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提交 adbf97b4 编写于 作者: Y Yibing Liu
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Merge branch 'develop' of upstream into deconv_group

上级 7a557040 01c0a81b
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CMakeLists.txt
浏览文件 @ adbf97b4
......@@ -100,6 +100,9 @@ endif()
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
"A path setting third party libraries download & build directories.")
set(FLUID_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_install_dir" CACHE STRING
"A path setting fluid shared and static libraries")
if (WITH_C_API AND WITH_PYTHON)
message(WARNING "It is suggest not embedded a python interpreter in Paddle "
"when using C-API. It will give an unpredictable behavior when using a "
......@@ -117,13 +120,14 @@ else()
endif()
set(WITH_MKLML ${WITH_MKL})
if (WITH_MKL AND AVX2_FOUND)
set(WITH_MKLDNN ON)
else()
message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
set(WITH_MKLDNN OFF)
if (NOT DEFINED WITH_MKLDNN)
if (WITH_MKL AND AVX2_FOUND)
set(WITH_MKLDNN ON)
else()
message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
set(WITH_MKLDNN OFF)
endif()
endif()
########################################################################################
include(external/mklml) # download mklml package
......
Dockerfile
浏览文件 @ adbf97b4
......@@ -70,7 +70,7 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
# specify sphinx version as 1.5.6 and remove -U option for [pip install -U
# sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
# version(1.7.1 for now), which causes building documentation failed.
RUN pip install --upgrade pip==9.0.3 && \
RUN easy_install -U pip && \
pip install -U wheel && \
pip install -U docopt PyYAML sphinx==1.5.6 && \
pip install sphinx-rtd-theme==0.1.9 recommonmark
......
benchmark/fluid/mnist.py
浏览文件 @ adbf97b4
......@@ -159,6 +159,7 @@ def run_benchmark(model, args):
paddle.dataset.mnist.train(), batch_size=args.batch_size)
accuracy = fluid.metrics.Accuracy()
train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
iters, num_samples, start_time = 0, 0, time.time()
for pass_id in range(args.pass_num):
accuracy.reset()
......@@ -175,17 +176,20 @@ def run_benchmark(model, args):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([len(y_data), 1])
outs = exe.run(
fluid.default_main_program(),
outs = train_exe.run(
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost, batch_acc, batch_size_tensor]
fetch_list=[
avg_cost.name, batch_acc.name, batch_size_tensor.name
]
) # The accuracy is the accumulation of batches, but not the current batch.
accuracy.update(value=outs[1], weight=outs[2])
accuracy.update(
value=np.array(np.mean(outs[1])),
weight=np.mean(np.array(outs[2])))
iters += 1
num_samples += len(y_data)
loss = np.array(outs[0])
acc = np.array(outs[1])
loss = np.mean(np.array(outs[0]))
acc = np.mean(np.array(outs[1]))
train_losses.append(loss)
train_accs.append(acc)
print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %
......
benchmark/fluid/resnet.py
浏览文件 @ adbf97b4
......@@ -241,6 +241,7 @@ def run_benchmark(model, args):
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
accuracy = fluid.average.WeightedAverage()
train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
if args.use_fake_data:
data = train_reader().next()
image = np.array(map(lambda x: x[0].reshape(dshape), data)).astype(
......@@ -264,14 +265,17 @@ def run_benchmark(model, args):
data)).astype('float32')
label = np.array(map(lambda x: x[1], data)).astype('int64')
label = label.reshape([-1, 1])
loss, acc, weight = exe.run(
fluid.default_main_program(),
loss, acc, weight = train_exe.run(
feed={'data': image,
'label': label},
fetch_list=[avg_cost, batch_acc, batch_size_tensor])
fetch_list=[
avg_cost.name, batch_acc.name, batch_size_tensor.name
])
iters += 1
num_samples += len(label)
accuracy.add(value=acc, weight=weight)
accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
loss = np.mean(np.array(loss))
acc = np.mean(np.array(acc))
train_losses.append(loss)
train_accs.append(acc)
print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %
......
benchmark/fluid/vgg.py
浏览文件 @ adbf97b4
......@@ -169,6 +169,7 @@ def main():
iters, num_samples, start_time = 0, 0, time.time()
accuracy = fluid.average.WeightedAverage()
train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
for pass_id in range(args.pass_num):
accuracy.reset()
train_accs = []
......@@ -184,14 +185,17 @@ def main():
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([-1, 1])
loss, acc, weight = exe.run(
fluid.default_main_program(),
loss, acc, weight = train_exe.run(
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost, batch_acc, batch_size_tensor])
accuracy.add(value=acc, weight=weight)
fetch_list=[
avg_cost.name, batch_acc.name, batch_size_tensor.name
])
accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
iters += 1
num_samples += len(y_data)
loss = np.mean(np.array(loss))
acc = np.mean(np.array(acc))
print(
"Pass = %d, Iter = %d, Loss = %f, Accuracy = %f" %
(pass_id, iters, loss, acc)
......
cmake/external/boost.cmake
浏览文件 @ adbf97b4
......@@ -23,8 +23,12 @@ set(BOOST_PROJECT "extern_boost")
# checked that the devtools package of CentOS 6 installs boost 1.41.0.
# So we use 1.41.0 here.
set(BOOST_VER "1.41.0")
set(BOOST_TAR "boost_1_41_0")
set(BOOST_URL "http://paddlepaddledeps.bj.bcebos.com/${BOOST_TAR}.tar.gz")
if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
message(STATUS "use pre defined download url")
set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
set(BOOST_URL "http://paddlepaddledeps.bj.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
endif()
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
set(BOOST_DOWNLOAD_DIR "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
......
cmake/external/eigen.cmake
浏览文件 @ adbf97b4
......@@ -21,11 +21,12 @@ else()
ExternalProject_Add(
extern_eigen3
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/RLovelett/eigen.git"
GIT_REPOSITORY "https://github.com/eigenteam/eigen-git-mirror"
# eigen on cuda9.1 missing header of math_funtions.hpp
# https://stackoverflow.com/questions/43113508/math-functions-hpp-not-found-when-using-cuda-with-eigen
GIT_TAG 917060c364181f33a735dc023818d5a54f60e54c
PREFIX ${EIGEN_SOURCE_DIR}
DOWNLOAD_NAME "eigen"
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
......
cmake/external/mkldnn.cmake
浏览文件 @ adbf97b4
......@@ -53,11 +53,9 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "v0.14"
GIT_TAG "db3424ad44901513c03a1ea31ccaacdf633fbe9f"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
# Patch MKLDNN to compile with gcc 4.8, the related issue is in intel/mkl-dnn#237.
PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/mkldnn.hpp ${MKLDNN_SOURCES_DIR}/src/extern_mkldnn/include/mkldnn.hpp
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
CMAKE_ARGS -DMKLROOT=${MKLML_ROOT}
......
cmake/external/mklml.cmake
浏览文件 @ adbf97b4
......@@ -27,8 +27,12 @@ ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.3.20180406")
SET(MKLML_URL "http://paddlepaddledeps.bj.bcebos.com/${MKLML_VER}.tgz")
IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
MESSAGE(STATUS "use pre defined download url")
SET(MKLML_VER "mklml_lnx_2018.0.3.20180406" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.bj.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
ENDIF()
MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
......
cmake/generic.cmake
浏览文件 @ adbf97b4
......@@ -231,7 +231,7 @@ endfunction(cc_binary)
function(cc_test TARGET_NAME)
if(WITH_TESTING)
set(options "")
set(options SERIAL)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS ARGS)
cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
......@@ -241,6 +241,9 @@ function(cc_test TARGET_NAME)
add_test(NAME ${TARGET_NAME}
COMMAND ${TARGET_NAME} ${cc_test_ARGS}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
if (${cc_test_SERIAL})
set_property(TEST ${TARGET_NAME} PROPERTY SERIAL 1)
endif()
endif()
endfunction(cc_test)
......@@ -295,7 +298,7 @@ endfunction(nv_binary)
function(nv_test TARGET_NAME)
if (WITH_GPU AND WITH_TESTING)
set(options "")
set(options SERIAL)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
......@@ -303,6 +306,9 @@ function(nv_test TARGET_NAME)
target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main memory gtest gflags glog)
add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main memory gtest gflags glog)
add_test(${TARGET_NAME} ${TARGET_NAME})
if (nv_test_SERIAL)
set_property(TEST ${TARGET_NAME} PROPERTY SERIAL 1)
endif()
endif()
endfunction(nv_test)
......
cmake/inference_lib.cmake
浏览文件 @ adbf97b4
......@@ -52,32 +52,32 @@ function(copy TARGET)
endfunction()
# third party
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/eigen3")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/eigen3")
copy(eigen3_lib
SRCS ${EIGEN_INCLUDE_DIR}/Eigen/Core ${EIGEN_INCLUDE_DIR}/Eigen/src ${EIGEN_INCLUDE_DIR}/unsupported/Eigen
DSTS ${dst_dir}/Eigen ${dst_dir}/Eigen ${dst_dir}/unsupported
)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/gflags")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/gflags")
copy(gflags_lib
SRCS ${GFLAGS_INCLUDE_DIR} ${GFLAGS_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/glog")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/glog")
copy(glog_lib
SRCS ${GLOG_INCLUDE_DIR} ${GLOG_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/boost/")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/boost/")
copy(boost_lib
SRCS ${BOOST_INCLUDE_DIR}/boost
DSTS ${dst_dir}
)
if(NOT PROTOBUF_FOUND)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/protobuf")
copy(protobuf_lib
SRCS ${PROTOBUF_INCLUDE_DIR} ${PROTOBUF_LIBRARY}
DSTS ${dst_dir} ${dst_dir}/lib
......@@ -85,31 +85,39 @@ if(NOT PROTOBUF_FOUND)
endif()
if(NOT CBLAS_FOUND)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/openblas")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/openblas")
copy(openblas_lib
SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
DSTS ${dst_dir} ${dst_dir}
)
elseif (WITH_MKLML)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/mklml")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mklml")
copy(mklml_lib
SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
)
endif()
if(WITH_MKLDNN)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mkldnn")
copy(mkldnn_lib
SRCS ${MKLDNN_INC_DIR} ${MKLDNN_SHARED_LIB}
DSTS ${dst_dir} ${dst_dir}/lib
)
endif()
if(NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/snappy")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
copy(snappy_lib
SRCS ${SNAPPY_INCLUDE_DIR} ${SNAPPY_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/snappystream")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappystream")
copy(snappystream_lib
SRCS ${SNAPPYSTREAM_INCLUDE_DIR} ${SNAPPYSTREAM_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/zlib")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
copy(zlib_lib
SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib)
......@@ -117,7 +125,7 @@ endif()
# paddle fluid module
set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
set(dst_dir "${CMAKE_INSTALL_PREFIX}/paddle/fluid")
set(dst_dir "${FLUID_INSTALL_DIR}/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/fluid/framework/framework.pb.h
......@@ -148,4 +156,30 @@ copy(string_lib
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/tinyformat
)
set(module "pybind")
copy(pybind_lib
SRCS ${CMAKE_CURRENT_BINARY_DIR}/paddle/fluid/${module}/pybind.h
DSTS ${dst_dir}/${module}
)
# CMakeCache Info
copy(cmake_cache
SRCS ${CMAKE_CURRENT_BINARY_DIR}/CMakeCache.txt
DSTS ${FLUID_INSTALL_DIR})
add_custom_target(inference_lib_dist DEPENDS ${inference_lib_dist_dep})
# paddle fluid version
execute_process(
COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
OUTPUT_VARIABLE PADDLE_GIT_COMMIT)
set(version_file ${FLUID_INSTALL_DIR}/version.txt)
file(WRITE ${version_file}
"GIT COMMIT ID: ${PADDLE_GIT_COMMIT}\n"
"WITH_MKL: ${WITH_MKL}\n"
"WITH_GPU: ${WITH_GPU}\n")
if(WITH_GPU)
file(APPEND ${version_file}
"CUDA version: ${CUDA_VERSION}\n"
"CUDNN version: v${CUDNN_MAJOR_VERSION}\n")
endif()
doc/fluid/api/layers.rst
浏览文件 @ adbf97b4
......@@ -485,7 +485,7 @@ roi_pool
.. autofunction:: paddle.fluid.layers.roi_pool
:noindex:
ops
===
......@@ -828,4 +828,10 @@ topk
.. autofunction:: paddle.fluid.layers.topk
:noindex:
dice_loss
----
.. autofunction:: paddle.fluid.layers.dice_loss
:noindex:
doc/fluid/design/concepts/functions_operators_layers.md
浏览文件 @ adbf97b4
......@@ -40,7 +40,7 @@ template <typename T>
class FCOp : public OperatorBase {
public:
void Run(...) {
add(mul(Input<T>("X"), Input<T>("W")), Input<T>("b");
add(mul(Input<T>("X"), Input<T>("W")), Input<T>("b"));
}
};
REGISTER_OP(FCOp, "fc");
......
doc/fluid/howto/optimization/cpu_profiling_cn.md
浏览文件 @ adbf97b4
# CPU性能调优
此教程会介绍如何使用Python的cProfile包、Python库yep、Google perftools来进行性能分析 (profiling) 与调优（performance tuning）。
Profling 指发现性能瓶颈。系统中的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。Tuning 指消除瓶颈。性能优化的过程通常是不断重复地 profiling 和 tuning。
......@@ -8,7 +10,7 @@ PaddlePaddle 用户一般通过调用 Python API 编写深度学习程序。大
* Python 与 C++ 混合代码的性能分析
# Python代码的性能分析
## Python代码的性能分析
### 生成性能分析文件
......
doc/fluid/howto/optimization/cpu_profiling_en.md
浏览文件 @ adbf97b4
# Tune CPU performance
This tutorial introduces techniques we use to profile and tune the
CPU performance of PaddlePaddle. We will use Python packages
`cProfile` and `yep`, and Google's `perftools`.
......@@ -14,7 +16,7 @@ the profiling and tuning of
1. the Python code and
1. the mixture of Python and C++ code.
# Profiling the Python Code
## Profiling the Python Code
### Generate the Performance Profiling File
......
doc/v2/build_and_install/build_from_source_cn.rst
浏览文件 @ adbf97b4
......@@ -35,7 +35,7 @@ PaddlePaddle需要使用Docker环境完成编译，这样可以免去单独安
# 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
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/paddle_build.sh build
# 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
......
doc/v2/build_and_install/build_from_source_en.rst
浏览文件 @ adbf97b4
......@@ -34,7 +34,7 @@ Or you can build your own image from source as the optional step below:
# 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
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/paddle_build.sh build
# 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
......
doc/v2/build_and_install/pip_install_cn.rst
浏览文件 @ adbf97b4
......@@ -10,20 +10,38 @@ PaddlePaddle可以使用常用的Python包管理工具
使用pip安装
------------------------------
执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件，版本为cpu_avx_openblas。
执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件。
.. code-block:: bash
pip install paddlepaddle
当前的默认版本为0.12.0，cpu_avx_openblas，您可以通过指定版本号来安装其它版本，例如:
.. code-block:: bash
pip install paddlepaddle==0.11.0
如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
如果需要安装支持GPU的版本（cuda8.0_cudnn5_avx_openblas），需要执行：
.. code-block:: bash
pip install paddlepaddle-gpu
当前的默认版本也是0.12.0，PaddlePaddle针对不同需求提供了更多版本的安装包，部分列表如下：
================================= ========================================
版本号 版本说明
================================= ========================================
paddlepaddle-gpu==0.12.0 使用CUDA 8.0和cuDNN 5编译的0.12.0版本
paddlepaddle-gpu==0.11.0.post87 使用CUDA 8.0和cuDNN 7编译的0.11.0版本
paddlepaddle-gpu==0.11.0.post8 使用CUDA 8.0和cuDNN 5编译的0.11.0版本
paddlepaddle-gpu==0.11.0 使用CUDA 7.5和cuDNN 5编译的0.11.0版本
================================= ========================================
您可以在 `Release History <https://pypi.org/project/paddlepaddle-gpu/#history>`_ 中找到paddlepaddle-gpu的各个发行版本。
如果需要获取并安装最新的（开发分支）PaddlePaddle，可以从我们的CI系统中下载最新的whl安装包和c-api开发包并安装，
您可以从下面的表格中找到需要的版本：
......@@ -37,12 +55,11 @@ PaddlePaddle可以使用常用的Python包管理工具
:header: "版本说明", "cp27-cp27mu", "cp27-cp27m"
:widths: 1, 3, 3
"cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_mkl", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
.. _pip_dependency:
......@@ -69,7 +86,7 @@ PaddlePaddle发布的安装包会尽量对齐 `manylinux1 <https://www.python.or
------------------------------
- paddlepaddle*.whl is not a supported wheel on this platform.
出现这个问题的主要原因是，没有找到和当前系统匹配的paddlepaddle安装包。请检查Python版本是否为2.7系列。另外最新的pip官方源中的安装包默认是manylinux1标准，需要使用最新的pip (>9.0.0) 才可以安装。可以使用下面的命令更新您的pip：
.. code-block:: bash
......
doc/v2/build_and_install/pip_install_en.rst
浏览文件 @ adbf97b4
......@@ -12,20 +12,38 @@ Install using pip
------------------------------
Run the following command to install PaddlePaddle on the current
machine, it will also download requirements, the version is cpu_avx_openblas.
machine, it will also download requirements.
.. code-block:: bash
pip install paddlepaddle
the default version is 0.12.0, cpu_avx_openblas, you can specify the versions to satisfy your demands, like:
If you wish to install GPU version (cuda7.5_cudnn5_avx_openblas), just run:
.. code-block:: bash
pip install paddlepaddle==0.11.0
If you need to install a GPU-enabled version (cuda8.0_cudnn5_avx_openblas), you need to run:
.. code-block:: bash
pip install paddlepaddle-gpu
If you wish to install the latest develop branch PaddlePaddle,
The default version is also 0.12.0, PaddlePaddle provides several versions of packages for different needs, as shown in the table:
================================= ========================================
版本号 版本说明
================================= ========================================
paddlepaddle-gpu==0.12.0 0.12.0 built with CUDA 8.0 and cuDNN 5
paddlepaddle-gpu==0.11.0.post87 0.11.0 built with CUDA 8.0 and cuDNN 7
paddlepaddle-gpu==0.11.0.post8 0.11.0 built with CUDA 8.0 and cuDNN 5
paddlepaddle-gpu==0.11.0 0.11.0 built with CUDA 7.5 and cuDNN 5
================================= ========================================
You can find all versions released of paddlepaddle-gpu in `Release History <https://pypi.org/project/paddlepaddle-gpu/#history>`_ .
If you wish to install the latest develop branch PaddlePaddle,
you can download the latest whl package from our CI system. Access
the below links, log in as guest, then click at the "Artifact"
tab, you'll find the download link of whl packages.
......@@ -40,12 +58,11 @@ If the links below shows up the login form, just click "Log in as guest" to star
:header: "version", "cp27-cp27mu", "cp27-cp27m"
:widths: 1, 3, 3
"cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_mkl", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
.. _pip_dependency:
......@@ -79,7 +96,7 @@ FAQ
------------------------------
- paddlepaddle*.whl is not a supported wheel on this platform.
The main cause of this issue is that your current platform is
not supported. Please check that you are using Python 2.7 series.
Besides, pypi only supports manylinux1 standard, you'll need to
......
paddle/fluid/framework/data_device_transform.cc
浏览文件 @ adbf97b4
......@@ -36,9 +36,11 @@ void TransDataDevice(const Tensor& in, const platform::Place& dst_place,
VLOG(3) << "DeviceTransform in, src_place " << in.place()
<< " dst_place: " << dst_place;
auto* dev_ctx = GetDeviceContext(in.place(), dst_place);
dev_ctx->Wait();
TensorCopy(in, dst_place, *dev_ctx, out);
dev_ctx->Wait();
if (platform::is_gpu_place(in.place()) && platform::is_cpu_place(dst_place)) {
dev_ctx->Wait();
}
}
} // namespace framework
......
paddle/fluid/framework/data_type.cc
浏览文件 @ adbf97b4
......@@ -58,6 +58,7 @@ static DataTypeMap* InitDataTypeMap() {
RegType(bool, proto::VarType::BOOL);
RegType(size_t, proto::VarType::SIZE_T);
RegType(int16_t, proto::VarType::INT16);
RegType(uint8_t, proto::VarType::UINT8);
#undef RegType
return retv;
......
paddle/fluid/framework/data_type.h
浏览文件 @ adbf97b4
......@@ -47,8 +47,14 @@ inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
case proto::VarType::BOOL:
visitor.template operator()<bool>();
break;
case proto::VarType::UINT8:
visitor.template operator()<uint8_t>();
break;
case proto::VarType::INT16:
visitor.template operator()<int16_t>();
break;
default:
PADDLE_THROW("Not supported");
PADDLE_THROW("Not supported %d", type);
}
}
......
paddle/fluid/framework/data_type_transform.cc
浏览文件 @ adbf97b4
......@@ -91,6 +91,12 @@ void TransDataType(const OpKernelType& kernel_type_for_var,
case proto::VarType::BOOL:
framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
break;
case proto::VarType::INT16:
framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
break;
case proto::VarType::UINT8:
framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
break;
default:
PADDLE_THROW("Not support type %d", src_type);
}
......
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ adbf97b4
......@@ -98,7 +98,7 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(const OpDesc &op,
return false;
};
if (op.Type() == "split") {
if (op.Type() == "split" || op.Type() == "split_byref") {
return checker(op.OutputArgumentNames(), send_op->InputArgumentNames());
} else if (op.Type() == "concat") {
return checker(op.InputArgumentNames(), send_op->OutputArgumentNames());
......
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @ adbf97b4
......@@ -70,6 +70,14 @@ class OpHandleBase {
const std::vector<VarHandleBase *> &Inputs() const { return inputs_; }
size_t NoDupInputSize() const {
std::unordered_set<VarHandleBase *> res;
for (auto *var : inputs_) {
res.emplace(var);
}
return res.size();
}
const std::vector<VarHandleBase *> &Outputs() const { return outputs_; }
protected:
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ adbf97b4
......@@ -174,7 +174,7 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
void ThreadedSSAGraphExecutor::InsertPendingOp(
std::unordered_map<OpHandleBase *, size_t> *pending_ops,
OpHandleBase *op_instance) const {
pending_ops->insert({op_instance, op_instance->Inputs().size()});
pending_ops->insert({op_instance, op_instance->NoDupInputSize()});
}
void ThreadedSSAGraphExecutor::InsertPendingVar(
......
paddle/fluid/framework/executor.cc
浏览文件 @ adbf97b4
......@@ -228,7 +228,8 @@ static bool has_fetch_operators(
void Executor::Run(const ProgramDesc& program, Scope* scope,
std::map<std::string, const LoDTensor*>* feed_targets,
std::map<std::string, LoDTensor*>* fetch_targets,
bool create_vars, const std::string& feed_holder_name,
bool create_local_scope, bool create_vars,
const std::string& feed_holder_name,
const std::string& fetch_holder_name) {
platform::RecordBlock b(kProgramId);
bool has_feed_ops =
......@@ -290,8 +291,9 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
}
auto ctx = Prepare(*copy_program, 0);
RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets, create_vars,
feed_holder_name, fetch_holder_name);
RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets,
create_local_scope, create_vars, feed_holder_name,
fetch_holder_name);
}
std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
......@@ -366,8 +368,9 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
void Executor::RunPreparedContext(
ExecutorPrepareContext* ctx, Scope* scope,
std::map<std::string, const LoDTensor*>* feed_targets,
std::map<std::string, LoDTensor*>* fetch_targets, bool create_vars,
const std::string& feed_holder_name, const std::string& fetch_holder_name) {
std::map<std::string, LoDTensor*>* fetch_targets, bool create_local_scope,
bool create_vars, const std::string& feed_holder_name,
const std::string& fetch_holder_name) {
auto& global_block = ctx->prog_.Block(ctx->block_id_);
PADDLE_ENFORCE(
......@@ -387,7 +390,7 @@ void Executor::RunPreparedContext(
}
}
RunPreparedContext(ctx, scope, create_vars, create_vars);
RunPreparedContext(ctx, scope, create_local_scope, create_vars);
// obtain the data of fetch_targets from fetch_holder
for (auto* op : global_block.AllOps()) {
......
paddle/fluid/framework/executor.h
浏览文件 @ adbf97b4
......@@ -57,7 +57,7 @@ class Executor {
void Run(const ProgramDesc& program, Scope* scope,
std::map<std::string, const LoDTensor*>* feed_targets,
std::map<std::string, LoDTensor*>* fetch_targets,
bool create_vars = true,
bool create_local_scope = true, bool create_vars = true,
const std::string& feed_holder_name = "feed",
const std::string& fetch_holder_name = "fetch");
......@@ -76,6 +76,7 @@ class Executor {
void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
std::map<std::string, const LoDTensor*>* feed_targets,
std::map<std::string, LoDTensor*>* fetch_targets,
bool create_local_scope = true,
bool create_vars = true,
const std::string& feed_holder_name = "feed",
const std::string& fetch_holder_name = "fetch");
......
paddle/fluid/framework/framework.proto
浏览文件 @ adbf97b4
......@@ -103,6 +103,7 @@ message VarType {
FP64 = 6;
// Tensor<size_t> is used in C++.
SIZE_T = 19;
UINT8 = 20;
// Other types that may need additional descriptions
LOD_TENSOR = 7;
......
paddle/fluid/framework/lod_tensor_test.cc
浏览文件 @ adbf97b4
......@@ -228,11 +228,12 @@ TEST(LoD, CheckAbsLoD) {
ASSERT_FALSE(CheckAbsLoD(abs_lod0));
}
TEST(LoDTensor, RecordIO) {
template <typename T>
static void TestRecordIO() {
LoDTensor tensor;
int* tmp = tensor.mutable_data<int>(make_ddim({4, 5}), platform::CPUPlace());
T* tmp = tensor.mutable_data<T>(make_ddim({4, 5}), platform::CPUPlace());
for (int i = 0; i < 20; ++i) {
tmp[i] = i;
tmp[i] = static_cast<T>(i);
}
std::stringstream* stream = new std::stringstream();
......@@ -247,7 +248,7 @@ TEST(LoDTensor, RecordIO) {
auto assert_tensor_ok = [](const LoDTensor& tensor) {
for (int i = 0; i < 20; ++i) {
ASSERT_EQ(tensor.data<int>()[i], i);
ASSERT_EQ(tensor.data<T>()[i], static_cast<T>(i));
}
};
......@@ -265,5 +266,13 @@ TEST(LoDTensor, RecordIO) {
}
}
TEST(LoDTensor, RecordIO) {
TestRecordIO<int>();
TestRecordIO<int16_t>();
TestRecordIO<uint8_t>();
TestRecordIO<float>();
TestRecordIO<double>();
}
} // namespace framework
} // namespace paddle
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @ adbf97b4
nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS ${FLUID_CORE_MODULES})
nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc io_converter.cc
DEPS ${FLUID_CORE_MODULES} activation_op tensorrt_engine)
DEPS ${FLUID_CORE_MODULES} activation_op tensorrt_engine
SERIAL)
nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)
paddle/fluid/inference/tensorrt/convert/op_converter.h
浏览文件 @ adbf97b4
......@@ -49,7 +49,7 @@ class OpConverter {
// convert fluid block to tensorrt network
void ConvertBlock(const framework::proto::BlockDesc& block,
TensorRTEngine* engine) {
for (size_t i = 0; i < block.ops_size(); i++) {
for (int i = 0; i < block.ops_size(); i++) {
const auto& op = block.ops(i);
OpConverter::Run(op, engine);
}
......
paddle/fluid/inference/tests/book/test_inference_image_classification.cc
浏览文件 @ adbf97b4
......@@ -21,6 +21,7 @@ DEFINE_string(fp16_dirname, "", "Directory of the float16 inference model.");
DEFINE_int32(batch_size, 1, "Batch size of input data");
DEFINE_int32(repeat, 1, "Running the inference program repeat times");
DEFINE_bool(skip_cpu, false, "Skip the cpu test");
DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run inference");
TEST(inference, image_classification) {
if (FLAGS_dirname.empty() || FLAGS_batch_size < 1 || FLAGS_repeat < 1) {
......@@ -58,8 +59,10 @@ TEST(inference, image_classification) {
// Run inference on CPU
LOG(INFO) << "--- CPU Runs: ---";
LOG(INFO) << "Batch size is " << FLAGS_batch_size;
LOG(INFO) << "FLAGS_use_mkldnn: " << FLAGS_use_mkldnn;
TestInference<paddle::platform::CPUPlace, false, true>(
dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined);
dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined,
FLAGS_use_mkldnn);
LOG(INFO) << output1.dims();
}
......
paddle/fluid/inference/tests/test_helper.h
浏览文件 @ adbf97b4
......@@ -133,11 +133,24 @@ std::vector<std::vector<int64_t>> GetFeedTargetShapes(
return feed_target_shapes;
}
void EnableMKLDNN(
const std::unique_ptr<paddle::framework::ProgramDesc>& program) {
for (size_t bid = 0; bid < program->Size(); ++bid) {
auto* block = program->MutableBlock(bid);
for (auto* op : block->AllOps()) {
if (op->HasAttr("use_mkldnn")) {
op->SetAttr("use_mkldnn", true);
}
}
}
}
template <typename Place, bool CreateVars = true, bool PrepareContext = false>
void TestInference(const std::string& dirname,
const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
const std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
const int repeat = 1, const bool is_combined = false) {
const int repeat = 1, const bool is_combined = false,
const bool use_mkldnn = false) {
// 1. Define place, executor, scope
auto place = Place();
auto executor = paddle::framework::Executor(place);
......@@ -149,7 +162,7 @@ void TestInference(const std::string& dirname,
state = paddle::platform::ProfilerState::kCPU;
} else {
#ifdef PADDLE_WITH_CUDA
state = paddle::platform::ProfilerState::kCUDA;
state = paddle::platform::ProfilerState::kAll;
// The default device_id of paddle::platform::CUDAPlace is 0.
// Users can get the device_id using:
// int device_id = place.GetDeviceId();
......@@ -169,10 +182,13 @@ void TestInference(const std::string& dirname,
"init_program",
paddle::platform::DeviceContextPool::Instance().Get(place));
inference_program = InitProgram(&executor, scope, dirname, is_combined);
if (use_mkldnn) {
EnableMKLDNN(inference_program);
}
}
// Disable the profiler and print the timing information
paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,
"load_program_profiler.txt");
"load_program_profiler");
paddle::platform::ResetProfiler();
// 3. Get the feed_target_names and fetch_target_names
......@@ -208,10 +224,10 @@ void TestInference(const std::string& dirname,
if (PrepareContext) {
ctx = executor.Prepare(*inference_program, 0);
executor.RunPreparedContext(ctx.get(), scope, &feed_targets,
&fetch_targets, CreateVars);
&fetch_targets, true, CreateVars);
} else {
executor.Run(*inference_program, scope, &feed_targets, &fetch_targets,
CreateVars);
true, CreateVars);
}
// Enable the profiler
......@@ -236,8 +252,7 @@ void TestInference(const std::string& dirname,
// Disable the profiler and print the timing information
paddle::platform::DisableProfiler(
paddle::platform::EventSortingKey::kDefault,
"run_inference_profiler.txt");
paddle::platform::EventSortingKey::kDefault, "run_inference_profiler");
paddle::platform::ResetProfiler();
}
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ adbf97b4
......@@ -186,11 +186,7 @@ endif()
add_subdirectory(detail)
if(WITH_DISTRIBUTE)
if(WITH_GPU)
op_library(gen_nccl_id_op DEPS nccl_common)
else()
set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
endif()
set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
op_library(send_op DEPS ${DISTRIBUTE_DEPS})
......@@ -206,8 +202,17 @@ if(WITH_DISTRIBUTE)
op_library(send_barrier_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor)
cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op listen_and_serv_op executor)
cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op
listen_and_serv_op sum_op executor SERIAL)
if(WITH_GPU)
set_source_files_properties(test_send_nccl_id.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op
listen_and_serv_op executor SERIAL)
op_library(gen_nccl_id_op DEPS nccl_common sendrecvop_grpc)
set_source_files_properties(gen_nccl_id_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
endif()
else()
set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op gen_nccl_id_op)
endif()
......
paddle/fluid/operators/activation_mkldnn_op.cc
浏览文件 @ adbf97b4
......@@ -15,6 +15,7 @@
#include "mkldnn.hpp"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/mkldnn_activation_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
......@@ -23,6 +24,18 @@ using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
namespace {
std::string gethash(const mkldnn::memory::dims &operand_dims,
const mkldnn::algorithm algorithm) {
auto dim2str = [](const mkldnn::memory::dims &operand_dims) {
std::string dstr = "";
for (size_t i = 0; i < operand_dims.size(); ++i) {
dstr += std::to_string(operand_dims[i]) + "-";
}
return dstr;
};
return dim2str(operand_dims) + std::to_string(algorithm);
}
template <typename T, typename ExecContext>
void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm,
const T alpha = 0, const T beta = 0) {
......@@ -37,42 +50,70 @@ void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm,
const auto *src_data = src->template data<T>();
auto *dst = ctx.template Output<Tensor>("Out");
const T *dst_data = dst->template mutable_data<T>(ctx.GetPlace());
T *dst_data = dst->template mutable_data<T>(ctx.GetPlace());
// get memory dim
PADDLE_ENFORCE(src->dims().size() == 2 || src->dims().size() == 4,
"Input dim must be with 2 or 4");
std::vector<int> src_tz = framework::vectorize2int(src->dims());
// create memory description
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// create memory primitives
auto src_memory =
mkldnn::memory({data_md, mkldnn_engine},
static_cast<void *>(const_cast<float *>(src_data)));
auto dst_memory =
mkldnn::memory({data_md, mkldnn_engine},
static_cast<void *>(const_cast<float *>(dst_data)));
auto forward_desc = mkldnn::eltwise_forward::desc(
mkldnn::prop_kind::forward_training, algorithm, data_md, alpha, beta);
// save prim desc into global device context to be referred in backward path
const std::string key = ctx.op().Output("Out");
const std::string key_eltwise_pd = key + "@eltwise_pd";
auto forward_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
forward_desc, mkldnn_engine);
dev_ctx.SetBlob(key_eltwise_pd, forward_pd);
auto eltwise = mkldnn::eltwise_forward(*forward_pd, src_memory, dst_memory);
const std::string key = gethash(src_tz, algorithm);
const std::string key_src_data =
key + ctx.op().Output("Out") + "@eltwise_fwd_src_data";
const std::string key_src_mem = key + "@eltwise_fwd_src_mem";
const std::string key_dst_mem = key + "@eltwise_fwd_dst_mem";
const std::string key_fwd = key + "@eltwise_fwd";
auto p_fwd = std::static_pointer_cast<mkldnn::eltwise_forward>(
dev_ctx.GetBlob(key_fwd));
// save input data to be referred in backward path
auto p_src_data = std::make_shared<const T *>(src_data);
dev_ctx.SetBlob(key_src_data, p_src_data);
if (p_fwd == nullptr) {
// create memory description
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// create memory primitives
auto p_src_mem = std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(src_data)));
dev_ctx.SetBlob(key_src_mem, p_src_mem);
auto p_dst_mem = std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(dst_data)));
dev_ctx.SetBlob(key_dst_mem, p_dst_mem);
auto fwd_desc = mkldnn::eltwise_forward::desc(
mkldnn::prop_kind::forward_training, algorithm, data_md, alpha, beta);
auto p_fwd_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
fwd_desc, mkldnn_engine);
const std::string key_fwd_pd = key + "eltwise_fwd_pd";
dev_ctx.SetBlob(key_fwd_pd, p_fwd_pd);
p_fwd = std::make_shared<mkldnn::eltwise_forward>(
*p_fwd_pd, *(p_src_mem.get()), *(p_dst_mem.get()));
dev_ctx.SetBlob(key_fwd, p_fwd);
} else {
// primitives already exist
auto p_src_mem =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
PADDLE_ENFORCE(p_src_mem != nullptr,
"Fail to find eltwise p_src_mem in device context.");
auto p_dst_mem =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_dst_mem));
PADDLE_ENFORCE(p_dst_mem != nullptr,
"Fail to find eltwise p_src_mem in device context.");
p_src_mem->set_data_handle(platform::to_void_reinterpret_cast(src_data));
p_dst_mem->set_data_handle(dst_data);
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline = {eltwise};
std::vector<mkldnn::primitive> pipeline = {*(p_fwd.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
......@@ -83,8 +124,7 @@ void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm,
const auto &mkldnn_engine = dev_ctx.GetEngine();
// get buffers
const auto *x = ctx.template Input<Tensor>("X");
const auto *src = x->template data<T>();
const auto *out = ctx.template Input<Tensor>("Out");
auto *dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
const auto *diff_dst = dout->template data<T>();
......@@ -94,45 +134,73 @@ void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm,
const T *diff_src = dx->template mutable_data<T>(ctx.GetPlace());
// get memory dim
std::vector<int> src_tz = framework::vectorize2int(x->dims());
// create memory description
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// create memory primitives
auto src_memory = mkldnn::memory(
{data_md, mkldnn_engine}, static_cast<void *>(const_cast<float *>(src)));
auto diff_src_memory =
mkldnn::memory({data_md, mkldnn_engine},
static_cast<void *>(const_cast<float *>(diff_src)));
auto diff_dst_memory =
mkldnn::memory({data_md, mkldnn_engine},
static_cast<void *>(const_cast<float *>(diff_dst)));
auto backward_desc =
mkldnn::eltwise_backward::desc(algorithm, data_md, data_md, alpha, beta);
// retrieve eltwise primitive desc from device context
const std::string key = ctx.op().Input("Out");
const std::string key_eltwise_pd = key + "@eltwise_pd";
const std::shared_ptr<void> forward_pd = dev_ctx.GetBlob(key_eltwise_pd);
PADDLE_ENFORCE(forward_pd != nullptr,
"Fail to find eltwise_pd in device context");
auto *p_forward_pd =
static_cast<mkldnn::eltwise_forward::primitive_desc *>(forward_pd.get());
auto eltwise_bwd_prim_desc = mkldnn::eltwise_backward::primitive_desc(
backward_desc, mkldnn_engine, *p_forward_pd);
auto eltwise_bwd = mkldnn::eltwise_backward(eltwise_bwd_prim_desc, src_memory,
diff_dst_memory, diff_src_memory);
std::vector<int> src_tz = framework::vectorize2int(out->dims());
const std::string key = gethash(src_tz, algorithm);
const std::string key_diff_src_mem = key + "@eltwise_diff_src_mem";
const std::string key_diff_dst_mem = key + "@eltwise_diff_dst_mem";
const std::string key_grad = key + "@eltwise_grad";
const std::string key_src_data =
key + ctx.op().Input("Out") + "@eltwise_fwd_src_data";
const auto p_src_data =
std::static_pointer_cast<T *>(dev_ctx.GetBlob(key_src_data));
const std::string key_src_mem = key + "@eltwise_fwd_src_mem";
auto p_src_mem =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
p_src_mem->set_data_handle(*p_src_data.get());
auto p_grad = std::static_pointer_cast<mkldnn::eltwise_forward::primitive>(
dev_ctx.GetBlob(key_grad));
if (p_grad == nullptr) {
// create memory description
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// create memory primitives
std::shared_ptr<void> p_diff_src_mem =
std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(diff_src)));
dev_ctx.SetBlob(key_diff_src_mem, p_diff_src_mem);
std::shared_ptr<void> p_diff_dst_mem =
std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(diff_dst)));
dev_ctx.SetBlob(key_diff_dst_mem, p_diff_dst_mem);
auto bwd_desc = mkldnn::eltwise_backward::desc(algorithm, data_md, data_md,
alpha, beta);
const std::string key_fwd_pd = key + "eltwise_fwd_pd";
auto *p_fwd_pd = static_cast<mkldnn::eltwise_forward::primitive_desc *>(
dev_ctx.GetBlob(key_fwd_pd).get());
auto eltwise_bwd_prim_desc = mkldnn::eltwise_backward::primitive_desc(
bwd_desc, mkldnn_engine, *p_fwd_pd);
p_grad = std::make_shared<mkldnn::eltwise_backward>(
eltwise_bwd_prim_desc, *static_cast<mkldnn::memory *>(p_src_mem.get()),
*(static_cast<mkldnn::memory *>(p_diff_dst_mem.get())),
*(static_cast<mkldnn::memory *>(p_diff_src_mem.get())));
} else {
// primitives already exist
auto p_diff_src_mem = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_diff_src_mem));
auto p_diff_dst_mem = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_diff_dst_mem));
p_diff_src_mem->set_data_handle(
platform::to_void_reinterpret_cast(diff_src));
p_diff_dst_mem->set_data_handle(
platform::to_void_reinterpret_cast(diff_dst));
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline = {eltwise_bwd};
std::vector<mkldnn::primitive> pipeline = {*(p_grad.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
} // anonymous namespace
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ adbf97b4
......@@ -41,7 +41,7 @@ namespace operators {
\
protected: \
std::unique_ptr<::paddle::framework::OpDesc> Apply() const override { \
auto *op = new ::paddle::framework::OpDesc(); \
auto* op = new ::paddle::framework::OpDesc(); \
op->SetType(#KERNEL_TYPE "_grad"); \
op->SetInput("Out", Output("Out")); \
op->SetInput(::paddle::framework::GradVarName("Out"), \
......@@ -54,23 +54,50 @@ namespace operators {
} \
}
framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel& oper,
const std::string& name) {
framework::LibraryType library{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_MKLDNN
auto it = oper.Attrs().find("use_mkldnn");
if (library == framework::LibraryType::kPlain && it != oper.Attrs().end() &&
platform::CanMKLDNNBeUsed(ctx)) {
library = framework::LibraryType::kMKLDNN;
}
#endif
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>(name)->type()),
ctx.GetPlace(), layout, library);
}
class ActivationOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetKernelType(ctx, *this, "X");
}
};
class ActivationOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetKernelType(ctx, *this, "Out");
}
};
__attribute__((unused)) constexpr char SigmoidDoc[] = R"DOC(
......
paddle/fluid/operators/beam_search_op.h
浏览文件 @ adbf97b4
......@@ -14,10 +14,6 @@ limitations under the License. */
#pragma once
#ifdef PADDLE_WITH_TESTING
#include "gtest/gtest.h"
#endif
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
......
paddle/fluid/operators/detail/CMakeLists.txt
浏览文件 @ adbf97b4
......@@ -4,6 +4,8 @@ if(WITH_DISTRIBUTE)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(serde_test.cc grpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr
cares zlib protobuf sendrecvop_grpc)
cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_table_op)
cares zlib protobuf sendrecvop_grpc SERIAL)
cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc
grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor
proto_desc lookup_table_op SERIAL)
endif()
paddle/fluid/operators/detail/grpc_server.cc
浏览文件 @ adbf97b4
......@@ -184,7 +184,7 @@ class RequestPrefetch final : public RequestBase {
framework::Scope* local_scope = &scope_->NewScope();
auto* var = local_scope->FindVar(var_name);
InitializeVariable(var, var_desc->GetType());
executor_->RunPreparedContext(prefetch_ctx_, scope_, false, false);
executor_->RunPreparedContext(prefetch_ctx_, scope_);
SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply);
......
paddle/fluid/operators/detection_map_op.cc
浏览文件 @ adbf97b4
......@@ -51,7 +51,8 @@ class DetectionMAPOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(label_dims.size(), 2,
"The rank of Input(Label) must be 2, "
"the shape is [N, 6].");
PADDLE_ENFORCE_EQ(label_dims[1], 6, "The shape is of Input(Label) [N, 6].");
PADDLE_ENFORCE(label_dims[1] == 6 || label_dims[1] == 5,
"The shape of Input(Label) is [N, 6] or [N, 5].");
if (ctx->HasInput("PosCount")) {
PADDLE_ENFORCE(ctx->HasInput("TruePos"),
......@@ -88,9 +89,10 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
"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"
"(LoDTensor) A 2-D LoDTensor represents the"
"Labeled ground-truth data. Each row has 6 values: "
"[label, is_difficult, xmin, ymin, xmax, ymax], N is the total "
"[label, xmin, ymin, xmax, ymax, is_difficult] or 5 values: "
"[label, xmin, ymin, xmax, ymax], where 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, "
......
paddle/fluid/operators/detection_map_op.h
浏览文件 @ adbf97b4
......@@ -72,7 +72,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
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");
bool evaluate_difficult = ctx.Attr<bool>("evaluate_difficult");
auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
int class_num = ctx.Attr<int>("class_num");
......@@ -175,14 +175,20 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<Box>> boxes;
for (size_t 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);
if (input_label.dims()[1] == 6) {
Box box(labels(i, 2), labels(i, 3), labels(i, 4), labels(i, 5));
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);
} else {
PADDLE_ENFORCE_EQ(input_label.dims()[1], 5);
Box box(labels(i, 1), labels(i, 2), labels(i, 3), labels(i, 4));
boxes[label].push_back(box);
}
}
gt_boxes->push_back(boxes);
}
......
paddle/fluid/operators/is_empty_op.cc
浏览文件 @ adbf97b4
......@@ -12,45 +12,41 @@ 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/is_empty_op.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
namespace paddle {
namespace operators {
constexpr char kInput[] = "X";
constexpr char kOutput[] = "Out";
class IsEmptyOp : public framework::OperatorBase {
class IsEmptyOp : public framework::OperatorWithKernel {
public:
IsEmptyOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
using framework::OperatorWithKernel::OperatorWithKernel;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
// get input
auto *var = scope.FindVar(Input(kInput));
PADDLE_ENFORCE_NOT_NULL(var);
auto &tensor = var->Get<framework::LoDTensor>();
// get output
auto *out = scope.FindVar(Output(kOutput));
PADDLE_ENFORCE_NOT_NULL(out);
auto *out_tensor = out->GetMutable<framework::LoDTensor>();
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of IsEmptyOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of IsEmptyOp should not be null.");
ctx->SetOutputDim("Out", {1});
}
out_tensor->Resize({1});
out_tensor->mutable_data<bool>(platform::CPUPlace())[0] =
framework::product(tensor.dims()) == 0;
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
platform::CPUPlace());
return kt;
}
};
class IsEmptyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
class IsEmptyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(kInput, "(Tensor) Tensor which is to be checked.");
AddOutput(kOutput, "(Tensor) a boolean Tensor that indicate empty or not.");
AddInput("X", "(LoDTensor) Tensor which is to be checked.");
AddOutput("Out",
"(LoDTensor) a boolean Tensor that indicate empty or not.");
AddComment(R"DOC(
IsEmpty Operator which checks whether a tensor is empty.
......@@ -62,5 +58,12 @@ It will just return product(tensor.ddims()) > 0;
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(is_empty, paddle::operators::IsEmptyOp,
paddle::operators::IsEmptyOpProtoMaker);
namespace ops = paddle::operators;
REGISTER_OPERATOR(is_empty, ops::IsEmptyOp, ops::IsEmptyOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
is_empty, ops::IsEmptyOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::IsEmptyOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::IsEmptyOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::IsEmptyOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/is_empty_op.h 0 → 100644
浏览文件 @ adbf97b4
/* Copyright (c) 2016 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. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class IsEmptyOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
// get input
auto* input_tensor = context.Input<framework::LoDTensor>("X");
// get output
auto* output_tensor = context.Output<framework::LoDTensor>("Out");
output_tensor->mutable_data<bool>(platform::CPUPlace())[0] =
framework::product(input_tensor->dims()) == 0;
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ adbf97b4
......@@ -57,8 +57,7 @@ static void ParallelExecuteBlocks(
framework::Async([&executor, &prepared, &program, &scope, idx]() {
int run_block = idx; // thread local
try {
executor->RunPreparedContext(prepared[run_block].get(), scope,
false, false);
executor->RunPreparedContext(prepared[run_block].get(), scope);
} catch (std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
}
......@@ -211,8 +210,8 @@ static void AsyncUpdateThread(
}
auto fs = framework::Async([var_name, &executor, &v, prepared] {
try {
executor->RunPreparedContext(prepared, v.second->GetMutableLocalScope(),
false, false);
executor->RunPreparedContext(prepared,
v.second->GetMutableLocalScope());
} catch (std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
}
......
paddle/fluid/operators/math/math_function.cc
浏览文件 @ adbf97b4
......@@ -38,7 +38,9 @@ template struct SetConstant<platform::CPUDeviceContext, bool>;
template struct Transpose<platform::CPUDeviceContext, double, RANK>; \
template struct Transpose<platform::CPUDeviceContext, int, RANK>; \
template struct Transpose<platform::CPUDeviceContext, int64_t, RANK>; \
template struct Transpose<platform::CPUDeviceContext, bool, RANK>;
template struct Transpose<platform::CPUDeviceContext, bool, RANK>; \
template struct Transpose<platform::CPUDeviceContext, int16_t, RANK>; \
template struct Transpose<platform::CPUDeviceContext, uint8_t, RANK>;
DEFINE_CPU_TRANS(1);
DEFINE_CPU_TRANS(2);
......
paddle/fluid/operators/mkldnn_activation_op.h
浏览文件 @ adbf97b4
......@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
......@@ -60,52 +62,5 @@ class MKLDNNActivationGradKernel
}
};
namespace { // NOLINT
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel& oper) {
framework::LibraryType library{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_MKLDNN
if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library = framework::LibraryType::kMKLDNN;
}
#endif
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.GetPlace(), layout, library);
}
} // anonymous namespace
class ActivationWithMKLDNNOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetKernelType(ctx, *this);
}
};
class ActivationWithMKLDNNOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetKernelType(ctx, *this);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/pool_mkldnn_op.cc
浏览文件 @ adbf97b4
......@@ -18,6 +18,26 @@ limitations under the License. */
namespace paddle {
namespace operators {
using mkldnn::memory; // Note: paddle has also "memory" namespace
using mkldnn::pooling_forward;
using mkldnn::pooling_backward;
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Make hashing function more optimial
static std::string gethash(memory::dims& input_dims, std::string& pooling_type,
std::vector<int>& ksize, std::vector<int>& strides,
std::vector<int>& paddings, std::string suffix) {
auto dims2str = [](memory::dims& operand_dims) {
std::string dstr = "";
for (size_t i = 0; i < operand_dims.size(); ++i) {
dstr += std::to_string(operand_dims[i]) + "-";
}
return dstr;
};
return dims2str(input_dims) + dims2str(ksize) + dims2str(strides) +
dims2str(paddings) + pooling_type + suffix;
}
template <typename T>
class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
......@@ -34,10 +54,6 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when saving info into device context
const std::string key = ctx.op().Output("Out");
const std::string key_pool_pd = key + "@pool_pd";
const std::string key_pool_workspace_memory =
key + "@pool_workspace_memory";
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
......@@ -63,37 +79,71 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
// TODO(pzelazko-intel): support more formats
auto src_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
auto dst_md = platform::MKLDNNMemDesc(dst_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
std::shared_ptr<mkldnn::pooling_forward::primitive_desc> pool_pd =
CreatePrimitiveDesc(src_md, dst_md, strides, paddings, ksize,
pooling_type, mkldnn_engine);
// save pool_pd into global device context to be referred in backward path
dev_ctx.SetBlob(key_pool_pd, pool_pd);
std::shared_ptr<mkldnn::memory> workspace_memory =
CreateWorkspaceMemory(pool_pd, pooling_type, mkldnn_engine);
// save pool_workspace_memory to be referred in backward path
dev_ctx.SetBlob(key_pool_workspace_memory, workspace_memory);
auto src_memory =
mkldnn::memory({src_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(input_data)));
auto dst_memory =
mkldnn::memory({dst_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(output_data)));
const std::string key = gethash(src_tz, pooling_type, ksize, strides,
paddings, ctx.op().Output("Out"));
const std::string key_pool_p = key + "@pool_p";
const std::string key_pool_pd = key + "@pool_pd";
const std::string key_pool_src_mem_p = key + "@pool_src_mem_p";
const std::string key_pool_dst_mem_p = key + "@pool_dst_mem_p";
const std::string key_pool_workspace_memory =
key + "@pool_workspace_memory";
auto pool_prim = mkldnn::pooling_forward(*pool_pd, src_memory, dst_memory,
*workspace_memory);
auto pool_p =
std::static_pointer_cast<pooling_forward>(dev_ctx.GetBlob(key_pool_p));
if (pool_p == nullptr) {
// TODO(pzelazko-intel): support more formats
auto src_md =
platform::MKLDNNMemDesc(src_tz, platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw);
auto dst_md =
platform::MKLDNNMemDesc(dst_tz, platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw);
std::shared_ptr<pooling_forward::primitive_desc> pool_pd =
CreatePrimitiveDesc(src_md, dst_md, strides, paddings, ksize,
pooling_type, mkldnn_engine);
// save pool_pd into global device context to be referred in backward path
dev_ctx.SetBlob(key_pool_pd, pool_pd);
std::shared_ptr<mkldnn::memory> workspace_memory =
CreateWorkspaceMemory(pool_pd, pooling_type, mkldnn_engine);
// save pool_workspace_memory to be referred in backward path
dev_ctx.SetBlob(key_pool_workspace_memory, workspace_memory);
auto pool_src_memory_p = std::make_shared<memory>(
memory::primitive_desc{src_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(input_data)));
dev_ctx.SetBlob(key_pool_src_mem_p, pool_src_memory_p);
auto pool_dst_memory_p = std::make_shared<memory>(
memory::primitive_desc{dst_md, mkldnn_engine},
static_cast<void*>(output_data));
dev_ctx.SetBlob(key_pool_dst_mem_p, pool_dst_memory_p);
pool_p = std::make_shared<pooling_forward>(
*pool_pd, *(pool_src_memory_p.get()), *(pool_dst_memory_p.get()),
*workspace_memory);
dev_ctx.SetBlob(key_pool_p, pool_p);
} else {
// Primitives already exist
auto pool_src_memory_p =
std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_src_mem_p));
PADDLE_ENFORCE(pool_src_memory_p != nullptr,
"Fail to find pooling src mem_p in device context");
auto pool_dst_memory_p =
std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_dst_mem_p));
PADDLE_ENFORCE(pool_dst_memory_p != nullptr,
"Fail to find pooling dst mem_p in device context");
pool_src_memory_p->set_data_handle(
reinterpret_cast<void*>(const_cast<T*>(input_data)));
pool_dst_memory_p->set_data_handle(output_data);
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{pool_prim};
std::vector<mkldnn::primitive> pipeline{*(pool_p.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
......@@ -120,9 +170,10 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
mkldnn::memory::primitive_desc workspace_md =
pooling_type == "max"
? pool_pd->workspace_primitive_desc()
: mkldnn::memory::primitive_desc(
{{}, mkldnn::memory::f32, mkldnn::memory::format::nchw},
engine);
: mkldnn::memory::primitive_desc({{},
platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw},
engine);
auto p_workspace_memory = new mkldnn::memory(workspace_md);
return std::unique_ptr<mkldnn::memory>(p_workspace_memory);
......@@ -140,13 +191,6 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
Tensor* in_x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when referring info from device context
const std::string key = ctx.op().Input("Out");
const std::string key_pool_pd = key + "@pool_pd";
const std::string key_pool_workspace_memory =
key + "@pool_workspace_memory";
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
......@@ -171,43 +215,76 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
std::vector<int> diff_dst_tz =
paddle::framework::vectorize2int(out_grad->dims());
auto diff_src_md = platform::MKLDNNMemDesc(diff_src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
auto diff_dst_md = platform::MKLDNNMemDesc(diff_dst_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// Retrieve pool_pd/pool_workspace_memory from device context
auto pool_pd =
std::static_pointer_cast<mkldnn::pooling_forward::primitive_desc>(
dev_ctx.GetBlob(key_pool_pd));
PADDLE_ENFORCE(pool_pd != nullptr,
"Fail to find pool_pd in device context");
auto workspace_memory = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_pool_workspace_memory));
PADDLE_ENFORCE(workspace_memory != nullptr,
"Fail to find workspace_memory in device context");
auto pool_bwd_desc = mkldnn::pooling_backward::desc(
pooling_type == "max" ? mkldnn::algorithm::pooling_max
: mkldnn::algorithm::pooling_avg,
diff_src_md, diff_dst_md, strides, ksize, paddings, paddings,
mkldnn::padding_kind::zero);
auto pool_bwd_pd = mkldnn::pooling_backward::primitive_desc(
pool_bwd_desc, mkldnn_engine, *pool_pd);
auto diff_src_memory =
mkldnn::memory({diff_src_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(in_x_grad_data)));
auto diff_dst_memory =
mkldnn::memory({diff_dst_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(out_grad_data)));
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when referring info from device context
const std::string key = gethash(diff_src_tz, pooling_type, ksize, strides,
paddings, ctx.op().Input("Out"));
const std::string key_pool_bwd_p = key + "@pool_bwd_p";
const std::string key_pool_diff_src_mem_p = key + "@pool_diff_src_mem_p";
const std::string key_pool_diff_dst_mem_p = key + "@pool_diff_dst_mem_p";
const std::string key_pool_pd = key + "@pool_pd";
const std::string key_pool_workspace_memory =
key + "@pool_workspace_memory";
auto bwd_prim = mkldnn::pooling_backward(
pool_bwd_pd, diff_dst_memory, *workspace_memory, diff_src_memory);
auto pool_bwd_p = std::static_pointer_cast<pooling_backward>(
dev_ctx.GetBlob(key_pool_bwd_p));
if (pool_bwd_p == nullptr) {
auto diff_src_md =
platform::MKLDNNMemDesc(diff_src_tz, platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw);
auto diff_dst_md =
platform::MKLDNNMemDesc(diff_dst_tz, platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw);
// Retrieve pool_pd/pool_workspace_memory from device context
auto pool_pd =
std::static_pointer_cast<mkldnn::pooling_forward::primitive_desc>(
dev_ctx.GetBlob(key_pool_pd));
PADDLE_ENFORCE(pool_pd != nullptr,
"Fail to find pool_pd in device context");
auto workspace_memory = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_pool_workspace_memory));
PADDLE_ENFORCE(workspace_memory != nullptr,
"Fail to find workspace_memory in device context");
auto pool_diff_src_memory_p = std::make_shared<memory>(memory(
{diff_src_md, mkldnn_engine}, static_cast<void*>(in_x_grad_data)));
dev_ctx.SetBlob(key_pool_diff_src_mem_p, pool_diff_src_memory_p);
auto pool_diff_dst_memory_p = std::make_shared<memory>(
memory({diff_dst_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(out_grad_data))));
dev_ctx.SetBlob(key_pool_diff_dst_mem_p, pool_diff_dst_memory_p);
auto pool_bwd_desc = mkldnn::pooling_backward::desc(
pooling_type == "max" ? mkldnn::algorithm::pooling_max
: mkldnn::algorithm::pooling_avg,
diff_src_md, diff_dst_md, strides, ksize, paddings, paddings,
mkldnn::padding_kind::zero);
auto pool_bwd_pd = mkldnn::pooling_backward::primitive_desc(
pool_bwd_desc, mkldnn_engine, *pool_pd);
pool_bwd_p = std::make_shared<pooling_backward>(
pool_bwd_pd, *(pool_diff_dst_memory_p.get()), *workspace_memory,
*(pool_diff_src_memory_p));
dev_ctx.SetBlob(key_pool_bwd_p, pool_bwd_p);
} else {
// Primitives already exist
auto pool_diff_src_memory_p = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_pool_diff_src_mem_p));
PADDLE_ENFORCE(pool_diff_src_memory_p != nullptr,
"Fail to find pooling src mem_p in device context");
auto pool_diff_dst_memory_p = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_pool_diff_dst_mem_p));
PADDLE_ENFORCE(pool_diff_dst_memory_p != nullptr,
"Fail to find pooling dst mem_p in device context");
pool_diff_src_memory_p->set_data_handle(
reinterpret_cast<void*>(in_x_grad_data));
pool_diff_dst_memory_p->set_data_handle(const_cast<T*>(out_grad_data));
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{bwd_prim};
std::vector<mkldnn::primitive> pipeline{*(pool_bwd_p.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
} // Compute()
};
......
paddle/fluid/operators/roi_pool_op.cu
浏览文件 @ adbf97b4
......@@ -38,10 +38,10 @@ __global__ void GPUROIPoolForward(
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;
for (size_t i = index; i < nthreads; i += offset) {
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
int c = (index / pooled_width / pooled_height) % channels;
int n = index / pooled_width / pooled_height / channels;
int pw = i % pooled_width;
int ph = (i / pooled_width) % pooled_height;
int c = (i / pooled_width / pooled_height) % channels;
int n = i / pooled_width / pooled_height / channels;
const int64_t* offset_input_rois = input_rois + n * kROISize;
int roi_batch_ind = roi_batch_id_data[n];
......@@ -52,14 +52,19 @@ __global__ void GPUROIPoolForward(
int roi_width = max(roi_end_w - roi_start_w + 1, 1);
int roi_height = max(roi_end_h - roi_start_h + 1, 1);
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
int hstart = static_cast<int>(floor(static_cast<T>(ph) * bin_size_h));
int wstart = static_cast<int>(floor(static_cast<T>(pw) * bin_size_w));
int hend = static_cast<int>(ceil(static_cast<T>(ph + 1) * bin_size_h));
int wend = static_cast<int>(ceil(static_cast<T>(pw + 1) * bin_size_w));
int hstart = static_cast<int>(floor(static_cast<double>(ph) *
static_cast<double>(roi_height) /
static_cast<double>(pooled_height)));
int wstart = static_cast<int>(floor(static_cast<double>(pw) *
static_cast<double>(roi_width) /
static_cast<double>(pooled_width)));
int hend = static_cast<int>(ceil(static_cast<double>(ph + 1) *
static_cast<double>(roi_height) /
static_cast<double>(pooled_height)));
int wend = static_cast<int>(ceil(static_cast<double>(pw + 1) *
static_cast<double>(roi_width) /
static_cast<double>(pooled_width)));
hstart = min(max(hstart + roi_start_h, 0), height);
hend = min(max(hend + roi_start_h, 0), height);
wstart = min(max(wstart + roi_start_w, 0), width);
......@@ -79,9 +84,9 @@ __global__ void GPUROIPoolForward(
}
}
}
output_data[index] = maxval;
output_data[i] = maxval;
if (argmax_data) {
argmax_data[index] = maxidx;
argmax_data[i] = maxidx;
}
}
}
......@@ -96,10 +101,10 @@ __global__ void GPUROIPoolBackward(
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;
for (int i = index; i < nthreads; i += offset) {
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
int c = (index / pooled_width / pooled_height) % channels;
int n = index / pooled_width / pooled_height / channels;
int pw = i % pooled_width;
int ph = (i / pooled_width) % pooled_height;
int c = (i / pooled_width / pooled_height) % channels;
int n = i / pooled_width / pooled_height / channels;
int roi_batch_ind = roi_batch_id_data[n];
int input_offset = (roi_batch_ind * channels + c) * height * width;
......@@ -138,6 +143,7 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
int width = in_dims[3];
int rois_num = rois->dims()[0];
if (rois_num == 0) return;
int output_size = out->numel();
......
paddle/fluid/operators/send_recv_op_test.cc
浏览文件 @ adbf97b4
......@@ -92,12 +92,16 @@ void InitSelectedRowsInScope(const p::CPUPlace &place, f::Scope *scope) {
void AddOp(const std::string &type, const f::VariableNameMap &inputs,
const f::VariableNameMap &outputs, f::AttributeMap attrs,
f::BlockDesc *block) {
f::BlockDesc *block, bool is_sparse) {
// insert output
for (auto kv : outputs) {
for (auto v : kv.second) {
auto var = block->Var(v);
var->SetDataType(f::proto::VarType::FP32);
var->SetPersistable(true);
if (is_sparse) {
var->SetType(f::proto::VarType::SELECTED_ROWS);
}
}
}
......@@ -128,7 +132,8 @@ void StartServerNet(bool is_sparse, std::atomic<bool> *initialized) {
auto *optimize_block = program.AppendBlock(root_block);
auto *prefetch_block = program.AppendBlock(root_block);
// X for server side tensors, RX for received tensors, must be of same shape.
AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block);
AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block,
is_sparse);
f::AttributeMap attrs;
attrs.insert({"endpoint", std::string("127.0.0.1:0")});
attrs.insert({"Fanin", 1});
......
paddle/fluid/operators/smooth_l1_loss_op.cc
浏览文件 @ adbf97b4
......@@ -105,7 +105,7 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
auto in_dims = ctx->GetInputDim("X");
auto in_dims = ctx->GetInputDim("Diff");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_GE(out_dims.size(), 2,
......@@ -127,12 +127,33 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
}
};
class SmoothL1LossGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType("smooth_l1_loss_grad");
op->SetInput("InsideWeight", Input("InsideWeight"));
op->SetInput("OutsideWeight", Input("OutsideWeight"));
op->SetInput("Diff", Output("Diff"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetAttrMap(Attrs());
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
return std::unique_ptr<framework::OpDesc>(op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(smooth_l1_loss, ops::SmoothL1LossOp, ops::SmoothL1LossOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::SmoothL1LossGradMaker);
REGISTER_OPERATOR(smooth_l1_loss_grad, ops::SmoothL1LossGradOp);
REGISTER_OP_CPU_KERNEL(
smooth_l1_loss,
......
paddle/fluid/operators/warpctc_op.h
浏览文件 @ adbf97b4
......@@ -186,8 +186,7 @@ class WarpCTCKernel : public framework::OpKernel<T> {
// warpctc accesses labels in CPU memory
Tensor warpctc_label;
TensorCopy(*label, platform::CPUPlace(), ctx.device_context(),
&warpctc_label);
TensorCopySync(*label, platform::CPUPlace(), &warpctc_label);
const int* warpctc_label_data = warpctc_label.data<int>();
// warpctc stores loss in CPU memory
Tensor warpctc_loss;
......
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ adbf97b4
......@@ -49,7 +49,7 @@ nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_
nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
nv_test(transform_test SRCS transform_test.cu DEPS memory place device_context)
cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto ${GPU_CTX_DEPS})
cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto framework_proto ${GPU_CTX_DEPS})
cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
......
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @ adbf97b4
......@@ -38,6 +38,11 @@ void* to_void_cast(const Type* t) {
return static_cast<void*>(const_cast<Type*>(t));
}
template <typename Type>
void* to_void_reinterpret_cast(const Type* t) {
return reinterpret_cast<void*>(const_cast<Type*>(t));
}
template <class Type>
using tf_desc = typename Type::desc;
......@@ -71,5 +76,15 @@ inline bool CanMKLDNNBeUsed(const framework::ExecutionContext& ctx) {
return use_mkldnn && platform::is_cpu_place(ctx.GetPlace());
}
template <typename Type>
mkldnn::memory::data_type MKLDNNGetDataType() {
return mkldnn::memory::data_undef;
}
template <>
inline mkldnn::memory::data_type MKLDNNGetDataType<float>() {
return mkldnn::memory::f32;
}
} // namespace platform
} // namespace paddle
paddle/fluid/platform/profiler.cc
浏览文件 @ adbf97b4
......@@ -173,8 +173,9 @@ void PopEvent(const std::string& name, const DeviceContext* dev_ctx) {
}
RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
: start_ns_(PosixInNsec()) {
: is_enabled_(false), start_ns_(PosixInNsec()) {
if (g_state == ProfilerState::kDisabled) return;
is_enabled_ = true;
dev_ctx_ = dev_ctx;
name_ = name;
PushEvent(name_, dev_ctx_);
......@@ -183,7 +184,7 @@ RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
}
RecordEvent::~RecordEvent() {
if (g_state == ProfilerState::kDisabled) return;
if (g_state == ProfilerState::kDisabled || !is_enabled_) return;
DeviceTracer* tracer = GetDeviceTracer();
if (tracer) {
tracer->AddCPURecords(CurAnnotation(), start_ns_, PosixInNsec(),
......@@ -193,14 +194,16 @@ RecordEvent::~RecordEvent() {
PopEvent(name_, dev_ctx_);
}
RecordBlock::RecordBlock(int block_id) : start_ns_(PosixInNsec()) {
RecordBlock::RecordBlock(int block_id)
: is_enabled_(false), start_ns_(PosixInNsec()) {
if (g_state == ProfilerState::kDisabled) return;
is_enabled_ = true;
SetCurBlock(block_id);
name_ = string::Sprintf("block_%d", block_id);
}
RecordBlock::~RecordBlock() {
if (g_state == ProfilerState::kDisabled) return;
if (g_state == ProfilerState::kDisabled || !is_enabled_) return;
DeviceTracer* tracer = GetDeviceTracer();
if (tracer) {
// We try to put all blocks at the same nested depth in the
......
paddle/fluid/platform/profiler.h
浏览文件 @ adbf97b4
......@@ -74,6 +74,7 @@ struct RecordEvent {
~RecordEvent();
bool is_enabled_;
uint64_t start_ns_;
// The device context is used by Event to get the current cuda stream.
const DeviceContext* dev_ctx_;
......@@ -89,6 +90,7 @@ struct RecordBlock {
~RecordBlock();
private:
bool is_enabled_;
std::string name_;
uint64_t start_ns_;
};
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ adbf97b4
......@@ -238,6 +238,7 @@ void BindVarDsec(pybind11::module *m) {
pybind11::enum_<pd::proto::VarType::Type>(var_desc, "VarType", "")
.value("BOOL", pd::proto::VarType::BOOL)
.value("UINT8", pd::proto::VarType::UINT8)
.value("INT16", pd::proto::VarType::INT16)
.value("INT32", pd::proto::VarType::INT32)
.value("INT64", pd::proto::VarType::INT64)
......
paddle/fluid/train/demo/CMakeLists.txt 0 → 100644
浏览文件 @ adbf97b4
cmake_minimum_required(VERSION 3.0)
project(cpp_train_demo CXX C)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
if(NOT DEFINED PADDLE_LIB)
message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/paddle/lib/dir")
endif()
option(WITH_MKLDNN "Compile PaddlePaddle with MKLDNN" OFF)
option(WITH_MKL "Compile PaddlePaddle with MKL support, default use openblas." OFF)
include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
include_directories("${PADDLE_LIB}/third_party/boost")
include_directories("${PADDLE_LIB}/third_party/eigen3")
link_directories("${PADDLE_LIB}/third_party/install/snappy/lib")
link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
add_executable(demo_trainer demo_trainer.cc)
if(WITH_MKLDNN)
include_directories("${PADDLE_LIB}/third_party/install/mkldnn/include")
set(MKLDNN_LIB ${PADDLE_LIB}/third_party/install/mkldnn/lib/libmkldnn.so.0)
endif()
if(WITH_MKL)
include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel.so)
else()
if(APPLE)
set(MATH_LIB cblas)
else(APPLE)
set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas.a)
endif(APPLE)
endif()
if(APPLE)
set(MACOS_LD_FLAGS "-undefined dynamic_lookup -Wl,-all_load -framework CoreFoundation -framework Security")
else(APPLE)
set(ARCHIVE_START "-Wl,--whole-archive")
set(ARCHIVE_END "-Wl,--no-whole-archive")
set(EXTERNAL_LIB "-lrt -ldl -lpthread")
endif(APPLE)
target_link_libraries(demo_trainer
${MACOS_LD_FLAGS}
${ARCHIVE_START}
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.a
${ARCHIVE_END}
${MATH_LIB}
${MKLDNN_LIB}
glog gflags protobuf snappystream snappy z
${EXTERNAL_LIB})
paddle/fluid/train/demo/README.md 0 → 100644
浏览文件 @ adbf97b4
### step 1. build paddle lib
```
# WITH_MKL=ON|OFF
# WITH_MKLDNN=ON|OFF
PADDLE_LIB=/paddle/lib/dir
cmake .. -DFLUID_INSTALL_DIR=$PADDLE_LIB \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_FLUID_ONLY=ON \
-DWITH_GPU=OFF \
-DWITH_STYLE_CHECK=OFF \
-DWITH_MKL=OFF \
-DWITH_MKLDNN=OFF
make -j8
make -j8 inference_lib_dist
```
### step 2. generate program desc
```
# please install paddle before run this scripe
pip install --upgrade paddlepaddle-*.whl
python demo_network.py
```
This will generate two program desc files:
- startup_program: used to init all parameters
- main_program: main logic of the network
### step 3. build demo_trainer and run it.
```
# Make a build dir at the same dir of this README.md document.
# The demo dir can be put anywhere.
mkdir build
cd build
# WITH_MKL=ON|OFF
# WITH_MKLDNN=ON|OFF
PADDLE_LIB=/paddle/lib/dir
# PADDLE_LIB is the same with FLUID_INSTALL_DIR when building the lib
cmake .. -DPADDLE_LIB=$PADDLE_LIB \
-DWITH_MKLDNN=OFF \
-DWITH_MKL=OFF
make
# copy startup_program and main_program to this dir
cp ../startup_program .
cp ../main_program .
# run demo cpp trainer
./demo_trainer
```
The output will be:
```
step: 0 loss: 1069.02
step: 1 loss: 1069.02
step: 2 loss: 1069.02
....
```
paddle/fluid/train/demo/demo_network.py 0 → 100644
浏览文件 @ adbf97b4
# 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 paddle.fluid as fluid
import paddle.fluid.framework as framework
def train_network(with_optimize):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
if with_optimize:
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.00001)
sgd_optimizer.minimize(avg_cost)
else:
fluid.backward.append_backward(avg_cost)
def save_program_desc(network_func):
startup_program = framework.Program()
train_program = framework.Program()
with framework.program_guard(train_program, startup_program):
network_func(with_optimize=False)
with open("startup_program", "w") as f:
f.write(startup_program.desc.serialize_to_string())
with open("main_program", "w") as f:
f.write(train_program.desc.serialize_to_string())
save_program_desc(train_network)
paddle/fluid/train/demo/demo_trainer.cc 0 → 100644
浏览文件 @ adbf97b4
// 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.
#include <fstream>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/init.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/place.h"
namespace paddle {
namespace train {
void ReadBinaryFile(const std::string& filename, std::string* contents) {
std::ifstream fin(filename, std::ios::in | std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", filename);
fin.seekg(0, std::ios::end);
contents->clear();
contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size());
fin.close();
}
std::unique_ptr<paddle::framework::ProgramDesc> Load(
paddle::framework::Executor* executor, const std::string& model_filename) {
VLOG(3) << "loading model from " << model_filename;
std::string program_desc_str;
ReadBinaryFile(model_filename, &program_desc_str);
std::unique_ptr<paddle::framework::ProgramDesc> main_program(
new paddle::framework::ProgramDesc(program_desc_str));
return main_program;
}
} // namespace train
} // namespace paddle
int main() {
paddle::framework::InitDevices(false);
const auto cpu_place = paddle::platform::CPUPlace();
paddle::framework::Executor executor(cpu_place);
paddle::framework::Scope scope;
auto startup_program = paddle::train::Load(&executor, "startup_program");
auto train_program = paddle::train::Load(&executor, "main_program");
std::string loss_name = "";
for (auto op_desc : train_program->Block(0).AllOps()) {
if (op_desc->Type() == "mean") {
loss_name = op_desc->Output("Out")[0];
break;
}
}
PADDLE_ENFORCE_NE(loss_name, "", "loss not found");
// init all parameters
executor.Run(*startup_program.get(), &scope, 0);
// prepare data
auto x_var = scope.Var("x");
auto x_tensor = x_var->GetMutable<paddle::framework::LoDTensor>();
x_tensor->Resize({2, 13});
auto x_data = x_tensor->mutable_data<float>(cpu_place);
for (int i = 0; i < 2 * 13; ++i) {
x_data[i] = static_cast<float>(i);
}
auto y_var = scope.Var("y");
auto y_tensor = y_var->GetMutable<paddle::framework::LoDTensor>();
y_tensor->Resize({2, 1});
auto y_data = y_tensor->mutable_data<float>(cpu_place);
for (int i = 0; i < 2 * 1; ++i) {
y_data[i] = static_cast<float>(i);
}
auto loss_var = scope.Var(loss_name);
for (int i = 0; i < 10; ++i) {
executor.Run(*train_program.get(), &scope, 0, false, true);
std::cout << "step: " << i << " loss: "
<< loss_var->Get<paddle::framework::LoDTensor>().data<float>()[0]
<< std::endl;
}
return 0;
}
paddle/scripts/docker/build.sh
浏览文件 @ adbf97b4
......@@ -198,7 +198,7 @@ EOF
# run paddle version to install python packages first
RUN apt-get update &&\
${NCCL_DEPS}\
apt-get install -y wget python-pip dmidecode python-tk && pip install -U pip==9.0.3 && \
apt-get install -y wget python-pip dmidecode python-tk && easy_install -U pip && \
pip install /*.whl; apt-get install -f -y && \
apt-get clean -y && \
rm -f /*.whl && \
......
paddle/scripts/paddle_build.sh
浏览文件 @ adbf97b4
......@@ -20,19 +20,15 @@
#=================================================
function print_usage() {
RED='\033[0;31m'
BLUE='\033[0;34m'
BOLD='\033[1m'
NONE='\033[0m'
echo -e "\n${RED}Usage${NONE}:
${BOLD}$0${NONE} [OPTION]"
${BOLD}${SCRIPT_NAME}${NONE} [OPTION]"
echo -e "\n${RED}Options${NONE}:
${BLUE}build${NONE}: run build for x86 platform
${BLUE}build_android${NONE}: run build for android platform
${BLUE}build_ios${NONE}: run build for ios platform
${BLUE}test${NONE}: run all unit tests
${BLUE}single_test${NONE}: run a single unit test
${BLUE}bind_test${NONE}: parallel tests bind to different GPU
${BLUE}doc${NONE}: generate paddle documents
${BLUE}html${NONE}: convert C++ source code into HTML
......@@ -45,7 +41,15 @@ function print_usage() {
}
function init() {
RED='\033[0;31m'
BLUE='\033[0;34m'
BOLD='\033[1m'
NONE='\033[0m'
PADDLE_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}")/../../" && pwd )"
if [ -z "${SCRIPT_NAME}" ]; then
SCRIPT_NAME=$0
fi
}
function cmake_gen() {
......@@ -91,7 +95,6 @@ function cmake_gen() {
-DWITH_AVX=${WITH_AVX:-OFF}
-DWITH_GOLANG=${WITH_GOLANG:-OFF}
-DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All}
-DWITH_SWIG_PY=ON
-DWITH_C_API=${WITH_C_API:-OFF}
-DWITH_PYTHON=${WITH_PYTHON:-ON}
-DWITH_SWIG_PY=${WITH_SWIG_PY:-ON}
......@@ -309,6 +312,25 @@ EOF
fi
}
function single_test() {
TEST_NAME=$1
if [ -z "${TEST_NAME}" ]; then
echo -e "${RED}Usage:${NONE}"
echo -e "${BOLD}${SCRIPT_NAME}${NONE} ${BLUE}single_test${NONE} [test_name]"
exit 1
fi
mkdir -p ${PADDLE_ROOT}/build
cd ${PADDLE_ROOT}/build
if [ ${WITH_TESTING:-ON} == "ON" ] ; then
cat <<EOF
========================================
Running ${TEST_NAME} ...
========================================
EOF
ctest --output-on-failure -R ${TEST_NAME}
fi
}
function bind_test() {
# the number of process to run tests
NUM_PROC=6
......@@ -383,17 +405,19 @@ EOF
function gen_dockerfile() {
# Set BASE_IMAGE according to env variables
CUDA_MAJOR="$(echo $CUDA_VERSION | cut -d '.' -f 1).$(echo $CUDA_VERSION | cut -d '.' -f 2)"
CUDNN_MAJOR=$(echo $CUDNN_VERSION | cut -d '.' -f 1)
if [[ ${WITH_GPU} == "ON" ]]; then
BASE_IMAGE="nvidia/cuda:8.0-cudnn5-runtime-ubuntu16.04"
BASE_IMAGE="nvidia/cuda:${CUDA_MAJOR}-cudnn${CUDNN_MAJOR}-runtime-ubuntu16.04"
else
BASE_IMAGE="ubuntu:16.04"
BASE_IMAGE="ubuntu:16.04"
fi
DOCKERFILE_GPU_ENV=""
DOCKERFILE_CUDNN_DSO=""
if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then
DOCKERFILE_GPU_ENV="ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:\${LD_LIBRARY_PATH}"
DOCKERFILE_CUDNN_DSO="RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.5 /usr/lib/x86_64-linux-gnu/libcudnn.so"
DOCKERFILE_CUDNN_DSO="RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.${CUDNN_MAJOR} /usr/lib/x86_64-linux-gnu/libcudnn.so"
fi
cat <<EOF
......@@ -409,7 +433,7 @@ EOF
EOF
if [[ ${WITH_GPU} == "ON" ]]; then
NCCL_DEPS="apt-get install -y libnccl2=2.1.2-1+cuda8.0 libnccl-dev=2.1.2-1+cuda8.0 &&"
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.1.2-1+cuda${CUDA_MAJOR} libnccl-dev=2.1.2-1+cuda${CUDA_MAJOR} &&"
else
NCCL_DEPS=""
fi
......@@ -427,7 +451,7 @@ EOF
# run paddle version to install python packages first
RUN apt-get update &&\
${NCCL_DEPS}\
apt-get install -y wget python-pip dmidecode python-tk && pip install -U pip==9.0.3 && \
apt-get install -y wget python-pip dmidecode python-tk && easy_install -U pip && \
pip install /*.whl; apt-get install -f -y && \
apt-get clean -y && \
rm -f /*.whl && \
......@@ -468,8 +492,9 @@ function gen_fluid_inference_lib() {
Deploying fluid inference library ...
========================================
EOF
make inference_lib_dist
fi
make -j `nproc` inference_lib_dist
tar -cf ${PADDLE_ROOT}/build/fluid.tgz ${PADDLE_ROOT}/build/fluid_install_dir
fi
}
function main() {
......@@ -480,6 +505,7 @@ function main() {
build)
cmake_gen ${PYTHON_ABI:-""}
build
gen_dockerfile
;;
build_android)
build_android
......@@ -490,6 +516,9 @@ function main() {
test)
run_test
;;
single_test)
single_test $2
;;
bind_test)
bind_test
;;
......
paddle/scripts/paddle_docker_build.sh
浏览文件 @ adbf97b4
......@@ -14,20 +14,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
function container_running() {
name=$1
docker ps -a --format "{{.Names}}" | grep "${name}" > /dev/null
return $?
}
function start_build_docker() {
docker pull $IMG
if container_running "${CONTAINER_ID}"; then
docker stop "${CONTAINER_ID}" 1>/dev/null
docker rm -f "${CONTAINER_ID}" 1>/dev/null
fi
apt_mirror='s#http://archive.ubuntu.com/ubuntu#mirror://mirrors.ubuntu.com/mirrors.txt#g'
DOCKER_ENV=$(cat <<EOL
-e FLAGS_fraction_of_gpu_memory_to_use=0.15 \
......@@ -61,8 +50,8 @@ EOL
fi
set -ex
${DOCKER_CMD} run -it \
--name $CONTAINER_ID \
${DOCKER_ENV} \
-e SCRIPT_NAME=$0 \
-v $PADDLE_ROOT:/paddle \
-v ${HOME}/.ccache:/root/.ccache \
-w /paddle \
......@@ -74,10 +63,8 @@ EOL
function main() {
DOCKER_REPO="paddlepaddle/paddle"
VERSION="latest-dev"
CONTAINER_ID="${USER}_paddle_dev"
PADDLE_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}")/../../" && pwd )"
if [ "$1" == "build_android" ]; then
CONTAINER_ID="${USER}_paddle_dev_android"
VERSION="latest-dev-android"
fi
IMG=${DOCKER_REPO}:${VERSION}
......
patches/mkldnn.hpp 已删除 100644 → 0
浏览文件 @ 7a557040
此差异已折叠。
python/paddle/fluid/data_feeder.py
浏览文件 @ adbf97b4
......@@ -54,9 +54,9 @@ class DataToLoDTensorConverter(object):
self.data.append(data)
else:
cur_lod_len = len(data)
lod[-1].append(lod[-1][-1] + cur_lod_len)
lod[0].append(lod[0][-1] + cur_lod_len)
for each_data in data:
self._feed_impl_(each_data, lod[:-1], lod_level - 1)
self._feed_impl_(each_data, lod[1:], lod_level - 1)
def done(self):
arr = numpy.array(self.data, dtype=self.dtype).reshape(self.shape)
......
python/paddle/fluid/evaluator.py
浏览文件 @ adbf97b4
......@@ -273,10 +273,11 @@ class DetectionMAP(Evaluator):
[M, 6]. The layout is [label, confidence, xmin, ymin, xmax, ymax].
gt_label (Variable): The ground truth label index, which is a LoDTensor
with shape [N, 1].
gt_difficult (Variable): Whether this ground truth is a difficult
bounding box (bbox), which is a LoDTensor [N, 1].
gt_box (Variable): The ground truth bounding box (bbox), which is a
LoDTensor with shape [N, 6]. The layout is [xmin, ymin, xmax, ymax].
gt_difficult (Variable|None): Whether this ground truth is a difficult
bounding bbox, which can be a LoDTensor [N, 1] or not set. If None,
it means all the ground truth labels are not difficult bbox.
class_num (int): The class number.
background_label (int): The index of background label, the background
label will be ignored. If set to -1, then all categories will be
......@@ -284,7 +285,8 @@ class DetectionMAP(Evaluator):
overlap_threshold (float): The threshold for deciding true/false
positive, 0.5 by defalut.
evaluate_difficult (bool): Whether to consider difficult ground truth
for evaluation, True by defalut.
for evaluation, True by defalut. This argument does not work when
gt_difficult is None.
ap_version (string): The average precision calculation ways, it must be
'integral' or '11point'. Please check
https://sanchom.wordpress.com/tag/average-precision/ for details.
......@@ -295,7 +297,7 @@ class DetectionMAP(Evaluator):
exe = fluid.executor(place)
map_evaluator = fluid.Evaluator.DetectionMAP(input,
gt_label, gt_difficult, gt_box)
gt_label, gt_box, gt_difficult)
cur_map, accum_map = map_evaluator.get_map_var()
fetch = [cost, cur_map, accum_map]
for epoch in PASS_NUM:
......@@ -313,8 +315,8 @@ class DetectionMAP(Evaluator):
input,
gt_label,
gt_box,
gt_difficult,
class_num,
gt_difficult=None,
class_num=None,
background_label=0,
overlap_threshold=0.5,
evaluate_difficult=True,
......@@ -322,8 +324,11 @@ class DetectionMAP(Evaluator):
super(DetectionMAP, self).__init__("map_eval")
gt_label = layers.cast(x=gt_label, dtype=gt_box.dtype)
gt_difficult = layers.cast(x=gt_difficult, dtype=gt_box.dtype)
label = layers.concat([gt_label, gt_difficult, gt_box], axis=1)
if gt_difficult:
gt_difficult = layers.cast(x=gt_difficult, dtype=gt_box.dtype)
label = layers.concat([gt_label, gt_difficult, gt_box], axis=1)
else:
label = layers.concat([gt_label, gt_box], axis=1)
# calculate mean average precision (mAP) of current mini-batch
map = layers.detection_map(
......
python/paddle/fluid/framework.py
浏览文件 @ adbf97b4
......@@ -72,6 +72,8 @@ def convert_np_dtype_to_dtype_(np_dtype):
return core.VarDesc.VarType.INT64
elif dtype == np.bool:
return core.VarDesc.VarType.BOOL
elif dtype == np.uint8:
return core.VarDesc.VarType.UINT8
else:
raise ValueError("Not supported numpy dtype " + str(dtype))
......
python/paddle/fluid/inferencer.py
浏览文件 @ adbf97b4
......@@ -12,11 +12,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import core
import executor
import framework
import io
import parallel_executor
import unique_name
from trainer import check_and_get_place
......@@ -24,40 +27,53 @@ __all__ = ['Inferencer', ]
class Inferencer(object):
def __init__(self, infer_func, param_path, place=None):
def __init__(self, infer_func, param_path, place=None, parallel=False):
"""
:param infer_func: a function that will return predict Variable
:param param_path: the path where the inference model is saved by fluid.io.save_params
:param place: place to do the inference
:param parallel: use parallel_executor to run the inference, it will use multi CPU/GPU.
"""
self.param_path = param_path
self.scope = core.Scope()
self.parallel = parallel
self.place = check_and_get_place(place)
self.inference_program = framework.Program()
with framework.program_guard(self.inference_program):
with unique_name.guard():
self.predict_var = infer_func()
self.exe = executor.Executor(check_and_get_place(place))
with executor.scope_guard(self.scope):
with self._prog_and_scope_guard():
# load params from param_path into scope
io.load_params(self.exe, param_path, self.inference_program)
io.load_params(executor.Executor(self.place), param_path)
if parallel:
with self._prog_and_scope_guard():
self.exe = parallel_executor.ParallelExecutor(
use_cuda=isinstance(self.place, core.CUDAPlace),
loss_name=self.predict_var.name)
else:
self.exe = executor.Executor(self.place)
def infer(self, inputs, return_numpy=True):
def infer(self, inputs):
"""
:param inputs: a map of {"input_name": input_var} that will be feed into the inference program
to get the predict value
:param return_numpy: if return numpy value for row tensor
:return: the predict value of the inference model
"""
if not isinstance(inputs, dict):
raise ValueError(
"inputs should be a map of {'input_name': input_var}")
with executor.scope_guard(self.scope):
results = self.exe.run(self.inference_program,
feed=inputs,
fetch_list=[self.predict_var],
return_numpy=return_numpy)
with self._prog_and_scope_guard():
results = self.exe.run(feed=inputs,
fetch_list=[self.predict_var.name])
return results
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(main_program=self.inference_program):
with executor.scope_guard(self.scope):
yield
python/paddle/fluid/layers/control_flow.py
浏览文件 @ adbf97b4
......@@ -49,6 +49,7 @@ __all__ = [
'reorder_lod_tensor_by_rank',
'ParallelDo',
'Print',
'is_empty',
]
......@@ -1097,7 +1098,7 @@ class ConditionalBlock(object):
input_set = set([ipt.name for ipt in self.inputs])
param_list = [
parent_block.var(each_name) for each_name in params
parent_block.var_recursive(each_name) for each_name in params
if each_name not in input_set
]
......@@ -1562,3 +1563,40 @@ def reorder_lod_tensor_by_rank(x, rank_table):
'RankTable': [rank_table]},
outputs={'Out': [out]})
return out
def is_empty(x, cond=None, **ignored):
"""
**Is Empty**
This layer returns the truth value of whether the variable is empty.
Args:
x(Variable): Operand of *is_empty*
cond(Variable|None): Optional output variable to store the result
of *is_empty*
Returns:
Variable: The tensor variable storing the output of *is_empty*.
Raises:
TypeError: If input cond is not a variable, or cond's dtype is
not bool
Examples:
.. code-block:: python
less = fluid.layers.is_empty(x=input)
"""
helper = LayerHelper("is_empty", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond.stop_gradient = True
elif not isinstance(cond, Variable):
raise TypeError("cond takes a variable")
elif cond.dtype != 'bool':
raise TypeError("The data type of cond must be bool")
helper.append_op(
type='is_empty', inputs={'X': [x]}, outputs={'Out': [cond]})
return cond
python/paddle/fluid/layers/detection.py
浏览文件 @ adbf97b4
......@@ -23,6 +23,7 @@ import nn
import math
__all__ = [
'prior_box',
'multi_box_head',
'bipartite_match',
'target_assign',
......@@ -564,6 +565,115 @@ def ssd_loss(location,
return loss
def prior_box(input,
image,
min_sizes,
max_sizes=None,
aspect_ratios=[1.],
variance=[0.1, 0.1, 0.2, 0.2],
flip=False,
clip=False,
steps=[0.0, 0.0],
offset=0.5,
name=None):
"""
**Prior box operator**
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the input produce N prior boxes, N is determined by
the count of min_sizes, max_sizes and aspect_ratios, The size of the
box is in range(min_size, max_size) interval, which is generated in
sequence according to the aspect_ratios.
Args:
input(Variable): The Input Variables, the format is NCHW.
image(Variable): The input image data of PriorBoxOp,
the layout is NCHW.
min_sizes(list|tuple|float value): min sizes of generated prior boxes.
max_sizes(list|tuple|None): max sizes of generated prior boxes.
Default: None.
aspect_ratios(list|tuple|float value): the aspect ratios of generated
prior boxes. Default: [1.].
variance(list|tuple): the variances to be encoded in prior boxes.
Default:[0.1, 0.1, 0.2, 0.2].
flip(bool): Whether to flip aspect ratios. Default:False.
clip(bool): Whether to clip out-of-boundary boxes. Default: False.
step(list|turple): Prior boxes step across width and height, If
step[0] == 0.0/step[1] == 0.0, the prior boxes step across
height/weight of the input will be automatically calculated.
Default: [0., 0.]
offset(float): Prior boxes center offset. Default: 0.5
name(str): Name of the prior box op. Default: None.
Returns:
boxes(Variable): the output prior boxes of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input,
num_priors is the total
box count of each position of input.
Variances(Variable): the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_priors is the total
box count of each position of input
Examples:
.. code-block:: python
box, var = prior_box(
input=conv1,
image=images,
min_sizes=[100.],
flip=True,
clip=True)
"""
helper = LayerHelper("prior_box", **locals())
dtype = helper.input_dtype()
def _is_list_or_tuple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
if not _is_list_or_tuple_(min_sizes):
min_sizes = [min_sizes]
if not _is_list_or_tuple_(aspect_ratios):
aspect_ratios = [aspect_ratios]
if not (_is_list_or_tuple_(steps) and len(steps) == 2):
raise ValueError('steps should be a list or tuple ',
'with length 2, (step_width, step_height).')
min_sizes = list(map(float, min_sizes))
aspect_ratios = list(map(float, aspect_ratios))
steps = list(map(float, steps))
attrs = {
'min_sizes': min_sizes,
'aspect_ratios': aspect_ratios,
'variances': variance,
'flip': flip,
'clip': clip,
'step_w': steps[0],
'step_h': steps[1],
'offset': offset
}
if max_sizes is not None and len(max_sizes) > 0 and max_sizes[0] > 0:
if not _is_list_or_tuple_(max_sizes):
max_sizes = [max_sizes]
attrs['max_sizes'] = max_sizes
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=attrs, )
box.stop_gradient = True
var.stop_gradient = True
return box, var
def multi_box_head(inputs,
image,
base_size,
......@@ -660,47 +770,6 @@ def multi_box_head(inputs,
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()
attrs = {
'min_sizes': min_sizes,
'aspect_ratios': aspect_ratios,
'variances': variance,
'flip': flip,
'clip': clip,
'step_w': step_w,
'step_h': step_h,
'offset': offset
}
if len(max_sizes) > 0 and max_sizes[0] > 0:
attrs['max_sizes'] = max_sizes
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=attrs, )
box.stop_gradient = True
var.stop_gradient = True
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 "
......@@ -777,11 +846,10 @@ def multi_box_head(inputs,
aspect_ratio = aspect_ratios[i]
if not _is_list_or_tuple_(aspect_ratio):
aspect_ratio = [aspect_ratio]
step = [step_w[i] if step_w else 0.0, step_h[i] if step_w else 0.0]
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, var = prior_box(input, image, min_size, max_size, aspect_ratio,
variance, flip, clip, step, offset)
box_results.append(box)
var_results.append(var)
......
python/paddle/fluid/layers/nn.py
浏览文件 @ adbf97b4
......@@ -80,6 +80,7 @@ __all__ = [
'pad',
'label_smooth',
'roi_pool',
'dice_loss',
]
......@@ -699,8 +700,8 @@ def dynamic_gru(input,
def gru_unit(input,
hidden,
size,
weight=None,
bias=None,
param_attr=None,
bias_attr=None,
activation='tanh',
gate_activation='sigmoid'):
"""
......@@ -731,8 +732,8 @@ def gru_unit(input,
input (Variable): The fc transformed input value of current step.
hidden (Variable): The hidden value of lstm unit from previous step.
size (integer): The input dimension value.
weight (ParamAttr): The weight parameters for gru unit. Default: None
bias (ParamAttr): The bias parameters for gru unit. Default: None
param_attr (ParamAttr): The weight parameters for gru unit. Default: None
bias_attr (ParamAttr): The bias parameters for gru unit. Default: None
activation (string): The activation type for cell (actNode).
Default: 'tanh'
gate_activation (string): The activation type for gates (actGate).
......@@ -764,34 +765,31 @@ def gru_unit(input,
size = size / 3
# create weight
if weight is None:
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
gate = helper.create_tmp_variable(dtype)
reset_hidden_pre = helper.create_tmp_variable(dtype)
updated_hidden = helper.create_tmp_variable(dtype)
inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
# create bias
if bias is None:
if helper.bias_attr:
bias_size = [1, 3 * size]
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
gate = helper.create_tmp_variable(dtype)
reset_hidden_pre = helper.create_tmp_variable(dtype)
updated_hidden = helper.create_tmp_variable(dtype)
inputs['Bias'] = bias
helper.append_op(
type='gru_unit',
inputs={'Input': input,
'HiddenPrev': hidden,
'Weight': weight},
inputs=inputs,
outputs={
'Gate': gate,
'ResetHiddenPrev': reset_hidden_pre,
'Hidden': updated_hidden,
},
attrs={
'activation': 0,
'gate_activation': 1,
'activation': 2, # tanh
'gate_activation': 1, # sigmoid
})
return updated_hidden, reset_hidden_pre, gate
......@@ -1329,6 +1327,8 @@ def sequence_pool(input, pool_type):
sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
last : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
first : out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
......@@ -1348,6 +1348,8 @@ def sequence_pool(input, pool_type):
sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
"""
helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype()
......@@ -3271,35 +3273,35 @@ 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.
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
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].
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.
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
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
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
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')
label = fluid.layers.data(name='label', shape=[100], dtype='float32')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.smooth_l1(x=fc, y=label)
"""
......@@ -3777,13 +3779,13 @@ def label_smooth(label,
def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
"""
Region of interest pooling (also known as RoI pooling) is to perform
Region of interest pooling (also known as RoI pooling) is to perform
is to perform max pooling on inputs of nonuniform sizes to obtain
fixed-size feature maps (e.g. 7*7).
The operator has three steps:
1. Dividing each region proposal into equal-sized sections with
the pooled_width and pooled_height
2. Finding the largest value in each section
The operator has three steps:
1. Dividing each region proposal into equal-sized sections with
the pooled_width and pooled_height
2. Finding the largest value in each section
3. Copying these max values to the output buffer
Args:
......@@ -3791,8 +3793,8 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
rois (Variable): ROIs (Regions of Interest) to pool over. It should
be a 2-D one level LoTensor of shape [num_rois, 4].
The layout is [x1, y1, x2, y2], where (x1, y1)
is the top left coordinates, and (x2, y2) is the
bottom right coordinates. The num_rois is the
is the top left coordinates, and (x2, y2) is the
bottom right coordinates. The num_rois is the
total number of ROIs in this batch data.
pooled_height (integer): The pooled output height. Default: 1
pooled_width (integer): The pooled output width. Default: 1
......@@ -3801,11 +3803,11 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
to the scale used when pooling. Default: 1.0
Returns:
pool_out (Variable): The output is a 4-D tensor of the shape
pool_out (Variable): The output is a 4-D tensor of the shape
(num_rois, channels, pooled_h, pooled_w).
Examples:
pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
"""
helper = LayerHelper('roi_pool', **locals())
dtype = helper.input_dtype()
......@@ -3823,3 +3825,43 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
"spatial_scale": spatial_scale
})
return pool_out
def dice_loss(input, label, epsilon=0.00001):
"""
**Dice loss Layer**
Dice loss for comparing the similarity of two batch of data,
usually is used for binary image segmentation i.e. labels are binary.
The dice loss can be defined as below equation:
.. math::
dice\_loss &= 1 - \\frac{2 * intersection\_area}{total\_area} \\\\
&= \\frac{(total\_area - intersection\_area) - intersection\_area}{total\_area} \\\\
&= \\frac{(union\_area - intersection\_area)}{total\_area}
Args:
input (Variable): The predictions with rank>=2. The first dimension is batch size,
and the last dimension is class number.
label (Variable): The groud truth with the same rank with input. The first dimension
is batch size, and the last dimension is 1.
epsilon (float): The epsilon will be added to the numerator and denominator.
If both input and label are empty, it makes sure dice is 1.
Default: 0.00001
Returns:
dice_loss (Variable): The dice loss with shape [1].
Examples:
predictions = fluid.layers.softmax(x)
loss = fluid.layers.dice_loss(input=predictions, label=label, 2)
"""
label = one_hot(label, depth=input.shape[-1])
reduce_dim = range(1, len(input.shape))
inse = reduce_sum(input * label, dim=reduce_dim)
dice_denominator = reduce_sum(
input, dim=reduce_dim) + reduce_sum(
label, dim=reduce_dim)
dice_score = 1 - inse * 2 / (dice_denominator + epsilon)
return reduce_mean(dice_score)
python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt
浏览文件 @ adbf97b4
......@@ -8,3 +8,5 @@ endforeach()
add_subdirectory(fit_a_line)
add_subdirectory(recognize_digits)
add_subdirectory(image_classification)
add_subdirectory(understand_sentiment)
python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py
浏览文件 @ adbf97b4
......@@ -57,22 +57,20 @@ def train(use_cuda, train_program, save_dirname):
optimizer=fluid.optimizer.SGD(learning_rate=0.001))
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
test_metrics = trainer.test(
reader=test_reader, feed_order=['x', 'y'])
print test_metrics
'''
...
['25.768919467926025']
['15.343549569447836']
...
'''
if float(test_metrics[0]) < 20.0:
if isinstance(event, fluid.EndStepEvent):
if event.step == 10:
test_metrics = trainer.test(
reader=test_reader, feed_order=['x', 'y'])
print test_metrics
'''
...
['25.768919467926025']
['15.343549569447836']
...
'''
if save_dirname is not None:
trainer.save_params(save_dirname)
return
trainer.stop()
trainer.train(
reader=train_reader,
......@@ -94,7 +92,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
print("infer results: ", results[0])
print("infer results: ", numpy.array(results[0]))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/image_classification/CMakeLists.txt 0 → 100644
浏览文件 @ adbf97b4
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
python/paddle/fluid/tests/book/high-level-api/image_classification/cifar10_small_test_set.py 0 → 100644
浏览文件 @ adbf97b4
# Copyright (c) 2016 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.
"""
CIFAR dataset.
This module will download dataset from
https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into
paddle reader creators.
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test
images.
The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes
containing 600 images each. There are 500 training images and 100 testing
images per class.
"""
import cPickle
import itertools
import numpy
import paddle.v2.dataset.common
import tarfile
__all__ = ['train10']
URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/'
CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
def reader_creator(filename, sub_name, batch_size=None):
def read_batch(batch):
data = batch['data']
labels = batch.get('labels', batch.get('fine_labels', None))
assert labels is not None
for sample, label in itertools.izip(data, labels):
yield (sample / 255.0).astype(numpy.float32), int(label)
def reader():
with tarfile.open(filename, mode='r') as f:
names = (each_item.name for each_item in f
if sub_name in each_item.name)
batch_count = 0
for name in names:
batch = cPickle.load(f.extractfile(name))
for item in read_batch(batch):
if isinstance(batch_size, int) and batch_count > batch_size:
break
batch_count += 1
yield item
return reader
def train10(batch_size=None):
"""
CIFAR-10 training set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Training reader creator
:rtype: callable
"""
return reader_creator(
paddle.v2.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
'data_batch',
batch_size=batch_size)
python/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_resnet.py → python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py
浏览文件 @ adbf97b4
......@@ -17,6 +17,7 @@ from __future__ import print_function
import paddle
import paddle.fluid as fluid
import numpy
import cifar10_small_test_set
def resnet_cifar10(input, depth=32):
......@@ -81,46 +82,50 @@ def train_network():
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label)
return avg_cost, accuracy
return [avg_cost, accuracy]
def train(use_cuda, save_path):
def train(use_cuda, train_program, save_dirname):
BATCH_SIZE = 128
EPOCH_NUM = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=128 * 10),
cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
def event_handler(event):
if isinstance(event, fluid.EndIteration):
if (event.batch_id % 10) == 0:
avg_cost, accuracy = trainer.test(reader=test_reader)
if isinstance(event, fluid.EndStepEvent):
avg_cost, accuracy = trainer.test(
reader=test_reader, feed_order=['pixel', 'label'])
print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
event.batch_id + 1, avg_cost, accuracy))
print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
if accuracy > 0.01: # Low threshold for speeding up CI
trainer.params.save(save_path)
return
if accuracy > 0.01: # Low threshold for speeding up CI
if save_dirname is not None:
trainer.save_params(save_dirname)
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
train_network,
train_func=train_program,
optimizer=fluid.optimizer.Adam(learning_rate=0.001),
place=place,
event_handler=event_handler)
trainer.train(train_reader, EPOCH_NUM, event_handler=event_handler)
place=place)
trainer.train(
reader=train_reader,
num_epochs=EPOCH_NUM,
event_handler=event_handler,
feed_order=['pixel', 'label'])
def infer(use_cuda, save_path):
params = fluid.Params(save_path)
def infer(use_cuda, inference_program, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(inference_network, params, place=place)
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=save_dirname, place=place)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range
......@@ -135,8 +140,14 @@ def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "image_classification_resnet.inference.model"
train(use_cuda, save_path)
infer(use_cuda, save_path)
train(
use_cuda=use_cuda, train_program=train_network, save_dirname=save_path)
infer(
use_cuda=use_cuda,
inference_program=inference_network,
save_dirname=save_path)
if __name__ == '__main__':
......
python/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_vgg.py → python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py
浏览文件 @ adbf97b4
......@@ -17,6 +17,7 @@ from __future__ import print_function
import paddle
import paddle.fluid as fluid
import numpy
import cifar10_small_test_set
def vgg16_bn_drop(input):
......@@ -60,46 +61,48 @@ def train_network():
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label)
return avg_cost, accuracy
return [avg_cost, accuracy]
def train(use_cuda, save_path):
def train(use_cuda, train_program, save_dirname):
BATCH_SIZE = 128
EPOCH_NUM = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=128 * 10),
cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
def event_handler(event):
if isinstance(event, fluid.EndIteration):
if (event.batch_id % 10) == 0:
avg_cost, accuracy = trainer.test(reader=test_reader)
if isinstance(event, fluid.EndStepEvent):
avg_cost, accuracy = trainer.test(
reader=test_reader, feed_order=['pixel', 'label'])
print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
event.batch_id + 1, avg_cost, accuracy))
print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
if accuracy > 0.01: # Low threshold for speeding up CI
trainer.params.save(save_path)
return
if accuracy > 0.01: # Low threshold for speeding up CI
if save_dirname is not None:
trainer.save_params(save_dirname)
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
train_network,
optimizer=fluid.optimizer.Adam(learning_rate=0.001),
train_func=train_program,
place=place,
event_handler=event_handler)
trainer.train(train_reader, EPOCH_NUM, event_handler=event_handler)
optimizer=fluid.optimizer.Adam(learning_rate=0.001))
trainer.train(
reader=train_reader,
num_epochs=1,
event_handler=event_handler,
feed_order=['pixel', 'label'])
def infer(use_cuda, save_path):
params = fluid.Params(save_path)
def infer(use_cuda, inference_program, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(inference_network, params, place=place)
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=save_dirname, place=place)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range
......@@ -114,8 +117,14 @@ def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "image_classification_vgg.inference.model"
train(use_cuda, save_path)
infer(use_cuda, save_path)
train(
use_cuda=use_cuda, train_program=train_network, save_dirname=save_path)
infer(
use_cuda=use_cuda,
inference_program=inference_network,
save_dirname=save_path)
if __name__ == '__main__':
......
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py
浏览文件 @ adbf97b4
......@@ -62,31 +62,31 @@ def train(use_cuda, train_program, save_dirname):
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
trainer = fluid.Trainer(
train_func=train_program, place=place, optimizer=optimizer)
train_func=train_program,
place=place,
optimizer=optimizer,
parallel=True)
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
test_metrics = trainer.test(
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['img', 'label'])
avg_cost_set = test_metrics[0]
acc_set = test_metrics[1]
# get test acc and loss
acc = numpy.array(acc_set).mean()
avg_cost = numpy.array(avg_cost_set).mean()
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if float(acc) > 0.2: # Smaller value to increase CI speed
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, float(avg_cost), float(acc)))
if math.isnan(float(avg_cost)):
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(numpy.array, event.metrics)))
train_reader = paddle.batch(
paddle.reader.shuffle(
......@@ -112,7 +112,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
results = inferencer.infer({'img': tensor_img})
print("infer results: ", results[0])
print("infer results: ", numpy.array(results[0]))
def main(use_cuda):
......@@ -131,4 +131,4 @@ def main(use_cuda):
if __name__ == '__main__':
# for use_cuda in (False, True):
main(use_cuda=False)
main(use_cuda=True)
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py
浏览文件 @ adbf97b4
......@@ -55,24 +55,18 @@ def train(use_cuda, train_program, save_dirname):
if isinstance(event, fluid.EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
test_metrics = trainer.test(
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['img', 'label'])
avg_cost_set = test_metrics[0]
acc_set = test_metrics[1]
# get test acc and loss
acc = numpy.array(acc_set).mean()
avg_cost = numpy.array(avg_cost_set).mean()
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if float(acc) > 0.2: # Smaller value to increase CI speed
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, float(avg_cost), float(acc)))
if math.isnan(float(avg_cost)):
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
train_reader = paddle.batch(
......@@ -99,7 +93,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
results = inferencer.infer({'img': tensor_img})
print("infer results: ", results[0])
print("infer results: ", numpy.array(results[0]))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/CMakeLists.txt 0 → 100644
浏览文件 @ adbf97b4
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py 0 → 100644
浏览文件 @ adbf97b4
# 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from functools import partial
import numpy as np
CLASS_DIM = 2
EMB_DIM = 128
HID_DIM = 512
BATCH_SIZE = 128
def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
return prediction
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
return net
def train_program(word_dict):
prediction = inference_program(word_dict)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
return [avg_cost, accuracy]
def train(use_cuda, train_program, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
optimizer = fluid.optimizer.Adagrad(learning_rate=0.002)
word_dict = paddle.dataset.imdb.word_dict()
trainer = fluid.Trainer(
train_func=partial(train_program, word_dict),
place=place,
optimizer=optimizer)
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
trainer.stop()
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(np.array, event.metrics)))
if event.step == 1: # Run 2 iterations to speed CI
trainer.save_params(save_dirname)
trainer.stop()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['words', 'label'])
def infer(use_cuda, inference_program, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
inferencer = fluid.Inferencer(
infer_func=partial(inference_program, word_dict),
param_path=save_dirname,
place=place)
def create_random_lodtensor(lod, place, low, high):
data = np.random.random_integers(low, high,
[lod[-1], 1]).astype("int64")
res = fluid.LoDTensor()
res.set(data, place)
res.set_lod([lod])
return res
lod = [0, 4, 10]
tensor_words = create_random_lodtensor(
lod, place, low=0, high=len(word_dict) - 1)
results = inferencer.infer({'words': tensor_words})
print("infer results: ", results)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "understand_sentiment_conv.inference.model"
train(use_cuda, train_program, save_path)
infer(use_cuda, inference_program, save_path)
if __name__ == '__main__':
for use_cuda in (False, True):
main(use_cuda=use_cuda)
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py 0 → 100644
浏览文件 @ adbf97b4
# 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from functools import partial
import numpy as np
CLASS_DIM = 2
EMB_DIM = 128
BATCH_SIZE = 128
LSTM_SIZE = 128
def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size):
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
sentence = fluid.layers.fc(input=emb, size=lstm_size, act='tanh')
rnn = fluid.layers.DynamicRNN()
with rnn.block():
word = rnn.step_input(sentence)
prev_hidden = rnn.memory(value=0.0, shape=[lstm_size])
prev_cell = rnn.memory(value=0.0, shape=[lstm_size])
def gate_common(ipt, hidden, size):
gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True)
gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
return gate0 + gate1
forget_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
lstm_size))
input_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
lstm_size))
output_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
lstm_size))
cell_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
lstm_size))
cell = forget_gate * prev_cell + input_gate * cell_gate
hidden = output_gate * fluid.layers.tanh(x=cell)
rnn.update_memory(prev_cell, cell)
rnn.update_memory(prev_hidden, hidden)
rnn.output(hidden)
last = fluid.layers.sequence_last_step(rnn())
prediction = fluid.layers.fc(input=last, size=class_dim, act="softmax")
return prediction
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
pred = dynamic_rnn_lstm(data, dict_dim, CLASS_DIM, EMB_DIM, LSTM_SIZE)
return pred
def train_program(word_dict):
prediction = inference_program(word_dict)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
return [avg_cost, accuracy]
def train(use_cuda, train_program, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
optimizer = fluid.optimizer.Adagrad(learning_rate=0.002)
word_dict = paddle.dataset.imdb.word_dict()
trainer = fluid.Trainer(
train_func=partial(train_program, word_dict),
place=place,
optimizer=optimizer)
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
trainer.stop()
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(np.array, event.metrics)))
if event.step == 1: # Run 2 iterations to speed CI
trainer.save_params(save_dirname)
trainer.stop()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['words', 'label'])
def infer(use_cuda, inference_program, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
inferencer = fluid.Inferencer(
infer_func=partial(inference_program, word_dict),
param_path=save_dirname,
place=place)
def create_random_lodtensor(lod, place, low, high):
data = np.random.random_integers(low, high,
[lod[-1], 1]).astype("int64")
res = fluid.LoDTensor()
res.set(data, place)
res.set_lod([lod])
return res
lod = [0, 4, 10]
tensor_words = create_random_lodtensor(
lod, place, low=0, high=len(word_dict) - 1)
results = inferencer.infer({'words': tensor_words})
print("infer results: ", results)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "understand_sentiment_conv.inference.model"
train(use_cuda, train_program, save_path)
infer(use_cuda, inference_program, save_path)
if __name__ == '__main__':
for use_cuda in (False, True):
main(use_cuda=use_cuda)
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/notest_understand_sentiment_stacked_lstm.py → python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py
浏览文件 @ adbf97b4
......@@ -17,11 +17,13 @@ from __future__ import print_function
import paddle
import paddle.fluid as fluid
from functools import partial
import numpy as np
CLASS_DIM = 2
EMB_DIM = 128
HID_DIM = 512
STACKED_NUM = 3
BATCH_SIZE = 128
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
......@@ -50,7 +52,7 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
return prediction
def inference_network(word_dict):
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
......@@ -60,57 +62,71 @@ def inference_network(word_dict):
return net
def train_network(word_dict):
prediction = inference_network(word_dict)
def train_program(word_dict):
prediction = inference_program(word_dict)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
return avg_cost, accuracy
return [avg_cost, accuracy]
def train(use_cuda, save_path):
BATCH_SIZE = 128
EPOCH_NUM = 5
def train(use_cuda, train_program, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
optimizer = fluid.optimizer.Adagrad(learning_rate=0.002)
word_dict = paddle.dataset.imdb.word_dict()
trainer = fluid.Trainer(
train_func=partial(train_program, word_dict),
place=place,
optimizer=optimizer)
train_data = paddle.batch(
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
trainer.stop()
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(np.array, event.metrics)))
if event.step == 1: # Run 2 iterations to speed CI
trainer.save_params(save_dirname)
trainer.stop()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
test_data = paddle.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
def event_handler(event):
if isinstance(event, fluid.EndIteration):
if (event.batch_id % 10) == 0:
avg_cost, accuracy = trainer.test(reader=test_data)
print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
event.batch_id + 1, avg_cost, accuracy))
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['words', 'label'])
if accuracy > 0.01: # Low threshold for speeding up CI
trainer.params.save(save_path)
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
partial(train_network, word_dict),
optimizer=fluid.optimizer.Adagrad(learning_rate=0.002),
place=place,
event_handler=event_handler)
trainer.train(train_data, EPOCH_NUM, event_handler=event_handler)
def infer(use_cuda, save_path):
params = fluid.Params(save_path)
def infer(use_cuda, inference_program, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
inferencer = fluid.Inferencer(
partial(inference_network, word_dict), params, place=place)
infer_func=partial(inference_program, word_dict),
param_path=save_dirname,
place=place)
def create_random_lodtensor(lod, place, low, high):
data = np.random.random_integers(low, high,
......@@ -131,8 +147,8 @@ def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "understand_sentiment_stacked_lstm.inference.model"
train(use_cuda, save_path)
infer(use_cuda, save_path)
train(use_cuda, train_program, save_path)
infer(use_cuda, inference_program, save_path)
if __name__ == '__main__':
......
python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py → python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py
浏览文件 @ adbf97b4
......@@ -90,7 +90,7 @@ def train_program(is_sparse):
return avg_cost
def train(use_cuda, train_program, save_path):
def train(use_cuda, train_program, save_dirname):
train_reader = paddle.batch(
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
test_reader = paddle.batch(
......@@ -99,27 +99,36 @@ def train(use_cuda, train_program, save_path):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
outs = trainer.test(reader=test_reader)
if isinstance(event, fluid.EndStepEvent):
outs = trainer.test(
reader=test_reader,
feed_order=['firstw', 'secondw', 'thirdw', 'forthw', 'nextw'])
avg_cost = outs[0]
print("loss= ", avg_cost)
if avg_cost < 5.0:
trainer.save_params(save_path)
return
if avg_cost < 10.0:
trainer.save_params(save_dirname)
trainer.stop()
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
trainer = fluid.Trainer(
train_program, fluid.optimizer.SGD(learning_rate=0.001), place=place)
train_func=train_program,
optimizer=fluid.optimizer.SGD(learning_rate=0.001),
place=place)
trainer.train(
reader=train_reader, num_epochs=1, event_handler=event_handler)
reader=train_reader,
num_epochs=1,
event_handler=event_handler,
feed_order=['firstw', 'secondw', 'thirdw', 'forthw', 'nextw'])
def infer(use_cuda, inference_program, save_path):
def infer(use_cuda, inference_program, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=save_path, place=place)
infer_func=inference_program, param_path=save_dirname, place=place)
lod = [0, 1]
first_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
......@@ -142,9 +151,17 @@ def main(use_cuda, is_sparse):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "word2vec.params"
train(use_cuda, partial(train_program, is_sparse), save_path)
infer(use_cuda, partial(inference_program, is_sparse), save_path)
save_path = "word2vec.inference.model"
train(
use_cuda=use_cuda,
train_program=partial(train_program, is_sparse),
save_dirname=save_path)
infer(
use_cuda=use_cuda,
inference_program=partial(inference_program, is_sparse),
save_dirname=save_path)
if __name__ == '__main__':
......
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ adbf97b4
......@@ -182,12 +182,6 @@ def train(use_cuda, save_dirname=None, is_local=True):
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
chunk_evaluator = fluid.evaluator.ChunkEvaluator(
input=crf_decode,
label=target,
chunk_scheme="IOB",
num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
......@@ -203,7 +197,6 @@ def train(use_cuda, save_dirname=None, is_local=True):
def train_loop(main_program):
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
......@@ -213,27 +206,19 @@ def train(use_cuda, save_dirname=None, is_local=True):
start_time = time.time()
batch_id = 0
for pass_id in xrange(PASS_NUM):
chunk_evaluator.reset(exe)
for data in train_data():
cost, precision, recall, f1_score = exe.run(
main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost] + chunk_evaluator.metrics)
pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
exe)
cost = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost:" + str(cost) + " precision:" + str(
precision) + " recall:" + str(recall) + " f1_score:" +
str(f1_score) + " pass_precision:" + str(
pass_precision) + " pass_recall:" + str(
pass_recall) + " pass_f1_score:" + str(
pass_f1_score))
print("avg_cost:" + str(cost))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(pass_precision) > 0.01:
if float(cost) < 60.0:
if save_dirname is not None:
# TODO(liuyiqun): Change the target to crf_decode
fluid.io.save_inference_model(save_dirname, [
......
python/paddle/fluid/tests/test_data_feeder.py
浏览文件 @ adbf97b4
......@@ -13,15 +13,62 @@
# limitations under the License.
import paddle.fluid as fluid
import unittest
def test_converter():
img = fluid.layers.data(name='image', shape=[1, 28, 28])
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
feeder = fluid.DataFeeder([img, label], fluid.CPUPlace())
result = feeder.feed([[[0] * 784, [9]], [[1] * 784, [1]]])
print(result)
class TestDataFeeder(unittest.TestCase):
def test_lod_level_0_converter(self):
img = fluid.layers.data(name='image', shape=[1, 28, 28])
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
feeder = fluid.DataFeeder([img, label], fluid.CPUPlace())
result = feeder.feed([([0] * 784, [9]), ([1] * 784, [1])])
print(result)
self.assertEqual(result['image'].shape(), [2, 1, 28, 28])
self.assertEqual(result['label'].shape(), [2, 1])
self.assertEqual(result['image'].lod(), [])
self.assertEqual(result['label'].lod(), [])
def test_lod_level_1_converter(self):
# lod_level = 1
# each sentence has a different number of words
sentences = fluid.layers.data(
name='sentences', shape=[1], dtype='int64', lod_level=1)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
feeder = fluid.DataFeeder([sentences, label], fluid.CPUPlace())
# lod = [[0, 3, 5, 9]]
# data = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]
# label = [1] * len(data)
result = feeder.feed(
[([1, 2, 3], [1]), ([4, 5], [1]), ([6, 7, 8, 9], [1])])
print(result)
self.assertEqual(result['sentences'].shape(), [9, 1])
self.assertEqual(result['label'].shape(), [3, 1])
self.assertEqual(result['sentences'].lod(), [[0, 3, 5, 9]])
self.assertEqual(result['label'].lod(), [])
def test_lod_level_2_converter(self):
# lod_level = 2
# paragraphs -> sentences -> words
paragraphs = fluid.layers.data(
name='paragraphs', shape=[1], dtype='int64', lod_level=2)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
feeder = fluid.DataFeeder([paragraphs, label], fluid.CPUPlace())
# lod = [[0, 2, 3], [0, 3, 5, 9]]
# data = [[[1, 2, 3], [4, 5]], [[6, 7, 8, 9]]]
# label = [1] * len(data)
result = feeder.feed(
[([[1, 2, 3], [4, 5]], [1]), ([[6, 7, 8, 9]], [1])])
print(result)
self.assertEqual(result['paragraphs'].shape(), [9, 1])
self.assertEqual(result['label'].shape(), [2, 1])
self.assertEqual(result['paragraphs'].lod(), [[0, 2, 3], [0, 3, 5, 9]])
self.assertEqual(result['label'].lod(), [])
if __name__ == '__main__':
test_converter()
unittest.main()
python/paddle/fluid/tests/test_detection.py
浏览文件 @ adbf97b4
......@@ -109,6 +109,24 @@ class TestDetection(unittest.TestCase):
print(str(program))
class TestPriorBox(unittest.TestCase):
def test_prior_box(self):
data_shape = [3, 224, 224]
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
box, var = layers.prior_box(
input=conv1,
image=images,
min_sizes=[100.0],
aspect_ratios=[1.],
flip=True,
clip=True)
assert len(box.shape) == 4
assert box.shape == var.shape
assert box.shape[3] == 4
class TestMultiBoxHead(unittest.TestCase):
def test_multi_box_head(self):
data_shape = [3, 224, 224]
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ adbf97b4
......@@ -26,14 +26,17 @@ list(REMOVE_ITEM TEST_OPS decorators) # decorators is a helper python file, not
function(py_test_modules TARGET_NAME)
if(WITH_TESTING)
set(options "")
set(options SERIAL)
set(oneValueArgs "")
set(multiValueArgs MODULES DEPS ARGS ENVS)
set(multiValueArgs MODULES DEPS ENVS)
cmake_parse_arguments(py_test_modules "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_test(NAME ${TARGET_NAME}
COMMAND env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_modules_ENVS}
${PYTHON_EXECUTABLE} -u -m unittest --verbose ${py_test_modules_MODULES} ${py_test_modules_ARGS}
${PYTHON_EXECUTABLE} ${PADDLE_SOURCE_DIR}/tools/test_runner.py ${py_test_modules_MODULES}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
if (py_test_modules_SERIAL)
set_property(TEST ${TARGET_NAME} PROPERTY SERIAL 1)
endif()
endif()
endfunction()
......@@ -81,7 +84,7 @@ endif(WITH_FAST_BUNDLE_TEST)
py_test_modules(test_sequence_expand MODULES test_sequence_expand)
# tests with high overhead
py_test_modules(test_parallel_executor MODULES test_parallel_executor)
py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR})
py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
py_test_modules(test_train_dyn_rnn MODULES test_dyn_rnn)
py_test_modules(test_mul_op MODULES test_mul_op)
py_test_modules(test_network_with_dtype MODULES test_network_with_dtype)
......@@ -106,4 +109,4 @@ py_test_modules(test_registry MODULES test_registry)
py_test_modules(test_fetch_var MODULES test_fetch_var)
py_test_modules(test_dynrnn_static_input MODULES test_dynrnn_static_input)
py_test_modules(test_parallel_op MODULES test_parallel_op)
py_test_modules(test_dist_train MODULES test_dist_train)
py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
python/paddle/fluid/tests/unittests/test_detection_map_op.py
浏览文件 @ adbf97b4
......@@ -160,7 +160,9 @@ class TestDetectionMAPOp(OpTest):
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:
for v in self.label:
label = v[0]
difficult = False if len(v) == 5 else v[1]
if self.evaluate_difficult:
label_count[label] += 1
elif not difficult:
......@@ -245,6 +247,15 @@ class TestDetectionMAPOpSkipDiff(TestDetectionMAPOp):
[2, 0.8, 0, 1], [2, 0.1, 1, 0], [3, 0.2, 0, 1]]
class TestDetectionMAPOpWithoutDiff(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOpWithoutDiff, self).init_test_case()
# label xmin ymin xmax ymax
self.label = [[1, 0.1, 0.1, 0.3, 0.3], [1, 0.6, 0.6, 0.8, 0.8],
[2, 0.3, 0.3, 0.6, 0.5], [1, 0.7, 0.1, 0.9, 0.3]]
class TestDetectionMAPOp11Point(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOp11Point, self).init_test_case()
......
python/paddle/fluid/tests/unittests/test_dist_train.py
浏览文件 @ adbf97b4
......@@ -52,15 +52,18 @@ class TestSendOp(unittest.TestCase):
serv = layers.ListenAndServ(
"127.0.0.1:0", ["X"], optimizer_mode=False)
with serv.do():
out_var = main.global_block().create_var(
name="scale_0.tmp_0",
psersistable=True,
dtype="float32",
shape=[32, 32])
x = layers.data(
shape=[32, 32],
dtype='float32',
name="X",
append_batch_size=False)
fluid.initializer.Constant(value=1.0)(x, main.global_block())
o = layers.scale(x=x, scale=10.0)
main.global_block().create_var(
name=o.name, psersistable=False, dtype=o.dtype, shape=o.shape)
layers.scale(x=x, scale=10.0, out=out_var)
self.server_exe = fluid.Executor(place)
self.server_exe.run(main)
......
python/paddle/fluid/tests/unittests/test_is_empty_op.py
浏览文件 @ adbf97b4
......@@ -14,42 +14,24 @@
import unittest
import numpy as np
from paddle.fluid.op import Operator
import paddle.fluid.core as core
from op_test import OpTest
def create_tensor(scope, name, np_data):
tensor = scope.var(name).get_tensor()
tensor.set_dims(np_data.shape)
tensor.set(np_data, core.CPUPlace())
return tensor
class TestIsEmptyOp(unittest.TestCase):
class TestEmpty(OpTest):
def setUp(self):
self.scope = core.Scope()
# create input variables
np_data0 = np.array([0, 1, 2])
create_tensor(self.scope, "X0", np_data0)
np_data1 = np.array([1])
t = create_tensor(self.scope, "X1", np_data1)
t.set_dims([0])
self.op_type = "is_empty"
self.inputs = {'X': np.array([1, 2, 3])}
self.outputs = {'Out': np.array([False])}
# create output variables
self.scope.var("out")
def test_check_output(self):
self.check_output()
def test_no_empty(self):
self.one_case("X0", False)
def test_empty(self):
self.one_case("X1", True)
def one_case(self, input, target):
op = Operator(type="is_empty", X=input, Out="out")
op.run(self.scope, core.CPUPlace())
out = self.scope.var("out").get_tensor()
self.assertEqual(np.array(out)[0], target)
class TestNotEmpty(TestEmpty):
def setUp(self):
self.op_type = "is_empty"
self.inputs = {'X': np.array([])}
self.outputs = {'Out': np.array([True])}
if __name__ == "__main__":
......
python/paddle/fluid/trainer.py
浏览文件 @ adbf97b4
此差异已折叠。
tools/test_runner.py 0 → 100644
浏览文件 @ adbf97b4
此差异已折叠。
tools/timeline.py
浏览文件 @ adbf97b4
此差异已折叠。
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