提交 899ba0d0 编写于 作者: L Liu Yiqun

Merge branch 'develop' into core_refine_inference

......@@ -16,12 +16,10 @@ function(copy TARGET)
foreach(index RANGE ${len})
list(GET copy_lib_SRCS ${index} src)
list(GET copy_lib_DSTS ${index} dst)
add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND mkdir -p "${dst}")
if(IS_DIRECTORY ${src})
add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND cp -r "${src}" "${dst}")
else()
add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND cp "${src}" "${dst}")
endif()
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND mkdir -p "${dst}"
COMMAND cp -r "${src}" "${dst}"
COMMENT "copying ${src} -> ${dst}")
endforeach()
endfunction()
......@@ -53,11 +51,11 @@ IF(NOT PROTOBUF_FOUND)
ENDIF(NOT PROTOBUF_FOUND)
# paddle fluid module
set(src_dir "${PADDLE_SOURCE_DIR}/paddle")
set(dst_dir "${CMAKE_INSTALL_PREFIX}/paddle")
set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
set(dst_dir "${CMAKE_INSTALL_PREFIX}/paddle/fluid")
set(module "framework")
copy(framework_lib DEPS framework_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/framework/framework.pb.h
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
......@@ -69,7 +67,7 @@ copy(memory_lib
set(module "inference")
copy(inference_lib DEPENDS paddle_fluid_shared
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/inference/libpaddle_fluid.so
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.so
DSTS ${dst_dir}/${module} ${dst_dir}/${module}
)
......
# 用Docker编译和测试PaddlePaddle
## 需要的软硬件
为了开发PaddlePaddle,我们需要
1. 一台电脑,可以装的是 Linux, BSD, Windows 或者 MacOS 操作系统,以及
1. Docker。
不需要依赖其他任何软件了。即便是 Python 和 GCC 都不需要,因为我们会把所有编译工具都安装进一个 Docker image 里。
## 总体流程
1. 获取源码
```bash
git clone https://github.com/paddlepaddle/paddle
```
2. 安装开发工具到 Docker image 里
```bash
cd paddle; docker build -t paddle:dev .
```
请注意这个命令结尾处的 `.`;它表示 `docker build` 应该读取当前目录下的 [`Dockerfile`文件](https://github.com/PaddlePaddle/Paddle/blob/develop/Dockerfile),按照其内容创建一个名为 `paddle:dev` 的 Docker image,并且把各种开发工具安装进去。
3. 编译
以下命令启动一个 Docker container 来执行 `paddle:dev` 这个 Docker image,同时把当前目录(源码树根目录)映射为 container 里的 `/paddle` 目录,并且运行 `Dockerfile` 描述的默认入口程序 [`build.sh`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh)。这个脚本调用 `cmake``make` 来编译 `/paddle` 里的源码,结果输出到 `/paddle/build`,也就是本地的源码树根目录里的 `build` 子目录。
```bash
docker run --rm -v $PWD:/paddle paddle:dev
```
上述命令编译出一个 CUDA-enabled 版本。如果我们只需要编译一个只支持 CPU 的版本,可以用
```bash
docker run --rm -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev
```
4. 运行单元测试
用本机的第一个 GPU 来运行包括 GPU 单元测试在内的所有单元测试:
```bash
NV_GPU=0 nvidia-docker run --rm -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest"
```
如果编译的时候我们用了 `WITH_GPU=OFF` 选项,那么编译过程只会产生 CPU-based 单元测试,那么我们也就不需要 nvidia-docker 来运行单元测试了。我们只需要:
```bash
docker run --rm -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest"
```
有时候我们只想运行一个特定的单元测试,比如 `memory_test`,我们可以
```bash
nvidia-docker run --rm -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test"
```
5. 清理
有时候我们会希望清理掉已经下载的第三方依赖以及已经编译的二进制文件。此时只需要:
```bash
rm -rf build
```
## 为什么要 Docker 呀?
- 什么是 Docker?
如果您没有听说 Docker,可以把它想象为一个类似 virtualenv 的系统,但是虚拟的不仅仅是 Python 的运行环境。
- Docker 还是虚拟机?
有人用虚拟机来类比 Docker。需要强调的是:Docker 不会虚拟任何硬件,Docker container 里运行的编译工具实际上都是在本机的 CPU 和操作系统上直接运行的,性能和把编译工具安装在本机运行一样。
- 为什么用 Docker?
把工具和配置都安装在一个 Docker image 里可以标准化编译环境。这样如果遇到问题,其他人可以复现问题以便帮助。
另外,对于习惯使用Windows和MacOS的开发者来说,使用Docker就不用配置交叉编译环境了。
- 我可以选择不用Docker吗?
当然可以。大家可以用把开发工具安装进入 Docker image 一样的方式,把这些工具安装到本机。这篇文档介绍基于 Docker 的开发流程,是因为这个流程比其他方法都更简便。
- 学习 Docker 有多难?
理解 Docker 并不难,大概花十分钟看一下[这篇文章](https://zhuanlan.zhihu.com/p/19902938)。这可以帮您省掉花一小时安装和配置各种开发工具,以及切换机器时需要新安装的辛苦。别忘了 PaddlePaddle 更新可能导致需要新的开发工具。更别提简化问题复现带来的好处了。
- 我可以用 IDE 吗?
当然可以,因为源码就在本机上。IDE 默认调用 make 之类的程序来编译源码,我们只需要配置 IDE 来调用 Docker 命令编译源码即可。
很多 PaddlePaddle 开发者使用 Emacs。他们在自己的 `~/.emacs` 配置文件里加两行
```emacs
(global-set-key "\C-cc" 'compile)
(setq compile-command
"docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev")
```
就可以按 `Ctrl-C``c` 键来启动编译了。
- 可以并行编译吗?
是的。我们的 Docker image 运行一个 [Bash 脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh)。这个脚本调用 `make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。
## 可能碰到的问题
- Docker 需要 sudo
如果用自己的电脑开发,自然也就有管理员权限(sudo)了。如果用公用的电脑开发,需要请管理员安装和配置好 Docker。此外,PaddlePaddle 项目在努力开始支持其他不需要 sudo 的集装箱技术,比如 rkt。
- 在 Windows/MacOS 上编译很慢
Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存,以保证编译高效。具体做法请参考[这个issue](https://github.com/PaddlePaddle/Paddle/issues/627)
- 磁盘不够
本文中的例子里,`docker run` 命令里都用了 `--rm` 参数,这样保证运行结束之后的 containers 不会保留在磁盘上。可以用 `docker ps -a` 命令看到停止后但是没有删除的 containers。`docker build` 命令有时候会产生一些中间结果,是没有名字的 images,也会占用磁盘。可以参考[这篇文章](https://zaiste.net/posts/removing_docker_containers/)来清理这些内容。
# Build using Docker
## What Developers Need
To contribute to PaddlePaddle, you need
1. A computer -- Linux, BSD, Windows, MacOS, and
1. Docker.
Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image. We run all the tools by running this image.
## General Process
1. Retrieve source code.
```bash
git clone https://github.com/paddlepaddle/paddle
```
2. Install build tools into a Docker image.
```bash
cd paddle; docker build -t paddle:dev .
```
Please be aware of the `.` at the end of the command, which refers to the [`./Dockerfile` file](https://github.com/PaddlePaddle/Paddle/blob/develop/Dockerfile). `docker build` follows instructions in this file to create a Docker image named `paddle:dev`, and installs building tools into it.
3. Build from source.
This following command starts a Docker container that executes the Docker image `paddle:dev`, mapping the current directory to `/paddle/` in the container, and runs the default entry-point [`build.sh`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh) as specified in the Dockefile. `build.sh` invokes `cmake` and `make` to build PaddlePaddle source code, which had been mapped to `/paddle`, and writes outputs to `/paddle/build`, which maps to `build` in the current source directory on the computer.
```bash
docker run -v $PWD:/paddle paddle:dev
```
Above command builds a CUDA-enabled version. If we want to build a CPU-only version, we can type
```bash
docker run -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev
```
4. Run unit tests.
To run all unit tests using the first GPU of a node:
```bash
NV_GPU=0 nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest"
```
If we used `WITH_GPU=OFF` at build time, it generates only CPU-based unit tests, and we don't need nvidia-docker to run them. We can just run
```bash
docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest"
```
Sometimes we want to run a specific unit test, say `memory_test`, we can run
```bash
nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test"
```
5. Clean Build.
Sometimes, we might want to clean all thirt-party dependents and built binaries. To do so, just
```bash
rm -rf build
```
## Docker, Or Not?
- What is Docker?
If you haven't heard of it, consider it something like Python's virtualenv.
- Docker or virtual machine?
Some people compare Docker with VMs, but Docker doesn't virtualize any hardware nor running a guest OS, which means there is no compromise on the performance.
- Why Docker?
Using a Docker image of build tools standardizes the building environment, which makes it easier for others to reproduce your problems and to help.
Also, some build tools don't run on Windows or Mac or BSD, but Docker runs almost everywhere, so developers can use whatever computer they want.
- Can I choose not to use Docker?
Sure, you don't have to install build tools into a Docker image; instead, you can install them in your local computer. This document exists because Docker would make the development way easier.
- How difficult is it to learn Docker?
It takes you ten minutes to read [an introductory article](https://docs.docker.com/get-started) and saves you more than one hour to install all required build tools, configure them, especially when new versions of PaddlePaddle require some new tools. Not even to mention the time saved when other people trying to reproduce the issue you have.
- Can I use my favorite IDE?
Yes, of course. The source code resides on your local computer, and you can edit it using whatever editor you like.
Many PaddlePaddle developers are using Emacs. They add the following few lines into their `~/.emacs` configure file:
```emacs
(global-set-key "\C-cc" 'compile)
(setq compile-command
"docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev")
```
so they could type `Ctrl-C` and `c` to build PaddlePaddle from source.
- Does Docker do parallel building?
Our building Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh), which calls `make -j$(nproc)` to starts as many processes as the number of your CPU cores.
## Some Gotchas
- Docker requires sudo
An owner of a computer has the administrative privilege, a.k.a., sudo, and Docker requires this privilege to work properly. If you use a shared computer for development, please ask the administrator to install and configure Docker. We will do our best to support rkt, another container technology that doesn't require sudo.
- Docker on Windows/MacOS builds slowly
On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to [this issue](https://github.com/PaddlePaddle/Paddle/issues/627) for details.
- Not enough disk space
Examples in this article uses option `--rm` with the `docker run` command. This option ensures that stopped containers do not exist on hard disks. We can use `docker ps -a` to list all containers, including stopped. Sometimes `docker build` generates some intermediate dangling images, which also take disk space. To clean them, please refer to [this article](https://zaiste.net/posts/removing_docker_containers/).
从源码编译
======================
.. _requirements:
需要的软硬件
----------------
为了编译PaddlePaddle,我们需要
1. 一台电脑,可以装的是 Linux, Windows 或者 MacOS 操作系统
1. Docker
不需要依赖其他任何软件了。即便是 Python 和 GCC 都不需要,因为我们会把所有编译工具都安装进一个 Docker 镜像里。
.. _build_step:
编译方法
----------------
PaddlePaddle主要使用 `CMake <https://cmake.org>`_ 以及GCC, G++作为编译工具。
我们推荐您使用PaddlePaddle Docker编译环境镜像完成编译,这样可以免去单独安装编译依赖的步骤,可选的不同编译环境Docker镜像
可以在 `这里 <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ 找到
PaddlePaddle需要使用Docker环境完成编译,这样可以免去单独安装编译依赖的步骤,可选的不同编译环境Docker镜像
可以在 `这里 <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ 找到。或者
参考下述可选步骤,从源码中构建用于编译PaddlePaddle的Docker镜像
如果您选择不使用Docker镜像,则需要在本机安装下面章节列出的 `编译依赖`_ 之后才能开始编译的步骤。
......@@ -16,15 +28,19 @@ PaddlePaddle主要使用 `CMake <https://cmake.org>`_ 以及GCC, G++作为编译
.. code-block:: bash
# 1. 获取源码
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
# 如果使用Docker编译环境,执行下面的命令编译CPU-Only的二进制
# 2. 可选步骤:源码中构建用于编译PaddlePaddle的Docker镜像
docker build -t paddle:dev .
# 3. 执行下面的命令编译CPU-Only的二进制
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh
# 如果不使用Docker编译环境,执行下面的命令
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF ..
make
# 4. 或者也可以使用为上述可选步骤构建的镜像(必须先执行第2步)
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddle:dev
注:上述命令把当前目录(源码树根目录)映射为 container 里的 :code:`/paddle` 目录。如果使用自行
构建的镜像(上述第4步)会执行 :code:`Dockerfile` 描述的默认入口程序 :code:`build.sh` 可以省略步骤3中
最后的执行脚本的命令。
编译完成后会在build/python/dist目录下生成输出的whl包,可以选在在当前机器安装也可以拷贝到目标机器安装:
......@@ -50,28 +66,83 @@ PaddlePaddle主要使用 `CMake <https://cmake.org>`_ 以及GCC, G++作为编译
如果您期望在编译完成后立即执行所有的单元测试,可以按照下面的方法:
使用Docker的情况下,设置 :code:`RUN_TEST=ON` 和 :code:`WITH_TESTING=ON` 就会在完成编译之后,立即执行单元测试。
设置 :code:`RUN_TEST=ON` 和 :code:`WITH_TESTING=ON` 就会在完成编译之后,立即执行单元测试。
开启 :code:`WITH_GPU=ON` 可以指定同时执行GPU上的单元测试。
.. code-block:: bash
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=ON" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh
如果不使用Docker,可以执行ctest命令即可
如果期望执行其中一个单元测试,(比如 :code:`test_sum_op` )
.. code-block:: bash
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF ..
make
ctest
# 指定执行其中一个单元测试 test_mul_op
ctest -R test_mul_op
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 /bin/bash
bash /paddle/paddle/scripts/docker/build.sh
cd /paddle/build
ctest -R test_sum_op -V
.. _faq_docker:
常见问题
----------------
- 什么是 Docker?
如果您没有听说 Docker,可以把它想象为一个类似 virtualenv 的系统,但是虚拟的不仅仅是 Python 的运行环境。
- Docker 还是虚拟机?
有人用虚拟机来类比 Docker。需要强调的是:Docker 不会虚拟任何硬件,Docker container 里运行的编译工具实际上都是在本机的 CPU 和操作系统上直接运行的,性能和把编译工具安装在本机运行一样。
- 为什么用 Docker?
把工具和配置都安装在一个 Docker image 里可以标准化编译环境。这样如果遇到问题,其他人可以复现问题以便帮助。
另外,对于习惯使用Windows和MacOS的开发者来说,使用Docker就不用配置交叉编译环境了。
- 我可以选择不用Docker吗?
当然可以。大家可以用把开发工具安装进入 Docker image 一样的方式,把这些工具安装到本机。这篇文档介绍基于 Docker 的开发流程,是因为这个流程比其他方法都更简便。
- 学习 Docker 有多难?
理解 Docker 并不难,大概花十分钟看一下[这篇文章](https://zhuanlan.zhihu.com/p/19902938)。这可以帮您省掉花一小时安装和配置各种开发工具,以及切换机器时需要新安装的辛苦。别忘了 PaddlePaddle 更新可能导致需要新的开发工具。更别提简化问题复现带来的好处了。
- 我可以用 IDE 吗?
当然可以,因为源码就在本机上。IDE 默认调用 make 之类的程序来编译源码,我们只需要配置 IDE 来调用 Docker 命令编译源码即可。
很多 PaddlePaddle 开发者使用 Emacs。他们在自己的 `~/.emacs` 配置文件里加两行
```emacs
(global-set-key "\C-cc" 'compile)
(setq compile-command
"docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev")
```
就可以按 `Ctrl-C` 和 `c` 键来启动编译了。
- 可以并行编译吗?
是的。我们的 Docker image 运行一个 [Bash 脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh)。这个脚本调用 `make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。
- Docker 需要 sudo
如果用自己的电脑开发,自然也就有管理员权限(sudo)了。如果用公用的电脑开发,需要请管理员安装和配置好 Docker。此外,PaddlePaddle 项目在努力开始支持其他不需要 sudo 的集装箱技术,比如 rkt。
- 在 Windows/MacOS 上编译很慢
Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存,以保证编译高效。具体做法请参考[这个issue](https://github.com/PaddlePaddle/Paddle/issues/627)。
- 磁盘不够
本文中的例子里,`docker run` 命令里都用了 `--rm` 参数,这样保证运行结束之后的 containers 不会保留在磁盘上。可以用 `docker ps -a` 命令看到停止后但是没有删除的 containers。`docker build` 命令有时候会产生一些中间结果,是没有名字的 images,也会占用磁盘。可以参考[这篇文章](https://zaiste.net/posts/removing_docker_containers/)来清理这些内容。
.. _compile_deps:
编译依赖
附录:编译依赖
----------------
PaddlePaddle编译需要使用到下面的依赖(包含但不限于),其他的依赖软件,会自动在编译时下载。
......@@ -91,7 +162,7 @@ PaddlePaddle编译需要使用到下面的依赖(包含但不限于),其
.. _build_options:
编译选项
附录:编译选项
----------------
PaddlePaddle的编译选项,包括生成CPU/GPU二进制文件、链接何种BLAS库等。
......
Build from Sources
==========================
.. _build_step:
.. _requirements:
How To Build
Requirements
----------------
PaddlePaddle mainly uses `CMake <https://cmake.org>`_ and GCC, G++ as compile
tools. We recommend you to use our pre-built Docker image to run the build
to avoid installing dependencies by yourself. We have several build environment
Docker images `here <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ .
To build PaddlePaddle, you need
1. A computer -- Linux, Windows, MacOS.
1. Docker.
Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image.
We run all the tools by running this image.
.. _build_step:
If you choose not to use Docker image for your build, you need to install the
below `Compile Dependencies`_ before run the build.
How To Build
----------------
Then run:
You need to use Docker to build PaddlePaddle
to avoid installing dependencies by yourself. We have several pre-built
Docker images `here <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ ,
Or you can build your own image from source as the optional step below:
.. code-block:: bash
# 1. clone the source code
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
# run the following command to build a CPU-Only binaries if you are using docker
# 2. Optional: build development docker image from source
docker build -t paddle:dev .
# 3. Run the following command to build a CPU-Only binaries
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh
# else run these commands
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF ..
make
# 4. Or, use your built Docker image to build PaddlePaddle (must run step 2)
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddle:dev
NOTE: The above command try to mount the current working directory (root directory of source code)
into :code:`/paddle` directory inside docker container. If you are using your own image
(Step 4) it will run default entry-point :code:`build.sh` , so you could omit the last
command in step 3.
When the compile finishes, you can get the output whl package under
build/python/dist, then you can choose to install the whl on local
......@@ -61,22 +74,75 @@ Set :code:`WITH_GPU=ON` Can also run tests on GPU.
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=ON" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/paddle/scripts/docker/build.sh
If you don't use Docker, just run ctest will start the tests:
If you wish to run only one unit test, like :code:`test_sum_op`:
.. code-block:: bash
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=ON ..
make
ctest
# run a single test like test_mul_op
ctest -R test_mul_op
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 /bin/bash
bash /paddle/paddle/scripts/docker/build.sh
cd /paddle/build
ctest -R test_sum_op -V
.. _faq_docker:
Frequently Asked Questions
----------------
- What is Docker?
If you haven't heard of it, consider it something like Python's virtualenv.
- Docker or virtual machine?
Some people compare Docker with VMs, but Docker doesn't virtualize any hardware nor running a guest OS, which means there is no compromise on the performance.
- Why Docker?
Using a Docker image of build tools standardizes the building environment, which makes it easier for others to reproduce your problems and to help.
Also, some build tools don't run on Windows or Mac or BSD, but Docker runs almost everywhere, so developers can use whatever computer they want.
- Can I choose not to use Docker?
Sure, you don't have to install build tools into a Docker image; instead, you can install them on your local computer. This document exists because Docker would make the development way easier.
- How difficult is it to learn Docker?
It takes you ten minutes to read [an introductory article](https://docs.docker.com/get-started) and saves you more than one hour to install all required build tools, configure them, especially when new versions of PaddlePaddle require some new tools. Not even to mention the time saved when other people trying to reproduce the issue you have.
- Can I use my favorite IDE?
Yes, of course. The source code resides on your local computer, and you can edit it using whatever editor you like.
Many PaddlePaddle developers are using Emacs. They add the following few lines into their `~/.emacs` configure file:
```emacs
(global-set-key "\C-cc" 'compile)
(setq compile-command
"docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev")
```
so they could type `Ctrl-C` and `c` to build PaddlePaddle from source.
- Does Docker do parallel building?
Our building Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh), which calls `make -j$(nproc)` to starts as many processes as the number of your CPU cores.
- Docker requires sudo
An owner of a computer has the administrative privilege, a.k.a., sudo, and Docker requires this privilege to work properly. If you use a shared computer for development, please ask the administrator to install and configure Docker. We will do our best to support rkt, another container technology that doesn't require sudo.
- Docker on Windows/MacOS builds slowly
On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to [this issue](https://github.com/PaddlePaddle/Paddle/issues/627) for details.
- Not enough disk space
Examples in this article use option `--rm` with the `docker run` command. This option ensures that stopped containers do not exist on hard disks. We can use `docker ps -a` to list all containers, including stopped. Sometimes `docker build` generates some intermediate dangling images, which also take disk space. To clean them, please refer to [this article](https://zaiste.net/posts/removing_docker_containers/).
.. _compile_deps:
Compile Dependencies
Appendix: Compile Dependencies
----------------
PaddlePaddle need the following dependencies when compiling, other dependencies
......@@ -97,17 +163,13 @@ will be downloaded automatically.
.. _build_options:
Build Options
Appendix: Build Options
----------------
Build options include whether build binaries for CPU or GPU, which BLAS
library to use etc. You may pass these settings when running cmake.
For detailed cmake tutorial please refer to `here <https://cmake.org/cmake-tutorial>`_ 。
.. _build_options_bool:
Bool Type Options
----------------
You can add :code:`-D` argument to pass such options, like:
......
......@@ -13,7 +13,6 @@ PaddlePaddle提供pip和Docker的安装方式:
pip_install_cn.rst
docker_install_cn.rst
build_cn.md
编译流程
++++++++
......
......@@ -13,8 +13,6 @@ You can choose either pip or Docker to complete your install:
pip_install_en.rst
docker_install_en.rst
build_en.md
Build from Source
-----------------
......
......@@ -106,9 +106,11 @@ class Vector {
// std::vector iterator methods. Based on CPU data access method
size_t size() const { return size_; }
T* begin() { return &this->operator[](0); }
T* begin() { return capacity() == 0 ? &EmptyDummy() : &this->operator[](0); }
T* end() { return &this->operator[](size()); }
T* end() {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
T& front() { return *begin(); }
......@@ -118,8 +120,13 @@ class Vector {
return *it;
}
const T* begin() const { return &this->operator[](0); }
const T* end() const { return &this->operator[](size()); }
const T* begin() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](0);
}
const T* end() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
const T* cbegin() const { return begin(); }
......@@ -358,6 +365,11 @@ class Vector {
}
}
static T& EmptyDummy() {
static T dummy = T();
return dummy;
}
mutable int flag_;
mutable Tensor cpu_vec_;
mutable Tensor cuda_vec_;
......
......@@ -98,3 +98,9 @@ TEST(mixed_vector, InitWithCount) {
ASSERT_EQ(vec[i], 10);
}
}
TEST(mixed_vector, ForEach) {
vec<int> tmp;
for (auto& v : tmp) {
}
}
......@@ -25,7 +25,10 @@ namespace framework {
class CosineOp : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void Run(const Scope& scope, const platform::Place& place) const override {}
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {}
};
class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......@@ -44,7 +47,10 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
class MyTestOp : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void Run(const Scope& scope, const platform::Place& place) const override {}
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {}
};
class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......
......@@ -64,6 +64,18 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
}
}
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("Cannot run operator on place %s", place);
#else
auto dev_id = boost::get<platform::CUDAPlace>(place).device;
platform::SetDeviceId(dev_id);
#endif
}
RunImpl(scope, place);
}
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_LE(ins.size(), 1UL,
......@@ -479,7 +491,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
const Scope& scope_;
};
void OperatorWithKernel::Run(const Scope& scope,
void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
......
......@@ -89,8 +89,9 @@ class OperatorBase {
std::string DebugString() const { return DebugStringEx(nullptr); }
/// Net will call this function to Run an op.
virtual void Run(const Scope& scope, const platform::Place& place) const = 0;
/// Net will call this interface function to Run an op.
// The implementation should be written at RunImpl
void Run(const Scope& scope, const platform::Place& place);
// FIXME(typhoonzero): this is only used for recv_op to stop event_loop.
virtual void Stop() {}
......@@ -144,6 +145,8 @@ class OperatorBase {
private:
void GenerateTemporaryNames();
void CheckAllInputOutputSet() const;
virtual void RunImpl(const Scope& scope,
const platform::Place& place) const = 0;
};
// Macro for define a clone method.
......@@ -168,10 +171,13 @@ class OperatorBase {
class NOP : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void Run(const Scope& scope, const platform::Place& place) const override {}
std::unique_ptr<OperatorBase> Clone() const override {
return std::unique_ptr<OperatorBase>(new NOP(*this));
}
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {}
};
class ExecutionContext {
......@@ -363,8 +369,6 @@ class OperatorWithKernel : public OperatorBase {
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const Scope& scope, const platform::Place& place) const final;
static std::unordered_map<std::string /* op_type */, OpKernelMap>&
AllOpKernels() {
static std::unordered_map<std::string, OpKernelMap> g_all_op_kernels;
......@@ -393,6 +397,7 @@ class OperatorWithKernel : public OperatorBase {
// indicate kernel DataType by input data. Defaultly all input data must be
// same.
proto::DataType IndicateDataType(const ExecutionContext& ctx) const;
void RunImpl(const Scope& scope, const platform::Place& place) const final;
};
extern bool OpSupportGPU(const std::string& op_type);
......
......@@ -28,7 +28,10 @@ class OpWithoutKernelTest : public OperatorBase {
OpWithoutKernelTest(const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs), x(1) {}
void Run(const Scope& scope, const platform::Place& place) const override {
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {
++op_run_num;
ASSERT_EQ(static_cast<int>(inputs_.size()), 1);
ASSERT_EQ(static_cast<int>(outputs_.size()), 1);
......@@ -259,7 +262,9 @@ class OperatorClone : public paddle::framework::OperatorBase {
const paddle::framework::VariableNameMap& outputs,
const paddle::framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const paddle::framework::Scope& scope,
private:
void RunImpl(const paddle::framework::Scope& scope,
const paddle::platform::Place& place) const override {}
};
......
......@@ -29,6 +29,6 @@ inference_test(image_classification ARGS vgg resnet)
inference_test(label_semantic_roles)
inference_test(recognize_digits ARGS mlp)
inference_test(recommender_system)
inference_test(rnn_encoder_decoder)
#inference_test(rnn_encoder_decoder)
inference_test(understand_sentiment)
inference_test(word2vec)
......@@ -31,7 +31,9 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
auto &rank_table =
......
......@@ -71,7 +71,9 @@ class AssignOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *x = scope.FindVar(Input("X"));
if (x == nullptr) {
......
......@@ -55,7 +55,9 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(dev_place);
......
......@@ -204,7 +204,8 @@ class BeamSearchOp : public framework::OperatorBase {
PADDLE_THROW("Not Implemented");
}
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
auto ids_var = scope.FindVar(Input("ids"));
auto scores_var = scope.FindVar(Input("scores"));
......
......@@ -102,3 +102,5 @@ REGISTER_LOGICAL_OP(less_equal, "Out = X <= Y");
REGISTER_LOGICAL_KERNEL(less_equal, CPU, paddle::operators::LessEqualFunctor);
REGISTER_LOGICAL_OP(equal, "Out = X == Y");
REGISTER_LOGICAL_KERNEL(equal, CPU, paddle::operators::EqualFunctor);
REGISTER_LOGICAL_OP(not_equal, "Out = X != Y");
REGISTER_LOGICAL_KERNEL(not_equal, CPU, paddle::operators::NotEqualFunctor);
......@@ -17,3 +17,4 @@ limitations under the License. */
REGISTER_LOGICAL_KERNEL(less_than, CUDA, paddle::operators::LessThanFunctor);
REGISTER_LOGICAL_KERNEL(less_equal, CUDA, paddle::operators::LessEqualFunctor);
REGISTER_LOGICAL_KERNEL(equal, CUDA, paddle::operators::EqualFunctor);
REGISTER_LOGICAL_KERNEL(not_equal, CUDA, paddle::operators::NotEqualFunctor);
......@@ -48,6 +48,14 @@ struct EqualFunctor {
}
};
template <typename T>
struct NotEqualFunctor {
using ELEM_TYPE = T;
HOSTDEVICE bool operator()(const T& a, const T& b) const {
return !EqualFunctor<T>()(a, b);
}
};
template <typename DeviceContext, typename Functor>
class CompareOpKernel
: public framework::OpKernel<typename Functor::ELEM_TYPE> {
......
......@@ -193,7 +193,7 @@ void CondOp::MergeDataFromSubnet(const framework::Scope& scope,
}
}
void CondOp::Run(const Scope& scope, const platform::Place& place) const {
void CondOp::RunImpl(const Scope& scope, const platform::Place& place) const {
// get device context from pool
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(place);
......
......@@ -77,7 +77,8 @@ class CondOp : public framework::OperatorBase {
sub_net_op_[FALSE_BRANCH] = std::move(net);
}
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override;
private:
......
......@@ -65,7 +65,9 @@ class ConditionalBlockOp : public ConditionalOp {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ConditionalOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto xs = InputTensors(scope);
......@@ -128,7 +130,9 @@ class ConditionalBlockGradOp : public ConditionalOp {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ConditionalOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto xs = this->InputTensors(scope);
......
......@@ -106,7 +106,9 @@ template <typename T>
class CreateRandomDataGeneratorOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& shape_concat = Attr<std::vector<int>>("shape_concat");
const auto& ranks = Attr<std::vector<int>>("ranks");
......@@ -155,7 +157,9 @@ class CreateRandomDataGeneratorOpMaker
class CreateShuffleReaderOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
......@@ -187,7 +191,9 @@ class CreateShuffleReaderOpMaker : public framework::OpProtoAndCheckerMaker {
class CreateBatchReaderOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
......
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/detection_map_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class DetectionMAPOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("DetectRes"),
"Input(DetectRes) of DetectionMAPOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Label"),
"Input(Label) of DetectionMAPOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("AccumPosCount"),
"Output(AccumPosCount) of DetectionMAPOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("AccumTruePos"),
"Output(AccumTruePos) of DetectionMAPOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("AccumFalsePos"),
"Output(AccumFalsePos) of DetectionMAPOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MAP"),
"Output(MAP) of DetectionMAPOp should not be null.");
auto det_dims = ctx->GetInputDim("DetectRes");
PADDLE_ENFORCE_EQ(det_dims.size(), 2UL,
"The rank of Input(DetectRes) must be 2, "
"the shape is [N, 6].");
PADDLE_ENFORCE_EQ(det_dims[1], 6UL,
"The shape is of Input(DetectRes) [N, 6].");
auto label_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
"The rank of Input(Label) must be 2, "
"the shape is [N, 6].");
PADDLE_ENFORCE_EQ(label_dims[1], 6UL,
"The shape is of Input(Label) [N, 6].");
if (ctx->HasInput("PosCount")) {
PADDLE_ENFORCE(ctx->HasInput("TruePos"),
"Input(TruePos) of DetectionMAPOp should not be null when "
"Input(TruePos) is not null.");
PADDLE_ENFORCE(
ctx->HasInput("FalsePos"),
"Input(FalsePos) of DetectionMAPOp should not be null when "
"Input(FalsePos) is not null.");
}
ctx->SetOutputDim("MAP", framework::make_ddim({1}));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.Input<framework::Tensor>("DetectRes")->type()),
ctx.device_context());
}
};
class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
public:
DetectionMAPOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("DetectRes",
"(LoDTensor) A 2-D LoDTensor with shape [M, 6] represents the "
"detections. Each row has 6 values: "
"[label, confidence, xmin, ymin, xmax, ymax], M is the total "
"number of detect results in this mini-batch. For each instance, "
"the offsets in first dimension are called LoD, the number of "
"offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
"no detected data.");
AddInput("Label",
"(LoDTensor) A 2-D LoDTensor with shape[N, 6] represents the"
"Labeled ground-truth data. Each row has 6 values: "
"[label, is_difficult, xmin, ymin, xmax, ymax], N is the total "
"number of ground-truth data in this mini-batch. For each "
"instance, the offsets in first dimension are called LoD, "
"the number of offset is N + 1, if LoD[i + 1] - LoD[i] == 0, "
"means there is no ground-truth data.");
AddInput("PosCount",
"(Tensor) A tensor with shape [Ncls, 1], store the "
"input positive example count of each class, Ncls is the count of "
"input classification. "
"This input is used to pass the AccumPosCount generated by the "
"previous mini-batch when the multi mini-batches cumulative "
"calculation carried out. "
"When the input(PosCount) is empty, the cumulative "
"calculation is not carried out, and only the results of the "
"current mini-batch are calculated.")
.AsDispensable();
AddInput("TruePos",
"(LoDTensor) A 2-D LoDTensor with shape [Ntp, 2], store the "
"input true positive example of each class."
"This input is used to pass the AccumTruePos generated by the "
"previous mini-batch when the multi mini-batches cumulative "
"calculation carried out. ")
.AsDispensable();
AddInput("FalsePos",
"(LoDTensor) A 2-D LoDTensor with shape [Nfp, 2], store the "
"input false positive example of each class."
"This input is used to pass the AccumFalsePos generated by the "
"previous mini-batch when the multi mini-batches cumulative "
"calculation carried out. ")
.AsDispensable();
AddOutput("AccumPosCount",
"(Tensor) A tensor with shape [Ncls, 1], store the "
"positive example count of each class. It combines the input "
"input(PosCount) and the positive example count computed from "
"input(Detection) and input(Label).");
AddOutput("AccumTruePos",
"(LoDTensor) A LoDTensor with shape [Ntp', 2], store the "
"true positive example of each class. It combines the "
"input(TruePos) and the true positive examples computed from "
"input(Detection) and input(Label).");
AddOutput("AccumFalsePos",
"(LoDTensor) A LoDTensor with shape [Nfp', 2], store the "
"false positive example of each class. It combines the "
"input(FalsePos) and the false positive examples computed from "
"input(Detection) and input(Label).");
AddOutput("MAP",
"(Tensor) A tensor with shape [1], store the mAP evaluate "
"result of the detection.");
AddAttr<float>(
"overlap_threshold",
"(float) "
"The lower bound jaccard overlap threshold of detection output and "
"ground-truth data.")
.SetDefault(.3f);
AddAttr<bool>("evaluate_difficult",
"(bool, default true) "
"Switch to control whether the difficult data is evaluated.")
.SetDefault(true);
AddAttr<std::string>("ap_type",
"(string, default 'integral') "
"The AP algorithm type, 'integral' or '11point'.")
.SetDefault("integral")
.InEnum({"integral", "11point"})
.AddCustomChecker([](const std::string& ap_type) {
PADDLE_ENFORCE_NE(GetAPType(ap_type), APType::kNone,
"The ap_type should be 'integral' or '11point.");
});
AddComment(R"DOC(
Detection mAP evaluate operator.
The general steps are as follows. First, calculate the true positive and
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(detection_map, ops::DetectionMAPOp,
ops::DetectionMAPOpMaker);
REGISTER_OP_CPU_KERNEL(
detection_map, ops::DetectionMAPOpKernel<paddle::platform::CPUPlace, float>,
ops::DetectionMAPOpKernel<paddle::platform::CPUPlace, double>);
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
enum APType { kNone = 0, kIntegral, k11point };
APType GetAPType(std::string str) {
if (str == "integral") {
return APType::kIntegral;
} else if (str == "11point") {
return APType::k11point;
} else {
return APType::kNone;
}
}
template <typename T>
inline bool SortScorePairDescend(const std::pair<float, T>& pair1,
const std::pair<float, T>& pair2) {
return pair1.first > pair2.first;
}
template <typename T>
inline void GetAccumulation(std::vector<std::pair<T, int>> in_pairs,
std::vector<int>* accu_vec) {
std::stable_sort(in_pairs.begin(), in_pairs.end(), SortScorePairDescend<int>);
accu_vec->clear();
size_t sum = 0;
for (size_t i = 0; i < in_pairs.size(); ++i) {
auto count = in_pairs[i].second;
sum += count;
accu_vec->push_back(sum);
}
}
template <typename Place, typename T>
class DetectionMAPOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_detect = ctx.Input<framework::LoDTensor>("DetectRes");
auto* in_label = ctx.Input<framework::LoDTensor>("Label");
auto* out_map = ctx.Output<framework::Tensor>("MAP");
auto* in_pos_count = ctx.Input<framework::Tensor>("PosCount");
auto* in_true_pos = ctx.Input<framework::LoDTensor>("TruePos");
auto* in_false_pos = ctx.Input<framework::LoDTensor>("FalsePos");
auto* out_pos_count = ctx.Output<framework::Tensor>("AccumPosCount");
auto* out_true_pos = ctx.Output<framework::LoDTensor>("AccumTruePos");
auto* out_false_pos = ctx.Output<framework::LoDTensor>("AccumFalsePos");
float overlap_threshold = ctx.Attr<float>("overlap_threshold");
float evaluate_difficult = ctx.Attr<bool>("evaluate_difficult");
auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
auto label_lod = in_label->lod();
auto detect_lod = in_detect->lod();
PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(),
"The batch_size of input(Label) and input(Detection) "
"must be the same.");
std::vector<std::map<int, std::vector<Box>>> gt_boxes;
std::vector<std::map<int, std::vector<std::pair<T, Box>>>> detect_boxes;
GetBoxes(*in_label, *in_detect, gt_boxes, detect_boxes);
std::map<int, int> label_pos_count;
std::map<int, std::vector<std::pair<T, int>>> true_pos;
std::map<int, std::vector<std::pair<T, int>>> false_pos;
if (in_pos_count != nullptr) {
GetInputPos(*in_pos_count, *in_true_pos, *in_false_pos, label_pos_count,
true_pos, false_pos);
}
CalcTrueAndFalsePositive(gt_boxes, detect_boxes, evaluate_difficult,
overlap_threshold, label_pos_count, true_pos,
false_pos);
T map = CalcMAP(ap_type, label_pos_count, true_pos, false_pos);
GetOutputPos(ctx, label_pos_count, true_pos, false_pos, *out_pos_count,
*out_true_pos, *out_false_pos);
T* map_data = out_map->mutable_data<T>(ctx.GetPlace());
map_data[0] = map;
}
protected:
struct Box {
Box(T xmin, T ymin, T xmax, T ymax)
: xmin(xmin), ymin(ymin), xmax(xmax), ymax(ymax), is_difficult(false) {}
T xmin, ymin, xmax, ymax;
bool is_difficult;
};
inline T JaccardOverlap(const Box& box1, const Box& box2) const {
if (box2.xmin > box1.xmax || box2.xmax < box1.xmin ||
box2.ymin > box1.ymax || box2.ymax < box1.ymin) {
return 0.0;
} else {
T inter_xmin = std::max(box1.xmin, box2.xmin);
T inter_ymin = std::max(box1.ymin, box2.ymin);
T inter_xmax = std::min(box1.xmax, box2.xmax);
T inter_ymax = std::min(box1.ymax, box2.ymax);
T inter_width = inter_xmax - inter_xmin;
T inter_height = inter_ymax - inter_ymin;
T inter_area = inter_width * inter_height;
T bbox_area1 = (box1.xmax - box1.xmin) * (box1.ymax - box1.ymin);
T bbox_area2 = (box2.xmax - box2.xmin) * (box2.ymax - box2.ymin);
return inter_area / (bbox_area1 + bbox_area2 - inter_area);
}
}
void GetBoxes(const framework::LoDTensor& input_label,
const framework::LoDTensor& input_detect,
std::vector<std::map<int, std::vector<Box>>>& gt_boxes,
std::vector<std::map<int, std::vector<std::pair<T, Box>>>>&
detect_boxes) const {
auto labels = framework::EigenTensor<T, 2>::From(input_label);
auto detect = framework::EigenTensor<T, 2>::From(input_detect);
auto label_lod = input_label.lod();
auto detect_lod = input_detect.lod();
int batch_size = label_lod[0].size() - 1;
auto label_index = label_lod[0];
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<Box>> boxes;
for (int i = label_index[n]; i < label_index[n + 1]; ++i) {
Box box(labels(i, 2), labels(i, 3), labels(i, 4), labels(i, 5));
int label = labels(i, 0);
auto is_difficult = labels(i, 1);
if (std::abs(is_difficult - 0.0) < 1e-6)
box.is_difficult = false;
else
box.is_difficult = true;
boxes[label].push_back(box);
}
gt_boxes.push_back(boxes);
}
auto detect_index = detect_lod[0];
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<std::pair<T, Box>>> boxes;
for (int i = detect_index[n]; i < detect_index[n + 1]; ++i) {
Box box(detect(i, 2), detect(i, 3), detect(i, 4), detect(i, 5));
int label = detect(i, 0);
auto score = detect(i, 1);
boxes[label].push_back(std::make_pair(score, box));
}
detect_boxes.push_back(boxes);
}
}
void GetOutputPos(
const framework::ExecutionContext& ctx,
const std::map<int, int>& label_pos_count,
const std::map<int, std::vector<std::pair<T, int>>>& true_pos,
const std::map<int, std::vector<std::pair<T, int>>>& false_pos,
framework::Tensor& output_pos_count,
framework::LoDTensor& output_true_pos,
framework::LoDTensor& output_false_pos) const {
int max_class_id = 0;
int true_pos_count = 0;
int false_pos_count = 0;
for (auto it = label_pos_count.begin(); it != label_pos_count.end(); ++it) {
int label = it->first;
if (label > max_class_id) max_class_id = label;
int label_num_pos = it->second;
if (label_num_pos == 0 || true_pos.find(label) == true_pos.end())
continue;
auto label_true_pos = true_pos.find(label)->second;
auto label_false_pos = false_pos.find(label)->second;
true_pos_count += label_true_pos.size();
false_pos_count += label_false_pos.size();
}
int* pos_count_data = output_pos_count.mutable_data<int>(
framework::make_ddim({max_class_id + 1, 1}), ctx.GetPlace());
T* true_pos_data = output_true_pos.mutable_data<T>(
framework::make_ddim({true_pos_count, 2}), ctx.GetPlace());
T* false_pos_data = output_false_pos.mutable_data<T>(
framework::make_ddim({false_pos_count, 2}), ctx.GetPlace());
true_pos_count = 0;
false_pos_count = 0;
std::vector<size_t> true_pos_starts = {0};
std::vector<size_t> false_pos_starts = {0};
for (int i = 0; i <= max_class_id; ++i) {
auto it_count = label_pos_count.find(i);
pos_count_data[i] = 0;
if (it_count != label_pos_count.end()) {
pos_count_data[i] = it_count->second;
}
auto it_true_pos = true_pos.find(i);
if (it_true_pos != true_pos.end()) {
const std::vector<std::pair<T, int>>& true_pos_vec =
it_true_pos->second;
for (const std::pair<T, int>& tp : true_pos_vec) {
true_pos_data[true_pos_count * 2] = tp.first;
true_pos_data[true_pos_count * 2 + 1] = static_cast<T>(tp.second);
true_pos_count++;
}
}
true_pos_starts.push_back(true_pos_count);
auto it_false_pos = false_pos.find(i);
if (it_false_pos != false_pos.end()) {
const std::vector<std::pair<T, int>>& false_pos_vec =
it_false_pos->second;
for (const std::pair<T, int>& fp : false_pos_vec) {
false_pos_data[false_pos_count * 2] = fp.first;
false_pos_data[false_pos_count * 2 + 1] = static_cast<T>(fp.second);
false_pos_count++;
}
}
false_pos_starts.push_back(false_pos_count);
}
framework::LoD true_pos_lod;
true_pos_lod.emplace_back(true_pos_starts);
framework::LoD false_pos_lod;
false_pos_lod.emplace_back(false_pos_starts);
output_true_pos.set_lod(true_pos_lod);
output_false_pos.set_lod(false_pos_lod);
return;
}
void GetInputPos(
const framework::Tensor& input_pos_count,
const framework::LoDTensor& input_true_pos,
const framework::LoDTensor& input_false_pos,
std::map<int, int>& label_pos_count,
std::map<int, std::vector<std::pair<T, int>>>& true_pos,
std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
constexpr T kEPS = static_cast<T>(1e-6);
int class_number = input_pos_count.dims()[0];
const int* pos_count_data = input_pos_count.data<int>();
for (int i = 0; i < class_number; ++i) {
label_pos_count[i] = pos_count_data[i];
}
auto SetData = [](const framework::LoDTensor& pos_tensor,
std::map<int, std::vector<std::pair<T, int>>>& pos) {
const T* pos_data = pos_tensor.data<T>();
auto pos_data_lod = pos_tensor.lod();
for (int i = 0; i < pos_data_lod.size(); ++i) {
for (int j = pos_data_lod[0][i]; j < pos_data_lod[0][i + 1]; ++j) {
T score = pos_data[j * 2];
int flag = 1;
if (pos_data[j * 2 + 1] < kEPS) flag = 0;
pos[i].push_back(std::make_pair(score, flag));
}
}
};
SetData(input_true_pos, true_pos);
SetData(input_false_pos, false_pos);
return;
}
void CalcTrueAndFalsePositive(
const std::vector<std::map<int, std::vector<Box>>>& gt_boxes,
const std::vector<std::map<int, std::vector<std::pair<T, Box>>>>&
detect_boxes,
bool evaluate_difficult, float overlap_threshold,
std::map<int, int>& label_pos_count,
std::map<int, std::vector<std::pair<T, int>>>& true_pos,
std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
int batch_size = gt_boxes.size();
for (int n = 0; n < batch_size; ++n) {
auto image_gt_boxes = gt_boxes[n];
for (auto it = image_gt_boxes.begin(); it != image_gt_boxes.end(); ++it) {
size_t count = 0;
auto labeled_bboxes = it->second;
if (evaluate_difficult) {
count = labeled_bboxes.size();
} else {
for (size_t i = 0; i < labeled_bboxes.size(); ++i)
if (!(labeled_bboxes[i].is_difficult)) ++count;
}
if (count == 0) {
continue;
}
int label = it->first;
if (label_pos_count.find(label) == label_pos_count.end()) {
label_pos_count[label] = count;
} else {
label_pos_count[label] += count;
}
}
}
for (size_t n = 0; n < detect_boxes.size(); ++n) {
auto image_gt_boxes = gt_boxes[n];
auto detections = detect_boxes[n];
if (image_gt_boxes.size() == 0) {
for (auto it = detections.begin(); it != detections.end(); ++it) {
auto pred_boxes = it->second;
int label = it->first;
for (size_t i = 0; i < pred_boxes.size(); ++i) {
auto score = pred_boxes[i].first;
true_pos[label].push_back(std::make_pair(score, 0));
false_pos[label].push_back(std::make_pair(score, 1));
}
}
continue;
}
for (auto it = detections.begin(); it != detections.end(); ++it) {
int label = it->first;
auto pred_boxes = it->second;
if (image_gt_boxes.find(label) == image_gt_boxes.end()) {
for (size_t i = 0; i < pred_boxes.size(); ++i) {
auto score = pred_boxes[i].first;
true_pos[label].push_back(std::make_pair(score, 0));
false_pos[label].push_back(std::make_pair(score, 1));
}
continue;
}
auto matched_bboxes = image_gt_boxes.find(label)->second;
std::vector<bool> visited(matched_bboxes.size(), false);
// Sort detections in descend order based on scores
std::sort(pred_boxes.begin(), pred_boxes.end(),
SortScorePairDescend<Box>);
for (size_t i = 0; i < pred_boxes.size(); ++i) {
T max_overlap = -1.0;
size_t max_idx = 0;
auto score = pred_boxes[i].first;
for (size_t j = 0; j < matched_bboxes.size(); ++j) {
T overlap = JaccardOverlap(pred_boxes[i].second, matched_bboxes[j]);
if (overlap > max_overlap) {
max_overlap = overlap;
max_idx = j;
}
}
if (max_overlap > overlap_threshold) {
bool match_evaluate_difficult =
evaluate_difficult ||
(!evaluate_difficult && !matched_bboxes[max_idx].is_difficult);
if (match_evaluate_difficult) {
if (!visited[max_idx]) {
true_pos[label].push_back(std::make_pair(score, 1));
false_pos[label].push_back(std::make_pair(score, 0));
visited[max_idx] = true;
} else {
true_pos[label].push_back(std::make_pair(score, 0));
false_pos[label].push_back(std::make_pair(score, 1));
}
}
} else {
true_pos[label].push_back(std::make_pair(score, 0));
false_pos[label].push_back(std::make_pair(score, 1));
}
}
}
}
}
T CalcMAP(
APType ap_type, const std::map<int, int>& label_pos_count,
const std::map<int, std::vector<std::pair<T, int>>>& true_pos,
const std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
T mAP = 0.0;
int count = 0;
for (auto it = label_pos_count.begin(); it != label_pos_count.end(); ++it) {
int label = it->first;
int label_num_pos = it->second;
if (label_num_pos == 0 || true_pos.find(label) == true_pos.end())
continue;
auto label_true_pos = true_pos.find(label)->second;
auto label_false_pos = false_pos.find(label)->second;
// Compute average precision.
std::vector<int> tp_sum;
GetAccumulation<T>(label_true_pos, &tp_sum);
std::vector<int> fp_sum;
GetAccumulation<T>(label_false_pos, &fp_sum);
std::vector<T> precision, recall;
size_t num = tp_sum.size();
// Compute Precision.
for (size_t i = 0; i < num; ++i) {
precision.push_back(static_cast<T>(tp_sum[i]) /
static_cast<T>(tp_sum[i] + fp_sum[i]));
recall.push_back(static_cast<T>(tp_sum[i]) / label_num_pos);
}
// VOC2007 style
if (ap_type == APType::k11point) {
std::vector<T> max_precisions(11, 0.0);
int start_idx = num - 1;
for (int j = 10; j >= 0; --j)
for (int i = start_idx; i >= 0; --i) {
if (recall[i] < j / 10.) {
start_idx = i;
if (j > 0) max_precisions[j - 1] = max_precisions[j];
break;
} else {
if (max_precisions[j] < precision[i])
max_precisions[j] = precision[i];
}
}
for (int j = 10; j >= 0; --j) mAP += max_precisions[j] / 11;
++count;
} else if (ap_type == APType::kIntegral) {
// Nature integral
float average_precisions = 0.;
float prev_recall = 0.;
for (size_t i = 0; i < num; ++i) {
if (fabs(recall[i] - prev_recall) > 1e-6)
average_precisions += precision[i] * fabs(recall[i] - prev_recall);
prev_recall = recall[i];
}
mAP += average_precisions;
++count;
} else {
LOG(FATAL) << "Unkown ap version: " << ap_type;
}
}
if (count != 0) mAP /= count;
return mAP * 100;
}
}; // namespace operators
} // namespace operators
} // namespace paddle
......@@ -24,7 +24,9 @@ class FeedOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto feed_var_name = Input("X");
auto *feed_var = scope.FindVar(feed_var_name);
......
......@@ -26,7 +26,8 @@ class FetchOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto fetch_var_name = Input("X");
auto *fetch_var = scope.FindVar(fetch_var_name);
......
......@@ -33,7 +33,9 @@ class FillConstantInferShape : public framework::InferShapeBase {
class FillConstantOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto data_type =
static_cast<framework::proto::DataType>(Attr<int>("dtype"));
......
......@@ -42,7 +42,9 @@ class FillOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &out =
detail::Ref(detail::Ref(scope.FindVar(Output("Out")),
......
......@@ -37,7 +37,9 @@ class GetPlacesOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
bool is_gpu;
if (Attr<std::string>("device_type") == "AUTO") {
......
......@@ -51,7 +51,8 @@ class IncrementOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
auto &out =
......
......@@ -28,7 +28,8 @@ class IsEmptyOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
// get input
auto *var = scope.FindVar(Input(kInput));
......
......@@ -26,7 +26,9 @@ class LoadCombineOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
......
......@@ -25,7 +25,9 @@ class LoadOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
std::ifstream fin(filename);
......
......@@ -25,7 +25,9 @@ class LoDArrayLengthOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
auto &out =
......
......@@ -23,7 +23,9 @@ class LoDRankTableOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
auto *out =
......
......@@ -32,7 +32,9 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x = detail::Ref(scope.FindVar(Input("X")), "Cannot find input %s",
Input("X"))
......
......@@ -27,7 +27,8 @@ class MaxSeqenceLenOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &rank_table =
scope.FindVar(Input("RankTable"))->Get<framework::LoDRankTable>();
......
......@@ -27,7 +27,9 @@ class MergeLoDTensorOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
......
......@@ -26,7 +26,8 @@ class NCCLInitOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
const auto &name = Output("Communicator");
PADDLE_ENFORCE_NOT_NULL(scope.FindVar(name),
......
......@@ -57,20 +57,6 @@ class NetOp : public framework::OperatorBase {
this->CompleteAddOp();
}
/**
* @brief Run the network.
*
* Run all the operators with the `scope`, if no scope is provided, default
* scope will be used instead. If no OpContext is provicded, default context
* will be used.
*/
void Run(const framework::Scope& scope,
const platform::Place& place) const override {
for (auto& op : ops_) {
op->Run(scope, place);
}
}
bool SupportGPU() const override {
for (auto& op : ops_) {
if (!op->SupportGPU()) {
......@@ -117,6 +103,20 @@ class NetOp : public framework::OperatorBase {
std::vector<std::unique_ptr<framework::OperatorBase>> ops_;
private:
/**
* @brief Run the network.
*
* Run all the operators with the `scope`, if no scope is provided, default
* scope will be used instead. If no OpContext is provicded, default context
* will be used.
*/
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
for (auto& op : ops_) {
op->Run(scope, place);
}
}
bool add_op_done_{false};
std::set<std::string> intermediate_outputs_;
......
......@@ -26,7 +26,10 @@ class TestOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
DEFINE_OP_CLONE_METHOD(TestOp);
void Run(const Scope& scope, const platform::Place& place) const override {
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {
++run_cnt;
}
};
......
......@@ -118,7 +118,8 @@ class ParallelDoOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
......@@ -207,7 +208,8 @@ class ParallelDoGradOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
auto *program = block->Program();
......
......@@ -130,7 +130,8 @@ class TensorPrintOp : public framework::OperatorBase {
PADDLE_THROW("Not implemented.");
}
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
const framework::Variable* in_var_ptr = nullptr;
std::string phase = kForward;
......
......@@ -38,8 +38,8 @@ class PriorBoxOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_LT(input_dims[3], image_dims[3],
"The width of input must smaller than image.");
auto min_sizes = ctx->Attrs().Get<std::vector<int>>("min_sizes");
auto max_sizes = ctx->Attrs().Get<std::vector<int>>("max_sizes");
auto min_sizes = ctx->Attrs().Get<std::vector<float>>("min_sizes");
auto max_sizes = ctx->Attrs().Get<std::vector<float>>("max_sizes");
auto variances = ctx->Attrs().Get<std::vector<float>>("variances");
auto aspect_ratios = ctx->Attrs().Get<std::vector<float>>("aspect_ratios");
bool flip = ctx->Attrs().Get<bool>("flip");
......@@ -47,15 +47,15 @@ class PriorBoxOp : public framework::OperatorWithKernel {
std::vector<float> aspect_ratios_vec;
ExpandAspectRatios(aspect_ratios, flip, aspect_ratios_vec);
int num_priors = aspect_ratios_vec.size() * min_sizes.size();
size_t num_priors = aspect_ratios_vec.size() * min_sizes.size();
if (max_sizes.size() > 0) {
PADDLE_ENFORCE_EQ(max_sizes.size(), min_sizes.size(),
"The number of min_size and max_size must be equal.");
for (size_t i = 0; i < min_sizes.size(); ++i) {
num_priors += max_sizes.size();
for (size_t i = 0; i < max_sizes.size(); ++i) {
PADDLE_ENFORCE_GT(max_sizes[i], min_sizes[i],
"max_size[%d] must be greater than min_size[%d].", i,
i);
num_priors += 1;
}
}
......@@ -90,20 +90,20 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
"H is the height of input, W is the width of input, num_priors "
"is the box count of each position.");
AddAttr<std::vector<int>>("min_sizes",
"(vector<int>) List of min sizes "
AddAttr<std::vector<float>>("min_sizes",
"(vector<float>) List of min sizes "
"of generated prior boxes.")
.AddCustomChecker([](const std::vector<int>& min_sizes) {
.AddCustomChecker([](const std::vector<float>& min_sizes) {
PADDLE_ENFORCE_GT(min_sizes.size(), 0,
"Size of min_sizes must be at least 1.");
for (size_t i = 0; i < min_sizes.size(); ++i) {
PADDLE_ENFORCE_GT(min_sizes[i], 0,
PADDLE_ENFORCE_GT(min_sizes[i], 0.0,
"min_sizes[%d] must be positive.", i);
}
});
AddAttr<std::vector<int>>(
AddAttr<std::vector<float>>(
"max_sizes",
"(vector<int>) List of max sizes of generated prior boxes.");
"(vector<float>) List of max sizes of generated prior boxes.");
AddAttr<std::vector<float>>(
"aspect_ratios",
"(vector<float>) List of aspect ratios of generated prior boxes.");
......@@ -125,16 +125,16 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(true);
AddAttr<float>("step_w",
"Prior boxes step across width, 0 for auto calculation.")
"Prior boxes step across width, 0.0 for auto calculation.")
.SetDefault(0.0)
.AddCustomChecker([](const float& step_w) {
PADDLE_ENFORCE_GT(step_w, 0.0, "step_w should be larger than 0.");
PADDLE_ENFORCE_GE(step_w, 0.0, "step_w should be larger than 0.");
});
AddAttr<float>("step_h",
"Prior boxes step across height, 0 for auto calculation.")
"Prior boxes step across height, 0.0 for auto calculation.")
.SetDefault(0.0)
.AddCustomChecker([](const float& step_h) {
PADDLE_ENFORCE_GT(step_h, 0.0, "step_h should be larger than 0.");
PADDLE_ENFORCE_GE(step_h, 0.0, "step_h should be larger than 0.");
});
AddAttr<float>("offset",
......
......@@ -60,8 +60,8 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
auto min_sizes = ctx.Attr<std::vector<int>>("min_sizes");
auto max_sizes = ctx.Attr<std::vector<int>>("max_sizes");
auto min_sizes = ctx.Attr<std::vector<float>>("min_sizes");
auto max_sizes = ctx.Attr<std::vector<float>>("max_sizes");
auto input_aspect_ratio = ctx.Attr<std::vector<float>>("aspect_ratios");
auto variances = ctx.Attr<std::vector<float>>("variances");
auto flip = ctx.Attr<bool>("flip");
......@@ -108,7 +108,7 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
T box_width, box_height;
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
int min_size = min_sizes[s];
auto min_size = min_sizes[s];
// first prior: aspect_ratio = 1, size = min_size
box_width = box_height = min_size;
// xmin
......@@ -124,7 +124,7 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
idx++;
if (max_sizes.size() > 0) {
int max_size = max_sizes[s];
auto max_size = max_sizes[s];
// second prior: aspect_ratio = 1,
// size = sqrt(min_size * max_size)
box_width = box_height = sqrt(min_size * max_size);
......
......@@ -54,7 +54,9 @@ class ReadInferVarType : public framework::VarTypeInference {
class ReadOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
framework::ReaderHolder* reader =
scope.FindVar(Input("Reader"))->GetMutable<framework::ReaderHolder>();
......
......@@ -226,7 +226,8 @@ class RecurrentOp : public RecurrentBase {
const framework::AttributeMap &attrs)
: RecurrentBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto seq_len = static_cast<size_t>(this->GetSequenceLength(scope));
VLOG(3) << "Static RNN input sequence length = " << seq_len;
......@@ -315,7 +316,8 @@ class RecurrentGradOp : public RecurrentBase {
const framework::AttributeMap &attrs)
: RecurrentBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto seq_len = static_cast<size_t>(GetSequenceLength(scope));
StepScopes scopes = CreateStepScopes(scope, seq_len);
......
......@@ -75,7 +75,9 @@ class ReorderLoDTensorByRankTableBase : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x =
detail::Ref(scope.FindVar(Input("X")),
......
......@@ -24,7 +24,9 @@ class RNNMemoryHelperOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto mem_var_name = Input("X");
auto *mem_var = scope.FindVar(mem_var_name);
......@@ -76,7 +78,9 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto out_grad_var_name = Input(framework::GradVarName("Out"));
auto *out_grad_var = scope.FindVar(out_grad_var_name);
......
......@@ -63,7 +63,9 @@ class SaveCombineOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
......
......@@ -62,7 +62,9 @@ class SaveOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
......
......@@ -27,7 +27,8 @@ class ShrinkRNNMemoryOp : public ArrayOp {
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *x_var = scope.FindVar(Input("X"));
PADDLE_ENFORCE(x_var != nullptr, "Input X must be set");
......@@ -108,7 +109,8 @@ class ShrinkRNNMemoryGradOp : public ArrayOp {
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *dout_var = scope.FindVar(Input(framework::GradVarName("Out")));
auto *dx_var = scope.FindVar(Output(framework::GradVarName("X")));
......
......@@ -44,7 +44,6 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
}
};
template <typename AttrType>
class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SmoothL1LossOpMaker(OpProto* proto, OpAttrChecker* op_checker)
......@@ -73,10 +72,10 @@ class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out",
"(Tensor, default Tensor<float>) A tensor with rank be 2. "
"The output smooth l1 loss with shape [batch_size, 1].");
AddAttr<AttrType>("sigma",
AddAttr<float>("sigma",
"Hyper parameter of smooth l1 loss op."
"A float scalar with default value 3.0.")
.SetDefault(3.0);
.SetDefault(1.0);
AddComment(R"DOC(
Smooth L1 Loss Operator.
......@@ -133,9 +132,8 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(smooth_l1_loss, ops::SmoothL1LossOp,
ops::SmoothL1LossOpMaker<float>, smooth_l1_loss_grad,
ops::SmoothL1LossGradOp);
REGISTER_OP(smooth_l1_loss, ops::SmoothL1LossOp, ops::SmoothL1LossOpMaker,
smooth_l1_loss_grad, ops::SmoothL1LossGradOp);
REGISTER_OP_CPU_KERNEL(
smooth_l1_loss,
ops::SmoothL1LossKernel<paddle::platform::CPUDeviceContext, float>);
......
......@@ -33,7 +33,9 @@ class SplitLoDTensorOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
auto &mask = scope.FindVar(Input("Mask"))->Get<framework::LoDTensor>();
......
......@@ -24,7 +24,8 @@ class WriteToArrayOp : public ArrayOp {
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *x = scope.FindVar(Input("X"));
if (x == nullptr) return;
......@@ -122,7 +123,9 @@ class ReadFromArrayOp : public ArrayOp {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *x = scope.FindVar(Input("X"));
PADDLE_ENFORCE(x != nullptr, "X must be set");
......
......@@ -39,7 +39,8 @@ class WhileOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
PADDLE_ENFORCE_NOT_NULL(scope.FindVar(Input(kCondition)));
auto &cond = scope.FindVar(Input(kCondition))->Get<LoDTensor>();
......@@ -99,7 +100,8 @@ class WhileGradOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
......
......@@ -204,6 +204,17 @@ function gen_capi_package() {
fi
}
function gen_fluid_inference_lib() {
if [ ${WITH_C_API:-OFF} == "OFF" ] ; then
cat <<EOF
========================================
Building fluid inference library ...
========================================
EOF
make inference_lib_dist
fi
}
set -xe
cmake_gen ${PYTHON_ABI:-""}
......@@ -212,6 +223,7 @@ run_test
gen_docs
gen_dockerfile
gen_capi_package
gen_fluid_inference_lib
if [[ ${WITH_C_API:-OFF} == "ON" ]]; then
printf "PaddlePaddle C-API libraries was generated on build/paddle.tgz\n"
......
......@@ -16,6 +16,8 @@ import ops
from ops import *
import nn
from nn import *
import detection
from detection import *
import io
from io import *
import tensor
......@@ -26,12 +28,16 @@ import device
from device import *
import math_op_patch
from math_op_patch import *
import detection
from detection import *
__all__ = []
__all__ += math_op_patch.__all__
__all__ += detection.__all__
__all__ += nn.__all__
__all__ += io.__all__
__all__ += tensor.__all__
__all__ += control_flow.__all__
__all__ += ops.__all__
__all__ += device.__all__
__all__ += math_op_patch.__all__
__all__ += detection.__all__
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
All layers just related to the detection neural network.
"""
from ..layer_helper import LayerHelper
from ..framework import Variable
from tensor import concat
from ops import reshape
import math
__all__ = [
'detection_output',
'prior_box',
]
def detection_output(scores,
loc,
prior_box,
prior_box_var,
background_label=0,
nms_threshold=0.3,
nms_top_k=400,
keep_top_k=200,
score_threshold=0.01,
nms_eta=1.0):
"""
**Detection Output Layer**
This layer applies the NMS to the output of network and computes the
predict bounding box location. The output's shape of this layer could
be zero if there is no valid bounding box.
Args:
scores(Variable): A 3-D Tensor with shape [N, C, M] represents the
predicted confidence predictions. N is the batch size, C is the
class number, M is number of bounding boxes. For each category
there are total M scores which corresponding M bounding boxes.
loc(Variable): A 3-D Tensor with shape [N, M, 4] represents the
predicted locations of M bounding bboxes. N is the batch size,
and each bounding box has four coordinate values and the layout
is [xmin, ymin, xmax, ymax].
prior_box(Variable): A 2-D Tensor with shape [M, 4] holds M boxes,
each box is represented as [xmin, ymin, xmax, ymax],
[xmin, ymin] is the left top coordinate of the anchor box,
if the input is image feature map, they are close to the origin
of the coordinate system. [xmax, ymax] is the right bottom
coordinate of the anchor box.
prior_box_var(Variable): A 2-D Tensor with shape [M, 4] holds M group
of variance.
background_label(float): The index of background label,
the background label will be ignored. If set to -1, then all
categories will be considered.
nms_threshold(float): The threshold to be used in NMS.
nms_top_k(int): Maximum number of detections to be kept according
to the confidences aftern the filtering detections based on
score_threshold.
keep_top_k(int): Number of total bboxes to be kept per image after
NMS step. -1 means keeping all bboxes after NMS step.
score_threshold(float): Threshold to filter out bounding boxes with
low confidence score. If not provided, consider all boxes.
nms_eta(float): The parameter for adaptive NMS.
Returns:
The detected bounding boxes which are a Tensor.
Examples:
.. code-block:: python
pb = layers.data(name='prior_box', shape=[10, 4],
append_batch_size=False, dtype='float32')
pbv = layers.data(name='prior_box_var', shape=[10, 4],
append_batch_size=False, dtype='float32')
loc = layers.data(name='target_box', shape=[21, 4],
append_batch_size=False, dtype='float32')
scores = layers.data(name='scores', shape=[2, 21, 10],
append_batch_size=False, dtype='float32')
nmsed_outs = fluid.layers.detection_output(scores=scores,
loc=loc,
prior_box=pb,
prior_box_var=pbv)
"""
helper = LayerHelper("detection_output", **locals())
decoded_box = helper.create_tmp_variable(dtype=loc.dtype)
helper.append_op(
type="box_coder",
inputs={
'PriorBox': prior_box,
'PriorBoxVar': prior_box_var,
'TargetBox': loc
},
outputs={'OutputBox': decoded_box},
attrs={'code_type': 'decode_center_size'})
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
helper.append_op(
type="multiclass_nms",
inputs={'Scores': scores,
'BBoxes': decoded_box},
outputs={'Out': nmsed_outs},
attrs={
'background_label': 0,
'nms_threshold': nms_threshold,
'nms_top_k': nms_top_k,
'keep_top_k': keep_top_k,
'score_threshold': score_threshold,
'nms_eta': 1.0
})
return nmsed_outs
def prior_box(inputs,
image,
min_ratio,
max_ratio,
aspect_ratios,
base_size,
steps=None,
step_w=None,
step_h=None,
offset=0.5,
variance=[0.1, 0.1, 0.1, 0.1],
flip=False,
clip=False,
min_sizes=None,
max_sizes=None,
name=None):
"""
**Prior_boxes**
Generate prior boxes for SSD(Single Shot MultiBox Detector)
algorithm. The details of this algorithm, please refer the
section 2.2 of SSD paper (SSD: Single Shot MultiBox Detector)
<https://arxiv.org/abs/1512.02325>`_ .
Args:
inputs(list): The list of input Variables, the format
of all Variables is NCHW.
image(Variable): The input image data of PriorBoxOp,
the layout is NCHW.
min_ratio(int): the min ratio of generated prior boxes.
max_ratio(int): the max ratio of generated prior boxes.
aspect_ratios(list): the aspect ratios of generated prior
boxes. The length of input and aspect_ratios must be equal.
base_size(int): the base_size is used to get min_size
and max_size according to min_ratio and max_ratio.
step_w(list, optional, default=None): Prior boxes step
across width. If step_w[i] == 0.0, the prior boxes step
across width of the inputs[i] will be automatically calculated.
step_h(list, optional, default=None): Prior boxes step
across height, If step_h[i] == 0.0, the prior boxes
step across height of the inputs[i] will be automatically calculated.
offset(float, optional, default=0.5): Prior boxes center offset.
variance(list, optional, default=[0.1, 0.1, 0.1, 0.1]): the variances
to be encoded in prior boxes.
flip(bool, optional, default=False): Whether to flip
aspect ratios.
clip(bool, optional, default=False): Whether to clip
out-of-boundary boxes.
min_sizes(list, optional, default=None): If `len(inputs) <=2`,
min_sizes must be set up, and the length of min_sizes
should equal to the length of inputs.
max_sizes(list, optional, default=None): If `len(inputs) <=2`,
max_sizes must be set up, and the length of min_sizes
should equal to the length of inputs.
name(str, optional, None): Name of the prior box layer.
Returns:
boxes(Variable): the output prior boxes of PriorBoxOp.
The layout is [num_priors, 4]. num_priors is the total
box count of each position of inputs.
Variances(Variable): the expanded variances of PriorBoxOp.
The layout is [num_priors, 4]. num_priors is the total
box count of each position of inputs
Examples:
.. code-block:: python
prior_box(
inputs = [conv1, conv2, conv3, conv4, conv5, conv6],
image = data,
min_ratio = 20, # 0.20
max_ratio = 90, # 0.90
offset = 0.5,
base_size = 300,
variance = [0.1,0.1,0.1,0.1],
aspect_ratios = [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
flip=True,
clip=True)
"""
def _prior_box_(input,
image,
min_sizes,
max_sizes,
aspect_ratios,
variance,
flip=False,
clip=False,
step_w=0.0,
step_h=0.0,
offset=0.5,
name=None):
helper = LayerHelper("prior_box", **locals())
dtype = helper.input_dtype()
box = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
helper.append_op(
type="prior_box",
inputs={"Input": input,
"Image": image},
outputs={"Boxes": box,
"Variances": var},
attrs={
'min_sizes': min_sizes,
'max_sizes': max_sizes,
'aspect_ratios': aspect_ratios,
'variances': variance,
'flip': flip,
'clip': clip,
'step_w': step_w,
'step_h': step_h,
'offset': offset
})
return box, var
def _reshape_with_axis_(input, axis=1):
if not (axis > 0 and axis < len(input.shape)):
raise ValueError("The axis should be smaller than "
"the arity of input and bigger than 0.")
new_shape = [
-1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
]
out = reshape(x=input, shape=new_shape)
return out
assert isinstance(inputs, list), 'inputs should be a list.'
num_layer = len(inputs)
if num_layer <= 2:
assert min_sizes is not None and max_sizes is not None
assert len(min_sizes) == num_layer and len(max_sizes) == num_layer
else:
min_sizes = []
max_sizes = []
step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
for ratio in xrange(min_ratio, max_ratio + 1, step):
min_sizes.append(base_size * ratio / 100.)
max_sizes.append(base_size * (ratio + step) / 100.)
min_sizes = [base_size * .10] + min_sizes
max_sizes = [base_size * .20] + max_sizes
if aspect_ratios:
if not (isinstance(aspect_ratios, list) and
len(aspect_ratios) == num_layer):
raise ValueError(
'aspect_ratios should be list and the length of inputs '
'and aspect_ratios should be the same.')
if step_h:
if not (isinstance(step_h, list) and len(step_h) == num_layer):
raise ValueError(
'step_h should be list and the length of inputs and '
'step_h should be the same.')
if step_w:
if not (isinstance(step_w, list) and len(step_w) == num_layer):
raise ValueError(
'step_w should be list and the length of inputs and '
'step_w should be the same.')
if steps:
if not (isinstance(steps, list) and len(steps) == num_layer):
raise ValueError(
'steps should be list and the length of inputs and '
'step_w should be the same.')
step_w = steps
step_h = steps
box_results = []
var_results = []
for i, input in enumerate(inputs):
min_size = min_sizes[i]
max_size = max_sizes[i]
aspect_ratio = []
if not isinstance(min_size, list):
min_size = [min_size]
if not isinstance(max_size, list):
max_size = [max_size]
if aspect_ratios:
aspect_ratio = aspect_ratios[i]
if not isinstance(aspect_ratio, list):
aspect_ratio = [aspect_ratio]
box, var = _prior_box_(input, image, min_size, max_size, aspect_ratio,
variance, flip, clip, step_w[i]
if step_w else 0.0, step_h[i]
if step_w else 0.0, offset)
box_results.append(box)
var_results.append(var)
if len(box_results) == 1:
box = box_results[0]
var = var_results[0]
else:
reshaped_boxes = []
reshaped_vars = []
for i in range(len(box_results)):
reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3))
reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3))
box = concat(reshaped_boxes)
var = concat(reshaped_vars)
return box, var
......@@ -152,7 +152,12 @@ def monkey_patch_variable():
("__div__", "elementwise_div", False),
("__rdiv__", "elementwise_div", True),
("__pow__", "elementwise_pow", False),
("__rpow__", "elementwise_pow", True)):
("__rpow__", "elementwise_pow", True),
# for logical compare
("__eq__", "equal", False),
("__ne__", "not_equal", False),
("__lt__", "less_than", False),
("__le__", "less_equal", False)):
setattr(Variable, method_name,
_elemwise_method_creator_(method_name, op_type, reverse))
......
......@@ -66,6 +66,8 @@ __all__ = [
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
]
......@@ -3091,3 +3093,122 @@ def multiplex(inputs, index):
'Ids': index},
outputs={'Out': [out]})
return out
def softmax_with_cross_entropy(logits, label, soft_label=False):
"""
**Softmax With Cross Entropy Operator.**
Cross entropy loss with softmax is used as the output layer extensively. This
operator computes the softmax normalized values for each row of the input
tensor, after which cross-entropy loss is computed. This provides a more
numerically stable gradient.
Because this operator performs a softmax on logits internally, it expects
unscaled logits. This operator should not be used with the output of
softmax operator since that would produce incorrect results.
When the attribute soft_label is set false, this operators expects mutually
exclusive hard labels, each sample in a batch is in exactly one class with a
probability of 1.0. Each sample in the batch will have a single label.
The equation is as follows:
1) Hard label (one-hot label, so every sample has exactly one class)
.. math::
loss_j = -\\text{logit}_{label_j} +
\\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logit}_i)\\right), j = 1,..., K
2) Soft label (each sample can have a distribution over all classes)
.. math::
loss_j = -\\sum_{i=0}^{K}\\text{label}_i
\\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
\\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K
Args:
logits (Variable): The unscaled log probabilities, which is a 2-D tensor
with shape [N x K]. N is the batch_size, and K is the class number.
label (Variable): The ground truth which is a 2-D tensor. If soft_label
is set to false, Label is a Tensor<int64> with shape [N x 1]. If
soft_label is set to true, Label is a Tensor<float/double> with
soft_label (bool): A flag to indicate whether to interpretate the given
labels as soft labels. By default, `soft_label` is set to False.
Returns:
Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.softmax_with_cross_entropy(logits=fc, label=label)
"""
helper = LayerHelper('softmax_with_cross_entropy', **locals())
softmax = helper.create_tmp_variable(dtype=logits.dtype)
loss = helper.create_tmp_variable(dtype=logits.dtype)
helper.append_op(
type='softmax_with_cross_entropy',
inputs={'Logits': logits,
'Label': label},
outputs={'Softmax': softmax,
'Loss': loss},
attrs={'soft_label': soft_label})
return loss
def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
"""
**Smooth L1 Loss Operator. **
This operator computes the smooth l1 loss for X and Y.
The operator takes the first dimension of X and Y as batch size.
For each instance, it computes the smooth l1 loss element by element first
and then sums all the losses. So the shape of Out is [batch_size, 1].
Args:
x (Variable): A tensor with rank at least 2. The input value of smooth
l1 loss op with shape [batch_size, dim1, ..., dimN].
y (Variable): A tensor with rank at least 2. The target value of smooth
l1 loss op with same shape as x.
inside_weight (Variable|None): A tensor with rank at least 2. This
input is optional and should have same shape with x. If provided,
the result of (x - y) will be multiplied by this tensor element by
element.
outside_weight (Variable|None): A tensor with rank at least 2. This
input is optional and should have same shape with x. If provided,
the out smooth l1 loss will be multiplied by this tensor element
by element.
sigma (float|None): Hyper parameter of smooth l1 loss op. A float scalar
with default value 1.0.
Returns:
Variable: A tensor with rank be 2. The output smooth l1 loss with
shape [batch_size, 1].
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data(name='label', shape=[100], dtype='int64')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.smooth_l1(logits=fc, label=label)
"""
helper = LayerHelper('smooth_l1_loss', **locals())
diff = helper.create_tmp_variable(dtype=x.dtype)
loss = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='smooth_l1_loss',
inputs={
'X': x,
'Y': y,
'InsideWeight': inside_weight,
'OutsideWeight': outside_weight
},
outputs={'Diff': diff,
'Out': loss},
attrs={'sigma': sigma})
return loss
......@@ -179,7 +179,7 @@ def polynomial_decay(learning_rate,
shape=[1], dtype='float32', value=1.0)
with layers.Switch() as switch:
with switch.case(layers.equal(x=global_step, y=zero_var)):
with switch.case(global_step == zero_var):
layers.assign(input=one_var, output=div_res)
decay_steps = decay_steps * div_res
else:
......@@ -229,7 +229,7 @@ def piecewise_decay(global_step, boundaries, values):
shape=[1], dtype='float32', value=float(boundaries[i]))
value_var = layers.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(layers.less_than(global_step, boundary_val)):
with switch.case(global_step < boundary_val):
layers.assign(value_var, lr)
last_value_var = layers.fill_constant(
shape=[1],
......
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.v2.fluid as fluid
import paddle.v2.fluid.core as core
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.layers.detection as detection
from paddle.v2.fluid.framework import Program, program_guard
import unittest
import numpy as np
class TestBook(unittest.TestCase):
def test_detection_output(self):
program = Program()
with program_guard(program):
pb = layers.data(
name='prior_box',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
pbv = layers.data(
name='prior_box_var',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
loc = layers.data(
name='target_box',
shape=[20, 4],
append_batch_size=False,
dtype='float32')
scores = layers.data(
name='scores',
shape=[2, 20, 10],
append_batch_size=False,
dtype='float32')
out = layers.detection_output(
scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
self.assertIsNotNone(out)
print(str(program))
class TestPriorBox(unittest.TestCase):
def test_prior_box(self):
data_shape = [3, 224, 224]
box, var = self.prior_box_output(data_shape)
assert len(box.shape) == 2
assert box.shape == var.shape
assert box.shape[1] == 4
def prior_box_output(self, data_shape):
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(
input=images,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
conv2 = fluid.layers.conv2d(
input=conv1,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
conv3 = fluid.layers.conv2d(
input=conv2,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
conv4 = fluid.layers.conv2d(
input=conv3,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
conv5 = fluid.layers.conv2d(
input=conv4,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
box, var = detection.prior_box(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
image=images,
min_ratio=20,
max_ratio=90,
# steps=[8, 16, 32, 64, 100, 300],
aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
base_size=300,
offset=0.5,
flip=True,
clip=True)
return box, var
if __name__ == '__main__':
unittest.main()
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import sys
import collections
import math
from op_test import OpTest
class TestDetectionMAPOp(OpTest):
def set_data(self):
self.init_test_case()
self.mAP = [self.calc_map(self.tf_pos, self.tf_pos_lod)]
self.label = np.array(self.label).astype('float32')
self.detect = np.array(self.detect).astype('float32')
self.mAP = np.array(self.mAP).astype('float32')
if (len(self.class_pos_count) > 0):
self.class_pos_count = np.array(self.class_pos_count).astype(
'int32')
self.true_pos = np.array(self.true_pos).astype('float32')
self.false_pos = np.array(self.false_pos).astype('float32')
self.inputs = {
'Label': (self.label, self.label_lod),
'DetectRes': (self.detect, self.detect_lod),
'PosCount': self.class_pos_count,
'TruePos': (self.true_pos, self.true_pos_lod),
'FalsePos': (self.false_pos, self.false_pos_lod)
}
else:
self.inputs = {
'Label': (self.label, self.label_lod),
'DetectRes': (self.detect, self.detect_lod),
}
self.attrs = {
'overlap_threshold': self.overlap_threshold,
'evaluate_difficult': self.evaluate_difficult,
'ap_type': self.ap_type
}
self.out_class_pos_count = np.array(self.out_class_pos_count).astype(
'int')
self.out_true_pos = np.array(self.out_true_pos).astype('float32')
self.out_false_pos = np.array(self.out_false_pos).astype('float32')
self.outputs = {
'MAP': self.mAP,
'AccumPosCount': self.out_class_pos_count,
'AccumTruePos': (self.out_true_pos, self.out_true_pos_lod),
'AccumFalsePos': (self.out_false_pos, self.out_false_pos_lod)
}
def init_test_case(self):
self.overlap_threshold = 0.3
self.evaluate_difficult = True
self.ap_type = "integral"
self.label_lod = [[0, 2, 4]]
# label difficult xmin ymin xmax ymax
self.label = [[1, 0, 0.1, 0.1, 0.3, 0.3], [1, 1, 0.6, 0.6, 0.8, 0.8],
[2, 0, 0.3, 0.3, 0.6, 0.5], [1, 0, 0.7, 0.1, 0.9, 0.3]]
# label score xmin ymin xmax ymax difficult
self.detect_lod = [[0, 3, 7]]
self.detect = [
[1, 0.3, 0.1, 0.0, 0.4, 0.3], [1, 0.7, 0.0, 0.1, 0.2, 0.3],
[1, 0.9, 0.7, 0.6, 0.8, 0.8], [2, 0.8, 0.2, 0.1, 0.4, 0.4],
[2, 0.1, 0.4, 0.3, 0.7, 0.5], [1, 0.2, 0.8, 0.1, 1.0, 0.3],
[3, 0.2, 0.8, 0.1, 1.0, 0.3]
]
# label score true_pos false_pos
self.tf_pos_lod = [[0, 3, 7]]
self.tf_pos = [[1, 0.9, 1, 0], [1, 0.7, 1, 0], [1, 0.3, 0, 1],
[1, 0.2, 1, 0], [2, 0.8, 0, 1], [2, 0.1, 1, 0],
[3, 0.2, 0, 1]]
self.class_pos_count = []
self.true_pos_lod = [[]]
self.true_pos = [[]]
self.false_pos_lod = [[]]
self.false_pos = [[]]
def calc_map(self, tf_pos, tf_pos_lod):
mAP = 0.0
count = 0
def get_input_pos(class_pos_count, true_pos, true_pos_lod, false_pos,
false_pos_lod):
class_pos_count_dict = collections.Counter()
true_pos_dict = collections.defaultdict(list)
false_pos_dict = collections.defaultdict(list)
for i, count in enumerate(class_pos_count):
class_pos_count_dict[i] = count
for i in range(len(true_pos_lod[0]) - 1):
start = true_pos_lod[0][i]
end = true_pos_lod[0][i + 1]
for j in range(start, end):
true_pos_dict[i].append(true_pos[j])
for i in range(len(false_pos_lod[0]) - 1):
start = false_pos_lod[0][i]
end = false_pos_lod[0][i + 1]
for j in range(start, end):
false_pos_dict[i].append(false_pos[j])
return class_pos_count_dict, true_pos_dict, false_pos_dict
def get_output_pos(label_count, true_pos, false_pos):
max_label = 0
for (label, label_pos_num) in label_count.items():
if max_label < label:
max_label = label
label_number = max_label + 1
out_class_pos_count = []
out_true_pos_lod = [0]
out_true_pos = []
out_false_pos_lod = [0]
out_false_pos = []
for i in range(label_number):
out_class_pos_count.append([label_count[i]])
true_pos_list = true_pos[i]
out_true_pos += true_pos_list
out_true_pos_lod.append(len(out_true_pos))
false_pos_list = false_pos[i]
out_false_pos += false_pos_list
out_false_pos_lod.append(len(out_false_pos))
return out_class_pos_count, out_true_pos, [
out_true_pos_lod
], out_false_pos, [out_false_pos_lod]
def get_accumulation(pos_list):
sorted_list = sorted(pos_list, key=lambda pos: pos[0], reverse=True)
sum = 0
accu_list = []
for (score, count) in sorted_list:
sum += count
accu_list.append(sum)
return accu_list
label_count, true_pos, false_pos = get_input_pos(
self.class_pos_count, self.true_pos, self.true_pos_lod,
self.false_pos, self.false_pos_lod)
for (label, difficult, xmin, ymin, xmax, ymax) in self.label:
if self.evaluate_difficult:
label_count[label] += 1
elif not difficult:
label_count[label] += 1
true_pos = collections.defaultdict(list)
false_pos = collections.defaultdict(list)
for (label, score, tp, fp) in tf_pos:
true_pos[label].append([score, tp])
false_pos[label].append([score, fp])
for (label, label_pos_num) in label_count.items():
if label_pos_num == 0 or label not in true_pos: continue
label_true_pos = true_pos[label]
label_false_pos = false_pos[label]
accu_tp_sum = get_accumulation(label_true_pos)
accu_fp_sum = get_accumulation(label_false_pos)
precision = []
recall = []
for i in range(len(accu_tp_sum)):
precision.append(
float(accu_tp_sum[i]) /
float(accu_tp_sum[i] + accu_fp_sum[i]))
recall.append(float(accu_tp_sum[i]) / label_pos_num)
if self.ap_type == "11point":
max_precisions = [0.0] * 11
start_idx = len(accu_tp_sum) - 1
for j in range(10, -1, -1):
for i in range(start_idx, -1, -1):
if recall[i] < float(j) / 10.0:
start_idx = i
if j > 0:
max_precisions[j - 1] = max_precisions[j]
break
else:
if max_precisions[j] < precision[i]:
max_precisions[j] = precision[i]
for j in range(10, -1, -1):
mAP += max_precisions[j] / 11
count += 1
elif self.ap_type == "integral":
average_precisions = 0.0
prev_recall = 0.0
for i in range(len(accu_tp_sum)):
if math.fabs(recall[i] - prev_recall) > 1e-6:
average_precisions += precision[i] * \
math.fabs(recall[i] - prev_recall)
prev_recall = recall[i]
mAP += average_precisions
count += 1
self.out_class_pos_count, self.out_true_pos, self.out_true_pos_lod, self.out_false_pos, self.out_false_pos_lod = get_output_pos(
label_count, true_pos, false_pos)
if count != 0:
mAP /= count
return mAP * 100.0
def setUp(self):
self.op_type = "detection_map"
self.set_data()
def test_check_output(self):
self.check_output()
class TestDetectionMAPOpSkipDiff(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOpSkipDiff, self).init_test_case()
self.evaluate_difficult = False
self.tf_pos_lod = [[0, 2, 6]]
# label score true_pos false_pos
self.tf_pos = [[1, 0.7, 1, 0], [1, 0.3, 0, 1], [1, 0.2, 1, 0],
[2, 0.8, 0, 1], [2, 0.1, 1, 0], [3, 0.2, 0, 1]]
class TestDetectionMAPOp11Point(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOp11Point, self).init_test_case()
self.ap_type = "11point"
class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOpMultiBatch, self).init_test_case()
self.class_pos_count = [0, 2, 1]
self.true_pos_lod = [[0, 0, 3, 5]]
self.true_pos = [[0.7, 1.], [0.3, 0.], [0.2, 1.], [0.8, 0.], [0.1, 1.]]
self.false_pos_lod = [[0, 0, 3, 5]]
self.false_pos = [[0.7, 0.], [0.3, 1.], [0.2, 0.], [0.8, 1.], [0.1, 0.]]
if __name__ == '__main__':
unittest.main()
......@@ -161,8 +161,8 @@ class TestBook(unittest.TestCase):
label=label,
chunk_scheme="IOB",
num_chunk_types=(label_dict_len - 1) / 2)
self.assertNotEqual(crf, None)
self.assertNotEqual(crf_decode, None)
self.assertFalse(crf is None)
self.assertFalse(crf_decode is None)
print(str(program))
......@@ -309,6 +309,24 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(out)
print(str(program))
def test_softmax_with_cross_entropy(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[16], dtype='float32')
y = layers.data(name='label', shape=[1], dtype='int64')
loss = layers.softmax_with_cross_entropy(x, y)
self.assertIsNotNone(loss)
print(str(program))
def test_smooth_l1(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[4], dtype='float32')
y = layers.data(name='label', shape=[4], dtype='float32')
loss = layers.smooth_l1(x, y)
self.assertIsNotNone(loss)
print(str(program))
if __name__ == '__main__':
unittest.main()
......@@ -65,9 +65,9 @@ class TestPriorBoxOp(OpTest):
self.batch_size = 10
self.min_sizes = [2, 4]
self.min_sizes = np.array(self.min_sizes).astype('int64')
self.min_sizes = np.array(self.min_sizes).astype('float32').tolist()
self.max_sizes = [5, 10]
self.max_sizes = np.array(self.max_sizes).astype('int64')
self.max_sizes = np.array(self.max_sizes).astype('float32').tolist()
self.aspect_ratios = [2.0, 3.0]
self.flip = True
self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
......
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid as fluid
class TestPythonOperatorOverride(unittest.TestCase):
def check_result(self, fn, place, dtype):
shape = [9, 10]
x_data = np.random.random(size=shape).astype(dtype)
y_data = np.random.random(size=shape).astype(dtype)
python_out = fn(x_data, y_data)
x_var = layers.create_global_var(
name='x', shape=shape, value=0.0, dtype=dtype, persistable=True)
y_var = layers.create_global_var(
name='y', shape=shape, value=0.0, dtype=dtype, persistable=True)
out = fn(x_var, y_var)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
fluid_out = exe.run(fluid.default_main_program(),
feed={'x': x_data,
'y': y_data},
fetch_list=[out])
np.testing.assert_array_equal(python_out, fluid_out[0])
def test_override(self):
# compare func to check
compare_fns = [
lambda _a, _b: _a == _b,
lambda _a, _b: _a != _b,
lambda _a, _b: _a < _b,
lambda _a, _b: _a <= _b,
lambda _a, _b: _a > _b,
lambda _a, _b: _a >= _b,
]
# places to check
places = [fluid.CPUPlace()]
if fluid.core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
# dtypes to check
dtypes = ['int32', 'float32']
for place in places:
for dtype in dtypes:
for compare_fn in compare_fns:
with framework.program_guard(framework.Program(),
framework.Program()):
self.check_result(compare_fn, place, dtype)
if __name__ == '__main__':
unittest.main()
......@@ -52,3 +52,5 @@ RUN wget -O /opt/swig-2.0.12.tar.gz https://sourceforge.net/projects/swig/files/
RUN mkdir -p /src && cd /src && git clone https://github.com/NVIDIA/nccl.git nccl && cd nccl &&\
make -j `nproc` install <NCCL_MAKE_OPTS> && cd .. && rm -rf nccl
CMD ["bash", "/paddle/paddle/scripts/docker/build.sh"]
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