: + static float_t f(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + float_t d{0}; + const float_t eps = float_t(1) / fraction; + + for (size_t i = 0; i < y.size(); ++i) { + float_t diff = std::abs(y[i] - t[i]); + if (diff > eps) d += diff; + } + return d / static_cast(y.size()); + } + + static vec_t df(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + vec_t d(t.size()); + const float_t factor = float_t(1) / static_cast(t.size()); + const float_t eps = float_t(1) / fraction; + + for (size_t i = 0; i < y.size(); ++i) { + float_t sign = y[i] - t[i]; + if (sign < -eps) + d[i] = -factor; + else if (sign > eps) + d[i] = factor; + else + d[i] = 0.f; + } + return d; + } +}; + +// cross-entropy loss function for (multiple independent) binary classifications +class cross_entropy { + public: + static float_t f(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + float_t d{0}; + + for (size_t i = 0; i < y.size(); ++i) + d += -t[i] * std::log(y[i]) - + (float_t(1) - t[i]) * std::log(float_t(1) - y[i]); + + return d; + } + + static vec_t df(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + vec_t d(t.size()); + + for (size_t i = 0; i < y.size(); ++i) + d[i] = (y[i] - t[i]) / (y[i] * (float_t(1) - y[i])); + + return d; + } +}; + +// cross-entropy loss function for multi-class classification +class cross_entropy_multiclass { + public: + static float_t f(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + float_t d{0.0}; + + for (size_t i = 0; i < y.size(); ++i) d += -t[i] * std::log(y[i]); + + return d; + } + + static vec_t df(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + vec_t d(t.size()); + + for (size_t i = 0; i < y.size(); ++i) d[i] = -t[i] / y[i]; + + return d; + } +}; + +template +vec_t gradient(const vec_t &y, const vec_t &t) { + assert(y.size() == t.size()); + return E::df(y, t); +} + +template +std::vector gradient(const std::vector &y, + const std::vector &t) { + std::vector grads(y.size()); + + assert(y.size() == t.size()); + + for (size_t i = 0; i < y.size(); i++) grads[i] = gradient(y[i], t[i]); + + return grads; +} + +inline void apply_cost_if_defined(std::vector &sample_gradient, + const std::vector &sample_cost) { + if (sample_gradient.size() == sample_cost.size()) { + // @todo consider adding parallelism + const size_t channel_count = sample_gradient.size(); + for (size_t channel = 0; channel < channel_count; ++channel) { + if (sample_gradient[channel].size() == sample_cost[channel].size()) { + const size_t element_count = sample_gradient[channel].size(); + + // @todo optimize? (use AVX or so) + for (size_t element = 0; element < element_count; ++element) { + sample_gradient[channel][element] *= sample_cost[channel][element]; + } + } + } + } +} + +// gradient for a minibatch +template +std::vector gradient(const std::vector &y, + const std::vector &t, + const std::vector &t_cost) { + const size_t sample_count = y.size(); + const size_t channel_count = y[0].size(); + + std::vector gradients(sample_count); + + CNN_UNREFERENCED_PARAMETER(channel_count); + assert(y.size() == t.size()); + assert(t_cost.empty() || t_cost.size() == t.size()); + + // @todo add parallelism + for (size_t sample = 0; sample < sample_count; ++sample) { + assert(y[sample].size() == channel_count); + assert(t[sample].size() == channel_count); + assert(t_cost.empty() || t_cost[sample].empty() || + t_cost[sample].size() == channel_count); + + gradients[sample] = gradient(y[sample], t[sample]); + + if (sample < t_cost.size()) { + apply_cost_if_defined(gradients[sample], t_cost[sample]); + } + } + + return gradients; +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/models/alexnet.h b/tiny_dnn/models/alexnet.h new file mode 100755 index 0000000000000..925b874473a5c --- /dev/null +++ b/tiny_dnn/models/alexnet.h @@ -0,0 +1,40 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +namespace models { + +// Based on: +// https://github.com/DeepMark/deepmark/blob/master/torch/image%2Bvideo/alexnet.lua +class alexnet : public tiny_dnn::network { + public: + explicit alexnet(const std::string &name = "") + : tiny_dnn::network(name) { + // todo: (karandesai) shift this to tiny_dnn::activation + using relu = tiny_dnn::activation::relu; + using conv = tiny_dnn::layers::conv; + using max_pool = tiny_dnn::layers::max_pool; + *this << conv(224, 224, 11, 11, 3, 64, padding::valid, true, 4, 4); + *this << relu(54, 54, 64); + *this << max_pool(54, 54, 64, 2); + *this << conv(27, 27, 5, 5, 64, 192, padding::valid, true, 1, 1); + *this << relu(23, 23, 192); + *this << max_pool(23, 23, 192, 1); + *this << conv(23, 23, 3, 3, 192, 384, padding::valid, true, 1, 1); + *this << relu(21, 21, 384); + *this << conv(21, 21, 3, 3, 384, 256, padding::valid, true, 1, 1); + *this << relu(19, 19, 256); + *this << conv(19, 19, 3, 3, 256, 256, padding::valid, true, 1, 1); + *this << relu(17, 17, 256); + *this << max_pool(17, 17, 256, 1); + } +}; + +} // namespace models diff --git a/tiny_dnn/network.h b/tiny_dnn/network.h new file mode 100755 index 0000000000000..07cfaecfdd398 --- /dev/null +++ b/tiny_dnn/network.h @@ -0,0 +1,1152 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#ifndef CNN_NO_SERIALIZATION +#include +#endif +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/lossfunctions/loss_function.h" +#include "tiny_dnn/nodes.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +enum class content_type { + weights, ///< save/load the weights + model, ///< save/load the network architecture + weights_and_model ///< save/load both the weights and the architecture +}; + +enum class file_format { binary, json }; + +struct result { + result() : num_success(0), num_total(0) {} + + float_t accuracy() const { return float_t(num_success * 100.0 / num_total); } + + template + void print_summary(std::basic_ostream &os) const { + os << "accuracy:" << accuracy() << "% (" << num_success << "/" << num_total + << ")" << std::endl; + } + + template + void print_detail(std::basic_ostream &os) const { + print_summary(os); + auto all_labels = labels(); + + os << std::setw(5) << "*" + << " "; + for (auto c : all_labels) os << std::setw(5) << c << " "; + os << std::endl; + + for (auto r : all_labels) { + os << std::setw(5) << r << " "; + const auto row_iter = confusion_matrix.find(r); + for (auto c : all_labels) { + int count = 0; + if (row_iter != confusion_matrix.end()) { + const auto &row = row_iter->second; + const auto col_iter = row.find(c); + if (col_iter != row.end()) { + count = col_iter->second; + } + } + os << std::setw(5) << count << " "; + } + os << std::endl; + } + } + + std::set labels() const { + std::set all_labels; + for (auto const &r : confusion_matrix) { + all_labels.insert(r.first); + for (auto const &c : r.second) all_labels.insert(c.first); + } + return all_labels; + } + + int num_success; + int num_total; + std::map> confusion_matrix; +}; + +enum grad_check_mode { + GRAD_CHECK_ALL, ///< check all elements of weights + GRAD_CHECK_RANDOM ///< check 10 randomly selected weights +}; + +template +class network; + +template +network &operator<<(network &n, Layer &&l); + +void construct_graph(network &graph, + const std::vector> &inputs, + const std::vector> &outputs); + +void construct_graph(network &graph, + const std::vector &inputs, + const std::vector &outputs); +/** + * A model of neural networks in tiny-dnn + * + * There are two types of network model available: sequential and graph. + * A graph representation describe network as computational graph - + * each node of graph is layer, and each directed edge holds tensor and + * its gradients. Sequential representation describe network as linked list - + * each layer has at most one predecessor and one successor layer. + * + * Two types of network is represented as network and network + *class. + * These two classes have same API, except for its construction. + * + * using namespace tiny_dnn; + * using namespace tiny_dnn::layers; + * + * std::vector data; + * std::vector label; + * + * network net("foo"); + * std::cout << net.name(); // "foo" + * + * // simply stack layers by operator << + * net << fc(50, 200) << tanh() << fc(200, 10) << tanh(); + * + * // prepare optimizer + * adagrad opt; + * + * // then train + * net.train(opt, data, label, 10, 20); + * + * + * @param NetType specify the network is "sequential" or "graph". + * "sequential" means the network doesn't have any branch or + *merge pass. + * if the network has branch/merge, "graph" can be used. + **/ +template +class network { + public: + typedef typename std::vector::iterator iterator; + typedef typename std::vector::const_iterator const_iterator; + + explicit network(const std::string &name = "") + : name_(name), stop_training_(false) {} + + /** + * name of the network + **/ + std::string name() const { return name_; } + + /** + * explicitly initialize weights of all layers + **/ + void init_weight() { net_.setup(true); } + + /** + * executes forward-propagation and returns output + **/ + vec_t predict(const vec_t &in) { return fprop(in); } + + /** + * executes forward-propagation and returns output + **/ + tensor_t predict(const tensor_t &in) { return fprop(in); } + + /** + * executes forward-propagation and returns output + **/ + std::vector predict(const std::vector &in) { + return fprop(in); + } + + /** + * executes forward-propagation and returns maximum output + **/ + float_t predict_max_value(const vec_t &in) { return fprop_max(in); } + + /** + * executes forward-propagation and returns maximum output index + **/ + label_t predict_label(const vec_t &in) { return fprop_max_index(in); } + + /** + * executes forward-propagation and returns output + * + * @param in input value range(double[], std::vector, + *std::list etc) + **/ + template + vec_t predict(const Range &in) { + using std::begin; // for ADL + using std::end; + return predict(vec_t(begin(in), end(in))); + } + + /** + * trains the network for a fixed number of epochs (for classification task) + * + * The difference between train and fit method is how to specify desired + * output. + * This method takes label_t argument and convert to target vector + * automatically. + * To train correctly, output dimension of last layer must be greater or + * equal + * to + * number of label-ids. + * + * @param optimizer optimizing algorithm for training + * @param inputs array of input data + * @param class_labels array of label-id for each input data(0-origin) + * @param batch_size number of samples per parameter update + * @param epoch number of training epochs + * @param on_batch_enumerate callback for each mini-batch enumerate + * @param on_epoch_enumerate callback for each epoch + * @param reset_weights set true if reset current network weights + * @param n_threads number of tasks + * @param t_cost target costs (leave to nullptr in order to + * assume + * equal cost for every target) + */ + template + bool train(Optimizer &optimizer, + const std::vector &inputs, + const std::vector &class_labels, + size_t batch_size, + int epoch, + OnBatchEnumerate on_batch_enumerate, + OnEpochEnumerate on_epoch_enumerate, + const bool reset_weights = false, + const int n_threads = CNN_TASK_SIZE, + const std::vector &t_cost = std::vector()) { + if (inputs.size() != class_labels.size()) { + return false; + } + if (inputs.size() < batch_size || class_labels.size() < batch_size) { + return false; + } + std::vector input_tensor, output_tensor, t_cost_tensor; + normalize_tensor(inputs, input_tensor); + normalize_tensor(class_labels, output_tensor); + if (!t_cost.empty()) normalize_tensor(t_cost, t_cost_tensor); + + return fit(optimizer, input_tensor, output_tensor, batch_size, epoch, + on_batch_enumerate, on_epoch_enumerate, reset_weights, + n_threads, t_cost_tensor); + } + + /** + * trains the network for a fixed number of epochs to generate desired + * output. + * + * This method executes fixed number of training steps and invoke callbacks + * for + * each mini-batch/epochs. + * The network is trained to minimize given loss function(specified by + * template + * parameter). + * + * Shape of inputs and desired_outputs must be same to network inputs. For + * example, if your network + * has 2 input layers that takes N dimensional array, for each element of + * inputs must be [2xN] + * array. + * + * @code + * network net; + * adagrad opt; + * + * net << layers::fc(2, 3) << activation::tanh() + * << layers::fc(3, 1) << activation::relu(); + * + * // 2training data, each data is float_t[2] + * std::vector data { { 1, 0 }, { 0, 2 } }; + * std::vector out { { 2 }, { 1 } }; + * + * net.fit(opt, data, out, 1, 1); + * + * // 2training data, each data is float_t[1][2] + * // this form is also valid + * std::vector data2{ { { 1, 0 } }, { { 0, 2 } } }; + * std::vector out2 { { { 2 } }, { { 1 } } }; + * + * net.fit(opt, data2, out2, 1, 1); + * @endcode + * + * + * @param optimizer optimizing algorithm for training + * @param inputs array of input data + * @param desired_outputs array of desired output + * @param batch_size number of samples per parameter update + * @param epoch number of training epochs + * @param on_batch_enumerate callback for each mini-batch enumerate + * @param on_epoch_enumerate callback for each epoch + * @param reset_weights set true if reset current network weights + * @param n_threads number of tasks + * @param t_cost target costs (leave to nullptr in order to + * assume + * equal cost for every target) + */ + template + bool fit(Optimizer &optimizer, + const std::vector &inputs, + const std::vector &desired_outputs, + size_t batch_size, + int epoch, + OnBatchEnumerate on_batch_enumerate, + OnEpochEnumerate on_epoch_enumerate, + const bool reset_weights = false, + const int n_threads = CNN_TASK_SIZE, + const std::vector &t_cost = std::vector()) { + std::vector input_tensor, output_tensor, t_cost_tensor; + normalize_tensor(inputs, input_tensor); + normalize_tensor(desired_outputs, output_tensor); + if (!t_cost.empty()) normalize_tensor(t_cost, t_cost_tensor); + + return fit(optimizer, input_tensor, output_tensor, batch_size, epoch, + on_batch_enumerate, on_epoch_enumerate, reset_weights, + n_threads, t_cost_tensor); + } + + /** + * @param optimizer optimizing algorithm for training + * @param inputs array of input data + * @param desired_outputs array of desired output + * @param batch_size number of samples per parameter update + * @param epoch number of training epochs + **/ + template + bool fit(Optimizer &optimizer, + const std::vector &inputs, + const std::vector &desired_outputs, + size_t batch_size = 1, + int epoch = 1) { + return fit(optimizer, inputs, desired_outputs, batch_size, epoch, + nop, nop); + } + + /** + * @param optimizer optimizing algorithm for training + * @param inputs array of input data + * @param class_labels array of label-id for each input data(0-origin) + * @param batch_size number of samples per parameter update + * @param epoch number of training epochs + **/ + template + bool train(Optimizer &optimizer, + const std::vector &inputs, + const std::vector &class_labels, + size_t batch_size = 1, + int epoch = 1) { + return train(optimizer, inputs, class_labels, batch_size, epoch, nop, + nop); + } + + /** + * @deprecated use fit instead for regression task + **/ + template + bool train(Optimizer &optimizer, + const std::vector &in, + const std::vector &t, + size_t batch_size = 1, + int epoch = 1) { + return fit(optimizer, in, t, batch_size, epoch, nop, nop); + } + + /** + * set the netphase to train or test + * @param phase phase of network, could be train or test + */ + void set_netphase(net_phase phase) { + for (auto n : net_) { + n->set_context(phase); + } + } + + /** + * request to finish an ongoing training + * + * It is safe to test the current network performance in @a + * on_batch_enumerate + * and + * @a on_epoch_enumerate callbacks during training. + */ + void stop_ongoing_training() { stop_training_ = true; } + + /** + * test and generate confusion-matrix for classification task + **/ + result test(const std::vector &in, const std::vector &t) { + result test_result; + set_netphase(net_phase::test); + for (size_t i = 0; i < in.size(); i++) { + const label_t predicted = fprop_max_index(in[i]); + const label_t actual = t[i]; + + if (predicted == actual) test_result.num_success++; + test_result.num_total++; + test_result.confusion_matrix[predicted][actual]++; + } + return test_result; + } + + /** + * generate output for each input + **/ + std::vector test(const std::vector &in) { + std::vector test_result(in.size()); + set_netphase(net_phase::test); + for (size_t i = 0; i < in.size(); i++) { + test_result[i] = predict(in[i]); + } + return test_result; + } + + /** + * calculate loss value (the smaller, the better) for regression task + **/ + template + float_t get_loss(const std::vector &in, const std::vector &t) { + float_t sum_loss = float_t(0); + + for (size_t i = 0; i < in.size(); i++) { + const vec_t predicted = predict(in[i]); + sum_loss += E::f(predicted, t[i]); + } + return sum_loss; + } + + /** + * calculate loss value (the smaller, the better) for regression task + **/ + template + float_t get_loss(const std::vector &in, const std::vector &t) { + float_t sum_loss = float_t(0); + std::vector in_tensor; + normalize_tensor(in, in_tensor); + + for (size_t i = 0; i < in.size(); i++) { + const tensor_t predicted = predict(in_tensor[i]); + for (size_t j = 0; j < predicted.size(); j++) { + sum_loss += E::f(predicted[j], t[i][j]); + } + } + return sum_loss; + } + + /** + * checking gradients calculated by bprop + * detail information: + * http://ufldl.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization + **/ + template + bool gradient_check(const std::vector &in, + const std::vector> &t, + float_t eps, + grad_check_mode mode) { + assert(in.size() == t.size()); + + std::vector v(t.size()); + const size_t sample_count = t.size(); + for (size_t sample = 0; sample < sample_count; ++sample) { + net_.label2vec(t[sample], v[sample]); + } + + for (auto current : net_) { // ignore first input layer + if (current->weights().size() < 2) { + continue; + } + std::vector weights = current->weights(); + std::vector grads = current->weights_grads(); + + if (weights.empty() || (*weights[0]).empty()) continue; + assert(weights.size() == grads.size()); + + switch (mode) { + case GRAD_CHECK_ALL: + for (size_t i = 0; i < weights.size(); i++) { + vec_t &w = *weights[i]; + tensor_t &dw = *grads[i]; + for (size_t j = 0; j < w.size(); j++) { + if (!calc_delta(in, v, w, dw, j, eps)) { + return false; + } + } + } + break; + case GRAD_CHECK_RANDOM: + for (size_t i = 0; i < grads.size(); i++) { + vec_t &w = *weights[i]; + tensor_t &dw = *grads[i]; + for (size_t j = 0; j < 10; j++) { + if (!calc_delta(in, v, w, dw, uniform_idx(w), eps)) { + return false; + } + } + } + break; + default: throw nn_error("unknown grad-check type"); + } + } + return true; + } + + /** + * return number of layers + **/ + size_t layer_size() const { return net_.size(); } + + /** + * @deprecated use layer_size() instread. + **/ + size_t depth() const { return layer_size(); } + + /** + * return raw pointer of index-th layer + **/ + const layer *operator[](size_t index) const { return net_[index]; } + + /** + * return raw pointer of index-th layer + **/ + layer *operator[](size_t index) { return net_[index]; } + + /** + * return index-th layer as + * throw nn_error if index-th layer cannot be converted to T + **/ + template + const T &at(size_t index) const { + return net_.template at(index); + } + + template + T &at(size_t index) { + return net_.template at(index); + } + + /** + * return total number of elements of output data + **/ + size_t out_data_size() const { return net_.out_data_size(); } + + /** + * return total number of elements of input data + */ + size_t in_data_size() const { return net_.in_data_size(); } + + /** + * set weight initializer to all layers + **/ + template + network &weight_init(const WeightInit &f) { + auto ptr = std::make_shared(f); + for (auto &l : net_) l->weight_init(ptr); + return *this; + } + + /** + * set bias initializer to all layers + **/ + template + network &bias_init(const BiasInit &f) { + auto ptr = std::make_shared(f); + for (auto &l : net_) l->bias_init(ptr); + return *this; + } + + /** + * returns if 2 networks have almost( + bool has_same_weights(const network &rhs, float_t eps) const { + auto first1 = net_.begin(); + auto first2 = rhs.net_.begin(); + auto last1 = net_.end(); + auto last2 = rhs.net_.end(); + + for (; first1 != last1 && first2 != last2; ++first1, ++first2) + if (!(*first1)->has_same_weights(**first2, eps)) return false; + return true; + } + + iterator begin() { return net_.begin(); } + iterator end() { return net_.end(); } + const_iterator begin() const { return net_.begin(); } + const_iterator end() const { return net_.end(); } + + void load(const std::string &filename, + content_type what = content_type::weights_and_model, + file_format format = file_format::binary) { +#ifndef CNN_NO_SERIALIZATION + std::ifstream ifs(filename.c_str(), std::ios::binary | std::ios::in); + if (ifs.fail() || ifs.bad()) throw nn_error("failed to open:" + filename); + + switch (format) { + case file_format::binary: { + cereal::BinaryInputArchive bi(ifs); + from_archive(bi, what); + } break; + case file_format::json: { + cereal::JSONInputArchive ji(ifs); + from_archive(ji, what); + } break; + default: throw nn_error("invalid serialization format"); + } +#else + throw nn_error("tiny-dnn was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION + } + + void save(const std::string &filename, + content_type what = content_type::weights_and_model, + file_format format = file_format::binary) const { +#ifndef CNN_NO_SERIALIZATION + std::ofstream ofs(filename.c_str(), std::ios::binary | std::ios::out); + if (ofs.fail() || ofs.bad()) throw nn_error("failed to open:" + filename); + + switch (format) { + case file_format::binary: { + cereal::BinaryOutputArchive bo(ofs); + to_archive(bo, what); + } break; + case file_format::json: { + cereal::JSONOutputArchive jo(ofs); + to_archive(jo, what); + } break; + default: throw nn_error("invalid serialization format"); + } +#else + throw nn_error("tiny-dnn was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION + } + + /** + * save the network architecture as json string + **/ + std::string to_json(content_type what = content_type::model) const { +#ifndef CNN_NO_SERIALIZATION + std::stringstream ss; + { + cereal::JSONOutputArchive oa(ss); + to_archive(oa, what); + } + return ss.str(); +#else + throw nn_error("tiny-dnn was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION + } + + /** + * load the network architecture from json string + **/ + void from_json(const std::string &json_string, + content_type what = content_type::model) { +#ifndef CNN_NO_SERIALIZATION + std::stringstream ss; + ss << json_string; + cereal::JSONInputArchive ia(ss); + from_archive(ia, what); +#else + throw nn_error("tiny-dnn was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION + } + + ///< @deprecated use save(filename,target,format) instead. + void save(std::ostream &os) const { + os.precision(std::numeric_limits::digits10); + net_.save(os); + } + + ///< @deprecated use load(filename,target,format) instead. + void load(std::istream &is) { + is.precision(std::numeric_limits::digits10); + net_.load(is); + } + + /** + * load network weights from filepath, 30 times faster than stream reading + * @deprecated use load_weights instead. + **/ + void fast_load(const char *filepath) { + FILE *stream = fopen(filepath, "r"); + std::vector data; + double temp; + while (fscanf(stream, "%lf", &temp) > 0) data.push_back(float_t(temp)); + fclose(stream); + + net_.load(data); + } + + template + void to_archive(OutputArchive &ar, + content_type what = content_type::weights_and_model) const { + if (what == content_type::model || + what == content_type::weights_and_model) { + net_.save_model(ar); + } + if (what == content_type::weights || + what == content_type::weights_and_model) { + net_.save_weights(ar); + } + } + + template + void from_archive(InputArchive &ar, + content_type what = content_type::weights_and_model) { + if (what == content_type::model || + what == content_type::weights_and_model) { + net_.load_model(ar); + } + if (what == content_type::weights || + what == content_type::weights_and_model) { + net_.load_weights(ar); + } + } + + protected: + float_t fprop_max(const vec_t &in) { + const vec_t &prediction = fprop(in); + return *std::max_element(std::begin(prediction), std::end(prediction)); + } + + label_t fprop_max_index(const vec_t &in) { + return label_t(max_index(fprop(in))); + } + + private: + template + friend network &operator<<(network &n, Layer &&l); + + friend void construct_graph( + network &graph, + const std::vector> &inputs, + const std::vector> &outputs); + + friend void construct_graph(network &graph, + const std::vector &inputs, + const std::vector &outputs); + + template + bool fit(Optimizer &optimizer, + const std::vector &inputs, + const std::vector &desired_outputs, + size_t batch_size, + int epoch, + OnBatchEnumerate on_batch_enumerate, + OnEpochEnumerate on_epoch_enumerate, + const bool reset_weights = false, + const int n_threads = CNN_TASK_SIZE, + const std::vector &t_cost = std::vector()) { + // check_training_data(in, t); + check_target_cost_matrix(desired_outputs, t_cost); + set_netphase(net_phase::train); + net_.setup(reset_weights); + + for (auto n : net_) n->set_parallelize(true); + optimizer.reset(); + stop_training_ = false; + in_batch_.resize(batch_size); + t_batch_.resize(batch_size); + for (int iter = 0; iter < epoch && !stop_training_; iter++) { + for (size_t i = 0; i < inputs.size() && !stop_training_; + i += batch_size) { + train_once( + optimizer, &inputs[i], &desired_outputs[i], + static_cast(std::min(batch_size, (size_t)inputs.size() - i)), + n_threads, get_target_cost_sample_pointer(t_cost, i)); + on_batch_enumerate(); + + /* if (i % 100 == 0 && layers_.is_exploded()) { + std::cout << "[Warning]Detected infinite value in weight. stop + learning." << std::endl; + return false; + } */ + } + on_epoch_enumerate(); + } + set_netphase(net_phase::test); + return true; + } + + /** + * train on one minibatch + * + * @param size is the number of data points to use in this batch + */ + template + void train_once(Optimizer &optimizer, + const tensor_t *in, + const tensor_t *t, + int size, + const int nbThreads, + const tensor_t *t_cost) { + if (size == 1) { + bprop(fprop(in[0]), t[0], t_cost ? t_cost[0] : tensor_t()); + net_.update_weights(&optimizer); + } else { + train_onebatch(optimizer, in, t, size, nbThreads, t_cost); + } + } + + /** + * trains on one minibatch, i.e. runs forward and backward propagation to + * calculate + * the gradient of the loss function with respect to the network parameters + * (weights), + * then calls the optimizer algorithm to update the weights + * + * @param batch_size the number of data points to use in this batch + */ + template + void train_onebatch(Optimizer &optimizer, + const tensor_t *in, + const tensor_t *t, + int batch_size, + const int num_tasks, + const tensor_t *t_cost) { + CNN_UNREFERENCED_PARAMETER(num_tasks); + std::copy(&in[0], &in[0] + batch_size, &in_batch_[0]); + std::copy(&t[0], &t[0] + batch_size, &t_batch_[0]); + std::vector t_cost_batch = + t_cost ? std::vector(&t_cost[0], &t_cost[0] + batch_size) + : std::vector(); + + bprop(fprop(in_batch_), t_batch_, t_cost_batch); + net_.update_weights(&optimizer); + } + + vec_t fprop(const vec_t &in) { + if (in.size() != (size_t)in_data_size()) data_mismatch(**net_.begin(), in); +#if 0 + return fprop(std::vector{ in })[0]; +#else + // a workaround to reduce memory consumption by skipping wrapper + // function + std::vector a(1); + a[0].emplace_back(in); + return fprop(a)[0][0]; +#endif + } + + // convenience wrapper for the function below + std::vector fprop(const std::vector &in) { + return fprop(std::vector{in})[0]; + } + + std::vector fprop(const std::vector &in) { + return net_.forward(in); + } + + // template + // float_t get_loss(const vec_t& out, const vec_t& t) { + // assert(out.size() == t.size()); + // return E::f(out, t); + // } + + template + bool calc_delta(const std::vector &in, + const std::vector &v, + vec_t &w, + tensor_t &dw, + size_t check_index, + double eps) { + static const float_t delta = + std::sqrt(std::numeric_limits::epsilon()); + + assert(in.size() == v.size()); + + const size_t sample_count = in.size(); + + assert(sample_count > 0); + + // at the moment, channel count must be 1 + assert(in[0].size() == 1); + assert(v[0].size() == 1); + + // clear previous results, if any + for (vec_t &dw_sample : dw) { + vectorize::fill(&dw_sample[0], dw_sample.size(), float_t(0)); + } + + // calculate dw/dE by numeric + float_t prev_w = w[check_index]; + + float_t f_p = float_t(0); + w[check_index] = prev_w + delta; + for (size_t i = 0; i < sample_count; i++) { + f_p += get_loss(in[i], v[i]); + } + + float_t f_m = float_t(0); + w[check_index] = prev_w - delta; + for (size_t i = 0; i < sample_count; i++) { + f_m += get_loss(in[i], v[i]); + } + + float_t delta_by_numerical = (f_p - f_m) / (float_t(2) * delta); + w[check_index] = prev_w; + + // calculate dw/dE by bprop + bprop(fprop(in), v, std::vector()); + + float_t delta_by_bprop = 0; + for (size_t sample = 0; sample < sample_count; ++sample) { + delta_by_bprop += dw[sample][check_index]; + } + net_.clear_grads(); + + return std::abs(delta_by_bprop - delta_by_numerical) <= eps; + } + + // convenience wrapper for the function below + template + void bprop(const std::vector &out, + const std::vector &t, + const std::vector &t_cost) { + bprop(std::vector{out}, std::vector{t}, + std::vector{t_cost}); + } + + template + void bprop(const std::vector &out, + const std::vector &t, + const std::vector &t_cost) { + std::vector delta = gradient(out, t, t_cost); + net_.backward(delta); + } + + void check_t(size_t i, label_t t, size_t dim_out) { + if (t >= dim_out) { + std::ostringstream os; + os << format_str("t[%u]=%u, dim(net output)=%u\n", i, t, dim_out); + os << "in classification task, dim(net output) "; + os << "must be greater than max class id.\n"; + if (dim_out == 1) { + os << "\n(for regression, use vector "; + os << "instead of vector for training signal)\n"; + } + + throw nn_error("output dimension mismatch!\n " + os.str()); + } + } + + void check_t(size_t i, const vec_t &t, size_t dim_out) { + if (t.size() != dim_out) { + throw nn_error( + format_str("output dimension mismatch!\n dim(target[%u])=%u, " + "dim(network output size=%u", + i, t.size(), dim_out)); + } + } + + template + void check_training_data(const std::vector &in, + const std::vector &t) { + size_t dim_in = in_data_size(); + size_t dim_out = out_data_size(); + + if (in.size() != t.size()) { + throw nn_error("size of training data must be equal to label data"); + } + + size_t num = in.size(); + + for (size_t i = 0; i < num; i++) { + if (in[i].size() != dim_in) { + throw nn_error( + format_str("input dimension mismatch!\n dim(data[%u])=%d, " + "dim(network input)=%u", + i, in[i].size(), dim_in)); + } + check_t(i, t[i], dim_out); + } + } + + void check_target_cost_matrix(const std::vector &t, + const std::vector &t_cost) { + if (!t_cost.empty()) { + if (t.size() != t_cost.size()) { + throw nn_error( + "if target cost is supplied, " + "its length must equal that of target data"); + } + + for (size_t i = 0, end = t.size(); i < end; i++) { + check_target_cost_element(t[i], t_cost[i]); + } + } + } + + // regression + void check_target_cost_element(const vec_t &t, const vec_t &t_cost) { + if (t.size() != t_cost.size()) { + throw nn_error( + "if target cost is supplied for a regression task, " + "its shape must be identical to the target data"); + } + } + + void check_target_cost_element(const tensor_t &t, const tensor_t &t_cost) { + if (t.size() != t_cost.size()) { + throw nn_error( + "if target cost is supplied for a regression task, " + "its shape must be identical to the target data"); + } + for (size_t i = 0; i < t.size(); i++) + check_target_cost_element(t[i], t_cost[i]); + } + + const tensor_t *get_target_cost_sample_pointer( + const std::vector &t_cost, size_t i) { + if (!t_cost.empty()) { + assert(i < t_cost.size()); + return &(t_cost[i]); + } else { + return nullptr; + } + } + + void normalize_tensor(const std::vector &inputs, + std::vector &normalized) { + normalized = inputs; + } + + void normalize_tensor(const std::vector &inputs, + std::vector &normalized) { + normalized.reserve(inputs.size()); + for (size_t i = 0; i < inputs.size(); i++) + normalized.emplace_back(tensor_t{inputs[i]}); + } + + void normalize_tensor(const std::vector &inputs, + std::vector &normalized) { + std::vector vec; + normalized.reserve(inputs.size()); + net_.label2vec(inputs, vec); + normalize_tensor(vec, normalized); + } + + std::string name_; + NetType net_; + bool stop_training_; + std::vector in_batch_; + std::vector t_batch_; +}; + +/** + * @brief [cut an image in samples to be tested (slow)] + * @details [long description] + * + * @param data [pointer to the data] + * @param rows [self explained] + * @param cols [self explained] + * @param sizepatch [size of the patch, such as the total number of pixel in the + * patch is sizepatch*sizepatch ] + * @return [vector of vec_c (sample) to be passed to test function] + */ +inline std::vector image2vec(const float_t *data, + const size_t rows, + const size_t cols, + const size_t sizepatch, + const size_t step = 1) { + assert(step > 0); + std::vector res( + (cols - sizepatch) * (rows - sizepatch) / (step * step), + vec_t(sizepatch * sizepatch)); + for_i((cols - sizepatch) * (rows - sizepatch) / (step * step), + [&](size_t count) { + const size_t j = step * (count / ((cols - sizepatch) / step)); + const size_t i = step * (count % ((cols - sizepatch) / step)); + + // vec_t sample(sizepatch*sizepatch); + + if ((i + sizepatch) < cols && (j + sizepatch) < rows) { + for (size_t k = 0; k < (sizepatch * sizepatch); k++) { + // for_i(sizepatch*sizepatch, [&](size_t k) { + size_t y = k / sizepatch + j; + size_t x = k % sizepatch + i; + res[count][k] = data[x + y * cols]; + } + //}); + // res[count] = (sample); + } + }); + return res; +} + +template +network &operator<<(network &n, Layer &&l) { + n.net_.add(std::forward(l)); + return n; +} + +template +std::basic_ostream &operator<<( + std::basic_ostream &os, const network &n) { + n.save(os); + return os; +} + +template +std::basic_istream &operator>>( + std::basic_istream &os, network &n) { + n.load(os); + return os; +} + +inline void construct_graph(network &graph, + const std::vector &inputs, + const std::vector &outputs) { + graph.net_.construct(inputs, outputs); +} + +inline void construct_graph( + network &graph, + const std::vector> &inputs, + const std::vector> &outputs) { + std::vector in_ptr, out_ptr; + auto shared2ptr = [](std::shared_ptr l) { return l.get(); }; + + std::transform(inputs.begin(), inputs.end(), std::back_inserter(in_ptr), + shared2ptr); + std::transform(outputs.begin(), outputs.end(), std::back_inserter(out_ptr), + shared2ptr); + + graph.net_.construct(in_ptr, out_ptr); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/node.h b/tiny_dnn/node.h new file mode 100755 index 0000000000000..4a39532c83d07 --- /dev/null +++ b/tiny_dnn/node.h @@ -0,0 +1,262 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/optimizers/optimizer.h" +#include "tiny_dnn/util/product.h" +#include "tiny_dnn/util/util.h" +#include "tiny_dnn/util/weight_init.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif + +namespace tiny_dnn { + +class node; +class layer; +class edge; + +typedef std::shared_ptr edgeptr_t; + +/** + * base class of all kind of tinny-cnn data + **/ +class node : public std::enable_shared_from_this { + public: + node(size_t in_size, size_t out_size) : prev_(in_size), next_(out_size) {} + virtual ~node() {} + + const std::vector &prev() const { return prev_; } + const std::vector &next() const { return next_; } + + size_t prev_port(const edge &e) const { + auto it = std::find_if(prev_.begin(), prev_.end(), + [&](edgeptr_t ep) { return ep.get() == &e; }); + return (size_t)std::distance(prev_.begin(), it); + } + + size_t next_port(const edge &e) const { + auto it = std::find_if(next_.begin(), next_.end(), + [&](edgeptr_t ep) { return ep.get() == &e; }); + return (size_t)std::distance(next_.begin(), it); + } + + std::vector prev_nodes() + const; // @todo refactor and remove this method + std::vector next_nodes() + const; // @todo refactor and remove this method + + protected: + node() = delete; + + friend void connect(layer *head, + layer *tail, + size_t head_index, + size_t tail_index); + + mutable std::vector prev_; + mutable std::vector next_; +}; + +/** + * class containing input/output data + **/ +class edge { + public: + edge(node *prev, const shape3d &shape, vector_type vtype) + : shape_(shape), + vtype_(vtype), + data_({vec_t(shape.size())}), + grad_({vec_t(shape.size())}), + prev_(prev) {} + + void merge_grads(vec_t *dst) { + assert(!grad_.empty()); + const auto &grad_head = grad_[0]; + size_t sz = grad_head.size(); + dst->resize(sz); + float_t *pdst = &(*dst)[0]; + // dst = 0 + std::fill((*dst).begin(), (*dst).end(), 0.0); + // @todo consider adding parallelism + size_t batch_size = grad_.size(); + float_t rcp_batch_size = 1.0 / batch_size; + for (size_t sample = 0; sample < batch_size; ++sample) { + // dst += grad_[sample] + vectorize::muladd(&grad_[sample][0], rcp_batch_size, sz, pdst); + } + } + + void clear_grads() { + for (size_t sample = 0, sample_count = grad_.size(); sample < sample_count; + ++sample) { + auto &g = grad_[sample]; + vectorize::fill(&g[0], g.size(), float_t{0}); + } + } + + tensor_t *get_data() { return &data_; } + + const tensor_t *get_data() const { return &data_; } + + tensor_t *get_gradient() { return &grad_; } + + const tensor_t *get_gradient() const { return &grad_; } + + const std::vector &next() const { return next_; } + node *prev() { return prev_; } + const node *prev() const { return prev_; } + + const shape3d &shape() const { return shape_; } + vector_type vtype() const { return vtype_; } + void add_next_node(node *next) { next_.push_back(next); } + + private: + shape3d shape_; + vector_type vtype_; + tensor_t data_; + tensor_t grad_; + node *prev_; // previous node, "producer" of this tensor + std::vector next_; // next nodes, "consumers" of this tensor +}; + +inline std::vector node::prev_nodes() const { + std::vector vecs; + for (auto &e : prev_) { + if (e && e->prev()) { + vecs.insert(vecs.end(), e->prev()); + } + } + return vecs; +} + +inline std::vector node::next_nodes() const { + std::vector vecs; + for (auto &e : next_) { + if (e) { + auto n = e->next(); + vecs.insert(vecs.end(), n.begin(), n.end()); + } + } + return vecs; +} + +template +struct layer_tuple { + layer_tuple(T l1, T l2) { + layers_.push_back(l1); + layers_.push_back(l2); + } + std::vector layers_; +}; + +template < + typename T, + typename U, + typename std::enable_if::value && + std::is_base_of::value>::type * = nullptr> +layer_tuple operator,(T &l1, U &l2) { + return layer_tuple(&l1, &l2); +} + +template < + typename T, + typename U, + typename std::enable_if::value && + std::is_base_of::value>::type * = nullptr> +layer_tuple> operator,(std::shared_ptr l1, + std::shared_ptr l2) { + return layer_tuple>(l1, l2); +} + +template < + typename T, + typename std::enable_if::value>::type * = nullptr> +layer_tuple operator,(layer_tuple lhs, T &rhs) { + lhs.layers_.push_back(&rhs); + return lhs; +} + +template < + typename T, + typename std::enable_if::value>::type * = nullptr> +layer_tuple> operator,( + layer_tuple> lhs, std::shared_ptr &rhs) { + lhs.layers_.push_back(rhs); + return lhs; +} + +template < + typename T, + typename std::enable_if::value>::type * = nullptr> +layer_tuple operator,(T &lhs, layer_tuple rhs) { + rhs.layers_.insert(rhs.layers_.begin(), &lhs); + return rhs; +} + +template < + typename T, + typename std::enable_if::value>::type * = nullptr> +layer_tuple> operator,( + std::shared_ptr &lhs, layer_tuple> rhs) { + rhs.layers_.insert(rhs.layers_.begin(), lhs); + return rhs; +} + +template +inline std::shared_ptr &operator<<(std::shared_ptr &lhs, + std::shared_ptr &rhs) { + connect(lhs.get(), rhs.get()); + return rhs; +} + +template +inline T &operator<<(const layer_tuple> &lhs, T &rhs) { + for (size_t i = 0; i < lhs.layers_.size(); i++) { + connect(&*lhs.layers_[i], &*rhs, 0, i); + } + return rhs; +} + +template +inline const layer_tuple> &operator<<( + T &lhs, const layer_tuple> &rhs) { + for (size_t i = 0; i < rhs.layers_.size(); i++) { + connect(&*lhs, &*rhs.layers_[i], i, 0); + } + return rhs; +} + +template +inline T &operator<<(const layer_tuple &lhs, T &rhs) { + for (size_t i = 0; i < lhs.layers_.size(); i++) { + connect(lhs.layers_[i], &rhs, 0, i); + } + return rhs; +} + +template +inline const layer_tuple &operator<<(T &lhs, + const layer_tuple &rhs) { + for (size_t i = 0; i < rhs.layers_.size(); i++) { + connect(&lhs, rhs.layers_[i], i, 0); + } + return rhs; +} +} // namespace tiny_dnn diff --git a/tiny_dnn/nodes.h b/tiny_dnn/nodes.h new file mode 100755 index 0000000000000..17f6ed5075866 --- /dev/null +++ b/tiny_dnn/nodes.h @@ -0,0 +1,618 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include + +#ifndef CNN_NO_SERIALIZATION +#include +#include +#endif + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/optimizers/optimizer.h" +#include "tiny_dnn/util/util.h" + +namespace cereal { + +template +void save(Archive &ar, const std::vector &v) { +#ifndef CNN_NO_SERIALIZATION + ar(cereal::make_size_tag(static_cast(v.size()))); + for (auto n : v) { + tiny_dnn::layer::save_layer(ar, *n); + } +#else + throw tiny_dnn::nn_error("tiny-dnn was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION +} + +template +void load(Archive &ar, std::vector> &v) { +#ifndef CNN_NO_SERIALIZATION + cereal::size_type size; + ar(cereal::make_size_tag(size)); + + for (size_t i = 0; i < size; i++) { + v.emplace_back(tiny_dnn::layer::load_layer(ar)); + } +#else + throw tiny_dnn::nn_error("tiny-dnn was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION +} + +} // namespace cereal + +namespace tiny_dnn { + +/** basic class of various network types (sequential, multi-in/multi-out). + * + * this class holds list of pointer of Node, and provides entry point of + * forward / backward operations. + * Node is a computational unit of tiny-dnn (for example, convolution). + * Currently 2 kinds of implementation are available: sequential and graph. + * + * Nodes can accept lvalue, rvalue and shared_ptr forms of node. + * If given type is rvalue or shared_ptr, nodes create shared_ptr to keep + * given node alive. If given type is lvalue, tiny-dnn holds raw-pointer only + * (to avoid double-free). + * + * sequential s; + * s.add(fc(100, 200)); // rvalue, moved into nodes + * + * s.add(std::make_shared(200, 100)); // shared_ptr, shared by + *nodes + * + * fc out(100, 10); + * softmax sft(); + * s.add(out); // lvalue, hold raw-pointer + *only + * + **/ +class nodes { + public: + typedef std::vector::iterator iterator; + typedef std::vector::const_iterator const_iterator; + + /** + * propagate gradient + * @param first : gradient of cost function(dE/dy) + * @param worker_index : id of worker-task + **/ + virtual void backward(const std::vector &first) = 0; + + /** + * @param first input : data vectors + * @param worker_index : id of worker-task + **/ + virtual std::vector forward( + const std::vector &first) = 0; // NOLINT + + /** + * update weights and clear all gradients + **/ + virtual void update_weights(optimizer *opt) { + for (auto l : nodes_) { + l->update_weight(opt); + } + } + + /** + * setup all weights, must be called before forward/backward + **/ + virtual void setup(bool reset_weight) { + for (auto l : nodes_) { + l->setup(reset_weight); + } + } + + void clear_grads() { + for (auto l : nodes_) { + l->clear_grads(); + } + } + + size_t size() const { return nodes_.size(); } + iterator begin() { return nodes_.begin(); } + iterator end() { return nodes_.end(); } + const_iterator begin() const { return nodes_.begin(); } + const_iterator end() const { return nodes_.end(); } + layer *operator[](size_t index) { return nodes_[index]; } + const layer *operator[](size_t index) const { return nodes_[index]; } + size_t in_data_size() const { return nodes_.front()->in_data_size(); } + size_t out_data_size() const { return nodes_.back()->out_data_size(); } + + template + const T &at(size_t index) const { + const T *v = dynamic_cast(nodes_[index]); + if (v) return *v; + throw nn_error("failed to cast"); + } + + template + T &at(size_t index) { + T *v = dynamic_cast(nodes_[index]); + if (v) return *v; + throw nn_error("failed to cast"); + } + + // @todo: multiple output + virtual float_t target_value_min(int out_channel = 0) const { + CNN_UNREFERENCED_PARAMETER(out_channel); + return nodes_.back()->out_value_range().first; + } + + virtual float_t target_value_max(int out_channel = 0) const { + CNN_UNREFERENCED_PARAMETER(out_channel); + return nodes_.back()->out_value_range().second; + } + + void save(std::ostream &os) const { // NOLINT + for (auto &l : nodes_) { + l->save(os); + } + } + + void load(std::istream &is) { // NOLINT + setup(false); + for (auto &l : nodes_) { + l->load(is); + } + } + + virtual void load(const std::vector &vec) { + int idx = 0; + setup(false); + for (auto &l : nodes_) { + l->load(vec, idx); + } + } + + void label2vec(const label_t *t, size_t num, std::vector &vec) const { + size_t outdim = out_data_size(); + + vec.reserve(num); + for (size_t i = 0; i < num; i++) { + assert(t[i] < outdim); + vec.emplace_back(outdim, target_value_min()); + vec.back()[t[i]] = target_value_max(); + } + } + + void label2vec(const std::vector &labels, + std::vector &vec) const { + return label2vec(&labels[0], labels.size(), vec); + } + + template + void save_model(OutputArchive &oa) const; + + template + void load_model(InputArchive &ia); + + template + void save_weights(OutputArchive &oa) const { + for (auto n : nodes_) { + oa(*n); + } + } + + template + void load_weights(InputArchive &ia) { + for (auto n : nodes_) { + ia(*n); + } + } + + protected: + template + void push_back(T &&node) { + push_back_impl( + std::forward(node), + typename std::is_rvalue_reference::type()); // NOLINT + } + + template + void push_back(std::shared_ptr node) { + own_nodes_.push_back(node); + nodes_.push_back(own_nodes_.back().get()); + } + + // transform indexing so that it's more suitable for per-layer operations + // input: [sample][channel][feature] + // output: [channel][sample][feature] + void reorder_for_layerwise_processing( + const std::vector &input, + std::vector> &output) { + size_t sample_count = input.size(); + size_t channel_count = input[0].size(); + + output.resize(channel_count); + for (size_t i = 0; i < channel_count; ++i) { + output[i].resize(sample_count); + } + + for (size_t sample = 0; sample < sample_count; ++sample) { + assert(input[sample].size() == channel_count); + for (size_t channel = 0; channel < channel_count; ++channel) { + output[channel][sample] = &input[sample][channel]; + } + } + } + + template + void push_back_impl(T &&node, std::true_type) { // is_rvalue_reference + own_nodes_.push_back( + std::make_shared::type>( + std::forward(node))); + nodes_.push_back(own_nodes_.back().get()); + } + + template + void push_back_impl(T &&node, std::false_type) { + nodes_.push_back(&node); + } + + /* Nodes which this class has ownership */ + std::vector> own_nodes_; + /* List of all nodes which includes own_nodes */ + std::vector nodes_; +}; + +/** + * single-input, single-output feedforward network + **/ +class sequential : public nodes { + public: + void backward(const std::vector &first) override { + std::vector> reordered_grad; + reorder_for_layerwise_processing(first, reordered_grad); + assert(reordered_grad.size() == 1); + + nodes_.back()->set_out_grads(&reordered_grad[0], 1); + + for (auto l = nodes_.rbegin(); l != nodes_.rend(); l++) { + (*l)->backward(); + } + } + + std::vector forward(const std::vector &first) override { + std::vector> reordered_data; + reorder_for_layerwise_processing(first, reordered_data); + assert(reordered_data.size() == 1); + + nodes_.front()->set_in_data(&reordered_data[0], 1); + + for (auto l : nodes_) { + l->forward(); + } + + std::vector out; + nodes_.back()->output(out); + + return normalize_out(out); + } + + template + void add(T &&layer) { + push_back(std::forward(layer)); + + if (nodes_.size() != 1) { + auto head = nodes_[nodes_.size() - 2]; + auto tail = nodes_[nodes_.size() - 1]; + connect(head, tail, 0, 0); + auto out = head->outputs(); + auto in = tail->inputs(); + } + check_connectivity(); + } + + void check_connectivity() { + for (size_t i = 0; i < nodes_.size() - 1; i++) { + auto out = nodes_[i]->outputs(); + auto in = nodes_[i + 1]->inputs(); + + if (out[0] != in[0]) { + throw nn_error(""); + } + } + } + + template + void load_connections(InputArchive &ia) { + CNN_UNREFERENCED_PARAMETER(ia); + for (size_t i = 0; i < nodes_.size() - 1; i++) { + auto head = nodes_[i]; + auto tail = nodes_[i + 1]; + connect(head, tail, 0, 0); + } + } + + template + void save_connections(OutputArchive &) const {} + + private: + friend class nodes; + + std::vector normalize_out( + const std::vector &out) { + // normalize indexing back to [sample][layer][feature] + std::vector normalized_output; + + const size_t sample_count = out[0]->size(); + normalized_output.resize(sample_count, tensor_t(1)); + + for (size_t sample = 0; sample < sample_count; ++sample) { + normalized_output[sample][0] = (*out[0])[sample]; + } + + return normalized_output; + } +}; + +/** + * generic graph network + * @todo not implemented + **/ +class graph : public nodes { + public: + void backward(const std::vector &out_grad) override { + size_t output_channel_count = out_grad[0].size(); + + if (output_channel_count != output_layers_.size()) { + throw nn_error("input size mismatch"); + } + + std::vector> reordered_grad; + reorder_for_layerwise_processing(out_grad, reordered_grad); + assert(reordered_grad.size() == output_channel_count); + + for (size_t i = 0; i < output_channel_count; i++) { + output_layers_[i]->set_out_grads(&reordered_grad[i], 1); + } + + for (auto l = nodes_.rbegin(); l != nodes_.rend(); l++) { + (*l)->backward(); + } + } + + std::vector forward(const std::vector &in_data) override { + size_t input_data_channel_count = in_data[0].size(); + + if (input_data_channel_count != input_layers_.size()) { + throw nn_error("input size mismatch"); + } + + std::vector> reordered_data; + reorder_for_layerwise_processing(in_data, reordered_data); + assert(reordered_data.size() == input_data_channel_count); + + for (size_t channel_index = 0; channel_index < input_data_channel_count; + channel_index++) { + input_layers_[channel_index]->set_in_data(&reordered_data[channel_index], + 1); + } + + for (auto l : nodes_) { + l->forward(); + } + return merge_outs(); + } + + void construct(const std::vector &input, + const std::vector &output) { + std::vector sorted; + std::vector input_nodes(input.begin(), input.end()); + std::unordered_map> removed_edge; + + // topological-sorting + while (!input_nodes.empty()) { + sorted.push_back(dynamic_cast(input_nodes.back())); + input_nodes.pop_back(); + + layer *curr = sorted.back(); + std::vector next = curr->next_nodes(); + + for (size_t i = 0; i < next.size(); i++) { + if (!next[i]) continue; + // remove edge between next[i] and current + if (removed_edge.find(next[i]) == removed_edge.end()) { + removed_edge[next[i]] = + std::vector(next[i]->prev_nodes().size(), 0); + } + + std::vector &removed = removed_edge[next[i]]; + removed[find_index(next[i]->prev_nodes(), curr)] = 1; + + if (std::all_of(removed.begin(), removed.end(), + [](uint8_t x) { return x == 1; })) { + input_nodes.push_back(next[i]); + } + } + } + + for (auto &n : sorted) { + nodes_.push_back(n); + } + + input_layers_ = input; + output_layers_ = output; + + setup(false); + } + + private: + friend class nodes; + + struct _graph_connection { + void add_connection(size_t head, + size_t tail, + size_t head_index, + size_t tail_index) { + if (!is_connected(head, tail, head_index, tail_index)) { + connections.emplace_back(head, tail, head_index, tail_index); + } + } + + bool is_connected(size_t head, + size_t tail, + size_t head_index, + size_t tail_index) const { + return std::find(connections.begin(), connections.end(), + std::make_tuple(head, tail, head_index, tail_index)) != + connections.end(); + } + + template + void serialize(Archive &ar) { +#ifndef CNN_NO_SERIALIZATION + ar(CEREAL_NVP(connections), CEREAL_NVP(in_nodes), CEREAL_NVP(out_nodes)); +#else + throw nn_error("TinyDNN was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION + } + + std::vector> connections; + std::vector in_nodes, out_nodes; + }; + + template + void save_connections(OutputArchive &oa) const { +#ifndef CNN_NO_SERIALIZATION + _graph_connection gc; + std::unordered_map node2id; + size_t idx = 0; + + for (auto n : nodes_) { + node2id[n] = idx++; + } + for (auto l : input_layers_) { + gc.in_nodes.push_back(node2id[l]); + } + for (auto l : output_layers_) { + gc.out_nodes.push_back(node2id[l]); + } + + for (auto l : input_layers_) { + graph_traverse(l, [=](layer &l) { CNN_UNREFERENCED_PARAMETER(l); }, + [&](edge &e) { + auto next = e.next(); + size_t head_index = e.prev()->next_port(e); + + for (auto n : next) { + size_t tail_index = n->prev_port(e); + gc.add_connection(node2id[e.prev()], node2id[n], + head_index, tail_index); + } + }); + } + + oa(cereal::make_nvp("graph", gc)); +#else + throw nn_error("TinyDNN was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION + } + + template + void load_connections(InputArchive &ia) { +#ifndef CNN_NO_SERIALIZATION + _graph_connection gc; + ia(cereal::make_nvp("graph", gc)); + + for (auto c : gc.connections) { + size_t head, tail, head_index, tail_index; + std::tie(head, tail, head_index, tail_index) = c; + connect(nodes_[head], nodes_[tail], head_index, tail_index); + } + for (auto in : gc.in_nodes) { + input_layers_.push_back(nodes_[in]); + } + for (auto out : gc.out_nodes) { + output_layers_.push_back(nodes_[out]); + } +#else + throw nn_error("TinyDNN was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION + } + + // normalize indexing back to [sample][layer][feature] + std::vector merge_outs() { + std::vector merged; + std::vector out; + size_t output_channel_count = output_layers_.size(); + for (size_t output_channel = 0; output_channel < output_channel_count; + ++output_channel) { + output_layers_[output_channel]->output(out); + + size_t sample_count = out[0]->size(); + if (output_channel == 0) { + assert(merged.empty()); + merged.resize(sample_count, tensor_t(output_channel_count)); + } + + assert(merged.size() == sample_count); + + for (size_t sample = 0; sample < sample_count; ++sample) { + merged[sample][output_channel] = (*out[0])[sample]; + } + } + return merged; + } + + size_t find_index(const std::vector &nodes, layer *target) { + for (size_t i = 0; i < nodes.size(); i++) { + if (nodes[i] == static_cast(&*target)) return i; + } + throw nn_error("invalid connection"); + } + std::vector input_layers_; + std::vector output_layers_; +}; + +template +void nodes::save_model(OutputArchive &oa) const { +#ifndef CNN_NO_SERIALIZATION + oa(cereal::make_nvp("nodes", nodes_)); + + if (typeid(*this) == typeid(sequential)) { + dynamic_cast(this)->save_connections(oa); + } else { + dynamic_cast(this)->save_connections(oa); + } +#else + throw nn_error("TinyDNN was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION +} + +template +void nodes::load_model(InputArchive &ia) { +#ifndef CNN_NO_SERIALIZATION + own_nodes_.clear(); + nodes_.clear(); + + ia(cereal::make_nvp("nodes", own_nodes_)); + + for (auto &n : own_nodes_) { + nodes_.push_back(&*n); + } + + if (typeid(*this) == typeid(sequential)) { + dynamic_cast(this)->load_connections(ia); + } else { + dynamic_cast(this)->load_connections(ia); + } +#else + throw nn_error("TinyDNN was not built with Serialization support"); +#endif // CNN_NO_SERIALIZATION +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/optimizers/optimizer.h b/tiny_dnn/optimizers/optimizer.h new file mode 100755 index 0000000000000..d19e21c1c4f93 --- /dev/null +++ b/tiny_dnn/optimizers/optimizer.h @@ -0,0 +1,247 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * base class of optimizer + * usesHessian : true if an optimizer uses hessian (2nd order derivative of loss + *function) + **/ +struct optimizer { + optimizer() = default; + optimizer(const optimizer &) = default; + optimizer(optimizer &&) = default; + optimizer &operator=(const optimizer &) = default; + optimizer &operator=(optimizer &&) = default; + virtual ~optimizer() = default; + virtual void update(const vec_t &dW, vec_t &W, bool parallelize) = 0; + virtual void reset() {} // override to implement pre-learning action +}; + +// helper class to hold N values for each weight +template +struct stateful_optimizer : public optimizer { + void reset() override { + for (auto &e : E_) e.clear(); + } + + protected: + template + vec_t &get(const vec_t &key) { + static_assert(Index < N, "index out of range"); + if (E_[Index][&key].empty()) E_[Index][&key].resize(key.size(), float_t()); + return E_[Index][&key]; + } + std::unordered_map E_[N]; +}; + +/** + * adaptive gradient method + * + * J Duchi, E Hazan and Y Singer, + * Adaptive subgradient methods for online learning and stochastic optimization + * The Journal of Machine Learning Research, pages 2121-2159, 2011. + **/ +struct adagrad : public stateful_optimizer<1> { + adagrad() : alpha(float_t(0.01)), eps(float_t(1e-8)) {} + + void update(const vec_t &dW, vec_t &W, bool parallelize) { + vec_t &g = get<0>(W); + for_i(parallelize, W.size(), [&](size_t i) { + g[i] += dW[i] * dW[i]; + W[i] -= alpha * dW[i] / (std::sqrt(g[i]) + eps); + }); + } + + float_t alpha; // learning rate + private: + float_t eps; +}; + +/** + * RMSprop + * + * T Tieleman, and G E Hinton, + * Lecture 6.5 - rmsprop, COURSERA: Neural Networks for Machine Learning (2012) + **/ +struct RMSprop : public stateful_optimizer<1> { + RMSprop() : alpha(float_t(0.0001)), mu(float_t(0.99)), eps(float_t(1e-8)) {} + + void update(const vec_t &dW, vec_t &W, bool parallelize) { + vec_t &g = get<0>(W); + + for_i(parallelize, W.size(), [&](size_t i) { + g[i] = mu * g[i] + (1 - mu) * dW[i] * dW[i]; + W[i] -= alpha * dW[i] / std::sqrt(g[i] + eps); + }); + } + + float_t alpha; // learning rate + float_t mu; // decay term + private: + float_t eps; // constant value to avoid zero-division +}; + +/** + * @brief [a new optimizer (2015)] + * @details [see Adam: A Method for Stochastic Optimization (Algorithm 1) + * http://arxiv.org/abs/1412.6980] + * + */ +struct adam : public stateful_optimizer<2> { + adam() + : alpha(float_t(0.001)), + b1(float_t(0.9)), + b2(float_t(0.999)), + b1_t(float_t(0.9)), + b2_t(float_t(0.999)), + eps(float_t(1e-8)) {} + + void update(const vec_t &dW, vec_t &W, bool parallelize) { + vec_t &mt = get<0>(W); + vec_t &vt = get<1>(W); + + for_i(parallelize, W.size(), [&](size_t i) { + mt[i] = b1 * mt[i] + (float_t(1) - b1) * dW[i]; + vt[i] = b2 * vt[i] + (float_t(1) - b2) * dW[i] * dW[i]; + + // L2 norm based update rule + W[i] -= alpha * (mt[i] / (float_t(1) - b1_t)) / + std::sqrt((vt[i] / (float_t(1) - b2_t)) + eps); + }); + + b1_t *= b1; + b2_t *= b2; + } + + float_t alpha; // learning rate + float_t b1; // decay term + float_t b2; // decay term + float_t b1_t; // decay term power t + float_t b2_t; // decay term power t + + private: + float_t eps; // constant value to avoid zero-division +}; + +/** + * @brief [a new optimizer (2015)] + * @details [see Adam: A Method for Stochastic Optimization (Algorithm 2) + * http://arxiv.org/abs/1412.6980] + * + */ +struct adamax : public stateful_optimizer<2> { + adamax() + : alpha(float_t(0.002)), + b1(float_t(0.9)), + b2(float_t(0.999)), + b1_t(b1), + eps(float_t(1e-8)) {} + + void update(const vec_t &dW, vec_t &W, bool parallelize) { + vec_t &mt = get<0>(W); + vec_t &ut = get<1>(W); + + for_i(parallelize, W.size(), [&](int i) { + mt[i] = b1 * mt[i] + (float_t(1) - b1) * dW[i]; + ut[i] = std::max(b2 * ut[i], std::abs(dW[i])); + + // Lp norm based update rule + W[i] -= (alpha / (1.0 - b1_t)) * (mt[i] / (ut[i] + eps)); + }); + + b1_t *= b1; + } + + float_t alpha; // learning rate + float_t b1; // decay term + float_t b2; // decay term + float_t b1_t; // decay term power t + + private: + float_t eps; // constant value to avoid zero-division +}; + +/** + * SGD without momentum + * + * slightly faster than tiny_dnn::momentum + **/ +struct gradient_descent : public optimizer { + gradient_descent() : alpha(float_t(0.01)), lambda(float_t(0)) {} + + void update(const vec_t &dW, vec_t &W, bool parallelize) { + for_i(parallelize, W.size(), + [&](size_t i) { W[i] = W[i] - alpha * (dW[i] + lambda * W[i]); }); + } + + float_t alpha; // learning rate + float_t lambda; // weight decay +}; + +/** + * SGD with momentum + * + * B T Polyak, + * Some methods of speeding up the convergence of iteration methods + * USSR Computational Mathematics and Mathematical Physics, 4(5):1-17, 1964. + **/ +struct momentum : public stateful_optimizer<1> { + public: + momentum() : alpha(float_t(0.01)), lambda(float_t(0)), mu(float_t(0.9)) {} + + void update(const vec_t &dW, vec_t &W, bool parallelize) { + vec_t &dWprev = get<0>(W); + + for_i(parallelize, W.size(), [&](size_t i) { + float_t V = mu * dWprev[i] - alpha * (dW[i] + W[i] * lambda); + W[i] += V; + dWprev[i] = V; + }); + } + + float_t alpha; // learning rate + float_t lambda; // weight decay + float_t mu; // momentum +}; + +/** + * SGD with Nesterov momentum + * + * Y Nesterov, + * A method for unconstrained convex minimization problem with the rate of + * convergence o(1/k2), Doklady ANSSSR, vol.269, pp.543-547, 1983. + **/ +struct nesterov_momentum : public stateful_optimizer<1> { + public: + nesterov_momentum() + : alpha(float_t(0.01)), lambda(float_t(0)), mu(float_t(0.9)) {} + + void update(const vec_t &dW, vec_t &W, bool parallelize) { + vec_t &dWprev = get<0>(W); + + for_i(parallelize, W.size(), [&](size_t i) { + float_t V = mu * dWprev[i] - alpha * (dW[i] + W[i] * lambda); + W[i] += (-mu) * dWprev[i] + (1 + mu) * V; + dWprev[i] = V; + }); + } + + float_t alpha; // learning rate + float_t lambda; // weight decay + float_t mu; // momentum +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/tiny_dnn.h b/tiny_dnn/tiny_dnn.h new file mode 100755 index 0000000000000..266fb97e8f37d --- /dev/null +++ b/tiny_dnn/tiny_dnn.h @@ -0,0 +1,168 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/config.h" +#include "tiny_dnn/network.h" +#include "tiny_dnn/nodes.h" + +#include "tiny_dnn/core/framework/tensor.h" + +#include "tiny_dnn/core/framework/device.h" +#include "tiny_dnn/core/framework/program_manager.h" + +#include "tiny_dnn/layers/arithmetic_layer.h" +#include "tiny_dnn/layers/average_pooling_layer.h" +#include "tiny_dnn/layers/average_unpooling_layer.h" +#include "tiny_dnn/layers/batch_normalization_layer.h" +#include "tiny_dnn/layers/concat_layer.h" +#include "tiny_dnn/layers/convolutional_layer.h" +#include "tiny_dnn/layers/deconvolutional_layer.h" +#include "tiny_dnn/layers/dropout_layer.h" +#include "tiny_dnn/layers/fully_connected_layer.h" +#include "tiny_dnn/layers/global_average_pooling_layer.h" +#include "tiny_dnn/layers/input_layer.h" +#include "tiny_dnn/layers/linear_layer.h" +#include "tiny_dnn/layers/lrn_layer.h" +#include "tiny_dnn/layers/max_pooling_layer.h" +#include "tiny_dnn/layers/max_unpooling_layer.h" +#include "tiny_dnn/layers/power_layer.h" +#include "tiny_dnn/layers/quantized_convolutional_layer.h" +#include "tiny_dnn/layers/quantized_deconvolutional_layer.h" +#include "tiny_dnn/layers/recurrent_cell_layer.h" +#include "tiny_dnn/layers/slice_layer.h" + +#include "tiny_dnn/activations/elu_layer.h" +#include "tiny_dnn/activations/leaky_relu_layer.h" +#include "tiny_dnn/activations/relu_layer.h" +#include "tiny_dnn/activations/selu_layer.h" +#include "tiny_dnn/activations/sigmoid_layer.h" +#include "tiny_dnn/activations/softmax_layer.h" +#include "tiny_dnn/activations/softplus_layer.h" +#include "tiny_dnn/activations/softsign_layer.h" +#include "tiny_dnn/activations/tanh_layer.h" +#include "tiny_dnn/activations/tanh_p1m2_layer.h" + +#ifdef CNN_USE_GEMMLOWP +#include "tiny_dnn/layers/quantized_fully_connected_layer.h" +#endif // CNN_USE_GEMMLOWP + +#include "tiny_dnn/lossfunctions/loss_function.h" +#include "tiny_dnn/optimizers/optimizer.h" + +#include "tiny_dnn/util/deform.h" +#include "tiny_dnn/util/graph_visualizer.h" +#include "tiny_dnn/util/product.h" +#include "tiny_dnn/util/weight_init.h" + +#include "tiny_dnn/io/cifar10_parser.h" +#include "tiny_dnn/io/display.h" +#include "tiny_dnn/io/layer_factory.h" +#include "tiny_dnn/io/mnist_parser.h" + +#ifdef DNN_USE_IMAGE_API +#include "tiny_dnn/util/image.h" +#endif // DNN_USE_IMAGE_API + +#ifndef CNN_NO_SERIALIZATION +#include "tiny_dnn/util/deserialization_helper.h" +#include "tiny_dnn/util/serialization_helper.h" +// to allow upcasting +CEREAL_REGISTER_TYPE(tiny_dnn::elu_layer) +CEREAL_REGISTER_TYPE(tiny_dnn::leaky_relu_layer) +CEREAL_REGISTER_TYPE(tiny_dnn::relu_layer) +CEREAL_REGISTER_TYPE(tiny_dnn::sigmoid_layer) +CEREAL_REGISTER_TYPE(tiny_dnn::softmax_layer) +CEREAL_REGISTER_TYPE(tiny_dnn::softplus_layer) +CEREAL_REGISTER_TYPE(tiny_dnn::softsign_layer) +CEREAL_REGISTER_TYPE(tiny_dnn::tanh_layer) +CEREAL_REGISTER_TYPE(tiny_dnn::tanh_p1m2_layer) +#endif // CNN_NO_SERIALIZATION + +// shortcut version of layer names +namespace tiny_dnn { +namespace layers { + +using conv = tiny_dnn::convolutional_layer; + +using q_conv = tiny_dnn::quantized_convolutional_layer; + +using max_pool = tiny_dnn::max_pooling_layer; + +using ave_pool = tiny_dnn::average_pooling_layer; + +using fc = tiny_dnn::fully_connected_layer; + +using dense = tiny_dnn::fully_connected_layer; + +using recurrent_cell = tiny_dnn::recurrent_cell_layer; + +#ifdef CNN_USE_GEMMLOWP +using q_fc = tiny_dnn::quantized_fully_connected_layer; +#endif + +using add = tiny_dnn::elementwise_add_layer; + +using dropout = tiny_dnn::dropout_layer; + +using input = tiny_dnn::input_layer; + +using linear = linear_layer; + +using lrn = tiny_dnn::lrn_layer; + +using concat = tiny_dnn::concat_layer; + +using deconv = tiny_dnn::deconvolutional_layer; + +using max_unpool = tiny_dnn::max_unpooling_layer; + +using ave_unpool = tiny_dnn::average_unpooling_layer; + +} // namespace layers + +namespace activation { + +using sigmoid = tiny_dnn::sigmoid_layer; + +using tanh = tiny_dnn::tanh_layer; + +using relu = tiny_dnn::relu_layer; + +using rectified_linear = tiny_dnn::relu_layer; + +using softmax = tiny_dnn::softmax_layer; + +using leaky_relu = tiny_dnn::leaky_relu_layer; + +using elu = tiny_dnn::elu_layer; + +using selu = tiny_dnn::selu_layer; + +using tanh_p1m2 = tiny_dnn::tanh_p1m2_layer; + +using softplus = tiny_dnn::softplus_layer; + +using softsign = tiny_dnn::softsign_layer; + +} // namespace activation + +#include "tiny_dnn/models/alexnet.h" + +using batch_norm = tiny_dnn::batch_normalization_layer; + +using slice = tiny_dnn::slice_layer; + +using power = tiny_dnn::power_layer; + +} // namespace tiny_dnn + +#ifdef CNN_USE_CAFFE_CONVERTER +// experimental / require google protobuf +#include "tiny_dnn/io/caffe/layer_factory.h" +#endif diff --git a/tiny_dnn/util/aligned_allocator.h b/tiny_dnn/util/aligned_allocator.h new file mode 100755 index 0000000000000..ef94db67dbd0a --- /dev/null +++ b/tiny_dnn/util/aligned_allocator.h @@ -0,0 +1,128 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#ifdef _WIN32 +#include +#endif + +#ifdef __MINGW32__ +#include +#endif + +#include "tiny_dnn/util/nn_error.h" + +namespace tiny_dnn { + +template +class aligned_allocator { + public: + typedef T value_type; + typedef T *pointer; + typedef std::size_t size_type; + typedef std::ptrdiff_t difference_type; + typedef T &reference; + typedef const T &const_reference; + typedef const T *const_pointer; + + template + struct rebind { + typedef aligned_allocator other; + }; + + aligned_allocator() {} + + template + aligned_allocator(const aligned_allocator &) {} + + const_pointer address(const_reference value) const { + return std::addressof(value); + } + + pointer address(reference value) const { return std::addressof(value); } + + pointer allocate(size_type size, const void * = nullptr) { + void *p = aligned_alloc(alignment, sizeof(T) * size); + if (!p && size > 0) throw nn_error("failed to allocate"); + return static_cast(p); + } + + size_type max_size() const { + return ~static_cast(0) / sizeof(T); + } + + void deallocate(pointer ptr, size_type) { aligned_free(ptr); } + + template + void construct(U *ptr, const V &value) { + void *p = ptr; + ::new (p) U(value); + } + + template + void construct(U *ptr, Args &&... args) { + void *p = ptr; + ::new (p) U(std::forward(args)...); + } + + template + void construct(U *ptr) { + void *p = ptr; + ::new (p) U(); + } + + template + void destroy(U *ptr) { + ptr->~U(); + } + + private: + void *aligned_alloc(size_type align, size_type size) const { +#if defined(_MSC_VER) + return ::_aligned_malloc(size, align); +#elif defined(__ANDROID__) + return ::memalign(align, size); +#elif defined(__MINGW32__) + return ::_mm_malloc(size, align); +#else // posix assumed + void *p; + if (::posix_memalign(&p, align, size) != 0) { + p = 0; + } + return p; +#endif + } + + void aligned_free(pointer ptr) { +#if defined(_MSC_VER) + ::_aligned_free(ptr); +#elif defined(__MINGW32__) + ::_mm_free(ptr); +#else + ::free(ptr); +#endif + } +}; + +template +inline bool operator==(const aligned_allocator &, + const aligned_allocator &) { + return true; +} + +template +inline bool operator!=(const aligned_allocator &, + const aligned_allocator &) { + return false; +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/colored_print.h b/tiny_dnn/util/colored_print.h new file mode 100755 index 0000000000000..443c7f35fc3a3 --- /dev/null +++ b/tiny_dnn/util/colored_print.h @@ -0,0 +1,78 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include + +#include "tiny_dnn/config.h" + +#ifdef CNN_WINDOWS +#ifndef NOMINMAX +#define NOMINMAX +#endif // ifdef NOMINMAX +#include +#endif + +namespace tiny_dnn { + +enum class Color { RED, GREEN, BLUE, YELLOW }; + +#ifdef CNN_WINDOWS +inline WORD getColorAttr(Color c) { + switch (c) { + case Color::RED: return FOREGROUND_RED; + case Color::GREEN: return FOREGROUND_GREEN; + case Color::BLUE: return FOREGROUND_BLUE; + case Color::YELLOW: return FOREGROUND_GREEN | FOREGROUND_RED; + default: assert(0); return 0; + } +} +#else +inline const char *getColorEscape(Color c) { + switch (c) { + case Color::RED: return "\033[31m"; + case Color::GREEN: return "\033[32m"; + case Color::BLUE: return "\033[34m"; + case Color::YELLOW: return "\033[33m"; + default: assert(0); return ""; + } +} +#endif + +inline void coloredPrint(Color c, const char *fmt, ...) { + va_list args; + va_start(args, fmt); + +#ifdef CNN_WINDOWS + const HANDLE std_handle = ::GetStdHandle(STD_OUTPUT_HANDLE); + + CONSOLE_SCREEN_BUFFER_INFO buffer_info; + ::GetConsoleScreenBufferInfo(std_handle, &buffer_info); + const WORD old_color = buffer_info.wAttributes; + const WORD new_color = getColorAttr(c) | FOREGROUND_INTENSITY; + + fflush(stdout); + ::SetConsoleTextAttribute(std_handle, new_color); + + vprintf(fmt, args); + + fflush(stdout); + ::SetConsoleTextAttribute(std_handle, old_color); +#else + printf("%s", getColorEscape(c)); + vprintf(fmt, args); + printf("\033[m"); +#endif + va_end(args); +} + +inline void coloredPrint(Color c, const std::string &msg) { + coloredPrint(c, msg.c_str()); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/deform.h b/tiny_dnn/util/deform.h new file mode 100755 index 0000000000000..35a251fccee3a --- /dev/null +++ b/tiny_dnn/util/deform.h @@ -0,0 +1,20 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +inline vec_t corrupt(vec_t &&in, float_t corruption_level, float_t min_value) { + for (size_t i = 0; i < in.size(); i++) + if (bernoulli(corruption_level)) in[i] = min_value; + return in; +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/deserialization_helper.h b/tiny_dnn/util/deserialization_helper.h new file mode 100755 index 0000000000000..7665abb5c6b6b --- /dev/null +++ b/tiny_dnn/util/deserialization_helper.h @@ -0,0 +1,155 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include + +#include +#include + +#include "tiny_dnn/layers/layers.h" +#include "tiny_dnn/util/macro.h" +#include "tiny_dnn/util/nn_error.h" +#include "tiny_dnn/util/serialization_functions.h" +#include "tiny_dnn/util/serialization_layer_list.h" + +namespace tiny_dnn { + +template +class deserialization_helper { + public: + void register_loader( + const std::string &name, + std::function(InputArchive &)> func) { + loaders_[name] = [=](void *ar) { + return func(*reinterpret_cast(ar)); + }; + } + + template + void register_type(const std::string &name) { + type_names_[typeid(T)] = name; + } + + std::shared_ptr load(const std::string &layer_name, InputArchive &ar) { + check_if_enabled(); + + if (loaders_.find(layer_name) == loaders_.end()) { + throw nn_error("Failed to load layer. Loader for " + layer_name + + " is not found.\n" + "Please use CNN_REGISTER_LAYER macro to register " + "appropriate loader."); + } + + return loaders_[layer_name](reinterpret_cast(&ar)); + } + + const std::string &type_name(std::type_index index) const { + if (type_names_.find(index) == type_names_.end()) { + throw nn_error("Typename is not registered"); + } + return type_names_.at(index); + } + + static deserialization_helper &get_instance() { + static deserialization_helper instance; + return instance; + } + + private: + void check_if_enabled() const { +#ifdef CNN_NO_SERIALIZATION + static_assert( + sizeof(InputArchive) == 0, + "You are using load functions, but deserialization function is " + "disabled in current configuration.\n\n" + "You need to undef CNN_NO_SERIALIZATION to enable these functions.\n" + "If you are using cmake, you can use -DUSE_SERIALIZER=ON " + "option.\n\n"); +#endif + } + + /** layer-type -> generator */ + std::map(void *)>> loaders_; + + std::map type_names_; + + template + static std::shared_ptr load_layer_impl(InputArchive &ia); + + template + friend void register_layers(T *h); + + template + void register_layer(const char *layer_name) { + register_loader(layer_name, load_layer_impl); + register_type(layer_name); + } + + deserialization_helper() { register_layers(this); } +}; // class deserialization_helper + +template +template +std::shared_ptr deserialization_helper::load_layer_impl( + InputArchive &ia) { + using ST = typename std::aligned_storage::type; + + std::unique_ptr bn(new ST()); + + cereal::memory_detail::LoadAndConstructLoadWrapper wrapper( + reinterpret_cast(bn.get())); + + wrapper.CEREAL_SERIALIZE_FUNCTION_NAME(ia); + + std::shared_ptr t; + t.reset(reinterpret_cast(bn.get())); + bn.release(); + + return t; +} + +template +void start_loading_layer(T &ar) { + CNN_UNREFERENCED_PARAMETER(ar); +} + +template +void finish_loading_layer(T &ar) { + CNN_UNREFERENCED_PARAMETER(ar); +} + +inline void start_loading_layer(cereal::JSONInputArchive &ia) { + ia.startNode(); +} + +inline void finish_loading_layer(cereal::JSONInputArchive &ia) { + ia.finishNode(); +} + +/** + * generate layer from cereal's Archive + **/ +template +std::shared_ptr layer::load_layer(InputArchive &ia) { + start_loading_layer(ia); + + std::string p; + ia(cereal::make_nvp("type", p)); + auto l = deserialization_helper::get_instance().load(p, ia); + + finish_loading_layer(ia); + + return l; +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/gradient_check.h b/tiny_dnn/util/gradient_check.h new file mode 100755 index 0000000000000..b5bdd9d43bb77 --- /dev/null +++ b/tiny_dnn/util/gradient_check.h @@ -0,0 +1,134 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +/** + * Auxiliar function to convert a vector of tensors to a vector of tensor + * pointers. + * @param input vector of tensors. + * @return vector of tensor pointers. + */ +std::vector tensor2ptr(std::vector &input) { + std::vector ret(input.size()); + for (size_t i = 0; i < input.size(); i++) { + ret[i] = &input[i]; + } + return ret; +} + +/** + * Computes the numeric gradient of a given layer + * http://karpathy.github.io/neuralnets/ + * http://cs231n.github.io/neural-networks-3/#gradcheck + * @param layer Reference to a layer type. + * @param in_data Input (data, weights, biases, etc). + * @param in_edge Input edge index to perturb to obtain the gradient (data, + *weights, biases, etc.) + * @param in_pos Input position to perturb for retrieving the gradient of a + *given edge. + * @param out_data Output matrices (to calculate the increment after + *perturbation). + * @param out_grads Output gradients. + * @param out_edge Output matrix index to calculate the increment after + *perturbation). + * @param out_pos Position in the matrix to calculate the increment after + *perturbation. + * @return The numeric gradient for the desired position and matrix. +**/ +float_t numeric_gradient(layer &layer, + std::vector in_data, // copy is safer + const size_t in_edge, + const size_t in_pos, + std::vector &out_data, + std::vector &out_grads, + const size_t out_edge, + const size_t out_pos) { + // sqrt(machine epsilon) is assumed to be safe + float_t h = std::sqrt(std::numeric_limits::epsilon()); + // initialize input/output + std::vector in_data_ = tensor2ptr(in_data); + std::vector out_data_ = tensor2ptr(out_data); + for (auto &tensor : out_grads) fill_tensor(tensor, 0.0); + for (auto &tensor : out_data) fill_tensor(tensor, 0.0); + std::vector out_grads_ = tensor2ptr(out_grads); + // Set output gradient to 1 so that input grad is 1*f'(x) + out_grads[out_edge][0][out_pos] = 1.0; + // Save current input value to perturb + float_t prev_in = in_data[in_edge][0][in_pos]; + // Perturb by a small amount (-h) + in_data[in_edge][0][in_pos] = prev_in - h; + layer.forward_propagation(in_data_, out_data_); + float_t out_1 = (*out_data_[out_edge])[0][out_pos]; + // Perturb by a small amount (+h) + in_data[in_edge][0][in_pos] = prev_in + h; + layer.forward_propagation(in_data_, out_data_); + float_t out_2 = (*out_data_[out_edge])[0][out_pos]; + // numerical gradient + return (out_2 - out_1) / (2 * h); +} + +/** + * Gets the gradient from the implemented backward pass. + * @param layer Reference to a layer type. + * @param in_data Input (data, weights, biases, etc). + * @param in_edge Input edge index for retrieving the gradient (data, weights, + * biases, etc.) + * @param in_pos Input position for retrieving the gradient. + * @param out_data Output data matrices. + * @param out_grads Next layer gradients (will be 1 for the tested position). + * @param out_edge Output matrix to put the gradient to 1. + * @param out_pos Output position to put the gradient to 1. + * @return The computed gradient for the desired position and matrix. + */ +float_t analytical_gradient(layer &layer, + std::vector in_data, + const size_t in_edge, + const size_t in_pos, + std::vector &out_data, + std::vector &out_grads, + const size_t out_edge, + const size_t out_pos) { + // initialize input/output + std::vector in_data_ = tensor2ptr(in_data); + std::vector in_grads = in_data; // copy constructor + std::vector in_grads_ = tensor2ptr(in_grads); + std::vector out_data_ = tensor2ptr(out_data); + for (auto &tensor : in_grads) fill_tensor(tensor, 0.0); + for (auto &tensor : out_grads) fill_tensor(tensor, 0.0); + for (auto &tensor : out_data) fill_tensor(tensor, 0.0); + std::vector out_grads_ = tensor2ptr(out_grads); + out_grads[out_edge][0][out_pos] = 1.0; // set target grad to 1. + // get gradient by plain backpropagation + layer.forward_propagation(in_data_, out_data_); + layer.back_propagation(in_data_, out_data_, out_grads_, in_grads_); + return in_grads[in_edge][0][in_pos]; +} + +/** + * Calculates the relative error between the real and the numeric gradient. + * |d1 - d2| / max(|d1|, |d2|) + * http://cs231n.github.io/neural-networks-3/#gradcheck + * @param analytical_gradient + * @param numeric_gradient + * @return the relative error. + */ +float_t relative_error(const float_t analytical_grad, + const float_t numeric_grad) { + float_t max = std::max(std::abs(analytical_grad), std::abs(numeric_grad)); + return (max == 0) ? 0.0 : std::abs(analytical_grad - numeric_grad) / max; +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/graph_visualizer.h b/tiny_dnn/util/graph_visualizer.h new file mode 100755 index 0000000000000..f697da45de16e --- /dev/null +++ b/tiny_dnn/util/graph_visualizer.h @@ -0,0 +1,119 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/layers/layer.h" +#include "tiny_dnn/network.h" +#include "tiny_dnn/node.h" + +namespace tiny_dnn { + +/** + * utility for graph visualization + **/ +class graph_visualizer { + public: + explicit graph_visualizer(layer *root_node, + const std::string &graph_name = "graph") + : root_(root_node), name_(graph_name) {} + + template + explicit graph_visualizer(network &network, + const std::string &graph_name = "graph") + : root_(network[0]), name_(graph_name) {} + + /** + * generate graph structure in dot language format + **/ + void generate(std::ostream &stream) { + generate_header(stream); + generate_nodes(stream); + generate_footer(stream); + } + + private: + typedef std::unordered_map node2name_t; + + void generate_header(std::ostream &stream) { + stream << "digraph \"" << name_ << "\" {" << std::endl; + stream << " node [ shape=record ];" << std::endl; + } + + void generate_nodes(std::ostream &stream) { + node2name_t node2name; + get_layer_names(node2name); + + graph_traverse( + root_, [&](const layer &l) { generate_layer(stream, l, node2name); }, + [&](const edge &e) { generate_edge(stream, e, node2name); }); + } + + void get_layer_names(node2name_t &node2name) { + std::unordered_map layer_counts; // [layer_type -> num] + + auto namer = [&](const layer &l) { + std::string ltype = l.layer_type(); + + // add quote and sequential-id + node2name[&l] = + "\"" + ltype + to_string(layer_counts[l.layer_type()]++) + "\""; + }; + + graph_traverse(root_, namer, [&](const edge &) {}); + } + + void generate_edge(std::ostream &stream, + const edge &e, + node2name_t &node2name) { + auto next = e.next(); + auto prev = e.prev(); + + for (auto n : next) { + size_t dst_port = n->prev_port(e); + size_t src_port = prev->next_port(e); + stream << " " << node2name[prev] << ":out" << src_port << " -> " + << node2name[n] << ":in" << dst_port << ";" << std::endl; + } + } + + void generate_layer(std::ostream &stream, + const layer &layer, + node2name_t &node2name) { + stream << " " << node2name[&layer] << " [" << std::endl; + stream << " label= \""; + stream << layer.layer_type() << "|{{in"; + generate_layer_channels(stream, layer.in_shape(), layer.in_types(), "in"); + stream << "}|{out"; + generate_layer_channels(stream, layer.out_shape(), layer.out_types(), + "out"); + stream << "}}\"" << std::endl; + stream << " ];" << std::endl; + } + + void generate_layer_channels(std::ostream &stream, + const std::vector &shapes, + const std::vector &vtypes, + const std::string &port_prefix) { + CNN_UNREFERENCED_PARAMETER(vtypes); + for (size_t i = 0; i < shapes.size(); i++) { + stream << "|<" << port_prefix << i << ">" << shapes[i] << "(" << vtypes[i] + << ")"; + } + } + + void generate_footer(std::ostream &stream) { stream << "}" << std::endl; } + + layer *root_; + std::string name_; +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/image.h b/tiny_dnn/util/image.h new file mode 100755 index 0000000000000..2e2b6bd9467f2 --- /dev/null +++ b/tiny_dnn/util/image.h @@ -0,0 +1,464 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/util/util.h" + +#ifdef _MSC_VER +#pragma warning(push) +#pragma warning(disable : 4996) // suppress warnings about using fopen +#endif + +#define STB_IMAGE_IMPLEMENTATION +#define STB_IMAGE_STATIC +#define STB_IMAGE_INLINE +#include "third_party/stb/stb_image.h" + +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#define STB_IMAGE_RESIZE_STATIC +#define STB_IMAGE_RESIZE_INLINE +#include "third_party/stb/stb_image_resize.h" + +#define STB_IMAGE_WRITE_IMPLEMENTATION +#define STB_IMAGE_WRITE_STATIC +#define STB_IMAGE_WRITE_INLINE +#include "third_party/stb/stb_image_write.h" + +namespace tiny_dnn { + +namespace detail { + +template +typename std::enable_if::value, T>::type saturated_sub( + T s1, T s2) { + return s1 > s2 ? static_cast(s1 - s2) : 0; +} + +template +typename std::enable_if::value, T>::type saturated_sub( + T s1, T s2) { + return static_cast(s1 - s2); +} + +inline bool ends_with(std::string const &value, std::string const &ending) { + if (ending.size() > value.size()) return false; + return std::equal(ending.rbegin(), ending.rend(), value.rbegin()); +} + +inline void resize_image_core(const uint8_t *src, + int srcw, + int srch, + uint8_t *dst, + int dstw, + int dsth, + int channels) { + stbir_resize_uint8(src, srcw, srch, 0, dst, dstw, dsth, 0, channels); +} + +inline void resize_image_core(const float *src, + int srcw, + int srch, + float *dst, + int dstw, + int dsth, + int channels) { + stbir_resize_float(src, srcw, srch, 0, dst, dstw, dsth, 0, channels); +} + +} // namespace detail + +enum class image_type { + grayscale, ///< load image and convert automatically to 8-bit grayscale + rgb, ///< load image and keep original color channels + bgr +}; + +/** + * Simple image utility class + */ +template +class image { + public: + typedef T intensity_t; + typedef typename std::vector::iterator iterator; + typedef typename std::vector::const_iterator const_iterator; + + image() : width_(0), height_(0), depth_(1) {} + + /** + * create image from raw pointer + */ + image(const T *data, size_t width, size_t height, image_type type) + : width_(width), + height_(height), + depth_(type == image_type::grayscale ? 1 : 3), + type_(type), + data_(depth_ * width_ * height_, 0) { + std::copy(data, data + width * height * depth_, &data_[0]); + } + + /** + * create WxHxD image filled with 0 + */ + image(const shape3d &size, image_type type) + : width_(size.width_), + height_(size.height_), + depth_(size.depth_), + type_(type), + data_(depth_ * width_ * height_, 0) { + if (type == image_type::grayscale && size.depth_ != 1) { + throw nn_error("depth must be 1 in grayscale"); + } else if (type != image_type::grayscale && size.depth_ != 3) { + throw nn_error("depth must be 3 in rgb/bgr"); + } + } + + template + image(const image &rhs) + : width_(rhs.width()), + height_(rhs.height()), + depth_(rhs.depth()), + type_(rhs.type()), + data_(rhs.shape().size()) { + std::transform(rhs.begin(), rhs.end(), data_.begin(), + [](T src) { return static_cast(src); }); + } + + /** + * create image from file + * supported file format: JPEG/PNG/TGA/BMP/PSD/GIF/HDR/PIC/PNM + * (see detail at the comments in + * thrid_party/stb/stb_image.h) + */ + image(const std::string &filename, image_type type) { + int w, h, d; + stbi_uc *input_pixels = stbi_load(filename.c_str(), &w, &h, &d, + type == image_type::grayscale ? 1 : 3); + if (input_pixels == nullptr) { + throw nn_error("failed to open image:" + + std::string(stbi_failure_reason())); + } + + width_ = w; + height_ = h; + depth_ = type == image_type::grayscale ? 1 : 3; + type_ = type; + + data_.resize(width_ * height_ * depth_); + + // reorder to HxWxD -> DxHxW + from_rgb(input_pixels, input_pixels + data_.size()); + + stbi_image_free(input_pixels); + } + + void save(const std::string &path) const { + int ret; + std::vector buf = to_rgb(); + + if (detail::ends_with(path, "png")) { + ret = stbi_write_png(path.c_str(), static_cast(width_), + static_cast(height_), static_cast(depth_), + (const void *)&buf[0], 0); + } else { + ret = stbi_write_bmp(path.c_str(), static_cast(width_), + static_cast(height_), static_cast(depth_), + (const void *)&buf[0]); + } + if (ret == 0) { + throw nn_error("failed to save image:" + path); + } + } + + void write(const std::string &path) const { save(path); } + + void resize(size_t width, size_t height) { + data_.resize(width * height * depth_); + width_ = width; + height_ = height; + // depth_ = depth; + } + + void fill(intensity_t value) { std::fill(data_.begin(), data_.end(), value); } + + intensity_t &at(size_t x, size_t y, size_t z = 0) { + assert(x < width_); + assert(y < height_); + assert(z < depth_); + return data_[z * width_ * height_ + y * width_ + x]; + } + + const intensity_t &at(size_t x, size_t y, size_t z = 0) const { + assert(x < width_); + assert(y < height_); + assert(z < depth_); + return data_[z * width_ * height_ + y * width_ + x]; + } + + bool empty() const { return data_.empty(); } + iterator begin() { return data_.begin(); } + iterator end() { return data_.end(); } + const_iterator begin() const { return data_.begin(); } + const_iterator end() const { return data_.end(); } + + intensity_t &operator[](std::size_t idx) { return data_[idx]; } + const intensity_t &operator[](std::size_t idx) const { return data_[idx]; } + + size_t width() const { return width_; } + size_t height() const { return height_; } + size_t depth() const { return depth_; } + image_type type() const { return type_; } + shape3d shape() const { return shape3d(width_, height_, depth_); } + const std::vector &data() const { return data_; } + vec_t to_vec() const { return vec_t(begin(), end()); } + + template + std::vector to_rgb() const { + if (depth_ == 1) { + return std::vector(data_.begin(), data_.end()); + } else { + std::vector buf(shape().size()); + auto order = depth_order(type_); + auto dst = buf.begin(); + + for (size_t y = 0; y < height_; y++) + for (size_t x = 0; x < width_; x++) + for (size_t i = 0; i < depth_; i++) + *dst++ = static_cast(at(x, y, order[i])); + return buf; + } + } + + template + void from_rgb(Iter begin, Iter end) { + if (depth_ == 1) { + std::copy(begin, end, data_.begin()); + } else { + auto order = depth_order(type_); + assert(std::distance(begin, end) == static_cast(data_.size())); + + for (size_t y = 0; y < height_; y++) + for (size_t x = 0; x < width_; x++) + for (size_t i = 0; i < depth_; i++) + at(x, y, order[i]) = static_cast(*begin++); + } + } + + private: + std::array depth_order(image_type img) const { + if (img == image_type::rgb) { + return {{0, 1, 2}}; + } else { + assert(img == image_type::bgr); + return {{2, 1, 0}}; + } + } + size_t width_; + size_t height_; + size_t depth_; + image_type type_; + std::vector data_; +}; + +template +image mean_image(const image &src) { + image mean(shape3d(1, 1, (size_t)src.depth()), src.type()); + + for (size_t i = 0; i < src.depth(); i++) { + float_t sum{0.0}; + for (size_t y = 0; y < src.height(); y++) { + for (size_t x = 0; x < src.width(); x++) { + sum += src.at(x, y, i); + } + } + mean.at(0, 0, i) = sum / (src.width() * src.height()); + } + + return mean; +} + +/** + * resize image into width x height + * This function use Mitchell-Netrevalli filter with B=1/3, C=1/3 for + * downsampling, and + * and cubic spline algorithm for upsampling. + */ +template +inline image resize_image(const image &src, int width, int height) { + image resized(shape3d(width, height, src.depth()), src.type()); + std::vector src_rgb = src.template to_rgb(); + std::vector dst_rgb(resized.shape().size()); + + detail::resize_image_core(&src_rgb[0], static_cast(src.width()), + static_cast(src.height()), &dst_rgb[0], width, + height, static_cast(src.depth())); + + resized.from_rgb(dst_rgb.begin(), dst_rgb.end()); + + return resized; +} + +// dst[x,y,d] = lhs[x,y,d] - rhs[x,y,d] +template +image subtract_image(const image &lhs, const image &rhs) { + if (lhs.shape() != rhs.shape()) { + throw nn_error("Shapes of lhs/rhs must be same. lhs:" + + to_string(lhs.shape()) + ",rhs:" + to_string(rhs.shape())); + } + + image dst(lhs.shape(), lhs.type()); + + auto dstit = dst.begin(); + auto lhsit = lhs.begin(); + auto rhsit = rhs.begin(); + + for (; dstit != dst.end(); ++dstit, ++lhsit, ++rhsit) { + *dstit = detail::saturated_sub(*lhsit, *rhsit); + } + return dst; +} + +template +image subtract_scalar(const image &lhs, const image &rhs) { + if (lhs.depth() != rhs.depth()) { + throw nn_error("Depth of lhs/rhs must be same. lhs:" + + to_string(lhs.depth()) + ",rhs:" + to_string(rhs.depth())); + } + if (rhs.width() != 1 || rhs.height() != 1) { + throw nn_error("rhs must be 1x1xN"); + } + + image dst(lhs.shape(), lhs.type()); + + auto dstit = dst.begin(); + auto lhsit = lhs.begin(); + auto rhsit = rhs.begin(); + + for (size_t i = 0; i < lhs.depth(); i++, ++rhsit) { + for (size_t j = 0; j < lhs.width() * lhs.height(); j++, ++dstit, ++lhsit) { + *dstit = detail::saturated_sub(*lhsit, *rhsit); + } + } + + return dst; +} + +/** + * visualize 1d-vector + * + * @example + * + * vec:[1,5,3] + * + * img: + * ---------- + * -11-55-33- + * -11-55-33- + * ---------- + **/ +template +inline image vec2image(const vec_t &vec, + size_t block_size = 2, + size_t max_cols = 20) { + if (vec.empty()) throw nn_error("failed to visialize image: vector is empty"); + + image img; + const size_t border_width = 1; + const auto cols = + vec.size() >= (size_t)max_cols ? (size_t)max_cols : vec.size(); + const auto rows = (vec.size() - 1) / cols + 1; + const auto pitch = block_size + border_width; + const auto width = pitch * cols + border_width; + const auto height = pitch * rows + border_width; + const typename image::intensity_t bg_color = 255; + size_t current_idx = 0; + + img.resize(width, height); + img.fill(bg_color); + + auto minmax = std::minmax_element(vec.begin(), vec.end()); + + for (unsigned int r = 0; r < rows; r++) { + size_t topy = pitch * r + border_width; + + for (unsigned int c = 0; c < cols; c++, current_idx++) { + size_t leftx = pitch * c + border_width; + const float_t src = vec[current_idx]; + image<>::intensity_t dst = static_cast::intensity_t>( + rescale(src, *minmax.first, *minmax.second, 0, 255)); + + for (size_t y = 0; y < block_size; y++) + for (size_t x = 0; x < block_size; x++) + img.at(x + leftx, y + topy) = dst; + + if (current_idx == vec.size()) return img; + } + } + return img; +} + +/** + * visualize 1d-vector + * + * @example + * + * vec:[5,2,1,3,6,3,0,9,8,7,4,2] maps:[width=2,height=3,depth=2] + * + * img: + * ------- + * -52-09- + * -13-87- + * -63-42- + * ------- + **/ +template +inline image vec2image(const vec_t &vec, const index3d &maps) { + if (vec.empty()) throw nn_error("failed to visualize image: vector is empty"); + if (vec.size() != maps.size()) + throw nn_error("failed to visualize image: vector size invalid"); + + const size_t border_width = 1; + const auto pitch = maps.width_ + border_width; + const auto width = maps.depth_ * pitch + border_width; + const auto height = maps.height_ + 2 * border_width; + const typename image::intensity_t bg_color = 255; + image img; + + img.resize(width, height); + img.fill(bg_color); + + auto minmax = std::minmax_element(vec.begin(), vec.end()); + + for (size_t c = 0; c < maps.depth_; ++c) { + const auto top = border_width; + const auto left = c * pitch + border_width; + + for (size_t y = 0; y < maps.height_; ++y) { + for (size_t x = 0; x < maps.width_; ++x) { + const float_t val = vec[maps.get_index(x, y, c)]; + + img.at(left + x, top + y) = static_cast::intensity_t>( + rescale(val, *minmax.first, *minmax.second, 0, 255)); + } + } + } + return img; +} + +} // namespace tiny_dnn + +#ifdef _MSC_VER +#pragma warning(pop) +#endif diff --git a/tiny_dnn/util/macro.h b/tiny_dnn/util/macro.h new file mode 100755 index 0000000000000..2df454e52a5ac --- /dev/null +++ b/tiny_dnn/util/macro.h @@ -0,0 +1,22 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#define CNN_UNREFERENCED_PARAMETER(x) (void)(x) + +#if defined _WIN32 && !defined(__MINGW32__) +#define CNN_WINDOWS +#endif + +#if defined(_MSC_VER) +#define CNN_MUST_INLINE __forceinline +#elif defined(__GNUC__) || defined(__clang__) || defined(__ICC) +#define CNN_MUST_INLINE __attribute__((always_inline)) inline +#else +#define CNN_MUST_INLINE inline +#endif diff --git a/tiny_dnn/util/math_functions.h b/tiny_dnn/util/math_functions.h new file mode 100755 index 0000000000000..8f8b05ca6b1f9 --- /dev/null +++ b/tiny_dnn/util/math_functions.h @@ -0,0 +1,98 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include + +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +// x = x / denom +inline void vector_div(vec_t &x, float_t denom) { + std::transform(x.begin(), x.end(), x.begin(), + [=](float_t x) { return x / denom; }); +} + +namespace detail { + +inline void moments_impl_calc_mean(size_t num_examples, + size_t channels, + size_t spatial_dim, + const tensor_t &in, + vec_t &mean) { + for (size_t i = 0; i < num_examples; i++) { + for (size_t j = 0; j < channels; j++) { + float_t &rmean = mean.at(j); + const auto it = in[i].begin() + (j * spatial_dim); + rmean = std::accumulate(it, it + spatial_dim, rmean); + } + } +} + +inline void moments_impl_calc_variance(size_t num_examples, + size_t channels, + size_t spatial_dim, + const tensor_t &in, + const vec_t &mean, + vec_t &variance) { + assert(mean.size() >= channels); + for (size_t i = 0; i < num_examples; i++) { + for (size_t j = 0; j < channels; j++) { + float_t &rvar = variance[j]; + const auto it = in[i].begin() + (j * spatial_dim); + const float_t ex = mean[j]; + rvar = std::accumulate(it, it + spatial_dim, rvar, + [ex](float_t current, float_t x) { + return current + pow(x - ex, float_t{2.0}); + }); + } + } + vector_div( + variance, + std::max(float_t{1.0f}, + static_cast(num_examples * spatial_dim) - float_t{1.0f})); +} + +} // namespace detail + +/** + * calculate mean/variance across channels + */ +inline void moments(const tensor_t &in, + size_t spatial_dim, + size_t channels, + vec_t &mean) { + const size_t num_examples = in.size(); + assert(in[0].size() == spatial_dim * channels); + + mean.resize(channels); + vectorize::fill(&mean[0], mean.size(), float_t{0.0}); + detail::moments_impl_calc_mean(num_examples, channels, spatial_dim, in, mean); + vector_div(mean, (float_t)num_examples * spatial_dim); +} + +inline void moments(const tensor_t &in, + size_t spatial_dim, + size_t channels, + vec_t &mean, + vec_t &variance) { + const size_t num_examples = in.size(); + assert(in[0].size() == spatial_dim * channels); + + // calc mean + moments(in, spatial_dim, channels, mean); + + variance.resize(channels); + vectorize::fill(&variance[0], variance.size(), float_t{0.0}); + detail::moments_impl_calc_variance(num_examples, channels, spatial_dim, in, + mean, variance); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/nn_error.h b/tiny_dnn/util/nn_error.h new file mode 100755 index 0000000000000..1d170b75e7b9e --- /dev/null +++ b/tiny_dnn/util/nn_error.h @@ -0,0 +1,66 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include "tiny_dnn/util/colored_print.h" + +namespace tiny_dnn { + +/** + * error exception class for tiny-dnn + **/ +class nn_error : public std::exception { + public: + explicit nn_error(const std::string &msg) : msg_(msg) {} + const char *what() const throw() override { return msg_.c_str(); } + + private: + std::string msg_; +}; + +/** + * warning class for tiny-dnn (for debug) + **/ +class nn_warn { + public: + explicit nn_warn(const std::string &msg) : msg_(msg) { +#ifdef CNN_USE_STDOUT + coloredPrint(Color::YELLOW, msg_h_ + msg_); +#endif + } + + private: + std::string msg_; + std::string msg_h_ = std::string("[WARNING] "); +}; + +/** + * info class for tiny-dnn (for debug) + **/ +class nn_info { + public: + explicit nn_info(const std::string &msg) : msg_(msg) { +#ifdef CNN_USE_STDOUT + std::cout << msg_h + msg_ << std::endl; +#endif + } + + private: + std::string msg_; + std::string msg_h = std::string("[INFO] "); +}; + +class nn_not_implemented_error : public nn_error { + public: + explicit nn_not_implemented_error(const std::string &msg = "not implemented") + : nn_error(msg) {} +}; + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/parallel_for.h b/tiny_dnn/util/parallel_for.h new file mode 100755 index 0000000000000..b05d6732f0b37 --- /dev/null +++ b/tiny_dnn/util/parallel_for.h @@ -0,0 +1,206 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/config.h" +#include "tiny_dnn/util/aligned_allocator.h" +#include "tiny_dnn/util/nn_error.h" + +#ifdef CNN_USE_TBB +#ifndef NOMINMAX +#define NOMINMAX // tbb includes windows.h in tbb/machine/windows_api.h +#endif +#include +#include +#endif + +#if !defined(CNN_USE_OMP) && !defined(CNN_SINGLE_THREAD) +#include // NOLINT +#include // NOLINT +#endif + +#if defined(CNN_USE_GCD) && !defined(CNN_SINGLE_THREAD) +#include +#endif + +namespace tiny_dnn { + +#ifdef CNN_USE_TBB + +static tbb::task_scheduler_init tbbScheduler( + tbb::task_scheduler_init::automatic); // tbb::task_scheduler_init::deferred); + +typedef tbb::blocked_range blocked_range; + +template +void parallel_for(size_t begin, size_t end, const Func &f, size_t grainsize) { + assert(end >= begin); + tbb::parallel_for( + blocked_range(begin, end, end - begin > grainsize ? grainsize : 1), f); +} + +template +void xparallel_for(size_t begin, size_t end, const Func &f) { + f(blocked_range(begin, end, 100)); +} + +#else + +struct blocked_range { + typedef size_t const_iterator; + + blocked_range(size_t begin, size_t end) : begin_(begin), end_(end) {} + blocked_range(int begin, int end) : begin_(begin), end_(end) {} + + const_iterator begin() const { return begin_; } + const_iterator end() const { return end_; } + + private: + size_t begin_; + size_t end_; +}; + +template +void xparallel_for(size_t begin, size_t end, const Func &f) { + blocked_range r(begin, end); + f(r); +} + +#if defined(CNN_USE_OMP) + +template +void parallel_for(size_t begin, + size_t end, + const Func &f, + size_t /*grainsize*/) { + assert(end >= begin); +#pragma omp parallel for + for (size_t i = begin; i < end; ++i) f(blocked_range(i, i + 1)); +} + +#elif defined(CNN_USE_GCD) + +template +void parallel_for(size_t begin, size_t end, const Func &f, size_t grainsize) { + assert(end >= begin); + size_t count = end - begin; + size_t blockSize = grainsize; + if (count < blockSize || blockSize == 0) { + blockSize = 1; + } + size_t blockCount = (count + blockSize - 1) / blockSize; + assert(blockCount > 0); + + dispatch_apply(blockCount, dispatch_get_global_queue(QOS_CLASS_DEFAULT, 0), + ^(size_t block) { + size_t blockStart = block * blockSize; + size_t blockEnd = blockStart + blockSize; + if (blockEnd > end) { + blockEnd = end; + } + assert(blockStart < blockEnd); + + f(blocked_range(blockStart, blockEnd)); + }); +} + +#elif defined(CNN_SINGLE_THREAD) + +template +void parallel_for(size_t begin, + size_t end, + const Func &f, + size_t /*grainsize*/) { + xparallel_for(begin, end, f); +} + +#else + +template +void parallel_for(size_t begin, + size_t end, + const Func &f, + size_t /*grainsize*/) { + assert(end >= begin); + size_t nthreads = std::thread::hardware_concurrency(); + size_t blockSize = (end - begin) / nthreads; + if (blockSize * nthreads < end - begin) blockSize++; + + std::vector > futures; + + size_t blockBegin = begin; + size_t blockEnd = blockBegin + blockSize; + if (blockEnd > end) blockEnd = end; + + for (size_t i = 0; i < nthreads; i++) { + futures.push_back( + std::move(std::async(std::launch::async, [blockBegin, blockEnd, &f] { + f(blocked_range(blockBegin, blockEnd)); + }))); + + blockBegin += blockSize; + blockEnd = blockBegin + blockSize; + if (blockBegin >= end) break; + if (blockEnd > end) blockEnd = end; + } + + for (auto &future : futures) future.wait(); +} + +#endif + +#endif // CNN_USE_TBB + +template +bool value_representation(U const &value) { + return static_cast(static_cast(value)) == value; +} + +template +inline void for_( + bool parallelize, size_t begin, T end, Func f, size_t grainsize = 100) { + static_assert(std::is_integral::value, "end must be integral type"); + parallelize = parallelize && value_representation(end); + parallelize ? parallel_for(begin, end, f, grainsize) + : xparallel_for(begin, end, f); +} + +template +inline void for_i(bool parallelize, T size, Func f, size_t grainsize = 100u) { +#ifdef CNN_SINGLE_THREAD + for (size_t i = 0; i < size; ++i) { + f(i); + } +#else // #ifdef CNN_SINGLE_THREAD + for_(parallelize, 0u, size, + [&](const blocked_range &r) { +#ifdef CNN_USE_OMP +#pragma omp parallel for +#endif + for (size_t i = r.begin(); i < r.end(); i++) { + f(i); + } + }, + grainsize); +#endif // #ifdef CNN_SINGLE_THREAD +} + +template +inline void for_i(T size, Func f, size_t grainsize = 100) { + for_i(true, size, f, grainsize); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/product.h b/tiny_dnn/util/product.h new file mode 100755 index 0000000000000..fb6d817f78497 --- /dev/null +++ b/tiny_dnn/util/product.h @@ -0,0 +1,789 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#if defined(CNN_USE_SSE) || defined(CNN_USE_AVX) +#include +#endif + +#include +#include +#include + +#ifdef CNN_USE_AVX +#include "tiny_dnn/core/kernels/avx_kernel_common.h" +#endif + +#include "tiny_dnn/util/macro.h" + +namespace vectorize { +namespace detail { + +// traits +template +struct scalar_generic { + typedef T register_type; + typedef T value_type; + enum { unroll_size = 1 }; + static CNN_MUST_INLINE register_type set1(const value_type &x) { return x; } + static CNN_MUST_INLINE register_type zero() { return register_type(0); } + static CNN_MUST_INLINE register_type mul(const register_type &v1, + const register_type &v2) { + return v1 * v2; + } + static CNN_MUST_INLINE register_type add(const register_type &v1, + const register_type &v2) { + return v1 + v2; + } + static CNN_MUST_INLINE register_type madd(const register_type &v1, + const register_type &v2, + const register_type &v3) { + return v1 * v2 + v3; + } + + template + static CNN_MUST_INLINE register_type load(const value_type *px) { + return *px; + } + template + static CNN_MUST_INLINE void store(value_type *px, const register_type &v) { + *px = v; + } + + static CNN_MUST_INLINE value_type resemble(const register_type &x) { + return x; + } + + static CNN_MUST_INLINE bool is_aligned(value_type *p) { return true; } +}; + +#ifdef CNN_USE_SSE + +struct float_sse { + typedef __m128 register_type; + typedef float value_type; + enum { unroll_size = 4 }; + static CNN_MUST_INLINE register_type set1(const value_type &x) { + return _mm_set1_ps(x); + } + static CNN_MUST_INLINE register_type zero() { return _mm_setzero_ps(); } + static CNN_MUST_INLINE register_type mul(const register_type &v1, + const register_type &v2) { + return _mm_mul_ps(v1, v2); + } + static CNN_MUST_INLINE register_type add(const register_type &v1, + const register_type &v2) { + return _mm_add_ps(v1, v2); + } + static CNN_MUST_INLINE register_type madd(const register_type &v1, + const register_type &v2, + const register_type &v3) { + return _mm_add_ps(_mm_mul_ps(v1, v2), v3); + } + + template + static CNN_MUST_INLINE register_type load(const value_type *px); + + template + static CNN_MUST_INLINE void store(value_type *px, const register_type &v); + + static CNN_MUST_INLINE value_type resemble(const register_type &x) { + alignas(16) float tmp[4]; + _mm_store_ps(tmp, x); + return tmp[0] + tmp[1] + tmp[2] + tmp[3]; + } + static CNN_MUST_INLINE bool is_aligned(value_type *p) { + return reinterpret_cast(p) % 16 == 0; + } +}; + +template <> +CNN_MUST_INLINE __m128 float_sse::load(const float *px) { + return _mm_load_ps(px); +} +template <> +CNN_MUST_INLINE __m128 float_sse::load(const float *px) { + return _mm_loadu_ps(px); +} + +template <> +CNN_MUST_INLINE void float_sse::store(float *px, + const __m128 &v) { + _mm_store_ps(px, v); +} +template <> +CNN_MUST_INLINE void float_sse::store(float *px, + const __m128 &v) { + _mm_storeu_ps(px, v); +} + +struct double_sse { + typedef __m128d register_type; + typedef double value_type; + enum { unroll_size = 2 }; + static CNN_MUST_INLINE register_type set1(const value_type &x) { + return _mm_set1_pd(x); + } + static CNN_MUST_INLINE register_type zero() { return _mm_setzero_pd(); } + static CNN_MUST_INLINE register_type mul(const register_type &v1, + const register_type &v2) { + return _mm_mul_pd(v1, v2); + } + static CNN_MUST_INLINE register_type add(const register_type &v1, + const register_type &v2) { + return _mm_add_pd(v1, v2); + } + static CNN_MUST_INLINE register_type madd(const register_type &v1, + const register_type &v2, + const register_type &v3) { + return _mm_add_pd(_mm_mul_pd(v1, v2), v3); + } + + template + static CNN_MUST_INLINE register_type load(const value_type *px); + + template + static CNN_MUST_INLINE void store(value_type *px, const register_type &v); + + static CNN_MUST_INLINE value_type resemble(const register_type &x) { + alignas(16) double tmp[2]; + _mm_store_pd(tmp, x); + return tmp[0] + tmp[1]; + } + + static CNN_MUST_INLINE bool is_aligned(value_type *p) { + return reinterpret_cast(p) % 16 == 0; + } +}; + +template <> +CNN_MUST_INLINE __m128d double_sse::load(const double *px) { + return _mm_load_pd(px); +} +template <> +CNN_MUST_INLINE __m128d double_sse::load(const double *px) { + return _mm_loadu_pd(px); +} + +template <> +CNN_MUST_INLINE void double_sse::store(double *px, + const __m128d &v) { + _mm_store_pd(px, v); +} +template <> +CNN_MUST_INLINE void double_sse::store(double *px, + const __m128d &v) { + _mm_storeu_pd(px, v); +} + +#endif // CNN_USE_SSE + +#ifdef CNN_USE_AVX + +struct float_avx { + typedef __m256 register_type; + typedef float value_type; + enum { unroll_size = 8 }; + static CNN_MUST_INLINE register_type set1(const value_type &x) { + return _mm256_set1_ps(x); + } + static CNN_MUST_INLINE register_type zero() { return _mm256_setzero_ps(); } + static CNN_MUST_INLINE register_type mul(const register_type &v1, + const register_type &v2) { + return _mm256_mul_ps(v1, v2); + } + static CNN_MUST_INLINE register_type add(const register_type &v1, + const register_type &v2) { + return _mm256_add_ps(v1, v2); + } +#ifdef CNN_USE_AVX2 + static CNN_MUST_INLINE register_type madd(const register_type &v1, + const register_type &v2, + const register_type &v3) { + return _mm256_fmadd_ps(v1, v2, v3); + } +#else + static CNN_MUST_INLINE register_type madd(const register_type &v1, + const register_type &v2, + const register_type &v3) { + return _mm256_add_ps(_mm256_mul_ps(v1, v2), v3); + } +#endif + + template + static CNN_MUST_INLINE register_type load(const value_type *px); + + template + static CNN_MUST_INLINE void store(value_type *px, const register_type &v); + + static CNN_MUST_INLINE value_type resemble(const register_type &x) { + return _mm_cvtss_f32(hsum256_ps(x)); + } + static CNN_MUST_INLINE bool is_aligned(value_type *p) { + return reinterpret_cast(p) % 32 == 0; + } +}; + +template <> +CNN_MUST_INLINE __m256 float_avx::load(const float *px) { + return _mm256_load_ps(px); +} +template <> +CNN_MUST_INLINE __m256 float_avx::load(const float *px) { + return _mm256_loadu_ps(px); +} + +template <> +CNN_MUST_INLINE void float_avx::store(float *px, + const __m256 &v) { + _mm256_store_ps(px, v); +} +template <> +CNN_MUST_INLINE void float_avx::store(float *px, + const __m256 &v) { + _mm256_storeu_ps(px, v); +} + +struct double_avx { + typedef __m256d register_type; + typedef double value_type; + enum { unroll_size = 4 }; + static CNN_MUST_INLINE register_type set1(const value_type &x) { + return _mm256_set1_pd(x); + } + static CNN_MUST_INLINE register_type zero() { return _mm256_setzero_pd(); } + static CNN_MUST_INLINE register_type mul(const register_type &v1, + const register_type &v2) { + return _mm256_mul_pd(v1, v2); + } + static CNN_MUST_INLINE register_type add(const register_type &v1, + const register_type &v2) { + return _mm256_add_pd(v1, v2); + } +#ifdef CNN_USE_AVX2 + static CNN_MUST_INLINE register_type madd(const register_type &v1, + const register_type &v2, + const register_type &v3) { + return _mm256_fmadd_pd(v1, v2, v3); + } +#else + static CNN_MUST_INLINE register_type madd(const register_type &v1, + const register_type &v2, + const register_type &v3) { + return _mm256_add_pd(_mm256_mul_pd(v1, v2), v3); + } +#endif + + template + static CNN_MUST_INLINE register_type load(const value_type *px); + + template + static CNN_MUST_INLINE void store(value_type *px, const register_type &v); + + static CNN_MUST_INLINE value_type resemble(const register_type &x) { + alignas(32) double tmp[4]; + _mm256_store_pd(tmp, x); + return std::accumulate(tmp, tmp + 4, 0.0); + } + static CNN_MUST_INLINE bool is_aligned(value_type *p) { + return reinterpret_cast(p) % 32 == 0; + } +}; + +template <> +CNN_MUST_INLINE __m256d double_avx::load(const double *px) { + return _mm256_load_pd(px); +} +template <> +CNN_MUST_INLINE __m256d double_avx::load(const double *px) { + return _mm256_loadu_pd(px); +} + +template <> +CNN_MUST_INLINE void double_avx::store(double *px, + const __m256d &v) { + _mm256_store_pd(px, v); +} +template <> +CNN_MUST_INLINE void double_avx::store(double *px, + const __m256d &v) { + _mm256_storeu_pd(px, v); +} + +#endif // CNN_USE_AVX + +// generic dot-product +template +CNN_MUST_INLINE typename T::value_type dot_product( + const typename T::value_type *f1, + const typename T::value_type *f2, + std::size_t size) { + typename T::register_type r0 = T::zero(); + typename T::register_type r1 = T::zero(); + typename T::register_type r2 = T::zero(); + typename T::register_type r3 = T::zero(); + auto sz = T::unroll_size; + auto sz4 = T::unroll_size * 4; + auto n4 = size / sz4; + auto n1 = (size % sz4) / sz; + auto remain = size % sz; + for (size_t i = 0; i < n4; ++i) { + auto s10 = T::template load(&f1[i * sz4 + sz * 0]); + auto s11 = T::template load(&f1[i * sz4 + sz * 1]); + auto s12 = T::template load(&f1[i * sz4 + sz * 2]); + auto s13 = T::template load(&f1[i * sz4 + sz * 3]); + auto s20 = T::template load(&f2[i * sz4 + sz * 0]); + auto s21 = T::template load(&f2[i * sz4 + sz * 1]); + auto s22 = T::template load(&f2[i * sz4 + sz * 2]); + auto s23 = T::template load(&f2[i * sz4 + sz * 3]); + r0 = T::madd(s10, s20, r0); + r1 = T::madd(s11, s21, r1); + r2 = T::madd(s12, s22, r2); + r3 = T::madd(s13, s23, r3); + } + size_t idx = n4 * sz4; + for (size_t i = 0; i < n1; ++i) { + auto s1 = T::template load(&f1[idx + i * sz]); + auto s2 = T::template load(&f2[idx + i * sz]); + r0 = T::madd(s1, s2, r0); + } + r0 = T::add(r0, r1); + r2 = T::add(r2, r3); + r0 = T::add(r0, r2); + typename T::value_type sum = T::resemble(r0); + idx += n1 * sz; + for (size_t i = 0; i < remain; ++i) { + sum += f1[idx + i] * f2[idx + i]; + } + return sum; +} + +template +CNN_MUST_INLINE void add(typename T::value_type c, + std::size_t size, + typename T::value_type *dst) { + typename T::register_type c2 = T::set1(c); + auto sz = T::unroll_size; + auto sz4 = T::unroll_size * 4; + auto n4 = size / sz4; + auto n1 = (size % sz4) / sz; + auto remain = size % sz; + for (size_t i = 0; i < n4; ++i) { + auto d0 = T::template load(&dst[i * sz4 + sz * 0]); + auto d1 = T::template load(&dst[i * sz4 + sz * 1]); + auto d2 = T::template load(&dst[i * sz4 + sz * 2]); + auto d3 = T::template load(&dst[i * sz4 + sz * 3]); + d0 = T::add(c2, d0); + d1 = T::add(c2, d1); + d2 = T::add(c2, d2); + d3 = T::add(c2, d3); + T::template store(&dst[i * sz4 + sz * 0], d0); + T::template store(&dst[i * sz4 + sz * 1], d1); + T::template store(&dst[i * sz4 + sz * 2], d2); + T::template store(&dst[i * sz4 + sz * 3], d3); + } + size_t idx = n4 * sz4; + for (size_t i = 0; i < n1; ++i) { + auto d = T::template load(&dst[idx + i * sz]); + d = T::add(c2, d); + T::template store(&dst[idx + i * sz], d); + } + idx += n1 * sz; + for (size_t i = 0; i < remain; ++i) { + dst[idx + i] += c; + } +} + +template +CNN_MUST_INLINE void add(const typename T::value_type *src, + std::size_t size, + typename T::value_type *dst) { + auto sz = T::unroll_size; + auto sz4 = T::unroll_size * 4; + auto n4 = size / sz4; + auto n1 = (size % sz4) / sz; + auto remain = size % sz; + for (size_t i = 0; i < n4; ++i) { + auto d0 = T::template load(&dst[i * sz4 + sz * 0]); + auto d1 = T::template load(&dst[i * sz4 + sz * 1]); + auto d2 = T::template load(&dst[i * sz4 + sz * 2]); + auto d3 = T::template load(&dst[i * sz4 + sz * 3]); + auto s0 = T::template load(&src[i * sz4 + sz * 0]); + auto s1 = T::template load(&src[i * sz4 + sz * 1]); + auto s2 = T::template load(&src[i * sz4 + sz * 2]); + auto s3 = T::template load(&src[i * sz4 + sz * 3]); + d0 = T::add(s0, d0); + d1 = T::add(s1, d1); + d2 = T::add(s2, d2); + d3 = T::add(s3, d3); + T::template store(&dst[i * sz4 + sz * 0], d0); + T::template store(&dst[i * sz4 + sz * 1], d1); + T::template store(&dst[i * sz4 + sz * 2], d2); + T::template store(&dst[i * sz4 + sz * 3], d3); + } + size_t idx = n4 * sz4; + for (size_t i = 0; i < n1; ++i) { + auto d = T::template load(&dst[idx + i * sz]); + auto s = T::template load(&src[idx + i * sz]); + d = T::add(s, d); + T::template store(&dst[idx + i * sz], d); + } + idx += n1 * sz; + for (size_t i = 0; i < remain; ++i) { + dst[idx + i] += src[idx + i]; + } +} + +// TODO(beru): documentation +/** + * + * @tparam T + * @tparam src_aligned + * @tparam dst_aligned + * @param src + * @param c + * @param size + * @param dst + */ +template +CNN_MUST_INLINE void muladd(const typename T::value_type *src, + typename T::value_type c, + std::size_t size, + typename T::value_type *dst) { + auto factor = T::set1(c); + auto sz = T::unroll_size; + auto sz4 = T::unroll_size * 4; + auto n4 = size / sz4; + auto n1 = (size % sz4) / sz; + auto remain = size % sz; + for (size_t i = 0; i < n4; ++i) { + auto d0 = T::template load(&dst[i * sz4 + sz * 0]); + auto d1 = T::template load(&dst[i * sz4 + sz * 1]); + auto d2 = T::template load(&dst[i * sz4 + sz * 2]); + auto d3 = T::template load(&dst[i * sz4 + sz * 3]); + auto s0 = T::template load(&src[i * sz4 + sz * 0]); + auto s1 = T::template load(&src[i * sz4 + sz * 1]); + auto s2 = T::template load(&src[i * sz4 + sz * 2]); + auto s3 = T::template load(&src[i * sz4 + sz * 3]); + d0 = T::madd(s0, factor, d0); + d1 = T::madd(s1, factor, d1); + d2 = T::madd(s2, factor, d2); + d3 = T::madd(s3, factor, d3); + T::template store(&dst[i * sz4 + sz * 0], d0); + T::template store(&dst[i * sz4 + sz * 1], d1); + T::template store(&dst[i * sz4 + sz * 2], d2); + T::template store(&dst[i * sz4 + sz * 3], d3); + } + size_t idx = n4 * sz4; + for (size_t i = 0; i < n1; ++i) { + auto d = T::template load(&dst[idx + i * sz]); + auto s = T::template load(&src[idx + i * sz]); + d = T::madd(s, factor, d); + T::template store(&dst[idx + i * sz], d); + } + idx += n1 * sz; + for (size_t i = 0; i < remain; ++i) { + dst[idx + i] += src[idx + i] * c; + } +} + +template +CNN_MUST_INLINE void reduce(const typename T::value_type *src, + std::size_t size, + typename T::value_type *dst) { + auto sz = T::unroll_size; + auto sz4 = T::unroll_size * 4; + auto n4 = size / sz4; + auto n1 = (size % sz4) / sz; + auto remain = size % sz; + for (size_t i = 0; i < n4; ++i) { + auto d0 = T::template load(&dst[i * sz4 + sz * 0]); + auto d1 = T::template load(&dst[i * sz4 + sz * 1]); + auto d2 = T::template load(&dst[i * sz4 + sz * 2]); + auto d3 = T::template load(&dst[i * sz4 + sz * 3]); + auto s0 = T::template load(&src[i * sz4 + sz * 0]); + auto s1 = T::template load(&src[i * sz4 + sz * 1]); + auto s2 = T::template load(&src[i * sz4 + sz * 2]); + auto s3 = T::template load(&src[i * sz4 + sz * 3]); + d0 = T::add(s0, d0); + d1 = T::add(s1, d1); + d2 = T::add(s2, d2); + d3 = T::add(s3, d3); + T::template store(&dst[i * sz4 + sz * 0], d0); + T::template store(&dst[i * sz4 + sz * 1], d1); + T::template store(&dst[i * sz4 + sz * 2], d2); + T::template store(&dst[i * sz4 + sz * 3], d3); + } + size_t idx = n4 * sz4; + for (size_t i = 0; i < n1; ++i) { + auto d = T::template load(&dst[idx + i * sz]); + auto s = T::template load(&src[idx + i * sz]); + d = T::add(s, d); + T::template store(&dst[idx + i * sz], d); + } + idx += n1 * sz; + for (size_t i = 0; i < remain; ++i) { + dst[idx + i] += src[idx + i]; + } +} + +template +void fill(T *dst, size_t size, T value) { + std::fill(dst, dst + size, value); +} + +#if defined(CNN_USE_AVX) +#ifdef CNN_USE_DOUBLE +#define CNN_VECTORIZE_TYPE detail::double_avx +#else +#define CNN_VECTORIZE_TYPE detail::float_avx +#endif +#elif defined(CNN_USE_SSE) +#ifdef CNN_USE_DOUBLE +#define CNN_VECTORIZE_TYPE detail::double_sse +#else +#define CNN_VECTORIZE_TYPE detail::float_sse +#endif +#else +#ifdef CNN_USE_DOUBLE +#define CNN_VECTORIZE_TYPE detail::scalar_generic +#else +#define CNN_VECTORIZE_TYPE detail::scalar_generic +#endif +#endif + +} // namespace detail + +#ifdef CNN_USE_AVX +// vertically accumulate 'n' AVX registers into single register. +template +#ifdef CNN_USE_DOUBLE +CNN_MUST_INLINE CNN_VECTORIZE_TYPE::register_type accumulate( + const double *start, const size_t &nblocks) { +#else +CNN_MUST_INLINE CNN_VECTORIZE_TYPE::register_type accumulate( + const float *start, const size_t &nblocks) { +#endif + const size_t n4 = nblocks / 4; + const size_t n2 = (nblocks % 4) / 2; + const size_t n1 = nblocks % 2; + CNN_VECTORIZE_TYPE::register_type v0 = CNN_VECTORIZE_TYPE::load( + start + CNN_VECTORIZE_TYPE::unroll_size * 0); + CNN_VECTORIZE_TYPE::register_type v1 = CNN_VECTORIZE_TYPE::load( + start + CNN_VECTORIZE_TYPE::unroll_size * 1); + CNN_VECTORIZE_TYPE::register_type v2 = CNN_VECTORIZE_TYPE::load( + start + CNN_VECTORIZE_TYPE::unroll_size * 2); + CNN_VECTORIZE_TYPE::register_type v3 = CNN_VECTORIZE_TYPE::load( + start + CNN_VECTORIZE_TYPE::unroll_size * 3); + CNN_VECTORIZE_TYPE::register_type sum0 = CNN_VECTORIZE_TYPE::zero(); + CNN_VECTORIZE_TYPE::register_type sum1 = CNN_VECTORIZE_TYPE::zero(); + CNN_VECTORIZE_TYPE::register_type sum2 = CNN_VECTORIZE_TYPE::zero(); + CNN_VECTORIZE_TYPE::register_type sum3 = CNN_VECTORIZE_TYPE::zero(); + for (size_t j = 0; j < n4; ++j) { + CNN_VECTORIZE_TYPE::register_type f0 = CNN_VECTORIZE_TYPE::load( + start + CNN_VECTORIZE_TYPE::unroll_size * 4); + CNN_VECTORIZE_TYPE::register_type f1 = CNN_VECTORIZE_TYPE::load( + start + CNN_VECTORIZE_TYPE::unroll_size * 5); + CNN_VECTORIZE_TYPE::register_type f2 = CNN_VECTORIZE_TYPE::load( + start + CNN_VECTORIZE_TYPE::unroll_size * 6); + CNN_VECTORIZE_TYPE::register_type f3 = CNN_VECTORIZE_TYPE::load( + start + CNN_VECTORIZE_TYPE::unroll_size * 7); + sum0 = CNN_VECTORIZE_TYPE::add(sum0, v0); + sum1 = CNN_VECTORIZE_TYPE::add(sum1, v1); + sum2 = CNN_VECTORIZE_TYPE::add(sum2, v2); + sum3 = CNN_VECTORIZE_TYPE::add(sum3, v3); + v0 = f0; + v1 = f1; + v2 = f2; + v3 = f3; + start += CNN_VECTORIZE_TYPE::unroll_size * 4; + } + if (n2) { + sum0 = CNN_VECTORIZE_TYPE::add(sum0, v0); + sum1 = CNN_VECTORIZE_TYPE::add(sum1, v1); + start += CNN_VECTORIZE_TYPE::unroll_size * 2; + } + if (n1) { + sum2 = CNN_VECTORIZE_TYPE::add( + sum2, CNN_VECTORIZE_TYPE::load(start + 0)); + start += CNN_VECTORIZE_TYPE::unroll_size * 1; + } + sum0 = CNN_VECTORIZE_TYPE::add(sum0, sum1); + sum2 = CNN_VECTORIZE_TYPE::add(sum2, sum3); + return CNN_VECTORIZE_TYPE::add(sum0, sum2); +} +#endif // CNN_USE_AVX + +// dst[i] += c +template +void add(T c, std::size_t size, T *dst) { + bool is_dst_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)dst); + if (is_dst_aligned) { + detail::add(c, size, dst); + } else { + detail::add(c, size, dst); + } +} + +// dst[i] += src[i] +template +void add(const T *src, std::size_t size, T *dst) { + bool src_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)src); + bool dst_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)dst); + if (src_aligned) { + if (dst_aligned) { + detail::add(src, size, + dst); + } else { + detail::add( + src, size, dst); + } + } else { + if (dst_aligned) { + detail::add( + src, size, dst); + } else { + detail::add( + src, size, dst); + } + } +} + +// dst[i] += c * src[i] +template +void muladd(const T *src, T c, std::size_t size, T *dst) { + bool src_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)src); + bool dst_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)dst); + if (src_aligned) { + if (dst_aligned) { + detail::muladd( + src, c, size, dst); + } else { + detail::muladd( + src, c, size, dst); + } + } else { + if (dst_aligned) { + detail::muladd( + src, c, size, dst); + } else { + detail::muladd( + src, c, size, dst); + } + } +} + +// sum(s1[i] * s2[i]) +template +T dot(const T *s1, const T *s2, std::size_t size) { + bool s1_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)s1); + bool s2_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)s2); + if (s1_aligned) { + if (s2_aligned) { + return detail::dot_product(s1, s2, size); + } else { + return detail::dot_product(s1, s2, size); + } + } else { + if (s2_aligned) { + return detail::dot_product(s1, s2, size); + } else { + return detail::dot_product(s1, s2, size); + } + } +} + +/// dst[i] += src[i] +template +void reduce(const T *src, std::size_t size, T *dst) { + bool src_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)src); + bool dst_aligned = + CNN_VECTORIZE_TYPE::is_aligned((CNN_VECTORIZE_TYPE::value_type *)dst); + if (src_aligned) { + if (dst_aligned) { + detail::reduce( + src, size, dst); + } else { + detail::reduce( + src, size, dst); + } + } else { + if (dst_aligned) { + detail::reduce( + src, size, dst); + } else { + detail::reduce( + src, size, dst); + } + } +} + +template +CNN_MUST_INLINE void fill(T *dst, std::size_t size, T value) { +#if defined(_MSC_VER) +#if defined(_M_AMD64) + +// On recent x86/x64 processors, REP string instructions are faster +// than SSE/AVX store instructions. +// CPUID feature flag (ERMSB) checking omitted +#if defined(CNN_USE_DOUBLE) + union { + unsigned __int64 dat; + T value; + } u; + static_assert(sizeof(T) == sizeof(unsigned __int64), "size mismatch."); + u.value = value; + __stosq((unsigned __int64 *)dst, u.dat, size); +#else // #if defined(CNN_USE_DOUBLE) + union { + unsigned long dat; // NOLINT + T value; + } u; + static_assert(sizeof(T) == sizeof(unsigned long), // NOLINT + "size mismatch."); + u.value = value; + __stosd((unsigned long *)dst, u.dat, size); // NOLINT +#endif // #if defined(CNN_USE_DOUBLE) + +#elif defined(_M_IX86) + +#if defined(CNN_USE_DOUBLE) + detail::fill(dst, size, value); +#else // #if defined(CNN_USE_DOUBLE) + union { + unsigned long dat; // NOLINT + T value; + } u; + static_assert(sizeof(T) == sizeof(unsigned long), // NOLINT + "size mismatch."); + u.value = value; + __stosd((unsigned long *)dst, u.dat, size); // NOLINT +#endif // #if defined(CNN_USE_DOUBLE) + +#else // !x86 && !x64 + detail::fill(dst, size, value); +#endif + +#else // #if defined(_MSC_VER) + detail::fill(dst, size, value); +#endif +} + +} // namespace vectorize diff --git a/tiny_dnn/util/random.h b/tiny_dnn/util/random.h new file mode 100755 index 0000000000000..325c8820ebf32 --- /dev/null +++ b/tiny_dnn/util/random.h @@ -0,0 +1,81 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include "tiny_dnn/config.h" +#include "tiny_dnn/util/nn_error.h" + +namespace tiny_dnn { + +class random_generator { + public: + static random_generator &get_instance() { + static random_generator instance; + return instance; + } + + std::mt19937 &operator()() { return gen_; } + + void set_seed(unsigned int seed) { gen_.seed(seed); } + + private: + // avoid gen_(0) for MSVC known issue + // https://connect.microsoft.com/VisualStudio/feedback/details/776456 + random_generator() : gen_(1) {} + std::mt19937 gen_; +}; + +template +inline typename std::enable_if::value, T>::type +uniform_rand(T min, T max) { + std::uniform_int_distribution dst(min, max); + return dst(random_generator::get_instance()()); +} + +template +inline typename std::enable_if::value, T>::type +uniform_rand(T min, T max) { + std::uniform_real_distribution dst(min, max); + return dst(random_generator::get_instance()()); +} + +template +inline typename std::enable_if::value, T>::type +gaussian_rand(T mean, T sigma) { + std::normal_distribution dst(mean, sigma); + return dst(random_generator::get_instance()()); +} + +inline void set_random_seed(unsigned int seed) { + random_generator::get_instance().set_seed(seed); +} + +template +inline int uniform_idx(const Container &t) { + return uniform_rand(0, static_cast(t.size() - 1)); +} + +inline bool bernoulli(float_t p) { + return uniform_rand(float_t{0}, float_t{1}) <= p; +} + +template +void uniform_rand(Iter begin, Iter end, float_t min, float_t max) { + for (Iter it = begin; it != end; ++it) *it = uniform_rand(min, max); +} + +template +void gaussian_rand(Iter begin, Iter end, float_t mean, float_t sigma) { + for (Iter it = begin; it != end; ++it) *it = gaussian_rand(mean, sigma); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/serialization_functions.h b/tiny_dnn/util/serialization_functions.h new file mode 100755 index 0000000000000..a13e78966692c --- /dev/null +++ b/tiny_dnn/util/serialization_functions.h @@ -0,0 +1,903 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include + +#include // For LoadAndConstruct +#include "tiny_dnn/tiny_dnn.h" + +namespace detail { + +/** + * size of layer, model, data etc. + * change to smaller type if memory footprint is severe + **/ +typedef std::uint32_t serial_size_t; + +typedef tiny_dnn::index3d shape3d_serial; + +template +static inline cereal::NameValuePair make_nvp(const char *name, T &&value) { + return cereal::make_nvp(name, value); +} + +template +void arc(Archive &ar, cereal::NameValuePair &&arg) { + ar(arg); +} + +template +void arc(Archive &ar, T &&arg) { + ar(arg); +} + +template ::value>::type = 0> +void arc(Archive &ar, cereal::NameValuePair &&arg) { + cereal::NameValuePair arg2(arg.name, arg.value); + ar(arg2); +} + +template ::value>::type = 0> +void arc(Archive &ar, cereal::NameValuePair &&arg) { + cereal::NameValuePair arg2(arg.name, 0); + ar(arg2); + arg.value = arg2.value; +} + +template +inline void arc(Archive &ar) {} + +template +inline void arc(Archive &ar, Type &&arg, Type2 &&arg2) { + arc(ar, std::forward(arg)); + arc(ar, std::forward(arg2)); +} + +template +inline void arc(Archive &ar, Type &&arg, Types &&... args) { + arc(ar, std::forward(arg)); + arc(ar, std::forward(args)...); +} + +} // namespace detail + +namespace cereal { + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + size_t num_args, dim; + + ::detail::arc(ar, ::detail::make_nvp("num_args", num_args), + ::detail::make_nvp("dim", dim)); + construct(num_args, dim); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + tiny_dnn::shape3d in; + size_t stride_x, stride_y, pool_size_x, pool_size_y; + tiny_dnn::padding pad_type; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in), + ::detail::make_nvp("pool_size_x", pool_size_x), + ::detail::make_nvp("pool_size_y", pool_size_y), + ::detail::make_nvp("stride_x", stride_x), + ::detail::make_nvp("stride_y", stride_y), + ::detail::make_nvp("pad_type", pad_type)); + construct(in.width_, in.height_, in.depth_, pool_size_x, pool_size_y, + stride_x, stride_y, pad_type); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + tiny_dnn::shape3d in; + size_t pool_size, stride; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in), + ::detail::make_nvp("pool_size", pool_size), + ::detail::make_nvp("stride", stride)); + construct(in.width_, in.height_, in.depth_, pool_size, stride); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + size_t in_spatial_size, in_channels; + tiny_dnn::float_t eps, momentum; + tiny_dnn::net_phase phase; + tiny_dnn::vec_t mean, variance; + + ::detail::arc(ar, ::detail::make_nvp("in_spatial_size", in_spatial_size), + ::detail::make_nvp("in_channels", in_channels), + ::detail::make_nvp("epsilon", eps), + ::detail::make_nvp("momentum", momentum), + ::detail::make_nvp("phase", phase), + ::detail::make_nvp("mean", mean), + ::detail::make_nvp("variance", variance)); + construct(in_spatial_size, in_channels, eps, momentum, phase); + construct->set_mean(mean); + construct->set_variance(variance); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + std::vector in_shapes; + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shapes)); + construct(in_shapes); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + size_t w_width, w_height, out_ch, w_stride, h_stride; + bool has_bias; + tiny_dnn::shape3d in; + tiny_dnn::padding pad_type; + tiny_dnn::core::connection_table tbl; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in), + ::detail::make_nvp("window_width", w_width), + ::detail::make_nvp("window_height", w_height), + ::detail::make_nvp("out_channels", out_ch), + ::detail::make_nvp("connection_table", tbl), + ::detail::make_nvp("pad_type", pad_type), + ::detail::make_nvp("has_bias", has_bias), + ::detail::make_nvp("w_stride", w_stride), + ::detail::make_nvp("h_stride", h_stride)); + + construct(in.width_, in.height_, w_width, w_height, in.depth_, out_ch, tbl, + pad_type, has_bias, w_stride, h_stride); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + size_t w_width, w_height, out_ch, w_stride, h_stride; + bool has_bias; + tiny_dnn::shape3d in; + tiny_dnn::padding pad_type; + tiny_dnn::core::connection_table tbl; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in), + ::detail::make_nvp("window_width", w_width), + ::detail::make_nvp("window_height", w_height), + ::detail::make_nvp("out_channels", out_ch), + ::detail::make_nvp("connection_table", tbl), + ::detail::make_nvp("pad_type", pad_type), + ::detail::make_nvp("has_bias", has_bias), + ::detail::make_nvp("w_stride", w_stride), + ::detail::make_nvp("h_stride", h_stride)); + + construct(in.width_, in.height_, w_width, w_height, in.depth_, out_ch, tbl, + pad_type, has_bias, w_stride, h_stride); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::net_phase phase; + tiny_dnn::float_t dropout_rate; + size_t in_size; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_size), + ::detail::make_nvp("dropout_rate", dropout_rate), + ::detail::make_nvp("phase", phase)); + construct(in_size, dropout_rate, phase); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + size_t in_dim, out_dim; + bool has_bias; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_dim), + ::detail::make_nvp("out_size", out_dim), + ::detail::make_nvp("has_bias", has_bias)); + construct(in_dim, out_dim, has_bias); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + + ::detail::arc(ar, ::detail::make_nvp("in_shape", in_shape)); + construct(in_shape); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d shape; + + ::detail::arc(ar, ::detail::make_nvp("shape", shape)); + construct(shape); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + size_t dim; + tiny_dnn::float_t scale, bias; + + ::detail::arc(ar, ::detail::make_nvp("in_size", dim), + ::detail::make_nvp("scale", scale), + ::detail::make_nvp("bias", bias)); + + construct(dim, scale, bias); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + size_t size; + tiny_dnn::float_t alpha, beta; + tiny_dnn::norm_region region; + + ::detail::arc( + ar, ::detail::make_nvp("in_shape", in_shape), + ::detail::make_nvp("size", size), ::detail::make_nvp("alpha", alpha), + ::detail::make_nvp("beta", beta), ::detail::make_nvp("region", region)); + construct(in_shape, size, alpha, beta, region); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in; + size_t stride_x, stride_y, pool_size_x, pool_size_y; + tiny_dnn::padding pad_type; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in), + ::detail::make_nvp("pool_size_x", pool_size_x), + ::detail::make_nvp("pool_size_y", pool_size_y), + ::detail::make_nvp("stride_x", stride_x), + ::detail::make_nvp("stride_y", stride_y), + ::detail::make_nvp("pad_type", pad_type)); + construct(in.width_, in.height_, in.depth_, pool_size_x, pool_size_y, + stride_x, stride_y, pad_type); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in; + size_t stride, unpool_size; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in), + ::detail::make_nvp("unpool_size", unpool_size), + ::detail::make_nvp("stride", stride)); + construct(in, unpool_size, stride); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + tiny_dnn::float_t factor; + tiny_dnn::float_t scale(1.0f); + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape), + ::detail::make_nvp("factor", factor), + ::detail::make_nvp("scale", scale)); + construct(in_shape, factor, scale); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + size_t w_width, w_height, out_ch, w_stride, h_stride; + bool has_bias; + tiny_dnn::shape3d in; + tiny_dnn::padding pad_type; + tiny_dnn::core::connection_table tbl; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in), + ::detail::make_nvp("window_width", w_width), + ::detail::make_nvp("window_height", w_height), + ::detail::make_nvp("out_channels", out_ch), + ::detail::make_nvp("connection_table", tbl), + ::detail::make_nvp("pad_type", pad_type), + ::detail::make_nvp("has_bias", has_bias), + ::detail::make_nvp("w_stride", w_stride), + ::detail::make_nvp("h_stride", h_stride)); + + construct(in.width_, in.height_, w_width, w_height, in.depth_, out_ch, tbl, + pad_type, has_bias, w_stride, h_stride); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + size_t w_width, w_height, out_ch, w_stride, h_stride; + bool has_bias; + tiny_dnn::shape3d in; + tiny_dnn::padding pad_type; + tiny_dnn::core::connection_table tbl; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in), + ::detail::make_nvp("window_width", w_width), + ::detail::make_nvp("window_height", w_height), + ::detail::make_nvp("out_channels", out_ch), + ::detail::make_nvp("connection_table", tbl), + ::detail::make_nvp("pad_type", pad_type), + ::detail::make_nvp("has_bias", has_bias), + ::detail::make_nvp("w_stride", w_stride), + ::detail::make_nvp("h_stride", h_stride)); + + construct(in.width_, in.height_, w_width, w_height, in.depth_, out_ch, tbl, + pad_type, has_bias, w_stride, h_stride); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, + cereal::construct &construct) { + size_t in_dim, out_dim; + bool has_bias; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_dim), + ::detail::make_nvp("out_size", out_dim), + ::detail::make_nvp("has_bias", has_bias)); + construct(in_dim, out_dim, has_bias); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + size_t in_dim, out_dim; + bool has_bias; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_dim), + ::detail::make_nvp("out_size", out_dim), + ::detail::make_nvp("has_bias", has_bias)); + auto al = tiny_dnn::layer::load_layer(ar); + // a nullptr is passed to avoid creating unused activation layer. + construct(in_dim, out_dim, has_bias, nullptr); + // set the activation to the loaded value + construct->set_activation( + std::static_pointer_cast(al)); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + tiny_dnn::slice_type slice_type; + size_t num_outputs; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape), + ::detail::make_nvp("slice_type", slice_type), + ::detail::make_nvp("num_outputs", num_outputs)); + construct(in_shape, slice_type, num_outputs); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape)); + construct(in_shape); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape)); + construct(in_shape); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape)); + construct(in_shape); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape)); + construct(in_shape); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + tiny_dnn::float_t epsilon; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape), + ::detail::make_nvp("epsilon", epsilon)); + + construct(in_shape, epsilon); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + tiny_dnn::float_t lambda; + tiny_dnn::float_t alpha; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape), + ::detail::make_nvp("lambda", lambda), + ::detail::make_nvp("alpha", alpha)); + construct(in_shape, lambda, alpha); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape)); + construct(in_shape); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape)); + construct(in_shape); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + tiny_dnn::float_t beta; + tiny_dnn::float_t threshold; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape), + ::detail::make_nvp("beta", beta), + ::detail::make_nvp("threshold", threshold)); + construct(in_shape, beta, threshold); + } +}; + +template <> +struct LoadAndConstruct { + template + static void load_and_construct( + Archive &ar, cereal::construct &construct) { + tiny_dnn::shape3d in_shape; + + ::detail::arc(ar, ::detail::make_nvp("in_size", in_shape)); + construct(in_shape); + } +}; + +} // namespace cereal + +namespace tiny_dnn { + +struct serialization_buddy { +#ifndef CNN_NO_SERIALIZATION + + template + static inline void serialize(Archive &ar, tiny_dnn::layer &layer) { + auto all_weights = layer.weights(); + for (auto weight : all_weights) { + ar(*weight); + } + layer.initialized_ = true; + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::elementwise_add_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("num_args", layer.num_args_), + ::detail::make_nvp("dim", layer.dim_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::average_pooling_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_), + ::detail::make_nvp("pool_size_x", layer.pool_size_x_), + ::detail::make_nvp("pool_size_y", layer.pool_size_y_), + ::detail::make_nvp("stride_x", layer.stride_x_), + ::detail::make_nvp("stride_y", layer.stride_y_), + ::detail::make_nvp("pad_type", layer.pad_type_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::average_unpooling_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_), + ::detail::make_nvp("pool_size", layer.w_.width_), + ::detail::make_nvp("stride", layer.stride_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::batch_normalization_layer &layer) { + ::detail::arc(ar, + ::detail::make_nvp("in_spatial_size", layer.in_spatial_size_), + ::detail::make_nvp("in_channels", layer.in_channels_), + ::detail::make_nvp("epsilon", layer.eps_), + ::detail::make_nvp("momentum", layer.momentum_), + ::detail::make_nvp("phase", layer.phase_), + ::detail::make_nvp("mean", layer.mean_), + ::detail::make_nvp("variance", layer.variance_)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::concat_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shapes_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::convolutional_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_size", params_.in), + ::detail::make_nvp("window_width", params_.weight.width_), + ::detail::make_nvp("window_height", params_.weight.height_), + ::detail::make_nvp("out_channels", params_.out.depth_), + ::detail::make_nvp("connection_table", params_.tbl), + ::detail::make_nvp("pad_type", params_.pad_type), + ::detail::make_nvp("has_bias", params_.has_bias), + ::detail::make_nvp("w_stride", params_.w_stride), + ::detail::make_nvp("h_stride", params_.h_stride)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::deconvolutional_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_size", params_.in), + ::detail::make_nvp("window_width", params_.weight.width_), + ::detail::make_nvp("window_height", params_.weight.height_), + ::detail::make_nvp("out_channels", params_.out.depth_), + ::detail::make_nvp("connection_table", params_.tbl), + ::detail::make_nvp("pad_type", params_.pad_type), + ::detail::make_nvp("has_bias", params_.has_bias), + ::detail::make_nvp("w_stride", params_.w_stride), + ::detail::make_nvp("h_stride", params_.h_stride)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::dropout_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_size_), + ::detail::make_nvp("dropout_rate", layer.dropout_rate_), + ::detail::make_nvp("phase", layer.phase_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::fully_connected_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_size", params_.in_size_), + ::detail::make_nvp("out_size", params_.out_size_), + ::detail::make_nvp("has_bias", params_.has_bias_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::global_average_pooling_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_shape", params_.in)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::input_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("shape", layer.shape_)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::linear_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.dim_), + ::detail::make_nvp("scale", layer.scale_), + ::detail::make_nvp("bias", layer.bias_)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::lrn_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_shape", layer.in_shape_), + ::detail::make_nvp("size", layer.size_), + ::detail::make_nvp("alpha", layer.alpha_), + ::detail::make_nvp("beta", layer.beta_), + ::detail::make_nvp("region", layer.region_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::max_pooling_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_size", params_.in), + ::detail::make_nvp("pool_size_x", params_.pool_size_x), + ::detail::make_nvp("pool_size_y", params_.pool_size_y), + ::detail::make_nvp("stride_x", params_.stride_x), + ::detail::make_nvp("stride_y", params_.stride_y), + ::detail::make_nvp("pad_type", params_.pad_type)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::max_unpooling_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_), + ::detail::make_nvp("unpool_size", layer.unpool_size_), + ::detail::make_nvp("stride", layer.stride_)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::power_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape_), + ::detail::make_nvp("factor", layer.factor_), + ::detail::make_nvp("scale", layer.scale_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::quantized_convolutional_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_size", params_.in), + ::detail::make_nvp("window_width", params_.weight.width_), + ::detail::make_nvp("window_height", params_.weight.height_), + ::detail::make_nvp("out_channels", params_.out.depth_), + ::detail::make_nvp("connection_table", params_.tbl), + ::detail::make_nvp("pad_type", params_.pad_type), + ::detail::make_nvp("has_bias", params_.has_bias), + ::detail::make_nvp("w_stride", params_.w_stride), + ::detail::make_nvp("h_stride", params_.h_stride)); + } + + template + static inline void serialize( + Archive &ar, tiny_dnn::quantized_deconvolutional_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_size", params_.in), + ::detail::make_nvp("window_width", params_.weight.width_), + ::detail::make_nvp("window_height", params_.weight.height_), + ::detail::make_nvp("out_channels", params_.out.depth_), + ::detail::make_nvp("connection_table", params_.tbl), + ::detail::make_nvp("pad_type", params_.pad_type), + ::detail::make_nvp("has_bias", params_.has_bias), + ::detail::make_nvp("w_stride", params_.w_stride), + ::detail::make_nvp("h_stride", params_.h_stride)); + } + + template + static inline void serialize( + Archive &ar, tiny_dnn::quantized_fully_connected_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_size", params_.in_size_), + ::detail::make_nvp("out_size", params_.out_size_), + ::detail::make_nvp("has_bias", params_.has_bias_)); + } + + template + static inline void serialize(Archive &ar, + tiny_dnn::recurrent_cell_layer &layer) { + auto ¶ms_ = layer.params_; + ::detail::arc(ar, ::detail::make_nvp("in_size", params_.in_size_), + ::detail::make_nvp("out_size", params_.out_size_), + ::detail::make_nvp("has_bias", params_.has_bias_)); + tiny_dnn::layer::save_layer(ar, *params_.activation_); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::slice_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape_), + ::detail::make_nvp("slice_type", layer.slice_type_), + ::detail::make_nvp("num_outputs", layer.num_outputs_)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::sigmoid_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0])); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::tanh_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0])); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::relu_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0])); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::softmax_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0])); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::leaky_relu_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0]), + ::detail::make_nvp("epsilon", layer.epsilon_)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::elu_layer &layer) { + tiny_dnn::shape3d shape = layer.in_shape()[0]; + ::detail::arc(ar, ::detail::make_nvp("in_size", shape)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::selu_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0]), + ::detail::make_nvp("lambda", layer.lambda_), + ::detail::make_nvp("alpha", layer.alpha_)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::tanh_p1m2_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0])); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::softplus_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0]), + ::detail::make_nvp("beta", layer.beta_), + ::detail::make_nvp("threshold", layer.threshold_)); + } + + template + static inline void serialize(Archive &ar, tiny_dnn::softsign_layer &layer) { + ::detail::arc(ar, ::detail::make_nvp("in_size", layer.in_shape()[0])); + } + +#endif // #ifndef CNN_NO_SERIALIZATION +}; // struct serialization_buddy + +template +typename std::enable_if::value>::type +serialize(Archive &ar, T &layer) { + auto &inconstant_layer = + const_cast::type &>(layer); + inconstant_layer.serialize_prolog(ar); + serialization_buddy::serialize(ar, inconstant_layer); +} + +template +void serialize(Archive &ar, tiny_dnn::index3d &idx) { + ::detail::arc(ar, ::detail::make_nvp("width", idx.width_), + ::detail::make_nvp("height", idx.height_), + ::detail::make_nvp("depth", idx.depth_)); +} + +namespace core { + +template +void serialize(Archive &ar, tiny_dnn::core::connection_table &tbl) { + ::detail::arc(ar, ::detail::make_nvp("rows", tbl.rows_), + ::detail::make_nvp("cols", tbl.cols_)); + if (tbl.is_empty()) { + std::string all("all"); + ::detail::arc(ar, ::detail::make_nvp("connection", all)); + } else { + ::detail::arc(ar, ::detail::make_nvp("connection", tbl.connected_)); + } +} + +} // namespace core + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/serialization_helper.h b/tiny_dnn/util/serialization_helper.h new file mode 100755 index 0000000000000..121095b76c229 --- /dev/null +++ b/tiny_dnn/util/serialization_helper.h @@ -0,0 +1,125 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include + +#include +#include + +#include "tiny_dnn/layers/layers.h" +#include "tiny_dnn/util/macro.h" +#include "tiny_dnn/util/nn_error.h" +#include "tiny_dnn/util/serialization_functions.h" +#include "tiny_dnn/util/serialization_layer_list.h" + +namespace tiny_dnn { + +template +class serialization_helper { + public: + void register_saver( + const std::string &name, + std::function func) { + savers_[name] = [=](void *ar, const layer *l) { + return func(*reinterpret_cast(ar), l); + }; + } + + template + void register_type(const std::string &name) { + type_names_[typeid(T)] = name; + } + + void save(const std::string &layer_name, OutputArchive &ar, const layer *l) { + check_if_enabled(); + + if (savers_.find(layer_name) == savers_.end()) { + throw nn_error("Failed to save layer. Saver for " + layer_name + + " is not found.\n" + "Please use CNN_REGISTER_LAYER macro to register " + "appropriate saver."); + } + + savers_[layer_name](reinterpret_cast(&ar), l); + } + + const std::string &type_name(std::type_index index) const { + if (type_names_.find(index) == type_names_.end()) { + throw nn_error("Typename is not registered"); + } + return type_names_.at(index); + } + + static serialization_helper &get_instance() { + static serialization_helper instance; + return instance; + } + + private: + void check_if_enabled() const { +#ifdef CNN_NO_SERIALIZATION + static_assert( + sizeof(OutputArchive) == 0, + "You are using save functions, but serialization function is " + "disabled " + "in current configuration.\n\n" + "You need to undef CNN_NO_SERIALIZATION to enable these functions.\n" + "If you are using cmake, you can use -DUSE_SERIALIZER=ON " + "option.\n\n"); +#endif + } + + /** layer-type -> generator */ + std::map> savers_; + + std::map type_names_; + + template + static void save_layer_impl(OutputArchive &oa, const layer *layer); + + template + friend void register_layers(T *h); + + template + void register_layer(const char *layer_name) { + register_type(layer_name); + register_saver(layer_name, save_layer_impl); + } + + serialization_helper() { register_layers(this); } +}; // class serialization_helper + +template +template +void serialization_helper::save_layer_impl(OutputArchive &oa, + const layer *layer) { + oa(cereal::make_nvp( + serialization_helper::get_instance().type_name(typeid(T)), + *dynamic_cast(layer))); +} + +template +void layer::save_layer(OutputArchive &oa, const layer &l) { + const std::string &name = + serialization_helper::get_instance().type_name(typeid(l)); + serialization_helper::get_instance().save(name, oa, &l); +} + +template +void layer::serialize_prolog(Archive &ar) { + std::string name = + serialization_helper::get_instance().type_name(typeid(*this)); + ar(cereal::make_nvp("type", name)); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/serialization_layer_list.h b/tiny_dnn/util/serialization_layer_list.h new file mode 100755 index 0000000000000..ba3ddcb6c290f --- /dev/null +++ b/tiny_dnn/util/serialization_layer_list.h @@ -0,0 +1,52 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include "tiny_dnn/layers/layers.h" + +namespace tiny_dnn { + +template +void register_layers(T* h) { + h->template register_layer("elementwise_add"); + h->template register_layer("avepool"); + h->template register_layer("aveunpool"); + h->template register_layer("batchnorm"); + h->template register_layer("concat"); + h->template register_layer("conv"); + h->template register_layer("deconv"); + h->template register_layer("dropout"); + h->template register_layer("fully_connected"); + h->template register_layer( + "global_average_pooling"); + h->template register_layer("input"); + h->template register_layer("linear"); + h->template register_layer("lrn"); + h->template register_layer("maxpool"); + h->template register_layer("maxunpool"); + h->template register_layer("power"); + h->template register_layer("q_conv"); + h->template register_layer("q_deconv"); + h->template register_layer( + "q_fully_connected"); + h->template register_layer("recurrent_cell"); + h->template register_layer("slice"); + + h->template register_layer("sigmoid"); + h->template register_layer("tanh"); + h->template register_layer("relu"); + h->template register_layer("softmax"); + h->template register_layer("leaky_relu"); + h->template register_layer("elu"); + h->template register_layer("tanh_scaled"); + h->template register_layer("softplus"); + h->template register_layer("softsign"); + h->template register_layer("selu"); +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/target_cost.h b/tiny_dnn/util/target_cost.h new file mode 100755 index 0000000000000..e30f71cee9e7d --- /dev/null +++ b/tiny_dnn/util/target_cost.h @@ -0,0 +1,82 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include // std::accumulate +#include + +#include "tiny_dnn/util/util.h" + +namespace tiny_dnn { + +// calculate the number of samples for each class label +// - for example, if there are 10 samples having label 0, and +// 20 samples having label 1, returns a vector [10, 20] +inline std::vector calculate_label_counts( + const std::vector &t) { + std::vector label_counts; + for (label_t label : t) { + if (label >= label_counts.size()) { + label_counts.resize(label + 1); + } + label_counts[label]++; + } + assert(std::accumulate(label_counts.begin(), label_counts.end(), 0u) == + t.size()); + return label_counts; +} + +// calculate the weight of a given sample needed for a balanced target cost +// NB: we call a target cost matrix "balanced", if the cost of each *class* is +// equal +// (this happens when the product weight * sample count is equal between the +// different +// classes, and the sum of these products equals the total number of +// samples) +inline float_t get_sample_weight_for_balanced_target_cost( + size_t classes, size_t total_samples, size_t this_class_samples) { + assert(this_class_samples <= total_samples); + return total_samples / static_cast(classes * this_class_samples); +} + +// create a target cost matrix implying equal cost for each *class* (distinct +// label) +// - by default, each *sample* has an equal cost, which means e.g. that a +// classifier +// may prefer to always guess the majority class (in case the degree of +// imbalance +// is relatively high, and the classification task is relatively difficult) +// - the parameter w can be used to fine-tune the balance: +// * use 0 to have an equal cost for each *sample* (equal to not supplying +// any target costs at all) +// * use 1 to have an equal cost for each *class* (default behaviour of this +// function) +// * use a value between 0 and 1 to have something between the two extremes +inline std::vector create_balanced_target_cost( + const std::vector &t, float_t w = 1.0) { + const auto label_counts = calculate_label_counts(t); + const size_t total_sample_count = t.size(); + const size_t class_count = label_counts.size(); + + std::vector target_cost(t.size()); + + for (size_t i = 0; i < total_sample_count; ++i) { + vec_t &sample_cost = target_cost[i]; + sample_cost.resize(class_count); + const float_t balanced_weight = get_sample_weight_for_balanced_target_cost( + class_count, total_sample_count, label_counts[t[i]]); + const float_t unbalanced_weight = 1; + const float_t sample_weight = + w * balanced_weight + (1 - w) * unbalanced_weight; + vectorize::fill(&sample_cost[0], sample_cost.size(), sample_weight); + } + + return target_cost; +} + +} // namespace tiny_dnn diff --git a/tiny_dnn/util/util.h b/tiny_dnn/util/util.h new file mode 100755 index 0000000000000..9e73798aa5675 --- /dev/null +++ b/tiny_dnn/util/util.h @@ -0,0 +1,442 @@ +/* + Copyright (c) 2013, Taiga Nomi and the respective contributors + All rights reserved. + + Use of this source code is governed by a BSD-style license that can be found + in the LICENSE file. +*/ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "tiny_dnn/xtensor/xarray.hpp" +#include "tiny_dnn/xtensor/xview.hpp" + +#include "tiny_dnn/config.h" + +#ifndef CNN_NO_SERIALIZATION +#include +#include +#include +#include +#include +#include +#include +#endif + +#include "tiny_dnn/util/aligned_allocator.h" +#include "tiny_dnn/util/macro.h" +#include "tiny_dnn/util/nn_error.h" +#include "tiny_dnn/util/parallel_for.h" +#include "tiny_dnn/util/product.h" +#include "tiny_dnn/util/random.h" + +#if defined(USE_OPENCL) || defined(USE_CUDA) +#ifdef USE_OPENCL +#include "third_party/CLCudaAPI/clpp11.h" +#else +#include "third_party/CLCudaAPI/cupp11.h" +#endif +#endif + +namespace tiny_dnn { + +///< output label(class-index) for classification +///< must be equal to size_t, because size of last layer is equal to num. +/// of classes +typedef size_t label_t; + +typedef size_t layer_size_t; // for backward compatibility + +typedef std::vector> vec_t; + +typedef std::vector tensor_t; + +template +using xtensor_t = xt::xexpression; + +enum class net_phase { train, test }; + +enum class padding { + valid, ///< use valid pixels of input + same ///< add zero-padding around input so as to keep image size +}; + +template +T *reverse_endian(T *p) { + std::reverse(reinterpret_cast(p), + reinterpret_cast(p) + sizeof(T)); + return p; +} + +inline bool is_little_endian() { + int x = 1; + return *reinterpret_cast(&x) != 0; +} + +template +size_t max_index(const T &vec) { + auto begin_iterator = std::begin(vec); + return std::max_element(begin_iterator, std::end(vec)) - begin_iterator; +} + +template +U rescale(T x, T src_min, T src_max, U dst_min, U dst_max) { + U value = static_cast( + ((x - src_min) * (dst_max - dst_min)) / (src_max - src_min) + dst_min); + return std::min(dst_max, std::max(value, dst_min)); +} + +inline void nop() { + // do nothing +} + +template +inline T sqr(T value) { + return value * value; +} + +inline bool isfinite(float_t x) { return x == x; } + +template +inline bool has_infinite(const Container &c) { + for (auto v : c) + if (!isfinite(v)) return true; + return false; +} + +template +size_t max_size(const Container &c) { + typedef typename Container::value_type value_t; + const auto max_size = + std::max_element(c.begin(), c.end(), [](const value_t &left, + const value_t &right) { + return left.size() < right.size(); + })->size(); + assert(max_size <= std::numeric_limits::max()); + return max_size; +} + +inline std::string format_str(const char *fmt, ...) { + static char buf[2048]; + +#ifdef _MSC_VER +#pragma warning(disable : 4996) +#endif + va_list args; + va_start(args, fmt); + vsnprintf(buf, sizeof(buf), fmt, args); + va_end(args); +#ifdef _MSC_VER +#pragma warning(default : 4996) +#endif + return std::string(buf); +} + +template +struct index3d { + index3d(T width, T height, T depth) { reshape(width, height, depth); } + + index3d() : width_(0), height_(0), depth_(0) {} + + void reshape(T width, T height, T depth) { + width_ = width; + height_ = height; + depth_ = depth; + + if ((int64_t)width * height * depth > std::numeric_limits::max()) + throw nn_error(format_str( + "error while constructing layer: layer size too large for " + "tiny-dnn\nWidthxHeightxChannels=%dx%dx%d >= max size of " + "[%s](=%d)", + width, height, depth, typeid(T).name(), std::numeric_limits::max())); + } + + T get_index(T x, T y, T channel) const { + assert(x >= 0 && x < width_); + assert(y >= 0 && y < height_); + assert(channel >= 0 && channel < depth_); + return (height_ * channel + y) * width_ + x; + } + + T area() const { return width_ * height_; } + + T size() const { return width_ * height_ * depth_; } + + T width_; + T height_; + T depth_; +}; + +typedef index3d shape3d; + +template +bool operator==(const index3d &lhs, const index3d &rhs) { + return (lhs.width_ == rhs.width_) && (lhs.height_ == rhs.height_) && + (lhs.depth_ == rhs.depth_); +} + +template +bool operator!=(const index3d &lhs, const index3d &rhs) { + return !(lhs == rhs); +} + +template +Stream &operator<<(Stream &s, const index3d &d) { + s << d.width_ << "x" << d.height_ << "x" << d.depth_; + return s; +} + +template +std::ostream &operator<<(std::ostream &s, const index3d &d) { + s << d.width_ << "x" << d.height_ << "x" << d.depth_; + return s; +} + +template +Stream &operator<<(Stream &s, const std::vector> &d) { + s << "["; + for (size_t i = 0; i < d.size(); i++) { + if (i) s << ","; + s << "[" << d[i] << "]"; + } + s << "]"; + return s; +} + +// equivalent to std::to_string, which android NDK doesn't support +template +std::string to_string(T value) { + std::ostringstream os; + os << value; + return os.str(); +} + +#define CNN_LOG_VECTOR(vec, name) +/* +void CNN_LOG_VECTOR(const vec_t& vec, const std::string& name) { + std::cout << name << ","; + + if (vec.empty()) { + std::cout << "(empty)" << std::endl; + } + else { + for (size_t i = 0; i < vec.size(); i++) { + std::cout << vec[i] << ","; + } + } + + std::cout << std::endl; +} +*/ + +template +size_t sumif(const std::vector &vec, Pred p, Sum s) { + size_t sum = 0; + for (size_t i = 0; i < vec.size(); i++) { + if (p(i)) sum += s(vec[i]); + } + return sum; +} + +template +std::vector filter(const std::vector &vec, Pred p) { + std::vector res; + for (size_t i = 0; i < vec.size(); i++) { + if (p(i)) res.push_back(vec[i]); + } + return res; +} + +template +std::vector map_(const std::vector &vec, Pred p) { + std::vector res(vec.size()); + for (size_t i = 0; i < vec.size(); ++i) { + res[i] = p(vec[i]); + } + return res; +} + +enum class vector_type : int32_t { + // 0x0001XXX : in/out data + data = 0x0001000, // input/output data, fed by other layer or input channel + + // 0x0002XXX : trainable parameters, updated for each back propagation + weight = 0x0002000, + bias = 0x0002001, + + label = 0x0004000, + aux = 0x0010000 // layer-specific storage +}; + +inline std::string to_string(vector_type vtype) { + switch (vtype) { + case tiny_dnn::vector_type::data: return "data"; + case tiny_dnn::vector_type::weight: return "weight"; + case tiny_dnn::vector_type::bias: return "bias"; + case tiny_dnn::vector_type::label: return "label"; + case tiny_dnn::vector_type::aux: return "aux"; + default: return "unknown"; + } +} + +inline std::ostream &operator<<(std::ostream &os, vector_type vtype) { + os << to_string(vtype); + return os; +} + +inline vector_type operator&(vector_type lhs, vector_type rhs) { + return (vector_type)(static_cast(lhs) & static_cast(rhs)); +} + +inline bool is_trainable_weight(vector_type vtype) { + return (vtype & vector_type::weight) == vector_type::weight; +} + +inline std::vector std_input_order(bool has_bias) { + if (has_bias) { + return {vector_type::data, vector_type::weight, vector_type::bias}; + } else { + return {vector_type::data, vector_type::weight}; + } +} + +inline void fill_tensor(tensor_t &tensor, float_t value) { + for (auto &t : tensor) { + vectorize::fill(&t[0], t.size(), value); + } +} + +inline void fill_tensor(tensor_t &tensor, float_t value, size_t size) { + for (auto &t : tensor) { + t.resize(size, value); + } +} + +inline size_t conv_out_length(size_t in_length, + size_t window_size, + size_t stride, + padding pad_type) { + size_t output_length; + + if (pad_type == padding::same) { + output_length = in_length; + } else if (pad_type == padding::valid) { + output_length = in_length - window_size + 1; + } else { + throw nn_error("Not recognized pad_type."); + } + return (output_length + stride - 1) / stride; +} + +// get all platforms (drivers), e.g. NVIDIA +// https://github.com/CNugteren/CLCudaAPI/blob/master/samples/device_info.cc + +inline void printAvailableDevice(const size_t platform_id, + const size_t device_id) { +#if defined(USE_OPENCL) || defined(USE_CUDA) + // Initializes the CLCudaAPI platform and device. This initializes the + // OpenCL/CUDA back-end and + // selects a specific device on the platform. + auto platform = CLCudaAPI::Platform(platform_id); + auto device = CLCudaAPI::Device(platform, device_id); + + // Prints information about the chosen device. Most of these results should + // stay the same when + // switching between the CUDA and OpenCL back-ends. + printf("\n## Printing device information...\n"); + printf(" > Platform ID %zu\n", platform_id); + printf(" > Device ID %zu\n", device_id); + printf(" > Framework version %s\n", device.Version().c_str()); + printf(" > Vendor %s\n", device.Vendor().c_str()); + printf(" > Device name %s\n", device.Name().c_str()); + printf(" > Device type %s\n", device.Type().c_str()); + printf(" > Max work-group size %zu\n", device.MaxWorkGroupSize()); + printf(" > Max thread dimensions %zu\n", + device.MaxWorkItemDimensions()); + printf(" > Max work-group sizes:\n"); + for (auto i = size_t{0}; i < device.MaxWorkItemDimensions(); ++i) { + printf(" - in the %zu-dimension %zu\n", i, + device.MaxWorkItemSizes()[i]); + } + printf(" > Local memory per work-group %zu bytes\n", device.LocalMemSize()); + printf(" > Device capabilities %s\n", device.Capabilities().c_str()); + printf(" > Core clock rate %zu MHz\n", device.CoreClock()); + printf(" > Number of compute units %zu\n", device.ComputeUnits()); + printf(" > Total memory size %zu bytes\n", device.MemorySize()); + printf(" > Maximum allocatable memory %zu bytes\n", device.MaxAllocSize()); + printf(" > Memory clock rate %zu MHz\n", device.MemoryClock()); + printf(" > Memory bus width %zu bits\n", device.MemoryBusWidth()); +#else + CNN_UNREFERENCED_PARAMETER(platform_id); + CNN_UNREFERENCED_PARAMETER(device_id); + nn_warn("TinyDNN was not build with OpenCL or CUDA support."); +#endif +} + +template +std::unique_ptr make_unique(Args &&... args) { + return std::unique_ptr(new T(std::forward(args)...)); +} + +// TODO(Randl): Remove after full integration of xtensor +inline xt::xarray to_xtensor(const tensor_t &t) { + if (t.size() == 0) return xt::xarray({0, 0}); + xt::xarray result = xt::zeros({t.size(), t[0].size()}); + for (size_t i = 0; i < t.size(); ++i) + for (size_t j = 0; j < t[0].size(); ++j) result(i, j) = t[i][j]; + return result; +} + +// TODO(Randl): Remove after full integration +inline tensor_t from_xtensor(const xt::xarray &t) { + tensor_t result; + for (size_t i = 0; i < t.shape()[0]; ++i) { + result.push_back(vec_t()); + for (size_t j = 0; j < t.shape()[1]; ++j) result.back().push_back(t(i, j)); + } + return result; +} + +// check for value type being some particular type +template +using value_type_is = + std::enable_if_t::value>; + +template +using value_is_float = value_type_is; + +template +using value_is_double = value_type_is; + +// check that whole tuple are xexpressions +template +struct is_xexpression : std::false_type {}; + +template +struct is_xexpression> : std::true_type {}; + +template