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PaddleDetection

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PaddleDetection
提交 df8fbf80 编写于 作者: F fengjiayi
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into dev_CustomReader

上级 b48eba19 01c0a81b
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Showing with 466 addition and 64 deletion
CMakeLists.txt
浏览文件 @ df8fbf80
......@@ -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 ${WITH_MKL})
if (WITH_MKL AND AVX2_FOUND)
if (NOT DEFINED WITH_MKLDNN)
if (WITH_MKL AND AVX2_FOUND)
set(WITH_MKLDNN ON)
else()
else()
message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
set(WITH_MKLDNN OFF)
endif()
endif()
########################################################################################
include(external/mklml) # download mklml package
......
cmake/external/boost.cmake
浏览文件 @ df8fbf80
......@@ -23,8 +23,12 @@ set(BOOST_PROJECT "extern_boost")
# checked that the devtools package of CentOS 6 installs boost 1.41.0.
# So we use 1.41.0 here.
set(BOOST_VER "1.41.0")
set(BOOST_TAR "boost_1_41_0")
set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${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}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
......
cmake/external/mklml.cmake
浏览文件 @ df8fbf80
......@@ -27,8 +27,12 @@ ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.3.20180406")
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${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 @ df8fbf80
......@@ -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 ${TARGET_NAME} PROPERTY SERIAL 1)
endif()
endif()
endfunction(nv_test)
......
cmake/inference_lib.cmake
浏览文件 @ df8fbf80
......@@ -52,32 +52,32 @@ function(copy TARGET)
endfunction()
# third party
set(dst_dir "${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} ${dst_dir}/lib
......@@ -85,13 +85,13 @@ if(NOT PROTOBUF_FOUND)
endif()
if(NOT CBLAS_FOUND)
set(dst_dir "${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 ${dst_dir}
......@@ -99,7 +99,7 @@ elseif (WITH_MKLML)
endif()
if(WITH_MKLDNN)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/mkldnn")
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mkldnn")
copy(mkldnn_lib
SRCS ${MKLDNN_INC_DIR} ${MKLDNN_SHARED_LIB}
DSTS ${dst_dir} ${dst_dir}/lib
......@@ -107,17 +107,17 @@ if(WITH_MKLDNN)
endif()
if(NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${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} ${dst_dir}/lib)
......@@ -125,7 +125,7 @@ endif()
# paddle fluid module
set(src_dir "${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
......@@ -165,7 +165,7 @@ copy(pybind_lib
# CMakeCache Info
copy(cmake_cache
SRCS ${CMAKE_CURRENT_BINARY_DIR}/CMakeCache.txt
DSTS ${CMAKE_INSTALL_PREFIX})
DSTS ${FLUID_INSTALL_DIR})
add_custom_target(inference_lib_dist DEPENDS ${inference_lib_dist_dep})
......@@ -173,7 +173,7 @@ add_custom_target(inference_lib_dist DEPENDS ${inference_lib_dist_dep})
execute_process(
COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
OUTPUT_VARIABLE PADDLE_GIT_COMMIT)
set(version_file ${CMAKE_INSTALL_PREFIX}/version.txt)
set(version_file ${FLUID_INSTALL_DIR}/version.txt)
file(WRITE ${version_file}
"GIT COMMIT ID: ${PADDLE_GIT_COMMIT}\n"
"WITH_MKL: ${WITH_MKL}\n"
......
doc/fluid/api/layers.rst
浏览文件 @ df8fbf80
......@@ -828,4 +828,10 @@ topk
.. autofunction:: paddle.fluid.layers.topk
:noindex:
dice_loss
----
.. autofunction:: paddle.fluid.layers.dice_loss
:noindex:
doc/v2/build_and_install/build_from_source_cn.rst
浏览文件 @ df8fbf80
......@@ -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 @ df8fbf80
......@@ -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
......
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @ df8fbf80
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/tests/book/test_inference_image_classification.cc
浏览文件 @ df8fbf80
......@@ -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 @ df8fbf80
......@@ -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);
......@@ -169,6 +182,9 @@ 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,
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ df8fbf80
......@@ -202,10 +202,12 @@ 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_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)
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()
......
paddle/fluid/operators/detail/CMakeLists.txt
浏览文件 @ df8fbf80
......@@ -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 ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr
cares zlib protobuf sendrecvop_grpc)
cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_table_op)
cares zlib protobuf sendrecvop_grpc SERIAL)
cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc
grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor
proto_desc lookup_table_op SERIAL)
endif()
paddle/fluid/operators/warpctc_op.h
浏览文件 @ df8fbf80
......@@ -186,8 +186,7 @@ class WarpCTCKernel : public framework::OpKernel<T> {
// warpctc accesses labels in CPU memory
Tensor warpctc_label;
TensorCopy(*label, platform::CPUPlace(), ctx.device_context(),
&warpctc_label);
TensorCopySync(*label, platform::CPUPlace(), &warpctc_label);
const int* warpctc_label_data = warpctc_label.data<int>();
// warpctc stores loss in CPU memory
Tensor warpctc_loss;
......
paddle/fluid/train/demo/README.md
浏览文件 @ df8fbf80
......@@ -7,7 +7,7 @@
# WITH_MKLDNN=ON|OFF
PADDLE_LIB=/paddle/lib/dir
cmake .. -DCMAKE_INSTALL_PREFIX=$PADDLE_LIB \
cmake .. -DFLUID_INSTALL_DIR=$PADDLE_LIB \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_FLUID_ONLY=ON \
-DWITH_GPU=OFF \
......@@ -42,7 +42,7 @@ cd build
# WITH_MKLDNN=ON|OFF
PADDLE_LIB=/paddle/lib/dir
# PADDLE_LIB is the same with CMAKE_INSTALL_PREFIX when building the lib
# PADDLE_LIB is the same with FLUID_INSTALL_DIR when building the lib
cmake .. -DPADDLE_LIB=$PADDLE_LIB \
-DWITH_MKLDNN=OFF \
-DWITH_MKL=OFF
......
paddle/scripts/paddle_build.sh
浏览文件 @ df8fbf80
......@@ -433,7 +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
......@@ -493,6 +493,7 @@ function gen_fluid_inference_lib() {
========================================
EOF
make -j `nproc` inference_lib_dist
tar -cf ${PADDLE_ROOT}/build/fluid.tgz ${PADDLE_ROOT}/build/fluid_install_dir
fi
}
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ df8fbf80
......@@ -1098,7 +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
]
......
python/paddle/fluid/layers/nn.py
浏览文件 @ df8fbf80
......@@ -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)
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
......@@ -3819,3 +3817,43 @@ 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/understand_sentiment/test_understand_sentiment_conv.py 0 → 100644
浏览文件 @ df8fbf80
# 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 @ df8fbf80
# 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/unittests/CMakeLists.txt
浏览文件 @ df8fbf80
......@@ -26,7 +26,7 @@ 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 ENVS)
cmake_parse_arguments(py_test_modules "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
......@@ -34,6 +34,9 @@ function(py_test_modules TARGET_NAME)
COMMAND env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_modules_ENVS}
${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)
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