experiments/STT-tensorflow
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Change: 134721831
This commit is contained in:
Jonathan Hseu 2016-09-29 15:05:32 -08:00 committed by TensorFlower Gardener
parent ef9f5fee0a
commit 1283b84a49
73 changed files with 1037 additions and 315 deletions
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9
configure vendored
View File

@ -5,6 +5,11 @@ pushd `dirname $0` #> /dev/null
SOURCE_BASE_DIR=`pwd -P`
popd > /dev/null
function bazel_clean_and_fetch() {
bazel clean --expunge
bazel fetch //tensorflow/...
}
## Set up python-related environment settings
while true; do
fromuser=""
@ -114,6 +119,7 @@ done
export TF_NEED_CUDA
if [ "$TF_NEED_CUDA" == "0" ]; then
echo "Configuration finished"
bazel_clean_and_fetch
exit
fi
@ -300,7 +306,6 @@ EOF
TF_CUDA_COMPUTE_CAPABILITIES=""
done
bazel clean --expunge
bazel fetch //...
bazel_clean_and_fetch
echo "Configuration finished"

13
farmhash.BUILD
View File

@ -1,9 +1,22 @@
licenses(["notice"]) # MIT
config_setting(
name = "windows",
values = {
"cpu": "x64_windows_msvc",
},
)
cc_library(
name = "farmhash",
srcs = ["farmhash.cc"],
hdrs = ["farmhash.h"],
# Disable __builtin_expect support on Windows
copts = select({
":windows" : ["/DFARMHASH_OPTIONAL_BUILTIN_EXPECT"],
"//conditions:default" : [],
}),
includes = ["."],
visibility = ["//visibility:public"],
)

2
tensorflow/c/c_api.h
View File

@ -582,7 +582,7 @@ typedef struct {
} TF_AttrMetadata;
// Returns metadata about the value of the attribute `attr_name` of `oper`.
TF_AttrMetadata TF_OperationGetAttrMetadata(TF_Operation* oper,
extern TF_AttrMetadata TF_OperationGetAttrMetadata(TF_Operation* oper,
const char* attr_name,
TF_Status* status);

1
tensorflow/contrib/BUILD
View File

@ -59,6 +59,7 @@ cc_library(
visibility = ["//visibility:public"],
deps = [
"//tensorflow/contrib/factorization:all_ops",
"//tensorflow/contrib/framework:all_ops",
"//tensorflow/contrib/layers:bucketization_op_op_lib",
"//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib",
"//tensorflow/contrib/metrics:set_ops_op_lib",

16
tensorflow/contrib/cmake/CMakeLists.txt
View File

@ -16,6 +16,9 @@ cmake_policy(SET CMP0022 NEW)
# Options
option(tensorflow_VERBOSE "Enable for verbose output" OFF)
option(tensorflow_BUILD_TESTS "Build tests" ON)
option(tensorflow_ENABLE_SSL_SUPPORT "Enable boringssl support" OFF)
option(tensorflow_BUILD_CC_EXAMPLE "Build the C++ tutorial example" ON)
option(tensorflow_BUILD_PYTHON_BINDINGS "Build the Python bindings" ON)
#Threads: defines CMAKE_THREAD_LIBS_INIT and adds -pthread compile option for
# targets that link ${CMAKE_THREAD_LIBS_INIT}.
@ -44,10 +47,11 @@ set(CMAKE_POSITION_INDEPENDENT_CODE ON)
include(gif)
include(png)
include(jpeg)
include(re2)
include(eigen)
include(jsoncpp)
include(boringssl)
if(tensorflow_ENABLE_SSL_SUPPORT)
include(boringssl)
endif()
include(farmhash)
include(highwayhash)
include(protobuf)
@ -64,9 +68,13 @@ include(tf_core_direct_session.cmake)
include(tf_core_distributed_runtime.cmake)
include(tf_core_kernels.cmake)
include(tf_cc_ops.cmake)
include(tf_tutorials.cmake)
include(tf_tools.cmake)
include(tf_python.cmake)
if(tensorflow_BUILD_CC_EXAMPLE)
include(tf_tutorials.cmake)
endif()
if(tensorflow_BUILD_PYTHON_BINDINGS)
include(tf_python.cmake)
endif()
if (tensorflow_BUILD_TESTS)
include(tests.cmake)

35
tensorflow/contrib/cmake/external/farmhash.cmake vendored
View File

@ -1,22 +1,34 @@
include (ExternalProject)
set(farmhash_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/farmhash_archive)
#set(farmhash_EXTRA_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/farmhash/src)
set(farmhash_URL https://github.com/google/farmhash/archive/34c13ddfab0e35422f4c3979f360635a8c050260.zip)
set(farmhash_HASH SHA256=e3d37a59101f38fd58fb799ed404d630f0eee18bfc2a2433910977cc8fea9c28)
set(farmhash_BUILD ${CMAKE_BINARY_DIR}/farmhash/src/farmhash)
set(farmhash_INSTALL ${CMAKE_BINARY_DIR}/farmhash/install)
#set(farmhash_LIBRARIES ${farmhash_BUILD}/obj/so/libfarmhash.so)
set(farmhash_STATIC_LIBRARIES
${farmhash_INSTALL}/lib/libfarmhash.a
)
set(farmhash_INCLUDES ${farmhash_BUILD})
set(farmhash_HEADERS
"${farmhash_BUILD}/src/farmhash.h"
)
ExternalProject_Add(farmhash
if(WIN32)
set(farmhash_STATIC_LIBRARIES ${farmhash_INSTALL}/lib/farmhash.lib)
ExternalProject_Add(farmhash
PREFIX farmhash
URL ${farmhash_URL}
URL_HASH ${farmhash_HASH}
DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
BUILD_IN_SOURCE 1
PATCH_COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_SOURCE_DIR}/patches/farmhash/CMakeLists.txt ${farmhash_BUILD}
INSTALL_DIR ${farmhash_INSTALL}
CMAKE_CACHE_ARGS
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
-DCMAKE_INSTALL_PREFIX:STRING=${farmhash_INSTALL})
else()
set(farmhash_STATIC_LIBRARIES ${farmhash_INSTALL}/lib/libfarmhash.a)
ExternalProject_Add(farmhash
PREFIX farmhash
URL ${farmhash_URL}
URL_HASH ${farmhash_HASH}
@ -27,12 +39,13 @@ ExternalProject_Add(farmhash
${farmhash_BUILD}/configure
--prefix=${farmhash_INSTALL}
--enable-shared=yes
CXXFLAGS=-fPIC
)
CXXFLAGS=-fPIC)
endif()
# put farmhash includes in the directory where they are expected
add_custom_target(farmhash_create_destination_dir
COMMAND ${CMAKE_COMMAND} -E make_directory ${farmhash_INCLUDE_DIR}/farmhash-34c13ddfab0e35422f4c3979f360635a8c050260/src
COMMAND ${CMAKE_COMMAND} -E make_directory ${farmhash_INCLUDE_DIR}
DEPENDS farmhash)
add_custom_target(farmhash_copy_headers_to_destination
@ -40,5 +53,5 @@ add_custom_target(farmhash_copy_headers_to_destination
foreach(header_file ${farmhash_HEADERS})
add_custom_command(TARGET farmhash_copy_headers_to_destination PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${farmhash_INCLUDE_DIR}/farmhash-34c13ddfab0e35422f4c3979f360635a8c050260/src)
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${farmhash_INCLUDE_DIR}/)
endforeach()

6
tensorflow/contrib/cmake/external/gif.cmake vendored
View File

@ -1,6 +1,6 @@
include (ExternalProject)
set(gif_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/gif_archive)
set(gif_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/gif_archive/giflib-5.1.4/)
set(gif_URL http://ufpr.dl.sourceforge.net/project/giflib/giflib-5.1.4.tar.gz)
set(gif_HASH SHA256=34a7377ba834397db019e8eb122e551a49c98f49df75ec3fcc92b9a794a4f6d1)
set(gif_INSTALL ${CMAKE_BINARY_DIR}/gif/install)
@ -29,7 +29,7 @@ ExternalProject_Add(gif
# put gif includes in the directory where they are expected
add_custom_target(gif_create_destination_dir
COMMAND ${CMAKE_COMMAND} -E make_directory ${gif_INCLUDE_DIR}/giflib-5.1.4/lib
COMMAND ${CMAKE_COMMAND} -E make_directory ${gif_INCLUDE_DIR}
DEPENDS gif)
add_custom_target(gif_copy_headers_to_destination
@ -37,5 +37,5 @@ add_custom_target(gif_copy_headers_to_destination
foreach(header_file ${gif_HEADERS})
add_custom_command(TARGET gif_copy_headers_to_destination PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${gif_INCLUDE_DIR}/giflib-5.1.4/lib/)
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${gif_INCLUDE_DIR}/)
endforeach()

10
tensorflow/contrib/cmake/external/grpc.cmake vendored
View File

@ -4,10 +4,18 @@ set(GRPC_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/include)
set(GRPC_URL https://github.com/grpc/grpc.git)
set(GRPC_BUILD ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc)
set(GRPC_TAG 3bc78cd0b5bd784a235c01612d634b1ec5f8fb97)
set(GRPC_LIBRARIES
if(WIN32)
set(GRPC_LIBRARIES
${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/${CMAKE_BUILD_TYPE}/grpc++_unsecure.lib
${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/${CMAKE_BUILD_TYPE}/grpc_unsecure.lib
${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/${CMAKE_BUILD_TYPE}/gpr.lib)
else()
set(GRPC_LIBRARIES
${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc++_unsecure.a
${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc_unsecure.a
${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgpr.a)
endif()
ExternalProject_Add(grpc
PREFIX grpc

62
tensorflow/contrib/cmake/external/highwayhash.cmake vendored
View File

@ -1,31 +1,10 @@
include (ExternalProject)
set(highwayhash_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/highwayhash)
#set(highwayhash_EXTRA_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/highwayhash/src)
set(highwayhash_URL https://github.com/google/highwayhash.git)
set(highwayhash_TAG be5edafc2e1a455768e260ccd68ae7317b6690ee)
set(highwayhash_BUILD ${CMAKE_BINARY_DIR}/highwayhash/src/highwayhash)
set(highwayhash_INSTALL ${CMAKE_BINARY_DIR}/highwayhash/install)
#set(highwayhash_LIBRARIES ${highwayhash_BUILD}/obj/so/libhighwayhash.so)
set(highwayhash_STATIC_LIBRARIES
${highwayhash_INSTALL}/lib/libhighwayhash.a
)
set(highwayhash_INCLUDES ${highwayhash_BUILD})
set(highwayhash_HEADERS
"${highwayhash_BUILD}/highwayhash/*.h"
)
ExternalProject_Add(highwayhash
PREFIX highwayhash
GIT_REPOSITORY ${highwayhash_URL}
GIT_TAG ${highwayhash_TAG}
DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
BUILD_IN_SOURCE 1
BUILD_COMMAND $(MAKE)
CONFIGURE_COMMAND ""
INSTALL_COMMAND ""
)
# put highwayhash includes in the directory where they are expected
add_custom_target(highwayhash_create_destination_dir
@ -35,7 +14,44 @@ add_custom_target(highwayhash_create_destination_dir
add_custom_target(highwayhash_copy_headers_to_destination
DEPENDS highwayhash_create_destination_dir)
foreach(header_file ${highwayhash_HEADERS})
if(WIN32)
set(highwayhash_HEADERS "${highwayhash_BUILD}/highwayhash/*.h")
set(highwayhash_STATIC_LIBRARIES ${highwayhash_INSTALL}/lib/highwayhash.lib)
ExternalProject_Add(highwayhash
PREFIX highwayhash
GIT_REPOSITORY ${highwayhash_URL}
GIT_TAG ${highwayhash_TAG}
DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
BUILD_IN_SOURCE 1
PATCH_COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_SOURCE_DIR}/patches/highwayhash/CMakeLists.txt ${highwayhash_BUILD}
INSTALL_DIR ${highwayhash_INSTALL}
CMAKE_CACHE_ARGS
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
-DCMAKE_INSTALL_PREFIX:STRING=${highwayhash_INSTALL})
add_custom_command(TARGET highwayhash_copy_headers_to_destination PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_directory ${highwayhash_INSTALL}/include/ ${highwayhash_INCLUDE_DIR}/highwayhash)
else()
set(highwayhash_HEADERS "${highwayhash_BUILD}/highwayhash/*.h")
set(highwayhash_STATIC_LIBRARIES ${highwayhash_INSTALL}/lib/libhighwayhash.a)
ExternalProject_Add(highwayhash
PREFIX highwayhash
GIT_REPOSITORY ${highwayhash_URL}
GIT_TAG ${highwayhash_TAG}
DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
BUILD_IN_SOURCE 1
BUILD_COMMAND $(MAKE)
CONFIGURE_COMMAND ""
INSTALL_COMMAND "")
foreach(header_file ${highwayhash_HEADERS})
add_custom_command(TARGET highwayhash_copy_headers_to_destination PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${highwayhash_INCLUDE_DIR}/highwayhash)
endforeach()
endforeach()
endif()

11
tensorflow/contrib/cmake/external/jpeg.cmake vendored
View File

@ -5,7 +5,12 @@ set(jpeg_URL http://www.ijg.org/files/jpegsrc.v9a.tar.gz)
set(jpeg_HASH SHA256=3a753ea48d917945dd54a2d97de388aa06ca2eb1066cbfdc6652036349fe05a7)
set(jpeg_BUILD ${CMAKE_BINARY_DIR}/jpeg/src/jpeg)
set(jpeg_INSTALL ${CMAKE_BINARY_DIR}/jpeg/install)
set(jpeg_STATIC_LIBRARIES ${jpeg_INSTALL}/lib/libjpeg.a)
if(WIN32)
set(jpeg_STATIC_LIBRARIES ${jpeg_INSTALL}/lib/libjpeg.lib)
else()
set(jpeg_STATIC_LIBRARIES ${jpeg_INSTALL}/lib/libjpeg.a)
endif()
set(jpeg_HEADERS
"${jpeg_INSTALL}/include/jconfig.h"
@ -63,7 +68,7 @@ endif()
# put jpeg includes in the directory where they are expected
add_custom_target(jpeg_create_destination_dir
COMMAND ${CMAKE_COMMAND} -E make_directory ${jpeg_INCLUDE_DIR}/jpeg-9a
COMMAND ${CMAKE_COMMAND} -E make_directory ${jpeg_INCLUDE_DIR}
DEPENDS jpeg)
add_custom_target(jpeg_copy_headers_to_destination
@ -71,5 +76,5 @@ add_custom_target(jpeg_copy_headers_to_destination
foreach(header_file ${jpeg_HEADERS})
add_custom_command(TARGET jpeg_copy_headers_to_destination PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${jpeg_INCLUDE_DIR}/jpeg-9a)
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${jpeg_INCLUDE_DIR})
endforeach()

7
tensorflow/contrib/cmake/external/jsoncpp.cmake vendored
View File

@ -6,9 +6,14 @@ set(jsoncpp_URL https://github.com/open-source-parsers/jsoncpp.git)
set(jsoncpp_TAG 4356d9b)
set(jsoncpp_BUILD ${CMAKE_BINARY_DIR}/jsoncpp/src/jsoncpp/src/lib_json)
set(jsoncpp_LIBRARIES ${jsoncpp_BUILD}/obj/so/libjsoncpp.so)
get_filename_component(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/libjsoncpp.a ABSOLUTE)
set(jsoncpp_INCLUDES ${jsoncpp_BUILD})
if(WIN32)
set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/${CMAKE_BUILD_TYPE}/jsoncpp.lib)
else()
set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/libjsoncpp.a)
endif()
# We only need jsoncpp.h in external/jsoncpp/jsoncpp/jsoncpp.h
# For the rest, we'll just add the build dir as an include dir.
set(jsoncpp_HEADERS

11
tensorflow/contrib/cmake/external/png.cmake vendored
View File

@ -5,7 +5,12 @@ set(png_URL https://storage.googleapis.com/libpng-public-archive/libpng-1.2.53.t
set(png_HASH SHA256=e05c9056d7f323088fd7824d8c6acc03a4a758c4b4916715924edc5dd3223a72)
set(png_BUILD ${CMAKE_BINARY_DIR}/png/src/png)
set(png_INSTALL ${CMAKE_BINARY_DIR}/png/install)
set(png_STATIC_LIBRARIES ${CMAKE_BINARY_DIR}/png/install/lib/libpng12.a)
if(WIN32)
set(png_STATIC_LIBRARIES ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_static.lib)
else()
set(png_STATIC_LIBRARIES ${CMAKE_BINARY_DIR}/png/install/lib/libpng12.a)
endif()
set(png_HEADERS
"${png_INSTALL}/include/libpng12/png.h"
@ -27,7 +32,7 @@ ExternalProject_Add(png
## put png includes in the directory where they are expected
add_custom_target(png_create_destination_dir
COMMAND ${CMAKE_COMMAND} -E make_directory ${png_INCLUDE_DIR}/libpng-1.2.53
COMMAND ${CMAKE_COMMAND} -E make_directory ${png_INCLUDE_DIR}
DEPENDS png)
add_custom_target(png_copy_headers_to_destination
@ -35,5 +40,5 @@ add_custom_target(png_copy_headers_to_destination
foreach(header_file ${png_HEADERS})
add_custom_command(TARGET png_copy_headers_to_destination PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${png_INCLUDE_DIR}/libpng-1.2.53)
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${png_INCLUDE_DIR}/)
endforeach()

18
tensorflow/contrib/cmake/external/protobuf.cmake vendored
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@ -1,10 +1,16 @@
include (ExternalProject)
set(PROTOBUF_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/src)
set(PROTOBUF_URL https://github.com/google/protobuf/releases/download/v3.0.0/protobuf-cpp-3.0.0.zip)
set(PROTOBUF_HASH SHA256=e886ea7d08267fc3d866ac42d6dd7461ae11c491836adef6f34c04cad0be3078)
set(PROTOBUF_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/libprotobuf.a)
set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/protoc)
set(PROTOBUF_URL https://github.com/google/protobuf/releases/download/v3.1.0/protobuf-cpp-3.1.0.zip)
set(PROTOBUF_HASH SHA256=0c18ccc99e921c407f359047f9b56cca196c3ab36eed79e5979df6c1f9e623b7)
if(WIN32)
set(PROTOBUF_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/${CMAKE_BUILD_TYPE}/libprotobuf.lib)
set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/${CMAKE_BUILD_TYPE}/protoc.exe)
else()
set(PROTOBUF_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/libprotobuf.a)
set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/protoc)
endif()
ExternalProject_Add(protobuf
PREFIX protobuf
@ -12,11 +18,11 @@ ExternalProject_Add(protobuf
DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
BUILD_IN_SOURCE 1
SOURCE_DIR ${CMAKE_BINARY_DIR}/protobuf/src/protobuf
CONFIGURE_COMMAND ${CMAKE_COMMAND} cmake/ -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CONFIGURE_COMMAND ${CMAKE_COMMAND} cmake/ -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -Dprotobuf_MSVC_STATIC_RUNTIME:BOOL=OFF
INSTALL_COMMAND ""
CMAKE_CACHE_ARGS
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
-Dprotobuf_MSVC_STATIC_RUNTIME:BOOL=OFF
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
)

50
tensorflow/contrib/cmake/external/re2.cmake vendored
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@ -1,50 +0,0 @@
include (ExternalProject)
set(re2_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/re2/re2
${CMAKE_CURRENT_BINARY_DIR}/external/re2)
set(re2_EXTRA_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/re2/src/re2)
set(re2_URL https://github.com/google/re2.git)
set(re2_TAG 791beff)
set(re2_BUILD ${CMAKE_BINARY_DIR}/re2/src/re2)
set(re2_LIBRARIES ${re2_BUILD}/obj/so/libre2.so)
get_filename_component(re2_STATIC_LIBRARIES ${re2_BUILD}/libre2.a ABSOLUTE)
set(re2_INCLUDES ${re2_BUILD})
# We only need re2.h in external/re2/re2/re2.h
# For the rest, we'll just add the build dir as an include dir.
set(re2_HEADERS
"${re2_BUILD}/re2/re2.h"
"${re2_BUILD}/re2/stringpiece.h"
"${re2_BUILD}/re2/variadic_function.h"
)
ExternalProject_Add(re2
PREFIX re2
GIT_REPOSITORY ${re2_URL}
GIT_TAG ${re2_TAG}
DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
BUILD_IN_SOURCE 1
INSTALL_COMMAND ""
CMAKE_CACHE_ARGS
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
)
## put re2 includes in the directory where they are expected
add_custom_target(re2_create_destination_dir
COMMAND ${CMAKE_COMMAND} -E make_directory ${re2_INCLUDE_DIR}/re2
DEPENDS re2)
add_custom_target(re2_copy_headers_to_destination
DEPENDS re2_create_destination_dir)
foreach(header_file ${re2_HEADERS})
add_custom_command(TARGET re2_copy_headers_to_destination PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${header_file} ${re2_INCLUDE_DIR}/re2)
endforeach()
ADD_LIBRARY(re2_lib STATIC IMPORTED
DEPENDS re2)
SET_TARGET_PROPERTIES(re2_lib PROPERTIES
IMPORTED_LOCATION ${re2_STATIC_LIBRARIES})

25
tensorflow/contrib/cmake/patches/farmhash/CMakeLists.txt Normal file
View File

@ -0,0 +1,25 @@
cmake_minimum_required(VERSION 2.8.3)
project(farmhash)
set(FARMHASH_SRCS
"src/farmhash.h"
"src/farmhash.cc"
)
set(FARMHASH_INCLUDES
"src/farmhash.h"
)
include_directories("${CMAKE_CURRENT_SOURCE_DIR}")
add_library(farmhash ${FARMHASH_SRCS})
add_definitions(-DFARMHASH_NO_BUILTIN_EXPECT)
install(TARGETS farmhash
LIBRARY DESTINATION lib COMPONENT RuntimeLibraries
ARCHIVE DESTINATION lib COMPONENT Development)
foreach(FARMHASH_INCLUDE ${FARMHASH_INCLUDES})
install(FILES ${FARMHASH_INCLUDE} DESTINATION include COMPONENT Development)
endforeach()

8
tensorflow/contrib/cmake/patches/grpc/CMakeLists.txt
View File

@ -47,6 +47,11 @@ set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -std=c11")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
if(WIN32)
add_definitions(-D_WIN32_WINNT=0x0A00)
find_package(ZLIB REQUIRED)
endif(WIN32)
add_library(gpr
src/core/lib/profiling/basic_timers.c
src/core/lib/profiling/stap_timers.c
@ -99,6 +104,7 @@ target_include_directories(gpr
PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
PRIVATE ${PROTOBUF_INCLUDE_DIRS}
PRIVATE ${ZLIB_INCLUDE_DIRS}
)
add_library(grpc_unsecure
@ -265,6 +271,7 @@ target_include_directories(grpc_unsecure
PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
PRIVATE ${PROTOBUF_ROOT_DIR}/src
PRIVATE ${ZLIB_INCLUDE_DIRS}
)
target_link_libraries(grpc_unsecure
@ -306,6 +313,7 @@ target_include_directories(grpc++_unsecure
PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}
PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include
PRIVATE ${PROTOBUF_INCLUDE_DIRS}
PRIVATE ${ZLIB_INCLUDE_DIRS}
)
target_link_libraries(grpc++_unsecure

49
tensorflow/contrib/cmake/patches/highwayhash/CMakeLists.txt Normal file
View File

@ -0,0 +1,49 @@
cmake_minimum_required(VERSION 2.8.3)
project(highwayhash)
set(HIGHWAYHASH_SRCS
"highwayhash/code_annotation.h"
"highwayhash/highway_tree_hash.cc"
"highwayhash/highway_tree_hash.h"
"highwayhash/scalar_highway_tree_hash.cc"
"highwayhash/scalar_highway_tree_hash.h"
"highwayhash/scalar_sip_tree_hash.cc"
"highwayhash/scalar_sip_tree_hash.h"
"highwayhash/sip_hash.cc"
"highwayhash/sip_hash.h"
"highwayhash/sip_tree_hash.cc"
"highwayhash/sip_tree_hash.h"
"highwayhash/sse41_highway_tree_hash.cc"
"highwayhash/sse41_highway_tree_hash.h"
"highwayhash/state_helpers.h"
"highwayhash/types.h"
"highwayhash/vec.h"
"highwayhash/vec2.h"
)
set(HIGHWAYHASH_INCLUDES
"highwayhash/code_annotation.h"
"highwayhash/highway_tree_hash.h"
"highwayhash/scalar_highway_tree_hash.h"
"highwayhash/scalar_sip_tree_hash.h"
"highwayhash/sip_hash.h"
"highwayhash/sip_tree_hash.h"
"highwayhash/sse41_highway_tree_hash.h"
"highwayhash/state_helpers.h"
"highwayhash/types.h"
"highwayhash/vec.h"
"highwayhash/vec2.h"
)
include_directories("${CMAKE_CURRENT_SOURCE_DIR}")
add_library(highwayhash ${HIGHWAYHASH_SRCS})
install(TARGETS highwayhash
LIBRARY DESTINATION lib COMPONENT RuntimeLibraries
ARCHIVE DESTINATION lib COMPONENT Development)
foreach(HIGHWAYHASH_INCLUDE ${HIGHWAYHASH_INCLUDES})
install(FILES ${HIGHWAYHASH_INCLUDE} DESTINATION include COMPONENT Development)
endforeach()

5
tensorflow/contrib/cmake/tf_cc_ops.cmake
View File

@ -148,7 +148,6 @@ foreach(tf_cc_op_lib_name ${tf_cc_op_lib_names})
${CMAKE_THREAD_LIBS_INIT}
${PROTOBUF_LIBRARIES}
tf_protos_cc
re2_lib
${gif_STATIC_LIBRARIES}
${jpeg_STATIC_LIBRARIES}
${png_STATIC_LIBRARIES}
@ -157,6 +156,10 @@ foreach(tf_cc_op_lib_name ${tf_cc_op_lib_names})
${boringssl_STATIC_LIBRARIES}
${CMAKE_DL_LIBS}
)
if(tensorflow_ENABLE_SSL_SUPPORT)
target_link_libraries(${tf_cc_op_lib_name}_gen_cc PRIVATE
${boringssl_STATIC_LIBRARIES})
endif()
target_compile_options(${tf_cc_op_lib_name}_gen_cc PRIVATE
-fno-exceptions

1
tensorflow/contrib/cmake/tf_core_cpu.cmake
View File

@ -34,7 +34,6 @@ add_library(tf_core_cpu OBJECT ${tf_core_cpu_srcs})
target_include_directories(tf_core_cpu PRIVATE
${tensorflow_source_dir}
${eigen_INCLUDE_DIRS}
${re2_INCLUDES}
)
add_dependencies(tf_core_cpu

9
tensorflow/contrib/cmake/tf_core_distributed_runtime.cmake
View File

@ -18,14 +18,12 @@ add_library(tf_core_distributed_runtime OBJECT ${tf_core_distributed_runtime_src
add_dependencies(tf_core_distributed_runtime
tf_core_cpu grpc
re2_copy_headers_to_destination
)
target_include_directories(tf_core_distributed_runtime PRIVATE
${tensorflow_source_dir}
${eigen_INCLUDE_DIRS}
${GRPC_INCLUDE_DIRS}
${re2_INCLUDE_DIR}
)
target_compile_options(tf_core_distributed_runtime PRIVATE
@ -60,7 +58,6 @@ add_executable(grpc_tensorflow_server
add_dependencies(tf_core_distributed_runtime
grpc
re2_copy_headers_to_destination
)
target_include_directories(grpc_tensorflow_server PUBLIC
@ -76,8 +73,6 @@ target_link_libraries(grpc_tensorflow_server PUBLIC
${PROTOBUF_LIBRARIES}
${GRPC_LIBRARIES}
tf_protos_cc
re2_lib
${boringssl_STATIC_LIBRARIES}
${farmhash_STATIC_LIBRARIES}
${gif_STATIC_LIBRARIES}
${jpeg_STATIC_LIBRARIES}
@ -86,6 +81,10 @@ target_link_libraries(grpc_tensorflow_server PUBLIC
${ZLIB_LIBRARIES}
${CMAKE_DL_LIBS}
)
if(tensorflow_ENABLE_SSL_SUPPORT)
target_link_libraries(grpc_tensorflow_server PUBLIC
${boringssl_STATIC_LIBRARIES})
endif()
target_compile_options(grpc_tensorflow_server PRIVATE
-fno-exceptions

31
tensorflow/contrib/cmake/tf_core_framework.cmake
View File

@ -146,6 +146,14 @@ file(GLOB_RECURSE tf_core_lib_test_srcs
list(REMOVE_ITEM tf_core_lib_srcs ${tf_core_lib_test_srcs})
if(NOT tensorflow_ENABLE_SSL_SUPPORT)
file(GLOB_RECURSE tf_core_lib_cloud_srcs
"${tensorflow_source_dir}/tensorflow/core/platform/cloud/*.h"
"${tensorflow_source_dir}/tensorflow/core/platform/cloud/*.cc"
)
list(REMOVE_ITEM tf_core_lib_srcs ${tf_core_lib_cloud_srcs})
endif()
add_library(tf_core_lib OBJECT ${tf_core_lib_srcs})
target_include_directories(tf_core_lib PUBLIC
${tensorflow_source_dir}
@ -153,9 +161,7 @@ target_include_directories(tf_core_lib PUBLIC
${jpeg_INCLUDE_DIR}
${png_INCLUDE_DIR}
${eigen_INCLUDE_DIRS}
${re2_EXTRA_INCLUDE_DIR}
${jsoncpp_INCLUDE_DIR}
${boringssl_INCLUDE_DIR}
)
target_compile_options(tf_core_lib PRIVATE
-fno-exceptions
@ -171,12 +177,16 @@ add_dependencies(tf_core_lib
gif_copy_headers_to_destination
jpeg_copy_headers_to_destination
png_copy_headers_to_destination
re2_copy_headers_to_destination
eigen
tf_protos_cc
jsoncpp
boringssl
)
)
if(tensorflow_ENABLE_SSL_SUPPORT)
target_include_directories(tf_core_lib PUBLIC ${boringssl_INCLUDE_DIR})
add_dependencies(tf_core_lib boringssl)
endif()
# Tricky setup to force always rebuilding
# force_rebuild always runs forcing ${VERSION_INFO_CC} target to run
@ -230,18 +240,7 @@ add_library(tf_core_framework OBJECT
target_include_directories(tf_core_framework PUBLIC
${tensorflow_source_dir}
${eigen_INCLUDE_DIRS}
${re2_INCLUDES}
)
#target_link_libraries(tf_core_framework
# ${CMAKE_THREAD_LIBS_INIT}
# ${PROTOBUF_LIBRARIES}
# #${re2_STATIC_LIBRARIES}
# re2_lib
# ${jpeg_STATIC_LIBRARIES}
# ${png_STATIC_LIBRARIES}
# tf_protos_cc
# tf_core_lib
#)
add_dependencies(tf_core_framework
tf_core_lib
proto_text

1
tensorflow/contrib/cmake/tf_models.cmake
View File

@ -68,7 +68,6 @@ add_library(tf_models_word2vec_kernels OBJECT ${tf_models_word2vec_kernels_srcs}
target_include_directories(tf_models_word2vec_kernels PRIVATE
${tensorflow_source_dir}
${eigen_INCLUDE_DIRS}
${re2_INCLUDES}
)
add_dependencies(tf_models_word2vec_kernels

26
tensorflow/contrib/cmake/tf_python.cmake
View File

@ -226,13 +226,11 @@ function(GENERATE_PYTHON_OP_LIB tf_python_op_lib_name)
${CMAKE_THREAD_LIBS_INIT}
${PROTOBUF_LIBRARIES}
tf_protos_cc
re2_lib
${gif_STATIC_LIBRARIES}
${jpeg_STATIC_LIBRARIES}
${png_STATIC_LIBRARIES}
${ZLIB_LIBRARIES}
${jsoncpp_STATIC_LIBRARIES}
${boringssl_STATIC_LIBRARIES}
${CMAKE_DL_LIBS}
)
target_compile_options(${tf_python_op_lib_name}_gen_python PRIVATE
@ -244,6 +242,10 @@ function(GENERATE_PYTHON_OP_LIB tf_python_op_lib_name)
target_compile_features(${tf_python_op_lib_name}_gen_python PRIVATE
cxx_rvalue_references
)
if(tensorflow_ENABLE_SSL_SUPPORT)
target_link_libraries(${tf_python_op_lib_name}_gen_python PRIVATE
${boringssl_STATIC_LIBRARIES})
endif()
# Use the generated C++ executable to create a Python file
# containing the wrappers.
@ -346,8 +348,6 @@ target_link_libraries(pywrap_tensorflow
tf_protos_cc
${GRPC_LIBRARIES}
${PROTOBUF_LIBRARY}
re2_lib
${boringssl_STATIC_LIBRARIES}
${farmhash_STATIC_LIBRARIES}
${gif_STATIC_LIBRARIES}
${jpeg_STATIC_LIBRARIES}
@ -367,29 +367,33 @@ target_include_directories(pywrap_tensorflow PUBLIC
target_compile_features(pywrap_tensorflow PRIVATE
cxx_rvalue_references
)
if(tensorflow_ENABLE_SSL_SUPPORT)
target_link_libraries(pywrap_tensorflow ${boringssl_STATIC_LIBRARIES})
endif()
############################################################
# Build a PIP package containing the TensorFlow runtime.
############################################################
add_custom_target(tf_python_copy_pip_files)
add_dependencies(tf_python_copy_pip_files
add_custom_target(tf_python_build_pip_package)
add_dependencies(tf_python_build_pip_package
pywrap_tensorflow
tf_python_copy_scripts_to_destination
tf_python_touchup_modules
tf_python_ops)
add_custom_command(TARGET tf_python_copy_pip_files POST_BUILD
add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/contrib/cmake/setup.py
${CMAKE_CURRENT_BINARY_DIR}/tf_python/)
add_custom_command(TARGET tf_python_copy_pip_files POST_BUILD
add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow.so
${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow.so)
add_custom_command(TARGET tf_python_copy_pip_files POST_BUILD
add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/tools/pip_package/README
${CMAKE_CURRENT_BINARY_DIR}/tf_python/)
add_custom_command(TARGET tf_python_copy_pip_files POST_BUILD
add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/tools/pip_package/MANIFEST.in
${CMAKE_CURRENT_BINARY_DIR}/tf_python/)
add_custom_command(TARGET tf_python_copy_pip_files POST_BUILD
add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/tf_python/setup.py bdist_wheel
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/tf_python)

17
tensorflow/contrib/cmake/tf_tools.cmake
View File

@ -17,30 +17,25 @@ target_include_directories(${proto_text} PUBLIC
${tensorflow_source_dir}
)
# TODO(mrry): Cut down the dependencies of this tool.
target_link_libraries(${proto_text} PUBLIC
${CMAKE_THREAD_LIBS_INIT}
${PROTOBUF_LIBRARIES}
# tf_protos_cc
# re2_lib
${gif_STATIC_LIBRARIES}
${jpeg_STATIC_LIBRARIES}
${png_STATIC_LIBRARIES}
${ZLIB_LIBRARIES}
${jsoncpp_STATIC_LIBRARIES}
${boringssl_STATIC_LIBRARIES}
${CMAKE_DL_LIBS}
)
)
if(tensorflow_ENABLE_SSL_SUPPORT)
target_link_libraries(${proto_text} PUBLIC ${boringssl_STATIC_LIBRARIES})
endif()
add_dependencies(${proto_text}
tf_core_lib
protobuf
# jpeg_copy_headers_to_destination
# png_copy_headers_to_destination
# re2_copy_headers_to_destination
# eigen
# tf_protos_cc
# jsoncpp
# boringssl
)
target_compile_options(${proto_text} PRIVATE

1
tensorflow/contrib/cmake/tf_tutorials.cmake
View File

@ -38,7 +38,6 @@ target_link_libraries(tf_tutorials_example_trainer PUBLIC
${CMAKE_THREAD_LIBS_INIT}
${PROTOBUF_STATIC_LIBRARIES}
tf_protos_cc
re2_lib
${boringssl_STATIC_LIBRARIES}
${farmhash_STATIC_LIBRARIES}
${gif_STATIC_LIBRARIES}

37
tensorflow/contrib/framework/BUILD
View File

@ -8,6 +8,9 @@ exports_files(["LICENSE"])
package(default_visibility = ["//tensorflow:__subpackages__"])
load("//tensorflow:tensorflow.bzl", "cuda_py_test")
load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs")
py_library(
name = "framework_py",
@ -23,7 +26,41 @@ py_library(
"python/ops/prettyprint_ops.py",
"python/ops/variables.py",
],
data = [
":python/ops/_variable_ops.so",
],
srcs_version = "PY2AND3",
deps = [
":gen_variable_ops",
],
)
tf_custom_op_library(
name = "python/ops/_variable_ops.so",
srcs = [
"kernels/zero_initializer_op.cc",
"kernels/zero_initializer_op.h",
"ops/variable_ops.cc",
],
)
tf_gen_op_libs(
op_lib_names = ["variable_ops"],
)
cc_library(
name = "all_ops",
deps = [
":variable_ops_op_lib",
],
)
tf_gen_op_wrapper_py(
name = "gen_variable_ops",
out = "python/ops/gen_variable_ops.py",
deps = [
":variable_ops_op_lib",
],
)
py_test(

1
tensorflow/contrib/framework/__init__.py
View File

@ -60,6 +60,7 @@
@@model_variable
@@variable
@@VariableDeviceChooser
@@zero_initializer
"""
from __future__ import absolute_import

37
tensorflow/contrib/framework/kernels/zero_initializer_op.cc Normal file
View File

@ -0,0 +1,37 @@
/* Copyright 2016 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.
==============================================================================*/
#include "tensorflow/contrib/framework/kernels/zero_initializer_op.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/op_kernel.h"
namespace tensorflow {
#define REGISTER_KERNELS(D, T) \
REGISTER_KERNEL_BUILDER(Name("ZeroInitializer") \
.Device(DEVICE_##D) \
.TypeConstraint<T>("T"), \
ZeroInitializerOp<T>);
#define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
TF_CALL_REAL_NUMBER_TYPES(REGISTER_CPU_KERNELS);
#undef REGISTER_CPU_KERNELS
#if GOOGLE_CUDA
#define REGISTER_GPU_KERNELS(T) REGISTER_KERNELS(GPU, T);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
#undef REGISTER_GPU_KERNELS
#endif // GOOGLE_CUDA
#undef REGISTER_KERNELS
} // namespace tensorflow

59
tensorflow/contrib/framework/kernels/zero_initializer_op.h Normal file
View File

@ -0,0 +1,59 @@
/* Copyright 2016 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.
==============================================================================*/
#ifndef TENSORFLOW_CONTRIB_FRAMEWORK_KERNELS_ZERO_INITIALIZER_OP_H_
#define TENSORFLOW_CONTRIB_FRAMEWORK_KERNELS_ZERO_INITIALIZER_OP_H_
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/op_kernel.h"
namespace tensorflow {
template <typename T>
class ZeroInitializerOp : public OpKernel {
public:
explicit ZeroInitializerOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
OP_REQUIRES(ctx, IsRefType(ctx->input_type(0)),
errors::InvalidArgument("input needs to be a ref type"));
}
void Compute(OpKernelContext* ctx) override {
mutex_lock l(*ctx->input_ref_mutex(0));
Tensor input = ctx->mutable_input(0, true);
OP_REQUIRES(ctx, !input.IsInitialized(),
errors::InvalidArgument("input is already initialized"));
AllocatorAttributes attr;
attr.set_gpu_compatible(true);
attr.set_nic_compatible(true);
PersistentTensor out_persistent;
Tensor* out_tensor = nullptr;
OP_REQUIRES_OK(ctx, ctx->allocate_persistent(
input.dtype(), input.shape(), &out_persistent,
&out_tensor, attr));
auto out_tensor_flat = out_tensor->flat<T>();
int total_size = static_cast<int>(1);
for (int d = static_cast<int>(0); d < out_tensor->dims(); d++) {
total_size *= out_tensor->dim_size(d);
}
for (int idx = static_cast<int>(0); idx < total_size; idx++) {
out_tensor_flat(idx) = static_cast<T>(0);
}
ctx->replace_ref_input(0, *out_tensor, true);
// we always return the input ref.
ctx->forward_ref_input_to_ref_output(0, 0);
}
};
} // end namespace tensorflow
#endif // TENSORFLOW_CONTRIB_FRAMEWORK_KERNELS_ZERO_INITIALIZER_OP_H_

43
tensorflow/contrib/framework/ops/variable_ops.cc Normal file
View File

@ -0,0 +1,43 @@
/* Copyright 2016 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.
==============================================================================*/
#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/op.h"
namespace tensorflow {
using shape_inference::InferenceContext;
using shape_inference::Shape;
REGISTER_OP("ZeroInitializer")
.Input("ref: Ref(T)")
.Output("output_ref: Ref(T)")
.Attr("T: realnumbertype")
.SetAllowsUninitializedInput()
.SetShapeFn([](InferenceContext* c) {
c->set_output(0, c->input(0));
return Status::OK();
})
.Doc(R"doc(
Initialize 'ref' with all zeros. This op requires that the tensor is not
initialized. The tensor will first be allocated memory, then be filled with all
zeros. This op is intended to save memory during initialization,
if you use this op, you should not run initializer of the 'ref' tensor.
ref: Should be from a `Variable` node.
output_ref:= Same as "ref".
)doc");
} // namespace tensorflow

30
tensorflow/contrib/framework/python/ops/variables.py
View File

@ -20,6 +20,7 @@ from __future__ import division
from __future__ import print_function
from tensorflow.contrib.framework.python.ops import add_arg_scope as contrib_add_arg_scope
from tensorflow.contrib.framework.python.ops import gen_variable_ops
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.framework import device as tf_device
from tensorflow.python.framework import dtypes
@ -29,8 +30,11 @@ from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.ops import gen_state_ops
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training import saver as tf_saver
from tensorflow.python.framework.load_library import load_op_library
from tensorflow.python.platform import resource_loader
__all__ = ['add_model_variable',
@ -53,9 +57,33 @@ __all__ = ['add_model_variable',
'local_variable',
'model_variable',
'variable',
'VariableDeviceChooser']
'VariableDeviceChooser',
'zero_initializer']
def zero_initializer(ref, use_locking=True, name="zero_initializer"):
"""Initialize 'ref' with all zeros, ref tensor should be uninitialized.
If already initialized, you will get ValueError. This op is intended to
save memory during initialization.
Args:
ref: ref of the tensor need to be zero initialized.
name: optional name for this operation.
Returns:
ref that initialized.
Raises:
ValueError: If ref tensor is initialized.
"""
_variable_ops = load_op_library(resource_loader.get_path_to_datafile(
"_variable_ops.so"))
assert _variable_ops, "Could not load _variable_ops.so"
return gen_variable_ops.zero_initializer(ref, name=name)
# shape function for _ZeroInitializerOp
@ops.RegisterShape("ZeroInitializer")
def _ZeroInitializerShape(op):
var_shape = op.inputs[0].get_shape()
return [var_shape]
def assert_global_step(global_step_tensor):
"""Asserts `global_step_tensor` is a scalar int `Variable` or `Tensor`.

23
tensorflow/contrib/framework/python/ops/variables_test.py
View File

@ -1053,5 +1053,28 @@ class AssignFromCheckpointFnTest(tf.test.TestCase):
self.assertEqual(init_value0, var0.eval())
self.assertEqual(init_value1, var1.eval())
class ZeroInitializerOpTest(tf.test.TestCase):
def _testZeroInitializer(self, shape, initializer, use_init):
var = tf.Variable(initializer)
var_zero = tf.contrib.framework.zero_initializer(var)
with self.test_session() as sess:
with self.assertRaisesOpError("Attempting to use uninitialized value"):
var.eval()
if use_init:
sess.run(var.initializer)
with self.assertRaisesOpError("input is already initialized"):
var_zero.eval()
self.assertAllClose(np.ones(shape), var.eval())
else:
var_zero.eval()
self.assertAllClose(np.zeros(shape), var.eval())
def testZeroInitializer(self):
for dtype in (tf.int32, tf.int64, tf.float32, tf.float64):
for use_init in (False, True):
self._testZeroInitializer(
[10, 20], tf.ones([10, 20], dtype = dtype), use_init)
if __name__ == '__main__':
tf.test.main()

4
tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj
View File

@ -281,7 +281,7 @@
"$(SRCROOT)/../../makefile/downloads/protobuf/src/",
"$(SRCROOT)/../../makefile/downloads",
"$(SRCROOT)/../../makefile/gen/proto",
"$(SRCROOT)/../../makefile/downloads/eigen-latest",
"$(SRCROOT)/../../makefile/downloads/eigen",
);
INFOPLIST_FILE = "$(SRCROOT)/Benchmark-Info.plist";
IPHONEOS_DEPLOYMENT_TARGET = 9.2;
@ -315,7 +315,7 @@
"$(SRCROOT)/../../makefile/downloads/protobuf/src/",
"$(SRCROOT)/../../makefile/downloads",
"$(SRCROOT)/../../makefile/gen/proto",
"$(SRCROOT)/../../makefile/downloads/eigen-latest",
"$(SRCROOT)/../../makefile/downloads/eigen",
);
INFOPLIST_FILE = "$(SRCROOT)/Benchmark-Info.plist";
IPHONEOS_DEPLOYMENT_TARGET = 9.2;

2
tensorflow/contrib/ios_examples/camera/Info.plist
View File

@ -36,5 +36,7 @@
<array>
<string>UIInterfaceOrientationPortrait</string>
</array>
<key>NSCameraUsageDescription</key>
<string>Capture images to detect object</string>
</dict>
</plist>

4
tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj
View File

@ -289,7 +289,7 @@
GCC_WARN_UNUSED_VARIABLE = YES;
HEADER_SEARCH_PATHS = (
"$(SRCROOT)/../../makefile/gen/proto",
"$(SRCROOT)/../../makefile/downloads/eigen-latest",
"$(SRCROOT)/../../makefile/downloads/eigen",
"$(SRCROOT)/../../makefile/downloads",
"$(SRCROOT)/../../makefile/downloads/protobuf/src/",
"$(SRCROOT)/../../../..",
@ -348,7 +348,7 @@
GCC_WARN_UNUSED_VARIABLE = YES;
HEADER_SEARCH_PATHS = (
"$(SRCROOT)/../../makefile/gen/proto",
"$(SRCROOT)/../../makefile/downloads/eigen-latest",
"$(SRCROOT)/../../makefile/downloads/eigen",
"$(SRCROOT)/../../makefile/downloads",
"$(SRCROOT)/../../makefile/downloads/protobuf/src/",
"$(SRCROOT)/../../../..",

4
tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj
View File

@ -284,7 +284,7 @@
"$(SRCROOT)/../../makefile/downloads/protobuf/src/",
"$(SRCROOT)/../../makefile/downloads",
"$(SRCROOT)/../../makefile/gen/proto",
"$(SRCROOT)/../../makefile/downloads/eigen-latest",
"$(SRCROOT)/../../makefile/downloads/eigen",
);
INFOPLIST_FILE = "$(SRCROOT)/RunModel-Info.plist";
IPHONEOS_DEPLOYMENT_TARGET = 9.2;
@ -323,7 +323,7 @@
"$(SRCROOT)/../../makefile/downloads/protobuf/src/",
"$(SRCROOT)/../../makefile/downloads",
"$(SRCROOT)/../../makefile/gen/proto",
"$(SRCROOT)/../../makefile/downloads/eigen-latest",
"$(SRCROOT)/../../makefile/downloads/eigen",
);
INFOPLIST_FILE = "$(SRCROOT)/RunModel-Info.plist";
IPHONEOS_DEPLOYMENT_TARGET = 9.2;

13
tensorflow/contrib/layers/python/layers/layers.py
View File

@ -117,6 +117,7 @@ def batch_norm(inputs,
scale=False,
epsilon=0.001,
activation_fn=None,
initializers={},
updates_collections=ops.GraphKeys.UPDATE_OPS,
is_training=True,
reuse=None,
@ -211,39 +212,43 @@ def batch_norm(inputs,
if center:
beta_collections = utils.get_variable_collections(variables_collections,
'beta')
beta_initializer = initializers.get('beta', init_ops.zeros_initializer)
beta = variables.model_variable('beta',
shape=params_shape,
dtype=dtype,
initializer=init_ops.zeros_initializer,
initializer=beta_initializer,
collections=beta_collections,
trainable=trainable)
if scale:
gamma_collections = utils.get_variable_collections(variables_collections,
'gamma')
gamma_initializer = initializers.get('gamma', init_ops.ones_initializer)
gamma = variables.model_variable('gamma',
shape=params_shape,
dtype=dtype,
initializer=init_ops.ones_initializer,
initializer=gamma_initializer,
collections=gamma_collections,
trainable=trainable)
# Create moving_mean and moving_variance variables and add them to the
# appropiate collections.
moving_mean_collections = utils.get_variable_collections(
variables_collections, 'moving_mean')
moving_mean_initializer = initializers.get('moving_mean', init_ops.zeros_initializer)
moving_mean = variables.model_variable(
'moving_mean',
shape=params_shape,
dtype=dtype,
initializer=init_ops.zeros_initializer,
initializer=moving_mean_initializer,
trainable=False,
collections=moving_mean_collections)
moving_variance_collections = utils.get_variable_collections(
variables_collections, 'moving_variance')
moving_variance_initializer = initializers.get('moving_variance', init_ops.ones_initializer)
moving_variance = variables.model_variable(
'moving_variance',
shape=params_shape,
dtype=dtype,
initializer=init_ops.ones_initializer,
initializer=moving_variance_initializer,
trainable=False,
collections=moving_variance_collections)

23
tensorflow/contrib/layers/python/layers/layers_test.py
View File

@ -1639,6 +1639,29 @@ class BatchNormTest(tf.test.TestCase):
self.assertAllClose(moving_mean.eval(), expected_mean)
self.assertAllClose(moving_variance.eval(), expected_var)
def testCustomInitializer(self):
height, width = 3, 3
channels = 3
with self.test_session() as sess:
images = np.ones((5, height, width, channels))*9.0
beta = tf.constant_initializer(np.ones(channels)*5.0)
gamma = tf.constant_initializer(np.ones(channels)*2.0)
mean = tf.constant_initializer(np.ones(channels)*5.0)
variance = tf.constant_initializer(np.ones(channels)*4.0)
output = tf.contrib.layers.batch_norm(images,
is_training=False,
scale=True,
epsilon=0.0,
initializers={
'beta': beta,
'gamma': gamma,
'moving_mean': mean,
'moving_variance': variance,
})
sess.run(tf.initialize_all_variables())
outs = sess.run(output)
self.assertAllClose(outs, images)
class LayerNormTest(tf.test.TestCase):

8
tensorflow/contrib/layers/python/layers/optimizers.py
View File

@ -61,7 +61,8 @@ def optimize_loss(loss,
update_ops=None,
variables=None,
name=None,
summaries=None):
summaries=None,
colocate_gradients_with_ops=False):
"""Given loss and parameters for optimizer, returns a training op.
Various ways of passing optimizers, include:
@ -112,6 +113,8 @@ def optimize_loss(loss,
summaries: List of internal quantities to visualize on tensorboard. If not
set only the loss and the learning rate will be reported. The
complete list is in OPTIMIZER_SUMMARIES.
colocate_gradients_with_ops: If True, try colocating gradients with the
corresponding op.
Returns:
Training op.
@ -185,7 +188,8 @@ def optimize_loss(loss,
variables = vars_.trainable_variables()
# Compute gradients.
gradients = opt.compute_gradients(loss, variables)
gradients = opt.compute_gradients(loss, variables,
colocate_gradients_with_ops=colocate_gradients_with_ops)
# Optionally add gradient noise.
if gradient_noise_scale is not None:

1
tensorflow/contrib/learn/BUILD
View File

@ -408,6 +408,7 @@ py_test(
size = "small",
srcs = ["python/learn/estimators/classifier_test.py"],
srcs_version = "PY2AND3",
tags = ["manual"], # http://b/31032996
deps = [
":learn",
"//tensorflow:tensorflow_py",

2
tensorflow/contrib/makefile/Makefile
View File

@ -426,6 +426,8 @@ $(wildcard tensorflow/core/platform/*/*/*.cc) \
$(wildcard tensorflow/core/util/*.cc) \
$(wildcard tensorflow/core/util/*/*.cc) \
tensorflow/core/util/version_info.cc
# Remove duplicates (for version_info.cc)
CORE_CC_ALL_SRCS := $(sort $(CORE_CC_ALL_SRCS))
CORE_CC_EXCLUDE_SRCS := \
$(wildcard tensorflow/core/*/*test.cc) \
$(wildcard tensorflow/core/*/*testutil*) \

6
tensorflow/contrib/makefile/README.md
View File

@ -141,6 +141,12 @@ xcode-select --install
If this is a new install, you will need to run XCode once to agree to the
license before continuing.
Then install [automake](https://en.wikipedia.org/wiki/Automake):
```bash
brew install automake
```
Also, download the graph if you haven't already:
```bash

7
tensorflow/contrib/makefile/download_dependencies.sh
View File

@ -19,7 +19,12 @@ set -e
DOWNLOADS_DIR=tensorflow/contrib/makefile/downloads
BZL_FILE_PATH=tensorflow/workspace.bzl
EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/.*tar\.gz' "${BZL_FILE_PATH}")"
EIGEN_VERSION="$(sed -ne 's/^[ \t]*eigen_version = "\(.*\)".*$/\1/p' "${BZL_FILE_PATH}")"
if [ "${EIGEN_VERSION}" == '' ]; then
echo "Cannot extract eigen_version from ${BZL_FILE_PATH}" >&2
exit 1
fi
EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/' "${BZL_FILE_PATH}")${EIGEN_VERSION}.tar.gz"
GEMMLOWP_URL="$(grep -o 'http.*github.com/google/gemmlowp/.*tar\.gz' "${BZL_FILE_PATH}")"
GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz"
PROTOBUF_URL="$(grep -o 'http.*github.com/google/protobuf/.*tar\.gz' "${BZL_FILE_PATH}")"

4
tensorflow/contrib/slim/README.md
View File

@ -69,7 +69,7 @@ and
models.
* [preprocess](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/preprocess.py):
is a module with various preprocessing utilities.
* [queues](https://www.tensorflow.org/code/tensorflow/contrib/slim/queues.py):
* [queues](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/queues.py):
provides a context manager for easily and safely starting and closing
QueueRunners.
* [regularizers](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/regularizers.py):
@ -503,7 +503,7 @@ pose_loss = MyCustomLossFunction(pose_predictions, pose_labels)
slim.losses.add_loss(pose_loss) # Letting TF-Slim know about the additional loss.
# The following two ways to compute the total loss are equivalent:
regularization_loss = tf.add_n(slim.get_regularization_losses())
regularization_loss = tf.add_n(slim.losses.get_regularization_losses())
total_loss1 = classification_loss + sum_of_squares_loss + pose_loss + regularization_loss
# (Regularization Loss is included in the total loss by default).

4
tensorflow/core/common_runtime/simple_placer.h
View File

@ -33,8 +33,8 @@ namespace tensorflow {
// devices the given DeviceSet, respecting the following constraints:
//
// 1. Existing device assignments remain unchanged.
// 2. Requested (partial or complete) device specifications in the
// are granted.
// 2. Requested (partial or complete) device specifications given by device name
// for each node are granted.
// 3. Nodes connected by edges of a reference type are colocated on
// the same device.
// 4. Given nodes "A" and "B", if node "B" has a colocation group

82
tensorflow/core/kernels/conv_ops_fused.cc
View File

@ -46,9 +46,16 @@ namespace {
// In this case, we've picked 16 megabytes as a reasonable limit.
const size_t kMaxChunkSize = (16 * 1024 * 1024);
// Lookup method used when resizing.
enum SamplingMode {
BILINEAR = 0,
NEAREST = 1,
};
// Combines bilinear resizing and mirror padding into the im2col transformation
// stage of convolution,
template <class T1, class T2, class T3, class TGemmFunctor>
// stage of convolution.
template <class T1, class T2, class T3, class TGemmFunctor,
SamplingMode SampleMode>
class FusedResizeAndPadConvFunctor {
public:
void operator()(OpKernelContext* context, const Tensor& input,
@ -78,6 +85,9 @@ class FusedResizeAndPadConvFunctor {
<< output_width << ", " << output_height;
return;
}
OP_REQUIRES(
context, ((SampleMode == NEAREST) || (SampleMode == BILINEAR)),
errors::InvalidArgument("Bad sample mode passed in", SampleMode));
// These calculations define how the patches will be positioned within the
// input image. The actual definitions are quite complex, and rely on the
@ -183,18 +193,24 @@ class FusedResizeAndPadConvFunctor {
T1 in_value;
if ((conv_in_x >= 0) && (conv_in_x < padded_width) &&
(conv_in_y >= 0) && (conv_in_y < padded_height)) {
const T1 top_left(
input_data(batch, top_y_index, left_x_index, in_channel));
if (SampleMode == NEAREST) {
const T1 top_left(input_data(batch, top_y_index,
left_x_index, in_channel));
in_value = top_left;
} else if (SampleMode == BILINEAR) {
const T1 top_left(input_data(batch, top_y_index,
left_x_index, in_channel));
const T1 top_right(input_data(batch, top_y_index,
right_x_index, in_channel));
const T1 bottom_left(input_data(batch, bottom_y_index,
left_x_index, in_channel));
const T1 bottom_right(input_data(batch, bottom_y_index,
right_x_index, in_channel));
const T1 bottom_right(input_data(
batch, bottom_y_index, right_x_index, in_channel));
const T1 top = top_left + (top_right - top_left) * x_lerp;
const T1 bottom =
bottom_left + (bottom_right - bottom_left) * x_lerp;
in_value = top + (bottom - top) * y_lerp;
}
} else {
in_value = T1(0);
}
@ -208,8 +224,8 @@ class FusedResizeAndPadConvFunctor {
((batch == (input_batches - 1)) &&
(out_y == (output_height - 1)) && (out_x == (output_width - 1)));
if (is_last_in_chunk || is_last_overall) {
// Now we've assembled a set of image patches into a matrix, apply a
// GEMM matrix multiply of the patches as rows, times the filter
// Now we've assembled a set of image patches into a matrix, apply
// a GEMM matrix multiply of the patches as rows, times the filter
// weights in columns, to get partial results in the output matrix.
const int how_many_patches = patch_index_within_chunk + 1;
const int m = how_many_patches;
@ -236,13 +252,15 @@ class FusedResizeAndPadConvFunctor {
// Implements a version of convolution with bilinear resizing and mirror padding
// included.
template <class T, class TConvFunctor>
template <class T, class TConvFunctor, bool DoResize>
class FusedResizeConv2DUsingGemmOp : public OpKernel {
public:
explicit FusedResizeConv2DUsingGemmOp(OpKernelConstruction* context)
: OpKernel(context) {
if (DoResize) {
OP_REQUIRES_OK(context,
context->GetAttr("resize_align_corners", &align_corners_));
}
MirrorPadMode mode;
OP_REQUIRES_OK(context, context->GetAttr("mode", &mode));
@ -280,13 +298,34 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel {
OP_REQUIRES(context, (input.shape().num_elements() > 0),
errors::InvalidArgument("Input tensor can't be empty"));
ImageResizerState st(align_corners_);
ImageResizerState st(false);
if (DoResize) {
st = ImageResizerState(align_corners_);
st.ValidateAndCalculateOutputSize(context, input);
if (!context->status().ok()) return;
const TensorShape resized_shape(
} else {
// Set up the resize parameters to do no scaling at all.
st.batch_size = input.dim_size(0);
st.out_height = input.dim_size(1);
st.out_width = input.dim_size(2);
st.in_height = input.dim_size(1);
st.in_width = input.dim_size(2);
st.channels = input.dim_size(3);
st.height_scale = 1.0f;
st.width_scale = 1.0f;
}
TensorShape resized_shape(
{input.dim_size(0), st.out_height, st.out_width, input.dim_size(3)});
const Tensor& paddings = context->input(2);
int paddings_index;
int filter_index;
if (DoResize) {
paddings_index = 2;
filter_index = 3;
} else {
paddings_index = 1;
filter_index = 2;
}
const Tensor& paddings = context->input(paddings_index);
const int dims = resized_shape.dims();
OP_REQUIRES(
@ -365,7 +404,7 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel {
// Input filter is of the following dimensions:
// [ filter_rows, filter_cols, in_depth, out_depth]
const Tensor& filter = context->input(3);
const Tensor& filter = context->input(filter_index);
// For 2D convolution, there should be 4 dimensions.
OP_REQUIRES(context, padded_shape.dims() == 4,
@ -479,9 +518,20 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel {
.Device(DEVICE_CPU) \
.TypeConstraint<T>("T"), \
FusedResizeConv2DUsingGemmOp< \
T, \
FusedResizeAndPadConvFunctor<T, T, T, FastGemmFunctor<T, T, T>>>);
T, FusedResizeAndPadConvFunctor<T, T, T, FastGemmFunctor<T, T, T>, \
BILINEAR>, \
true>);
TF_CALL_float(REGISTER_FUSED);
#define REGISTER_PAD_ONLY_FUSED(T) \
REGISTER_KERNEL_BUILDER( \
Name("FusedPadConv2D").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
FusedResizeConv2DUsingGemmOp< \
T, FusedResizeAndPadConvFunctor<T, T, T, FastGemmFunctor<T, T, T>, \
NEAREST>, \
false>);
TF_CALL_float(REGISTER_PAD_ONLY_FUSED);
} // namespace tensorflow

79
tensorflow/core/kernels/conv_ops_test.cc
View File

@ -121,22 +121,15 @@ class FusedResizePadConvOpTest : public OpsTestBase {
auto root = tensorflow::Scope::NewRootScope();
using namespace ::tensorflow::ops; // NOLINT(build/namespaces)
const size_t input_data_size = input_height * input_width * input_depth;
Tensor input_data(DT_FLOAT,
TensorShape({1, input_height, input_width, input_depth}));
for (int i = 0; i < input_data_size; ++i) {
input_data.flat<float>()(i) = i + 1.0f;
}
test::FillIota<float>(&input_data, 1.0f);
Output input =
Const(root.WithOpName("input"), Input::Initializer(input_data));
const size_t filter_data_size =
filter_size * filter_size * filter_count * input_depth;
Tensor filter_data(DT_FLOAT, TensorShape({filter_size, filter_size,
input_depth, filter_count}));
for (int i = 0; i < filter_data_size; ++i) {
filter_data.flat<float>()(i) = i + 1.0f;
}
test::FillIota<float>(&filter_data, 1.0f);
Output filter =
Const(root.WithOpName("filter"), Input::Initializer(filter_data));
@ -173,6 +166,54 @@ class FusedResizePadConvOpTest : public OpsTestBase {
test::ExpectTensorNear<float>(unfused_tensors[0], fused_tensors[0], 1e-5);
}
void CompareFusedPadOnlyAndSeparate(int input_width, int input_height,
int input_depth, int y_padding,
int x_padding, int filter_size,
int filter_count, string pad_mode,
int stride, string padding) {
auto root = tensorflow::Scope::NewRootScope();
using namespace ::tensorflow::ops; // NOLINT(build/namespaces)
Tensor input_data(DT_FLOAT,
TensorShape({1, input_height, input_width, input_depth}));
test::FillIota<float>(&input_data, 1.0f);
Output input =
Const(root.WithOpName("input"), Input::Initializer(input_data));
Tensor filter_data(DT_FLOAT, TensorShape({filter_size, filter_size,
input_depth, filter_count}));
test::FillIota<float>(&filter_data, 1.0f);
Output filter =
Const(root.WithOpName("filter"), Input::Initializer(filter_data));
Output paddings =
Const(root.WithOpName("paddings"),
{{0, 0}, {y_padding, y_padding}, {x_padding, x_padding}, {0, 0}});
Output mirror_pad =
MirrorPad(root.WithOpName("mirror_pad"), input, paddings, pad_mode);
Output conv = Conv2D(root.WithOpName("conv"), mirror_pad, filter,
{1, stride, stride, 1}, padding);
Output fused_conv =
FusedPadConv2D(root.WithOpName("fused_conv"), input, paddings, filter,
pad_mode, {1, stride, stride, 1}, padding);
tensorflow::GraphDef graph;
TF_ASSERT_OK(root.ToGraphDef(&graph));
std::unique_ptr<tensorflow::Session> session(
tensorflow::NewSession(tensorflow::SessionOptions()));
TF_ASSERT_OK(session->Create(graph));
std::vector<Tensor> unfused_tensors;
TF_ASSERT_OK(session->Run({}, {"conv"}, {}, &unfused_tensors));
std::vector<Tensor> fused_tensors;
TF_ASSERT_OK(session->Run({}, {"fused_conv"}, {}, &fused_tensors));
test::ExpectTensorNear<float>(unfused_tensors[0], fused_tensors[0], 1e-5);
}
};
TEST_F(FusedResizePadConvOpTest, HandwrittenConv) { HandwrittenConv(); }
@ -237,4 +278,24 @@ TEST_F(FusedResizePadConvOpTest, ResizeAndPadSymmetricComparative) {
"SAME");
}
TEST_F(FusedResizePadConvOpTest, NoResizeIdentityComparative) {
CompareFusedPadOnlyAndSeparate(10, 10, 1, 0, 0, 1, 1, "REFLECT", 1, "SAME");
}
TEST_F(FusedResizePadConvOpTest, NoResizeConvOnlyComparative) {
CompareFusedPadOnlyAndSeparate(10, 10, 3, 0, 0, 4, 4, "REFLECT", 1, "SAME");
}
TEST_F(FusedResizePadConvOpTest, NoResizePadOnlyComparative) {
CompareFusedPadOnlyAndSeparate(4, 4, 1, 2, 2, 1, 1, "REFLECT", 1, "SAME");
}
TEST_F(FusedResizePadConvOpTest, NoResizePadOnlyWithChannelsComparative) {
CompareFusedPadOnlyAndSeparate(4, 4, 3, 2, 2, 1, 1, "REFLECT", 1, "SAME");
}
TEST_F(FusedResizePadConvOpTest, NoResizePadOnlySymmetricComparative) {
CompareFusedPadOnlyAndSeparate(4, 4, 1, 2, 2, 1, 1, "SYMMETRIC", 1, "SAME");
}
} // namespace tensorflow

34
tensorflow/core/ops/nn_ops.cc
View File

@ -654,6 +654,40 @@ strides: 1-D of length 4. The stride of the sliding window for each dimension
padding: The type of padding algorithm to use.
)doc");
REGISTER_OP("FusedPadConv2D")
.Input("input: T")
.Input("paddings: int32")
.Input("filter: T")
.Output("output: T")
.Attr("T: {half, float, double}")
.Attr(GetMirrorPadModeAttrString())
.Attr("strides: list(int)")
.Attr(GetPaddingAttrString())
.Doc(R"doc(
Performs a padding as a preprocess during a convolution.
Similar to FusedResizeAndPadConv2d, this op allows for an optimized
implementation where the spatial padding transformation stage is fused with the
im2col lookup, but in this case without the bilinear filtering required for
resizing. Fusing the padding prevents the need to write out the intermediate
results as whole tensors, reducing memory pressure, and we can get some latency
gains by merging the transformation calculations.
The data_format attribute for Conv2D isn't supported by this op, and 'NHWC'
order is used instead.
Internally this op uses a single per-graph scratch buffer, which means that it
will block if multiple versions are being run in parallel. This is because this
operator is primarily an optimization to minimize memory usage.
input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
paddings: A two-column matrix specifying the padding sizes. The number of
rows must be the same as the rank of `input`.
filter: 4-D with shape
`[filter_height, filter_width, in_channels, out_channels]`.
strides: 1-D of length 4. The stride of the sliding window for each dimension
of `input`. Must be in the same order as the dimension specified with format.
padding: The type of padding algorithm to use.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("DepthwiseConv2dNative")

2
tensorflow/examples/tutorials/deepdream/deepdream.ipynb
View File

@ -623,7 +623,7 @@
"<a id=\"laplacian\"></a>\n",
"## Laplacian Pyramid Gradient Normalization\n",
"\n",
"This looks better, but the resulting images mostly contain high frequencies. Can we improve it? One way is to add a smoothness prior into the optimization objective. This will effectively blur the image a little every iteration, suppressing the higher frequencies, so that the lower frequencies can catch up. This will require more iterations to produce a nice image. Why don't we just boost lower frequencies of the gradient instead? One way to achieve this is through the [Laplacian pyramid](https://en.wikipedia.org/wiki/Pyramid_%28image_processing%29#Laplacian_pyramid) decomposition. We call the resulting technique _Laplacian Pyramid Gradient Normailzation_."
"This looks better, but the resulting images mostly contain high frequencies. Can we improve it? One way is to add a smoothness prior into the optimization objective. This will effectively blur the image a little every iteration, suppressing the higher frequencies, so that the lower frequencies can catch up. This will require more iterations to produce a nice image. Why don't we just boost lower frequencies of the gradient instead? One way to achieve this is through the [Laplacian pyramid](https://en.wikipedia.org/wiki/Pyramid_%28image_processing%29#Laplacian_pyramid) decomposition. We call the resulting technique _Laplacian Pyramid Gradient Normalization_."
]
},
{

8
tensorflow/examples/tutorials/estimators/abalone.py
View File

@ -18,7 +18,7 @@ from __future__ import division
from __future__ import print_function
import tempfile
import urllib
from six.moves import urllib
import numpy as np
import tensorflow as tf
@ -51,7 +51,7 @@ def maybe_download():
train_file_name = FLAGS.train_data
else:
train_file = tempfile.NamedTemporaryFile(delete=False)
urllib.urlretrieve("http://download.tensorflow.org/data/abalone_train.csv", train_file.name) # pylint: disable=line-too-long
urllib.request.urlretrieve("http://download.tensorflow.org/data/abalone_train.csv", train_file.name) # pylint: disable=line-too-long
train_file_name = train_file.name
train_file.close()
print("Training data is downloaded to %s" % train_file_name)
@ -60,7 +60,7 @@ def maybe_download():
test_file_name = FLAGS.test_data
else:
test_file = tempfile.NamedTemporaryFile(delete=False)
urllib.urlretrieve("http://download.tensorflow.org/data/abalone_test.csv", test_file.name) # pylint: disable=line-too-long
urllib.request.urlretrieve("http://download.tensorflow.org/data/abalone_test.csv", test_file.name) # pylint: disable=line-too-long
test_file_name = test_file.name
test_file.close()
print("Test data is downloaded to %s" % test_file_name)
@ -69,7 +69,7 @@ def maybe_download():
predict_file_name = FLAGS.predict_data
else:
predict_file = tempfile.NamedTemporaryFile(delete=False)
urllib.urlretrieve("http://download.tensorflow.org/data/abalone_predict.csv", predict_file.name) # pylint: disable=line-too-long
urllib.request.urlretrieve("http://download.tensorflow.org/data/abalone_predict.csv", predict_file.name) # pylint: disable=line-too-long
predict_file_name = predict_file.name
predict_file.close()
print("Prediction data is downloaded to %s" % predict_file_name)

2
tensorflow/examples/tutorials/input_fn/boston.py
View File

@ -64,7 +64,7 @@ def main(unused_argv):
# Print out predictions
y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
print ("Predictions: {}".format(str(y)))
print("Predictions: {}".format(str(y)))
if __name__ == "__main__":
tf.app.run()

15
tensorflow/examples/tutorials/word2vec/word2vec_basic.py
View File

@ -31,6 +31,7 @@ import tensorflow as tf
# Step 1: Download the data.
url = 'http://mattmahoney.net/dc/'
def maybe_download(filename, expected_bytes):
"""Download a file if not present, and make sure it's the right size."""
if not os.path.exists(filename):
@ -60,6 +61,7 @@ print('Data size', len(words))
# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 50000
def build_dataset(words):
count = [['UNK', -1]]
count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
@ -101,7 +103,7 @@ def generate_batch(batch_size, num_skips, skip_window):
data_index = (data_index + 1) % len(data)
for i in range(batch_size // num_skips):
target = skip_window # target label at the center of the buffer
targets_to_avoid = [ skip_window ]
targets_to_avoid = [skip_window]
for j in range(num_skips):
while target in targets_to_avoid:
target = random.randint(0, span - 1)
@ -187,7 +189,7 @@ with tf.Session(graph=graph) as session:
for step in xrange(num_steps):
batch_inputs, batch_labels = generate_batch(
batch_size, num_skips, skip_window)
feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}
feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}
# We perform one update step by evaluating the optimizer op (including it
# in the list of returned values for session.run()
@ -207,7 +209,7 @@ with tf.Session(graph=graph) as session:
for i in xrange(valid_size):
valid_word = reverse_dictionary[valid_examples[i]]
top_k = 8 # number of nearest neighbors
nearest = (-sim[i, :]).argsort()[1:top_k+1]
nearest = (-sim[i, :]).argsort()[1:top_k + 1]
log_str = "Nearest to %s:" % valid_word
for k in xrange(top_k):
close_word = reverse_dictionary[nearest[k]]
@ -217,11 +219,12 @@ with tf.Session(graph=graph) as session:
# Step 6: Visualize the embeddings.
def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
plt.figure(figsize=(18, 18)) #in inches
plt.figure(figsize=(18, 18)) # in inches
for i, label in enumerate(labels):
x, y = low_dim_embs[i,:]
x, y = low_dim_embs[i, :]
plt.scatter(x, y)
plt.annotate(label,
xy=(x, y),
@ -238,7 +241,7 @@ try:
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
plot_only = 500
low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])
low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
labels = [reverse_dictionary[i] for i in xrange(plot_only)]
plot_with_labels(low_dim_embs, labels)

2
tensorflow/g3doc/api_docs/python/client.md
View File

@ -134,7 +134,7 @@ Example:
# v is the numpy array [10, 20]
# 'fetches' can be a list.
v = session.run([a, b])
# v a Python list with 2 numpy arrays: the numpy array [10, 20] and the
# v is a Python list with 2 numpy arrays: the numpy array [10, 20] and the
# 1-D array [1.0, 2.0]
# 'fetches' can be arbitrary lists, tuples, namedtuple, dicts:
MyData = collections.namedtuple('MyData', ['a', 'b'])

4
tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.conv2d_transpose.md
View File

@ -11,9 +11,9 @@ deconvolution.
* <b>`value`</b>: A 4-D `Tensor` of type `float` and shape
`[batch, height, width, in_channels]`.
`[batch, in_height, in_width, in_channels]`.
* <b>`filter`</b>: A 4-D `Tensor` with the same type as `value` and shape
`[height, width, output_channels, in_channels]`. `filter`'s
`[filter_height, filter_width, output_channels, in_channels]`. `filter`'s
`in_channels` dimension must match that of `value`.
* <b>`output_shape`</b>: A 1-D `Tensor` representing the output shape of the
deconvolution op.

27
tensorflow/g3doc/get_started/os_setup.md
View File

@ -859,7 +859,7 @@ and you left the Cuda or cuDNN version empty, try specifying them explicitly.
### Protobuf library related issues
TensorFlow pip package depends on protobuf pip package version
3.0.0b2. Protobuf's pip package downloaded from [PyPI](https://pypi.python.org)
3.1.0. Protobuf's pip package downloaded from [PyPI](https://pypi.python.org)
(when running `pip install protobuf`) is a Python only library, that has
Python implementations of proto serialization/deserialization which can be
10x-50x slower than the C++ implementation. Protobuf also supports a binary
@ -877,14 +877,33 @@ $ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/prot
$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.0.0-cp27-cp27m-macosx_10_11_x86_64.whl
```
And for Python 3:
And for Python 3.5:
```bash
# Ubuntu/Linux 64-bit:
$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0-cp3-none-linux_x86_64.whl
$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0-cp35-cp35m-linux_x86_64.whl
# Mac OS X:
$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.0.0-cp3-cp3m-macosx_10_11_x86_64.whl
$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.0.0-cp35-cp35m-macosx_10_11_x86_64.whl
```
If your system/configuration is not listed above, you can use the following
instructions to build your own protobuf wheel file.
To install its prerequisites, [see
here](https://github.com/google/protobuf/blob/master/src/README.md):
Then:
```bash
$ git clone https://github.com/google/protobuf.git
$ cd protobuf
$ ./autogen.sh
$ CXXFLAGS="-fPIC -g -O2" ./configure
$ make -j12
$ export PROTOC=$PWD/src/protoc
$ cd python
$ python setup.py bdist_wheel --cpp_implementation --compile_static_extension
$ pip uninstall protobuf
$ pip install dist/<wheel file name>
```
Install the above package _after_ you have installed TensorFlow via pip, as the

2
tensorflow/g3doc/tutorials/image_recognition/index.md
View File

@ -262,7 +262,7 @@ output data.
This gives us a vector of `Tensor` objects, which in this case we know will only be a
single object long. You can think of a `Tensor` as a multi-dimensional array in this
context, and it holds a 299 pixel high, 299 pixel width, 3 channel image as float
context, and it holds a 299 pixel high, 299 pixel wide, 3 channel image as float
values. If you have your own image-processing framework in your product already, you
should be able to use that instead, as long as you apply the same transformations
before you feed images into the main graph.

2
tensorflow/g3doc/tutorials/word2vec/index.md
View File

@ -227,7 +227,7 @@ When we inspect these visualizations it becomes apparent that the vectors
capture some general, and in fact quite useful, semantic information about
words and their relationships to one another. It was very interesting when we
first discovered that certain directions in the induced vector space specialize
towards certain semantic relationships, e.g. *male-female*, *gender* and
towards certain semantic relationships, e.g. *male-female*, *verb tense* and
even *country-capital* relationships between words, as illustrated in the figure
below (see also for example
[Mikolov et al., 2013](http://www.aclweb.org/anthology/N13-1090)).

2
tensorflow/python/kernel_tests/BUILD
View File

@ -576,7 +576,7 @@ cuda_py_test(
cuda_py_test(
name = "division_past_test",
size = "small",
size = "medium",
srcs = ["division_past_test.py"],
additional_deps = [
"//tensorflow:tensorflow_py",

12
tensorflow/python/kernel_tests/bias_op_test.py
View File

@ -129,6 +129,12 @@ class BiasAddTest(tf.test.TestCase):
bias_jacob_t, bias_jacob_n = tf.test.compute_gradient(
bias_tensor, bias.shape, output_tensor, np_input.shape)
# Test gradient of BiasAddGrad
bias_add_grad = tf.gradients(tf.nn.l2_loss(output_tensor),
bias_tensor)[0]
grad_jacob_t, grad_jacob_n = tf.test.compute_gradient(
output_tensor, np_input.shape, bias_add_grad, bias.shape)
if dtype == np.float16:
# Compare fp16 theoretical gradients to fp32 numerical gradients,
# since fp16 numerical gradients are too imprecise unless great
@ -145,11 +151,17 @@ class BiasAddTest(tf.test.TestCase):
_, bias_jacob_n = tf.test.compute_gradient(
bias_tensor, bias.shape, output_tensor, np_input.shape)
bias_add_grad = tf.gradients(tf.nn.l2_loss(output_tensor),
bias_tensor)[0]
_, grad_jacob_n = tf.test.compute_gradient(
output_tensor, np_input.shape, bias_add_grad, bias.shape)
threshold = 2e-3
if dtype == tf.float64:
threshold = 1e-10
self.assertAllClose(tensor_jacob_t, tensor_jacob_n, threshold, threshold)
self.assertAllClose(bias_jacob_t, bias_jacob_n, threshold, threshold)
self.assertAllClose(grad_jacob_t, grad_jacob_n, threshold, threshold)
def testGradientTensor(self):
for (data_format, use_gpu) in GetTestConfigs():

4
tensorflow/python/ops/array_ops.py
View File

@ -1162,7 +1162,7 @@ def zeros(shape, dtype=dtypes.float32, name=None):
For example:
```python
tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
tf.zeros([3, 4], tf.int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
```
Args:
@ -1266,7 +1266,7 @@ def ones(shape, dtype=dtypes.float32, name=None):
For example:
```python
tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
tf.ones([2, 3], tf.int32) ==> [[1, 1, 1], [1, 1, 1]]
```
Args:

37
tensorflow/python/ops/nn_grad.py
View File

@ -203,6 +203,43 @@ def _BiasAddGrad(op, received_grad):
return (received_grad, gen_nn_ops.bias_add_grad(out_backprop=received_grad,
data_format=data_format))
@ops.RegisterGradient("BiasAddGrad")
def _BiasAddGradGrad(op, received_grad):
"""Gradient for the BiasAddGrad op.
Args:
op: BiasAddGrad op for which we are calculating gradients.
received_grad: The gradients passed to the BiasAddGrad op.
Returns:
A single gradient Tensor for the input to BiasAddGrad (which
is the gradient of the bias term in BiasAdd)
"""
try:
data_format = op.get_attr("data_format")
except ValueError:
data_format = None
shape = array_ops.shape(op.inputs[0])
rank = array_ops.rank(op.inputs[0])
bias_shape = array_ops.shape(received_grad)
if data_format == "NCHW":
expanded_shape = array_ops.concat(
0,
[array_ops.ones_like(shape[:-3]), bias_shape, array_ops.ones_like(shape[-2:])]
)
tile_mults = array_ops.concat(0, [shape[:-3], [1], shape[-2:]])
else:
expanded_shape = array_ops.concat(0, [array_ops.ones_like(shape[:-1]), bias_shape])
tile_mults = array_ops.concat(0, [shape[:-1], [1]])
expanded_grad = array_ops.reshape(received_grad, expanded_shape)
return array_ops.tile(expanded_grad, tile_mults)
@ops.RegisterGradient("BiasAddV1")
def _BiasAddGradV1(unused_bias_op, received_grad):

30
tensorflow/python/ops/nn_ops.py
View File

@ -1429,11 +1429,11 @@ ops.RegisterShape("AvgPool")(common_shapes.call_cpp_shape_fn)
ops.RegisterShape("MaxPool")(common_shapes.call_cpp_shape_fn)
@ops.RegisterShape("FusedResizeAndPadConv2D")
def _FusedResizeAndPadConv2DShape(op):
"""Shape function for FusedResizeAndPadConv2D op."""
def _CommonFusedConvCalculations(op, has_resize):
"""Shape function for Fused*Conv2D ops."""
# The bilinear resize shape calculation.
input_shape = op.inputs[0].get_shape().with_rank(4)
if has_resize:
unused_size_shape = op.inputs[1].get_shape().merge_with([2])
size = tensor_util.constant_value(op.inputs[1])
if size is not None:
@ -1444,13 +1444,19 @@ def _FusedResizeAndPadConv2DShape(op):
width = None
resized_shape = tensor_shape.TensorShape(
[input_shape[0], height, width, input_shape[3]])
paddings_index = 2
filter_index = 3
else:
resized_shape = input_shape
paddings_index = 1
filter_index = 2
# Calculates the effect of the padding.
paddings_shape = op.inputs[2].get_shape().with_rank(2)
paddings_shape = op.inputs[paddings_index].get_shape().with_rank(2)
resized_shape = resized_shape.with_rank(paddings_shape[0].value)
paddings_shape = paddings_shape.merge_with(
tensor_shape.matrix(resized_shape.ndims, 2))
paddings = tensor_util.constant_value(op.inputs[2])
paddings = tensor_util.constant_value(op.inputs[paddings_index])
if paddings is None:
padded_shape = tensor_shape.unknown_shape(ndims=resized_shape.ndims)
else:
@ -1462,7 +1468,7 @@ def _FusedResizeAndPadConv2DShape(op):
padded_shape = tensor_shape.TensorShape(output_dims)
# Finally work out the convolution's effect.
filter_shape = op.inputs[3].get_shape().with_rank(4)
filter_shape = op.inputs[filter_index].get_shape().with_rank(4)
batch_size = padded_shape[0]
in_rows = padded_shape[1]
@ -1494,6 +1500,18 @@ def _FusedResizeAndPadConv2DShape(op):
return [tensor_shape.TensorShape(output_shape)]
@ops.RegisterShape("FusedResizeAndPadConv2D")
def _FusedResizeAndPadConv2DShape(op):
"""Shape function for FusedResizeAndPadConv2D op."""
return _CommonFusedConvCalculations(op, True)
@ops.RegisterShape("FusedPadConv2D")
def _FusedPadConv2DShape(op):
"""Shape function for FusedResizeAndPadConv2D op."""
return _CommonFusedConvCalculations(op, False)
ops.RegisterShape("MaxPoolWithArgmax")(common_shapes.call_cpp_shape_fn)

2
tensorflow/python/ops/variable_scope.py
View File

@ -1348,7 +1348,7 @@ def variable_scope(name_or_scope,
a reuse scope, or if reuse is not `None` or `True`.
TypeError: when the types of some arguments are not appropriate.
"""
if default_name is None and not name_or_scope:
if default_name is None and name_or_scope is None:
raise TypeError("If default_name is None then name_or_scope is required")
if values is None:
values = []

42
tensorflow/python/tools/optimize_for_inference_lib.py
View File

@ -48,6 +48,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import math
import re
import numpy as np
@ -84,7 +85,8 @@ def optimize_for_inference(input_graph_def, input_node_names,
placeholder_type_enum)
optimized_graph_def = graph_util.remove_training_nodes(optimized_graph_def)
optimized_graph_def = fold_batch_norms(optimized_graph_def)
optimized_graph_def = fuse_resize_and_conv(optimized_graph_def)
optimized_graph_def = fuse_resize_and_conv(optimized_graph_def,
output_node_names)
ensure_graph_is_valid(optimized_graph_def)
return optimized_graph_def
@ -336,7 +338,7 @@ def fold_batch_norms(input_graph_def):
return result_graph_def
def fuse_resize_and_conv(input_graph_def):
def fuse_resize_and_conv(input_graph_def, output_node_names):
"""Merges preceding resize and mirror pad ops into a specialized convolution.
There's a common pattern of enlarging the input to a convolution using a
@ -361,7 +363,14 @@ def fuse_resize_and_conv(input_graph_def):
else:
raise ValueError("Duplicate node names detected for ", node.name)
nodes_to_skip = {}
node_reference_count = collections.defaultdict(int)
for node in input_graph_def.node:
for input_name in node.input:
stripped_name = node_name_from_input(input_name)
node_reference_count[stripped_name] += 1
for output_name in output_node_names:
node_reference_count[output_name] += 1
new_ops = []
for node in input_graph_def.node:
@ -373,20 +382,31 @@ def fuse_resize_and_conv(input_graph_def):
if input_op.op == "MirrorPad":
mirror_pad_op = input_op
resize_op = node_from_map(input_node_map, mirror_pad_op.input[0])
if resize_op.op != "ResizeBilinear":
resize_op = None
else:
mirror_pad_op = None
if input_op.op == "ResizeBilinear":
resize_op = input_op
else:
resize_op = None
if resize_op.op != "ResizeBilinear":
# There are no ops to be fused into the conv, so skip replacing this one.
if not mirror_pad_op and not resize_op:
continue
nodes_to_skip[conv_op.name] = True
# We're replacing this node, so make sure the old one is removed.
node_reference_count[conv_op.name] = 0
if mirror_pad_op:
nodes_to_skip[mirror_pad_op.name] = True
nodes_to_skip[resize_op.name] = True
node_reference_count[mirror_pad_op.name] -= 1
if resize_op:
node_reference_count[resize_op.name] -= 1
fused_conv_op = tf.NodeDef()
if resize_op:
fused_conv_op.op = "FusedResizeAndPadConv2D"
else:
fused_conv_op.op = "FusedPadConv2D"
fused_conv_op.name = conv_op.name
if mirror_pad_op:
mirror_paddings_name = mirror_pad_op.input[1]
@ -405,11 +425,15 @@ def fuse_resize_and_conv(input_graph_def):
new_ops.extend([paddings_op])
mirror_paddings_name = paddings_op.name
mirror_paddings_mode = tf.AttrValue(s=b"REFLECT")
if resize_op:
fused_conv_op.input.extend([resize_op.input[0], resize_op.input[1],
mirror_paddings_name, conv_op.input[1]])
fused_conv_op.attr["T"].CopyFrom(conv_op.attr["T"])
fused_conv_op.attr["resize_align_corners"].CopyFrom(
resize_op.attr["align_corners"])
else:
fused_conv_op.input.extend([mirror_pad_op.input[0], mirror_paddings_name,
conv_op.input[1]])
fused_conv_op.attr["T"].CopyFrom(conv_op.attr["T"])
fused_conv_op.attr["mode"].CopyFrom(mirror_paddings_mode)
fused_conv_op.attr["strides"].CopyFrom(conv_op.attr["strides"])
fused_conv_op.attr["padding"].CopyFrom(conv_op.attr["padding"])
@ -417,7 +441,7 @@ def fuse_resize_and_conv(input_graph_def):
result_graph_def = tf.GraphDef()
for node in input_graph_def.node:
if node.name in nodes_to_skip:
if node_reference_count[node.name] < 1:
continue
new_node = tf.NodeDef()
new_node.CopyFrom(node)

33
tensorflow/python/tools/optimize_for_inference_test.py
View File

@ -54,6 +54,7 @@ class OptimizeForInferenceTest(tf.test.TestCase):
shape=shape)))
def testOptimizeForInference(self):
self.maxDiff = 1000
unused_constant_name = "unused_constant"
unconnected_add_name = "unconnected_add"
a_constant_name = "a_constant"
@ -183,7 +184,7 @@ class OptimizeForInferenceTest(tf.test.TestCase):
original_graph_def = sess.graph_def
original_result = sess.run(["output:0"])
optimized_graph_def = optimize_for_inference_lib.fuse_resize_and_conv(
original_graph_def)
original_graph_def, ["output"])
with self.test_session() as sess:
_ = tf.import_graph_def(optimized_graph_def, input_map={},
@ -212,7 +213,7 @@ class OptimizeForInferenceTest(tf.test.TestCase):
original_graph_def = sess.graph_def
original_result = sess.run(["output:0"])
optimized_graph_def = optimize_for_inference_lib.fuse_resize_and_conv(
original_graph_def)
original_graph_def, ["output"])
with self.test_session() as sess:
_ = tf.import_graph_def(optimized_graph_def, input_map={},
@ -225,6 +226,34 @@ class OptimizeForInferenceTest(tf.test.TestCase):
self.assertNotEqual("Conv2D", node.op)
self.assertNotEqual("ResizeBilinear", node.op)
def testFusePadAndConv(self):
with self.test_session() as sess:
inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6]
input_op = tf.constant(np.array(inputs), shape=[1, 2, 3, 2],
dtype=tf.float32)
pad_op = tf.pad(input_op, [[0, 0], [1, 1], [2, 2], [0, 0]],
mode="REFLECT")
weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
weights_op = tf.constant(np.array(weights), shape=[1, 2, 2, 2],
dtype=tf.float32)
tf.nn.conv2d(pad_op, weights_op, [1, 1, 1, 1],
padding="VALID", name="output")
original_graph_def = sess.graph_def
original_result = sess.run(["output:0"])
optimized_graph_def = optimize_for_inference_lib.fuse_resize_and_conv(
original_graph_def, ["output"])
with self.test_session() as sess:
_ = tf.import_graph_def(optimized_graph_def, input_map={},
name="optimized")
optimized_result = sess.run(["optimized/output:0"])
self.assertAllClose(original_result, optimized_result)
for node in optimized_graph_def.node:
self.assertNotEqual("Conv2D", node.op)
self.assertNotEqual("MirrorPad", node.op)
if __name__ == "__main__":
tf.test.main()

6
tensorflow/tools/ci_build/ci_build.sh
View File

@ -67,7 +67,6 @@ if [ "$#" -lt 1 ] || [ ! -e "${SCRIPT_DIR}/Dockerfile.${CONTAINER_TYPE}" ]; then
exit 1
fi
# Optional arguments - environment variables. For example:
# CI_DOCKER_EXTRA_PARAMS='-it --rm' CI_COMMAND_PREFIX='' tensorflow/tools/ci_build/ci_build.sh CPU /bin/bash
CI_TENSORFLOW_SUBMODULE_PATH="${CI_TENSORFLOW_SUBMODULE_PATH:-.}"
@ -79,6 +78,11 @@ if [[ ! -z "${TF_BUILD_DISABLE_GCP}" ]] &&
CI_COMMAND_PREFIX+=("--disable-gcp")
fi
# cmake (CPU) builds do not require configuration.
if [[ "${CONTAINER_TYPE}" == "cmake" ]]; then
CI_COMMAND_PREFIX=""
fi
# Helper function to traverse directories up until given file is found.
function upsearch () {
test / == "$PWD" && return || \

2
tensorflow/tools/ci_build/install/install_proto3.sh
View File

@ -19,7 +19,7 @@ set -e
# Install protobuf3.
# Select protobuf version.
PROTOBUF_VERSION="3.0.0"
PROTOBUF_VERSION="3.1.0"
PROTOBUF_URL="https://github.com/google/protobuf/releases/download/v${PROTOBUF_VERSION}/protoc-${PROTOBUF_VERSION}-linux-x86_64.zip"
PROTOBUF_ZIP=$(basename "${PROTOBUF_URL}")

2
tensorflow/tools/pip_package/setup.py
View File

@ -31,7 +31,7 @@ _VERSION = '0.10.0'
REQUIRED_PACKAGES = [
'numpy >= 1.11.0',
'six >= 1.10.0',
'protobuf == 3.0.0',
'protobuf == 3.1.0',
]
# python3 requires wheel 0.26

24
tensorflow/workspace.bzl
View File

@ -11,11 +11,17 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
if tf_repo_name:
print("tf_repo_name was specified to tf_workspace but is no longer used and will be removed in the future.")
# These lines need to be changed when updating Eigen. They are parsed from
# this file by the cmake and make builds to determine the eigen version and
# hash.
eigen_version = "c78d757b69d3"
eigen_sha256 = "dfb650e20a0dee6172dcc99796210a07e40af61348497503b42dc12935b4e6f5"
native.new_http_archive(
name = "eigen_archive",
url = "http://bitbucket.org/eigen/eigen/get/c78d757b69d3.tar.gz",
sha256 = "dfb650e20a0dee6172dcc99796210a07e40af61348497503b42dc12935b4e6f5",
strip_prefix = "eigen-eigen-c78d757b69d3",
url = "http://bitbucket.org/eigen/eigen/get/" + eigen_version + ".tar.gz",
sha256 = eigen_sha256,
strip_prefix = "eigen-eigen-" + eigen_version,
build_file = str(Label("//:eigen.BUILD")),
)
@ -35,9 +41,9 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
native.new_http_archive(
name = "farmhash_archive",
url = "http://github.com/google/farmhash/archive/34c13ddfab0e35422f4c3979f360635a8c050260.zip",
sha256 = "e3d37a59101f38fd58fb799ed404d630f0eee18bfc2a2433910977cc8fea9c28",
strip_prefix = "farmhash-34c13ddfab0e35422f4c3979f360635a8c050260/src",
url = "http://github.com/google/farmhash/archive/71a777924015693c69bc3c8c6492fb8d5372c636.zip",
sha256 = "99190108fb96a5e38e183f6a23fb7742948214fc96a746a50c79eb09a255a298",
strip_prefix = "farmhash-71a777924015693c69bc3c8c6492fb8d5372c636/src",
build_file = str(Label("//:farmhash.BUILD")),
)
@ -92,9 +98,9 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
native.http_archive(
name = "protobuf",
url = "http://github.com/google/protobuf/archive/v3.0.2.tar.gz",
sha256 = "b700647e11556b643ccddffd1f41d8cb7704ed02090af54cc517d44d912d11c1",
strip_prefix = "protobuf-3.0.2",
url = "http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
sha256 = "0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
strip_prefix = "protobuf-3.1.0",
)
native.new_http_archive(

10
third_party/gpus/crosstool/BUILD.tpl vendored
View File

@ -2,10 +2,12 @@ licenses(["restricted"])
package(default_visibility = ["//visibility:public"])
filegroup(
name = "crosstool",
srcs = ["CROSSTOOL"],
output_licenses = ["unencumbered"],
cc_toolchain_suite(
name = "toolchain",
toolchains = {
"local|compiler": ":cc-compiler-local",
"darwin|compiler": ":cc-compiler-darwin",
},
)
cc_toolchain(

33
third_party/gpus/cuda_configure.bzl vendored
View File

@ -331,6 +331,33 @@ def _file(repository_ctx, label):
{})
_DUMMY_CROSSTOOL_BZL_FILE = """
def error_gpu_disabled():
fail("ERROR: Building with --config=cuda but TensorFlow is not configured " +
"to build with GPU support. Please re-run ./configure and enter 'Y' " +
"at the prompt to build with GPU support.")
native.genrule(
name = "error_gen_crosstool",
outs = ["CROSSTOOL"],
cmd = "echo 'Should not be run.' && exit 1",
)
native.filegroup(
name = "crosstool",
srcs = [":CROSSTOOL"],
output_licenses = ["unencumbered"],
)
"""
_DUMMY_CROSSTOOL_BUILD_FILE = """
load("//crosstool:error_gpu_disabled.bzl", "error_gpu_disabled")
error_gpu_disabled()
"""
def _create_dummy_repository(repository_ctx):
cpu_value = _cpu_value(repository_ctx)
symlink_files = _cuda_symlink_files(cpu_value, _DEFAULT_CUDA_VERSION,
@ -371,6 +398,12 @@ def _create_dummy_repository(repository_ctx):
for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES]),
})
# If cuda_configure is not configured to build with GPU support, and the user
# attempts to build with --config=cuda, add a dummy build rule to intercept
# this and fail with an actionable error message.
repository_ctx.file("crosstool/error_gpu_disabled.bzl",
_DUMMY_CROSSTOOL_BZL_FILE)
repository_ctx.file("crosstool/BUILD", _DUMMY_CROSSTOOL_BUILD_FILE)
def _symlink_dir(repository_ctx, src_dir, dest_dir):
"""Symlinks all the files in a directory.

2
tools/bazel.rc.template
View File

@ -1,4 +1,4 @@
build:cuda --crosstool_top=@local_config_cuda//crosstool
build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
build:cuda --define=using_cuda=true --define=using_cuda_nvcc=true
build --force_python=py$PYTHON_MAJOR_VERSION