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提交 3932cd67 编写于 作者: D dzhwinter
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Merge remote-tracking branch 'origin/develop' into enhance/memory

上级 94dd50c3 bec68fa0
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Showing with 253 addition and 155 deletion
CMakeLists.txt
浏览文件 @ 3932cd67
......@@ -25,12 +25,18 @@ message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
"${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
if(WIN32)
set(CMAKE_SUPPRESS_REGENERATION ON)
set(CMAKE_STATIC_LIBRARY_PREFIX lib)
add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
add_compile_options(/wd4068 /wd4129 /wd4244 /wd4267 /wd4297 /wd4530 /wd4577 /wd4819 /wd4838)
set(PADDLE_LINK_FLAGS "/IGNORE:4006 /IGNORE:4098 /IGNORE:4217 /IGNORE:4221")
set(CMAKE_STATIC_LINKER_FLAGS "${CMAKE_STATIC_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
endif(WIN32)
find_package(CUDA QUIET)
......
cmake/configure.cmake
浏览文件 @ 3932cd67
......@@ -152,7 +152,12 @@ endif()
if (WITH_MKLML AND MKLML_IOMP_LIB)
message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
set(OPENMP_FLAGS "-fopenmp")
if(WIN32)
# openmp not support well for now on windows
set(OPENMP_FLAGS "")
else(WIN32)
set(OPENMP_FLAGS "-fopenmp")
endif(WIN32)
set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
......
cmake/cuda.cmake
浏览文件 @ 3932cd67
......@@ -203,25 +203,26 @@ list(APPEND CUDA_NVCC_FLAGS "-w")
list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
if (NOT WIN32)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel")
# nvcc 9 does not support -Os. Use Release flags instead
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
endif()
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel")
# nvcc 9 does not support -Os. Use Release flags instead
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
endif()
else(NOT WIN32)
list(APPEND CUDA_NVCC_FLAGS "--compiler-options;/bigobj")
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
list(APPEND CUDA_NVCC_FLAGS "-g -G")
# match the cl's _ITERATOR_DEBUG_LEVEL
list(APPEND CUDA_NVCC_FLAGS "-D_DEBUG")
elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
list(APPEND CUDA_NVCC_FLAGS "-O3 -DNDEBUG")
else()
list(APPEND CUDA_NVCC_FLAGS "-Xcompiler \"/wd 4244 /wd 4267 /wd 4819\"")
list(APPEND CUDA_NVCC_FLAGS "--compiler-options;/bigobj")
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
list(APPEND CUDA_NVCC_FLAGS "-g -G")
# match the cl's _ITERATOR_DEBUG_LEVEL
list(APPEND CUDA_NVCC_FLAGS "-D_DEBUG")
elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
list(APPEND CUDA_NVCC_FLAGS "-O3 -DNDEBUG")
else()
message(FATAL "Windows only support Release or Debug build now. Please set visual studio build type to Release/Debug, x64 build.")
endif()
endif(NOT WIN32)
......
cmake/external/glog.cmake
浏览文件 @ 3932cd67
......@@ -20,8 +20,10 @@ SET(GLOG_INCLUDE_DIR "${GLOG_INSTALL_DIR}/include" CACHE PATH "glog include dire
IF(WIN32)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.lib" CACHE FILEPATH "glog library." FORCE)
SET(GLOG_CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4267 /wd4530")
ELSE(WIN32)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.a" CACHE FILEPATH "glog library." FORCE)
SET(GLOG_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
ENDIF(WIN32)
INCLUDE_DIRECTORIES(${GLOG_INCLUDE_DIR})
......@@ -39,7 +41,7 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS=${GLOG_CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
......
cmake/external/mkldnn.cmake
浏览文件 @ 3932cd67
......@@ -49,6 +49,8 @@ IF(NOT WIN32)
SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
ELSE()
SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} /EHsc")
ENDIF(NOT WIN32)
ExternalProject_Add(
......@@ -61,7 +63,6 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
CMAKE_ARGS -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
......
cmake/external/snappy.cmake
浏览文件 @ 3932cd67
......@@ -20,6 +20,12 @@ set(SNAPPY_SOURCES_DIR ${THIRD_PARTY_PATH}/snappy)
set(SNAPPY_INSTALL_DIR ${THIRD_PARTY_PATH}/install/snappy)
set(SNAPPY_INCLUDE_DIR "${SNAPPY_INSTALL_DIR}/include" CACHE PATH "snappy include directory." FORCE)
if(WIN32)
SET(SNAPPY_CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4244 /wd4267")
else()
SET(SNAPPY_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
endif()
ExternalProject_Add(
extern_snappy
GIT_REPOSITORY "https://github.com/google/snappy"
......@@ -31,7 +37,7 @@ ExternalProject_Add(
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS=${SNAPPY_CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_INSTALL_PREFIX=${SNAPPY_INSTALL_DIR}
......
cmake/flags.cmake
浏览文件 @ 3932cd67
......@@ -147,12 +147,6 @@ set(GPU_COMMON_FLAGS
-Wno-error=unused-function # Warnings in Numpy Header.
-Wno-error=array-bounds # Warnings in Eigen::array
)
else(NOT WIN32)
set(COMMON_FLAGS
"/w") #disable all warnings.
set(GPU_COMMON_FLAGS
"/w") #disable all warnings
endif(NOT WIN32)
if (APPLE)
......@@ -193,8 +187,7 @@ safe_set_static_flag()
CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO
CMAKE_C_FLAGS CMAKE_C_FLAGS_DEBUG CMAKE_C_FLAGS_RELEASE
CMAKE_C_FLAGS_MINSIZEREL CMAKE_C_FLAGS_RELWITHDEBINFO)
if(${flag_var} MATCHES "/W3")
string(REGEX REPLACE "/W3" "/w" ${flag_var} "${${flag_var}}")
endif(${flag_var} MATCHES "/W3")
string(REGEX REPLACE "(^| )/W[0-9]( |$)" " " ${flag_var} "${${flag_var}}")
set(flag_var "${flag_var} /w")
endforeach(flag_var)
endif(WIN32)
paddle/fluid/framework/ir/graph.h
浏览文件 @ 3932cd67
......@@ -141,7 +141,8 @@ class Graph {
ir::Node *CreateControlDepVar() {
// TODO(panyx0718): control var name should be really unique.
const std::string name = string::Sprintf(
"%s@%llu", ir::Node::kControlDepVarName, node_set_.size());
"%s@%llu", static_cast<const char *>(ir::Node::kControlDepVarName),
node_set_.size());
auto *x = AddNode(new ir::Node(name, ir::Node::Type::kVariable));
x->SetId(num_node_created_++);
return x;
......
paddle/fluid/imperative/CMakeLists.txt
浏览文件 @ 3932cd67
if(WITH_PYTHON)
cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas)
cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context)
cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas pybind)
cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context pybind)
cc_library(engine SRCS engine.cc)
endif()
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ 3932cd67
......@@ -58,12 +58,13 @@ if(WIN32)
sep_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array
analysis_config paddle_pass_builder)
target_link_libraries(paddle_fluid_shared shlwapi)
else(WIN32)
cc_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array
analysis_config paddle_pass_builder)
endif()
get_property(os_dependency_modules GLOBAL PROPERTY OS_DEPENDENCY_MODULES)
target_link_libraries(paddle_fluid_shared ${os_dependency_modules})
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
if(NOT APPLE AND NOT WIN32)
......
paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt
浏览文件 @ 3932cd67
cc_library(subgraph_detector SRCS subgraph_detector.cc DEPS proto_desc)
if(WITH_TESTING)
add_dependencies(subgraph_detector gtest)
endif()
if (WITH_GPU AND TENSORRT_FOUND)
cc_library(tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass.cc DEPS subgraph_detector tensorrt_op_teller)
......
paddle/fluid/operators/detection/box_coder_op.cc
浏览文件 @ 3932cd67
......@@ -38,20 +38,12 @@ class BoxCoderOp : public framework::OperatorWithKernel {
"The shape of PriorBox is [N, 4]");
if (ctx->HasInput("PriorBoxVar")) {
auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
PADDLE_ENFORCE(
prior_box_var_dims.size() == 1 || prior_box_var_dims.size() == 2,
"Input(PriorBoxVar) of BoxCoderOp should be 1 or 2.");
if (prior_box_var_dims.size() == 1) {
PADDLE_ENFORCE_EQ(
prior_box_var_dims[0], 4,
"The 1st dimension of Input(PriorBoxVar) should be 4"
"when the rank is 1.");
} else {
PADDLE_ENFORCE_EQ(
prior_box_dims, prior_box_var_dims,
"The dimension of Input(PriorBoxVar) should be equal to"
"the dimension of Input(PriorBox when the rank is 2.)");
}
PADDLE_ENFORCE(prior_box_var_dims.size() == 2,
"Input(PriorBoxVar) of BoxCoderOp should be 2.");
PADDLE_ENFORCE_EQ(
prior_box_dims, prior_box_var_dims,
"The dimension of Input(PriorBoxVar) should be equal to"
"the dimension of Input(PriorBox) when the rank is 2.");
}
}
......
paddle/fluid/operators/detection/box_coder_op.cu
浏览文件 @ 3932cd67
......@@ -56,10 +56,7 @@ __global__ void EncodeCenterSizeKernel(
output[idx * len + 2] = log(fabs(target_box_width / prior_box_width));
output[idx * len + 3] = log(fabs(target_box_height / prior_box_height));
if (prior_box_var_data) {
int prior_var_offset = 0;
if (prior_box_var_size == 2) {
prior_var_offset = col_idx * len;
}
int prior_var_offset = col_idx * len;
output[idx * len] /= prior_box_var_data[prior_var_offset];
output[idx * len + 1] /= prior_box_var_data[prior_var_offset + 1];
output[idx * len + 2] /= prior_box_var_data[prior_var_offset + 2];
......@@ -99,10 +96,7 @@ __global__ void DecodeCenterSizeKernel(
T box_var_x = T(1), box_var_y = T(1);
T box_var_w = T(1), box_var_h = T(1);
if (prior_box_var_data) {
int prior_var_offset = 0;
if (prior_box_var_size == 2) {
prior_var_offset = axis == 0 ? col_idx * len : row_idx * len;
}
int prior_var_offset = axis == 0 ? col_idx * len : row_idx * len;
box_var_x = prior_box_var_data[prior_var_offset];
box_var_y = prior_box_var_data[prior_var_offset + 1];
box_var_w = prior_box_var_data[prior_var_offset + 2];
......
paddle/fluid/operators/detection/box_coder_op.h
浏览文件 @ 3932cd67
......@@ -79,10 +79,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
output[offset + 3] =
std::log(std::fabs(target_box_height / prior_box_height));
if (prior_box_var) {
int prior_var_offset = 0;
if (prior_box_var->dims().size() == 2) {
prior_var_offset = j * len;
}
int prior_var_offset = j * len;
output[offset] /= prior_box_var_data[prior_var_offset];
output[offset + 1] /= prior_box_var_data[prior_var_offset + 1];
output[offset + 2] /= prior_box_var_data[prior_var_offset + 2];
......@@ -95,11 +92,12 @@ class BoxCoderKernel : public framework::OpKernel<T> {
}
}
}
template <int axis, int var_size>
void DecodeCenterSize(const framework::Tensor* target_box,
const framework::Tensor* prior_box,
const framework::Tensor* prior_box_var,
const bool normalized, const int axis,
const std::vector<float> variance, T* output) const {
const bool normalized, std::vector<float> variance,
T* output) const {
int64_t row = target_box->dims()[0];
int64_t col = target_box->dims()[1];
int64_t len = target_box->dims()[2];
......@@ -107,19 +105,17 @@ class BoxCoderKernel : public framework::OpKernel<T> {
auto* target_box_data = target_box->data<T>();
auto* prior_box_data = prior_box->data<T>();
const T* prior_box_var_data = nullptr;
if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
if (var_size == 2) prior_box_var_data = prior_box_var->data<T>();
int prior_box_offset = 0;
T var_data[4] = {1., 1., 1., 1.};
T* var_ptr = var_data;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
size_t offset = i * col * len + j * len;
if (axis == 0) {
prior_box_offset = j * len;
} else if (axis == 1) {
prior_box_offset = i * len;
}
prior_box_offset = axis == 0 ? j * len : i * len;
T prior_box_width = prior_box_data[prior_box_offset + 2] -
prior_box_data[prior_box_offset] +
(normalized == false);
......@@ -133,26 +129,18 @@ class BoxCoderKernel : public framework::OpKernel<T> {
T target_box_center_x = 0, target_box_center_y = 0;
T target_box_width = 0, target_box_height = 0;
T box_var_x = T(1), box_var_y = T(1);
T box_var_w = T(1), box_var_h = T(1);
if (prior_box_var) {
int prior_var_offset = 0;
if (prior_box_var->dims().size() == 2) {
if (axis == 0)
prior_var_offset = j * len;
else if (axis == 1)
prior_var_offset = i * len;
}
box_var_x = prior_box_var_data[prior_var_offset];
box_var_y = prior_box_var_data[prior_var_offset + 1];
box_var_w = prior_box_var_data[prior_var_offset + 2];
box_var_h = prior_box_var_data[prior_var_offset + 3];
} else if (!(variance.empty())) {
box_var_x = static_cast<T>(variance[0]);
box_var_y = static_cast<T>(variance[1]);
box_var_w = static_cast<T>(variance[2]);
box_var_h = static_cast<T>(variance[3]);
int prior_var_offset = axis == 0 ? j * len : i * len;
if (var_size == 2) {
std::memcpy(var_ptr, prior_box_var_data + prior_var_offset,
4 * sizeof(T));
} else if (var_size == 1) {
var_ptr = reinterpret_cast<T*>(variance.data());
}
T box_var_x = *var_ptr;
T box_var_y = *(var_ptr + 1);
T box_var_w = *(var_ptr + 2);
T box_var_h = *(var_ptr + 3);
target_box_center_x =
box_var_x * target_box_data[offset] * prior_box_width +
prior_box_center_x;
......@@ -211,8 +199,31 @@ class BoxCoderKernel : public framework::OpKernel<T> {
EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
variance, output);
} else if (code_type == BoxCodeType::kDecodeCenterSize) {
DecodeCenterSize(target_box, prior_box, prior_box_var, normalized, axis,
variance, output);
if (prior_box_var) {
if (axis == 0) {
DecodeCenterSize<0, 2>(target_box, prior_box, prior_box_var,
normalized, variance, output);
} else {
DecodeCenterSize<1, 2>(target_box, prior_box, prior_box_var,
normalized, variance, output);
}
} else if (!(variance.empty())) {
if (axis == 0) {
DecodeCenterSize<0, 1>(target_box, prior_box, prior_box_var,
normalized, variance, output);
} else {
DecodeCenterSize<1, 1>(target_box, prior_box, prior_box_var,
normalized, variance, output);
}
} else {
if (axis == 0) {
DecodeCenterSize<0, 0>(target_box, prior_box, prior_box_var,
normalized, variance, output);
} else {
DecodeCenterSize<1, 0>(target_box, prior_box, prior_box_var,
normalized, variance, output);
}
}
}
}
};
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 3932cd67
......@@ -37,7 +37,7 @@ math_library(concat_and_split)
math_library(context_project DEPS im2col math_function)
math_library(cross_entropy)
math_library(cos_sim_functor)
math_library(depthwise_conv)
math_library(depthwise_conv DEPS cub)
math_library(im2col)
math_library(sampler)
......
paddle/fluid/operators/ngraph/ngraph_bridge.cc
浏览文件 @ 3932cd67
......@@ -31,6 +31,7 @@ std::map<std::string,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {
{"accuracy", NG_OPS::BuildAccuracyNode},
{"conv2d", NG_OPS::BuildConv2dNode},
{"conv2d_grad", NG_OPS::BuildConv2dGradNode},
{"elementwise_add", NG_OPS::BuildElementwiseAddNode},
......
paddle/fluid/operators/ngraph/ngraph_ops.h
浏览文件 @ 3932cd67
......@@ -21,7 +21,8 @@ limitations under the License. */
#pragma once
#include "ops/binary_unnary_op.h"
#include "ops/accuracy_op.h"
#include "ops/binary_unary_op.h"
#include "ops/conv2d_op.h"
#include "ops/elementwise_add_op.h"
#include "ops/fill_constant_op.h"
......
paddle/fluid/operators/ngraph/ops/accuracy_op.h 0 → 100644
浏览文件 @ 3932cd67
/*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. */
#pragma once
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildAccuracyNode(
const std::shared_ptr<framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto indices = platform::GetInputNode(op, "Indices", ngb_node_map);
auto label = platform::GetInputNode(op, "Label", ngb_node_map);
auto inference = platform::GetInputNode(op, "Out", ngb_node_map);
auto inference_shape = inference->get_shape();
size_t num_samples = inference_shape.at(0);
size_t k = inference_shape.at(1);
std::shared_ptr<ngraph::Node> label_k = label;
if (k > 1) {
auto label_1d = std::make_shared<ngraph::op::Reshape>(
label, ngraph::AxisVector{0, 1}, ngraph::Shape{num_samples});
label_k = std::make_shared<ngraph::op::Broadcast>(label_1d, inference_shape,
ngraph::AxisSet{1});
}
auto node_equal = std::make_shared<ngraph::op::Equal>(indices, label_k);
auto node_eq_int =
std::make_shared<ngraph::op::Convert>(node_equal, ngraph::element::i64);
auto num_correct_0d =
std::make_shared<ngraph::op::Sum>(node_eq_int, ngraph::AxisSet{0, 1});
std::shared_ptr<ngraph::Node> num_correct =
platform::NgReshaper(num_correct_0d, ngraph::Shape{1});
std::shared_ptr<ngraph::Node> n_samples = ngraph::op::Constant::create(
ngraph::element::i64, ngraph::Shape{1}, {num_samples});
std::shared_ptr<ngraph::Node> accuracy = std::make_shared<ngraph::op::Divide>(
std::make_shared<ngraph::op::Convert>(num_correct, ngraph::element::f32),
std::make_shared<ngraph::op::Convert>(n_samples, ngraph::element::f32));
platform::SetOutputNode(op, "Accuracy", accuracy, ngb_node_map);
platform::SetOutputNode(op, "Correct", num_correct, ngb_node_map);
platform::SetOutputNode(op, "Total", n_samples, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
paddle/fluid/operators/ngraph/ops/top_k_op.h
浏览文件 @ 3932cd67
......@@ -36,11 +36,6 @@ void BuildTopKNode(
std::make_shared<ngraph::op::GetOutputElement>(top_k, 0);
std::shared_ptr<ngraph::Node> out =
std::make_shared<ngraph::op::GetOutputElement>(top_k, 1);
auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map);
if (dummy_out && dummy_out->get_element_type() != out->get_element_type()) {
out = std::make_shared<ngraph::op::Convert>(out,
dummy_out->get_element_type());
}
paddle::platform::SetOutputNode(op, "Indices", indices, ngb_node_map);
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
......
paddle/fluid/operators/pool_op.cc
浏览文件 @ 3932cd67
......@@ -259,7 +259,7 @@ Example:
W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
$$
For exclusive = true:
For exclusive = false:
$$
hstart = i * strides[0] - paddings[0]
hend = hstart + ksize[0]
......@@ -267,7 +267,7 @@ Example:
wend = wstart + ksize[1]
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$
For exclusive = false:
For exclusive = true:
$$
hstart = max(0, i * strides[0] - paddings[0])
hend = min(H, hstart + ksize[0])
......@@ -403,7 +403,7 @@ Example:
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
For exclusive = true:
For exclusive = false:
$$
dstart = i * strides[0] - paddings[0]
dend = dstart + ksize[0]
......@@ -413,7 +413,7 @@ Example:
wend = wstart + ksize[2]
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
For exclusive = false:
For exclusive = true:
$$
dstart = max(0, i * strides[0] - paddings[0])
dend = min(D, dstart + ksize[0])
......
paddle/fluid/operators/reader/ctr_reader.cc
浏览文件 @ 3932cd67
......@@ -213,7 +213,7 @@ void ReadSvmData(const DataDesc& data_desc, std::shared_ptr<Reader> reader,
framework::LoD lod{lod_data};
lod_tensor.set_lod(lod);
int64_t* tensor_data = lod_tensor.mutable_data<int64_t>(
framework::make_ddim({1, static_cast<int64_t>(batch_feasign.size())}),
framework::make_ddim({static_cast<int64_t>(batch_feasign.size()), 1}),
platform::CPUPlace());
memcpy(tensor_data, batch_feasign.data(),
batch_feasign.size() * sizeof(int64_t));
......@@ -223,7 +223,7 @@ void ReadSvmData(const DataDesc& data_desc, std::shared_ptr<Reader> reader,
// insert label tensor
framework::LoDTensor label_tensor;
auto* label_tensor_data = label_tensor.mutable_data<int64_t>(
framework::make_ddim({1, static_cast<int64_t>(batch_label.size())}),
framework::make_ddim({static_cast<int64_t>(batch_label.size()), 1}),
platform::CPUPlace());
memcpy(label_tensor_data, batch_label.data(),
batch_label.size() * sizeof(int64_t));
......
paddle/fluid/operators/reader/ctr_reader_test.cc
浏览文件 @ 3932cd67
......@@ -123,7 +123,7 @@ TEST(CTR_READER, read_data) {
std::vector<std::tuple<LoD, std::vector<int64_t>>> data_slot_6003{b1, b2, b3,
b4};
std::vector<DDim> label_dims = {{1, 3}, {1, 3}, {1, 3}, {1, 1}};
std::vector<DDim> label_dims = {{3, 1}, {3, 1}, {3, 1}, {1, 1}};
LoDTensorBlockingQueueHolder queue_holder;
int capacity = 64;
......
paddle/fluid/operators/reduce_ops/CMakeLists.txt
浏览文件 @ 3932cd67
include(operators)
register_operators()
if(WITH_GPU)
register_operators(DEPS cub)
else()
register_operators()
endif()
if(WITH_GPU)
file(GLOB OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.part.cu")
......
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 3932cd67
proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto)
proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto simple_threadpool)
py_proto_compile(profiler_py_proto SRCS profiler.proto)
add_custom_target(profiler_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
......@@ -36,7 +36,7 @@ cc_test(cpu_info_test SRCS cpu_info_test.cc DEPS cpu_info)
nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog enforce)
cc_library(place SRCS place.cc DEPS enforce boost)
cc_library(place SRCS place.cc DEPS enforce boost lib_any)
cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
add_subdirectory(dynload)
......
paddle/fluid/platform/ngraph_helper.h
浏览文件 @ 3932cd67
......@@ -43,13 +43,14 @@ std::shared_ptr<ngraph::Node> NgReshaper(std::shared_ptr<ngraph::Node> input,
std::shared_ptr<ngraph::Node> GetNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm, const paddle::framework::VariableNameMap& var_map,
const std::string name, const paddle::framework::VariableNameMap& var_map,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = var_map.at(prm);
auto& var_names = var_map.at(name);
PADDLE_ENFORCE_EQ(var_names.size(), 1,
"op %s prm %s expects one associated var", op->Type(), prm);
"op %s name %s expects one associated var", op->Type(),
name);
if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
return (*ngb_node_map)[var_names[0]];
} else {
......@@ -59,43 +60,53 @@ std::shared_ptr<ngraph::Node> GetNode(
std::shared_ptr<ngraph::Node> GetInputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm,
const std::string name,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, prm, op->Inputs(), ngb_node_map);
return GetNode(op, name, op->Inputs(), ngb_node_map);
}
std::shared_ptr<ngraph::Node> GetOutputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm,
const std::string name,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, prm, op->Outputs(), ngb_node_map);
return GetNode(op, name, op->Outputs(), ngb_node_map);
}
void SetOutputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm, std::shared_ptr<ngraph::Node> node,
const std::string name, std::shared_ptr<ngraph::Node> node,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = op->Outputs().at(prm);
auto& var_names = op->Outputs().at(name);
if (var_names.size() == 1) {
/* */
auto dummy_out = GetOutputNode(op, name, ngb_node_map);
if (dummy_out && dummy_out->get_shape() != node->get_shape()) {
node = NgReshaper(node, dummy_out->get_shape());
}
if (dummy_out &&
dummy_out->get_element_type() != node->get_element_type()) {
node = std::make_shared<ngraph::op::Convert>(
node, dummy_out->get_element_type());
}
(*ngb_node_map)[var_names[0]] = node;
} else if (var_names.size() == 0) {
(*ngb_node_map)[""] = node;
} else {
PADDLE_THROW("prm %s has more than 1 var_names.", prm);
PADDLE_THROW("name %s has more than 1 var_names.", name);
}
}
bool HasOutput(const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm) {
const std::string name) {
auto& outputs = op->Outputs();
if (outputs.find(prm) == outputs.end()) return false;
return outputs.at(prm).size() > 0;
if (outputs.find(name) == outputs.end()) return false;
return outputs.at(name).size() > 0;
}
inline void GetMidDims(const ngraph::Shape& x_shape,
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 3932cd67
......@@ -26,5 +26,5 @@ if(WITH_PYTHON)
get_property (os_dependency_modules GLOBAL PROPERTY OS_DEPENDENCY_MODULES)
target_link_libraries(paddle_pybind ${os_dependency_modules})
cc_test(tensor_py_test SRCS tensor_py_test.cc DEPS python)
cc_test(tensor_py_test SRCS tensor_py_test.cc DEPS python pybind)
endif(WITH_PYTHON)
python/CMakeLists.txt
浏览文件 @ 3932cd67
......@@ -54,7 +54,7 @@ ELSE(WIN32)
DEPENDS copy_paddle_pybind ${FLUID_CORE} framework_py_proto profiler_py_proto ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER})
ENDIF()
set(paddle_python_deps ${PADDLE_PYTHON_BUILD_DIR}/.timestamp ${MKL_DEPENDS})
set(paddle_python_deps ${PADDLE_PYTHON_BUILD_DIR}/.timestamp ${MKL_DEPENDS} ${external_project_dependencies})
add_custom_target(paddle_python ALL DEPENDS ${paddle_python_deps})
set(PADDLE_PYTHON_PACKAGE_DIR ${CMAKE_CURRENT_BINARY_DIR}/dist/)
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 3932cd67
......@@ -397,10 +397,10 @@ def box_coder(prior_box,
input is image feature map, they are close to
the origin of the coordinate system. [xmax, ymax]
is the right bottom coordinate of the anchor box.
prior_box_var(Variable|list): prior_box_var supports two types of input.
One is variable with shape [M, 4] holds M group.
The other one is list consist of 4 elements
shared by all boxes.
prior_box_var(Variable|list|None): prior_box_var supports two types
of input. One is variable with shape [M, 4]
holds M group. The other one is list consist of
4 elements shared by all boxes.
target_box(Variable): This input can be a 2-D LoDTensor with shape
[N, 4] when code_type is 'encode_center_size'.
This input also can be a 3-D Tensor with shape
......
python/paddle/fluid/tests/unittests/ngraph/test_accuracy_ngraph_op.py 0 → 100644
浏览文件 @ 3932cd67
# 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 unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_accuracy_op import TestAccuracyOp
class TestNGRAPHAccuracyOp(TestAccuracyOp):
def setUp(self):
super(TestNGRAPHAccuracyOp, self).setUp()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_box_coder_op.py
浏览文件 @ 3932cd67
......@@ -34,7 +34,9 @@ def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0):
pb_y = pb_y.reshape(shape)
if pb_v.ndim == 2:
pb_v = pb_v.reshape(1, pb_v.shape[0], pb_v.shape[1])
var_shape = (1, pb_v.shape[0], pb_v.shape[1]) if axis == 0 else (
pb_v.shape[0], 1, pb_v.shape[1])
pb_v = pb_v.reshape(var_shape)
if pb_v.ndim == 1:
tb_x = pb_v[0] * t_box[:, :, 0] * pb_w + pb_x
tb_y = pb_v[1] * t_box[:, :, 1] * pb_h + pb_y
......@@ -125,33 +127,6 @@ class TestBoxCoderOp(OpTest):
self.outputs = {'OutputBox': output_box}
class TestBoxCoderOpWithOneRankVar(OpTest):
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((81, 4)).astype('float32')
prior_box_var = np.random.random((4)).astype('float32')
target_box = np.random.random((20, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
output_box = batch_box_coder(prior_box, prior_box_var, target_box,
lod[0], code_type, box_normalized)
self.inputs = {
'PriorBox': prior_box,
'PriorBoxVar': prior_box_var,
'TargetBox': target_box,
}
self.attrs = {
'code_type': 'decode_center_size',
'box_normalized': False
}
self.outputs = {'OutputBox': output_box}
class TestBoxCoderOpWithoutBoxVar(OpTest):
def test_check_output(self):
self.check_output()
......@@ -210,7 +185,7 @@ class TestBoxCoderOpWithAxis(OpTest):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((30, 4)).astype('float32')
prior_box_var = np.random.random((4)).astype('float32')
prior_box_var = np.random.random((30, 4)).astype('float32')
target_box = np.random.random((30, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
......
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