Skip to content

P
Paddle-Lite

PaddlePaddle / Paddle-Lite

通知 338
Star 4
Fork 1
P
Paddle-Lite
提交 45de1e84 编写于 作者: 朔-望's avatar 朔-望 提交者： GitHub
浏览文件
操作

Merge pull request #198 from allonli/develop 

Add clang-tidy and clang-format hook
上级 9b7798f9 63dbcb6f
隐藏空白更改
内联 并排
Showing with 19033 addition and 19606 deletion
.clang-format
浏览文件 @ 45de1e84
...@@ -3,5 +3,4 @@ Language: Cpp ...@@ -3,5 +3,4 @@ Language: Cpp
BasedOnStyle: LLVM BasedOnStyle: LLVM
Standard: Cpp11 Standard: Cpp11
IndentWidth: 4 IndentWidth: 4
NamespaceIndentation: All
... ...
.clang-tidy 0 → 100644
浏览文件 @ 45de1e84
Checks: >
*
-android-*
-bugprone-bool-pointer-implicit-conversion
-cert-env33-c
-cert-dcl50-cpp
-cert-dcl59-cpp
-cppcoreguidelines-*
-fuchsia-*
-google-*
google-default-arguments
google-explicit-constructor
google-runtime-member-string-references
google-runtime-operator
-hicpp-braces-around-statements
-hicpp-named-parameter
-hicpp-no-array-decay
-hicpp-no-assembler
-hicpp-no-malloc
-hicpp-function-size
-hicpp-special-member-functions
-hicpp-vararg
-llvm-*
-objc-*
-readability-else-after-return
-readability-implicit-bool-conversion
-readability-named-parameter
-readability-simplify-boolean-expr
-readability-braces-around-statements
-readability-identifier-naming
-readability-function-size
-readability-redundant-member-init
-misc-bool-pointer-implicit-conversion
-misc-definitions-in-headers
-misc-unused-alias-decls
-misc-unused-parameters
-misc-unused-using-decls
-modernize-use-using
-modernize-use-default-member-init
-clang-diagnostic-*
-clang-analyzer-*
WarningsAsErrors: ''
HeaderFilterRegex: ''
AnalyzeTemporaryDtors: false
FormatStyle: none
User: allonli
CheckOptions:
- key: google-readability-braces-around-statements.ShortStatementLines
value: '1'
- key: google-readability-function-size.StatementThreshold
value: '800'
- key: google-readability-namespace-comments.ShortNamespaceLines
value: '10'
- key: google-readability-namespace-comments.SpacesBeforeComments
value: '2'
- key: modernize-loop-convert.MaxCopySize
value: '16'
- key: modernize-loop-convert.MinConfidence
value: reasonable
- key: modernize-loop-convert.NamingStyle
value: CamelCase
- key: modernize-pass-by-value.IncludeStyle
value: llvm
- key: modernize-replace-auto-ptr.IncludeStyle
value: llvm
- key: modernize-use-nullptr.NullMacros
value: 'NULL'
.pre-commit-config.yaml
浏览文件 @ 45de1e84
...@@ -22,13 +22,22 @@ repos: ...@@ -22,13 +22,22 @@ repos:
- repo: local - repo: local
hooks: hooks:
- id: clang-format-with-version-check - id: clang-format
name: clang-format name: clang-format
description: Format files with ClangFormat. description: Format files with ClangFormat.
entry: bash ./tools/pre-commit.hooks/.clang_format.hook -i entry: bash ./tools/pre-commit.hooks/.clang-format.hook -i
language: system language: system
files: (src).*\.(c|cc|cxx|cpp|h|hpp|hxx)$ files: \.(c|cc|cxx|cpp|h|hpp|hxx)$
- repo: local
hooks:
- id: clang-tidy
name: clang-tidy
description: Check C++ code style using clang-tidy.
entry: bash ./tools/pre-commit.hooks/.clang-tidy.hook -i
language: system
files: (src).*\.(c|cc|cxx|cpp|h|hpp|hxx)$
#
#- repo: local #- repo: local
# hooks: # hooks:
# - id: copyright_checker # - id: copyright_checker
......
.travis.yml
浏览文件 @ 45de1e84
...@@ -6,26 +6,54 @@ dist: trusty ...@@ -6,26 +6,54 @@ dist: trusty
os: os:
- linux - linux
env: env:
- JOB=check_style global:
- CMAKE_URL=https://cmake.org/files/v3.11/cmake-3.11.1-Linux-x86_64.tar.gz
addons: addons:
apt: apt:
sources:
- llvm-toolchain-trusty-6.0
- ubuntu-toolchain-r-test
packages: packages:
- git - git
- python - python
- python-pip - python-pip
- python2.7-dev - python2.7-dev
- clang-format-3.8 - libc6-i386
- clang-6.0
- libclang-6.0
- llvm-6.0
- llvm-6.0-dev
- curl
compiler:
- clang
before_install: before_install:
- sudo pip install -U virtualenv pre-commit pip - sudo pip install -U virtualenv pre-commit pip
# Download and install recent cmake
- |
if [[ ${TRAVIS_OS_NAME} == "linux" ]]; then
CMAKE_URL=${CMAKE_URL}
mkdir -p ${DEPS_DIR}/cmake
travis_retry wget --no-check-certificate --quiet -O - ${CMAKE_URL} | tar --strip-components=1 -xz -C ${DEPS_DIR}/cmake
export PATH=${DEPS_DIR}/cmake/bin:${PATH}
fi
script: #install:
- if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi # - if [ "$CXX" = "g++" ]; then export CXX="g++-5" CC="gcc-5"; fi
# - if [ "$CXX" = "clang++" ]; then export CXX="clang++-6.0" CC="clang-6.0"; fi
before_script:
- | - |
echo "cmake generate compile_commands.json for clang-tidy"
ls -l -a
clang-tidy -version
clang-format -version
script:
- |
function timeout() { perl -e 'alarm shift; exec @ARGV' "$@"; } function timeout() { perl -e 'alarm shift; exec @ARGV' "$@"; }
- | - |
timeout 600 .travis/${JOB}.sh # 10min timeout timeout 600 .travis/pre-commit-job.sh # 10min timeout
RESULT=$?; if [ $RESULT -eq 0 ] || [ $RESULT -eq 142 ]; then true; else exit 1; fi; RESULT=$?; if [ $RESULT -eq 0 ] || [ $RESULT -eq 142 ]; then true; else exit 1; fi;
notifications: notifications:
......
.travis/check_style.sh → .travis/pre-commit-job.sh
浏览文件 @ 45de1e84
#!/bin/bash #!/bin/bash
function abort(){ function abort(){
echo "Your change doesn't follow PaddlePaddle's code style" 1>&2 echo "Your change doesn't follow Paddle-Moible's code style" 1>&2
echo "Please use pre-commit to auto-format your code." 1>&2 echo "Please use pre-commit to auto-format your code." 1>&2
exit 1 exit 1
} }
...@@ -11,7 +11,6 @@ cd `dirname $0` ...@@ -11,7 +11,6 @@ cd `dirname $0`
cd .. cd ..
export PATH=/usr/bin:$PATH export PATH=/usr/bin:$PATH
pre-commit install pre-commit install
clang-format --version
if ! pre-commit run -a ; then if ! pre-commit run -a ; then
ls -lh ls -lh
......
CMakeLists.txt
浏览文件 @ 45de1e84
...@@ -5,7 +5,8 @@ add_definitions(-DPADDLE_MOBILE_DEBUG="true") ...@@ -5,7 +5,8 @@ add_definitions(-DPADDLE_MOBILE_DEBUG="true")
set(CMAKE_BUILD_TYPE RelWithDebInfo) set(CMAKE_BUILD_TYPE RelWithDebInfo)
set(CMAKE_VERBOSE_MAKEFILE on) set(CMAKE_VERBOSE_MAKEFILE ON)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY build) set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY build)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY build) set(CMAKE_LIBRARY_OUTPUT_DIRECTORY build)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY build) set(CMAKE_RUNTIME_OUTPUT_DIRECTORY build)
......
build.sh
浏览文件 @ 45de1e84
#!/bin/bash #!/bin/bash
build_for_linux() { build_for_linux() {
echo "linux" if [ ! `which brew` ]; then
echo "building failed! homebrew not found, please install homebrew."
return
fi
if [ ! `which cmake` ]; then
echo "installing cmake."
brew install cmake
if [ ! $? ]; then
echo "cmake install failed."
return
fi
fi
PLATFORM="x86"
MODE="Release"
CXX_FLAGS="-std=c++11 -O3 -s"
BUILD_DIR=build/release/"${PLATFORM}"
mkdir -p ${BUILD_DIR}/build
mkdir -p ${BUILD_DIR}/test
cp -r test/models ${BUILD_DIR}/test/models
cmake . \
-B"${BUILD_DIR}" \
-DCMAKE_BUILD_TYPE="${MODE}" \
-DCMAKE_CXX_FLAGS="${CXX_FLAGS}" \
-DIS_MAC=true
cd ${BUILD_DIR}
make -j 8
} }
build_for_mac() { build_for_mac() {
......
cmake-build-release/compile_commands.json 0 → 100644
浏览文件 @ 45de1e84
[
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/ddim.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/ddim.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/ddim.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/lod_tensor.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/lod_tensor.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/lod_tensor.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/scope.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/scope.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/scope.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/tensor_util.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/tensor_util.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/tensor_util.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/memory/t_malloc.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/memory/t_malloc.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/memory/t_malloc.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/math/im2col.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/im2col.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/im2col.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/math/math_function.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/math_function.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/math_function.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/math/vol2col.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/vol2col.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/vol2col.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/common/variant.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/common/variant.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/common/variant.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/attribute.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/attribute.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/attribute.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/block_desc.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/block_desc.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/block_desc.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/data_transform.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/data_transform.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/data_transform.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/executor.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/executor.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/executor.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/framework.pb.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/framework.pb.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/framework.pb.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/op_desc.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/op_desc.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/op_desc.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/operator.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/operator.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/operator.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/paddle_mobile_object.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/paddle_mobile_object.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/paddle_mobile_object.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/program.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/program.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/program.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/program_desc.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/program_desc.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/program_desc.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/framework/var_desc.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/var_desc.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/var_desc.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/io.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/io.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/io.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/conv_op.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/conv_op.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/conv_op.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/elementwise_add_op.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/elementwise_add_op.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/elementwise_add_op.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/kernel/arm/conv_kernel.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/conv_kernel.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/conv_kernel.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/kernel/arm/elementwise_add_kernel.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/elementwise_add_kernel.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/elementwise_add_kernel.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/kernel/arm/mul_kernel.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/mul_kernel.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/mul_kernel.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/kernel/fpga/conv_kernel.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/fpga/conv_kernel.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/fpga/conv_kernel.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/mul_op.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/mul_op.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/mul_op.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-static.dir/src/operators/op_param.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/op_param.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/op_param.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/ddim.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/ddim.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/ddim.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/lod_tensor.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/lod_tensor.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/lod_tensor.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/scope.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/scope.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/scope.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/tensor_util.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/tensor_util.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/tensor_util.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/memory/t_malloc.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/memory/t_malloc.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/memory/t_malloc.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/math/im2col.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/im2col.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/im2col.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/math/math_function.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/math_function.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/math_function.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/math/vol2col.cc.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/vol2col.cc",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/math/vol2col.cc"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/common/variant.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/common/variant.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/common/variant.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/attribute.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/attribute.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/attribute.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/block_desc.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/block_desc.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/block_desc.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/data_transform.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/data_transform.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/data_transform.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/executor.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/executor.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/executor.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/framework.pb.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/framework.pb.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/framework.pb.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/op_desc.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/op_desc.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/op_desc.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/operator.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/operator.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/operator.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/paddle_mobile_object.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/paddle_mobile_object.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/paddle_mobile_object.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/program.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/program.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/program.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/program_desc.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/program_desc.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/program_desc.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/framework/var_desc.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/framework/var_desc.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/framework/var_desc.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/io.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/io.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/io.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/conv_op.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/conv_op.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/conv_op.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/elementwise_add_op.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/elementwise_add_op.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/elementwise_add_op.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/kernel/arm/conv_kernel.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/conv_kernel.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/conv_kernel.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/kernel/arm/elementwise_add_kernel.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/elementwise_add_kernel.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/elementwise_add_kernel.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/kernel/arm/mul_kernel.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/mul_kernel.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/arm/mul_kernel.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/kernel/fpga/conv_kernel.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/fpga/conv_kernel.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/kernel/fpga/conv_kernel.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/mul_op.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/mul_op.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/mul_op.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -Dpaddle_mobile_EXPORTS -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -fPIC -std=c++11 -o CMakeFiles/paddle-mobile.dir/src/operators/op_param.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/src/operators/op_param.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/src/operators/op_param.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release/test",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/test-log.dir/common/test_log.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/test/common/test_log.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/test/common/test_log.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release/test",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/test-conv-op.dir/operators/test_cov_op.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/test/operators/test_cov_op.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/test/operators/test_cov_op.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release/test",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/test-load.dir/framework/test_load.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/test/framework/test_load.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/test/framework/test_load.cpp"
},
{
"directory": "/Users/allonli/Documents/workspace/paddle-mobile/cmake-build-release/test",
"command": "/Library/Developer/CommandLineTools/usr/bin/c++ -DPADDLE_MOBILE_DEBUG=\\\"true\\\" -I/Users/allonli/Documents/workspace/paddle-mobile/src -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/openblas/include -I/Users/allonli/Documents/workspace/paddle-mobile/third-party/protobuf/include -O2 -g -DNDEBUG -std=c++11 -o CMakeFiles/paddle-mobile-test.dir/main.cpp.o -c /Users/allonli/Documents/workspace/paddle-mobile/test/main.cpp",
"file": "/Users/allonli/Documents/workspace/paddle-mobile/test/main.cpp"
}
]
\ No newline at end of file
src/common/log.h
浏览文件 @ 45de1e84
...@@ -27,146 +27,145 @@ SOFTWARE. ...@@ -27,146 +27,145 @@ SOFTWARE.
namespace paddle_mobile { namespace paddle_mobile {
enum LogLevel { enum LogLevel {
kNO_LOG, kNO_LOG,
kLOG_ERROR, kLOG_ERROR,
kLOG_WARNING, kLOG_WARNING,
kLOG_INFO, kLOG_INFO,
kLOG_DEBUG, kLOG_DEBUG,
kLOG_DEBUG1, kLOG_DEBUG1,
kLOG_DEBUG2, kLOG_DEBUG2,
kLOG_DEBUG3, kLOG_DEBUG3,
kLOG_DEBUG4 kLOG_DEBUG4
}; };
// log level // log level
static LogLevel log_level = kLOG_DEBUG4; static LogLevel log_level = kLOG_DEBUG4;
static std::vector<std::string> logs{"NO", "ERROR ", "WARNING", static std::vector<std::string> logs{"NO", "ERROR ", "WARNING",
"INFO ", "DEBUG ", "DEBUG1 ", "INFO ", "DEBUG ", "DEBUG1 ",
"DEBUG2 ", "DEBUG3 ", "DEBUG4 "}; "DEBUG2 ", "DEBUG3 ", "DEBUG4 "};
struct ToLog; struct ToLog;
struct Print; struct Print;
struct Print { struct Print {
friend struct ToLog; friend struct ToLog;
template <typename T> Print &operator<<(T const &value) { template <typename T> Print &operator<<(T const &value) {
buffer_ << value; buffer_ << value;
return *this; return *this;
}
private:
void print(LogLevel level) {
buffer_ << std::endl;
if (level == kLOG_ERROR) {
std::cerr << buffer_.str();
} else {
std::cout << buffer_.str();
} }
}
private: std::ostringstream buffer_;
void print(LogLevel level) { };
buffer_ << std::endl;
if (level == kLOG_ERROR) { struct ToLog {
std::cerr << buffer_.str(); ToLog(LogLevel level = kLOG_DEBUG, const std::string &info = "")
} else { : level_(level) {
std::cout << buffer_.str(); unsigned blanks =
} (unsigned)(level > kLOG_DEBUG ? (level - kLOG_DEBUG) * 4 : 1);
} printer_ << logs[level] << " " << info << ":"
std::ostringstream buffer_; << std::string(blanks, ' ');
}; }
struct ToLog { template <typename T> ToLog &operator<<(T const &value) {
ToLog(LogLevel level = kLOG_DEBUG, const std::string &info = "") printer_ << value;
: level_(level) { return *this;
unsigned blanks = }
(unsigned)(level > kLOG_DEBUG ? (level - kLOG_DEBUG) * 4 : 1);
printer_ << logs[level] << " " << info << ":" ~ToLog() { printer_.print(level_); }
<< std::string(blanks, ' ');
} private:
LogLevel level_;
template <typename T> ToLog &operator<<(T const &value) { Print printer_;
printer_ << value; };
return *this;
}
~ToLog() { printer_.print(level_); }
private:
LogLevel level_;
Print printer_;
};
#define LOG(level) \ #define LOG(level) \
if (level > paddle_mobile::log_level) { \ if (level > paddle_mobile::log_level) { \
} else \ } else \
paddle_mobile::ToLog( \ paddle_mobile::ToLog( \
level, \ level, (std::stringstream() \
(std::stringstream() \ << "[file: " \
<< "[file: " \ << (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) : __FILE__) \ : __FILE__) \
<< "] [line: " << __LINE__ << "] ") \ << "] [line: " << __LINE__ << "] ") \
.str()) .str())
#define DLOG \ #define DLOG \
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \ if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \ } else \
paddle_mobile::ToLog( \ paddle_mobile::ToLog( \
paddle_mobile::kLOG_DEBUG, \ paddle_mobile::kLOG_DEBUG, \
(std::stringstream() \ (std::stringstream() \
<< "[file: " \ << "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) : __FILE__) \ << (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) \
<< "] [line: " << __LINE__ << "] ") \ : __FILE__) \
.str()) << "] [line: " << __LINE__ << "] ") \
} .str())
} // namespace paddle_mobile
#define LOGF(level, format, ...) \ #define LOGF(level, format, ...) \
if (level > paddle_mobile::log_level) { \ if (level > paddle_mobile::log_level) { \
} else \ } else \
printf(format, ##__VA_ARGS__) printf(format, ##__VA_ARGS__)
#define DLOGF(format, ...) \ #define DLOGF(format, ...) \
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \ if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \ } else \
printf(format, ##__VA_ARGS__) printf(format, ##__VA_ARGS__)
#else #else
namespace paddle_mobile { namespace paddle_mobile {
enum LogLevel { enum LogLevel {
kNO_LOG, kNO_LOG,
kLOG_ERROR, kLOG_ERROR,
kLOG_WARNING, kLOG_WARNING,
kLOG_INFO, kLOG_INFO,
kLOG_DEBUG, kLOG_DEBUG,
kLOG_DEBUG1, kLOG_DEBUG1,
kLOG_DEBUG2, kLOG_DEBUG2,
kLOG_DEBUG3, kLOG_DEBUG3,
kLOG_DEBUG4 kLOG_DEBUG4
}; };
struct ToLog; struct ToLog;
struct Print { struct Print {
friend struct ToLog; friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {} template <typename T> Print &operator<<(T const &value) {}
private: private:
}; };
struct ToLog { struct ToLog {
ToLog(LogLevel level) {} ToLog(LogLevel level) {}
template <typename T> ToLog &operator<<(T const &value) { template <typename T> ToLog &operator<<(T const &value) { return *this; }
return *this; };
}
};
#define LOG(level) \ #define LOG(level) \
if (true) { \ if (true) { \
} else \ } else \
paddle_mobile::ToLog(level) paddle_mobile::ToLog(level)
#define DLOG \ #define DLOG \
if (true) { \ if (true) { \
} else \ } else \
paddle_mobile::ToLog(paddle_mobile::kLOG_DEBUG) paddle_mobile::ToLog(paddle_mobile::kLOG_DEBUG)
#define LOGF(level, format, ...) #define LOGF(level, format, ...)
#define DLOGF(format, ...) #define DLOGF(format, ...)
} } // namespace paddle_mobile
#endif #endif
src/common/type_define.h
浏览文件 @ 45de1e84
...@@ -23,31 +23,30 @@ SOFTWARE. ...@@ -23,31 +23,30 @@ SOFTWARE.
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename Dtype> class OperatorBase; template <typename Dtype> class OperatorBase;
class OpDesc; class OpDesc;
class BlockDesc; class BlockDesc;
class InferShapeContext; class InferShapeContext;
} } // namespace framework
using VariableNameMap = std::map<std::string, std::vector<std::string>>; using VariableNameMap = std::map<std::string, std::vector<std::string>>;
template <typename Dtype> template <typename Dtype>
using OpCreator = std::function<framework::OperatorBase<Dtype> *( using OpCreator = std::function<framework::OperatorBase<Dtype> *(
const std::string & /*type*/, const VariableNameMap & /*inputs*/, const std::string & /*type*/, const VariableNameMap & /*inputs*/,
const VariableNameMap & /*outputs*/, const VariableNameMap & /*outputs*/,
const framework::AttributeMap & /*attrs*/)>; const framework::AttributeMap & /*attrs*/)>;
using GradOpMakerFN = using GradOpMakerFN =
std::function<std::vector<std::unique_ptr<framework::OpDesc>>( std::function<std::vector<std::unique_ptr<framework::OpDesc>>(
const framework::OpDesc &, const framework::OpDesc &,
const std::unordered_set<std::string> & /*no_grad_set*/, const std::unordered_set<std::string> & /*no_grad_set*/,
std::unordered_map<std::string, std::string> * /*grad_to_var*/, std::unordered_map<std::string, std::string> * /*grad_to_var*/,
const std::vector<framework::BlockDesc *> &grad_block)>; const std::vector<framework::BlockDesc *> &grad_block)>;
using InferVarTypeFN = using InferVarTypeFN = std::function<void(const framework::OpDesc & /*op_desc*/,
std::function<void(const framework::OpDesc & /*op_desc*/, framework::BlockDesc * /*block*/)>;
framework::BlockDesc * /*block*/)>;
using InferShapeFN = std::function<void(framework::InferShapeContext *)>;
using InferShapeFN = std::function<void(framework::InferShapeContext *)>; }; // namespace paddle_mobile
};
src/common/types.h
浏览文件 @ 45de1e84
...@@ -19,45 +19,45 @@ SOFTWARE. ...@@ -19,45 +19,45 @@ SOFTWARE.
#pragma once; #pragma once;
namespace paddle_mobile { namespace paddle_mobile {
enum class Precision : int { FP32 = 0 }; enum class Precision : int { FP32 = 0 };
//! device type //! device type
enum DeviceTypeEnum { kINVALID = -1, kCPU = 0, kFPGA = 1, kGPU_MALI = 2 }; enum DeviceTypeEnum { kINVALID = -1, kCPU = 0, kFPGA = 1, kGPU_MALI = 2 };
template <DeviceTypeEnum T> struct DeviceType {}; template <DeviceTypeEnum T> struct DeviceType {};
typedef DeviceType<kCPU> CPU; typedef DeviceType<kCPU> CPU;
typedef DeviceType<kFPGA> FPGA; typedef DeviceType<kFPGA> FPGA;
typedef DeviceType<kGPU_MALI> GPU_MALI; typedef DeviceType<kGPU_MALI> GPU_MALI;
//! data type //! data type
enum DataType { enum DataType {
PM_INVALID = -1, PM_INVALID = -1,
PM_HALF = 0, PM_HALF = 0,
PM_FLOAT = 1, PM_FLOAT = 1,
PM_DOUBLE = 2, PM_DOUBLE = 2,
PM_INT8 = 3, PM_INT8 = 3,
PM_INT16 = 4, PM_INT16 = 4,
PM_INT32 = 5, PM_INT32 = 5,
PM_INT64 = 6, PM_INT64 = 6,
PM_UINT8 = 7, PM_UINT8 = 7,
PM_UINT16 = 8, PM_UINT16 = 8,
PM_UINT32 = 9, PM_UINT32 = 9,
PM_STRING = 10, PM_STRING = 10,
PM_BOOL = 11, PM_BOOL = 11,
PM_SHAPE = 12, PM_SHAPE = 12,
PM_TENSOR = 13 PM_TENSOR = 13
}; };
//! //!
enum PMStatus { enum PMStatus {
PMSuccess = 0xFF, /*!< No errors */ PMSuccess = 0xFF, /*!< No errors */
PMNotInitialized = 0x01, /*!< Data not initialized. */ PMNotInitialized = 0x01, /*!< Data not initialized. */
PMInvalidValue = 0x02, /*!< Incorrect variable value. */ PMInvalidValue = 0x02, /*!< Incorrect variable value. */
PMMemAllocFailed = 0x03, /*!< Memory allocation error. */ PMMemAllocFailed = 0x03, /*!< Memory allocation error. */
PMUnKownError = 0x04, /*!< Unknown error. */ PMUnKownError = 0x04, /*!< Unknown error. */
PMOutOfAuthority = 0x05, /*!< Try to modified data not your own*/ PMOutOfAuthority = 0x05, /*!< Try to modified data not your own*/
PMOutOfMem = 0x06, /*!< OOM error*/ PMOutOfMem = 0x06, /*!< OOM error*/
PMUnImplError = 0x07, /*!< Unimplement error. */ PMUnImplError = 0x07, /*!< Unimplement error. */
PMWrongDevice = 0x08 /*!< un-correct device. */ PMWrongDevice = 0x08 /*!< un-correct device. */
}; };
} } // namespace paddle_mobile
src/common/variant.cpp
浏览文件 @ 45de1e84
...@@ -15,5 +15,3 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, ...@@ -15,5 +15,3 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. SOFTWARE.
==============================================================================*/ ==============================================================================*/
#include "variant.h"
src/common/variant.h
浏览文件 @ 45de1e84
...@@ -21,79 +21,79 @@ SOFTWARE. ...@@ -21,79 +21,79 @@ SOFTWARE.
#pragma once #pragma once
namespace paddle_mobile { namespace paddle_mobile {
template <int ID, typename Type> struct IDToType { typedef Type type_t; }; template <int ID, typename Type> struct IDToType { typedef Type type_t; };
template <typename F, typename... Ts> struct VariantHelper { template <typename F, typename... Ts> struct VariantHelper {
static const size_t size = sizeof(F) > VariantHelper<Ts...>::size static const size_t size = sizeof(F) > VariantHelper<Ts...>::size
? sizeof(F) ? sizeof(F)
: VariantHelper<Ts...>::size; : VariantHelper<Ts...>::size;
inline static void Destroy(size_t id, void *data) { inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) { if (id == typeid(F).hash_code()) {
reinterpret_cast<F *>(data)->~F(); reinterpret_cast<F *>(data)->~F();
} else { } else {
VariantHelper<Ts...>::Destroy(id, data); VariantHelper<Ts...>::Destroy(id, data);
}
} }
}; }
};
template <typename F> struct VariantHelper<F> { template <typename F> struct VariantHelper<F> {
static const size_t size = sizeof(F); static const size_t size = sizeof(F);
inline static void Destroy(size_t id, void *data) { inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) { if (id == typeid(F).hash_code()) {
// reinterpret_cast<F*>(data)->~F(); // reinterpret_cast<F*>(data)->~F();
} else { } else {
// std::cout << "未匹配到 " << std::endl; // std::cout << "未匹配到 " << std::endl;
}
} }
}; }
};
template <size_t size> class RawData { template <size_t size> class RawData {
public: public:
char data[size]; char data[size];
RawData() {} RawData() {}
RawData(const RawData &raw_data) { strcpy(data, raw_data.data); } RawData(const RawData &raw_data) { strcpy(data, raw_data.data); }
// void operator=(const RawData &raw_data){ // void operator=(const RawData &raw_data){
// strcpy(data, raw_data.data); // strcpy(data, raw_data.data);
// } // }
}; };
template <typename... Ts> struct Variant { template <typename... Ts> struct Variant {
Variant(const Variant &variant) { Variant(const Variant &variant) {
// std::cout << " 赋值构造函数 " << std::endl; // std::cout << " 赋值构造函数 " << std::endl;
type_id = variant.type_id; type_id = variant.type_id;
data = variant.data; data = variant.data;
} }
Variant() : type_id(invalid_type()) {} Variant() : type_id(invalid_type()) {}
~Variant() { ~Variant() {
// helper::Destroy(type_id, &data); // helper::Destroy(type_id, &data);
} }
template <typename T, typename... Args> void Set(Args &&... args) { template <typename T, typename... Args> void Set(Args &&... args) {
helper::Destroy(type_id, &data); helper::Destroy(type_id, &data);
new (&data) T(std::forward<Args>(args)...); new (&data) T(std::forward<Args>(args)...);
type_id = typeid(T).hash_code(); type_id = typeid(T).hash_code();
} }
template <typename T> T &Get() const { template <typename T> T &Get() const {
if (type_id == typeid(T).hash_code()) { if (type_id == typeid(T).hash_code()) {
return *const_cast<T *>(reinterpret_cast<const T *>(&data)); return *const_cast<T *>(reinterpret_cast<const T *>(&data));
} else { } else {
// std::cout << " bad cast in variant " << std::endl; // std::cout << " bad cast in variant " << std::endl;
throw std::bad_cast(); throw std::bad_cast();
}
} }
}
size_t TypeId() const { return type_id; } size_t TypeId() const { return type_id; }
private: private:
static inline size_t invalid_type() { return typeid(void).hash_code(); } static inline size_t invalid_type() { return typeid(void).hash_code(); }
typedef VariantHelper<Ts...> helper; typedef VariantHelper<Ts...> helper;
size_t type_id; size_t type_id;
RawData<helper::size> data; RawData<helper::size> data;
}; };
template <typename T> struct Vistor { typedef T type_t; }; template <typename T> struct Vistor { typedef T type_t; };
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/attribute.cpp
浏览文件 @ 45de1e84
...@@ -16,8 +16,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE ...@@ -16,8 +16,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. SOFTWARE.
==============================================================================*/ ==============================================================================*/
#include "attribute.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework {} namespace framework {}
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/attribute.h
浏览文件 @ 45de1e84
...@@ -22,110 +22,108 @@ SOFTWARE. ...@@ -22,110 +22,108 @@ SOFTWARE.
#include "framework.pb.h" #include "framework.pb.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class BlockDesc; class BlockDesc;
class Attribute { class Attribute {
public: public:
static Attribute static Attribute GetAttrValue(const proto::OpDesc::Attr &attr_desc) {
GetAttrValue(const proto::OpDesc::Attr &attr_desc) { // std::cout << "begin get attr value" << std::endl;
// std::cout << "begin get attr value" << std::endl; Attribute attr;
Attribute attr; switch (attr_desc.type()) {
switch (attr_desc.type()) { case proto::AttrType::BOOLEAN: {
case proto::AttrType::BOOLEAN: { attr.Set<bool>(attr_desc.b());
attr.Set<bool>(attr_desc.b()); break;
break; }
} case proto::AttrType::INT: {
case proto::AttrType::INT: { attr.Set<int>(attr_desc.i());
attr.Set<int>(attr_desc.i()); break;
break; }
} case proto::AttrType::FLOAT: {
case proto::AttrType::FLOAT: { attr.Set<float>(attr_desc.f());
attr.Set<float>(attr_desc.f()); break;
break; }
} case proto::AttrType::STRING: {
case proto::AttrType::STRING: { attr.Set<std::string>(attr_desc.s());
attr.Set<std::string>(attr_desc.s()); break;
break; }
} case proto::AttrType::BOOLEANS: {
case proto::AttrType::BOOLEANS: { std::vector<bool> val(attr_desc.bools_size());
std::vector<bool> val(attr_desc.bools_size()); for (int i = 0; i < attr_desc.bools_size(); ++i) {
for (int i = 0; i < attr_desc.bools_size(); ++i) { val[i] = attr_desc.bools(i);
val[i] = attr_desc.bools(i);
}
attr.Set<std::vector<bool>>(val);
break;
}
case proto::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
attr.Set<std::vector<int>>(val);
break;
}
case proto::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
attr.Set<std::vector<float>>(val);
break;
}
case proto::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
attr.Set<std::vector<std::string>>(val);
break;
}
case proto::AttrType::LONG: {
attr.Set<int64_t>(attr_desc.l());
break;
}
default:
// std::cout << " not support " << std::endl;
break;
}
// std::cout << "end get attr value" << std::endl;
return attr;
} }
attr.Set<std::vector<bool>>(val);
Attribute() {} break;
template <typename T, typename... Args> }
Attribute &Set(Args &&... args) { case proto::AttrType::INTS: {
variant_.Set<T>(args...); std::vector<int> val(attr_desc.ints_size());
return *this; for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
attr.Set<std::vector<int>>(val);
break;
}
case proto::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
attr.Set<std::vector<float>>(val);
break;
}
case proto::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
} }
attr.Set<std::vector<std::string>>(val);
break;
}
case proto::AttrType::LONG: {
attr.Set<int64_t>(attr_desc.l());
break;
}
default:
// std::cout << " not support " << std::endl;
break;
}
// std::cout << "end get attr value" << std::endl;
return attr;
}
template <typename T> T &Get() const { return variant_.Get<T>(); } Attribute() {}
template <typename T, typename... Args> Attribute &Set(Args &&... args) {
variant_.Set<T>(args...);
return *this;
}
private: template <typename T> T &Get() const { return variant_.Get<T>(); }
Variant<int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>, bool,
std::vector<bool>, BlockDesc *, int64_t>
variant_;
};
using AttributeMap = std::unordered_map<std::string, Attribute>; private:
Variant<int, float, std::string, std::vector<int>, std::vector<float>,
std::vector<std::string>, bool, std::vector<bool>, BlockDesc *,
int64_t>
variant_;
};
class AttrReader { using AttributeMap = std::unordered_map<std::string, Attribute>;
public:
explicit AttrReader(const AttributeMap &attrs) : attrs_(attrs) {}
template <typename T> inline T Get(const std::string &name) const { class AttrReader {
// PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should public:
// be in explicit AttrReader(const AttributeMap &attrs) : attrs_(attrs) {}
// AttributeMap",
// name); template <typename T> inline T Get(const std::string &name) const {
return ((Attribute)attrs_.at(name)).Get<T>(); // PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should
} // be in
// AttributeMap",
// name);
return ((Attribute)attrs_.at(name)).Get<T>();
}
private: private:
const AttributeMap &attrs_; const AttributeMap &attrs_;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/block_desc.cpp
浏览文件 @ 45de1e84
...@@ -19,32 +19,32 @@ SOFTWARE. ...@@ -19,32 +19,32 @@ SOFTWARE.
#include "block_desc.h" #include "block_desc.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
std::vector<std::shared_ptr<VarDesc>> BlockDesc::Vars() const { std::vector<std::shared_ptr<VarDesc>> BlockDesc::Vars() const {
std::vector<std::shared_ptr<VarDesc>> res; std::vector<std::shared_ptr<VarDesc>> res;
for (const auto &p : vars_) { for (const auto &p : vars_) {
res.push_back(p.second); res.push_back(p.second);
} }
return res; return res;
} }
std::vector<std::shared_ptr<OpDesc>> BlockDesc::Ops() const { std::vector<std::shared_ptr<OpDesc>> BlockDesc::Ops() const {
std::vector<std::shared_ptr<OpDesc>> res; std::vector<std::shared_ptr<OpDesc>> res;
for (const auto &op : ops_) { for (const auto &op : ops_) {
res.push_back(op); res.push_back(op);
} }
return res; return res;
} }
BlockDesc::BlockDesc(const proto::BlockDesc &desc) : desc_(desc) { BlockDesc::BlockDesc(const proto::BlockDesc &desc) : desc_(desc) {
for (const proto::VarDesc &var_desc : desc_.vars()) { for (const proto::VarDesc &var_desc : desc_.vars()) {
vars_[var_desc.name()].reset(new VarDesc(var_desc)); vars_[var_desc.name()].reset(new VarDesc(var_desc));
} }
for (const proto::OpDesc &op_desc : desc_.ops()) { for (const proto::OpDesc &op_desc : desc_.ops()) {
ops_.emplace_back(new framework::OpDesc(op_desc)); ops_.emplace_back(new framework::OpDesc(op_desc));
} }
} }
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/block_desc.h
浏览文件 @ 45de1e84
...@@ -24,50 +24,47 @@ SOFTWARE. ...@@ -24,50 +24,47 @@ SOFTWARE.
#include "var_desc.h" #include "var_desc.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class BlockDesc : PaddleMobileObject { class BlockDesc : PaddleMobileObject {
public: public:
BlockDesc(const proto::BlockDesc &desc); BlockDesc(const proto::BlockDesc &desc);
const int &ID() const { return desc_.idx(); } const int &ID() const { return desc_.idx(); }
const int &Parent() const { return desc_.parent_idx(); } const int &Parent() const { return desc_.parent_idx(); }
bool operator==( bool operator==(const paddle_mobile::framework::BlockDesc &in_block) const {
const paddle_mobile::framework::BlockDesc &in_block) const { return this->ID() == in_block.ID() &&
return this->ID() == in_block.ID() && this->Parent() == in_block.Parent();
this->Parent() == in_block.Parent(); }
}
bool operator<( bool operator<(const paddle_mobile::framework::BlockDesc &in_block) const {
const paddle_mobile::framework::BlockDesc &in_block) const { return this->ID() < in_block.ID() && this->Parent() < in_block.Parent();
return this->ID() < in_block.ID() && }
this->Parent() < in_block.Parent();
}
std::vector<std::shared_ptr<VarDesc>> Vars() const; std::vector<std::shared_ptr<VarDesc>> Vars() const;
std::vector<std::shared_ptr<OpDesc>> Ops() const; std::vector<std::shared_ptr<OpDesc>> Ops() const;
private: private:
proto::BlockDesc desc_; proto::BlockDesc desc_;
std::vector<std::shared_ptr<OpDesc>> ops_; std::vector<std::shared_ptr<OpDesc>> ops_;
std::unordered_map<std::string, std::shared_ptr<VarDesc>> vars_; std::unordered_map<std::string, std::shared_ptr<VarDesc>> vars_;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
namespace std { namespace std {
template <> struct hash<paddle_mobile::framework::BlockDesc> { template <> struct hash<paddle_mobile::framework::BlockDesc> {
typedef paddle_mobile::framework::BlockDesc argument_type; typedef paddle_mobile::framework::BlockDesc argument_type;
typedef std::size_t result_type; typedef std::size_t result_type;
result_type operator()(argument_type const &s) const noexcept { result_type operator()(argument_type const &s) const noexcept {
result_type const h1(std::hash<int>{}(s.ID())); result_type const h1(std::hash<int>{}(s.ID()));
result_type const h2(std::hash<int>{}(s.ID())); result_type const h2(std::hash<int>{}(s.ID()));
return h1 ^ (h2 << 1); return h1 ^ (h2 << 1);
} }
}; };
} // namespace std } // namespace std
src/framework/data_layout.h
浏览文件 @ 45de1e84
...@@ -19,50 +19,49 @@ limitations under the License. */ ...@@ -19,50 +19,49 @@ limitations under the License. */
#include <string> #include <string>
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
enum class DataLayout { enum class DataLayout {
kNHWC = 0, kNHWC = 0,
kNCHW = 1, kNCHW = 1,
kAnyLayout = 2, kAnyLayout = 2,
}; };
inline DataLayout StringToDataLayout(const std::string &str) { inline DataLayout StringToDataLayout(const std::string &str) {
std::string s(str); std::string s(str);
for (size_t i = 0; i < s.size(); ++i) { for (size_t i = 0; i < s.size(); ++i) {
s[i] = toupper(s[i]); s[i] = toupper(s[i]);
} }
if (s == "NHWC") { if (s == "NHWC") {
return DataLayout::kNHWC; return DataLayout::kNHWC;
} else if (s == "NCHW") { } else if (s == "NCHW") {
return DataLayout::kNCHW; return DataLayout::kNCHW;
} else if (s == "ANYLAYOUT") { } else if (s == "ANYLAYOUT") {
return DataLayout::kAnyLayout; return DataLayout::kAnyLayout;
} else { } else {
// std::cout << "Unknown storage order string: %s", s; // std::cout << "Unknown storage order string: %s", s;
} }
} }
inline std::string DataLayoutToString(const DataLayout &data_layout) { inline std::string DataLayoutToString(const DataLayout &data_layout) {
switch (data_layout) { switch (data_layout) {
case DataLayout::kNHWC: case DataLayout::kNHWC:
return "NHWC"; return "NHWC";
case DataLayout::kNCHW: case DataLayout::kNCHW:
return "NCHW"; return "NCHW";
case DataLayout::kAnyLayout: case DataLayout::kAnyLayout:
return "ANY_LAYOUT"; return "ANY_LAYOUT";
default: default:
break; break;
// std::cout << "unknown DataLayou %d", data_layout; // std::cout << "unknown DataLayou %d", data_layout;
} }
} }
inline std::ostream &operator<<(std::ostream &out, inline std::ostream &operator<<(std::ostream &out, const DataLayout &l) {
const DataLayout &l) { out << DataLayoutToString(l);
out << DataLayoutToString(l); return out;
return out; }
}
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/data_transform.cpp
浏览文件 @ 45de1e84
...@@ -21,72 +21,72 @@ SOFTWARE. ...@@ -21,72 +21,72 @@ SOFTWARE.
#include "data_transform.h" #include "data_transform.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
static void PassTensorData(Tensor *from, Tensor *to) { static void PassTensorData(Tensor *from, Tensor *to) {
to->ShareDataWith(*from); to->ShareDataWith(*from);
*from = Tensor(); *from = Tensor();
} }
void DataTransform(const OpKernelType &expected_kernel_type, void DataTransform(const OpKernelType &expected_kernel_type,
const OpKernelType &kernel_type_for_var, const OpKernelType &kernel_type_for_var,
const Tensor &input_tensor, Tensor *output_tensor) { const Tensor &input_tensor, Tensor *output_tensor) {
bool transformed = false; bool transformed = false;
Tensor in; Tensor in;
in.ShareDataWith(input_tensor); in.ShareDataWith(input_tensor);
Tensor out; Tensor out;
// // do layout transform // // do layout transform
// if (NeedTransformLayout(expected_kernel_type.data_layout_, // if (NeedTransformLayout(expected_kernel_type.data_layout_,
// kernel_type_for_var.data_layout_)) { // kernel_type_for_var.data_layout_)) {
// TransDataLayout(kernel_type_for_var, expected_kernel_type, in, // TransDataLayout(kernel_type_for_var, expected_kernel_type, in,
// &out); // &out);
// transformed = true; // transformed = true;
// PassTensorData(&out, &in); // PassTensorData(&out, &in);
// } // }
// //
// // do data type transform // // do data type transform
// if (expected_kernel_type.data_type_ != // if (expected_kernel_type.data_type_ !=
// kernel_type_for_var.data_type_) { // kernel_type_for_var.data_type_) {
// TransDataType(kernel_type_for_var, expected_kernel_type, in, // TransDataType(kernel_type_for_var, expected_kernel_type, in,
// &out); // &out);
// transformed = true; // transformed = true;
// PassTensorData(&out, &in); // PassTensorData(&out, &in);
// } // }
// //
// // do device transform // // do device transform
// if (!platform::is_same_place(kernel_type_for_var.place_, // if (!platform::is_same_place(kernel_type_for_var.place_,
// expected_kernel_type.place_)) { // expected_kernel_type.place_)) {
// TransDataDevice(in, expected_kernel_type.place_, &out); // TransDataDevice(in, expected_kernel_type.place_, &out);
// transformed = true; // transformed = true;
// PassTensorData(&out, &in); // PassTensorData(&out, &in);
// } // }
// //
// PADDLE_ENFORCE(transformed, "No transform is applied, please // PADDLE_ENFORCE(transformed, "No transform is applied, please
// check!"); // check!");
// get output data // get output data
output_tensor->ShareDataWith(in); output_tensor->ShareDataWith(in);
} }
void CopyVariableWithTensor(const Variable &in_var, void CopyVariableWithTensor(const Variable &in_var, const Tensor &tensor,
const Tensor &tensor, Variable &out_var) { Variable &out_var) {
// if (in_var.IsType<LoDTensor>()) { // if (in_var.IsType<LoDTensor>()) {
// auto& in_lod_tensor = in_var.Get<LoDTensor>(); // auto& in_lod_tensor = in_var.Get<LoDTensor>();
// auto* tran_lod_tensor = out_var.GetMutable<LoDTensor>(); // auto* tran_lod_tensor = out_var.GetMutable<LoDTensor>();
// tran_lod_tensor->set_lod(in_lod_tensor.lod()); // tran_lod_tensor->set_lod(in_lod_tensor.lod());
// tran_lod_tensor->set_layout(in_lod_tensor.layout()); // tran_lod_tensor->set_layout(in_lod_tensor.layout());
// tran_lod_tensor->ShareDataWith(tensor); // tran_lod_tensor->ShareDataWith(tensor);
// } else if (in_var.IsType<SelectedRows>()) { // } else if (in_var.IsType<SelectedRows>()) {
// auto& in_selected_rows = in_var.Get<SelectedRows>(); // auto& in_selected_rows = in_var.Get<SelectedRows>();
// auto* trans_selected_rows = // auto* trans_selected_rows =
// out_var.GetMutable<SelectedRows>(); // out_var.GetMutable<SelectedRows>();
// trans_selected_rows->set_height(in_selected_rows.height()); // trans_selected_rows->set_height(in_selected_rows.height());
// trans_selected_rows->set_rows(in_selected_rows.rows()); // trans_selected_rows->set_rows(in_selected_rows.rows());
// trans_selected_rows->mutable_value()->ShareDataWith(tensor); // trans_selected_rows->mutable_value()->ShareDataWith(tensor);
// } else { // } else {
// PADDLE_THROW("unknown var type"); // PADDLE_THROW("unknown var type");
// } // }
} }
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/data_transform.h
浏览文件 @ 45de1e84
...@@ -28,14 +28,14 @@ SOFTWARE. ...@@ -28,14 +28,14 @@ SOFTWARE.
#include "variable.h" #include "variable.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
void DataTransform(const OpKernelType &expected_kernel_type, void DataTransform(const OpKernelType &expected_kernel_type,
const OpKernelType &kernel_type_for_var, const OpKernelType &kernel_type_for_var,
const Tensor &input_tensor, Tensor *out); const Tensor &input_tensor, Tensor *out);
void CopyVariableWithTensor(const Variable &in_var, void CopyVariableWithTensor(const Variable &in_var, const Tensor &tensor,
const Tensor &tensor, Variable &out_var); Variable &out_var);
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/data_type.h
浏览文件 @ 45de1e84
...@@ -21,23 +21,23 @@ SOFTWARE. ...@@ -21,23 +21,23 @@ SOFTWARE.
#include "framework.pb.h" #include "framework.pb.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
// inline proto::VarType::Type ToDataType(std::type_index type) { // inline proto::VarType::Type ToDataType(std::type_index type) {
// using namespace paddle_mobile::framework::proto; // using namespace paddle_mobile::framework::proto;
// if (typeid(float).hash_code() == type.hash_code()) { // if (typeid(float).hash_code() == type.hash_code()) {
// return proto::VarType::FP32; // return proto::VarType::FP32;
// } else if (typeid(double).hash_code() == type.hash_code()) { // } else if (typeid(double).hash_code() == type.hash_code()) {
// return proto::VarType::FP64; // return proto::VarType::FP64;
// } else if (typeid(int).hash_code() == type.hash_code()) { // } else if (typeid(int).hash_code() == type.hash_code()) {
// return proto::VarType::INT32; // return proto::VarType::INT32;
// } else if (typeid(int64_t).hash_code() == type.hash_code()) { // } else if (typeid(int64_t).hash_code() == type.hash_code()) {
// return proto::VarType::INT64; // return proto::VarType::INT64;
// } else if (typeid(bool).hash_code() == type.hash_code()) { // } else if (typeid(bool).hash_code() == type.hash_code()) {
// return proto::VarType::BOOL; // return proto::VarType::BOOL;
// } else { // } else {
//// PADDLE_THROW("Not supported"); //// PADDLE_THROW("Not supported");
// } // }
// } // }
} }
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/ddim.cc
浏览文件 @ 45de1e84
...@@ -15,320 +15,318 @@ limitations under the License. */ ...@@ -15,320 +15,318 @@ limitations under the License. */
#include "ddim.h" #include "ddim.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
/// @cond HIDDEN /// @cond HIDDEN
template <int i> Dim<i> make_dim(const int64_t *d) { template <int i> Dim<i> make_dim(const int64_t *d) {
return Dim<i>(*d, make_dim<i - 1>(d + 1)); return Dim<i>(*d, make_dim<i - 1>(d + 1));
} }
template <> Dim<0> make_dim<0>(const int64_t *d) { return Dim<0>(*d); } template <> Dim<0> make_dim<0>(const int64_t *d) { return Dim<0>(*d); }
void make_ddim(DDim &ddim, const int64_t *dims, int n) { void make_ddim(DDim &ddim, const int64_t *dims, int n) {
switch (n) { switch (n) {
case 0: case 0:
ddim = make_dim<0>(dims); ddim = make_dim<0>(dims);
break; break;
case 1: case 1:
ddim = make_dim<1>(dims); ddim = make_dim<1>(dims);
break; break;
case 2: case 2:
ddim = make_dim<2>(dims); ddim = make_dim<2>(dims);
break; break;
case 3: case 3:
ddim = make_dim<3>(dims); ddim = make_dim<3>(dims);
break; break;
case 4: case 4:
ddim = make_dim<4>(dims); ddim = make_dim<4>(dims);
break; break;
case 5: case 5:
ddim = make_dim<5>(dims); ddim = make_dim<5>(dims);
break; break;
case 6: case 6:
ddim = make_dim<6>(dims); ddim = make_dim<6>(dims);
break; break;
case 7: case 7:
ddim = make_dim<7>(dims); ddim = make_dim<7>(dims);
break; break;
case 8: case 8:
ddim = make_dim<8>(dims); ddim = make_dim<8>(dims);
break; break;
case 9: case 9:
ddim = make_dim<9>(dims); ddim = make_dim<9>(dims);
break; break;
default: default:
// std::cout << "Dynamic dimensions must have between [1, // std::cout << "Dynamic dimensions must have between [1,
// 9] // 9]
// dimensions."; // dimensions.";
break; break;
} }
} }
/// @endcond /// @endcond
DDim make_ddim(std::initializer_list<int64_t> dims) { DDim make_ddim(std::initializer_list<int64_t> dims) {
DDim result(make_dim(0)); DDim result(make_dim(0));
make_ddim(result, dims.begin(), dims.size()); make_ddim(result, dims.begin(), dims.size());
return result; return result;
} }
DDim make_ddim(const std::vector<int64_t> &dims) { DDim make_ddim(const std::vector<int64_t> &dims) {
DDim result(make_dim(0)); DDim result(make_dim(0));
make_ddim(result, &dims[0], dims.size()); make_ddim(result, &dims[0], dims.size());
return result; return result;
} }
DDim make_ddim(const std::vector<int> &dims) { DDim make_ddim(const std::vector<int> &dims) {
std::vector<int64_t> res(dims.size()); std::vector<int64_t> res(dims.size());
std::transform(dims.begin(), dims.end(), res.begin(), std::transform(dims.begin(), dims.end(), res.begin(),
[](int d) { return static_cast<int64_t>(d); }); [](int d) { return static_cast<int64_t>(d); });
return make_ddim(res); return make_ddim(res);
} }
/// @cond HIDDEN /// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes // XXX For some reason, putting this in an anonymous namespace causes
// errors // errors
struct DynamicMutableIndexer : Vistor<int64_t &> { struct DynamicMutableIndexer : Vistor<int64_t &> {
public: public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {} explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
template <int D> int64_t &operator()(Dim<D> &dim) const { template <int D> int64_t &operator()(Dim<D> &dim) const {
return dim[idx_]; return dim[idx_];
} }
private: private:
int idx_; int idx_;
}; };
struct DynamicConstIndexer : public Vistor<int64_t> { struct DynamicConstIndexer : public Vistor<int64_t> {
public: public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {} explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D> int64_t operator()(const Dim<D> &dim) const { template <int D> int64_t operator()(const Dim<D> &dim) const {
return dim[idx_]; return dim[idx_];
} }
private: private:
int idx_; int idx_;
}; };
/// @endcond /// @endcond
int64_t &DDim::operator[](int idx) { int64_t &DDim::operator[](int idx) {
return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this); return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this);
} }
int64_t DDim::operator[](int idx) const { int64_t DDim::operator[](int idx) const {
return DDim::ApplyVistor(DynamicConstIndexer(idx), *this); return DDim::ApplyVistor(DynamicConstIndexer(idx), *this);
} }
int DDim::size() const { return arity(*this); } int DDim::size() const { return arity(*this); }
bool DDim::operator==(DDim d) const { bool DDim::operator==(DDim d) const {
// if (var.which() != d.getVar().which()) { // if (var.which() != d.getVar().which()) {
// return false; // return false;
// } else { // } else {
std::vector<int64_t> v1 = vectorize(*this); std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d); std::vector<int64_t> v2 = vectorize(d);
for (unsigned int i = 0; i < v1.size(); i++) { for (unsigned int i = 0; i < v1.size(); i++) {
if (v1[i] != v2[i]) { if (v1[i] != v2[i]) {
return false; return false;
}
}
return true;
// }
}
bool DDim::operator!=(DDim d) const { return !(*this == d); }
DDim DDim::operator+(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] + v2[i]);
}
return make_ddim(v3);
}
DDim DDim::operator*(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
}
return make_ddim(v3);
} }
}
int64_t get(const DDim &ddim, int idx) { return ddim[idx]; } return true;
// }
void set(DDim &ddim, int idx, int value) { ddim[idx] = value; } }
/// @cond HIDDEN bool DDim::operator!=(DDim d) const { return !(*this == d); }
struct VectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector; DDim DDim::operator+(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
explicit VectorizeVisitor(std::vector<int64_t> &v) : vector(v) {} std::vector<int64_t> v2 = vectorize(d);
template <typename T> void operator()(const T &t) { std::vector<int64_t> v3;
vector.push_back(t.head);
this->operator()(t.tail); assert(v1.size() == v2.size());
}
for (unsigned int i = 0; i < v1.size(); i++) {
void operator()(const Dim<0> &t) {} v3.push_back(v1[i] + v2[i]);
}; }
/// @endcond
return make_ddim(v3);
std::vector<int64_t> vectorize(const DDim &ddim) { }
std::vector<int64_t> result;
VectorizeVisitor visitor(result); DDim DDim::operator*(DDim d) const {
DDim::ApplyVistor(visitor, ddim); std::vector<int64_t> v1 = vectorize(*this);
return result; std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
}
return make_ddim(v3);
}
int64_t get(const DDim &ddim, int idx) { return ddim[idx]; }
void set(DDim &ddim, int idx, int value) { ddim[idx] = value; }
/// @cond HIDDEN
struct VectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
explicit VectorizeVisitor(std::vector<int64_t> &v) : vector(v) {}
template <typename T> void operator()(const T &t) {
vector.push_back(t.head);
this->operator()(t.tail);
}
void operator()(const Dim<0> &t) {}
};
/// @endcond
std::vector<int64_t> vectorize(const DDim &ddim) {
std::vector<int64_t> result;
VectorizeVisitor visitor(result);
DDim::ApplyVistor(visitor, ddim);
return result;
}
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> vectorize2int(const DDim &ddim) {
std::vector<int64_t> temp = vectorize(ddim);
std::vector<int> result(temp.begin(), temp.end());
return result;
}
struct ProductVisitor : Vistor<int64_t> {
template <int D> int64_t operator()(const Dim<D> &dim) {
return product(dim);
}
};
int64_t product(const DDim &ddim) {
ProductVisitor visitor;
return DDim::ApplyVistor(visitor, ddim);
}
struct SliceVectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
int begin;
int end;
SliceVectorizeVisitor(std::vector<int64_t> &v, int b, int e)
: vector(v), begin(b), end(e) {
// PADDLE_ENFORCE(begin < end,
// "Begin index must be less than end index in
// ddim
// slice.");
// PADDLE_ENFORCE(begin >= 0,
// "Begin index can't be less than zero in
// ddim slice.");
}
template <int S> void operator()(const Dim<S> &dim) {
if (begin == 0) {
vector.push_back(dim.head);
} else {
--begin;
} }
--end;
// NOTE: framework::vectorize converts to type int64_t if (end > 0) {
// which does not fit cudnn inputs. this->operator()(dim.tail);
std::vector<int> vectorize2int(const DDim &ddim) {
std::vector<int64_t> temp = vectorize(ddim);
std::vector<int> result(temp.begin(), temp.end());
return result;
} }
}
struct ProductVisitor : Vistor<int64_t> {
template <int D> int64_t operator()(const Dim<D> &dim) { void operator()(const Dim<0> &dim) {
return product(dim); // PADDLE_ENFORCE(end == 0, "End index in ddim slice is out
} // of bound.");
}; }
};
int64_t product(const DDim &ddim) {
ProductVisitor visitor; DDim slice_ddim(const DDim &ddim, int begin, int end) {
return DDim::ApplyVistor(visitor, ddim); std::vector<int64_t> vec;
} vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
struct SliceVectorizeVisitor : Vistor<void> { // boost::apply_visitor(visitor, dim);
std::vector<int64_t> &vector; DDim::ApplyVistor(visitor, ddim);
int begin; // visitor(ddim.var.Get<Dim<4>>());
int end; return make_ddim(vec);
}
SliceVectorizeVisitor(std::vector<int64_t> &v, int b, int e)
: vector(v), begin(b), end(e) { /// \cond HIDDEN
// PADDLE_ENFORCE(begin < end,
// "Begin index must be less than end index in struct ArityVisitor : Vistor<int> {
// ddim template <int D> int operator()(Dim<D>) const { return D; }
// slice."); };
// PADDLE_ENFORCE(begin >= 0,
// "Begin index can't be less than zero in /// \endcond
// ddim slice.");
} int arity(const DDim &d) {
ArityVisitor arityVisitor = ArityVisitor();
template <int S> void operator()(const Dim<S> &dim) { return DDim::ApplyVistor(arityVisitor, d);
if (begin == 0) { // return arityVisitor(d.var.Get<Dim<4>>());
vector.push_back(dim.head); // return boost::apply_visitor(ArityVisitor(), d); }
} else { }
--begin; /// \cond HIDDEN
}
--end; /// \endcond
if (end > 0) {
this->operator()(dim.tail); struct OSVistor : Vistor<std::ostream &> {
} OSVistor(std::ostream &os) : os_(os) {}
}
template <int D> std::ostream &operator()(Dim<D> dim) const {
void operator()(const Dim<0> &dim) { return os_ << dim;
// PADDLE_ENFORCE(end == 0, "End index in ddim slice is out }
// of bound.");
} private:
}; std::ostream &os_;
};
DDim slice_ddim(const DDim &ddim, int begin, int end) {
std::vector<int64_t> vec; std::ostream &operator<<(std::ostream &os, const DDim &ddim) {
vec.reserve(end - begin); auto vistor = OSVistor(os);
SliceVectorizeVisitor visitor(vec, begin, end); DDim::ApplyVistor(vistor, ddim);
// boost::apply_visitor(visitor, dim); return os;
DDim::ApplyVistor(visitor, ddim); }
// visitor(ddim.var.Get<Dim<4>>());
return make_ddim(vec); DDim::DDim(std::initializer_list<int64_t> init_list) {
} *this = make_ddim(init_list);
}
/// \cond HIDDEN
DDim flatten_to_2d(const DDim &src, int num_col_dims) {
struct ArityVisitor : Vistor<int> { int rank = src.size();
template <int D> int operator()(Dim<D>) const { return D; } return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
}; product(slice_ddim(src, num_col_dims, rank))});
}
/// \endcond
DDim flatten_to_1d(const DDim &src) { return make_ddim({product(src)}); }
int arity(const DDim &d) {
ArityVisitor arityVisitor = ArityVisitor(); DDim stride(const DDim &ddim) {
return DDim::ApplyVistor(arityVisitor, d); std::vector<int64_t> strides(ddim.size());
// return arityVisitor(d.var.Get<Dim<4>>()); strides[ddim.size() - 1] = 1;
// return boost::apply_visitor(ArityVisitor(), d); } for (int i = ddim.size() - 2; i >= 0; --i) {
} strides[i] = strides[i + 1] * ddim[i + 1];
/// \cond HIDDEN }
return framework::make_ddim(strides);
/// \endcond }
struct OSVistor : Vistor<std::ostream &> { DDim stride_numel(const framework::DDim &ddim) {
OSVistor(std::ostream &os) : os_(os) {} std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = ddim[ddim.size() - 1];
template <int D> std::ostream &operator()(Dim<D> dim) const { for (int i = ddim.size() - 2; i >= 0; --i) {
return os_ << dim; strides[i] = strides[i + 1] * ddim[i];
} }
return framework::make_ddim(strides);
private: }
std::ostream &os_;
}; } // namespace framework
std::ostream &operator<<(std::ostream &os, const DDim &ddim) {
auto vistor = OSVistor(os);
DDim::ApplyVistor(vistor, ddim);
return os;
}
DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(init_list);
}
DDim flatten_to_2d(const DDim &src, int num_col_dims) {
int rank = src.size();
return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
product(slice_ddim(src, num_col_dims, rank))});
}
DDim flatten_to_1d(const DDim &src) {
return make_ddim({product(src)});
}
DDim stride(const DDim &ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = 1;
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i + 1];
}
return framework::make_ddim(strides);
}
DDim stride_numel(const framework::DDim &ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = ddim[ddim.size() - 1];
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i];
}
return framework::make_ddim(strides);
}
} // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/ddim.h
浏览文件 @ 45de1e84
...@@ -22,145 +22,142 @@ limitations under the License. */ ...@@ -22,145 +22,142 @@ limitations under the License. */
#include <vector> #include <vector>
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
/** /**
* \brief A dynamically sized dimension. * \brief A dynamically sized dimension.
* *
* The number of dimensions must be between [1, 9]. * The number of dimensions must be between [1, 9].
*/ */
struct DDim { struct DDim {
typedef Variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, typedef Variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>,
Dim<6>, Dim<7>, Dim<8>, Dim<9>> Dim<7>, Dim<8>, Dim<9>>
DDimVar; DDimVar;
DDimVar var; DDimVar var;
template <typename Vistor> template <typename Vistor>
static typename Vistor::type_t ApplyVistor(Vistor vistor, static typename Vistor::type_t ApplyVistor(Vistor vistor, const DDim &d) {
const DDim &d) { if (d.var.TypeId() == typeid(Dim<0>).hash_code()) {
if (d.var.TypeId() == typeid(Dim<0>).hash_code()) { return vistor(d.var.Get<Dim<0>>());
return vistor(d.var.Get<Dim<0>>()); } else if (d.var.TypeId() == typeid(Dim<1>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<1>).hash_code()) { return vistor(d.var.Get<Dim<1>>());
return vistor(d.var.Get<Dim<1>>()); } else if (d.var.TypeId() == typeid(Dim<2>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<2>).hash_code()) { return vistor(d.var.Get<Dim<2>>());
return vistor(d.var.Get<Dim<2>>()); } else if (d.var.TypeId() == typeid(Dim<3>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<3>).hash_code()) { return vistor(d.var.Get<Dim<3>>());
return vistor(d.var.Get<Dim<3>>()); } else if (d.var.TypeId() == typeid(Dim<4>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<4>).hash_code()) { return vistor(d.var.Get<Dim<4>>());
return vistor(d.var.Get<Dim<4>>()); } else if (d.var.TypeId() == typeid(Dim<5>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<5>).hash_code()) { return vistor(d.var.Get<Dim<5>>());
return vistor(d.var.Get<Dim<5>>()); } else if (d.var.TypeId() == typeid(Dim<6>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<6>).hash_code()) { return vistor(d.var.Get<Dim<6>>());
return vistor(d.var.Get<Dim<6>>()); } else if (d.var.TypeId() == typeid(Dim<7>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<7>).hash_code()) { return vistor(d.var.Get<Dim<7>>());
return vistor(d.var.Get<Dim<7>>()); } else if (d.var.TypeId() == typeid(Dim<8>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<8>).hash_code()) { return vistor(d.var.Get<Dim<8>>());
return vistor(d.var.Get<Dim<8>>()); } else if (d.var.TypeId() == typeid(Dim<9>).hash_code()) {
} else if (d.var.TypeId() == typeid(Dim<9>).hash_code()) { return vistor(d.var.Get<Dim<9>>());
return vistor(d.var.Get<Dim<9>>()); } else {
} else { printf(" dim not support \n");
printf(" dim not support \n"); throw std::bad_exception();
throw std::bad_exception(); // return typename Vistor::type_t();
// return typename Vistor::type_t(); }
} }
}
DDim() { var.Set<Dim<1>>(Dim<1>()); }
DDim() { var.Set<Dim<1>>(Dim<1>()); }
template <int D> explicit DDim(const Dim<D> &in) { var.Set<Dim<D>>(in); }
template <int D> explicit DDim(const Dim<D> &in) {
var.Set<Dim<D>>(in); /*implicit*/ DDim(std::initializer_list<int64_t> init_list);
}
template <int D> DDim &operator=(const Dim<D> &in) {
/*implicit*/ DDim(std::initializer_list<int64_t> init_list); var.Set<Dim<D>>(in);
return *this;
template <int D> DDim &operator=(const Dim<D> &in) { }
var.Set<Dim<D>>(in);
return *this; int64_t &operator[](int idx);
}
int64_t operator[](int idx) const;
int64_t &operator[](int idx);
// template <typename Visitor>
int64_t operator[](int idx) const; // typename Visitor::result_type apply_visitor(Visitor& visitor) {
// return var.apply_visitor(visitor);
// template <typename Visitor> // }
// typename Visitor::result_type apply_visitor(Visitor& visitor) { //
// return var.apply_visitor(visitor); // template <typename Visitor>
// } // typename Visitor::result_type apply_visitor(Visitor& visitor)
// // const {
// template <typename Visitor> // return var.apply_visitor(visitor);
// typename Visitor::result_type apply_visitor(Visitor& visitor) // }
// const {
// return var.apply_visitor(visitor); DDimVar getVar() { return var; }
// }
bool operator==(DDim d) const;
DDimVar getVar() { return var; }
bool operator!=(DDim d) const;
bool operator==(DDim d) const;
DDim operator+(DDim d) const;
bool operator!=(DDim d) const;
DDim operator*(DDim d) const;
DDim operator+(DDim d) const;
int size() const;
DDim operator*(DDim d) const; };
int size() const; /**
}; * \brief Make a DDim from std::vector<int64_t>
*
/** * \param dims An vector of ints. Must be sized between [1, 9]
* \brief Make a DDim from std::vector<int64_t> */
* DDim make_ddim(const std::vector<int64_t> &dims);
* \param dims An vector of ints. Must be sized between [1, 9]
*/ DDim make_ddim(const std::vector<int> &dims);
DDim make_ddim(const std::vector<int64_t> &dims);
/**
DDim make_ddim(const std::vector<int> &dims); * \brief Make a DDim from an initializer list
*
/** * \param dims An initializer list of ints. Must be sized between [1, 9]
* \brief Make a DDim from an initializer list *
* */
* \param dims An initializer list of ints. Must be sized between [1, 9] DDim make_ddim(std::initializer_list<int64_t> dims);
*
*/ int64_t get(const DDim &dim, int idx);
DDim make_ddim(std::initializer_list<int64_t> dims);
void set(DDim &dim, int idx, int val);
int64_t get(const DDim &dim, int idx);
std::vector<int64_t> vectorize(const DDim &ddim);
void set(DDim &dim, int idx, int val);
std::vector<int> vectorize2int(const DDim &ddim);
std::vector<int64_t> vectorize(const DDim &ddim);
int64_t product(const DDim &ddim);
std::vector<int> vectorize2int(const DDim &ddim);
/**
int64_t product(const DDim &ddim); * \brief Slice a ddim
*
/** * Slice dim with [begin, end).
* \brief Slice a ddim * e.g. DDim d = make_ddim({1,2,3,4,5});
* * slice_ddim(d, 1, 3); ====> {2,3}
* Slice dim with [begin, end). */
* e.g. DDim d = make_ddim({1,2,3,4,5}); DDim slice_ddim(const DDim &dim, int begin, int end);
* slice_ddim(d, 1, 3); ====> {2,3}
*/ /**
DDim slice_ddim(const DDim &dim, int begin, int end); * \brief What is the length of this dimension?
*
/** * \param Dynamic dimension to inspect
* \brief What is the length of this dimension? */
*
* \param Dynamic dimension to inspect
*/
int arity(const DDim &ddim); int arity(const DDim &ddim);
std::ostream &operator<<(std::ostream &, const DDim &); std::ostream &operator<<(std::ostream &, const DDim &);
// Reshape a tensor to a matrix. The matrix's first dimension(column // Reshape a tensor to a matrix. The matrix's first dimension(column
// length) // length)
// will be the product of tensor's first `num_col_dims` dimensions. // will be the product of tensor's first `num_col_dims` dimensions.
DDim flatten_to_2d(const DDim &src, int num_col_dims); DDim flatten_to_2d(const DDim &src, int num_col_dims);
DDim flatten_to_1d(const DDim &src); DDim flatten_to_1d(const DDim &src);
DDim stride(const DDim &ddim); DDim stride(const DDim &ddim);
DDim stride_numel(const DDim &ddim); DDim stride_numel(const DDim &ddim);
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/dim.h
浏览文件 @ 45de1e84
...@@ -21,410 +21,392 @@ ...@@ -21,410 +21,392 @@
#include "platform/hostdevice.h" #include "platform/hostdevice.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
// Statically sized, statically indexed dimension // Statically sized, statically indexed dimension
template <int i> struct Dim { template <int i> struct Dim {
static constexpr int dimensions = i; static constexpr int dimensions = i;
template <typename... Args> template <typename... Args>
HOSTDEVICE Dim(int64_t _head, Args... _tail) HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
: head(_head), tail(_tail...) { static_assert(sizeof...(_tail) == i - 1,
static_assert( "Dim initialized with the wrong number of parameters");
sizeof...(_tail) == i - 1, }
"Dim initialized with the wrong number of parameters");
}
HOSTDEVICE HOSTDEVICE
Dim(int64_t _head, const Dim<i - 1> &_tail) Dim(int64_t _head, const Dim<i - 1> &_tail) : head(_head), tail(_tail) {}
: head(_head), tail(_tail) {}
HOSTDEVICE HOSTDEVICE
Dim() : head(0), tail() {} Dim() : head(0), tail() {}
/** Construct a Dim from a linear index and size. Uses Fortran /** Construct a Dim from a linear index and size. Uses Fortran
* order * order
* indexing. */ * indexing. */
HOSTDEVICE HOSTDEVICE
Dim(int64_t idx, const Dim<i> &size) Dim(int64_t idx, const Dim<i> &size)
: head(idx % size.head), tail(idx / size.head, size.tail) {} : head(idx % size.head), tail(idx / size.head, size.tail) {}
/** Construct a Dim with each dimension set to the given index */ /** Construct a Dim with each dimension set to the given index */
HOSTDEVICE HOSTDEVICE
Dim(int64_t idx) : head(idx), tail(idx) {} Dim(int64_t idx) : head(idx), tail(idx) {}
HOSTDEVICE HOSTDEVICE
bool operator==(const Dim<i> &o) const { bool operator==(const Dim<i> &o) const {
return (head == o.head) && (tail == o.tail); return (head == o.head) && (tail == o.tail);
} }
HOSTDEVICE HOSTDEVICE
bool operator!=(const Dim<i> &o) const { return !(*this == o); } bool operator!=(const Dim<i> &o) const { return !(*this == o); }
HOSTDEVICE HOSTDEVICE
int64_t &operator[](int idx); int64_t &operator[](int idx);
HOSTDEVICE HOSTDEVICE
int64_t operator[](int idx) const; int64_t operator[](int idx) const;
HOST std::string to_string() const; HOST std::string to_string() const;
int64_t head; int64_t head;
Dim<i - 1> tail; Dim<i - 1> tail;
}; };
// Base case specialization // Base case specialization
template <> struct Dim<0> { template <> struct Dim<0> {
static constexpr int dimensions = 0; static constexpr int dimensions = 0;
HOSTDEVICE HOSTDEVICE
Dim(int64_t _head) {} Dim(int64_t _head) {}
HOSTDEVICE HOSTDEVICE
Dim() {} Dim() {}
HOSTDEVICE HOSTDEVICE
Dim(int idx, const Dim<0> &size) { Dim(int idx, const Dim<0> &size) {
#ifndef __CUDA_ARCH__ #ifndef __CUDA_ARCH__
if (idx > 0) { if (idx > 0) {
throw std::invalid_argument("Index out of range."); throw std::invalid_argument("Index out of range.");
} }
#else #else
PADDLE_ASSERT(idx == 0); PADDLE_ASSERT(idx == 0);
#endif #endif
} }
HOSTDEVICE HOSTDEVICE
bool operator==(const Dim<0> &o) const { return true; } bool operator==(const Dim<0> &o) const { return true; }
HOSTDEVICE HOSTDEVICE
bool operator!=(const Dim<0> &o) const { return false; } bool operator!=(const Dim<0> &o) const { return false; }
HOSTDEVICE HOSTDEVICE
int64_t &operator[](int idx); int64_t &operator[](int idx);
HOSTDEVICE HOSTDEVICE
int64_t operator[](int idx) const; int64_t operator[](int idx) const;
}; };
namespace { namespace {
// Helper for accessing Dim classes // Helper for accessing Dim classes
template <int i> struct DimGetter { template <int i> struct DimGetter {
// Return a copy if Dim is const // Return a copy if Dim is const
template <typename D> template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
HOSTDEVICE static int64_t impl(const D &d) { return DimGetter<i - 1>::impl(d.tail);
return DimGetter<i - 1>::impl(d.tail); }
} // Return a reference if Dim is mutable
// Return a reference if Dim is mutable template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
template <typename D> HOSTDEVICE static int64_t &impl(D &d) { return DimGetter<i - 1>::impl(d.tail);
return DimGetter<i - 1>::impl(d.tail); }
} };
};
// Eureka! We found the element!
// Eureka! We found the element! template <> struct DimGetter<0> {
template <> struct DimGetter<0> { // Return a copy if Dim is const
// Return a copy if Dim is const template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
template <typename D> return d.head;
HOSTDEVICE static int64_t impl(const D &d) { }
return d.head; // Return a reference if Dim is mutable
} template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
// Return a reference if Dim is mutable return d.head;
template <typename D> HOSTDEVICE static int64_t &impl(D &d) { }
return d.head; };
}
}; template <int D> HOSTDEVICE int64_t &indexer(Dim<D> &dim, int idx) {
template <int D> HOSTDEVICE int64_t &indexer(Dim<D> &dim, int idx) {
#ifndef __CUDA_ARCH__ #ifndef __CUDA_ARCH__
if (idx < 0) { if (idx < 0) {
throw std::invalid_argument( throw std::invalid_argument("Tried to access a negative dimension");
"Tried to access a negative dimension"); }
}
#else #else
PADDLE_ASSERT(idx >= 0); PADDLE_ASSERT(idx >= 0);
#endif #endif
if (idx == 0) { if (idx == 0) {
return dim.head; return dim.head;
} }
return indexer(dim.tail, idx - 1); return indexer(dim.tail, idx - 1);
} }
template <> HOSTDEVICE int64_t &indexer<0>(Dim<0> &dim, int idx) { template <> HOSTDEVICE int64_t &indexer<0>(Dim<0> &dim, int idx) {
#ifndef __CUDA_ARCH__ #ifndef __CUDA_ARCH__
throw std::invalid_argument("Invalid index"); throw std::invalid_argument("Invalid index");
#else #else
PADDLE_ASSERT(false); PADDLE_ASSERT(false);
#if CUDA_VERSION < 8000 #if CUDA_VERSION < 8000
// On CUDA versions previous to 8.0, only __shared__ variables // On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code. // could be declared as static in the device code.
int64_t head = 0; int64_t head = 0;
#else #else
static int64_t head = 0; static int64_t head = 0;
#endif #endif
return head; return head;
#endif #endif
} }
template <int D> template <int D> HOSTDEVICE int64_t indexer(const Dim<D> &dim, int idx) {
HOSTDEVICE int64_t indexer(const Dim<D> &dim, int idx) {
#ifndef __CUDA_ARCH__ #ifndef __CUDA_ARCH__
if (idx < 0) { if (idx < 0) {
throw std::invalid_argument( throw std::invalid_argument("Tried to access a negative dimension");
"Tried to access a negative dimension"); }
}
#else #else
PADDLE_ASSERT(idx >= 0); PADDLE_ASSERT(idx >= 0);
#endif #endif
if (idx == 0) { if (idx == 0) {
return dim.head; return dim.head;
} }
return indexer(dim.tail, idx - 1); return indexer(dim.tail, idx - 1);
} }
template <> template <> HOSTDEVICE int64_t indexer<0>(const Dim<0> &dim, int idx) {
HOSTDEVICE int64_t indexer<0>(const Dim<0> &dim, int idx) {
#ifndef __CUDA_ARCH__ #ifndef __CUDA_ARCH__
throw std::invalid_argument("Invalid index"); throw std::invalid_argument("Invalid index");
#else #else
PADDLE_ASSERT(false); PADDLE_ASSERT(false);
#if CUDA_VERSION < 8000 #if CUDA_VERSION < 8000
// On CUDA versions previous to 8.0, only __shared__ variables // On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code. // could be declared as static in the device code.
int64_t head = 0; int64_t head = 0;
#else #else
static int64_t head = 0; static int64_t head = 0;
#endif #endif
return head; return head;
#endif #endif
} }
} // namespace } // namespace
// Static access to constant Dim // Static access to constant Dim
template <int i, int l> HOSTDEVICE int64_t get(const Dim<l> &d) { template <int i, int l> HOSTDEVICE int64_t get(const Dim<l> &d) {
return DimGetter<i>::impl(d); return DimGetter<i>::impl(d);
} }
// Static access to mutable Dim // Static access to mutable Dim
template <int i, int l> HOSTDEVICE int64_t &get(Dim<l> &d) { template <int i, int l> HOSTDEVICE int64_t &get(Dim<l> &d) {
return DimGetter<i>::impl(d); return DimGetter<i>::impl(d);
} }
// Dynamic access to constant Dim // Dynamic access to constant Dim
template <int l> HOSTDEVICE int64_t Dim<l>::operator[](int i) const { template <int l> HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
// std::cout << "l: " << l << std::endl; // std::cout << "l: " << l << std::endl;
return indexer(*this, i); return indexer(*this, i);
} }
// Dynamic access to mutable Dim // Dynamic access to mutable Dim
template <int l> HOSTDEVICE int64_t &Dim<l>::operator[](int i) { template <int l> HOSTDEVICE int64_t &Dim<l>::operator[](int i) {
return indexer(*this, i); return indexer(*this, i);
} }
// Dynamic access to constant Dim // Dynamic access to constant Dim
inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const { inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const {
return indexer(*this, i); return indexer(*this, i);
} }
// Dynamic access to mutable Dim // Dynamic access to mutable Dim
inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) { inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
return indexer(*this, i); return indexer(*this, i);
} }
// Dynamic access to constant Dim // Dynamic access to constant Dim
// without std::enable_if will try to instantiate this on get<0>(d) // without std::enable_if will try to instantiate this on get<0>(d)
template <int l> template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l> &d,
get(const Dim<l> &d, int i) { int i) {
return d[i]; return d[i];
} }
// Dynamic access to mutable Dim // Dynamic access to mutable Dim
template <int l> template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t &>::type HOSTDEVICE typename std::enable_if<(l > 0), int64_t &>::type get(Dim<l> &d,
get(Dim<l> &d, int i) { int i) {
return d[i]; return d[i];
} }
// Dot product of two dims // Dot product of two dims
template <int i> template <int i>
HOSTDEVICE int64_t linearize(const Dim<i> &a, const Dim<i> &b) { HOSTDEVICE int64_t linearize(const Dim<i> &a, const Dim<i> &b) {
return a.head * b.head + linearize(a.tail, b.tail); return a.head * b.head + linearize(a.tail, b.tail);
} }
// Base case dot product of two Dims // Base case dot product of two Dims
// Notice it is inline because it is no longer a template // Notice it is inline because it is no longer a template
template <> template <>
HOSTDEVICE inline int64_t linearize(const Dim<0> &a, const Dim<0> &b) { HOSTDEVICE inline int64_t linearize(const Dim<0> &a, const Dim<0> &b) {
return 0; return 0;
} }
// Product of a Dim // Product of a Dim
template <int i> template <int i> HOSTDEVICE int64_t product(const Dim<i> &a, int prod = 1) {
HOSTDEVICE int64_t product(const Dim<i> &a, int prod = 1) { return prod * a.head * product(a.tail);
return prod * a.head * product(a.tail); }
}
// Base case product of a Dim
// Base case product of a Dim // Notice it is inline because it is no longer a template
// Notice it is inline because it is no longer a template template <> HOSTDEVICE inline int64_t product(const Dim<0> &a, int prod) {
template <> return prod;
HOSTDEVICE inline int64_t product(const Dim<0> &a, int prod) { }
return prod;
} // Is 0 <= idx_i < size_i for all i?
template <int i>
// Is 0 <= idx_i < size_i for all i? HOSTDEVICE bool contained(const Dim<i> &idx, const Dim<i> &size) {
template <int i> return ((0 <= idx.head) && (idx.head < size.head) &&
HOSTDEVICE bool contained(const Dim<i> &idx, const Dim<i> &size) { contained(idx.tail, size.tail));
return ((0 <= idx.head) && (idx.head < size.head) && }
contained(idx.tail, size.tail));
} // Base case of is 0 <= idx_i < size_i ?
// Notice it is inline because it is no longer a template
// Base case of is 0 <= idx_i < size_i ? template <>
// Notice it is inline because it is no longer a template HOSTDEVICE inline bool contained(const Dim<0> &idx, const Dim<0> &size) {
template <> return true;
HOSTDEVICE inline bool contained(const Dim<0> &idx, }
const Dim<0> &size) {
return true; /**
} * \brief Compute exclusive prefix-multiply of a Dim.
*/
/** template <int i>
* \brief Compute exclusive prefix-multiply of a Dim. HOSTDEVICE Dim<i> ex_prefix_mul(const Dim<i> &src, int mul = 1) {
*/ return Dim<i>(mul, ex_prefix_mul(src.tail, mul * src.head));
template <int i> }
HOSTDEVICE Dim<i> ex_prefix_mul(const Dim<i> &src, int mul = 1) {
return Dim<i>(mul, ex_prefix_mul(src.tail, mul * src.head)); ///\cond HIDDEN
} // Base case of ex_prefix_mul
// Notice it is inline because it is no longer a template
///\cond HIDDEN template <> HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
// Base case of ex_prefix_mul return Dim<0>();
// Notice it is inline because it is no longer a template }
template <> ///\endcond
HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
return Dim<0>(); /**
} * Add two dimensions together
///\endcond */
template <int i> HOSTDEVICE Dim<i> dim_plus(const Dim<i> &a, const Dim<i> &b) {
/** return Dim<i>(a.head + b.head, dim_plus(a.tail, b.tail));
* Add two dimensions together }
*/
template <int i> // Base case
HOSTDEVICE Dim<i> dim_plus(const Dim<i> &a, const Dim<i> &b) { template <>
return Dim<i>(a.head + b.head, dim_plus(a.tail, b.tail)); HOSTDEVICE inline Dim<0> dim_plus(const Dim<0> &a, const Dim<0> &b) {
} return Dim<0>();
}
// Base case
template <> template <int i>
HOSTDEVICE inline Dim<0> dim_plus(const Dim<0> &a, const Dim<0> &b) { HOSTDEVICE Dim<i> operator+(const Dim<i> &lhs, const Dim<i> &rhs) {
return Dim<0>(); return dim_plus(lhs, rhs);
} }
template <int i> /**
HOSTDEVICE Dim<i> operator+(const Dim<i> &lhs, const Dim<i> &rhs) { * Multiply two dimensions together
return dim_plus(lhs, rhs); */
} template <int i> HOSTDEVICE Dim<i> dim_mult(const Dim<i> &a, const Dim<i> &b) {
return Dim<i>(a.head * b.head, dim_mult(a.tail, b.tail));
/** }
* Multiply two dimensions together
*/ // Base case
template <int i> template <>
HOSTDEVICE Dim<i> dim_mult(const Dim<i> &a, const Dim<i> &b) { HOSTDEVICE inline Dim<0> dim_mult(const Dim<0> &a, const Dim<0> &b) {
return Dim<i>(a.head * b.head, dim_mult(a.tail, b.tail)); return Dim<0>();
} }
// Base case template <int i>
template <> HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
HOSTDEVICE inline Dim<0> dim_mult(const Dim<0> &a, const Dim<0> &b) { return dim_mult(lhs, rhs);
return Dim<0>(); }
}
/**
template <int i> * \brief Normalize strides to ensure any dimension with extent 1
HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) { * has stride 0.
return dim_mult(lhs, rhs); *
} * \param size Dim object containing the size of an array
* \param stride Dim object containing stride of an array
/** * \return Dim object the same size as \p size with normalized strides
* \brief Normalize strides to ensure any dimension with extent 1 *
* has stride 0. */
*
* \param size Dim object containing the size of an array template <int i>
* \param stride Dim object containing stride of an array HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size, const Dim<i> &stride) {
* \return Dim object the same size as \p size with normalized strides int norm_stride = size.head == 1 ? 0 : stride.head;
* return Dim<i>(norm_stride, normalize_strides(size.tail, stride.tail));
*/ }
template <int i> ///\cond HIDDEN
HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size,
const Dim<i> &stride) { template <>
int norm_stride = size.head == 1 ? 0 : stride.head; HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
return Dim<i>(norm_stride, const Dim<0> &stride) {
normalize_strides(size.tail, stride.tail)); return Dim<0>();
} }
///\cond HIDDEN ///\endcond
template <> /**
HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size, * Helper function to create a Dim
const Dim<0> &stride) { *
return Dim<0>(); * \param idxes The type of Dim constructed depends on the number of
} * params
*
///\endcond */
/** template <typename... Args>
* Helper function to create a Dim HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
* return Dim<sizeof...(Args)>(idxes...);
* \param idxes The type of Dim constructed depends on the number of }
* params
* // Allows us to output a Dim
*/ // XXX For some reason, overloading fails to resolve this correctly
template <int i>
template <typename... Args> typename std::enable_if<(i > 1), std::ostream &>::type
HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) { operator<<(std::ostream &os, const Dim<i> &d) {
return Dim<sizeof...(Args)>(idxes...); os << d.head << ", " << d.tail;
} return os;
}
// Allows us to output a Dim
// XXX For some reason, overloading fails to resolve this correctly // Base case that allows us to output a Dim
template <int i> // XXX I wish this could be an overload instead of a template
typename std::enable_if<(i > 1), std::ostream &>::type template <int i>
operator<<(std::ostream &os, const Dim<i> &d) { typename std::enable_if<(i == 1), std::ostream &>::type
os << d.head << ", " << d.tail; operator<<(std::ostream &os, const Dim<i> &d) {
return os; os << d.head;
} return os;
}
// Base case that allows us to output a Dim
// XXX I wish this could be an overload instead of a template inline std::ostream &operator<<(std::ostream &os, const Dim<0> &d) {
template <int i> return os;
typename std::enable_if<(i == 1), std::ostream &>::type }
operator<<(std::ostream &os, const Dim<i> &d) {
os << d.head; template <int i> HOST std::string Dim<i>::to_string() const {
return os; std::stringstream stream;
}
stream << *this;
inline std::ostream &operator<<(std::ostream &os, const Dim<0> &d) {
return os; return stream.str();
} }
template <int i> HOST std::string Dim<i>::to_string() const { template <int D>
std::stringstream stream; HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, Dim<D> extents) {
Dim<D> result;
stream << *this;
for (int i = 0; i < D - 1; ++i) {
return stream.str(); result[i] = linear_index % extents[i];
} linear_index /= extents[i];
}
template <int D>
HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, result[D - 1] = linear_index;
Dim<D> extents) {
Dim<D> result; return result;
}
for (int i = 0; i < D - 1; ++i) {
result[i] = linear_index % extents[i]; } // namespace framework
linear_index /= extents[i];
}
result[D - 1] = linear_index;
return result;
}
} // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/executor.cpp
浏览文件 @ 45de1e84
...@@ -20,78 +20,74 @@ SOFTWARE. ...@@ -20,78 +20,74 @@ SOFTWARE.
#include "executor.h" #include "executor.h"
#include "lod_tensor.h" #include "lod_tensor.h"
#include "operators/conv_op.h" #include "operators/conv_op.h"
#include "variable.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename Dtype> template <typename Dtype>
Executor<Dtype>::Executor(const Program<Dtype> p) : program_(p) { Executor<Dtype>::Executor(const Program<Dtype> p) : program_(p) {
if (use_optimize_) { if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram; to_predict_program_ = program_.optimizeProgram;
} else { } else {
to_predict_program_ = program_.originProgram; to_predict_program_ = program_.originProgram;
} }
const std::vector<std::shared_ptr<BlockDesc>> blocks = const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks(); to_predict_program_->Blocks();
for (int i = 0; i < blocks.size(); ++i) { for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i]; std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops(); std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
for (int j = 0; j < ops.size(); ++j) { for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j]; std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "conv2d" && if (op->Type() == "conv2d" && op->Input("Input")[0] == "pixel") {
op->Input("Input")[0] == "pixel") { Attribute strides_attr = op->GetAttrMap().at("strides");
Attribute strides_attr = op->GetAttrMap().at("strides"); std::vector<int> stride = strides_attr.Get<std::vector<int>>();
std::vector<int> stride = for (int k = 0; k < stride.size(); ++k) {
strides_attr.Get<std::vector<int>>();
for (int k = 0; k < stride.size(); ++k) {
}
std::shared_ptr<operators::ConvOp<Dtype, float>> conv =
std::make_shared<operators::ConvOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(conv);
}
} }
std::shared_ptr<operators::ConvOp<Dtype, float>> conv =
std::make_shared<operators::ConvOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(conv);
} }
} }
}
}
template <typename Dtype> template <typename Dtype>
std::shared_ptr<Tensor> Executor<Dtype>::predict(Tensor &t) { std::shared_ptr<Tensor> Executor<Dtype>::predict(Tensor &t) {
// feed // feed
auto scope = program_.scope; auto scope = program_.scope;
Variable *g_feed_value = scope->Var("pixel"); Variable *g_feed_value = scope->Var("pixel");
auto tensor = g_feed_value->GetMutable<Tensor>(); auto tensor = g_feed_value->GetMutable<Tensor>();
tensor->ShareDataWith(t); tensor->ShareDataWith(t);
Variable *con_output = scope->Var("conv2d_0.tmp_0"); Variable *con_output = scope->Var("conv2d_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>(); Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 16, 32, 32}); output_tensor->mutable_data<float>({1, 16, 32, 32});
// std::cout << typeid(output_tensor).name() << std::endl; // std::cout << typeid(output_tensor).name() << std::endl;
// std::cout << "output_tensor dims: " << output_tensor->dims() << // std::cout << "output_tensor dims: " << output_tensor->dims() <<
// std::endl; // std::endl;
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>(); std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor); out_tensor.reset(output_tensor);
predict(t, 0); predict(t, 0);
return out_tensor; return out_tensor;
} }
template <typename Dtype> template <typename Dtype>
void Executor<Dtype>::predict(const Tensor &t, int block_id) { void Executor<Dtype>::predict(const Tensor &t, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block = std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id); to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
++j) { auto op = ops_of_block_[*to_predict_block.get()][j];
auto op = ops_of_block_[*to_predict_block.get()][j]; // std::cout << "开始run" << std::endl;
// std::cout << "开始run" << std::endl; op->Run();
op->Run(); }
} }
}
template class Executor<CPU>; template class Executor<CPU>;
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/executor.h
浏览文件 @ 45de1e84
...@@ -32,22 +32,22 @@ SOFTWARE. ...@@ -32,22 +32,22 @@ SOFTWARE.
#include "variable.h" #include "variable.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename Dtype> class Executor { template <typename Dtype> class Executor {
public: public:
Executor(const Program<Dtype> p); Executor(const Program<Dtype> p);
std::shared_ptr<Tensor> predict(Tensor &t); std::shared_ptr<Tensor> predict(Tensor &t);
private: private:
const framework::Program<Dtype> program_; const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_; std::shared_ptr<ProgramDesc> to_predict_program_;
void predict(const Tensor &t, int block_id); void predict(const Tensor &t, int block_id);
std::map<framework::BlockDesc, std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>> std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_; ops_of_block_;
bool use_optimize_ = false; bool use_optimize_ = false;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/framework.pb.cpp
浏览文件 @ 45de1e84
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
src/framework/framework.pb.h
浏览文件 @ 45de1e84
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
src/framework/lod_tensor.cc
浏览文件 @ 45de1e84
...@@ -19,304 +19,295 @@ limitations under the License. */ ...@@ -19,304 +19,295 @@ limitations under the License. */
#include <string.h> #include <string.h>
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
std::ostream &operator<<(std::ostream &os, const LoD &lod) { std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os << "{"; os << "{";
for (auto &v : lod) { for (auto &v : lod) {
os << "{"; os << "{";
bool is_first = true; bool is_first = true;
for (auto &i : v) { for (auto &i : v) {
if (is_first) { if (is_first) {
os << i; os << i;
is_first = false; is_first = false;
} else { } else {
os << ", " << i; os << ", " << i;
}
}
os << "}";
} }
os << "}";
return os;
} }
os << "}";
std::ostream &operator<<(std::ostream &os, const LoDTensor &t) { }
// PADDLE_ENFORCE(t.type().hash_code() == os << "}";
// typeid(float).hash_code());
return os;
// if (!platform::is_cpu_place(t.place())) { }
// LoDTensor tt;
// framework::TensorCopy(t, platform::CPUPlace(), &tt); std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
// platform::DeviceContextPool &pool = // PADDLE_ENFORCE(t.type().hash_code() ==
// platform::DeviceContextPool::Instance(); auto &dev_ctx = // typeid(float).hash_code());
// *pool.Get(t.place()); dev_ctx.Wait();
// // if (!platform::is_cpu_place(t.place())) {
// os << tt; // LoDTensor tt;
// return os; // framework::TensorCopy(t, platform::CPUPlace(), &tt);
// } // platform::DeviceContextPool &pool =
// platform::DeviceContextPool::Instance(); auto &dev_ctx =
os << "dim: " << t.dims() << "\n"; // *pool.Get(t.place()); dev_ctx.Wait();
os << "lod: " << t.lod() << "\n"; //
// os << tt;
// only print first ten elements // return os;
int64_t size = t.numel() < 10 ? t.numel() : 10; // }
for (int64_t i = 0; i < size; ++i) {
os << t.data<float>()[i] << " "; os << "dim: " << t.dims() << "\n";
} os << "lod: " << t.lod() << "\n";
return os; // only print first ten elements
int64_t size = t.numel() < 10 ? t.numel() : 10;
for (int64_t i = 0; i < size; ++i) {
os << t.data<float>()[i] << " ";
}
return os;
}
std::string LoDToString(const LoD &lod) {
std::ostringstream stream;
stream << lod;
return stream.str();
}
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end) {
// PADDLE_ENFORCE_LT(level, in.size());
// PADDLE_ENFORCE_LT(elem_end, in[level].size());
LoD res;
res.resize(in.size() - level);
// copy the first level
res[0].assign(in[level].begin() + elem_begin,
in[level].begin() + elem_end + 1);
for (size_t lvl = 1; lvl < res.size(); lvl++) {
const auto &in_level = in[level + lvl];
const auto &above_level = res[lvl - 1];
auto &out_level = res[lvl];
out_level.assign(in_level.begin() + above_level.front(),
in_level.begin() + above_level.back() + 1);
}
for (size_t lvl = 0; lvl < res.size(); lvl++) {
// to make the first offset equals 0, all the elements minus the
// first
// element
size_t front = res[lvl].front();
for (auto &ele : res[lvl]) {
ele -= front;
} }
}
std::string LoDToString(const LoD &lod) { return res;
std::ostringstream stream; }
stream << lod;
return stream.str(); LoD ToAbsOffset(const LoD &in) {
// the lowest level stores relative offsets
if (in.empty() || in.size() == 1)
return in;
LoD result = in;
for (auto level = static_cast<int>(in.size() - 2); level >= 0; level--) {
for (size_t i = 0; i < in[level].size(); ++i) {
size_t index = in[level][i];
result[level][i] = result[level + 1][index];
} }
}
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin, return result;
size_t elem_end) { }
// PADDLE_ENFORCE_LT(level, in.size());
// PADDLE_ENFORCE_LT(elem_end, in[level].size()); bool operator==(const LoD &a, const LoD &b) {
if (a.size() != b.size()) {
LoD res; return false;
res.resize(in.size() - level); }
// copy the first level
res[0].assign(in[level].begin() + elem_begin, for (size_t i = 0; i < a.size(); i++) {
in[level].begin() + elem_end + 1); const auto &a_level = a[i];
for (size_t lvl = 1; lvl < res.size(); lvl++) { const auto &b_level = b[i];
const auto &in_level = in[level + lvl]; if (a_level.size() != b_level.size()) {
const auto &above_level = res[lvl - 1]; return false;
auto &out_level = res[lvl];
out_level.assign(in_level.begin() + above_level.front(),
in_level.begin() + above_level.back() + 1);
}
for (size_t lvl = 0; lvl < res.size(); lvl++) {
// to make the first offset equals 0, all the elements minus the
// first
// element
size_t front = res[lvl].front();
for (auto &ele : res[lvl]) {
ele -= front;
}
}
return res;
} }
for (size_t j = 0; j < a_level.size(); j++) {
LoD ToAbsOffset(const LoD &in) { if (a_level[j] != b_level[j]) {
// the lowest level stores relative offsets
if (in.empty() || in.size() == 1)
return in;
LoD result = in;
for (auto level = static_cast<int>(in.size() - 2); level >= 0;
level--) {
for (size_t i = 0; i < in[level].size(); ++i) {
size_t index = in[level][i];
result[level][i] = result[level + 1][index];
}
}
return result;
}
bool operator==(const LoD &a, const LoD &b) {
if (a.size() != b.size()) {
return false; return false;
} }
for (size_t i = 0; i < a.size(); i++) {
const auto &a_level = a[i];
const auto &b_level = b[i];
if (a_level.size() != b_level.size()) {
return false;
}
for (size_t j = 0; j < a_level.size(); j++) {
if (a_level[j] != b_level[j]) {
return false;
}
}
}
return true;
} }
}
bool CheckLoD(const LoD &in, int tensor_height) { return true;
if (in.empty()) }
return true;
for (const auto &level : in) { bool CheckLoD(const LoD &in, int tensor_height) {
// check: there should be more than 2 offsets existing in each if (in.empty())
// level. return true;
if (level.size() < 2) for (const auto &level : in) {
return false; // check: there should be more than 2 offsets existing in each
// check: the first offset(the begin offset) of each level // level.
// should be 0. if (level.size() < 2)
if (level.front() != 0) return false;
return false; // check: the first offset(the begin offset) of each level
// check: all the offsets in a level should be ascending(no same // should be 0.
// items if (level.front() != 0)
// allows). return false;
if (!std::is_sorted(level.begin(), level.begin(), // check: all the offsets in a level should be ascending(no same
[](size_t a, size_t b) { // items
if (a < b) // allows).
return true; if (!std::is_sorted(level.begin(), level.begin(),
return false; [](size_t a, size_t b) {
})) { if (a < b)
std::cout << "ascending error"; return true;
return false; return false;
} })) {
} std::cout << "ascending error";
// check: the lowest level's last offset should equals return false;
// `tensor_height` if
// tensor_height>0.
if (tensor_height > 0 && (size_t)tensor_height != in.back().back())
return false;
// check: the higher level's last offset should equals the lower
// level's
// size-1.
// NOTE LoD store the levels from top to bottom, so the higher level
// goes
// first.
for (size_t level = 0; level < in.size() - 1; level++) {
if (in[level].back() != in[level + 1].size() - 1)
return false;
}
return true;
} }
}
bool CheckAbsLoD(const LoD &in, int tensor_height) { // check: the lowest level's last offset should equals
if (in.empty()) // `tensor_height` if
return true; // tensor_height>0.
for (const auto &level : in) { if (tensor_height > 0 && (size_t)tensor_height != in.back().back())
// check: all the offsets in a level should be ascending(no same return false;
// items
// allows). // check: the higher level's last offset should equals the lower
if (!std::is_sorted(level.begin(), level.begin(), // level's
[](size_t a, size_t b) { // size-1.
if (a < b) // NOTE LoD store the levels from top to bottom, so the higher level
return true; // goes
return false; // first.
})) { for (size_t level = 0; level < in.size() - 1; level++) {
return false; if (in[level].back() != in[level + 1].size() - 1)
} return false;
}
// check: there should be more than 2 offsets existing in each return true;
// level. }
if (level.size() < 2)
return false; bool CheckAbsLoD(const LoD &in, int tensor_height) {
if (in.empty())
// check: the first offset of each level should be 0, and the return true;
// last should be for (const auto &level : in) {
// the same(the height of underlying tensor). // check: all the offsets in a level should be ascending(no same
if (level.front() != 0) // items
return false; // allows).
if (tensor_height < 0) { if (!std::is_sorted(level.begin(), level.begin(),
tensor_height = level.back(); [](size_t a, size_t b) {
} else if ((size_t)tensor_height != level.back()) { if (a < b)
return false; return true;
} return false;
} })) {
return true; return false;
} }
using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>; // check: there should be more than 2 offsets existing in each
// level.
LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, if (level.size() < 2)
size_t start_idx, return false;
size_t end_idx,
size_t start_level) { // check: the first offset of each level should be 0, and the
LoD sub_lod; // last should be
// the same(the height of underlying tensor).
for (size_t level_idx = start_level; level_idx < lod.size(); if (level.front() != 0)
++level_idx) { return false;
// PADDLE_ENFORCE_LE(start_idx, end_idx); if (tensor_height < 0) {
// PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size()); tensor_height = level.back();
std::vector<size_t> level_lens; } else if ((size_t)tensor_height != level.back()) {
for (size_t i = start_idx; i < end_idx; ++i) { return false;
level_lens.push_back(lod[level_idx][i + 1] -
lod[level_idx][i]);
}
sub_lod.emplace_back(level_lens);
start_idx = lod[level_idx][start_idx];
end_idx = lod[level_idx][end_idx];
}
return LoDAndOffset{sub_lod, {start_idx, end_idx}};
} }
}
void AppendLoD(LoD *lod, const LoD &lod_length) { return true;
// PADDLE_ENFORCE( }
// lod->empty() || lod->size() == lod_length.size(),
// "The lod_length should has the same size with the appended using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
// lod.");
if (lod->empty()) { LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
for (size_t i = 0; i < lod_length.size(); ++i) { size_t end_idx, size_t start_level) {
lod->emplace_back(1, 0); // size = 1, value = 0; LoD sub_lod;
}
*lod = LoD(lod_length.size(), std::vector<size_t>({0})); for (size_t level_idx = start_level; level_idx < lod.size(); ++level_idx) {
} // PADDLE_ENFORCE_LE(start_idx, end_idx);
for (size_t i = 0; i < lod->size(); ++i) { // PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size());
auto &level = (*lod)[i]; std::vector<size_t> level_lens;
for (size_t len : lod_length[i]) { for (size_t i = start_idx; i < end_idx; ++i) {
level.push_back(level.back() + len); level_lens.push_back(lod[level_idx][i + 1] - lod[level_idx][i]);
}
}
} }
sub_lod.emplace_back(level_lens);
void SerializeToStream(std::ostream &os, const LoDTensor &tensor) { start_idx = lod[level_idx][start_idx];
{ // the 1st field, uint32_t version for LoDTensor end_idx = lod[level_idx][end_idx];
constexpr uint32_t version = 0; }
os.write(reinterpret_cast<const char *>(&version),
sizeof(version)); return LoDAndOffset{sub_lod, {start_idx, end_idx}};
} }
{
// the 2st field, LoD information void AppendLoD(LoD *lod, const LoD &lod_length) {
// uint64_t lod_level // PADDLE_ENFORCE(
// uint64_t lod_level_1 size in byte. // lod->empty() || lod->size() == lod_length.size(),
// int* lod_level_1 data // "The lod_length should has the same size with the appended
// ... // lod.");
auto lod = tensor.lod(); if (lod->empty()) {
uint64_t size = lod.size(); for (size_t i = 0; i < lod_length.size(); ++i) {
os.write(reinterpret_cast<const char *>(&size), sizeof(size)); lod->emplace_back(1, 0); // size = 1, value = 0;
for (auto &each : lod) {
size = each.size() *
sizeof(framework::LoD::value_type::value_type);
os.write(reinterpret_cast<const char *>(&size),
sizeof(size));
os.write(reinterpret_cast<const char *>(each.data()),
static_cast<std::streamsize>(size));
}
}
// the 3st field, Tensor
TensorToStream(os, static_cast<Tensor>(tensor));
} }
*lod = LoD(lod_length.size(), std::vector<size_t>({0}));
void DeserializeFromStream(std::istream &is, LoDTensor *tensor) { }
{ for (size_t i = 0; i < lod->size(); ++i) {
// the 1st field, unit32_t version for LoDTensor auto &level = (*lod)[i];
uint32_t version; for (size_t len : lod_length[i]) {
is.read(reinterpret_cast<char *>(&version), sizeof(version)); level.push_back(level.back() + len);
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is }
// supported"); }
} }
{
// the 2st field, LoD information void SerializeToStream(std::ostream &os, const LoDTensor &tensor) {
uint64_t lod_level; { // the 1st field, uint32_t version for LoDTensor
is.read(reinterpret_cast<char *>(&lod_level), constexpr uint32_t version = 0;
sizeof(lod_level)); os.write(reinterpret_cast<const char *>(&version), sizeof(version));
auto &lod = *tensor->mutable_lod(); }
lod.resize(lod_level); {
for (uint64_t i = 0; i < lod_level; ++i) { // the 2st field, LoD information
uint64_t size; // uint64_t lod_level
is.read(reinterpret_cast<char *>(&size), sizeof(size)); // uint64_t lod_level_1 size in byte.
std::vector<size_t> tmp(size / sizeof(size_t)); // int* lod_level_1 data
is.read(reinterpret_cast<char *>(tmp.data()), // ...
static_cast<std::streamsize>(size)); auto lod = tensor.lod();
lod[i] = tmp; uint64_t size = lod.size();
} os.write(reinterpret_cast<const char *>(&size), sizeof(size));
}
// the 3st filed, Tensor for (auto &each : lod) {
TensorFromStream(is, static_cast<Tensor *>(tensor)); size = each.size() * sizeof(framework::LoD::value_type::value_type);
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
os.write(reinterpret_cast<const char *>(each.data()),
static_cast<std::streamsize>(size));
}
}
// the 3st field, Tensor
TensorToStream(os, static_cast<Tensor>(tensor));
}
void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
{
// the 1st field, unit32_t version for LoDTensor
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is
// supported");
}
{
// the 2st field, LoD information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
lod[i] = tmp;
} }
}
// the 3st filed, Tensor
TensorFromStream(is, static_cast<Tensor *>(tensor));
}
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/lod_tensor.h
浏览文件 @ 45de1e84
...@@ -23,190 +23,186 @@ limitations under the License. */ ...@@ -23,190 +23,186 @@ limitations under the License. */
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
/* /*
* LoD is short for Level of Details. * LoD is short for Level of Details.
* *
* - in a level, each element indicates relative offset of the lower * - in a level, each element indicates relative offset of the lower
* level * level
* - the first element should be 0 and that indicates that this sequence * - the first element should be 0 and that indicates that this sequence
* start * start
* from 0 * from 0
* - each sequence's begin and end(no-inclusive) is level[id, id+1] * - each sequence's begin and end(no-inclusive) is level[id, id+1]
* *
* For example: * For example:
* 3-level LoD stores * 3-level LoD stores
* *
* 0 2 3 * 0 2 3
* 0 2 4 7 * 0 2 4 7
* 0 2 5 7 10 12 15 20 * 0 2 5 7 10 12 15 20
*/ */
using LoD = std::vector<std::vector<size_t>>; using LoD = std::vector<std::vector<size_t>>;
std::ostream &operator<<(std::ostream &os, const LoD &lod); std::ostream &operator<<(std::ostream &os, const LoD &lod);
std::ostream &operator<<(std::ostream &os, const LoDTensor &t); std::ostream &operator<<(std::ostream &os, const LoDTensor &t);
std::string LoDToString(const LoD &lod); std::string LoDToString(const LoD &lod);
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin, LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end); size_t elem_end);
/* /*
* Transform an LoD from relative offsets to absolute offsets. * Transform an LoD from relative offsets to absolute offsets.
*/ */
LoD ToAbsOffset(const LoD &in); LoD ToAbsOffset(const LoD &in);
bool operator==(const LoD &a, const LoD &b); bool operator==(const LoD &a, const LoD &b);
/* /*
* Check whether this lod's format is valid. * Check whether this lod's format is valid.
* *
* ATTENTION: * ATTENTION:
* - Empty lod is treated as valid. * - Empty lod is treated as valid.
* *
* It will check two things: * It will check two things:
* *
* 1. all the offsets in a level should be ascending(no same items * 1. all the offsets in a level should be ascending(no same items
* allows). * allows).
* 2. there should be more than 2 offsets existing in each level. * 2. there should be more than 2 offsets existing in each level.
* 3. the higher level's last offset should equals the lower level's * 3. the higher level's last offset should equals the lower level's
* size-1. * size-1.
* 4. the first offset(the begin offset) of each level should be 0. * 4. the first offset(the begin offset) of each level should be 0.
* 5. the lowest level's last offset should equals `tensor_height` if * 5. the lowest level's last offset should equals `tensor_height` if
* tensor_height>0. * tensor_height>0.
*/ */
bool CheckLoD(const LoD &in, int tensor_height = -1); bool CheckLoD(const LoD &in, int tensor_height = -1);
/* /*
* Check whether this absolute lod's format is valid. * Check whether this absolute lod's format is valid.
* *
* ATTENTION: * ATTENTION:
* - Empty lod is treated as valid. * - Empty lod is treated as valid.
* *
* It will check two things: * It will check two things:
* 1. all the offsets in a level should be ascending(no same items * 1. all the offsets in a level should be ascending(no same items
* allows) * allows)
* 2. there should be more than 2 offsets existing in each level. * 2. there should be more than 2 offsets existing in each level.
* 3. the first offset of each level should be 0, and the last should * 3. the first offset of each level should be 0, and the last should
* be the * be the
* same(the height of underlying tensor) or `tensor_height` if * same(the height of underlying tensor) or `tensor_height` if
* tensor_height>0. * tensor_height>0.
*/ */
bool CheckAbsLoD(const LoD &in, int tensor_height = -1); bool CheckAbsLoD(const LoD &in, int tensor_height = -1);
/* /*
* LoDTensor (Level of details Tensor) * LoDTensor (Level of details Tensor)
* see https://en.wikipedia.org/wiki/Level_of_details for reference. * see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/ */
class LoDTensor : public Tensor { class LoDTensor : public Tensor {
public: public:
LoDTensor() : Tensor() {} LoDTensor() : Tensor() {}
explicit LoDTensor(const LoD &lod) : lod_(lod) {} explicit LoDTensor(const LoD &lod) : lod_(lod) {}
void set_lod(const LoD &lod) { lod_ = lod; } void set_lod(const LoD &lod) { lod_ = lod; }
const LoD &lod() const { return lod_; } const LoD &lod() const { return lod_; }
LoD *mutable_lod() { return &lod_; } LoD *mutable_lod() { return &lod_; }
/* /*
* Get the start offset and end offset of an element from LoD. * Get the start offset and end offset of an element from LoD.
*/ */
std::pair<size_t, size_t> lod_element(size_t level, std::pair<size_t, size_t> lod_element(size_t level, size_t elem) const {
size_t elem) const { // PADDLE_ENFORCE_LT(level, NumLevels());
// PADDLE_ENFORCE_LT(level, NumLevels()); // PADDLE_ENFORCE_LT(elem, NumElements(level));
// PADDLE_ENFORCE_LT(elem, NumElements(level)); return std::make_pair((lod_)[level][elem], (lod_)[level][elem + 1]);
return std::make_pair((lod_)[level][elem], }
(lod_)[level][elem + 1]);
} /*
* Number of LoDTensor's levels, each level has units of data, for
/* * example,
* Number of LoDTensor's levels, each level has units of data, for * in the sentence's view, article, paragraph, sentence are 3
* example, * levels.
* in the sentence's view, article, paragraph, sentence are 3 */
* levels. size_t NumLevels() const { return lod_.size(); }
*/
size_t NumLevels() const { return lod_.size(); } /*
* Number of elements in a level.
/* */
* Number of elements in a level. size_t NumElements(size_t level = 0) const {
*/ // PADDLE_ENFORCE_LT(level, NumLevels());
size_t NumElements(size_t level = 0) const { // the last offset is the end of last element
// PADDLE_ENFORCE_LT(level, NumLevels()); return (lod_)[level].size() - 1;
// the last offset is the end of last element }
return (lod_)[level].size() - 1;
} private:
LoD lod_;
private: };
LoD lod_;
}; /*
* Expand the `source` to fit the LoD of `lod`. For example, a `source`
/* * LoDTensor is
* Expand the `source` to fit the LoD of `lod`. For example, a `source` * - LoD: [0, 2]
* LoDTensor is * - tensor: [a0, a1]
* - LoD: [0, 2] * a `lod` is
* - tensor: [a0, a1] * - LoD: [0 3 5]
* a `lod` is * returns a new LoDTensor
* - LoD: [0 3 5] * - [a0 a0 a0 a1 a1]
* returns a new LoDTensor */
* - [a0 a0 a0 a1 a1] template <typename T>
*/ LoDTensor LodExpand(const LoDTensor &source, const LoD &lod, size_t level) {
template <typename T> LoD abs_lod = ToAbsOffset(lod);
LoDTensor LodExpand(const LoDTensor &source, const LoD &lod, const auto &lod_level = lod[level];
size_t level) { size_t num_instances = source.dims()[0];
LoD abs_lod = ToAbsOffset(lod);
const auto &lod_level = lod[level]; // new tensor
size_t num_instances = source.dims()[0]; LoDTensor tensor;
tensor.set_lod(lod);
// new tensor auto dims = source.dims();
LoDTensor tensor; dims[0] = lod_level.back();
tensor.set_lod(lod); tensor.Resize(dims);
auto dims = source.dims(); tensor.mutable_data<T>();
dims[0] = lod_level.back();
tensor.Resize(dims); // PADDLE_ENFORCE_EQ(num_instances, lod_level.size() - 1);
tensor.mutable_data<T>(); for (size_t ins = 0; ins < num_instances; ins++) {
for (size_t elem = lod_level[ins]; elem < lod_level[ins + 1]; elem++) {
// PADDLE_ENFORCE_EQ(num_instances, lod_level.size() - 1); auto slice = tensor.Slice(elem, elem + 1);
for (size_t ins = 0; ins < num_instances; ins++) { TensorCopy(source.Slice(ins, ins + 1), &slice);
for (size_t elem = lod_level[ins]; elem < lod_level[ins + 1];
elem++) {
auto slice = tensor.Slice(elem, elem + 1);
TensorCopy(source.Slice(ins, ins + 1), &slice);
}
}
return tensor;
} }
}
// Get the absolute offset of a lod[start_level][start_idx:end_idx] and return tensor;
// relative length of details for every levels(i.e., [start_level: ]). }
//
// For example, // Get the absolute offset of a lod[start_level][start_idx:end_idx] and
// lod = [[0, 3, 4, 8], [0, 9, 10, 11, 13, 17, 19, 22, 24]] // relative length of details for every levels(i.e., [start_level: ]).
// start_level = 0 //
// start_idx = 1 // For example,
// end_idx = 3 // lod = [[0, 3, 4, 8], [0, 9, 10, 11, 13, 17, 19, 22, 24]]
// // start_level = 0
// Returns: // start_idx = 1
// LoD = [[1, 4], [2, 4, 2, 3, 2]] // end_idx = 3
// pair<size_t, size_t> = {11, 24} //
std::pair<LoD, std::pair<size_t, size_t>> // Returns:
GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx, // LoD = [[1, 4], [2, 4, 2, 3, 2]]
size_t end_idx, size_t start_level); // pair<size_t, size_t> = {11, 24}
std::pair<LoD, std::pair<size_t, size_t>>
void AppendLoD(LoD *lod, const LoD &lod_length); GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx, size_t end_idx,
size_t start_level);
/*
* Serialize/Desiralize LoDTensor to std::ostream void AppendLoD(LoD *lod, const LoD &lod_length);
* You can pass ofstream or ostringstream to serilize to file
* or to a in memory string. GPU tensor will be copied to CPU. /*
*/ * Serialize/Desiralize LoDTensor to std::ostream
void SerializeToStream(std::ostream &os, const LoDTensor &tensor); * You can pass ofstream or ostringstream to serilize to file
* or to a in memory string. GPU tensor will be copied to CPU.
void DeserializeFromStream(std::istream &is, LoDTensor *tensor); */
void SerializeToStream(std::ostream &os, const LoDTensor &tensor);
} // namespace framework
void DeserializeFromStream(std::istream &is, LoDTensor *tensor);
} // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/op_desc.cpp
浏览文件 @ 45de1e84
...@@ -5,58 +5,55 @@ ...@@ -5,58 +5,55 @@
#include "op_desc.h" #include "op_desc.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
OpDesc::OpDesc(const proto::OpDesc &desc) : desc_(desc) { OpDesc::OpDesc(const proto::OpDesc &desc) : desc_(desc) {
for (int i = 0; i < desc_.inputs_size(); ++i) { for (int i = 0; i < desc_.inputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.inputs(i); const proto::OpDesc::Var &var = desc_.inputs(i);
std::vector<std::string> &args = inputs_[var.parameter()]; std::vector<std::string> &args = inputs_[var.parameter()];
int arg_size = var.arguments_size(); int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) { for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j)); args.push_back(var.arguments(j));
}
}
for (int i = 0; i < desc_.outputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.outputs(i);
std::vector<std::string> &args = outputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
}
for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
if (attr.type() != proto::AttrType::BLOCK) {
attrs_[attr_name] = Attribute::GetAttrValue(attr);
// if (attr.type() == proto::AttrType::INT){
// std::cout << " attrName " << attr_name << " " <<
// attrs_[attr_name].Get<int>() << std::endl;
// }
}
}
} }
}
const std::vector<std::string> &
OpDesc::Input(const std::string &name) const { for (int i = 0; i < desc_.outputs_size(); ++i) {
return inputs_.find(name)->second; const proto::OpDesc::Var &var = desc_.outputs(i);
std::vector<std::string> &args = outputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
} }
}
const std::vector<std::string> &
OpDesc::Output(const std::string &name) const { for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
return outputs_.find(name)->second; std::string attr_name = attr.name();
if (attr.type() != proto::AttrType::BLOCK) {
attrs_[attr_name] = Attribute::GetAttrValue(attr);
// if (attr.type() == proto::AttrType::INT){
// std::cout << " attrName " << attr_name << " " <<
// attrs_[attr_name].Get<int>() << std::endl;
// }
} }
}
}
Attribute OpDesc::GetAttr(const std::string &name) const { const std::vector<std::string> &OpDesc::Input(const std::string &name) const {
auto it = attrs_.find(name); return inputs_.find(name)->second;
return it->second; }
}
const std::unordered_map<std::string, Attribute> & const std::vector<std::string> &OpDesc::Output(const std::string &name) const {
OpDesc::GetAttrMap() const { return outputs_.find(name)->second;
return attrs_; }
}
Attribute OpDesc::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
return it->second;
}
const std::unordered_map<std::string, Attribute> &OpDesc::GetAttrMap() const {
return attrs_;
}
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/op_desc.h
浏览文件 @ 45de1e84
...@@ -23,31 +23,29 @@ SOFTWARE. ...@@ -23,31 +23,29 @@ SOFTWARE.
#include "paddle_mobile_object.h" #include "paddle_mobile_object.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class OpDesc : PaddleMobileObject { class OpDesc : PaddleMobileObject {
public: public:
OpDesc(const proto::OpDesc &desc); OpDesc(const proto::OpDesc &desc);
const std::vector<std::string> & const std::vector<std::string> &Input(const std::string &name) const;
Input(const std::string &name) const; const std::vector<std::string> &Output(const std::string &name) const;
const std::vector<std::string> & Attribute GetAttr(const std::string &name) const;
Output(const std::string &name) const;
Attribute GetAttr(const std::string &name) const;
const VariableNameMap &GetInputs() { return inputs_; } const VariableNameMap &GetInputs() { return inputs_; }
const VariableNameMap &GetOutputs() { return outputs_; } const VariableNameMap &GetOutputs() { return outputs_; }
const AttributeMap &GetAttrMap() const; const AttributeMap &GetAttrMap() const;
const std::string &Type() { return desc_.type(); }; const std::string &Type() { return desc_.type(); };
private: private:
proto::OpDesc desc_; proto::OpDesc desc_;
VariableNameMap inputs_; VariableNameMap inputs_;
VariableNameMap outputs_; VariableNameMap outputs_;
AttributeMap attrs_; AttributeMap attrs_;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/op_info.h
浏览文件 @ 45de1e84
...@@ -22,74 +22,73 @@ SOFTWARE. ...@@ -22,74 +22,73 @@ SOFTWARE.
#include "framework.pb.h" #include "framework.pb.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename Dtype> struct OpInfo { template <typename Dtype> struct OpInfo {
OpCreator<Dtype> creator_; OpCreator<Dtype> creator_;
const OpCreator<Dtype> &Creator() const { const OpCreator<Dtype> &Creator() const {
// PADDLE_ENFORCE_NOT_NULL(creator_, // PADDLE_ENFORCE_NOT_NULL(creator_,
// "Operator Creator has not been // "Operator Creator has not been
// registered"); // registered");
return creator_; return creator_;
} }
}; };
template <typename Dtype> class OpInfoMap; template <typename Dtype> class OpInfoMap;
template <typename Dtype> template <typename Dtype> static OpInfoMap<Dtype> *g_op_info_map = nullptr;
static OpInfoMap<Dtype> *g_op_info_map = nullptr;
template <typename Dtype> class OpInfoMap {
template <typename Dtype> class OpInfoMap { public:
public: static OpInfoMap &Instance() {
static OpInfoMap &Instance() { if (g_op_info_map<Dtype> == nullptr) {
if (g_op_info_map<Dtype> == nullptr) { g_op_info_map<Dtype> = new OpInfoMap();
g_op_info_map<Dtype> = new OpInfoMap(); }
} return *g_op_info_map<Dtype>;
return *g_op_info_map<Dtype>; };
};
bool Has(const std::string &op_type) const {
bool Has(const std::string &op_type) const { return map_.find(op_type) != map_.end();
return map_.find(op_type) != map_.end(); }
}
void Insert(const std::string &type, const OpInfo<Dtype> &info) {
void Insert(const std::string &type, const OpInfo<Dtype> &info) { // PADDLE_ENFORCE(!Has(type), "Operator %s has been
// PADDLE_ENFORCE(!Has(type), "Operator %s has been // registered", type);
// registered", type); map_.insert({type, info});
map_.insert({type, info}); }
}
const OpInfo<Dtype> &Get(const std::string &type) const {
const OpInfo<Dtype> &Get(const std::string &type) const { auto op_info_ptr = GetNullable(type);
auto op_info_ptr = GetNullable(type); // PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not
// PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not // been
// been // registered",
// registered", // type);
// type); return *op_info_ptr;
return *op_info_ptr; }
}
const OpInfo<Dtype> *GetNullable(const std::string &type) const {
const OpInfo<Dtype> *GetNullable(const std::string &type) const { auto it = map_.find(type);
auto it = map_.find(type); if (it == map_.end()) {
if (it == map_.end()) { return nullptr;
return nullptr; } else {
} else { return &it->second;
return &it->second; }
} }
}
const std::unordered_map<std::string, OpInfo<Dtype>> &map() const {
const std::unordered_map<std::string, OpInfo<Dtype>> &map() const { return map_;
return map_; }
}
std::unordered_map<std::string, OpInfo<Dtype>> *mutable_map() {
std::unordered_map<std::string, OpInfo<Dtype>> *mutable_map() { return &map_;
return &map_; }
}
private:
private: OpInfoMap() = default;
OpInfoMap() = default; std::unordered_map<std::string, OpInfo<Dtype>> map_;
std::unordered_map<std::string, OpInfo<Dtype>> map_;
// DISABLE_COPY_AND_ASSIGN(OpInfoMap);
// DISABLE_COPY_AND_ASSIGN(OpInfoMap); };
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/op_kernel_type.h
浏览文件 @ 45de1e84
...@@ -22,51 +22,44 @@ SOFTWARE. ...@@ -22,51 +22,44 @@ SOFTWARE.
#include "framework.pb.h" #include "framework.pb.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
struct OpKernelType { struct OpKernelType {
struct Hash { struct Hash {
size_t operator()(const OpKernelType &key) const { size_t operator()(const OpKernelType &key) const {
int data_type = static_cast<int>(key.data_type_) int data_type = static_cast<int>(key.data_type_) << LEFT_SHIFT;
<< LEFT_SHIFT; int data_layout = static_cast<int>(key.data_layout_)
int data_layout = static_cast<int>(key.data_layout_) << (LEFT_SHIFT * 2);
<< (LEFT_SHIFT * 2);
std::hash<int> hasher; std::hash<int> hasher;
return hasher(data_type + data_layout); return hasher(data_type + data_layout);
} }
}; };
// place, data_type, library_type kinds less than 2^8 // place, data_type, library_type kinds less than 2^8
constexpr static int LEFT_SHIFT = 8; constexpr static int LEFT_SHIFT = 8;
proto::VarType::Type data_type_; proto::VarType::Type data_type_;
DataLayout data_layout_; DataLayout data_layout_;
OpKernelType(proto::VarType::Type data_type, OpKernelType(proto::VarType::Type data_type,
DataLayout data_layout = DataLayout::kAnyLayout) DataLayout data_layout = DataLayout::kAnyLayout)
: data_type_(data_type), data_layout_(data_layout) {} : data_type_(data_type), data_layout_(data_layout) {}
bool operator==(const OpKernelType &o) const { bool operator==(const OpKernelType &o) const {
return data_type_ == o.data_type_ && return data_type_ == o.data_type_ && data_layout_ == o.data_layout_;
data_layout_ == o.data_layout_; }
}
bool operator!=(const OpKernelType &o) const { bool operator!=(const OpKernelType &o) const { return !(*this == o); }
return !(*this == o); };
}
};
inline bool NeedTransformLayout(const DataLayout &l, inline bool NeedTransformLayout(const DataLayout &l, const DataLayout &r) {
const DataLayout &r) { return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout && l != r;
return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout && }
l != r;
}
inline bool TransFromNeeded(const OpKernelType &l, inline bool TransFromNeeded(const OpKernelType &l, const OpKernelType &r) {
const OpKernelType &r) { return (l.data_type_ != r.data_type_) ||
return (l.data_type_ != r.data_type_) || NeedTransformLayout(l.data_layout_, r.data_layout_);
NeedTransformLayout(l.data_layout_, r.data_layout_); }
}
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/op_proto_maker.h
浏览文件 @ 45de1e84
...@@ -19,8 +19,8 @@ SOFTWARE. ...@@ -19,8 +19,8 @@ SOFTWARE.
#pragma once #pragma once
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
// this class not only make proto but also init attribute checkers. // this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {}; class OpProtoAndCheckerMaker {};
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/operator.cpp
浏览文件 @ 45de1e84
...@@ -20,23 +20,23 @@ SOFTWARE. ...@@ -20,23 +20,23 @@ SOFTWARE.
#include "op_info.h" #include "op_info.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename Dtype> template <typename Dtype>
OperatorBase<Dtype>::OperatorBase(const std::string &type, OperatorBase<Dtype>::OperatorBase(const std::string &type,
const VariableNameMap &inputs, const VariableNameMap &inputs,
const VariableNameMap &outputs, const VariableNameMap &outputs,
const AttributeMap &attrs, const AttributeMap &attrs,
std::shared_ptr<Scope> scope) std::shared_ptr<Scope> scope)
: type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs), : type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs),
scope_(scope) { scope_(scope) {
CheckAllInputOutputSet(); CheckAllInputOutputSet();
} }
template <typename Dtype> template <typename Dtype>
void OperatorBase<Dtype>::CheckAllInputOutputSet() const {} void OperatorBase<Dtype>::CheckAllInputOutputSet() const {}
template class OperatorBase<CPU>; template class OperatorBase<CPU>;
template class OperatorWithKernel<CPU>; template class OperatorWithKernel<CPU>;
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/operator.h
浏览文件 @ 45de1e84
...@@ -33,68 +33,64 @@ SOFTWARE. ...@@ -33,68 +33,64 @@ SOFTWARE.
#include "variable.h" #include "variable.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
static std::unordered_map<std::string, std::vector<std::string>> static std::unordered_map<std::string, std::vector<std::string>>
op_input_output_key = { op_input_output_key = {
{"conv2d", {"Input", "Output"}}, {"relu", {"X", "Out"}}, {"conv2d", {"Input", "Output"}}, {"relu", {"X", "Out"}},
{"softmax", {"X", "Out"}}, {"mul", {"X", "Out"}}, {"softmax", {"X", "Out"}}, {"mul", {"X", "Out"}},
{"elementwise_add", {"X", "Out"}}, {"pool2d", {"X", "Out"}}, {"elementwise_add", {"X", "Out"}}, {"pool2d", {"X", "Out"}},
{"batch_norm", {"X", "Y"}}, {"lrn", {"X", "Out"}}, {"batch_norm", {"X", "Y"}}, {"lrn", {"X", "Out"}},
{"concat", {"X", "Out"}}, {"concat", {"X", "Out"}},
}; };
template <typename Dtype> class OperatorBase : PaddleMobileObject { template <typename Dtype> class OperatorBase : PaddleMobileObject {
public: public:
OperatorBase(const std::string &type, const VariableNameMap &inputs, OperatorBase(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const VariableNameMap &outputs, const AttributeMap &attrs,
const AttributeMap &attrs, std::shared_ptr<Scope> scope);
std::shared_ptr<Scope> scope); virtual ~OperatorBase() {}
virtual ~OperatorBase() {} virtual void Run() const = 0;
virtual void Run() const = 0;
const VariableNameMap &Inputs() const { return inputs_; } const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; } const VariableNameMap &Outputs() const { return outputs_; }
const std::string &Type() const { return type_; } const std::string &Type() const { return type_; }
const AttributeMap &Attrs() const { return attrs_; } const AttributeMap &Attrs() const { return attrs_; }
void ClearVariables() const { void ClearVariables() const {
if (this->scope_) { if (this->scope_) {
this->scope_->EraseVars(this->inputs_.at("Filter")); this->scope_->EraseVars(this->inputs_.at("Filter"));
this->scope_->EraseVars(this->inputs_.at("Input")); this->scope_->EraseVars(this->inputs_.at("Input"));
} }
} }
protected: protected:
std::shared_ptr<Scope> scope_; std::shared_ptr<Scope> scope_;
std::string type_; std::string type_;
VariableNameMap inputs_; VariableNameMap inputs_;
VariableNameMap outputs_; VariableNameMap outputs_;
AttributeMap attrs_; AttributeMap attrs_;
private: private:
void CheckAllInputOutputSet() const; void CheckAllInputOutputSet() const;
}; };
template <typename Dtype> template <typename Dtype>
class OperatorWithKernel : public OperatorBase<Dtype> { class OperatorWithKernel : public OperatorBase<Dtype> {
public: public:
OperatorWithKernel(const std::string &type, OperatorWithKernel(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &inputs, const VariableNameMap &outputs,
const VariableNameMap &outputs, const AttributeMap &attrs, std::shared_ptr<Scope> scope)
const AttributeMap &attrs, : OperatorBase<Dtype>(type, inputs, outputs, attrs, scope) {}
std::shared_ptr<Scope> scope) virtual void InferShape() const = 0;
: OperatorBase<Dtype>(type, inputs, outputs, attrs, scope) {} virtual void Run() const = 0;
virtual void InferShape() const = 0; };
virtual void Run() const = 0;
};
template <typename Dtype, typename P> template <typename Dtype, typename P> class OpKernelBase : PaddleMobileObject {
class OpKernelBase : PaddleMobileObject { public:
public: virtual void Compute(const P &para) const = 0;
virtual void Compute(const P &para) const = 0;
virtual ~OpKernelBase() = default; virtual ~OpKernelBase() = default;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/paddle_mobile_object.h
浏览文件 @ 45de1e84
...@@ -23,14 +23,14 @@ SOFTWARE. ...@@ -23,14 +23,14 @@ SOFTWARE.
namespace paddle_mobile { namespace paddle_mobile {
class PaddleMobileObject { class PaddleMobileObject {
public: public:
virtual std::string ToString() { virtual std::string ToString() {
char address[128] = {0}; char address[128] = {0};
sprintf(address, "%p", this); sprintf(address, "%p", this);
return std::string(address); return std::string(address);
} }
private: private:
}; };
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/program-optimize/node.cpp
浏览文件 @ 45de1e84
...@@ -21,44 +21,44 @@ SOFTWARE. ...@@ -21,44 +21,44 @@ SOFTWARE.
#include "node.h" #include "node.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
Node &Node::operator>(const Node &out) { Node &Node::operator>(const Node &out) {
std::shared_ptr<Node> node = std::make_shared<Node>(Node(out)); std::shared_ptr<Node> node = std::make_shared<Node>(Node(out));
outputs_.push_back(node); outputs_.push_back(node);
return *node; return *node;
} }
bool Node::operator==(const Node &in) { bool Node::operator==(const Node &in) {
if (in.type_ == this->type_) { if (in.type_ == this->type_) {
if (this->outputs_.size() == in.outputs_.size()) { if (this->outputs_.size() == in.outputs_.size()) {
for (int i = 0; i < outputs_.size(); ++i) { for (int i = 0; i < outputs_.size(); ++i) {
if (!(*outputs_[i] == *in.outputs_[i])) { if (!(*outputs_[i] == *in.outputs_[i])) {
return false;
}
}
} else {
return false; return false;
} }
} else {
return false;
} }
return true; } else {
return false;
} }
} else {
return false;
}
return true;
}
std::string Node::ToString(std::string blank) const { std::string Node::ToString(std::string blank) const {
std::stringstream ss; std::stringstream ss;
ss << type_ << ": \n"; ss << type_ << ": \n";
for (int i = 0; i < outputs_.size(); ++i) { for (int i = 0; i < outputs_.size(); ++i) {
ss << blank << outputs_[i]->ToString(blank + " ") << ""; ss << blank << outputs_[i]->ToString(blank + " ") << "";
} }
return ss.str(); return ss.str();
} }
std::string Node::ToString() const { return this->ToString(" "); } std::string Node::ToString() const { return this->ToString(" "); }
Print &operator<<(Print &printer, const Node &node) { Print &operator<<(Print &printer, const Node &node) {
printer << node.ToString(); printer << node.ToString();
return printer; return printer;
}
}
} }
} // namespace framework
} // namespace paddle_mobile
src/framework/program-optimize/node.h
浏览文件 @ 45de1e84
...@@ -25,22 +25,22 @@ SOFTWARE. ...@@ -25,22 +25,22 @@ SOFTWARE.
#include "framework/paddle_mobile_object.h" #include "framework/paddle_mobile_object.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class Node : PaddleMobileObject { class Node : PaddleMobileObject {
public: public:
Node(const std::string &type) : type_(type) {} Node(const std::string &type) : type_(type) {}
Node &operator>(const Node &out); Node &operator>(const Node &out);
bool operator==(const Node &in); bool operator==(const Node &in);
std::string ToString() const; std::string ToString() const;
private: private:
std::string ToString(std::string blank) const; std::string ToString(std::string blank) const;
std::vector<std::shared_ptr<Node>> outputs_; std::vector<std::shared_ptr<Node>> outputs_;
std::string type_; std::string type_;
}; };
Print &operator<<(Print &printer, const Node &node); Print &operator<<(Print &printer, const Node &node);
} } // namespace framework
} } // namespace paddle_mobile
src/framework/program-optimize/program_optimize.cpp
浏览文件 @ 45de1e84
...@@ -19,7 +19,7 @@ SOFTWARE. ...@@ -19,7 +19,7 @@ SOFTWARE.
#include "program_optimize.h" #include "program_optimize.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
std::shared_ptr<ProgramDesc> ProgramOptimize::Optimize() {} std::shared_ptr<ProgramDesc> ProgramOptimize::Optimize() {}
} } // namespace framework
} } // namespace paddle_mobile
src/framework/program-optimize/program_optimize.h
浏览文件 @ 45de1e84
...@@ -21,15 +21,15 @@ SOFTWARE. ...@@ -21,15 +21,15 @@ SOFTWARE.
#include "framework/program_desc.h" #include "framework/program_desc.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class ProgramOptimize { class ProgramOptimize {
public: public:
ProgramOptimize(std::shared_ptr<ProgramDesc> ori_desc) ProgramOptimize(std::shared_ptr<ProgramDesc> ori_desc)
: ori_desc_(ori_desc) {} : ori_desc_(ori_desc) {}
std::shared_ptr<ProgramDesc> Optimize(); std::shared_ptr<ProgramDesc> Optimize();
private: private:
std::shared_ptr<ProgramDesc> ori_desc_; std::shared_ptr<ProgramDesc> ori_desc_;
}; };
} } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/program.cpp
浏览文件 @ 45de1e84
...@@ -17,5 +17,5 @@ SOFTWARE. ...@@ -17,5 +17,5 @@ SOFTWARE.
==============================================================================*/ ==============================================================================*/
namespace paddle_mobile { namespace paddle_mobile {
namespace framework {} namespace framework {}
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/program.h
浏览文件 @ 45de1e84
...@@ -24,17 +24,17 @@ SOFTWARE. ...@@ -24,17 +24,17 @@ SOFTWARE.
#include "scope.h" #include "scope.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename Dtype, Precision P = Precision::FP32> template <typename Dtype, Precision P = Precision::FP32>
class Program : PaddleMobileObject { class Program : PaddleMobileObject {
public: public:
std::shared_ptr<ProgramDesc> originProgram; std::shared_ptr<ProgramDesc> originProgram;
std::shared_ptr<ProgramDesc> optimizeProgram; std::shared_ptr<ProgramDesc> optimizeProgram;
std::shared_ptr<Scope> scope; std::shared_ptr<Scope> scope;
private: private:
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/program_desc.cpp
浏览文件 @ 45de1e84
...@@ -5,18 +5,18 @@ ...@@ -5,18 +5,18 @@
#include "program_desc.h" #include "program_desc.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) : desc_(desc) { ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) : desc_(desc) {
for (auto &block_desc : *desc_.mutable_blocks()) { for (auto &block_desc : *desc_.mutable_blocks()) {
// new framework::BlockDesc(block_desc) // new framework::BlockDesc(block_desc)
blocks_.emplace_back(std::make_shared<BlockDesc>(block_desc)); blocks_.emplace_back(std::make_shared<BlockDesc>(block_desc));
} }
} }
std::shared_ptr<BlockDesc> ProgramDesc::Block(size_t idx) { std::shared_ptr<BlockDesc> ProgramDesc::Block(size_t idx) {
return blocks_[idx]; return blocks_[idx];
} }
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/program_desc.h
浏览文件 @ 45de1e84
...@@ -25,20 +25,18 @@ SOFTWARE. ...@@ -25,20 +25,18 @@ SOFTWARE.
#include "paddle_mobile_object.h" #include "paddle_mobile_object.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class ProgramDesc : PaddleMobileObject { class ProgramDesc : PaddleMobileObject {
public: public:
ProgramDesc(const proto::ProgramDesc &desc); ProgramDesc(const proto::ProgramDesc &desc);
std::shared_ptr<BlockDesc> Block(size_t idx); std::shared_ptr<BlockDesc> Block(size_t idx);
const std::vector<std::shared_ptr<BlockDesc>> &Blocks() { const std::vector<std::shared_ptr<BlockDesc>> &Blocks() { return blocks_; };
return blocks_;
};
private: private:
std::vector<std::shared_ptr<BlockDesc>> blocks_; std::vector<std::shared_ptr<BlockDesc>> blocks_;
proto::ProgramDesc desc_; proto::ProgramDesc desc_;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/scope.cc
浏览文件 @ 45de1e84
...@@ -4,116 +4,116 @@ ...@@ -4,116 +4,116 @@
#include <vector> #include <vector>
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
Scope &Scope::NewScope() const { Scope &Scope::NewScope() const {
std::unique_lock<std::mutex> lock(mutex_); std::unique_lock<std::mutex> lock(mutex_);
kids_.push_back(new Scope(this)); kids_.push_back(new Scope(this));
return *kids_.back(); return *kids_.back();
} }
Variable *Scope::Var(const std::string &name) { Variable *Scope::Var(const std::string &name) {
auto *pvar = FindVarLocally(name); auto *pvar = FindVarLocally(name);
if (pvar != nullptr) { if (pvar != nullptr) {
return pvar; return pvar;
}; };
pvar = new Variable; pvar = new Variable;
vars_[name] = pvar; vars_[name] = pvar;
pvar->name_ = &(vars_.find(name)->first); pvar->name_ = &(vars_.find(name)->first);
return pvar; return pvar;
} }
// Variable* Scope::Var(std::string* name) { // Variable* Scope::Var(std::string* name) {
// auto var_name = string::Sprintf("%p.%d", this, // auto var_name = string::Sprintf("%p.%d", this,
// vars_.size()); // vars_.size());
// if (name != nullptr) { // if (name != nullptr) {
// *name = var_name; // *name = var_name;
// } // }
// return Var(var_name); // return Var(var_name);
// } // }
Variable *Scope::FindVar(const std::string &name) const { Variable *Scope::FindVar(const std::string &name) const {
auto *pvar = FindVarLocally(name); auto *pvar = FindVarLocally(name);
if (pvar != nullptr) { if (pvar != nullptr) {
return pvar; return pvar;
} }
return (parent_ == nullptr) ? nullptr : parent_->FindVar(name); return (parent_ == nullptr) ? nullptr : parent_->FindVar(name);
} }
const Scope *Scope::FindScope(const Variable *var) const { const Scope *Scope::FindScope(const Variable *var) const {
for (auto &name_var : vars_) { for (auto &name_var : vars_) {
if (name_var.second == var) { if (name_var.second == var) {
return this; return this;
}
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
} }
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
}
void Scope::DropKids() { void Scope::DropKids() {
for (Scope *s : kids_) { for (Scope *s : kids_) {
delete s; delete s;
} }
kids_.clear(); kids_.clear();
} }
std::vector<std::string> Scope::LocalVarNames() const { std::vector<std::string> Scope::LocalVarNames() const {
std::vector<std::string> known_vars; std::vector<std::string> known_vars;
known_vars.reserve(vars_.size()); known_vars.reserve(vars_.size());
for (auto &name_var : vars_) { for (auto &name_var : vars_) {
known_vars.emplace_back(name_var.first); known_vars.emplace_back(name_var.first);
} }
return known_vars; return known_vars;
} }
void Scope::DeleteScope(Scope *scope) const { void Scope::DeleteScope(Scope *scope) const {
std::unique_lock<std::mutex> lock(mutex_); std::unique_lock<std::mutex> lock(mutex_);
auto it = std::find(kids_.begin(), kids_.end(), scope); auto it = std::find(kids_.begin(), kids_.end(), scope);
kids_.erase(it); kids_.erase(it);
delete scope; delete scope;
// deferent // deferent
} }
void Scope::EraseVars(const std::vector<std::string> &var_names) { void Scope::EraseVars(const std::vector<std::string> &var_names) {
std::set<std::string> var_set(var_names.begin(), var_names.end()); std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) { for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) { if (var_set.find(it->first) != var_set.end()) {
delete it->second; delete it->second;
it = vars_.erase(it); it = vars_.erase(it);
} else { } else {
++it; ++it;
}
}
} }
}
}
void Scope::Rename(const std::string &origin_name, void Scope::Rename(const std::string &origin_name,
const std::string &new_name) const { const std::string &new_name) const {
auto origin_it = vars_.find(origin_name); auto origin_it = vars_.find(origin_name);
if (origin_it == vars_.end()) { if (origin_it == vars_.end()) {
return; return;
} }
auto new_it = vars_.find(new_name); auto new_it = vars_.find(new_name);
if (new_it != vars_.end()) { if (new_it != vars_.end()) {
return; return;
} }
vars_[new_name] = origin_it->second; vars_[new_name] = origin_it->second;
vars_.erase(origin_it); vars_.erase(origin_it);
} }
// //
// std::string Scope::Rename(const std::string& origin_name) // std::string Scope::Rename(const std::string& origin_name)
// const { // const {
// auto var_name = string::Sprintf("%p.%d", this, // auto var_name = string::Sprintf("%p.%d", this,
// vars_.size()); // vars_.size());
// Rename(origin_name, var_name); // Rename(origin_name, var_name);
// return var_name; // return var_name;
// } // }
Variable *Scope::FindVarLocally(const std::string &name) const { Variable *Scope::FindVarLocally(const std::string &name) const {
auto it = vars_.find(name); auto it = vars_.find(name);
if (it != vars_.end()) { if (it != vars_.end()) {
return it->second; return it->second;
} }
return nullptr; return nullptr;
} }
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/scope.h
浏览文件 @ 45de1e84
...@@ -24,58 +24,58 @@ SOFTWARE. ...@@ -24,58 +24,58 @@ SOFTWARE.
#include <unordered_map> //std::unordered_map #include <unordered_map> //std::unordered_map
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class Scope { class Scope {
public: public:
Scope() {} Scope() {}
~Scope() {} ~Scope() {}
Scope &NewScope() const; Scope &NewScope() const;
/// Create a variable with given name if it doesn't exist. /// Create a variable with given name if it doesn't exist.
Variable *Var(const std::string &name); Variable *Var(const std::string &name);
/// Create a variable with a scope-unique name. /// Create a variable with a scope-unique name.
Variable *Var(std::string *name = nullptr); Variable *Var(std::string *name = nullptr);
void EraseVars(const std::vector<std::string> &var_names); void EraseVars(const std::vector<std::string> &var_names);
/// Find a variable in the scope or any of its ancestors. Returns /// Find a variable in the scope or any of its ancestors. Returns
/// nullptr if cannot find. /// nullptr if cannot find.
Variable *FindVar(const std::string &name) const; Variable *FindVar(const std::string &name) const;
const Scope *parent() const { return parent_; } const Scope *parent() const { return parent_; }
/// Find the scope or an ancestor scope that contains the given /// Find the scope or an ancestor scope that contains the given
/// variable. /// variable.
const Scope *FindScope(const Variable *var) const; const Scope *FindScope(const Variable *var) const;
void DeleteScope(Scope *scope) const; void DeleteScope(Scope *scope) const;
/// Drop all kids scopes belonged to this scope. /// Drop all kids scopes belonged to this scope.
void DropKids(); void DropKids();
// enumerate all the variables current contains. // enumerate all the variables current contains.
std::vector<std::string> LocalVarNames() const; std::vector<std::string> LocalVarNames() const;
// Rename variable to a new name // Rename variable to a new name
void Rename(const std::string &origin_name, void Rename(const std::string &origin_name,
const std::string &new_name) const; const std::string &new_name) const;
// Rename variable to a new name and return the new name // Rename variable to a new name and return the new name
std::string Rename(const std::string &origin_name) const; std::string Rename(const std::string &origin_name) const;
Variable *FindVarLocally(const std::string &name) const; Variable *FindVarLocally(const std::string &name) const;
private: private:
// Call Scope::NewScope for a sub-scope. // Call Scope::NewScope for a sub-scope.
explicit Scope(Scope const *parent) : parent_(parent) {} explicit Scope(Scope const *parent) : parent_(parent) {}
mutable std::unordered_map<std::string, Variable *> vars_; mutable std::unordered_map<std::string, Variable *> vars_;
mutable std::list<Scope *> kids_; mutable std::list<Scope *> kids_;
Scope const *parent_{nullptr}; Scope const *parent_{nullptr};
mutable std::mutex mutex_; mutable std::mutex mutex_;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/selected_rows.h
浏览文件 @ 45de1e84
...@@ -24,59 +24,58 @@ SOFTWARE. ...@@ -24,59 +24,58 @@ SOFTWARE.
#include "tensor.h" #include "tensor.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class SelectedRows { class SelectedRows {
public: public:
SelectedRows(const std::vector<int64_t> &rows, SelectedRows(const std::vector<int64_t> &rows, const int64_t &height)
const int64_t &height) : rows_(rows), height_(height) {
: rows_(rows), height_(height) { value_.reset(new Tensor());
value_.reset(new Tensor()); }
}
SelectedRows() { SelectedRows() {
height_ = 0; height_ = 0;
value_.reset(new Tensor()); value_.reset(new Tensor());
} }
const Tensor &value() const { return *value_; } const Tensor &value() const { return *value_; }
Tensor *mutable_value() { return value_.get(); } Tensor *mutable_value() { return value_.get(); }
int64_t height() const { return height_; } int64_t height() const { return height_; }
void set_height(int64_t height) { height_ = height; } void set_height(int64_t height) { height_ = height; }
const std::vector<int64_t> &rows() const { return rows_; } const std::vector<int64_t> &rows() const { return rows_; }
std::vector<int64_t> *mutable_rows() { return &rows_; } std::vector<int64_t> *mutable_rows() { return &rows_; }
void set_rows(const std::vector<int64_t> &rows) { rows_ = rows; } void set_rows(const std::vector<int64_t> &rows) { rows_ = rows; }
/** /**
* get the index of id in rows * get the index of id in rows
*/ */
int64_t index(int64_t id) const { int64_t index(int64_t id) const {
auto it = std::find(rows_.begin(), rows_.end(), id); auto it = std::find(rows_.begin(), rows_.end(), id);
// PADDLE_ENFORCE(it != rows_.end(), "id should be in rows"); // PADDLE_ENFORCE(it != rows_.end(), "id should be in rows");
return static_cast<int64_t>(std::distance(rows_.begin(), it)); return static_cast<int64_t>(std::distance(rows_.begin(), it));
} }
DDim GetCompleteDims() const { DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims()); std::vector<int64_t> dims = vectorize(value_->dims());
dims[0] = height_; dims[0] = height_;
return make_ddim(dims); return make_ddim(dims);
} }
private: private:
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} // Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9}
// here. // here.
// SelectedRows are simply concated when adding together. Until a // SelectedRows are simply concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled. // SelectedRows add a Tensor, will the duplicate rows be handled.
std::vector<int64_t> rows_; std::vector<int64_t> rows_;
std::unique_ptr<Tensor> value_{nullptr}; std::unique_ptr<Tensor> value_{nullptr};
int64_t height_; int64_t height_;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/tensor.h
浏览文件 @ 45de1e84
...@@ -25,316 +25,310 @@ limitations under the License. */ ...@@ -25,316 +25,310 @@ limitations under the License. */
#include "memory/t_malloc.h" #include "memory/t_malloc.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename... T> struct SizeOfTypeFunctor; template <typename... T> struct SizeOfTypeFunctor;
template <typename T> struct SizeOfTypeFunctor<T> { template <typename T> struct SizeOfTypeFunctor<T> {
size_t operator()(std::type_index type) const { size_t operator()(std::type_index type) const {
if (typeid(T).hash_code() == type.hash_code()) { if (typeid(T).hash_code() == type.hash_code()) {
return sizeof(T); return sizeof(T);
} else { } else {
return 0UL; return 0UL;
}
}
};
template <> struct SizeOfTypeFunctor<> {
size_t operator()(std::type_index type) const { return 0UL; }
};
template <typename HEAD, typename... TAIL>
struct SizeOfTypeFunctor<HEAD, TAIL...> {
size_t operator()(std::type_index type) const {
SizeOfTypeFunctor<HEAD> head;
size_t head_size = head(type);
if (head_size != 0) {
return head_size;
}
SizeOfTypeFunctor<TAIL...> tail;
return tail(type);
}
};
static inline size_t SizeOfType(std::type_index type) {
SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool,
size_t>
functor;
size_t size = functor(type);
// PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s",
// type.name());
return size;
} }
}
class LoDTensor; };
class Tensor { template <> struct SizeOfTypeFunctor<> {
public: size_t operator()(std::type_index type) const { return 0UL; }
Tensor() : offset_(0) {} };
/*! Return a pointer to mutable memory block. */ template <typename HEAD, typename... TAIL>
template <typename T> inline T *data() { struct SizeOfTypeFunctor<HEAD, TAIL...> {
check_memory_size(); size_t operator()(std::type_index type) const {
// PADDLE_ENFORCE(std::is_same<T, void>::value || SizeOfTypeFunctor<HEAD> head;
// holder_->type().hash_code() == size_t head_size = head(type);
// typeid(T).hash_code(), if (head_size != 0) {
// "Tensor holds the wrong type, it holds %s", return head_size;
// this->holder_->type().name()); }
return reinterpret_cast<T *>( SizeOfTypeFunctor<TAIL...> tail;
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_); return tail(type);
} }
};
/*! Return a pointer to constant memory block. */
template <typename T> inline const T *data() const { static inline size_t SizeOfType(std::type_index type) {
check_memory_size(); SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool, size_t>
// PADDLE_ENFORCE(std::is_same<T, void>::value || functor;
// holder_->type().hash_code() == size_t size = functor(type);
// typeid(T).hash_code(), // PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s",
// "Tensor holds the wrong type, it holds %s", // type.name());
// this->holder_->type().name()); return size;
}
return reinterpret_cast<const T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_); class LoDTensor;
}
class Tensor {
inline bool IsInitialized() const { return holder_ != nullptr; } public:
Tensor() : offset_(0) {}
/**
* @brief Return a pointer to mutable memory block. /*! Return a pointer to mutable memory block. */
* @note If not exist, then allocation. template <typename T> inline T *data() {
*/ check_memory_size();
template <typename T> inline T *mutable_data() { // PADDLE_ENFORCE(std::is_same<T, void>::value ||
static_assert(std::is_pod<T>::value, "T must be POD"); // holder_->type().hash_code() ==
return reinterpret_cast<T *>(mutable_data(typeid(T))); // typeid(T).hash_code(),
} // "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
inline void *mutable_data(std::type_index type) { return reinterpret_cast<T *>(
if (holder_ != nullptr) { reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
holder_->set_type(type); }
}
// PADDLE_ENFORCE_GE(numel(), 0, /*! Return a pointer to constant memory block. */
// "When calling this method, the Tensor's template <typename T> inline const T *data() const {
// numel must be check_memory_size();
// " "equal or larger than zero. " "Please // PADDLE_ENFORCE(std::is_same<T, void>::value ||
// check // holder_->type().hash_code() ==
// Tensor::Resize has been called first."); // typeid(T).hash_code(),
int64_t size = numel() * SizeOfType(type); // "Tensor holds the wrong type, it holds %s",
/* some versions of boost::variant don't have operator!= */ // this->holder_->type().name());
if (holder_ == nullptr || holder_->size() < size + offset_) {
holder_.reset(new PlaceholderImpl(size, type)); return reinterpret_cast<const T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
offset_ = 0; }
}
return reinterpret_cast<void *>( inline bool IsInitialized() const { return holder_ != nullptr; }
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
} /**
* @brief Return a pointer to mutable memory block.
inline void *mutable_data() { * @note If not exist, then allocation.
// PADDLE_ENFORCE(this->holder_ != nullptr, */
// "Cannot invoke mutable data if current hold template <typename T> inline T *mutable_data() {
// nothing."); static_assert(std::is_pod<T>::value, "T must be POD");
return mutable_data(holder_->type()); return reinterpret_cast<T *>(mutable_data(typeid(T)));
} }
/** inline void *mutable_data(std::type_index type) {
* @brief Return a pointer to mutable memory block. if (holder_ != nullptr) {
* holder_->set_type(type);
* @param[in] dims The dimensions of the memory block. }
* @param[in] place The place of the memory block. // PADDLE_ENFORCE_GE(numel(), 0,
* // "When calling this method, the Tensor's
* @note If not exist, then allocation. // numel must be
*/ // " "equal or larger than zero. " "Please
template <typename T> inline T *mutable_data(DDim dims) { // check
static_assert(std::is_pod<T>::value, "T must be POD"); // Tensor::Resize has been called first.");
Resize(dims); int64_t size = numel() * SizeOfType(type);
return mutable_data<T>(); /* some versions of boost::variant don't have operator!= */
} if (holder_ == nullptr || holder_->size() < size + offset_) {
holder_.reset(new PlaceholderImpl(size, type));
/*! Return the dimensions of the memory block. */
inline const DDim &dims() const { return dims_; } offset_ = 0;
}
/*! Return the numel of the memory block. */ return reinterpret_cast<void *>(
inline int64_t numel() const { return product(dims_); } reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
/*! Resize the dimensions of the memory block. */
inline Tensor &Resize(const DDim &dims) { inline void *mutable_data() {
dims_ = dims; // PADDLE_ENFORCE(this->holder_ != nullptr,
return *this; // "Cannot invoke mutable data if current hold
} // nothing.");
return mutable_data(holder_->type());
/*! The internal of two tensors share the same memory block. */ }
inline Tensor &ShareDataWith(const Tensor &src) {
src.check_memory_size(); /**
*this = src; * @brief Return a pointer to mutable memory block.
return *this; *
} * @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
/** *
* @brief Return a sub-tensor of the given tensor. * @note If not exist, then allocation.
* */
* @param[in] begin_idx The index of the start row(inclusive) to template <typename T> inline T *mutable_data(DDim dims) {
* slice. static_assert(std::is_pod<T>::value, "T must be POD");
* The index number begins from 0. Resize(dims);
* @param[in] end_idx The index of the end row(exclusive) to return mutable_data<T>();
* slice. }
* The index number begins from 0.
*/ /*! Return the dimensions of the memory block. */
inline Tensor Slice(int begin_idx, int end_idx) const { inline const DDim &dims() const { return dims_; }
check_memory_size();
// PADDLE_ENFORCE_GE(begin_idx, 0, /*! Return the numel of the memory block. */
// "The start row index must be greater than inline int64_t numel() const { return product(dims_); }
// 0.");
// PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is /*! Resize the dimensions of the memory block. */
// out of inline Tensor &Resize(const DDim &dims) {
// bound."); PADDLE_ENFORCE_LT( dims_ = dims;
// begin_idx, end_idx, return *this;
// "The start row index must be lesser than the end row }
// index.");
/*! The internal of two tensors share the same memory block. */
if (dims_[0] == 1) { inline Tensor &ShareDataWith(const Tensor &src) {
return *this; src.check_memory_size();
} else { *this = src;
size_t base = numel() / dims_[0]; return *this;
Tensor dst; }
dst.holder_ = holder_;
dst.set_layout(layout_); /**
DDim dst_dims = dims_; * @brief Return a sub-tensor of the given tensor.
dst_dims[0] = end_idx - begin_idx; *
dst.Resize(dst_dims); * @param[in] begin_idx The index of the start row(inclusive) to
dst.offset_ = * slice.
offset_ + begin_idx * base * SizeOfType(type()); * The index number begins from 0.
return dst; * @param[in] end_idx The index of the end row(exclusive) to
} * slice.
} * The index number begins from 0.
*/
std::type_index type() const { inline Tensor Slice(int begin_idx, int end_idx) const {
// PADDLE_ENFORCE_NOT_NULL( check_memory_size();
// holder_, "Tensor not initialized yet // PADDLE_ENFORCE_GE(begin_idx, 0,
// when // "The start row index must be greater than
// Tensor::type() is called."); // 0.");
return holder_->type(); // PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is
} // out of
// bound."); PADDLE_ENFORCE_LT(
// memory size returns the holding memory size in byte. // begin_idx, end_idx,
size_t memory_size() const { // "The start row index must be lesser than the end row
return holder_ == nullptr ? 0UL : holder_->size() - offset_; // index.");
}
if (dims_[0] == 1) {
inline void check_memory_size() const { return *this;
// PADDLE_ENFORCE_NOT_NULL( } else {
// holder_, "Tensor holds no memory. Call size_t base = numel() / dims_[0];
// Tensor::mutable_data Tensor dst;
// first."); dst.holder_ = holder_;
// PADDLE_ENFORCE_LE( dst.set_layout(layout_);
// numel() * SizeOfType(type()), memory_size(), DDim dst_dims = dims_;
// "Tensor's dims_ is out of bound. Call dst_dims[0] = end_idx - begin_idx;
// Tensor::mutable_data " dst.Resize(dst_dims);
// "first to re-allocate memory.\n" dst.offset_ = offset_ + begin_idx * base * SizeOfType(type());
// "or maybe the required data-type mismatches the data return dst;
// already }
// stored."); }
}
std::type_index type() const {
inline DataLayout layout() const { return layout_; } // PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor not initialized yet
inline void set_layout(const DataLayout layout) { // when
layout_ = layout; // Tensor::type() is called.");
} return holder_->type();
}
private:
/** // memory size returns the holding memory size in byte.
* @note Placeholder hides type T, so it doesn't appear as a size_t memory_size() const {
* template return holder_ == nullptr ? 0UL : holder_->size() - offset_;
* parameter of Variable. }
*/
struct Placeholder { inline void check_memory_size() const {
virtual ~Placeholder() = default; // PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor holds no memory. Call
virtual void *ptr() const = 0; // Tensor::mutable_data
// first.");
virtual size_t size() const = 0; // PADDLE_ENFORCE_LE(
// numel() * SizeOfType(type()), memory_size(),
virtual std::type_index type() const = 0; // "Tensor's dims_ is out of bound. Call
// Tensor::mutable_data "
virtual void set_type(std::type_index type) = 0; // "first to re-allocate memory.\n"
}; // "or maybe the required data-type mismatches the data
// already
struct PlaceholderImpl : public Placeholder { // stored.");
PlaceholderImpl(size_t size, std::type_index type) }
: ptr_(static_cast<uint8_t *>(memory::Alloc(size)),
memory::PODDeleter<uint8_t>()), inline DataLayout layout() const { return layout_; }
size_(size), type_(type) {
// PADDLE_ENFORCE_NOT_NULL(ptr_, inline void set_layout(const DataLayout layout) { layout_ = layout; }
// "Insufficient %s
// memory to allocation.", private:
// (is_cpu_place(place_) /**
// ? * @note Placeholder hides type T, so it doesn't appear as a
// "CPU" : * template
// "GPU")); * parameter of Variable.
} */
struct Placeholder {
virtual size_t size() const { return size_; } virtual ~Placeholder() = default;
virtual void *ptr() const { virtual void *ptr() const = 0;
return static_cast<void *>(ptr_.get());
} virtual size_t size() const = 0;
virtual std::type_index type() const { return type_; } virtual std::type_index type() const = 0;
virtual void set_type(std::type_index type) { type_ = type; } virtual void set_type(std::type_index type) = 0;
};
/*! the pointer of memory block. */
std::unique_ptr<uint8_t, memory::PODDeleter<uint8_t>> ptr_; struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(size_t size, std::type_index type)
/*! the size of memory block. */ : ptr_(static_cast<uint8_t *>(memory::Alloc(size)),
size_t size_; memory::PODDeleter<uint8_t>()),
size_(size), type_(type) {
/* the current type of memory */ // PADDLE_ENFORCE_NOT_NULL(ptr_,
std::type_index type_; // "Insufficient %s
}; // memory to allocation.",
// (is_cpu_place(place_)
/*! holds the memory block if allocated. */ // ?
std::shared_ptr<Placeholder> holder_; // "CPU" :
// "GPU"));
/**
* @brief points to elements dimensions.
*
* @note dims_ do not indicate the memory block size.
*/
DDim dims_;
/**
* @brief the layout of memory block, default is NHWC.
*
* @note the memory allocation order, describe how weight/data is
* stored
* For example, in 4-D Tensor(rank=4), there are three
* commonly
* used layout. They are
* NCHW, NHWC, CHWN.
* N,C,H,W for respectively the batch size, the number of
* feature maps, the height, the width.
*/
DataLayout layout_ = DataLayout::kNHWC;
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
* @note Some of them may be slices of the others. So the offset_
* is introduced here to indicate the byte offset between
* PlaceHolder::ptr_ and where the tensor data really
* begins.
*/
size_t offset_;
};
inline Tensor ReshapeToMatrix(const Tensor &src, int num_col_dims) {
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
return res;
} }
} // namespace framework virtual size_t size() const { return size_; }
virtual void *ptr() const { return static_cast<void *>(ptr_.get()); }
virtual std::type_index type() const { return type_; }
virtual void set_type(std::type_index type) { type_ = type; }
/*! the pointer of memory block. */
std::unique_ptr<uint8_t, memory::PODDeleter<uint8_t>> ptr_;
/*! the size of memory block. */
size_t size_;
/* the current type of memory */
std::type_index type_;
};
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
/**
* @brief points to elements dimensions.
*
* @note dims_ do not indicate the memory block size.
*/
DDim dims_;
/**
* @brief the layout of memory block, default is NHWC.
*
* @note the memory allocation order, describe how weight/data is
* stored
* For example, in 4-D Tensor(rank=4), there are three
* commonly
* used layout. They are
* NCHW, NHWC, CHWN.
* N,C,H,W for respectively the batch size, the number of
* feature maps, the height, the width.
*/
DataLayout layout_ = DataLayout::kNHWC;
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
* @note Some of them may be slices of the others. So the offset_
* is introduced here to indicate the byte offset between
* PlaceHolder::ptr_ and where the tensor data really
* begins.
*/
size_t offset_;
};
inline Tensor ReshapeToMatrix(const Tensor &src, int num_col_dims) {
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
return res;
}
} // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/tensor_util.cc
浏览文件 @ 45de1e84
...@@ -18,189 +18,187 @@ ...@@ -18,189 +18,187 @@
#include <vector> #include <vector>
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
void TensorCopy(const Tensor &src, Tensor *dst) { void TensorCopy(const Tensor &src, Tensor *dst) {
// VLOG(3) << "TensorCopy " << src.dims() << " from " << // VLOG(3) << "TensorCopy " << src.dims() << " from " <<
// src.place() << " to // src.place() << " to
// " // "
// << dst_place; // << dst_place;
src.check_memory_size(); src.check_memory_size();
dst->Resize(src.dims()); dst->Resize(src.dims());
dst->set_layout(src.layout()); dst->set_layout(src.layout());
auto src_ptr = src.data<void>(); auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type()); auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type()); auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size); memory::Copy(dst_ptr, src_ptr, size);
} }
void TensorCopySync(const Tensor &src, Tensor *dst) { void TensorCopySync(const Tensor &src, Tensor *dst) {
// VLOG(3) << "TensorCopySync " << src.dims() << " from " << // VLOG(3) << "TensorCopySync " << src.dims() << " from " <<
// src.place() // src.place()
// << " to " << dst_place; // << " to " << dst_place;
src.check_memory_size(); src.check_memory_size();
dst->Resize(src.dims()); dst->Resize(src.dims());
dst->set_layout(src.layout()); dst->set_layout(src.layout());
auto src_ptr = src.data<void>(); auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type()); auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type()); auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size); memory::Copy(dst_ptr, src_ptr, size);
} }
template <typename Predicate> struct AnyDTypeVisitor { template <typename Predicate> struct AnyDTypeVisitor {
Predicate predicate_; Predicate predicate_;
const Tensor &tensor_; const Tensor &tensor_;
Tensor *out_; Tensor *out_;
AnyDTypeVisitor(Predicate predicate, const Tensor &tensor, AnyDTypeVisitor(Predicate predicate, const Tensor &tensor, Tensor *out)
Tensor *out) : predicate_(predicate), tensor_(tensor), out_(out) {}
: predicate_(predicate), tensor_(tensor), out_(out) {}
template <typename T> void operator()() const {
template <typename T> void operator()() const { // auto t = EigenVector<T>::Flatten(tensor_);
// auto t = EigenVector<T>::Flatten(tensor_); // auto o = EigenScalar<bool>::From(*out_);
// auto o = EigenScalar<bool>::From(*out_); // return any of predicate_(t) is true.
// return any of predicate_(t) is true. // o.device(*ctx_.eigen_device()) = predicate_(t).any();
// o.device(*ctx_.eigen_device()) = predicate_(t).any(); }
} };
};
template <typename Predicate>
template <typename Predicate> inline void AnyImpl(Predicate predicate, const Tensor &tensor,
inline void AnyImpl(Predicate predicate, const Tensor &tensor, framework::Tensor *out) {
framework::Tensor *out) { VisitDataType(ToDataType(tensor.type()),
VisitDataType(ToDataType(tensor.type()), AnyDTypeVisitor<Predicate>(predicate, tensor, out));
AnyDTypeVisitor<Predicate>(predicate, tensor, out)); }
}
template <typename Predicate> struct AnyVisitor {
template <typename Predicate> struct AnyVisitor { const framework::Tensor &tensor_;
const framework::Tensor &tensor_; Predicate predicate_;
Predicate predicate_;
AnyVisitor(const framework::Tensor &tensor, Predicate predicate)
AnyVisitor(const framework::Tensor &tensor, Predicate predicate) : tensor_(tensor), predicate_(std::move(predicate)) {}
: tensor_(tensor), predicate_(std::move(predicate)) {}
bool operator()(void) const {
bool operator()(void) const { framework::Tensor out;
framework::Tensor out; out.Resize({1});
out.Resize({1}); out.mutable_data<bool>();
out.mutable_data<bool>(); AnyImpl(predicate_, tensor_, &out);
AnyImpl(predicate_, tensor_, &out); return this->GetResult(out);
return this->GetResult(out); }
}
bool GetResult(const framework::Tensor &out) const {
bool GetResult(const framework::Tensor &out) const { return *out.data<bool>();
return *out.data<bool>(); }
} };
};
template <typename Predicate>
template <typename Predicate> inline bool Any(const framework::Tensor &tensor, Predicate predicate) {
inline bool Any(const framework::Tensor &tensor, Predicate predicate) { AnyVisitor<Predicate> visitor(tensor, predicate);
AnyVisitor<Predicate> visitor(tensor, predicate); // return platform::VisitPlace(visitor);
// return platform::VisitPlace(visitor); return visitor();
return visitor(); }
}
struct ContainsNANPredicate {
struct ContainsNANPredicate { template <typename T>
template <typename T> auto operator()(const T &eigen_vec) const
auto operator()(const T &eigen_vec) const -> decltype(std::declval<T>().isnan()) {
-> decltype(std::declval<T>().isnan()) { // Cast eigen_vector to vector of bool. true if is inf.
// Cast eigen_vector to vector of bool. true if is inf. return eigen_vec.isnan();
return eigen_vec.isnan(); }
} };
};
bool TensorContainsNAN(const framework::Tensor &tensor) {
bool TensorContainsNAN(const framework::Tensor &tensor) { ContainsNANPredicate predicate;
ContainsNANPredicate predicate; return Any(tensor, predicate);
return Any(tensor, predicate); }
}
struct ContainsInfPredicate {
struct ContainsInfPredicate { template <typename T>
template <typename T> auto operator()(const T &eigen_vec) const
auto operator()(const T &eigen_vec) const -> decltype(std::declval<T>().isinf()) {
-> decltype(std::declval<T>().isinf()) { // Cast eigen_vector to vector of bool. true if is inf.
// Cast eigen_vector to vector of bool. true if is inf. return eigen_vec.isinf();
return eigen_vec.isinf(); }
} };
};
bool TensorContainsInf(const framework::Tensor &tensor) {
bool TensorContainsInf(const framework::Tensor &tensor) { ContainsInfPredicate predicate;
ContainsInfPredicate predicate; return Any(tensor, predicate);
return Any(tensor, predicate); }
}
void TensorToStream(std::ostream &os, const Tensor &tensor) {
void TensorToStream(std::ostream &os, const Tensor &tensor) { { // the 1st field, uint32_t version
{ // the 1st field, uint32_t version constexpr uint32_t version = 0;
constexpr uint32_t version = 0; os.write(reinterpret_cast<const char *>(&version), sizeof(version));
os.write(reinterpret_cast<const char *>(&version), }
sizeof(version)); { // the 2nd field, tensor description
} // int32_t size
{ // the 2nd field, tensor description // void* protobuf message
// int32_t size proto::VarType::TensorDesc desc;
// void* protobuf message desc.set_data_type(framework::ToDataType(tensor.type()));
proto::VarType::TensorDesc desc; auto dims = framework::vectorize(tensor.dims());
desc.set_data_type(framework::ToDataType(tensor.type())); auto *pb_dims = desc.mutable_dims();
auto dims = framework::vectorize(tensor.dims()); pb_dims->Resize(static_cast<int>(dims.size()), 0);
auto *pb_dims = desc.mutable_dims(); std::copy(dims.begin(), dims.end(), pb_dims->begin());
pb_dims->Resize(static_cast<int>(dims.size()), 0); int32_t size = desc.ByteSize();
std::copy(dims.begin(), dims.end(), pb_dims->begin()); os.write(reinterpret_cast<const char *>(&size), sizeof(size));
int32_t size = desc.ByteSize(); auto out = desc.SerializeAsString();
os.write(reinterpret_cast<const char *>(&size), sizeof(size)); os.write(out.data(), size);
auto out = desc.SerializeAsString(); }
os.write(out.data(), size); { // the 3rd field, tensor data
} uint64_t size = tensor.memory_size();
{ // the 3rd field, tensor data auto *data_ptr = tensor.data<void>();
uint64_t size = tensor.memory_size(); // PADDLE_ENFORCE(size <
auto *data_ptr = tensor.data<void>(); // std::numeric_limits<std::streamsize>::max(),
// PADDLE_ENFORCE(size < // "Index overflow when writing tensor");
// std::numeric_limits<std::streamsize>::max(),
// "Index overflow when writing tensor"); os.write(static_cast<const char *>(data_ptr),
static_cast<std::streamsize>(size));
os.write(static_cast<const char *>(data_ptr), }
static_cast<std::streamsize>(size)); }
}
} struct DeserializedDataFunctor {
DeserializedDataFunctor(void **buf, Tensor *tensor)
struct DeserializedDataFunctor { : buf_(buf), tensor_(tensor) {}
DeserializedDataFunctor(void **buf, Tensor *tensor)
: buf_(buf), tensor_(tensor) {} template <typename T> void operator()() {
*buf_ = tensor_->mutable_data<T>();
template <typename T> void operator()() { }
*buf_ = tensor_->mutable_data<T>();
} void **buf_;
Tensor *tensor_;
void **buf_; };
Tensor *tensor_;
}; void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
uint32_t version;
void TensorFromStream(std::istream &is, framework::Tensor *tensor) { is.read(reinterpret_cast<char *>(&version), sizeof(version));
uint32_t version; // PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
is.read(reinterpret_cast<char *>(&version), sizeof(version)); proto::VarType::TensorDesc desc;
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported"); { // int32_t size
proto::VarType::TensorDesc desc; // proto buffer
{ // int32_t size int32_t size;
// proto buffer is.read(reinterpret_cast<char *>(&size), sizeof(size));
int32_t size; std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(&size), sizeof(size)); is.read(reinterpret_cast<char *>(buf.get()), size);
std::unique_ptr<char[]> buf(new char[size]); // PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
is.read(reinterpret_cast<char *>(buf.get()), size); // "Cannot parse tensor desc");
// PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size), }
// "Cannot parse tensor desc"); { // read tensor
} std::vector<int64_t> dims;
{ // read tensor dims.reserve(static_cast<size_t>(desc.dims().size()));
std::vector<int64_t> dims; std::copy(desc.dims().begin(), desc.dims().end(),
dims.reserve(static_cast<size_t>(desc.dims().size())); std::back_inserter(dims));
std::copy(desc.dims().begin(), desc.dims().end(), tensor->Resize(framework::make_ddim(dims));
std::back_inserter(dims)); void *buf;
tensor->Resize(framework::make_ddim(dims));
void *buf; framework::VisitDataType(desc.data_type(),
DeserializedDataFunctor(&buf, tensor));
framework::VisitDataType(desc.data_type(), is.read(static_cast<char *>(buf), tensor->memory_size());
DeserializedDataFunctor(&buf, tensor)); }
is.read(static_cast<char *>(buf), tensor->memory_size()); }
}
} } // namespace framework
} // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/tensor_util.h
浏览文件 @ 45de1e84
...@@ -20,47 +20,47 @@ limitations under the License. */ ...@@ -20,47 +20,47 @@ limitations under the License. */
#include <vector> #include <vector>
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
void TensorCopy(const Tensor &src, Tensor *dst); void TensorCopy(const Tensor &src, Tensor *dst);
void TensorCopySync(const Tensor &src, Tensor *dst); void TensorCopySync(const Tensor &src, Tensor *dst);
template <typename T> template <typename T>
void TensorFromVector(const std::vector<T> &src, Tensor *dst); void TensorFromVector(const std::vector<T> &src, Tensor *dst);
template <typename T> template <typename T>
void TesnorToVector(const Tensor &src, std::vector<T> *dst); void TesnorToVector(const Tensor &src, std::vector<T> *dst);
bool TensorContainsNAN(const framework::Tensor &tensor); bool TensorContainsNAN(const framework::Tensor &tensor);
bool TensorContainsInf(const framework::Tensor &tensor); bool TensorContainsInf(const framework::Tensor &tensor);
void TensorToStream(std::ostream &os, const Tensor &tensor); void TensorToStream(std::ostream &os, const Tensor &tensor);
void TensorFromStream(std::istream &is, Tensor *tensor); void TensorFromStream(std::istream &is, Tensor *tensor);
// //
// The implementation of template functions. // The implementation of template functions.
// //
template <typename T> template <typename T>
void TensorFromVector(const std::vector<T> &src, Tensor *dst) { void TensorFromVector(const std::vector<T> &src, Tensor *dst) {
auto src_ptr = static_cast<const void *>(src.data()); auto src_ptr = static_cast<const void *>(src.data());
dst->Resize({static_cast<int64_t>(src.size())}); dst->Resize({static_cast<int64_t>(src.size())});
auto dst_ptr = static_cast<void *>(dst->mutable_data<T>()); auto dst_ptr = static_cast<void *>(dst->mutable_data<T>());
auto size = src.size() * sizeof(T); auto size = src.size() * sizeof(T);
memory::Copy(dst_ptr, src_ptr, size); memory::Copy(dst_ptr, src_ptr, size);
} }
template <typename T> template <typename T>
void TensorToVector(const Tensor &src, std::vector<T> *dst) { void TensorToVector(const Tensor &src, std::vector<T> *dst) {
auto src_ptr = static_cast<const void *>(src.data<T>()); auto src_ptr = static_cast<const void *>(src.data<T>());
auto size = src.numel() * sizeof(T); auto size = src.numel() * sizeof(T);
dst->resize(src.numel()); dst->resize(src.numel());
auto dst_ptr = static_cast<void *>(dst->data()); auto dst_ptr = static_cast<void *>(dst->data());
memory::Copy(dst_ptr, src_ptr, size); memory::Copy(dst_ptr, src_ptr, size);
} }
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/var_desc.cpp
浏览文件 @ 45de1e84
...@@ -20,9 +20,9 @@ SOFTWARE. ...@@ -20,9 +20,9 @@ SOFTWARE.
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
VarDesc::VarDesc(const proto::VarDesc &desc) : desc_(desc) {} VarDesc::VarDesc(const proto::VarDesc &desc) : desc_(desc) {}
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/var_desc.h
浏览文件 @ 45de1e84
...@@ -22,68 +22,67 @@ SOFTWARE. ...@@ -22,68 +22,67 @@ SOFTWARE.
#include "paddle_mobile_object.h" #include "paddle_mobile_object.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class VarDesc { class VarDesc {
public: public:
VarDesc(const proto::VarDesc &desc); VarDesc(const proto::VarDesc &desc);
std::string Name() const { return desc_.name(); } std::string Name() const { return desc_.name(); }
proto::VarType::Type GetType() const { return desc_.type().type(); } proto::VarType::Type GetType() const { return desc_.type().type(); }
bool Persistable() const { return desc_.persistable(); } bool Persistable() const { return desc_.persistable(); }
const proto::VarType::ChannelDesc &channel_desc() const { const proto::VarType::ChannelDesc &channel_desc() const {
switch (desc_.type().type()) { switch (desc_.type().type()) {
case proto::VarType::CHANNEL: case proto::VarType::CHANNEL:
return desc_.type().channel(); return desc_.type().channel();
default: default:
break; break;
} }
} }
const proto::VarType::TensorDesc &tensor_desc() const { const proto::VarType::TensorDesc &tensor_desc() const {
switch (desc_.type().type()) { switch (desc_.type().type()) {
case proto::VarType::SELECTED_ROWS: case proto::VarType::SELECTED_ROWS:
return desc_.type().selected_rows(); return desc_.type().selected_rows();
case proto::VarType::LOD_TENSOR: case proto::VarType::LOD_TENSOR:
return desc_.type().lod_tensor().tensor(); return desc_.type().lod_tensor().tensor();
case proto::VarType::LOD_TENSOR_ARRAY: case proto::VarType::LOD_TENSOR_ARRAY:
return desc_.type().tensor_array().tensor(); return desc_.type().tensor_array().tensor();
default: default:
break; break;
} }
} }
proto::VarType::Type GetDataType() const { proto::VarType::Type GetDataType() const {
switch (desc_.type().type()) { switch (desc_.type().type()) {
case proto::VarType::CHANNEL: case proto::VarType::CHANNEL:
return channel_desc().data_type(); return channel_desc().data_type();
break; break;
default: default:
return tensor_desc().data_type(); return tensor_desc().data_type();
} }
} }
template <typename T> template <typename T>
std::vector<T> RepeatedToVector( std::vector<T> RepeatedToVector(
const google::protobuf::RepeatedField<T> &repeated_field) const google::protobuf::RepeatedField<T> &repeated_field) const {
const { std::vector<T> ret;
std::vector<T> ret; ret.reserve(repeated_field.size());
ret.reserve(repeated_field.size()); std::copy(repeated_field.begin(), repeated_field.end(),
std::copy(repeated_field.begin(), repeated_field.end(), std::back_inserter(ret));
std::back_inserter(ret)); return ret;
return ret; }
}
std::vector<int64_t> GetShape() const { std::vector<int64_t> GetShape() const {
return this->RepeatedToVector(tensor_desc().dims()); return this->RepeatedToVector(tensor_desc().dims());
} }
private: private:
proto::VarDesc desc_; proto::VarDesc desc_;
}; };
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/var_type.h
浏览文件 @ 45de1e84
...@@ -23,17 +23,17 @@ SOFTWARE. ...@@ -23,17 +23,17 @@ SOFTWARE.
#include "variable.h" #include "variable.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
inline proto::VarType::Type ToVarType(std::type_index type) { inline proto::VarType::Type ToVarType(std::type_index type) {
if (type.hash_code() == typeid(LoDTensor).hash_code()) { if (type.hash_code() == typeid(LoDTensor).hash_code()) {
return proto::VarType_Type_LOD_TENSOR; return proto::VarType_Type_LOD_TENSOR;
} else if (type.hash_code() == typeid(SelectedRows).hash_code()) { } else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
return proto::VarType_Type_SELECTED_ROWS; return proto::VarType_Type_SELECTED_ROWS;
} else { } else {
// PADDLE_THROW("ToVarType:Unsupported type %s", // PADDLE_THROW("ToVarType:Unsupported type %s",
// type.name()); // type.name());
} }
} }
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/framework/variable.h
浏览文件 @ 45de1e84
...@@ -26,71 +26,71 @@ SOFTWARE. ...@@ -26,71 +26,71 @@ SOFTWARE.
#include <typeinfo> #include <typeinfo>
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
class Variable : public PaddleMobileObject { class Variable : public PaddleMobileObject {
public: public:
template <typename T> const T *Get() const { template <typename T> const T *Get() const {
return static_cast<const T *>(holder_->Ptr()); return static_cast<const T *>(holder_->Ptr());
}
bool IsInitialized() const { return holder_ != nullptr; }
const std::string *Name() { return name_; }
template <typename T> T *GetMutable() {
if (!IsType<T>()) {
if (*Name() == "pixel") {
// std::cout << " reset " << *Name() <<
// std::endl;
} }
holder_.reset(new PlaceholderImp<T>(new T()));
bool IsInitialized() const { return holder_ != nullptr; } }
return static_cast<T *>(holder_->Ptr());
const std::string *Name() { return name_; } }
template <typename T> T *GetMutable() { template <typename T> bool IsType() const {
if (!IsType<T>()) { if (holder_) {
if (*Name() == "pixel") { // printf("not null \n");
// std::cout << " reset " << *Name() << printf(" holder type : %s, this type %s \n", holder_->Type().name(),
// std::endl; typeid(T).name());
} }
holder_.reset(new PlaceholderImp<T>(new T()));
} // std::cout << " " << holder_->Type() << " " <<
return static_cast<T *>(holder_->Ptr()); // typeid(T) <<
} // std::endl;
return holder_ != nullptr && holder_->Type() == typeid(T);
template <typename T> bool IsType() const { }
if (holder_) {
// printf("not null \n"); void Clear() { holder_.reset(); }
printf(" holder type : %s, this type %s \n",
holder_->Type().name(), typeid(T).name()); std::type_index Type() const { return holder_->Type(); }
}
void SetName(const std::string *name) { name_ = name; }
// std::cout << " " << holder_->Type() << " " <<
// typeid(T) << private:
// std::endl; struct Placeholder {
return holder_ != nullptr && holder_->Type() == typeid(T); Placeholder() = default;
} virtual ~Placeholder() = default;
void Clear() { holder_.reset(); } virtual const std::type_info &Type() const = 0;
virtual void *Ptr() const = 0;
std::type_index Type() const { return holder_->Type(); } };
void SetName(const std::string *name) { name_ = name; } template <typename T> struct PlaceholderImp : public Placeholder {
explicit PlaceholderImp(T *ptr) : ptr_(ptr), type_(typeid(T)) {}
private:
struct Placeholder { virtual const std::type_info &Type() const { return type_; }
Placeholder() = default; virtual void *Ptr() const override {
virtual ~Placeholder() = default; return static_cast<void *>(ptr_.get());
}
virtual const std::type_info &Type() const = 0;
virtual void *Ptr() const = 0; std::unique_ptr<T> ptr_;
}; const std::type_info &type_;
};
template <typename T> struct PlaceholderImp : public Placeholder {
explicit PlaceholderImp(T *ptr) : ptr_(ptr), type_(typeid(T)) {} std::unique_ptr<Placeholder> holder_;
friend class Scope;
virtual const std::type_info &Type() const { return type_; } const std::string *name_;
virtual void *Ptr() const override { };
return static_cast<void *>(ptr_.get()); } // namespace framework
}
std::unique_ptr<T> ptr_;
const std::type_info &type_;
};
std::unique_ptr<Placeholder> holder_;
friend class Scope;
const std::string *name_;
};
} // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
src/io.cpp
浏览文件 @ 45de1e84
...@@ -28,398 +28,386 @@ SOFTWARE. ...@@ -28,398 +28,386 @@ SOFTWARE.
namespace paddle_mobile { namespace paddle_mobile {
void ReadBinaryFile(const std::string &filename, std::string *contents) { void ReadBinaryFile(const std::string &filename, std::string *contents) {
std::ifstream fin(filename, std::ios::in | std::ios::binary); std::ifstream fin(filename, std::ios::in | std::ios::binary);
fin.seekg(0, std::ios::end); fin.seekg(0, std::ios::end);
contents->clear(); contents->clear();
contents->resize(fin.tellg()); contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg); fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size()); fin.read(&(contents->at(0)), contents->size());
fin.close(); fin.close();
} }
template <typename Dtype, Precision P> template <typename Dtype, Precision P>
void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor, void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
const std::string &file_path) { const std::string &file_path) {
// LOG(kLOG_DEBUG) << " to load " << file_path; // LOG(kLOG_DEBUG) << " to load " << file_path;
// Log(kLOG_DEBUG) << "123"; // Log(kLOG_DEBUG) << "123";
std::ifstream is(file_path); std::ifstream is(file_path);
std::streampos pos = is.tellg(); // save current position std::streampos pos = is.tellg(); // save current position
is.seekg(0, std::ios::end); is.seekg(0, std::ios::end);
// LOG(kLOG_DEBUG) << " file length = " << is.tellg(); // LOG(kLOG_DEBUG) << " file length = " << is.tellg();
is.seekg(pos); // restore saved position is.seekg(pos); // restore saved position
// 1. version // 1. version
uint32_t version; uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version)); is.read(reinterpret_cast<char *>(&version), sizeof(version));
// LOG(kLOG_INFO) << " version: " << version; // LOG(kLOG_INFO) << " version: " << version;
// 2 Lod information // 2 Lod information
uint64_t lod_level; uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level)); is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
// LOG(kLOG_DEBUG) << " load level: " << lod_level; // LOG(kLOG_DEBUG) << " load level: " << lod_level;
// LOG(kLOG_DEBUG) << " lod info: "; // LOG(kLOG_DEBUG) << " lod info: ";
auto &lod = *tensor->mutable_lod(); auto &lod = *tensor->mutable_lod();
lod.resize(lod_level); lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) { for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size; uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
LOG(kLOG_DEBUG1) << " lod - " << tmp[j];
}
lod[i] = tmp;
}
// 3. tensor version
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&tensor_version),
sizeof(tensor_version));
// std::cout << " tensor_version: " << tensor_version << std::endl;
// 4. tensor desc
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size)); is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size << std::endl; std::vector<size_t> tmp(size / sizeof(size_t));
std::unique_ptr<char[]> buf(new char[size]); is.read(reinterpret_cast<char *>(tmp.data()),
is.read(reinterpret_cast<char *>(buf.get()), size); static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
framework::proto::VarType::TensorDesc desc; LOG(kLOG_DEBUG1) << " lod - " << tmp[j];
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " << desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: " << desc.dims()[l]
// <<
// std::endl;
memory_size *= desc.dims()[l];
} }
lod[i] = tmp;
}
std::vector<int64_t> dims; // 3. tensor version
dims.reserve(static_cast<size_t>(desc.dims().size())); uint32_t tensor_version;
std::copy(desc.dims().begin(), desc.dims().end(), is.read(reinterpret_cast<char *>(&tensor_version), sizeof(tensor_version));
std::back_inserter(dims)); // std::cout << " tensor_version: " << tensor_version << std::endl;
tensor->Resize(framework::make_ddim(dims));
// 4. tensor desc
void *memory; int32_t size;
int type_size = 0; is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " desc pre type: "; // std::cout << " tensor desc size: " << size << std::endl;
switch (desc.data_type()) { std::unique_ptr<char[]> buf(new char[size]);
case framework::proto::VarType::FP16: is.read(reinterpret_cast<char *>(buf.get()), size);
// std::cout << "FP16" << std::endl;
type_size = 2; framework::proto::VarType::TensorDesc desc;
break; desc.ParseFromArray(buf.get(), size);
case framework::proto::VarType::FP32:
type_size = 4; // std::cout << " desc dims size " << desc.dims().size() <<
memory = tensor->mutable_data<float>(); // std::endl;
// std::cout << "FP32" << std::endl; int memory_size = 1;
break; for (int l = 0; l < desc.dims().size(); ++l) {
case framework::proto::VarType::FP64: // std::cout << " dim " << l << " value: " << desc.dims()[l]
type_size = 8; // <<
// std::cout << "FP64" << std::endl; // std::endl;
break; memory_size *= desc.dims()[l];
case framework::proto::VarType::INT32: }
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" << std::endl;
}
// std::cout << " malloc size: " << memory_size * type_size << std::vector<int64_t> dims;
// std::endl; dims.reserve(static_cast<size_t>(desc.dims().size()));
is.read(static_cast<char *>(memory), memory_size * type_size); std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
// std::cout << " memory: " << memory << std::endl; tensor->Resize(framework::make_ddim(dims));
is.close();
}; void *memory;
int type_size = 0;
template <typename Dtype, Precision P> // std::cout << " desc pre type: ";
const framework::Program<Dtype, P> switch (desc.data_type()) {
Loader<Dtype, P>::Load(const std::string &dirname) { case framework::proto::VarType::FP16:
std::string model_filename = dirname + "/__model__"; // std::cout << "FP16" << std::endl;
std::string program_desc_str; type_size = 2;
ReadBinaryFile(model_filename, &program_desc_str); break;
framework::proto::ProgramDesc program_desc_proto; case framework::proto::VarType::FP32:
program_desc_proto.ParseFromString(program_desc_str); type_size = 4;
memory = tensor->mutable_data<float>();
std::shared_ptr<framework::ProgramDesc> originProgramDesc = // std::cout << "FP32" << std::endl;
std::make_shared<framework::ProgramDesc>(program_desc_proto); break;
case framework::proto::VarType::FP64:
framework::Program<Dtype, P> program; type_size = 8;
program.originProgram = originProgramDesc; // std::cout << "FP64" << std::endl;
break;
std::shared_ptr<framework::Scope> scope = case framework::proto::VarType::INT32:
std::make_shared<framework::Scope>(); type_size = 4;
program.scope = scope; // std::cout << "INT32" << std::endl;
break;
auto block = originProgramDesc->Block(0); case framework::proto::VarType::INT64:
type_size = 8;
for (auto block : originProgramDesc->Blocks()) { // std::cout << "INT64" << std::endl;
// std::cout << "for block" << std::endl; break;
for (int i = 0; i < block->Vars().size(); ++i) { case framework::proto::VarType::BOOL:
std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i]; type_size = 1;
auto var = scope->Var(var_desc->Name()); // std::cout << "BOOL" << std::endl;
if (var_desc->GetType() == break;
framework::proto::VarType::LOD_TENSOR) { default:
if (var_desc->Persistable() && break;
var_desc->GetType() != // std::cout << " not support" << std::endl;
framework::proto::VarType::FEED_MINIBATCH && }
var_desc->GetType() !=
framework::proto::VarType::FETCH_LIST) { // std::cout << " malloc size: " << memory_size * type_size <<
framework::LoDTensor *tensor = // std::endl;
var->GetMutable<framework::LoDTensor>(); is.read(static_cast<char *>(memory), memory_size * type_size);
// to load // std::cout << " memory: " << memory << std::endl;
LoadVar(tensor, dirname + "/" + var_desc->Name()); is.close();
} };
} else {
// std::cout << "非 lod" << std::endl; template <typename Dtype, Precision P>
const framework::Program<Dtype, P>
Loader<Dtype, P>::Load(const std::string &dirname) {
std::string model_filename = dirname + "/__model__";
std::string program_desc_str;
ReadBinaryFile(model_filename, &program_desc_str);
framework::proto::ProgramDesc program_desc_proto;
program_desc_proto.ParseFromString(program_desc_str);
std::shared_ptr<framework::ProgramDesc> originProgramDesc =
std::make_shared<framework::ProgramDesc>(program_desc_proto);
framework::Program<Dtype, P> program;
program.originProgram = originProgramDesc;
std::shared_ptr<framework::Scope> scope =
std::make_shared<framework::Scope>();
program.scope = scope;
auto block = originProgramDesc->Block(0);
for (auto block : originProgramDesc->Blocks()) {
// std::cout << "for block" << std::endl;
for (int i = 0; i < block->Vars().size(); ++i) {
std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i];
auto var = scope->Var(var_desc->Name());
if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
if (var_desc->Persistable() &&
var_desc->GetType() !=
framework::proto::VarType::FEED_MINIBATCH &&
var_desc->GetType() !=
framework::proto::VarType::FETCH_LIST) {
framework::LoDTensor *tensor =
var->GetMutable<framework::LoDTensor>();
// to load
LoadVar(tensor, dirname + "/" + var_desc->Name());
} }
} else {
// std::cout << "非 lod" << std::endl;
} }
} }
}
#ifdef PADDLE_MOBILE_DEBUG #ifdef PADDLE_MOBILE_DEBUG
for (int i = 0; i < program_desc_proto.blocks().size(); ++i) { for (int i = 0; i < program_desc_proto.blocks().size(); ++i) {
framework::proto::BlockDesc block = program_desc_proto.blocks()[i]; framework::proto::BlockDesc block = program_desc_proto.blocks()[i];
LOG(kLOG_DEBUG) << "block: " << block.idx(); LOG(kLOG_DEBUG) << "block: " << block.idx();
for (int j = 0; j < block.ops().size(); ++j) { for (int j = 0; j < block.ops().size(); ++j) {
framework::proto::OpDesc op = block.ops()[j]; framework::proto::OpDesc op = block.ops()[j];
LOG(kLOG_DEBUG1) << " op: " << op.type(); LOG(kLOG_DEBUG1) << " op: " << op.type();
for (int m = 0; m < op.inputs_size(); ++m) { for (int m = 0; m < op.inputs_size(); ++m) {
const framework::proto::OpDesc::Var &var = op.inputs(m); const framework::proto::OpDesc::Var &var = op.inputs(m);
LOG(kLOG_DEBUG2) << " input parameter: " LOG(kLOG_DEBUG2) << " input parameter: " << var.parameter();
<< var.parameter(); for (int n = 0; n < var.arguments().size(); ++n) {
for (int n = 0; n < var.arguments().size(); ++n) { LOG(kLOG_DEBUG3) << " argument - " << var.arguments()[n];
LOG(kLOG_DEBUG3) << " argument - "
<< var.arguments()[n];
}
} }
}
for (int y = 0; y < op.outputs_size(); ++y) { for (int y = 0; y < op.outputs_size(); ++y) {
const framework::proto::OpDesc::Var &var = op.outputs(y); const framework::proto::OpDesc::Var &var = op.outputs(y);
LOG(kLOG_DEBUG2) << " out parameter: " << var.parameter(); LOG(kLOG_DEBUG2) << " out parameter: " << var.parameter();
for (int z = 0; z < var.arguments().size(); ++z) { for (int z = 0; z < var.arguments().size(); ++z) {
LOG(kLOG_DEBUG3) << " argument - " LOG(kLOG_DEBUG3) << " argument - " << var.arguments()[z];
<< var.arguments()[z];
}
} }
}
for (int x = 0; x < op.attrs().size(); ++x) { for (int x = 0; x < op.attrs().size(); ++x) {
const framework::proto::OpDesc_Attr attr = op.attrs()[x]; const framework::proto::OpDesc_Attr attr = op.attrs()[x];
// std::cout << " attr name: " << attr.name() << // std::cout << " attr name: " << attr.name() <<
// std::endl; // std::endl;
// std::cout << " attr type: " << attr.type() << // std::cout << " attr type: " << attr.type() <<
// std::endl; // std::endl;
switch (attr.type()) { switch (attr.type()) {
case framework::proto::AttrType::BOOLEAN: case framework::proto::AttrType::BOOLEAN:
// std::cout << " boolen: " << attr.b() << // std::cout << " boolen: " << attr.b() <<
// std::endl;
break;
case framework::proto::AttrType::INT:
// std::cout << " int: " << attr.i() <<
// std::endl;
break;
case framework::proto::AttrType::FLOAT:
// std::cout << " float: " << attr.f() <<
// std::endl; // std::endl;
case framework::proto::AttrType::STRING: break;
// std::cout << " string: " << attr.s() << case framework::proto::AttrType::INT:
// std::cout << " int: " << attr.i() <<
// std::endl; // std::endl;
case framework::proto::AttrType::BOOLEANS: break;
// std::vector<bool> case framework::proto::AttrType::FLOAT:
// bools(attr.bools_size()); // std::cout << " float: " << attr.f() <<
for (int y = 0; y < attr.bools_size(); ++y) { // std::endl;
// std::cout << " bool - " << case framework::proto::AttrType::STRING:
// attr.bools(y) << // std::cout << " string: " << attr.s() <<
// std::endl; // std::endl;
} case framework::proto::AttrType::BOOLEANS:
case framework::proto::AttrType::LONG: // std::vector<bool>
// std::cout << " long: " << attr.l() << // bools(attr.bools_size());
// std::endl; for (int y = 0; y < attr.bools_size(); ++y) {
case framework::proto::AttrType::FLOATS: // std::cout << " bool - " <<
for (int y = 0; y < attr.floats_size(); ++y) { // attr.bools(y) <<
// std::cout << " float - " << y << // std::endl;
// ": " << }
// attr.floats(y) case framework::proto::AttrType::LONG:
// << std::endl; // std::cout << " long: " << attr.l() <<
} // std::endl;
case framework::proto::AttrType::INTS: case framework::proto::AttrType::FLOATS:
for (int y = 0; y < attr.ints_size(); ++y) { for (int y = 0; y < attr.floats_size(); ++y) {
// std::cout << " int - " << y << ": // std::cout << " float - " << y <<
// " << // ": " <<
// attr.ints(y) // attr.floats(y)
// << std::endl; // << std::endl;
} }
case framework::proto::AttrType::STRINGS: case framework::proto::AttrType::INTS:
for (int y = 0; y < attr.strings_size(); ++y) { for (int y = 0; y < attr.ints_size(); ++y) {
// std::cout << " string - " << y << // std::cout << " int - " << y << ":
// ": " << // " <<
// attr.strings(y) // attr.ints(y)
// << std::endl; // << std::endl;
} }
case framework::proto::AttrType::STRINGS:
for (int y = 0; y < attr.strings_size(); ++y) {
// std::cout << " string - " << y <<
// ": " <<
// attr.strings(y)
// << std::endl;
} }
} }
} }
}
for (int k = 0; k < block.vars().size(); ++k) { for (int k = 0; k < block.vars().size(); ++k) {
framework::proto::VarDesc var = block.vars()[k]; framework::proto::VarDesc var = block.vars()[k];
if (var.type().type() == if (var.type().type() == framework::proto::VarType::LOD_TENSOR) {
framework::proto::VarType::LOD_TENSOR) { // std::cout << " var name: " << var.name() <<
// std::cout << " var name: " << var.name() << // std::endl;
// std::endl; const framework::proto::VarType::TensorDesc &tensor_desc =
const framework::proto::VarType::TensorDesc &tensor_desc = var.type().lod_tensor().tensor();
var.type().lod_tensor().tensor(); // std::cout << " in var tensor desc dims size "
// std::cout << " in var tensor desc dims size " // << tensor_desc.dims().size() <<
// << tensor_desc.dims().size() << // std::endl;
// std::endl; int memory_size = 1;
int memory_size = 1; for (int l = 0; l < tensor_desc.dims().size(); ++l) {
for (int l = 0; l < tensor_desc.dims().size(); ++l) { // std::cout << " var tensor desc dim " << l
// std::cout << " var tensor desc dim " << l // << " value: " <<
// << " value: " << // tensor_desc.dims()[l] <<
// tensor_desc.dims()[l] << // std::endl;
// std::endl;
}
} }
}
if (var.persistable() && if (var.persistable() &&
var.type().type() != var.type().type() !=
framework::proto::VarType::FEED_MINIBATCH && framework::proto::VarType::FEED_MINIBATCH &&
var.type().type() != var.type().type() != framework::proto::VarType::FETCH_LIST) {
framework::proto::VarType::FETCH_LIST) { // std::cout << " to load " << var.name() <<
// std::cout << " to load " << var.name() << // std::endl;
// std::endl; std::string file_path = dirname + "/" + var.name();
std::string file_path = dirname + "/" + var.name(); std::ifstream is(file_path);
std::ifstream is(file_path); std::streampos pos = is.tellg(); // save current position
std::streampos pos = is.seekg(0, std::ios::end);
is.tellg(); // save current position // std::cout << " file length = " << is.tellg() <<
is.seekg(0, std::ios::end); // std::endl;
// std::cout << " file length = " << is.tellg() << is.seekg(pos); // restore saved position
// std::endl;
is.seekg(pos); // restore saved position // 1. version
uint32_t version;
// 1. version is.read(reinterpret_cast<char *>(&version), sizeof(version));
uint32_t version; // std::cout << " version: " << version <<
is.read(reinterpret_cast<char *>(&version), // std::endl;
sizeof(version));
// std::cout << " version: " << version << // 2 Lod information
// std::endl; uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level),
// 2 Lod information sizeof(lod_level));
uint64_t lod_level; // std::cout << " load level: " << lod_level <<
is.read(reinterpret_cast<char *>(&lod_level), // std::endl;
sizeof(lod_level)); // std::cout << " lod info: " << std::endl;
// std::cout << " load level: " << lod_level << for (uint64_t i = 0; i < lod_level; ++i) {
// std::endl; uint64_t size;
// std::cout << " lod info: " << std::endl;
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
// std::cout << " lod - " << tmp[j] <<
// std::endl;
}
}
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&version),
sizeof(version));
// std::cout << " tensor_version: " <<
// tensor_version <<
// std::endl;
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size)); is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size << std::vector<size_t> tmp(size / sizeof(size_t));
// std::endl; is.read(reinterpret_cast<char *>(tmp.data()),
std::unique_ptr<char[]> buf(new char[size]); static_cast<std::streamsize>(size));
is.read(reinterpret_cast<char *>(buf.get()), size); for (int j = 0; j < tmp.size(); ++j) {
// std::cout << " lod - " << tmp[j] <<
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " <<
// desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: "
// <<
// desc.dims()[l]
// << std::endl;
memory_size *= desc.dims()[l];
}
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" <<
// std::endl; // std::endl;
} }
}
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// std::cout << " tensor_version: " <<
// tensor_version <<
// std::endl;
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size <<
// std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " <<
// desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: "
// <<
// desc.dims()[l]
// << std::endl;
memory_size *= desc.dims()[l];
}
// std::cout << " malloc size: " << memory_size * int type_size = 0;
// type_size // std::cout << " desc pre type: ";
// << std::endl; switch (desc.data_type()) {
void *memory = malloc(memory_size * type_size); case framework::proto::VarType::FP16:
is.read(static_cast<char *>(memory), // std::cout << "FP16" << std::endl;
memory_size * type_size); type_size = 2;
// std::cout << " memory: " << memory << break;
// std::endl; case framework::proto::VarType::FP32:
is.close(); type_size = 4;
} else { // std::cout << "FP32" << std::endl;
// std::cout << " *not load " break;
// << " var : " << var.name() << std::endl; case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" <<
// std::endl;
} }
// std::cout << " malloc size: " << memory_size *
// type_size
// << std::endl;
void *memory = malloc(memory_size * type_size);
is.read(static_cast<char *>(memory), memory_size * type_size);
// std::cout << " memory: " << memory <<
// std::endl;
is.close();
} else {
// std::cout << " *not load "
// << " var : " << var.name() << std::endl;
} }
} }
}
#endif #endif
return program; return program;
} }
template class Loader<CPU, Precision::FP32>; template class Loader<CPU, Precision::FP32>;
} // namespace paddle_mobile } // namespace paddle_mobile
src/io.h
浏览文件 @ 45de1e84
...@@ -27,14 +27,13 @@ SOFTWARE. ...@@ -27,14 +27,13 @@ SOFTWARE.
namespace paddle_mobile { namespace paddle_mobile {
template <typename Dtype, Precision P = Precision::FP32> template <typename Dtype, Precision P = Precision::FP32>
class Loader : PaddleMobileObject { class Loader : PaddleMobileObject {
public: public:
const framework::Program<Dtype, P> Load(const std::string &dirname); const framework::Program<Dtype, P> Load(const std::string &dirname);
private: private:
void LoadVar(framework::LoDTensor *tensor, void LoadVar(framework::LoDTensor *tensor, const std::string &file_path);
const std::string &file_path); };
};
} // namespace paddle_mobile } // namespace paddle_mobile
src/memory/t_malloc.cc
浏览文件 @ 45de1e84
...@@ -22,30 +22,30 @@ SOFTWARE. ...@@ -22,30 +22,30 @@ SOFTWARE.
#include <cstring> #include <cstring>
namespace paddle_mobile { namespace paddle_mobile {
namespace memory { namespace memory {
const int MALLOC_ALIGN = 16; const int MALLOC_ALIGN = 16;
void Copy(void *dst, const void *src, size_t num) { void Copy(void *dst, const void *src, size_t num) {
std::memcpy(dst, src, num); std::memcpy(dst, src, num);
}; };
void *Alloc(size_t size) { void *Alloc(size_t size) {
size_t offset = sizeof(void *) + MALLOC_ALIGN - 1; size_t offset = sizeof(void *) + MALLOC_ALIGN - 1;
char *p = static_cast<char *>(malloc(offset + size)); char *p = static_cast<char *>(malloc(offset + size));
if (!p) { if (!p) {
return nullptr; return nullptr;
} }
void *r = reinterpret_cast<void *>( void *r = reinterpret_cast<void *>(reinterpret_cast<size_t>(p + offset) &
reinterpret_cast<size_t>(p + offset) & (~(MALLOC_ALIGN - 1))); (~(MALLOC_ALIGN - 1)));
static_cast<void **>(r)[-1] = p; static_cast<void **>(r)[-1] = p;
return r; return r;
} }
void Free(void *ptr) { void Free(void *ptr) {
if (ptr) { if (ptr) {
free(static_cast<void **>(ptr)[-1]); free(static_cast<void **>(ptr)[-1]);
} }
} }
} // namespace memory } // namespace memory
} // namespace paddle_mobile } // namespace paddle_mobile
src/memory/t_malloc.h
浏览文件 @ 45de1e84
...@@ -21,44 +21,44 @@ SOFTWARE. ...@@ -21,44 +21,44 @@ SOFTWARE.
#include <type_traits> #include <type_traits>
namespace paddle_mobile { namespace paddle_mobile {
namespace memory { namespace memory {
void Copy(void *dst, const void *src, size_t num); void Copy(void *dst, const void *src, size_t num);
void *Alloc(size_t size); void *Alloc(size_t size);
void Free(void *ptr); void Free(void *ptr);
/** /**
* \brief Free memory block in one place. * \brief Free memory block in one place.
* *
* \note In some cases, custom deleter is used to * \note In some cases, custom deleter is used to
* deallocate the memory automatically for * deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h. * std::unique_ptr<T> in tensor.h.
* static_cast * static_cast
*/ */
template <typename T> class PODDeleter { template <typename T> class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD"); static_assert(std::is_pod<T>::value, "T must be POD");
public: public:
explicit PODDeleter(){}; explicit PODDeleter(){};
void operator()(T *ptr) { Free(static_cast<void *>(ptr)); } void operator()(T *ptr) { Free(static_cast<void *>(ptr)); }
}; };
/** /**
* \brief Free memory block in one place does not meet POD * \brief Free memory block in one place does not meet POD
* *
* \note In some cases, custom deleter is used to * \note In some cases, custom deleter is used to
* deallocate the memory automatically for * deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h. * std::unique_ptr<T> in tensor.h.
* reinterpret_cast * reinterpret_cast
*/ */
template <typename T> class PlainDeleter { template <typename T> class PlainDeleter {
public: public:
explicit PlainDeleter(){}; explicit PlainDeleter(){};
void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); } void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); }
}; };
} // namespace memory } // namespace memory
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/conv_op.cpp
浏览文件 @ 45de1e84
...@@ -19,58 +19,57 @@ SOFTWARE. ...@@ -19,58 +19,57 @@ SOFTWARE.
#include "conv_op.h" #include "conv_op.h"
#include "framework/data_type.h" #include "framework/data_type.h"
#include "framework/op_proto_maker.h" #include "framework/op_proto_maker.h"
#include "framework/operator.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
int ConvOutputSize(int input_size, int filter_size, int dilation, int ConvOutputSize(int input_size, int filter_size, int dilation, int padding,
int padding, int stride) { int stride) {
const int dkernel = dilation * (filter_size - 1) + 1; const int dkernel = dilation * (filter_size - 1) + 1;
int output_size = (input_size + 2 * padding - dkernel) / stride + 1; int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
return output_size; return output_size;
} }
template <typename Dtype, typename T> template <typename Dtype, typename T>
void ConvOp<Dtype, T>::InferShape() const { void ConvOp<Dtype, T>::InferShape() const {
// std::cout << " begin get dims: " << std::endl; // std::cout << " begin get dims: " << std::endl;
auto in_dims = param_.Input()->dims(); auto in_dims = param_.Input()->dims();
// std::cout << " end get in dims: " << std::endl; // std::cout << " end get in dims: " << std::endl;
// std::cout << " in_dims: " << in_dims << std::endl; // std::cout << " in_dims: " << in_dims << std::endl;
// std::cout << " begin get Filter " << std::endl; // std::cout << " begin get Filter " << std::endl;
auto filter_dims = param_.Filter()->dims(); auto filter_dims = param_.Filter()->dims();
// std::cout << " end get Filter " << std::endl; // std::cout << " end get Filter " << std::endl;
// std::cout << " begin get Attrs " << std::endl; // std::cout << " begin get Attrs " << std::endl;
const std::vector<int> &strides = param_.Strides(); const std::vector<int> &strides = param_.Strides();
// std::cout << " end get Attrs " << strides[0] << std::endl; // std::cout << " end get Attrs " << strides[0] << std::endl;
std::vector<int> paddings = param_.Paddings(); std::vector<int> paddings = param_.Paddings();
int groups = param_.Groups(); int groups = param_.Groups();
std::vector<int> dilations = param_.Dilations(); std::vector<int> dilations = param_.Dilations();
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]}); std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) { for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back( output_shape.push_back(ConvOutputSize(in_dims[i + 2],
ConvOutputSize(in_dims[i + 2], filter_dims[i + 2], filter_dims[i + 2], dilations[i],
dilations[i], paddings[i], strides[i])); paddings[i], strides[i]));
} }
framework::DDim ddim = framework::make_ddim(output_shape); framework::DDim ddim = framework::make_ddim(output_shape);
param_.Output()->Resize(ddim); param_.Output()->Resize(ddim);
} }
template class ConvOp<CPU, float>; template class ConvOp<CPU, float>;
} // namespace operators } // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/conv_op.h
浏览文件 @ 45de1e84
...@@ -22,33 +22,32 @@ SOFTWARE. ...@@ -22,33 +22,32 @@ SOFTWARE.
#include "operators/kernel/conv_kernel.h" #include "operators/kernel/conv_kernel.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
using namespace framework; using namespace framework;
template <typename DeviceType, typename T> template <typename DeviceType, typename T>
class ConvOp : public framework::OperatorWithKernel<DeviceType> { class ConvOp : public framework::OperatorWithKernel<DeviceType> {
public: public:
ConvOp(const std::string &type, const VariableNameMap &inputs, ConvOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const VariableNameMap &outputs, const framework::AttributeMap &attrs,
const framework::AttributeMap &attrs, std::shared_ptr<framework::Scope> scope)
std::shared_ptr<framework::Scope> scope) : framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
: framework::OperatorWithKernel<DeviceType>( attrs, scope),
type, inputs, outputs, attrs, scope), param_(inputs, outputs, attrs, *scope) {}
param_(inputs, outputs, attrs, *scope) {}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel; void InferShape() const override;
void InferShape() const override;
void Run() const {
void Run() const { operators::ConvKernel<DeviceType, T, ConvParam> kernel;
operators::ConvKernel<DeviceType, T, ConvParam> kernel; kernel.Compute(param_);
kernel.Compute(param_); this->ClearVariables();
this->ClearVariables(); }
}
private:
private: ConvParam param_;
ConvParam param_; };
};
} // namespace operators
} // operators } // namespace paddle_mobile
} // paddle_mobile
src/operators/elementwise_add_op.cpp
浏览文件 @ 45de1e84
...@@ -19,13 +19,13 @@ SOFTWARE. ...@@ -19,13 +19,13 @@ SOFTWARE.
#include "elementwise_add_op.h" #include "elementwise_add_op.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
template <typename Dtype, typename T> template <typename Dtype, typename T>
void ElementwiseAddOp<Dtype, T>::InferShape() const { void ElementwiseAddOp<Dtype, T>::InferShape() const {
auto x_dim = param_.InputX()->dims(); auto x_dim = param_.InputX()->dims();
param_.Out()->Resize(x_dim); param_.Out()->Resize(x_dim);
}
template class ElementwiseAddOp<CPU, float>;
}
} }
template class ElementwiseAddOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
src/operators/elementwise_add_op.h
浏览文件 @ 45de1e84
...@@ -21,35 +21,32 @@ SOFTWARE. ...@@ -21,35 +21,32 @@ SOFTWARE.
#include "op_param.h" #include "op_param.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
using namespace framework; using namespace framework;
template <typename DeviceType, typename T> template <typename DeviceType, typename T>
class ElementwiseAddOp class ElementwiseAddOp : public framework::OperatorWithKernel<DeviceType> {
: public framework::OperatorWithKernel<DeviceType> { public:
public: ElementwiseAddOp(const std::string &type, const VariableNameMap &inputs,
ElementwiseAddOp(const std::string &type, const VariableNameMap &outputs,
const VariableNameMap &inputs, const framework::AttributeMap attrs,
const VariableNameMap &outputs, std::shared_ptr<framework::Scope> scope)
const framework::AttributeMap attrs, : framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
std::shared_ptr<framework::Scope> scope) attrs, scope),
: framework::OperatorWithKernel<DeviceType>( param_(inputs, outputs, attrs, *scope) {}
type, inputs, outputs, attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const { void Run() const {
operators::ElementwiseAddKernel<DeviceType, T, operators::ElementwiseAddKernel<DeviceType, T, ElementwiseAddParam>
ElementwiseAddParam> kernel;
kernel; kernel.Compute(param_);
kernel.Compute(param_); }
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel; using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override; void InferShape() const override;
protected: protected:
ElementwiseAddParam param_; ElementwiseAddParam param_;
}; };
} } // namespace operators
} } // namespace paddle_mobile
src/operators/kernel/arm/conv_kernel.cpp
浏览文件 @ 45de1e84
...@@ -19,146 +19,137 @@ SOFTWARE. ...@@ -19,146 +19,137 @@ SOFTWARE.
#include "operators/kernel/conv_kernel.h" #include "operators/kernel/conv_kernel.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
bool IsExpand(const std::vector<int64_t> &filter_dim, bool IsExpand(const std::vector<int64_t> &filter_dim,
const std::vector<int> &strides, const std::vector<int> &strides, const std::vector<int> &paddings,
const std::vector<int> &paddings, const std::vector<int> &dilations) {
const std::vector<int> &dilations) { bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
bool filter_1 = true, strides_1 = true, padding_0 = true, for (size_t j = 0; j < strides.size(); ++j) {
dilation_1 = true; filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
for (size_t j = 0; j < strides.size(); ++j) { strides_1 = strides_1 && (strides[j] == 1);
filter_1 = padding_0 = padding_0 && (paddings[j] == 0);
filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1); dilation_1 = dilation_1 && (dilations[j] == 1);
strides_1 = strides_1 && (strides[j] == 1); }
padding_0 = padding_0 && (paddings[j] == 0); return !(filter_1 && strides_1 && padding_0 && dilation_1);
dilation_1 = dilation_1 && (dilations[j] == 1); }
}
return !(filter_1 && strides_1 && padding_0 && dilation_1); template <>
} void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
LOG(kLOG_DEBUG) << param;
template <>
void ConvKernel<CPU, float, ConvParam>::Compute( const Tensor *input = param.Input();
const ConvParam &param) const {
LOG(kLOG_DEBUG) << param; // The filter will be reshaped in the calculations,
// so here use an assignment operation,
const Tensor *input = param.Input(); // that avoids modifying the variable in the Scope.
Tensor filter = *param.Filter();
// The filter will be reshaped in the calculations,
// so here use an assignment operation, Tensor *output = param.Output();
// that avoids modifying the variable in the Scope. // output->mutable_data<T>(context.GetPlace());
Tensor filter = *param.Filter();
int groups = param.Groups();
Tensor *output = param.Output(); std::vector<int> strides = param.Strides();
// output->mutable_data<T>(context.GetPlace()); std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
int groups = param.Groups();
std::vector<int> strides = param.Strides(); DLOG << " compute end get Attrs " << strides[0];
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations(); const int batch_size = static_cast<int>(input->dims()[0]);
DLOG << " compute end get Attrs " << strides[0]; // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h,
// k_w}
const int batch_size = static_cast<int>(input->dims()[0]); std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
// output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h,
// filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, // o_w}
// k_w} std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
std::vector<int64_t> filter_shape_vec(
framework::vectorize(filter.dims())); // use col_shape in the im2col calculation
// output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h, // col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h,
// o_w} // k_w, o_d,
std::vector<int64_t> output_shape_vec( // o_h, o_w}
framework::vectorize(output->dims())); size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
// use col_shape in the im2col calculation col_shape_vec[0] = input->dims()[1] / groups;
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, for (size_t j = 0; j < data_dim; ++j) {
// k_w, o_d, col_shape_vec[j + 1] = filter_shape_vec[j + 2];
// o_h, o_w} col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
size_t data_dim = filter_shape_vec.size() - 2; }
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim); framework::DDim col_shape(framework::make_ddim(col_shape_vec));
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) { // use col_matrix_shape in the gemm calculation
col_shape_vec[j + 1] = filter_shape_vec[j + 2]; // size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w,
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2]; // o_d *
} // o_h * o_w)
framework::DDim col_shape(framework::make_ddim(col_shape_vec)); framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
// use col_matrix_shape in the gemm calculation
// size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w, bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
// o_d * Tensor col;
// o_h * o_w) // col_matrix shares the same piece of data with col,
framework::DDim col_matrix_shape = // but will be reshaped into a two-dimensional matrix shape
framework::flatten_to_2d(col_shape, data_dim + 1); // to call the matrix multiplication interface.
Tensor col_matrix;
bool is_expand = if (is_expand) {
IsExpand(filter_shape_vec, strides, paddings, dilations); col.mutable_data<float>(col_shape);
Tensor col; col_matrix.ShareDataWith(col);
// col_matrix shares the same piece of data with col, col_matrix.Resize(col_matrix_shape);
// but will be reshaped into a two-dimensional matrix shape }
// to call the matrix multiplication interface.
Tensor col_matrix; framework::DDim input_shape = framework::slice_ddim(
if (is_expand) { input->dims(), 1, static_cast<int>(input->dims().size()));
col.mutable_data<float>(col_shape);
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
// auto& dev_ctx = context.template
// device_context<DeviceContext>();
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col); col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape); col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
} }
framework::DDim input_shape = framework::slice_ddim( // gemm
input->dims(), 1, static_cast<int>(input->dims().size())); Tensor out_slice =
out_batch.Slice(g * out_step, (g + 1) * out_step);
framework::DDim filter_matrix_shape = { Tensor filter_slice =
filter.dims()[0], filter.numel() / filter.dims()[0]}; filter.Slice(g * out_step, (g + 1) * out_step);
filter.Resize(filter_matrix_shape); math::matmul<float>(filter_slice, false, col_matrix, false,
float(1.0), &out_slice, float(0.0));
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
// auto& dev_ctx = context.template
// device_context<DeviceContext>();
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch =
output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice =
in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1],
paddings[0], paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice =
out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice =
filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
float(1.0), &out_slice, float(0.0));
}
}
} }
}
}
template class ConvKernel<CPU, float, ConvParam>; template class ConvKernel<CPU, float, ConvParam>;
} // namespace operators } // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/kernel/arm/elementwise_add_kernel.cpp
浏览文件 @ 45de1e84
...@@ -17,25 +17,25 @@ limitations under the License. */ ...@@ -17,25 +17,25 @@ limitations under the License. */
#include "operators/kernel/elementwise_add_kernel.h" #include "operators/kernel/elementwise_add_kernel.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
template <typename T> struct AddFunctor { template <typename T> struct AddFunctor {
inline T operator()(T a, T b) const { return a + b; } inline T operator()(T a, T b) const { return a + b; }
}; };
template <> template <>
void ElementwiseAddKernel<CPU, float, ElementwiseAddParam>::Compute( void ElementwiseAddKernel<CPU, float, ElementwiseAddParam>::Compute(
const ElementwiseAddParam &param) const { const ElementwiseAddParam &param) const {
const Tensor *input_x = param.InputX(); const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY(); const Tensor *input_y = param.InputY();
Tensor *Out = param.Out(); Tensor *Out = param.Out();
Out->mutable_data<float>(); Out->mutable_data<float>();
const int axis = param.Axis(); const int axis = param.Axis();
ElementwiseComputeEx<AddFunctor<float>, float>( ElementwiseComputeEx<AddFunctor<float>, float>(input_x, input_y, axis,
input_x, input_y, axis, AddFunctor<float>(), Out); AddFunctor<float>(), Out);
} }
template class ElementwiseAddKernel<CPU, float, ElementwiseAddParam>; template class ElementwiseAddKernel<CPU, float, ElementwiseAddParam>;
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle_mobile
src/operators/kernel/arm/mul_kernel.cpp
浏览文件 @ 45de1e84
...@@ -21,36 +21,34 @@ SOFTWARE. ...@@ -21,36 +21,34 @@ SOFTWARE.
#include "operators/kernel/mul_kernel.h" #include "operators/kernel/mul_kernel.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
template <> template <>
void void MulKernel<CPU, float, MulParam>::Compute(const MulParam &param) const {
MulKernel<CPU, float, MulParam>::Compute(const MulParam &param) const { const Tensor *input_x = param.InputX();
const Tensor *input_x = param.InputX(); const Tensor *input_y = param.InputY();
const Tensor *input_y = param.InputY(); Tensor *out = param.Out();
Tensor *out = param.Out(); out->mutable_data<float>();
out->mutable_data<float>(); const Tensor x_matrix =
const Tensor x_matrix = input_x->dims().size() > 2
input_x->dims().size() > 2 ? framework::ReshapeToMatrix(*input_x, param.XNumColDims())
? framework::ReshapeToMatrix(*input_x, param.XNumColDims()) : *input_x;
: *input_x; const Tensor y_matrix =
const Tensor y_matrix = input_y->dims().size() > 2
input_y->dims().size() > 2 ? framework::ReshapeToMatrix(*input_y, param.YNumColDims())
? framework::ReshapeToMatrix(*input_y, param.YNumColDims()) : *input_y;
: *input_y; auto out_dim = out->dims();
auto out_dim = out->dims(); if (out_dim.size() != 2) {
if (out_dim.size() != 2) { out->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
out->Resize({x_matrix.dims()[0], y_matrix.dims()[1]}); }
} math::matmul<float>(x_matrix, false, y_matrix, false, static_cast<float>(1),
math::matmul<float>(x_matrix, false, y_matrix, false, out, static_cast<float>(0));
static_cast<float>(1), out, if (out_dim.size() != 2) {
static_cast<float>(0)); out->Resize(out_dim);
if (out_dim.size() != 2) { }
out->Resize(out_dim); }
}
}
template class MulKernel<CPU, float, MulParam>; template class MulKernel<CPU, float, MulParam>;
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle_mobile
src/operators/kernel/conv_kernel.h
浏览文件 @ 45de1e84
...@@ -25,15 +25,14 @@ SOFTWARE. ...@@ -25,15 +25,14 @@ SOFTWARE.
#pragma once; #pragma once;
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
using namespace framework; using namespace framework;
template <typename DeviceType, typename T, typename P> template <typename DeviceType, typename T, typename P>
class ConvKernel class ConvKernel : public framework::OpKernelBase<DeviceType, ConvParam> {
: public framework::OpKernelBase<DeviceType, ConvParam> { public:
public: void Compute(const ConvParam &param) const;
void Compute(const ConvParam &param) const; };
}; } // namespace operators
} } // namespace paddle_mobile
}
src/operators/kernel/elementwise_add_kernel.h
浏览文件 @ 45de1e84
...@@ -22,15 +22,15 @@ SOFTWARE. ...@@ -22,15 +22,15 @@ SOFTWARE.
#include "operators/op_param.h" #include "operators/op_param.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
using namespace framework; using namespace framework;
template <typename DeviceType, typename T, typename P> template <typename DeviceType, typename T, typename P>
class ElementwiseAddKernel class ElementwiseAddKernel
: public framework::OpKernelBase<DeviceType, ElementwiseAddParam> { : public framework::OpKernelBase<DeviceType, ElementwiseAddParam> {
public: public:
void Compute(const ElementwiseAddParam &param) const; void Compute(const ElementwiseAddParam &param) const;
}; };
} } // namespace operators
} } // namespace paddle_mobile
src/operators/kernel/fpga/conv_kernel.cpp
浏览文件 @ 45de1e84
...@@ -16,15 +16,13 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE ...@@ -16,15 +16,13 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. SOFTWARE.
==============================================================================*/ ==============================================================================*/
#include "operators/kernel/conv_kernel.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
// template<> // template<>
// void ConvKernel<FPGA, float>::Compute(const ConvParam &param) const // void ConvKernel<FPGA, float>::Compute(const ConvParam &param) const
// {} // {}
// //
// template class ConvKernel<FPGA, float>; // template class ConvKernel<FPGA, float>;
}
} }
} // namespace paddle_mobile
src/operators/kernel/mul_kernel.h
浏览文件 @ 45de1e84
...@@ -22,14 +22,14 @@ SOFTWARE. ...@@ -22,14 +22,14 @@ SOFTWARE.
#pragma once; #pragma once;
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
using namespace framework; using namespace framework;
template <typename DeviceType, typename T, typename P> template <typename DeviceType, typename T, typename P>
class MulKernel : public framework::OpKernelBase<DeviceType, MulParam> { class MulKernel : public framework::OpKernelBase<DeviceType, MulParam> {
public: public:
void Compute(const MulParam &param) const; void Compute(const MulParam &param) const;
}; };
} } // namespace operators
} } // namespace paddle_mobile
src/operators/math/elementwise_op_function.h
浏览文件 @ 45de1e84
...@@ -18,194 +18,190 @@ limitations under the License. */ ...@@ -18,194 +18,190 @@ limitations under the License. */
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0) #define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
/* /*
* Out = X ⊙ Y * Out = X ⊙ Y
* If Y's shape does not match X' shape, they will be reshaped. * If Y's shape does not match X' shape, they will be reshaped.
* For example: * For example:
* 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
* pre=2, n=3*4, post=5 * pre=2, n=3*4, post=5
* x.shape(2, 12, 5) * y.shape(1, 12, 1).broadcast(2, 12, 5) * x.shape(2, 12, 5) * y.shape(1, 12, 1).broadcast(2, 12, 5)
* 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5) * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
* pre=2*3, n=4*5, post=1 * pre=2*3, n=4*5, post=1
* x.shape(6, 20, 1) * y.shape(1, 20, 1).broadcast(6, 20, 1) * x.shape(6, 20, 1) * y.shape(1, 20, 1).broadcast(6, 20, 1)
*/ */
inline void get_mid_dims(const framework::DDim &x_dims, inline void get_mid_dims(const framework::DDim &x_dims,
const framework::DDim &y_dims, const int axis, const framework::DDim &y_dims, const int axis,
int *pre, int *n, int *post) { int *pre, int *n, int *post) {
*pre = 1; *pre = 1;
*n = 1; *n = 1;
*post = 1; *post = 1;
// compute pre // compute pre
for (int i = 0; i < axis; ++i) { for (int i = 0; i < axis; ++i) {
(*pre) *= x_dims[i]; (*pre) *= x_dims[i];
} }
for (int i = 0; i < y_dims.size(); ++i) { for (int i = 0; i < y_dims.size(); ++i) {
assert(x_dims[i + axis] == y_dims[i]); assert(x_dims[i + axis] == y_dims[i]);
/// "Broadcast dimension mismatch."); /// "Broadcast dimension mismatch.");
(*n) *= y_dims[i]; (*n) *= y_dims[i];
} }
for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) { for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
(*post) *= x_dims[i]; (*post) *= x_dims[i];
} }
}
/// remove dims tail 1. (4,20,1,1) -> (4,20)
inline void trim_trailing_singular_dims(framework::DDim *dims) {
// Remove trailing dimensions of size 1 for y
auto actual_dims_size = dims->size();
for (; actual_dims_size != 0; --actual_dims_size) {
if ((*dims)[actual_dims_size - 1] != 1)
break;
}
if (actual_dims_size != dims->size()) {
auto actual_dims = framework::vectorize(*dims);
actual_dims.resize(actual_dims_size);
*dims = framework::make_ddim(actual_dims);
}
}
template <typename T> class RowwiseTransformIterator {
public:
RowwiseTransformIterator(const T *ptr, int n) : ptr_(ptr), i_(0), n_(n) {}
RowwiseTransformIterator<T> &operator++() {
++i_;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
} }
return *this;
/// remove dims tail 1. (4,20,1,1) -> (4,20) }
inline void trim_trailing_singular_dims(framework::DDim *dims) {
// Remove trailing dimensions of size 1 for y bool operator==(const RowwiseTransformIterator<T> &rhs) const {
auto actual_dims_size = dims->size(); return (ptr_ + i_) == &(*rhs);
for (; actual_dims_size != 0; --actual_dims_size) { }
if ((*dims)[actual_dims_size - 1] != 1)
break; bool operator!=(const RowwiseTransformIterator<T> &rhs) const {
} return (ptr_ + i_) != &(*rhs);
if (actual_dims_size != dims->size()) { }
auto actual_dims = framework::vectorize(*dims);
actual_dims.resize(actual_dims_size); const T &operator*() { return ptr_[i_]; }
*dims = framework::make_ddim(actual_dims);
private:
const T *ptr_;
int i_;
int64_t n_;
};
/// (4,20,2)+(20,): (20,) just as (20,1), when move 2 strides in last
/// dimension
/// in (4,20,2) is 2 ,
/// (20,1) move 1 stride , to fill(add) 2 element with the same number.
template <typename T> class MidWiseTransformIterator {
public:
MidWiseTransformIterator(const T *ptr, int n, int post)
: ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}
MidWiseTransformIterator<T> &operator++() {
++j_;
if (UNLIKELY(j_ == post_)) {
++i_;
j_ = 0;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
} }
} }
return *this;
template <typename T> class RowwiseTransformIterator { }
public:
RowwiseTransformIterator(const T *ptr, int n) bool operator==(const MidWiseTransformIterator<T> &rhs) const {
: ptr_(ptr), i_(0), n_(n) {} return (ptr_ + i_) == &(*rhs);
}
RowwiseTransformIterator<T> &operator++() {
++i_; bool operator!=(const MidWiseTransformIterator<T> &rhs) const {
if (UNLIKELY(i_ == n_)) { return (ptr_ + i_) != &(*rhs);
i_ = 0; }
}
return *this; const T &operator*() { return ptr_[i_]; }
}
private:
bool operator==(const RowwiseTransformIterator<T> &rhs) const { const T *ptr_;
return (ptr_ + i_) == &(*rhs); int64_t i_;
} int64_t j_;
int64_t n_;
bool operator!=(const RowwiseTransformIterator<T> &rhs) const { int64_t post_;
return (ptr_ + i_) != &(*rhs); };
}
template <typename Functor, typename T, typename OutType = T>
const T &operator*() { return ptr_[i_]; } class TransformFunctor {
public:
private: TransformFunctor(const framework::Tensor *x, const framework::Tensor *y,
const T *ptr_; framework::Tensor *z, Functor func)
int i_; : x_(x->data<T>()), y_(y->data<T>()), z_(z->mutable_data<OutType>()),
int64_t n_; nx_(x->numel()), func_(func) {}
};
inline void Run() const {
/// (4,20,2)+(20,): (20,) just as (20,1), when move 2 strides in last math::Transform trans;
/// dimension // 同时执行func(x_, y_)传入z_。
/// in (4,20,2) is 2 , trans(x_, x_ + nx_, y_, z_, func_);
/// (20,1) move 1 stride , to fill(add) 2 element with the same number. }
template <typename T> class MidWiseTransformIterator {
public: inline void RunRowWise(int n, int pre) const {
MidWiseTransformIterator(const T *ptr, int n, int post) math::Transform trans;
: ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {} trans(x_, x_ + nx_, RowwiseTransformIterator<T>(y_, n), z_, func_);
}
MidWiseTransformIterator<T> &operator++() {
++j_; inline void RunMidWise(int n, int pre, int post) const {
if (UNLIKELY(j_ == post_)) { math::Transform trans;
++i_; trans(x_, x_ + nx_, MidWiseTransformIterator<T>(y_, n, post), z_,
j_ = 0; func_);
if (UNLIKELY(i_ == n_)) { }
i_ = 0;
} private:
} const T *x_;
return *this; const T *y_;
} OutType *z_;
int64_t nx_;
bool operator==(const MidWiseTransformIterator<T> &rhs) const { Functor func_;
return (ptr_ + i_) == &(*rhs); };
}
template <typename Functor, typename T, typename OutType = T>
bool operator!=(const MidWiseTransformIterator<T> &rhs) const { void ElementwiseComputeEx(const framework::Tensor *x,
return (ptr_ + i_) != &(*rhs); const framework::Tensor *y, int axis, Functor func,
} framework::Tensor *z) {
TransformFunctor<Functor, T, OutType> functor(x, y, z, func);
const T &operator*() { return ptr_[i_]; }
auto x_dims = x->dims();
private: auto y_dims = y->dims();
const T *ptr_; // PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
int64_t i_; // "Rank of first input must >= rank of second
int64_t j_; // input.");
int64_t n_;
int64_t post_; if (x_dims == y_dims) {
}; functor.Run();
return;
template <typename Functor, typename T, typename OutType = T> }
class TransformFunctor {
public: /// axis = -1 represent the last dimension.
TransformFunctor(const framework::Tensor *x, axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
const framework::Tensor *y, framework::Tensor *z, // PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
Functor func) // "Axis should be in range [0, x_dims)");
: x_(x->data<T>()), y_(y->data<T>()), trim_trailing_singular_dims(&y_dims);
z_(z->mutable_data<OutType>()), nx_(x->numel()), func_(func) { axis = (y_dims.size() == 0) ? x_dims.size() : axis;
}
int pre, n, post;
inline void Run() const { get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
math::Transform trans; if (post == 1) {
// 同时执行func(x_, y_)传入z_。 functor.RunRowWise(n, pre);
trans(x_, x_ + nx_, y_, z_, func_); return;
} } else {
functor.RunMidWise(n, pre, post);
inline void RunRowWise(int n, int pre) const { return;
math::Transform trans; }
trans(x_, x_ + nx_, RowwiseTransformIterator<T>(y_, n), z_, }
func_);
} } // namespace operators
} // namespace paddle_mobile
inline void RunMidWise(int n, int pre, int post) const {
math::Transform trans;
trans(x_, x_ + nx_, MidWiseTransformIterator<T>(y_, n, post),
z_, func_);
}
private:
const T *x_;
const T *y_;
OutType *z_;
int64_t nx_;
Functor func_;
};
template <typename Functor, typename T, typename OutType = T>
void ElementwiseComputeEx(const framework::Tensor *x,
const framework::Tensor *y, int axis,
Functor func, framework::Tensor *z) {
TransformFunctor<Functor, T, OutType> functor(x, y, z, func);
auto x_dims = x->dims();
auto y_dims = y->dims();
// PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
// "Rank of first input must >= rank of second
// input.");
if (x_dims == y_dims) {
functor.Run();
return;
}
/// axis = -1 represent the last dimension.
axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
// PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
// "Axis should be in range [0, x_dims)");
trim_trailing_singular_dims(&y_dims);
axis = (y_dims.size() == 0) ? x_dims.size() : axis;
int pre, n, post;
get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
if (post == 1) {
functor.RunRowWise(n, pre);
return;
} else {
functor.RunMidWise(n, pre, post);
return;
}
}
} // namespace operators
} // namespace paddle
src/operators/math/im2col.cc
浏览文件 @ 45de1e84
...@@ -16,349 +16,316 @@ limitations under the License. */ ...@@ -16,349 +16,316 @@ limitations under the License. */
#include "common/types.h" #include "common/types.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
/* /*
* im = [input_channels, input_height, input_width] * im = [input_channels, input_height, input_width]
* col = * col =
* [input_channels, filter_height, filter_width, output_height, * [input_channels, filter_height, filter_width, output_height,
* output_width] * output_width]
*/ */
template <class T> class Im2ColFunctor<ColFormat::kCFO, CPU, T> { template <class T> class Im2ColFunctor<ColFormat::kCFO, CPU, T> {
public: public:
void operator()(const framework::Tensor &im, void operator()(const framework::Tensor &im,
const std::vector<int> &dilation, const std::vector<int> &dilation,
const std::vector<int> &stride, const std::vector<int> &stride,
const std::vector<int> &padding, const std::vector<int> &padding, framework::Tensor *col) {
framework::Tensor *col) { // PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(im.dims().size() == 3); // PADDLE_ENFORCE(col->dims().size() == 5);
// PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0]; int im_channels = im.dims()[0];
int im_height = im.dims()[1]; int im_height = im.dims()[1];
int im_width = im.dims()[2]; int im_width = im.dims()[2];
int filter_height = col->dims()[1]; int filter_height = col->dims()[1];
int filter_width = col->dims()[2]; int filter_width = col->dims()[2];
int col_height = col->dims()[3]; int col_height = col->dims()[3];
int col_width = col->dims()[4]; int col_width = col->dims()[4];
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] // PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// - // -
// ((dilation[0] * (filter_height - 1) // ((dilation[0] * (filter_height - 1)
// + 1))) / // + 1))) /
// stride[0] + // stride[0] +
// 1, // 1,
// col_height, // col_height,
// "Output_height and // "Output_height and
// padding(padding_up, padding_down) // padding(padding_up, padding_down)
// are " "inconsistent."); // are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] // PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// - // -
// ((dilation[1] * (filter_width - 1) // ((dilation[1] * (filter_width - 1)
// + 1))) / // + 1))) /
// stride[1] + // stride[1] +
// 1, // 1,
// col_width, // col_width,
// "Output_height and // "Output_height and
// padding(padding_up, padding_down) // padding(padding_up, padding_down)
// are " "inconsistent."); // are " "inconsistent.");
int channels_col = int channels_col = im_channels * filter_height * filter_width;
im_channels * filter_height * filter_width;
const T *im_data = im.data<T>(); const T *im_data = im.data<T>();
T *col_data = col->data<T>(); T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) { for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width; int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height; int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height); int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) { for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + int im_row_idx =
h_offset * dilation[0]; h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) { for (int w = 0; w < col_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + int im_col_idx =
w_offset * dilation[1]; w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = int col_idx = (c * col_height + h) * col_width + w;
(c * col_height + h) * col_width + w; int im_idx =
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
(im_row_idx + c_im * im_height) * im_width +
im_col_idx;
col_data[col_idx] = col_data[col_idx] =
(im_row_idx < 0 || (im_row_idx < 0 || im_row_idx >= im_height ||
im_row_idx >= im_height || im_col_idx < 0 || im_col_idx >= im_width)
im_col_idx < 0 || im_col_idx >= im_width) ? static_cast<T>(0)
? static_cast<T>(0) : im_data[im_idx];
: im_data[im_idx];
}
}
}
} }
}; }
}
}
};
/* /*
* im = [input_channels, input_height, input_width] * im = [input_channels, input_height, input_width]
* col = * col =
* [input_channels, filter_height, filter_width, output_height, * [input_channels, filter_height, filter_width, output_height,
* output_width] * output_width]
*/ */
template <class T> class Col2ImFunctor<ColFormat::kCFO, CPU, T> { template <class T> class Col2ImFunctor<ColFormat::kCFO, CPU, T> {
public: public:
void operator()(const framework::Tensor &col, void operator()(const framework::Tensor &col,
const std::vector<int> &dilation, const std::vector<int> &dilation,
const std::vector<int> &stride, const std::vector<int> &stride,
const std::vector<int> &padding, const std::vector<int> &padding, framework::Tensor *im) {
framework::Tensor *im) { // PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(im->dims().size() == 3); // PADDLE_ENFORCE(col.dims().size() == 5);
// PADDLE_ENFORCE(col.dims().size() == 5); int im_channels = im->dims()[0];
int im_channels = im->dims()[0]; int im_height = im->dims()[1];
int im_height = im->dims()[1]; int im_width = im->dims()[2];
int im_width = im->dims()[2]; int filter_height = col.dims()[1];
int filter_height = col.dims()[1]; int filter_width = col.dims()[2];
int filter_width = col.dims()[2]; int col_height = col.dims()[3];
int col_height = col.dims()[3]; int col_width = col.dims()[4];
int col_width = col.dims()[4];
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] // PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// - // -
// ((dilation[0] * (filter_height - 1) // ((dilation[0] * (filter_height - 1)
// + 1))) / // + 1))) /
// stride[0] + // stride[0] +
// 1, // 1,
// col_height, // col_height,
// "Output_height and // "Output_height and
// padding(padding_up, padding_down) // padding(padding_up, padding_down)
// are " "inconsistent."); // are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] // PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// - // -
// ((dilation[1] * (filter_width - 1) // ((dilation[1] * (filter_width - 1)
// + 1))) / // + 1))) /
// stride[1] + // stride[1] +
// 1, // 1,
// col_width, // col_width,
// "Output_height and // "Output_height and
// padding(padding_up, padding_down) // padding(padding_up, padding_down)
// are " "inconsistent."); // are " "inconsistent.");
int channels_col = int channels_col = im_channels * filter_height * filter_width;
im_channels * filter_height * filter_width;
T *im_data = im->data<T>(); T *im_data = im->data<T>();
const T *col_data = col.data<T>(); const T *col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) { for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width; int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height; int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height); int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) { for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + int im_row_idx =
h_offset * dilation[0]; h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) { for (int w = 0; w < col_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + int im_col_idx =
w_offset * dilation[1]; w * stride[1] - padding[1] + w_offset * dilation[1];
if ((im_row_idx) >= 0 && if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
(im_row_idx) < im_height && (im_col_idx) >= 0 && (im_col_idx) < im_width) {
(im_col_idx) >= 0 && im_data[(im_row_idx + c_im * im_height) * im_width +
(im_col_idx) < im_width) { im_col_idx] +=
im_data[(im_row_idx + c_im * im_height) * col_data[(c * col_height + h) * col_width + w];
im_width +
im_col_idx] +=
col_data[(c * col_height + h) *
col_width +
w];
}
}
}
} }
} }
}; }
}
}
};
template class Im2ColFunctor<ColFormat::kCFO, CPU, float>; template class Im2ColFunctor<ColFormat::kCFO, CPU, float>;
template class Im2ColFunctor<ColFormat::kCFO, CPU, double>; template class Im2ColFunctor<ColFormat::kCFO, CPU, double>;
template class Col2ImFunctor<ColFormat::kCFO, CPU, float>; template class Col2ImFunctor<ColFormat::kCFO, CPU, float>;
template class Col2ImFunctor<ColFormat::kCFO, CPU, double>; template class Col2ImFunctor<ColFormat::kCFO, CPU, double>;
/* /*
* im = [input_channels, input_height, input_width] * im = [input_channels, input_height, input_width]
* col = * col =
* [output_height, output_width, input_channels, filter_height, * [output_height, output_width, input_channels, filter_height,
* filter_width] * filter_width]
*/ */
template <class T> class Im2ColFunctor<ColFormat::kOCF, CPU, T> { template <class T> class Im2ColFunctor<ColFormat::kOCF, CPU, T> {
public: public:
void operator()(const framework::Tensor &im, void operator()(const framework::Tensor &im,
const std::vector<int> &dilation, const std::vector<int> &dilation,
const std::vector<int> &stride, const std::vector<int> &stride,
const std::vector<int> &padding, const std::vector<int> &padding, framework::Tensor *col) {
framework::Tensor *col) { // PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(im.dims().size() == 3); // PADDLE_ENFORCE(col->dims().size() == 5);
// PADDLE_ENFORCE(col->dims().size() == 5); int im_channels = im.dims()[0];
int im_channels = im.dims()[0]; int im_height = im.dims()[1];
int im_height = im.dims()[1]; int im_width = im.dims()[2];
int im_width = im.dims()[2]; int filter_height = col->dims()[3];
int filter_height = col->dims()[3]; int filter_width = col->dims()[4];
int filter_width = col->dims()[4]; int col_height = col->dims()[0];
int col_height = col->dims()[0]; int col_width = col->dims()[1];
int col_width = col->dims()[1];
// PADDLE_ENFORCE_EQ( // PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] - // (im_height + padding[0] + padding[2] -
// filter_height) / stride[0] // filter_height) / stride[0]
// + 1, col_height, "Output_height and // + 1, col_height, "Output_height and
// padding(padding_up, // padding(padding_up,
// padding_down) are " "inconsistent."); // padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ( // PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] - // (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] + // filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left, // 1, col_width, "col_width and padding(padding_left,
// padding_right) // padding_right)
// are " "inconsistent."); // are " "inconsistent.");
const T *im_data = im.data<T>(); const T *im_data = im.data<T>();
T *col_data = col->data<T>(); T *col_data = col->data<T>();
for (int col_row_idx = 0; col_row_idx < col_height; for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
++col_row_idx) { for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; for (int channel = 0; channel < im_channels; ++channel) {
++col_col_idx) { for (int filter_row_idx = 0; filter_row_idx < filter_height;
for (int channel = 0; channel < im_channels; ++filter_row_idx) {
++channel) { int im_row_offset = col_row_idx * stride[0] +
for (int filter_row_idx = 0; filter_row_idx - padding[0];
filter_row_idx < filter_height; for (int filter_col_idx = 0;
++filter_row_idx) { filter_col_idx < filter_width; ++filter_col_idx) {
int im_row_offset = int im_col_offset = col_col_idx * stride[1] +
col_row_idx * stride[0] + filter_col_idx - padding[1];
filter_row_idx - padding[0];
for (int filter_col_idx = 0;
filter_col_idx < filter_width;
++filter_col_idx) {
int im_col_offset =
col_col_idx * stride[1] +
filter_col_idx - padding[1];
int col_offset = int col_offset =
((((col_row_idx)*col_width + ((((col_row_idx)*col_width + col_col_idx) *
col_col_idx) * im_channels +
im_channels + channel) *
channel) * filter_height +
filter_height + filter_row_idx) *
filter_row_idx) * filter_width +
filter_width + filter_col_idx;
filter_col_idx;
int im_offset = (channel * im_height + int im_offset =
im_row_offset) * (channel * im_height + im_row_offset) *
im_width + im_width +
im_col_offset; im_col_offset;
col_data[col_offset] = col_data[col_offset] =
(im_row_offset < 0 || (im_row_offset < 0 ||
im_row_offset >= im_height || im_row_offset >= im_height ||
im_col_offset < 0 || im_col_offset < 0 || im_col_offset >= im_width)
im_col_offset >= im_width) ? static_cast<T>(0)
? static_cast<T>(0) : im_data[im_offset];
: im_data[im_offset];
}
}
}
} }
} }
} }
}; }
}
}
};
/* /*
* im = [input_channels, input_height, input_width] * im = [input_channels, input_height, input_width]
* col = * col =
* [output_height, output_width, input_channels, filter_height, * [output_height, output_width, input_channels, filter_height,
* filter_width] * filter_width]
*/ */
template <class T> class Col2ImFunctor<ColFormat::kOCF, CPU, T> { template <class T> class Col2ImFunctor<ColFormat::kOCF, CPU, T> {
public: public:
void operator()(const framework::Tensor &col, void operator()(const framework::Tensor &col,
const std::vector<int> &dilation, const std::vector<int> &dilation,
const std::vector<int> &stride, const std::vector<int> &stride,
const std::vector<int> &padding, const std::vector<int> &padding, framework::Tensor *im) {
framework::Tensor *im) { // PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(im->dims().size() == 3); // PADDLE_ENFORCE(col.dims().size() == 5);
// PADDLE_ENFORCE(col.dims().size() == 5); int im_channels = im->dims()[0];
int im_channels = im->dims()[0]; int im_height = im->dims()[1];
int im_height = im->dims()[1]; int im_width = im->dims()[2];
int im_width = im->dims()[2]; int filter_height = col.dims()[3];
int filter_height = col.dims()[3]; int filter_width = col.dims()[4];
int filter_width = col.dims()[4]; int col_height = col.dims()[0];
int col_height = col.dims()[0]; int col_width = col.dims()[1];
int col_width = col.dims()[1];
// PADDLE_ENFORCE_EQ( // PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] - // (im_height + padding[0] + padding[2] -
// filter_height) / stride[0] // filter_height) / stride[0]
// + 1, col_height, "Output_height and // + 1, col_height, "Output_height and
// padding(padding_up, // padding(padding_up,
// padding_down) are " "inconsistent."); // padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ( // PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] - // (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] + // filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left, // 1, col_width, "col_width and padding(padding_left,
// padding_right) // padding_right)
// are " "inconsistent."); // are " "inconsistent.");
T *im_data = im->data<T>(); T *im_data = im->data<T>();
const T *col_data = col.data<T>(); const T *col_data = col.data<T>();
for (int col_row_idx = 0; col_row_idx < col_height; for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
++col_row_idx) { for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; for (int channel = 0; channel < im_channels; ++channel) {
++col_col_idx) { for (int filter_row_idx = 0; filter_row_idx < filter_height;
for (int channel = 0; channel < im_channels; ++filter_row_idx) {
++channel) { int im_row_offset = col_row_idx * stride[0] +
for (int filter_row_idx = 0; filter_row_idx - padding[0];
filter_row_idx < filter_height; for (int filter_col_idx = 0;
++filter_row_idx) { filter_col_idx < filter_width; ++filter_col_idx) {
int im_row_offset = int im_col_offset = col_col_idx * stride[1] +
col_row_idx * stride[0] + filter_col_idx - padding[1];
filter_row_idx - padding[0];
for (int filter_col_idx = 0;
filter_col_idx < filter_width;
++filter_col_idx) {
int im_col_offset =
col_col_idx * stride[1] +
filter_col_idx - padding[1];
int col_offset = int col_offset =
(((col_row_idx * col_width + (((col_row_idx * col_width + col_col_idx) *
col_col_idx) * im_channels +
im_channels + channel) *
channel) * filter_height +
filter_height + filter_row_idx) *
filter_row_idx) * filter_width +
filter_width + filter_col_idx;
filter_col_idx;
if (im_row_offset >= 0 && if (im_row_offset >= 0 &&
im_row_offset < im_height && im_row_offset < im_height &&
im_col_offset >= 0 && im_col_offset >= 0 &&
im_col_offset < im_width) { im_col_offset < im_width) {
int im_offset = int im_offset =
(channel * im_height + (channel * im_height + im_row_offset) *
im_row_offset) * im_width +
im_width + im_col_offset;
im_col_offset; im_data[im_offset] += col_data[col_offset];
im_data[im_offset] +=
col_data[col_offset];
}
}
}
} }
} }
} }
} }
}; }
}
}
};
template class Im2ColFunctor<ColFormat::kOCF, CPU, float>; template class Im2ColFunctor<ColFormat::kOCF, CPU, float>;
template class Im2ColFunctor<ColFormat::kOCF, CPU, double>; template class Im2ColFunctor<ColFormat::kOCF, CPU, double>;
template class Col2ImFunctor<ColFormat::kOCF, CPU, float>; template class Col2ImFunctor<ColFormat::kOCF, CPU, float>;
template class Col2ImFunctor<ColFormat::kOCF, CPU, double>; template class Col2ImFunctor<ColFormat::kOCF, CPU, double>;
} // namespace math } // namespace math
} // namespace operators } // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/math/im2col.h
浏览文件 @ 45de1e84
...@@ -17,96 +17,94 @@ limitations under the License. */ ...@@ -17,96 +17,94 @@ limitations under the License. */
#include "framework/tensor.h" #include "framework/tensor.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
/* The storage format of the coldata in the Im2ColFunctor and /* The storage format of the coldata in the Im2ColFunctor and
* Col2ImFunctor. */ * Col2ImFunctor. */
enum class ColFormat { kCFO = 0, kOCF = 1 }; enum class ColFormat { kCFO = 0, kOCF = 1 };
/* /*
* \brief Converts the image data of three dimensions(CHW) into a * \brief Converts the image data of three dimensions(CHW) into a
* colData of * colData of
* five dimensions in the Im2ColFunctor calculation, * five dimensions in the Im2ColFunctor calculation,
* And in the Col2ImFunctor calculation, it is reversed. * And in the Col2ImFunctor calculation, it is reversed.
* *
* \param imData Image data. * \param imData Image data.
* \param imShape The shape of imData, * \param imShape The shape of imData,
* [input_channels, input_height, input_width]. * [input_channels, input_height, input_width].
* \param colData Column data. * \param colData Column data.
* \param colShape The shape of colData. * \param colShape The shape of colData.
* *
* \param dilations dilation data. * \param dilations dilation data.
* \param 2-dimension [dilation_height, dilation_width]. * \param 2-dimension [dilation_height, dilation_width].
* *
* \param strides stride data. * \param strides stride data.
* \param 2-dimension [stride_height, stride_width]. * \param 2-dimension [stride_height, stride_width].
* *
* \param paddings padding data. * \param paddings padding data.
* \param 4-dimension [up_pad, left_pad, down_pad, right_pad]. * \param 4-dimension [up_pad, left_pad, down_pad, right_pad].
* *
* If the template argument Format is kCFO, the shape of colData is: * If the template argument Format is kCFO, the shape of colData is:
* [input_channels, filter_height, filter_width, output_height, * [input_channels, filter_height, filter_width, output_height,
* output_width] * output_width]
* So, it is easy to reshape into a convolution matrix for * So, it is easy to reshape into a convolution matrix for
* convolution * convolution
* calculation based on matrix multiplication. * calculation based on matrix multiplication.
* The shape of convolution matrix is [height, width], where the * The shape of convolution matrix is [height, width], where the
* height is equal * height is equal
* input_channels * filter_height * filter_width, and the width is * input_channels * filter_height * filter_width, and the width is
* equal * equal
* output_height * output_width. * output_height * output_width.
* *
* Reshape: * Reshape:
* shape of colData shape of convolution matrix * shape of colData shape of convolution matrix
* [input_channels, * [input_channels,
* filter_height, * filter_height,
* filter_width, ======> [height, width] * filter_width, ======> [height, width]
* output_height, * output_height,
* output_width] * output_width]
* *
* If the template argument Format is kOCF, the shape of colData is: * If the template argument Format is kOCF, the shape of colData is:
* [output_height, output_width, input_channels, filter_height, * [output_height, output_width, input_channels, filter_height,
* filter_width] * filter_width]
* So, it is easy to reshape into a sequence matrix for rnn * So, it is easy to reshape into a sequence matrix for rnn
* calculation. * calculation.
* The shape of sequence matrix is [seq_length, step_size], where * The shape of sequence matrix is [seq_length, step_size], where
* the seq_length * the seq_length
* is equal output_height * output_width, and the step_size is equal * is equal output_height * output_width, and the step_size is equal
* input_channels * filter_height * filter_width. * input_channels * filter_height * filter_width.
* *
* Reshape: * Reshape:
* shape of colData shape of sequence matrix * shape of colData shape of sequence matrix
* [output_height, * [output_height,
* output_width, * output_width,
* input_channels, ======> [seqLength, stepSize] * input_channels, ======> [seqLength, stepSize]
* filter_height, * filter_height,
* filter_width] * filter_width]
* *
* \note The caller needs to ensure that imShape.inputChannels is * \note The caller needs to ensure that imShape.inputChannels is
* equal to * equal to
* colShape.inputChannels. * colShape.inputChannels.
*/ */
template <ColFormat Format, typename DeviceType, typename T> template <ColFormat Format, typename DeviceType, typename T>
class Im2ColFunctor { class Im2ColFunctor {
public: public:
void operator()(const framework::Tensor &im, void operator()(const framework::Tensor &im,
const std::vector<int> &dilation, const std::vector<int> &dilation,
const std::vector<int> &stride, const std::vector<int> &stride,
const std::vector<int> &padding, const std::vector<int> &padding, framework::Tensor *col);
framework::Tensor *col); };
};
template <ColFormat Format, typename DeviceType, typename T> template <ColFormat Format, typename DeviceType, typename T>
class Col2ImFunctor { class Col2ImFunctor {
public: public:
void operator()(const framework::Tensor &col, void operator()(const framework::Tensor &col,
const std::vector<int> &dilation, const std::vector<int> &dilation,
const std::vector<int> &stride, const std::vector<int> &stride,
const std::vector<int> &padding, const std::vector<int> &padding, framework::Tensor *im);
framework::Tensor *im); };
};
} // namespace math } // namespace math
} // namespace operators } // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/math/math_function.cc
浏览文件 @ 45de1e84
...@@ -15,125 +15,110 @@ limitations under the License. */ ...@@ -15,125 +15,110 @@ limitations under the License. */
#include "math_function.h" #include "math_function.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
template <> template <>
void gemm<float>(const CBLAS_TRANSPOSE transA, void gemm<float>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const CBLAS_TRANSPOSE transB, const int M, const int M, const int N, const int K, const float alpha,
const int N, const int K, const float alpha, const float *A, const float *B, const float beta, float *C) {
const float *A, const float *B, const float beta, int lda = (transA == CblasNoTrans) ? K : M;
float *C) { int ldb = (transB == CblasNoTrans) ? N : K;
int lda = (transA == CblasNoTrans) ? K : M; int ldc = N;
int ldb = (transB == CblasNoTrans) ? N : K; cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
int ldc = N; beta, C, ldc);
cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, }
lda, B, ldb, beta, C, ldc);
} template <>
void gemm<double>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
template <> const int M, const int N, const int K, const double alpha,
void gemm<double>(const CBLAS_TRANSPOSE transA, const double *A, const double *B, const double beta,
const CBLAS_TRANSPOSE transB, const int M, double *C) {
const int N, const int K, const double alpha, int lda = (transA == CblasNoTrans) ? K : M;
const double *A, const double *B, int ldb = (transB == CblasNoTrans) ? N : K;
const double beta, double *C) { int ldc = N;
int lda = (transA == CblasNoTrans) ? K : M; cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
int ldb = (transB == CblasNoTrans) ? N : K; beta, C, ldc);
int ldc = N; }
cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc); template <>
} void gemm<float>(const bool transA, const bool transB, const int M, const int N,
const int K, const float alpha, const float *A, const int lda,
template <> const float *B, const int ldb, const float beta, float *C,
void gemm<float>(const bool transA, const bool transB, const int M, const int ldc) {
const int N, const int K, const float alpha, cblas_sgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
const float *A, const int lda, const float *B, transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
const int ldb, const float beta, float *C, lda, B, ldb, beta, C, ldc);
const int ldc) { }
cblas_sgemm(CblasRowMajor,
transA == false ? CblasNoTrans : CblasTrans, template <>
transB == false ? CblasNoTrans : CblasTrans, M, N, void gemm<double>(const bool transA, const bool transB, const int M,
K, alpha, A, lda, B, ldb, beta, C, ldc); const int N, const int K, const double alpha, const double *A,
} const int lda, const double *B, const int ldb,
const double beta, double *C, const int ldc) {
template <> cblas_dgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
void gemm<double>(const bool transA, const bool transB, const int M, transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
const int N, const int K, const double alpha, lda, B, ldb, beta, C, ldc);
const double *A, const int lda, const double *B, }
const int ldb, const double beta, double *C,
const int ldc) { template <>
cblas_dgemm(CblasRowMajor, void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
transA == false ? CblasNoTrans : CblasTrans, const framework::Tensor &matrix_b, bool trans_b, float alpha,
transB == false ? CblasNoTrans : CblasTrans, M, N, framework::Tensor *matrix_out, float beta) {
K, alpha, A, lda, B, ldb, beta, C, ldc); auto dim_a = matrix_a.dims();
} auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
template <> // PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
void matmul<float>(const framework::Tensor &matrix_a, bool trans_a, // dim_out.size() ==
const framework::Tensor &matrix_b, bool trans_b, // 2,
float alpha, framework::Tensor *matrix_out, // "The input and output of matmul be matrix");
float beta) { //
auto dim_a = matrix_a.dims(); // PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
auto dim_b = matrix_b.dims(); // platform::is_cpu_place(matrix_b.place())
auto dim_out = matrix_out->dims(); // &&
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && // platform::is_cpu_place(matrix_out->place()),
// dim_out.size() == // "Matrix must all be in CPUPlace");
// 2,
// "The input and output of matmul be matrix"); int M = dim_out[0];
// int N = dim_out[1];
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) && int K = (trans_a == false) ? dim_a[1] : dim_a[0];
// platform::is_cpu_place(matrix_b.place())
// && CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
// platform::is_cpu_place(matrix_out->place()), CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
// "Matrix must all be in CPUPlace");
gemm<float>(transA, transB, M, N, K, alpha, matrix_a.data<float>(),
int M = dim_out[0]; matrix_b.data<float>(), beta, matrix_out->data<float>());
int N = dim_out[1]; }
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
template <>
CBLAS_TRANSPOSE transA = void matmul<double>(const framework::Tensor &matrix_a, bool trans_a,
(trans_a == false) ? CblasNoTrans : CblasTrans; const framework::Tensor &matrix_b, bool trans_b,
CBLAS_TRANSPOSE transB = double alpha, framework::Tensor *matrix_out, double beta) {
(trans_b == false) ? CblasNoTrans : CblasTrans; auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
gemm<float>(transA, transB, M, N, K, alpha, auto dim_out = matrix_out->dims();
matrix_a.data<float>(), matrix_b.data<float>(), // PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
beta, matrix_out->data<float>()); // dim_out.size() ==
} // 2,
// "The input and output of matmul be matrix");
template <> //
void matmul<double>(const framework::Tensor &matrix_a, bool trans_a, // PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
const framework::Tensor &matrix_b, bool trans_b, // platform::is_cpu_place(matrix_b.place())
double alpha, framework::Tensor *matrix_out, // &&
double beta) { // platform::is_cpu_place(matrix_out->place()),
auto dim_a = matrix_a.dims(); // "Matrix must all be in CPUPlace");
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims(); int M = dim_out[0];
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && int N = dim_out[1];
// dim_out.size() == int K = (trans_a == false) ? dim_a[1] : dim_a[0];
// 2,
// "The input and output of matmul be matrix"); CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
// CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place()) gemm<double>(transA, transB, M, N, K, alpha, matrix_a.data<double>(),
// && matrix_b.data<double>(), beta, matrix_out->data<double>());
// platform::is_cpu_place(matrix_out->place()), }
// "Matrix must all be in CPUPlace");
} // namespace math
int M = dim_out[0]; } // namespace operators
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA =
(trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB =
(trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<double>(transA, transB, M, N, K, alpha,
matrix_a.data<double>(), matrix_b.data<double>(),
beta, matrix_out->data<double>());
}
} // namespace math
} // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/math/math_function.h
浏览文件 @ 45de1e84
...@@ -19,26 +19,24 @@ limitations under the License. */ ...@@ -19,26 +19,24 @@ limitations under the License. */
#include <cmath> #include <cmath>
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
template <typename T> template <typename T>
void gemm(const CBLAS_TRANSPOSE transA, void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int M, const int N, const int K, const T alpha, const T *A,
const int K, const T alpha, const T *A, const T *B, const T *B, const T beta, T *C);
const T beta, T *C);
template <typename T>
template <typename T> void gemm(const bool transA, const bool transB, const int M, const int N,
void gemm(const bool transA, const bool transB, const int M, const int K, const T alpha, const T *A, const int lda, const T *B,
const int N, const int K, const T alpha, const T *A, const int ldb, const T beta, T *C, const int ldc);
const int lda, const T *B, const int ldb, const T beta,
T *C, const int ldc); // matrix multiply with continuous memory
template <typename T>
// matrix multiply with continuous memory void matmul(const framework::Tensor &matrix_a, bool trans_a,
template <typename T> const framework::Tensor &matrix_b, bool trans_b, T alpha,
void matmul(const framework::Tensor &matrix_a, bool trans_a, framework::Tensor *matrix_out, T beta);
const framework::Tensor &matrix_b, bool trans_b, } // namespace math
T alpha, framework::Tensor *matrix_out, T beta); } // namespace operators
} // namespace math
} // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/math/transform.h
浏览文件 @ 45de1e84
...@@ -17,41 +17,39 @@ limitations under the License. */ ...@@ -17,41 +17,39 @@ limitations under the License. */
#include <algorithm> #include <algorithm>
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
// Transform applys a unary or a binary functor on each element in a // Transform applys a unary or a binary functor on each element in a
// range defined by a pair of iterators. // range defined by a pair of iterators.
// //
// - The specialization for CPU calls std::transform. // - The specialization for CPU calls std::transform.
// - The specialization for CUDA calls thrust::tranform. // - The specialization for CUDA calls thrust::tranform.
// //
// NOTE: We need to define InputIter and OutputIter defined as // NOTE: We need to define InputIter and OutputIter defined as
// different types, because the InputIter points op's inputs // different types, because the InputIter points op's inputs
// and // and
// OutputIter pints to op's outputs. // OutputIter pints to op's outputs.
// //
// NOTE: We don't assume that InputIter to be const InputType* and // NOTE: We don't assume that InputIter to be const InputType* and
// OutputIter to be OutputType*, because we might use a // OutputIter to be OutputType*, because we might use a
// iterator // iterator
// class, paddle::fluid::operators::RowwiseTRansformIterator. // class, paddle::fluid::operators::RowwiseTRansformIterator.
struct Transform { struct Transform {
template <typename InputIter, typename OutputIter, template <typename InputIter, typename OutputIter, typename UnaryOperation>
typename UnaryOperation> void operator()(InputIter first, InputIter last, OutputIter result,
void operator()(InputIter first, InputIter last, UnaryOperation op) {
OutputIter result, UnaryOperation op) { std::transform(first, last, result, op);
std::transform(first, last, result, op); }
}
template <typename InputIter1, typename InputIter2, typename OutputIter,
template <typename InputIter1, typename InputIter2, typename BinaryOperation>
typename OutputIter, typename BinaryOperation> void operator()(InputIter1 first1, InputIter1 last1, InputIter2 first2,
void operator()(InputIter1 first1, InputIter1 last1, OutputIter result, BinaryOperation op) {
InputIter2 first2, OutputIter result, std::transform(first1, last1, first2, result, op);
BinaryOperation op) { }
std::transform(first1, last1, first2, result, op); };
} } // namespace math
}; } // namespace operators
} } // namespace paddle_mobile
} // namespace platform
} // namespace paddle
src/operators/math/vol2col.cc
浏览文件 @ 45de1e84
...@@ -15,212 +15,200 @@ limitations under the License. */ ...@@ -15,212 +15,200 @@ limitations under the License. */
#include "vol2col.h" #include "vol2col.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
using Tensor = paddle_mobile::framework::Tensor; using Tensor = paddle_mobile::framework::Tensor;
/* /*
* vol = [input_channels, input_depth, input_height, input_width] * vol = [input_channels, input_depth, input_height, input_width]
* col = * col =
* [input_channels, filter_depth, filter_height, filter_width, * [input_channels, filter_depth, filter_height, filter_width,
* output_depth, output_height, output_width] * output_depth, output_height, output_width]
*/ */
template <typename T> class Vol2ColFunctor<CPU, T> { template <typename T> class Vol2ColFunctor<CPU, T> {
public: public:
void operator()(const Tensor &vol, void operator()(const Tensor &vol, const std::vector<int> &dilations,
const std::vector<int> &dilations, const std::vector<int> &strides,
const std::vector<int> &strides, const std::vector<int> &paddings, Tensor *col) const {
const std::vector<int> &paddings, // PADDLE_ENFORCE(vol.dims().size() == 4);
Tensor *col) const { // PADDLE_ENFORCE(col->dims().size() == 7);
// PADDLE_ENFORCE(vol.dims().size() == 4);
// PADDLE_ENFORCE(col->dims().size() == 7); int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_channels = vol.dims()[0]; int input_height = vol.dims()[2];
int input_depth = vol.dims()[1]; int input_width = vol.dims()[3];
int input_height = vol.dims()[2]; int filter_depth = col->dims()[1];
int input_width = vol.dims()[3]; int filter_height = col->dims()[2];
int filter_depth = col->dims()[1]; int filter_width = col->dims()[3];
int filter_height = col->dims()[2]; int output_depth = col->dims()[4];
int filter_width = col->dims()[3]; int output_height = col->dims()[5];
int output_depth = col->dims()[4]; int output_width = col->dims()[6];
int output_height = col->dims()[5]; int channels_col =
int output_width = col->dims()[6]; input_channels * filter_depth * filter_height * filter_width;
int channels_col = input_channels * filter_depth *
filter_height * filter_width; // PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] - // + 1))) /
// ((dilations[0] * (filter_depth - 1) // strides[0] +
// + 1))) / // 1,
// strides[0] + // output_depth,
// 1, // "input_depth and output_depth are "
// output_depth, // "mismatching.");
// "input_depth and output_depth are " // PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// "mismatching."); // ((dilations[1] * (filter_height -
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] - // 1) + 1))) /
// ((dilations[1] * (filter_height - // strides[1] +
// 1) + 1))) / // 1,
// strides[1] + // output_height,
// 1, // "input_height and output_height are
// output_height, // "
// "input_height and output_height are // "mismatching.");
// " // PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// "mismatching."); // ((dilations[2] * (filter_width - 1)
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] - // + 1))) /
// ((dilations[2] * (filter_width - 1) // strides[2] +
// + 1))) / // 1,
// strides[2] + // output_width,
// 1, // "input_width and output_width are "
// output_width, // "mismatching.");
// "input_width and output_width are "
// "mismatching."); const T *vol_data = vol.data<T>();
T *col_data = col->data<T>();
const T *vol_data = vol.data<T>();
T *col_data = col->data<T>(); for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
for (int c = 0; c < channels_col; ++c) { int h_offset = (c / filter_width) % filter_height;
int w_offset = c % filter_width; int d_offset = (c / filter_width / filter_height) % filter_depth;
int h_offset = (c / filter_width) % filter_height; int c_in = c / filter_width / filter_height / filter_depth;
int d_offset = for (int d = 0; d < output_depth; ++d) {
(c / filter_width / filter_height) % filter_depth; int d_pad =
int c_in = d * strides[0] - paddings[0] + d_offset * dilations[0];
c / filter_width / filter_height / filter_depth; for (int h = 0; h < output_height; ++h) {
for (int d = 0; d < output_depth; ++d) { int h_pad =
int d_pad = d * strides[0] - paddings[0] + h * strides[1] - paddings[1] + h_offset * dilations[1];
d_offset * dilations[0]; for (int w = 0; w < output_width; ++w) {
for (int h = 0; h < output_height; ++h) { int w_pad = w * strides[2] - paddings[2] +
int h_pad = h * strides[1] - paddings[1] + w_offset * dilations[2];
h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) { int col_idx =
int w_pad = w * strides[2] - paddings[2] + ((c * output_depth + d) * output_height + h) *
w_offset * dilations[2]; output_width +
w;
int col_idx = ((c * output_depth + d) * int vol_idx =
output_height + ((c_in * input_depth + d_pad) * input_height +
h) * h_pad) *
output_width + input_width +
w; w_pad;
int vol_idx = col_data[col_idx] =
((c_in * input_depth + d_pad) * (h_pad < 0 || h_pad >= input_height || w_pad < 0 ||
input_height + w_pad >= input_width || d_pad < 0 ||
h_pad) * d_pad >= input_depth)
input_width + ? static_cast<T>(0)
w_pad; : vol_data[vol_idx];
col_data[col_idx] =
(h_pad < 0 || h_pad >= input_height ||
w_pad < 0 || w_pad >= input_width ||
d_pad < 0 || d_pad >= input_depth)
? static_cast<T>(0)
: vol_data[vol_idx];
}
}
}
} }
} }
}; }
}
/* }
* vol = [input_channels,input_depth, input_height, input_width] };
* col =
* [input_channels, filter_depth, filter_height, filter_width, /*
* output_depth, output_height, output_width] * vol = [input_channels,input_depth, input_height, input_width]
*/ * col =
template <typename T> class Col2VolFunctor<CPU, T> { * [input_channels, filter_depth, filter_height, filter_width,
public: * output_depth, output_height, output_width]
void operator()(const Tensor &col, */
const std::vector<int> &dilations, template <typename T> class Col2VolFunctor<CPU, T> {
const std::vector<int> &strides, public:
const std::vector<int> &paddings, void operator()(const Tensor &col, const std::vector<int> &dilations,
Tensor *vol) const { const std::vector<int> &strides,
// PADDLE_ENFORCE(vol->dims().size() == 4); const std::vector<int> &paddings, Tensor *vol) const {
// PADDLE_ENFORCE(col.dims().size() == 7); // PADDLE_ENFORCE(vol->dims().size() == 4);
// PADDLE_ENFORCE(col.dims().size() == 7);
int input_channels = vol->dims()[0];
int input_depth = vol->dims()[1]; int input_channels = vol->dims()[0];
int input_height = vol->dims()[2]; int input_depth = vol->dims()[1];
int input_width = vol->dims()[3]; int input_height = vol->dims()[2];
int filter_depth = col.dims()[1]; int input_width = vol->dims()[3];
int filter_height = col.dims()[2]; int filter_depth = col.dims()[1];
int filter_width = col.dims()[3]; int filter_height = col.dims()[2];
int output_depth = col.dims()[4]; int filter_width = col.dims()[3];
int output_height = col.dims()[5]; int output_depth = col.dims()[4];
int output_width = col.dims()[6]; int output_height = col.dims()[5];
int channels_col = input_channels * filter_depth * int output_width = col.dims()[6];
filter_height * filter_width; int channels_col =
input_channels * filter_depth * filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1) // PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// + 1))) / // ((dilations[0] * (filter_depth - 1)
// strides[0] + // + 1))) /
// 1, // strides[0] +
// output_depth, // 1,
// "input_depth and output_depth are " // output_depth,
// "mismatching."); // "input_depth and output_depth are "
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] - // "mismatching.");
// ((dilations[1] * (filter_height - // PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// 1) + 1))) / // ((dilations[1] * (filter_height -
// strides[1] + // 1) + 1))) /
// 1, // strides[1] +
// output_height, // 1,
// "input_height and output_height are // output_height,
// " // "input_height and output_height are
// "mismatching."); // "
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] - // "mismatching.");
// ((dilations[2] * (filter_width - 1) // PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// + 1))) / // ((dilations[2] * (filter_width - 1)
// strides[2] + // + 1))) /
// 1, // strides[2] +
// output_width, // 1,
// "input_width and output_width are " // output_width,
// "mismatching."); // "input_width and output_width are "
T *vol_data = vol->data<T>(); // "mismatching.");
const T *col_data = col.data<T>(); T *vol_data = vol->data<T>();
const T *col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width; for (int c = 0; c < channels_col; ++c) {
int h_offset = (c / filter_width) % filter_height; int w_offset = c % filter_width;
int d_offset = int h_offset = (c / filter_width) % filter_height;
(c / filter_width / filter_height) % filter_depth; int d_offset = (c / filter_width / filter_height) % filter_depth;
int cIm = int cIm = c / filter_width / filter_height / filter_depth;
c / filter_width / filter_height / filter_depth; for (int d = 0; d < output_depth; ++d) {
for (int d = 0; d < output_depth; ++d) { int d_pad =
int d_pad = d * strides[0] - paddings[0] + d * strides[0] - paddings[0] + d_offset * dilations[0];
d_offset * dilations[0]; for (int h = 0; h < output_height; ++h) {
for (int h = 0; h < output_height; ++h) { int h_pad =
int h_pad = h * strides[1] - paddings[1] + h * strides[1] - paddings[1] + h_offset * dilations[1];
h_offset * dilations[1]; for (int w = 0; w < output_width; ++w) {
for (int w = 0; w < output_width; ++w) { int w_pad = w * strides[2] - paddings[2] +
int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2];
w_offset * dilations[2];
if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 &&
if (h_pad >= 0 && h_pad < input_height && w_pad < input_width && d_pad >= 0 &&
w_pad >= 0 && w_pad < input_width && d_pad < input_depth) {
d_pad >= 0 && d_pad < input_depth) { int vol_idx =
int vol_idx = ((cIm * input_depth + d_pad) * input_height +
((cIm * input_depth + d_pad) * h_pad) *
input_height + input_width +
h_pad) * w_pad;
input_width +
w_pad; int col_idx =
((c * output_depth + d) * output_height + h) *
int col_idx = ((c * output_depth + d) * output_width +
output_height + w;
h) * vol_data[vol_idx] += col_data[col_idx];
output_width +
w;
vol_data[vol_idx] += col_data[col_idx];
}
}
}
} }
} }
} }
}; }
}
template class Vol2ColFunctor<CPU, float>; }
template class Vol2ColFunctor<CPU, double>; };
template class Col2VolFunctor<CPU, float>;
template class Col2VolFunctor<CPU, double>; template class Vol2ColFunctor<CPU, float>;
template class Vol2ColFunctor<CPU, double>;
} // namespace math template class Col2VolFunctor<CPU, float>;
} // namespace operators template class Col2VolFunctor<CPU, double>;
} // namespace math
} // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/math/vol2col.h
浏览文件 @ 45de1e84
...@@ -18,78 +18,74 @@ limitations under the License. */ ...@@ -18,78 +18,74 @@ limitations under the License. */
#include "framework/tensor.h" #include "framework/tensor.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
/* /*
* \brief Converts the feature data of four dimensions(CDHW) into a * \brief Converts the feature data of four dimensions(CDHW) into a
* colData of * colData of
* seven dimensions in the Vol2ColFunctor calculation, * seven dimensions in the Vol2ColFunctor calculation,
* And in the Col2VolFunctor calculation, it is reversed. * And in the Col2VolFunctor calculation, it is reversed.
* *
* \param volData Vol data. * \param volData Vol data.
* \param volShape The shape of volData, * \param volShape The shape of volData,
* [input_channels, input_depth, input_height, * [input_channels, input_depth, input_height,
* input_width]. * input_width].
* \param colData Column data. * \param colData Column data.
* \param colShape The shape of colData. * \param colShape The shape of colData.
* *
* \param dilations dilation data. * \param dilations dilation data.
* \param 3-dimension [dilation_depth, dilation_height, * \param 3-dimension [dilation_depth, dilation_height,
* dilation_width]. * dilation_width].
* *
* \param strides stride data. * \param strides stride data.
* \param 3-dimension [stride_depth, stride_height, stride_width]. * \param 3-dimension [stride_depth, stride_height, stride_width].
* *
* \param paddings padding data. * \param paddings padding data.
* \param 3-dimension [d_pad, h_pad, w_pad]. * \param 3-dimension [d_pad, h_pad, w_pad].
* *
* The shape of colData is: * The shape of colData is:
* [input_channels, filter_depth, filter_height, filter_width, * [input_channels, filter_depth, filter_height, filter_width,
* output_depth, * output_depth,
* output_height, output_width] * output_height, output_width]
* So, it is easy to reshape into a convolution matrix for * So, it is easy to reshape into a convolution matrix for
* convolution * convolution
* calculation based on matrix multiplication. * calculation based on matrix multiplication.
* The shape of convolution matrix is [height, width], where the * The shape of convolution matrix is [height, width], where the
* height is equal * height is equal
* input_channels * filter_depth * filter_height * filter_width, and * input_channels * filter_depth * filter_height * filter_width, and
* the width * the width
* is equal output_depth * output_height * output_width. * is equal output_depth * output_height * output_width.
* *
* Reshape: * Reshape:
* shape of colData shape of convolution matrix * shape of colData shape of convolution matrix
* [input_channels, * [input_channels,
* filter_depth, * filter_depth,
* filter_height, * filter_height,
* filter_width, ======> [height, width] * filter_width, ======> [height, width]
* output_depth, * output_depth,
* output_height, * output_height,
* output_width] * output_width]
* *
* \note The caller needs to ensure that volShape.inputChannels is * \note The caller needs to ensure that volShape.inputChannels is
* equal to * equal to
* colShape.inputChannels. * colShape.inputChannels.
*/ */
using Tensor = paddle_mobile::framework::Tensor; using Tensor = paddle_mobile::framework::Tensor;
template <typename DeviceType, typename T> class Vol2ColFunctor { template <typename DeviceType, typename T> class Vol2ColFunctor {
public: public:
void operator()(const Tensor &vol, void operator()(const Tensor &vol, const std::vector<int> &dilations,
const std::vector<int> &dilations, const std::vector<int> &strides,
const std::vector<int> &strides, const std::vector<int> &paddings, Tensor *col) const;
const std::vector<int> &paddings, };
Tensor *col) const;
};
template <typename DeviceType, typename T> class Col2VolFunctor { template <typename DeviceType, typename T> class Col2VolFunctor {
public: public:
void operator()(const Tensor &col, void operator()(const Tensor &col, const std::vector<int> &dilations,
const std::vector<int> &dilations, const std::vector<int> &strides,
const std::vector<int> &strides, const std::vector<int> &paddings, Tensor *vol) const;
const std::vector<int> &paddings, };
Tensor *vol) const;
};
} // namespace math } // namespace math
} // namespace operators } // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/mul_op.cpp
浏览文件 @ 45de1e84
...@@ -19,39 +19,38 @@ SOFTWARE. ...@@ -19,39 +19,38 @@ SOFTWARE.
#include "mul_op.h" #include "mul_op.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
template <typename Dtype, typename T> template <typename Dtype, typename T> void MulOp<Dtype, T>::InferShape() const {
void MulOp<Dtype, T>::InferShape() const { auto x_dims = param_.InputX()->dims();
auto x_dims = param_.InputX()->dims(); auto y_dims = param_.InputY()->dims();
auto y_dims = param_.InputY()->dims(); int x_num_col_dims = param_.XNumColDims();
int x_num_col_dims = param_.XNumColDims(); int y_num_col_dims = param_.YNumColDims();
int y_num_col_dims = param_.YNumColDims();
assert(x_dims.size() > x_num_col_dims); assert(x_dims.size() > x_num_col_dims);
assert(y_dims.size() > y_num_col_dims); assert(y_dims.size() > y_num_col_dims);
/// (1,2,3,4) , x_num_col_dims = 2 -> (2,12) /// (1,2,3,4) , x_num_col_dims = 2 -> (2,12)
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims); auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims); auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
assert(x_mat_dims[1] == y_mat_dims[0]); assert(x_mat_dims[1] == y_mat_dims[0]);
std::vector<int64_t> output_dims; std::vector<int64_t> output_dims;
output_dims.reserve(static_cast<size_t>( output_dims.reserve(
x_num_col_dims + y_dims.size() - y_num_col_dims)); static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));
for (int i = 0; i < x_num_col_dims; ++i) { for (int i = 0; i < x_num_col_dims; ++i) {
output_dims.push_back(x_dims[i]); output_dims.push_back(x_dims[i]);
} }
for (int i = y_num_col_dims; i < y_dims.size(); ++i) {
output_dims.push_back(y_dims[i]);
}
framework::DDim ddim = framework::make_ddim(output_dims); for (int i = y_num_col_dims; i < y_dims.size(); ++i) {
param_.Out()->Resize(ddim); output_dims.push_back(y_dims[i]);
}
template class MulOp<CPU, float>;
} }
framework::DDim ddim = framework::make_ddim(output_dims);
param_.Out()->Resize(ddim);
} }
template class MulOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
src/operators/mul_op.h
浏览文件 @ 45de1e84
...@@ -21,32 +21,31 @@ SOFTWARE. ...@@ -21,32 +21,31 @@ SOFTWARE.
#include "operators/op_param.h" #include "operators/op_param.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
using namespace framework; using namespace framework;
template <typename DeviceType, typename T> template <typename DeviceType, typename T>
class MulOp : public framework::OperatorWithKernel<DeviceType> { class MulOp : public framework::OperatorWithKernel<DeviceType> {
public: public:
MulOp(const std::string &type, const VariableNameMap &inputs, MulOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const VariableNameMap &outputs, const framework::AttributeMap attrs,
const framework::AttributeMap attrs, std::shared_ptr<framework::Scope> scope)
std::shared_ptr<framework::Scope> scope) : framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
: framework::OperatorWithKernel<DeviceType>( attrs, scope),
type, inputs, outputs, attrs, scope), param_(inputs, outputs, attrs, *scope) {}
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
void Run() const { operators::MulKernel<DeviceType, T, MulParam> kernel;
operators::MulKernel<DeviceType, T, MulParam> kernel; kernel.Compute(param_);
kernel.Compute(param_); }
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel; void InferShape() const override;
void InferShape() const override;
protected:
protected: MulParam param_;
MulParam param_; };
};
} // namespace operators
} // namespace operators } // namespace paddle_mobile
} // namespace paddle
src/operators/op_param.cpp
浏览文件 @ 45de1e84
...@@ -19,27 +19,27 @@ SOFTWARE. ...@@ -19,27 +19,27 @@ SOFTWARE.
#include "op_param.h" #include "op_param.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
Print &operator<<(Print &printer, const ConvParam &conv_param) { Print &operator<<(Print &printer, const ConvParam &conv_param) {
printer << "parameter of conv: " printer << "parameter of conv: "
<< "\n"; << "\n";
printer << " stride: " printer << " stride: "
<< " (" << conv_param.Strides()[0] << " (" << conv_param.Strides()[0] << conv_param.Strides()[1]
<< conv_param.Strides()[1] << ") " << ") "
<< "\n"; << "\n";
printer << " paddings: " printer << " paddings: "
<< " (" << conv_param.Paddings()[0] << " (" << conv_param.Paddings()[0] << conv_param.Paddings()[1]
<< conv_param.Paddings()[1] << ") " << ") "
<< "\n"; << "\n";
printer << " dilations: " printer << " dilations: "
<< " (" << conv_param.Dilations()[0] << " (" << conv_param.Dilations()[0] << conv_param.Dilations()[1]
<< conv_param.Dilations()[1] << ") " << ") "
<< "\n"; << "\n";
printer << " groups: " << conv_param.Groups() << "\n"; printer << " groups: " << conv_param.Groups() << "\n";
printer << " input dims: " << conv_param.Input()->dims() << "\n"; printer << " input dims: " << conv_param.Input()->dims() << "\n";
printer << " filter dims: " << conv_param.Filter()->dims() << "\n"; printer << " filter dims: " << conv_param.Filter()->dims() << "\n";
printer << " output dims: " << conv_param.Output()->dims(); printer << " output dims: " << conv_param.Output()->dims();
return printer; return printer;
} }
} // namespace operators } // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/operators/op_param.h
浏览文件 @ 45de1e84
...@@ -26,211 +26,201 @@ SOFTWARE. ...@@ -26,211 +26,201 @@ SOFTWARE.
#include "framework/variable.h" #include "framework/variable.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
using namespace framework; using namespace framework;
class OpParam : PaddleMobileObject { class OpParam : PaddleMobileObject {
public: public:
protected: protected:
template <typename T> template <typename T>
static T *InputFrom(const VariableNameMap &inputs, static T *InputFrom(const VariableNameMap &inputs, const Scope &scope) {
const Scope &scope) { return GetVarValue<T>("Input", inputs, scope);
return GetVarValue<T>("Input", inputs, scope); }
}
template <typename T>
template <typename T> static T *InputXFrom(const VariableNameMap &inputs, const Scope &scope) {
static T *InputXFrom(const VariableNameMap &inputs, return GetVarValue<T>("X", inputs, scope);
const Scope &scope) { }
return GetVarValue<T>("X", inputs, scope);
} template <typename T>
static T *InputYFrom(const VariableNameMap &inputs, const Scope &scope) {
template <typename T> return GetVarValue<T>("Y", inputs, scope);
static T *InputYFrom(const VariableNameMap &inputs, }
const Scope &scope) {
return GetVarValue<T>("Y", inputs, scope); template <typename T>
} static std::vector<T *> InputMultiFrom(const VariableNameMap &inputs,
const Scope &scope) {
template <typename T> return GetMultiVarValue<T>("Input", inputs, scope);
static std::vector<T *> }
InputMultiFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetMultiVarValue<T>("Input", inputs, scope); template <typename T>
} static T *OutputFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Output", outputs, scope);
template <typename T> }
static T *OutputFrom(const VariableNameMap &outputs,
const Scope &scope) { template <typename T>
return GetVarValue<T>("Output", outputs, scope); static T *OutFrom(const VariableNameMap &outputs, const Scope &scope) {
} return GetVarValue<T>("Out", outputs, scope);
}
template <typename T>
static T *OutFrom(const VariableNameMap &outputs, template <typename T>
const Scope &scope) { static T *FilterFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Out", outputs, scope); return GetVarValue<T>("Filter", inputs, scope);
} }
template <typename T> template <typename T>
static T *FilterFrom(const VariableNameMap &inputs, static const T GetAttr(std::string key, const AttributeMap &map) {
const Scope &scope) { return ((Attribute)map.at(key)).Get<T>();
return GetVarValue<T>("Filter", inputs, scope); }
}
template <typename T>
template <typename T> static T *GetVarValue(std::string key, const VariableNameMap &var_map,
static const T GetAttr(std::string key, const AttributeMap &map) { const Scope &scope) {
return ((Attribute)map.at(key)).Get<T>(); auto var_vec = var_map.at(key);
} if (var_vec.size()) {
// std::cout << " get var value -- " << var_vec[0] <<
template <typename T> // std::endl;
static T *GetVarValue(std::string key, auto var = scope.FindVar(var_vec[0]);
const VariableNameMap &var_map, return var->GetMutable<T>();
const Scope &scope) { } else {
auto var_vec = var_map.at(key); return nullptr;
if (var_vec.size()) { }
// std::cout << " get var value -- " << var_vec[0] << }
// std::endl;
auto var = scope.FindVar(var_vec[0]); template <typename T>
return var->GetMutable<T>(); static std::vector<T *> GetMultiVarValue(std::string key,
} else { const VariableNameMap &var_map,
return nullptr; const Scope &scope) {
} auto var_vecs = var_map.at(key);
} assert(var_vecs.size() > 1);
std::vector<T *> var_res;
template <typename T> for (auto &var_vec : var_vecs) {
static std::vector<T *> auto var = scope.FindVar(var_vec);
GetMultiVarValue(std::string key, const VariableNameMap &var_map, var_res.push_back(var->GetMutable<T>());
const Scope &scope) { }
auto var_vecs = var_map.at(key); return var_res;
assert(var_vecs.size() > 1); }
std::vector<T *> var_res; };
for (auto &var_vec : var_vecs) {
auto var = scope.FindVar(var_vec); class ConvParam : OpParam {
var_res.push_back(var->GetMutable<T>()); public:
} ConvParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
return var_res; const framework::AttributeMap &attrs,
} const framework::Scope &scope) {
}; filter_ = FilterFrom<framework::LoDTensor>(inputs, scope);
input_ = InputFrom<framework::Tensor>(inputs, scope);
class ConvParam : OpParam { output_ = OutputFrom<framework::Tensor>(outputs, scope);
public: strides_ = GetAttr<std::vector<int>>("strides", attrs);
ConvParam(const VariableNameMap &inputs, paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
const VariableNameMap &outputs, dilations_ = GetAttr<std::vector<int>>("dilations", attrs);
const framework::AttributeMap &attrs, groups = GetAttr<int>("groups", attrs);
const framework::Scope &scope) { }
filter_ = FilterFrom<framework::LoDTensor>(inputs, scope);
input_ = InputFrom<framework::Tensor>(inputs, scope); const Tensor *Input() const { return input_; }
output_ = OutputFrom<framework::Tensor>(outputs, scope);
strides_ = GetAttr<std::vector<int>>("strides", attrs); const LoDTensor *Filter() const { return filter_; }
paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
dilations_ = GetAttr<std::vector<int>>("dilations", attrs); Tensor *Output() const { return output_; }
groups = GetAttr<int>("groups", attrs);
} const std::vector<int> &Strides() const { return strides_; }
const Tensor *Input() const { return input_; } const std::vector<int> &Paddings() const { return paddings_; }
const LoDTensor *Filter() const { return filter_; } const std::vector<int> &Dilations() const { return dilations_; }
Tensor *Output() const { return output_; } const int &Groups() const { return groups; }
const std::vector<int> &Strides() const { return strides_; } private:
Tensor *input_;
const std::vector<int> &Paddings() const { return paddings_; } Tensor *output_;
LoDTensor *filter_;
const std::vector<int> &Dilations() const { return dilations_; } std::vector<int> strides_;
std::vector<int> paddings_;
const int &Groups() const { return groups; } std::vector<int> dilations_;
int groups;
private: };
Tensor *input_;
Tensor *output_; Print &operator<<(Print &printer, const ConvParam &conv_param);
LoDTensor *filter_;
std::vector<int> strides_; class ElementwiseAddParam : OpParam {
std::vector<int> paddings_; public:
std::vector<int> dilations_; ElementwiseAddParam(const VariableNameMap &inputs,
int groups;
};
Print &operator<<(Print &printer, const ConvParam &conv_param);
class ElementwiseAddParam : OpParam {
public:
ElementwiseAddParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &Axis() const { return axis_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int axis_;
};
class MulParam : OpParam {
public:
MulParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
x_num_col_dims_ = GetAttr<int>("x_num_col_dims", attrs);
y_num_col_dims_ = GetAttr<int>("y_num_col_dims", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &XNumColDims() const { return x_num_col_dims_; }
const int &YNumColDims() const { return y_num_col_dims_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int x_num_col_dims_;
int y_num_col_dims_;
};
class ConcatParam : public OpParam {
public:
ConcatParam(const VariableNameMap &inputs,
const VariableNameMap &outputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs, const framework::AttributeMap &attrs,
const framework::Scope &scope) { const framework::Scope &scope) {
inputs_ = InputMultiFrom<framework::Tensor>(inputs, scope); input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope); input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
axis_ = GetAttr<int>("axis", attrs); out_ = OutFrom<framework::Tensor>(outputs, scope);
} axis_ = GetAttr<int>("axis", attrs);
}
std::vector<Tensor *> Inputs() const { return inputs_; } const Tensor *InputX() const { return input_x_; }
Tensor *Out() const { return out_; } const Tensor *InputY() const { return input_y_; }
const int &Axis() const { return axis_; } Tensor *Out() const { return out_; }
private: const int &Axis() const { return axis_; }
std::vector<Tensor *> inputs_;
Tensor *out_;
int axis_;
};
} // namespace operators private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int axis_;
};
class MulParam : OpParam {
public:
MulParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
x_num_col_dims_ = GetAttr<int>("x_num_col_dims", attrs);
y_num_col_dims_ = GetAttr<int>("y_num_col_dims", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &XNumColDims() const { return x_num_col_dims_; }
const int &YNumColDims() const { return y_num_col_dims_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int x_num_col_dims_;
int y_num_col_dims_;
};
class ConcatParam : public OpParam {
public:
ConcatParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
inputs_ = InputMultiFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
std::vector<Tensor *> Inputs() const { return inputs_; }
Tensor *Out() const { return out_; }
const int &Axis() const { return axis_; }
private:
std::vector<Tensor *> inputs_;
Tensor *out_;
int axis_;
};
} // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
src/platform/data_type.h
浏览文件 @ 45de1e84
...@@ -19,107 +19,107 @@ limitations under the License. */ ...@@ -19,107 +19,107 @@ limitations under the License. */
#include <typeindex> #include <typeindex>
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
inline proto::VarType::Type ToDataType(std::type_index type) { inline proto::VarType::Type ToDataType(std::type_index type) {
/*if (typeid(platform::float16).hash_code() == type.hash_code()) { /*if (typeid(platform::float16).hash_code() == type.hash_code()) {
return proto::VarType::FP16; return proto::VarType::FP16;
} else */ } else */
if (typeid(const float).hash_code() == type.hash_code()) { if (typeid(const float).hash_code() == type.hash_code()) {
// CPPLint complains Using C-style cast. Use // CPPLint complains Using C-style cast. Use
// static_cast<float>() instead // static_cast<float>() instead
// One fix to this is to replace float with const float because // One fix to this is to replace float with const float because
// typeid(T) == typeid(const T) // typeid(T) == typeid(const T)
// http://en.cppreference.com/w/cpp/language/typeid // http://en.cppreference.com/w/cpp/language/typeid
return proto::VarType::FP32; return proto::VarType::FP32;
} else if (typeid(const double).hash_code() == type.hash_code()) { } else if (typeid(const double).hash_code() == type.hash_code()) {
return proto::VarType::FP64; return proto::VarType::FP64;
} else if (typeid(const int).hash_code() == type.hash_code()) { } else if (typeid(const int).hash_code() == type.hash_code()) {
return proto::VarType::INT32; return proto::VarType::INT32;
} else if (typeid(const int64_t).hash_code() == type.hash_code()) { } else if (typeid(const int64_t).hash_code() == type.hash_code()) {
return proto::VarType::INT64; return proto::VarType::INT64;
} else if (typeid(const bool).hash_code() == type.hash_code()) { } else if (typeid(const bool).hash_code() == type.hash_code()) {
return proto::VarType::BOOL; return proto::VarType::BOOL;
} else { } else {
// PADDLE_THROW("Not supported"); // PADDLE_THROW("Not supported");
// std::cout << "Not supported"; // std::cout << "Not supported";
} }
} }
inline std::type_index ToTypeIndex(proto::VarType::Type type) { inline std::type_index ToTypeIndex(proto::VarType::Type type) {
switch (type) { switch (type) {
// case proto::VarType::FP16: // case proto::VarType::FP16:
// return typeid(platform::float16); // return typeid(platform::float16);
case proto::VarType::FP32: case proto::VarType::FP32:
return typeid(float); return typeid(float);
case proto::VarType::FP64: case proto::VarType::FP64:
return typeid(double); return typeid(double);
case proto::VarType::INT32: case proto::VarType::INT32:
return typeid(int); return typeid(int);
case proto::VarType::INT64: case proto::VarType::INT64:
return typeid(int64_t); return typeid(int64_t);
case proto::VarType::BOOL: case proto::VarType::BOOL:
return typeid(bool); return typeid(bool);
default: default:
// PADDLE_THROW("Not support type %d", type); // PADDLE_THROW("Not support type %d", type);
printf("Not support type %d", type); printf("Not support type %d", type);
} }
} }
template <typename Visitor> template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) { inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) { switch (type) {
// case proto::VarType::FP16: // case proto::VarType::FP16:
// visitor.template operator()<platform::float16>(); // visitor.template operator()<platform::float16>();
// break; // break;
case proto::VarType::FP32: case proto::VarType::FP32:
visitor.template operator()<float>(); visitor.template operator()<float>();
break; break;
case proto::VarType::FP64: case proto::VarType::FP64:
visitor.template operator()<double>(); visitor.template operator()<double>();
break; break;
case proto::VarType::INT32: case proto::VarType::INT32:
visitor.template operator()<int>(); visitor.template operator()<int>();
break; break;
case proto::VarType::INT64: case proto::VarType::INT64:
visitor.template operator()<int64_t>(); visitor.template operator()<int64_t>();
break; break;
case proto::VarType::BOOL: case proto::VarType::BOOL:
visitor.template operator()<bool>(); visitor.template operator()<bool>();
break; break;
default: default:
// PADDLE_THROW("Not supported"); // PADDLE_THROW("Not supported");
printf("Not supported"); printf("Not supported");
} }
} }
inline std::string DataTypeToString(const proto::VarType::Type type) { inline std::string DataTypeToString(const proto::VarType::Type type) {
switch (type) { switch (type) {
case proto::VarType::FP16: case proto::VarType::FP16:
return "float16"; return "float16";
case proto::VarType::FP32: case proto::VarType::FP32:
return "float32"; return "float32";
case proto::VarType::FP64: case proto::VarType::FP64:
return "float64"; return "float64";
case proto::VarType::INT16: case proto::VarType::INT16:
return "int16"; return "int16";
case proto::VarType::INT32: case proto::VarType::INT32:
return "int32"; return "int32";
case proto::VarType::INT64: case proto::VarType::INT64:
return "int64"; return "int64";
case proto::VarType::BOOL: case proto::VarType::BOOL:
return "bool"; return "bool";
default: default:
// PADDLE_THROW("Not support type %d", type); // PADDLE_THROW("Not support type %d", type);
printf("Not support type %d", type); printf("Not support type %d", type);
} }
} }
inline std::ostream &operator<<(std::ostream &out, inline std::ostream &operator<<(std::ostream &out,
const proto::VarType::Type &type) { const proto::VarType::Type &type) {
out << DataTypeToString(type); out << DataTypeToString(type);
return out; return out;
} }
} // namespace framework } // namespace framework
} // namespace paddle_mobile } // namespace paddle_mobile
test/common/test_log.cpp
浏览文件 @ 45de1e84
...@@ -22,9 +22,10 @@ int main() { ...@@ -22,9 +22,10 @@ int main() {
DLOGF("DASJFDAFJ%d -- %f", 12345, 344.234); DLOGF("DASJFDAFJ%d -- %f", 12345, 344.234);
LOGF( paddle_mobile::kLOG_DEBUG, "DASJFDAFJ%d -- %f", 12345, 344.234); LOGF(paddle_mobile::kLOG_DEBUG, "DASJFDAFJ%d -- %f", 12345, 344.234);
LOG(paddle_mobile::kLOG_DEBUG) << "test debug" << " next log"; LOG(paddle_mobile::kLOG_DEBUG) << "test debug"
<< " next log";
LOG(paddle_mobile::kLOG_DEBUG1) << "test debug1" LOG(paddle_mobile::kLOG_DEBUG1) << "test debug1"
<< " next log"; << " next log";
......
test/elementwise_add_op_test.h
浏览文件 @ 45de1e84
...@@ -21,149 +21,144 @@ SOFTWARE. ...@@ -21,149 +21,144 @@ SOFTWARE.
#include "test_include.h" #include "test_include.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename Dtype> class TestElementwiseAddOp { template <typename Dtype> class TestElementwiseAddOp {
public: public:
TestElementwiseAddOp(const Program<Dtype> p) : program_(p) { TestElementwiseAddOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) { if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram; to_predict_program_ = program_.optimizeProgram;
} else { } else {
to_predict_program_ = program_.originProgram; to_predict_program_ = program_.originProgram;
} }
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops =
block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() ==
// "elementwise_add") {
// if
// (op->GetAttrMap().at("axis").Get<int>()
// != -1) {
// DLOG << "attr: axis =
// "
// <<
// op->GetAttrMap().at("axis").Get<int>();
// }
// }
// DLOG << "op:" << op->Type();
if (op->Type() == "elementwise_add" &&
op->Input("X")[0] == "batch_norm_2.tmp_2") {
DLOG << " elementwise_add attr size: "
<< op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: "
<< op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
Attribute axis_attr = op->GetAttrMap().at("axis");
int axis = axis_attr.Get<int>();
DLOG << " Attr axis is : " << axis;
std::shared_ptr<
operators::ElementwiseAddOp<Dtype, float>>
add = std::make_shared<
operators::ElementwiseAddOp<Dtype, float>>(
op->Type(), op->GetInputs(),
op->GetOutputs(), op->GetAttrMap(),
program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
}
}
}
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 3, 224, 224});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor =
std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
private: const std::vector<std::shared_ptr<BlockDesc>> blocks =
const framework::Program<Dtype> program_; to_predict_program_->Blocks();
std::shared_ptr<ProgramDesc> to_predict_program_; // DLOG << " **block size " << blocks.size();
std::map<framework::BlockDesc, for (int i = 0; i < blocks.size(); ++i) {
std::vector<std::shared_ptr<OperatorBase<Dtype>>>> std::shared_ptr<BlockDesc> block_desc = blocks[i];
ops_of_block_; std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
bool use_optimize_ = false; // DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
void predict_add(const Tensor &t1, const Tensor &t2, int block_id) { std::shared_ptr<OpDesc> op = ops[j];
std::shared_ptr<BlockDesc> to_predict_block = // if (op->Type() ==
to_predict_program_->Block(block_id); // "elementwise_add") {
for (int j = 0; // if
j < ops_of_block_[*to_predict_block.get()].size(); ++j) { // (op->GetAttrMap().at("axis").Get<int>()
auto op = ops_of_block_[*to_predict_block.get()][j]; // != -1) {
DLOG << "op -> run()"; // DLOG << "attr: axis =
op->Run(); // "
// <<
// op->GetAttrMap().at("axis").Get<int>();
// }
// }
// DLOG << "op:" << op->Type();
if (op->Type() == "elementwise_add" &&
op->Input("X")[0] == "batch_norm_2.tmp_2") {
DLOG << " elementwise_add attr size: "
<< op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
Attribute axis_attr = op->GetAttrMap().at("axis");
int axis = axis_attr.Get<int>();
DLOG << " Attr axis is : " << axis;
std::shared_ptr<operators::ElementwiseAddOp<Dtype, float>>
add = std::make_shared<
operators::ElementwiseAddOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
} }
} }
};
template class TestElementwiseAddOp<CPU>;
} // namespace framework
namespace test {
void testElementwiseAdd() {
DLOG << "----------**********----------";
DLOG << "begin to run ElementAddOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(
std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (1,3,224,224)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
static_cast<float>(1));
float *inputx_ptr = inputx.data<float>();
/// input y (224,)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
static_cast<float>(1));
float *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
testElementwiseAddOp(program);
auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
float *output_add_ptr = output_add->data<float>();
// for (int j = 0; j < output_add->numel(); ++j) {
// DLOG << "value of output: " << output_add_ptr[j];
// }
/// output (1,3,224,224)
DLOG << "output memory size : " << output_add->memory_size();
DLOG << "output numel : " << output_add->numel();
DLOG << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
<< output_add_ptr[226];
} }
} // namespace test }
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 3, 224, 224});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestElementwiseAddOp<CPU>;
} // namespace framework
namespace test {
void testElementwiseAdd() {
DLOG << "----------**********----------";
DLOG << "begin to run ElementAddOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program =
loader.Load(std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (1,3,224,224)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
static_cast<float>(1));
float *inputx_ptr = inputx.data<float>();
/// input y (224,)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
static_cast<float>(1));
float *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
testElementwiseAddOp(program);
auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
float *output_add_ptr = output_add->data<float>();
// for (int j = 0; j < output_add->numel(); ++j) {
// DLOG << "value of output: " << output_add_ptr[j];
// }
/// output (1,3,224,224)
DLOG << "output memory size : " << output_add->memory_size();
DLOG << "output numel : " << output_add->numel();
DLOG << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
<< output_add_ptr[226];
}
} // namespace test
} // namespace paddle_mobile } // namespace paddle_mobile
test/framework/test_optimize.cpp
浏览文件 @ 45de1e84
...@@ -16,24 +16,22 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE ...@@ -16,24 +16,22 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. SOFTWARE.
==============================================================================*/ ==============================================================================*/
#include <iostream>
#include "framework/program-optimize/node.h" #include "framework/program-optimize/node.h"
#include <iostream>
using namespace paddle_mobile::framework; using namespace paddle_mobile::framework;
int main(){ int main() {
Node node("conv"); Node node("conv");
node > Node("add") > Node("relu"); node > Node("add") > Node("relu");
Node node1("conv"); Node node1("conv");
node1 > Node("add") > Node("relu"); node1 > Node("add") > Node("relu");
if (node == node1){ if (node == node1) {
DLOG << "equal"; DLOG << "equal";
} }
DLOG << "\n" << node1; DLOG << "\n" << node1;
// DLOG << node; // DLOG << node;
} }
test/mul_op_test.h
浏览文件 @ 45de1e84
...@@ -21,182 +21,170 @@ SOFTWARE. ...@@ -21,182 +21,170 @@ SOFTWARE.
#include "test_include.h" #include "test_include.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace framework { namespace framework {
template <typename Dtype> class TestMulOp { template <typename Dtype> class TestMulOp {
public: public:
TestMulOp(const Program<Dtype> p) : program_(p) { TestMulOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) { if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram; to_predict_program_ = program_.optimizeProgram;
} else { } else {
to_predict_program_ = program_.originProgram; to_predict_program_ = program_.originProgram;
} }
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops =
block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() == "mul") {
// DLOG << "x_num_col_dims :
// "
// << op->GetAttrMap()
// .at("x_num_col_dims")
// .Get<int>();
// DLOG << "y_num_col_dims :
// "
// << op->GetAttrMap()
// .at("y_num_col_dims")
// .Get<int>();
// DLOG << " Input X is : "
// << op->Input("X")[0];
// }
// DLOG << "op:" << op->Type();
if (op->Type() == "mul" &&
op->Input("X")[0] == "pool2d_0.tmp_0") {
DLOG << " mul attr size: "
<< op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: "
<< op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << "x_num_col_dims : "
<< op->GetAttrMap()
.at("x_num_col_dims")
.Get<int>();
DLOG << "y_num_col_dims : "
<< op->GetAttrMap()
.at("y_num_col_dims")
.Get<int>();
std::shared_ptr<operators::MulOp<Dtype, float>>
add = std::make_shared<
operators::MulOp<Dtype, float>>(
op->Type(), op->GetInputs(),
op->GetOutputs(), op->GetAttrMap(),
program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
}
}
}
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("pool2d_0.tmp_0");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("fc_0.w_0");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("fc_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({3, 3});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor =
std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0;
j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestMulOp<CPU>;
} // namespace framework
namespace test {
void testMul() {
DLOG << "----------**********----------";
DLOG << "begin to run MulOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(
std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (3,2,1,1)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {3, 2, 1, 1}, static_cast<float>(0),
static_cast<float>(1));
float *inputx_ptr = inputx.data<float>();
/// input y (2,3)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {2, 3}, static_cast<float>(0),
static_cast<float>(1));
float *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestMulOp<paddle_mobile::CPU> testMulOp(
program);
auto output_mul = testMulOp.predict_add(inputx, inputy);
float *output_mul_ptr = output_mul->data<float>();
auto dimx_1 = inputx.numel() / inputx.dims()[0];
DLOG << " inputx : ";
for (int i = 0; i < inputx.dims()[0]; ++i) {
for (int j = 0; j < dimx_1; ++j) {
DLOGF("%f ", inputx_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
}
auto dimy_1 = inputy.numel() / inputy.dims()[0];
DLOG << " inputy : ";
for (int i = 0; i < inputy.dims()[0]; ++i) {
for (int j = 0; j < dimy_1; ++j) {
DLOGF("%f ", inputy_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
}
auto dim_output_1 = output_mul->numel() / output_mul->dims()[0]; const std::vector<std::shared_ptr<BlockDesc>> blocks =
DLOG << " output : "; to_predict_program_->Blocks();
for (int i = 0; i < output_mul->dims()[0]; ++i) { // DLOG << " **block size " << blocks.size();
for (int j = 0; j < dim_output_1; ++j) { for (int i = 0; i < blocks.size(); ++i) {
DLOGF("%f ", output_mul_ptr[i * dimy_1 + j]); std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() == "mul") {
// DLOG << "x_num_col_dims :
// "
// << op->GetAttrMap()
// .at("x_num_col_dims")
// .Get<int>();
// DLOG << "y_num_col_dims :
// "
// << op->GetAttrMap()
// .at("y_num_col_dims")
// .Get<int>();
// DLOG << " Input X is : "
// << op->Input("X")[0];
// }
// DLOG << "op:" << op->Type();
if (op->Type() == "mul" &&
op->Input("X")[0] == "pool2d_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << "x_num_col_dims : "
<< op->GetAttrMap().at("x_num_col_dims").Get<int>();
DLOG << "y_num_col_dims : "
<< op->GetAttrMap().at("y_num_col_dims").Get<int>();
std::shared_ptr<operators::MulOp<Dtype, float>> add =
std::make_shared<operators::MulOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
} }
DLOGF("\n");
} }
}
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("pool2d_0.tmp_0");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("fc_0.w_0");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("fc_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({3, 3});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestMulOp<CPU>;
} // namespace framework
namespace test {
void testMul() {
DLOG << "----------**********----------";
DLOG << "begin to run MulOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program =
loader.Load(std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (3,2,1,1)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {3, 2, 1, 1}, static_cast<float>(0),
static_cast<float>(1));
float *inputx_ptr = inputx.data<float>();
/// input y (2,3)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {2, 3}, static_cast<float>(0),
static_cast<float>(1));
float *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestMulOp<paddle_mobile::CPU> testMulOp(program);
auto output_mul = testMulOp.predict_add(inputx, inputy);
float *output_mul_ptr = output_mul->data<float>();
auto dimx_1 = inputx.numel() / inputx.dims()[0];
DLOG << " inputx : ";
for (int i = 0; i < inputx.dims()[0]; ++i) {
for (int j = 0; j < dimx_1; ++j) {
DLOGF("%f ", inputx_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
}
auto dimy_1 = inputy.numel() / inputy.dims()[0];
DLOG << " inputy : ";
for (int i = 0; i < inputy.dims()[0]; ++i) {
for (int j = 0; j < dimy_1; ++j) {
DLOGF("%f ", inputy_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
}
auto dim_output_1 = output_mul->numel() / output_mul->dims()[0];
DLOG << " output : ";
for (int i = 0; i < output_mul->dims()[0]; ++i) {
for (int j = 0; j < dim_output_1; ++j) {
DLOGF("%f ", output_mul_ptr[i * dimy_1 + j]);
}
DLOGF("\n");
}
/// output (3,3) /// output (3,3)
DLOG << "output memory size : " << output_mul->memory_size(); DLOG << "output memory size : " << output_mul->memory_size();
DLOG << "output numel : " << output_mul->numel(); DLOG << "output numel : " << output_mul->numel();
DLOG << inputx_ptr[0] << " x " << inputy_ptr[0] << " + " DLOG << inputx_ptr[0] << " x " << inputy_ptr[0] << " + " << inputx_ptr[1]
<< inputx_ptr[1] << " x " << inputy_ptr[0 + 3] << " = " << " x " << inputy_ptr[0 + 3] << " = " << output_mul_ptr[0];
<< output_mul_ptr[0]; }
} } // namespace test
} // namespace test
} // namespace paddle_mobile } // namespace paddle_mobile
test/test_include.h
浏览文件 @ 45de1e84
...@@ -7,7 +7,6 @@ ...@@ -7,7 +7,6 @@
#include "framework/scope.h" #include "framework/scope.h"
#include "framework/tensor.h" #include "framework/tensor.h"
#include "framework/variable.h" #include "framework/variable.h"
#include "framework/variable.h"
#include "io.h" #include "io.h"
#include "test_helper.h" #include "test_helper.h"
#include <map> #include <map>
......
tools/pre-commit.hooks/.clang-format.hook 0 → 100644
浏览文件 @ 45de1e84
#!/bin/bash
#set -e
#
#readonly VERSION="3.8"
#
#version=$(clang-format -version)
#
#if ! [[ $version == *"$VERSION"* ]]; then
# echo "clang-format version check failed."
# echo "a version contains '$VERSION' is needed, but get '$version'"
# echo "you can install the right version, and make an soft-link to '\$PATH' env"
# exit -1
#fi
clang-format $@
tools/pre-commit.hooks/cpplint.bash → tools/pre-commit.hooks/.clang-tidy.hook
浏览文件 @ 45de1e84
#!/bin/bash #!/bin/bash
bash -c "cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON"
TOTAL_ERRORS=0 TOTAL_ERRORS=0
#iclang-tidy *.[ch]pp -checks=*
# The trick to remove deleted files: https://stackoverflow.com/a/2413151 # The trick to remove deleted files: https://stackoverflow.com/a/2413151
for file in $(git diff --cached --name-status | awk '$1 != "D" {print $2}'|grep -v ".pb." | grep -v "third-party/"); do for file in $(git diff --cached --name-status | awk '$1 != "D" {print $2}' | grep -v "third-party/" | grep -v ".pb."); do
cpplint $file python ./tools/pre-commit.hooks/run-clang-tidy.py $file;
echo "python ./tools/pre-commit.hooks/run-clang-tidy.py $file"
TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?); TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?);
done done
rm -f compile_commands.json
exit $TOTAL_ERRORS exit $TOTAL_ERRORS
tools/pre-commit.hooks/.clang_format.hook 已删除 100755 → 0
浏览文件 @ 9b7798f9
#!/bin/bash
set -e
readonly VERSION="3.8"
version=$(clang-format -version)
if ! [[ $version == *"$VERSION"* ]]; then
echo "clang-format version check failed."
echo "a version contains '$VERSION' is needed, but get '$version'"
echo "you can install the right version, and make an soft-link to '\$PATH' env"
exit -1
fi
clang-format $@
tools/pre-commit.hooks/clang-format.bash 已删除 100755 → 0
浏览文件 @ 9b7798f9
#!/bin/bash
set -e
readonly VERSION="version 3."
version=$(clang-format -version)
if ! [[ $version == *"$VERSION"* ]]; then
echo "clang-format version check failed."
echo "a version contains '$VERSION' is needed, but get '$version'"
echo "you can install the right version, and make an soft-link to '\$PATH' env"
exit -1
fi
clang-format $@
tools/pre-commit.hooks/run-clang-tidy.py 0 → 100755
浏览文件 @ 45de1e84
#!/usr/bin/env python
#
#===- run-clang-tidy.py - Parallel clang-tidy runner ---------*- python -*--===#
#
# The LLVM Compiler Infrastructure
#
# This file is distributed under the University of Illinois Open Source
# License. See LICENSE.TXT for details.
#
#===------------------------------------------------------------------------===#
# FIXME: Integrate with clang-tidy-diff.py
"""
Parallel clang-tidy runner
==========================
Runs clang-tidy over all files in a compilation database. Requires clang-tidy
and clang-apply-replacements in $PATH.
Example invocations.
- Run clang-tidy on all files in the current working directory with a default
set of checks and show warnings in the cpp files and all project headers.
run-clang-tidy.py $PWD
- Fix all header guards.
run-clang-tidy.py -fix -checks=-*,llvm-header-guard
- Fix all header guards included from clang-tidy and header guards
for clang-tidy headers.
run-clang-tidy.py -fix -checks=-*,llvm-header-guard extra/clang-tidy \
-header-filter=extra/clang-tidy
Compilation database setup:
http://clang.llvm.org/docs/HowToSetupToolingForLLVM.html
"""
from __future__ import print_function
import argparse
import glob
import json
import multiprocessing
import os
import re
import shutil
import subprocess
import sys
import tempfile
import threading
import traceback
import yaml
is_py2 = sys.version[0] == '2'
if is_py2:
import Queue as queue
else:
import queue as queue
def find_compilation_database(path):
"""Adjusts the directory until a compilation database is found."""
result = './'
while not os.path.isfile(os.path.join(result, path)):
if os.path.realpath(result) == '/':
print('Error: could not find compilation database.')
sys.exit(1)
result += '../'
return os.path.realpath(result)
def make_absolute(f, directory):
if os.path.isabs(f):
return f
return os.path.normpath(os.path.join(directory, f))
def get_tidy_invocation(f, clang_tidy_binary, checks, tmpdir, build_path,
header_filter, extra_arg, extra_arg_before, quiet):
"""Gets a command line for clang-tidy."""
start = [clang_tidy_binary]
if header_filter is not None:
start.append('-header-filter=' + header_filter)
else:
# Show warnings in all in-project headers by default.
start.append('-header-filter=^' + build_path + '/.*')
if checks:
start.append('-checks=' + checks)
if tmpdir is not None:
start.append('-export-fixes')
# Get a temporary file. We immediately close the handle so clang-tidy can
# overwrite it.
(handle, name) = tempfile.mkstemp(suffix='.yaml', dir=tmpdir)
os.close(handle)
start.append(name)
for arg in extra_arg:
start.append('-extra-arg=%s' % arg)
for arg in extra_arg_before:
start.append('-extra-arg-before=%s' % arg)
start.append('-p=' + build_path)
if quiet:
start.append('-quiet')
start.append(f)
return start
def merge_replacement_files(tmpdir, mergefile):
"""Merge all replacement files in a directory into a single file"""
# The fixes suggested by clang-tidy >= 4.0.0 are given under
# the top level key 'Diagnostics' in the output yaml files
mergekey="Diagnostics"
merged=[]
for replacefile in glob.iglob(os.path.join(tmpdir, '*.yaml')):
content = yaml.safe_load(open(replacefile, 'r'))
if not content:
continue # Skip empty files.
merged.extend(content.get(mergekey, []))
if merged:
# MainSourceFile: The key is required by the definition inside
# include/clang/Tooling/ReplacementsYaml.h, but the value
# is actually never used inside clang-apply-replacements,
# so we set it to '' here.
output = { 'MainSourceFile': '', mergekey: merged }
with open(mergefile, 'w') as out:
yaml.safe_dump(output, out)
else:
# Empty the file:
open(mergefile, 'w').close()
def check_clang_apply_replacements_binary(args):
"""Checks if invoking supplied clang-apply-replacements binary works."""
try:
subprocess.check_call([args.clang_apply_replacements_binary, '--version'])
except:
print('Unable to run clang-apply-replacements. Is clang-apply-replacements '
'binary correctly specified?', file=sys.stderr)
traceback.print_exc()
sys.exit(1)
def apply_fixes(args, tmpdir):
"""Calls clang-apply-fixes on a given directory."""
invocation = [args.clang_apply_replacements_binary]
if args.format:
invocation.append('-format')
if args.style:
invocation.append('-style=' + args.style)
invocation.append(tmpdir)
subprocess.call(invocation)
def run_tidy(args, tmpdir, build_path, queue):
"""Takes filenames out of queue and runs clang-tidy on them."""
while True:
name = queue.get()
invocation = get_tidy_invocation(name, args.clang_tidy_binary, args.checks,
tmpdir, build_path, args.header_filter,
args.extra_arg, args.extra_arg_before,
args.quiet)
sys.stdout.write(' '.join(invocation) + '\n')
subprocess.call(invocation)
queue.task_done()
def main():
parser = argparse.ArgumentParser(description='Runs clang-tidy over all files '
'in a compilation database. Requires '
'clang-tidy and clang-apply-replacements in '
'$PATH.')
parser.add_argument('-clang-tidy-binary', metavar='PATH',
default='clang-tidy',
help='path to clang-tidy binary')
parser.add_argument('-clang-apply-replacements-binary', metavar='PATH',
default='clang-apply-replacements',
help='path to clang-apply-replacements binary')
parser.add_argument('-checks', default=None,
help='checks filter, when not specified, use clang-tidy '
'default')
parser.add_argument('-header-filter', default=None,
help='regular expression matching the names of the '
'headers to output diagnostics from. Diagnostics from '
'the main file of each translation unit are always '
'displayed.')
parser.add_argument('-export-fixes', metavar='filename', dest='export_fixes',
help='Create a yaml file to store suggested fixes in, '
'which can be applied with clang-apply-replacements.')
parser.add_argument('-j', type=int, default=0,
help='number of tidy instances to be run in parallel.')
parser.add_argument('files', nargs='*', default=['.*'],
help='files to be processed (regex on path)')
parser.add_argument('-fix', action='store_true', help='apply fix-its')
parser.add_argument('-format', action='store_true', help='Reformat code '
'after applying fixes')
parser.add_argument('-style', default='file', help='The style of reformat '
'code after applying fixes')
parser.add_argument('-p', dest='build_path',
help='Path used to read a compile command database.')
parser.add_argument('-extra-arg', dest='extra_arg',
action='append', default=[],
help='Additional argument to append to the compiler '
'command line.')
parser.add_argument('-extra-arg-before', dest='extra_arg_before',
action='append', default=[],
help='Additional argument to prepend to the compiler '
'command line.')
parser.add_argument('-quiet', action='store_true',
help='Run clang-tidy in quiet mode')
args = parser.parse_args()
db_path = 'compile_commands.json'
if args.build_path is not None:
build_path = args.build_path
else:
# Find our database
build_path = find_compilation_database(db_path)
try:
invocation = [args.clang_tidy_binary, '-list-checks']
invocation.append('-p=' + build_path)
if args.checks:
invocation.append('-checks=' + args.checks)
invocation.append('-')
subprocess.check_call(invocation)
except:
print("Unable to run clang-tidy.", file=sys.stderr)
sys.exit(1)
# Load the database and extract all files.
database = json.load(open(os.path.join(build_path, db_path)))
files = [make_absolute(entry['file'], entry['directory'])
for entry in database]
max_task = args.j
if max_task == 0:
max_task = multiprocessing.cpu_count()
tmpdir = None
if args.fix or args.export_fixes:
check_clang_apply_replacements_binary(args)
tmpdir = tempfile.mkdtemp()
# Build up a big regexy filter from all command line arguments.
file_name_re = re.compile('|'.join(args.files))
try:
# Spin up a bunch of tidy-launching threads.
task_queue = queue.Queue(max_task)
for _ in range(max_task):
t = threading.Thread(target=run_tidy,
args=(args, tmpdir, build_path, task_queue))
t.daemon = True
t.start()
# Fill the queue with files.
for name in files:
if file_name_re.search(name):
task_queue.put(name)
# Wait for all threads to be done.
task_queue.join()
except KeyboardInterrupt:
# This is a sad hack. Unfortunately subprocess goes
# bonkers with ctrl-c and we start forking merrily.
print('\nCtrl-C detected, goodbye.')
if tmpdir:
shutil.rmtree(tmpdir)
os.kill(0, 9)
return_code = 0
if args.export_fixes:
print('Writing fixes to ' + args.export_fixes + ' ...')
try:
merge_replacement_files(tmpdir, args.export_fixes)
except:
print('Error exporting fixes.\n', file=sys.stderr)
traceback.print_exc()
return_code=1
if args.fix:
print('Applying fixes ...')
try:
apply_fixes(args, tmpdir)
except:
print('Error applying fixes.\n', file=sys.stderr)
traceback.print_exc()
return_code=1
if tmpdir:
shutil.rmtree(tmpdir)
sys.exit(return_code)
if __name__ == '__main__':
main()
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或