hedaoyuan
9 years ago
parent
148bd4d0b3
commit
5fddd99e18
@ -1,12 +1,27 @@
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file(GLOB FUNCTION_HEADERS . *.h)
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if(NOT WITH_GPU)
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file(GLOB FUNCTION_SOURCES . *.cpp)
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add_library(paddle_function STATIC ${FUNCTION_SOURCES})
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else()
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file(GLOB FUNCTION_SOURCES . *.cpp *.cu)
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cuda_add_library(paddle_function ${FUNCTION_SOURCES})
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file(GLOB h_files . *_op.h)
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file(GLOB cpp_files . *_op.cpp)
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list(APPEND h_files Function.h)
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list(APPEND cpp_files Function.cpp)
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if(WITH_GPU)
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file(GLOB cu_files . *_op_gpu.cu)
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cuda_compile(cu_objs ${cu_files})
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endif()
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add_style_check_target(paddle_function ${FUNCTION_SOURCES})
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add_style_check_target(paddle_function ${FUNCTION_HEADERS})
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add_library(paddle_function STATIC ${cpp_files} ${cu_objs})
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add_library(paddle_test_main STATIC TestMain.cpp)
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if(WITH_GPU)
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# TODO:
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# file(GLOB test_files . *_op_test.cpp)
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# add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files})
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add_simple_unittest(cross_map_normal_op_test)
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endif()
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add_style_check_target(paddle_function ${h_files})
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add_style_check_target(paddle_function ${cpp_files})
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if(WITH_GPU)
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add_style_check_target(paddle_function ${cu_files})
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endif()
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@ -0,0 +1,102 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "Function.h"
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#include "paddle/math/Vector.h"
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#include "paddle/math/tests/TensorCheck.h"
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namespace paddle {
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class FunctionCompare {
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public:
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FunctionCompare(const std::string& name, const FuncConfig& config)
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: cpu(FunctionBase::funcRegistrar_.createByType(name + "-CPU")),
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gpu(FunctionBase::funcRegistrar_.createByType(name + "-GPU")) {
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cpu->init(config);
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gpu->init(config);
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}
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void cmpWithArg(const Arguments& inputs,
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const Arguments& outputs,
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const Arguments& inouts) {
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// init cpu and gpu arguments
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auto initArgs = [=](
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Arguments& cpuArgs, Arguments& gpuArgs, const Arguments& inArgs) {
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for (auto arg : inArgs) {
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size_t size = sizeof(real);
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for (auto dim : arg.dims_) {
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size *= dim;
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}
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cpuMemory.emplace_back(std::make_shared<CpuMemoryHandle>(size));
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gpuMemory.emplace_back(std::make_shared<GpuMemoryHandle>(size));
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cpuArgs.emplace_back(
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Tensor((real*)cpuMemory.back()->getBuf(), arg.dims_));
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gpuArgs.emplace_back(
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Tensor((real*)gpuMemory.back()->getBuf(), arg.dims_));
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// will use an api to refactor this code.
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CpuVector cpuVector(size / sizeof(real),
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(real*)cpuArgs.back().getData());
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GpuVector gpuVector(size / sizeof(real),
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(real*)gpuArgs.back().getData());
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cpuVector.uniform(0.001, 1);
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gpuVector.copyFrom(cpuVector);
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}
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};
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initArgs(cpuInputs, gpuInputs, inputs);
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initArgs(cpuOutputs, gpuOutputs, outputs);
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initArgs(cpuInouts, gpuInouts, inouts);
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// function calculate
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cpu->calc(cpuInputs, cpuOutputs, cpuInouts);
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gpu->calc(gpuInputs, gpuOutputs, gpuInouts);
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// check outputs and inouts
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auto checkArgs = [=](const Arguments& cpuArgs, const Arguments& gpuArgs) {
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for (size_t i = 0; i < cpuArgs.size(); i++) {
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auto cpu = cpuArgs[i];
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auto gpu = gpuArgs[i];
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size_t size = 1;
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for (auto dim : cpu.dims_) {
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size *= dim;
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}
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CpuVector cpuVector(size, (real*)cpu.getData());
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GpuVector gpuVector(size, (real*)gpu.getData());
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autotest::TensorCheckErr(cpuVector, gpuVector);
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}
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};
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checkArgs(cpuOutputs, gpuOutputs);
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checkArgs(cpuInouts, gpuInouts);
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}
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protected:
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std::shared_ptr<FunctionBase> cpu;
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std::shared_ptr<FunctionBase> gpu;
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std::vector<CpuMemHandlePtr> cpuMemory;
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std::vector<GpuMemHandlePtr> gpuMemory;
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Arguments cpuInputs;
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Arguments cpuOutputs;
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Arguments cpuInouts;
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Arguments gpuInputs;
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Arguments gpuOutputs;
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Arguments gpuInouts;
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};
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} // namespace paddle
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using paddle::FunctionCompare;
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using paddle::FuncConfig;
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using paddle::Dims;
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using paddle::Tensor;
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@ -0,0 +1,22 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <gtest/gtest.h>
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#include "paddle/utils/Util.h"
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int main(int argc, char** argv) {
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testing::InitGoogleTest(&argc, argv);
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paddle::initMain(argc, argv);
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return RUN_ALL_TESTS();
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}
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@ -0,0 +1,71 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <gtest/gtest.h>
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#include "FunctionTest.h"
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TEST(CrossMapNormal, real) {
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for (size_t numSamples : {5, 32}) {
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for (size_t channels : {1, 5, 32}) {
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for (size_t imgSizeH : {5, 33, 100}) {
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for (size_t imgSizeW : {5, 32, 96}) {
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for (size_t size : {1, 2, 3, 5, 7}) {
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VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
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<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW
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<< " size=" << size;
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FunctionCompare compare("CrossMapNormal",
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FuncConfig()
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.set("size", size)
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.set("scale", (real)1.5)
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.set("pow", (real)0.5));
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Dims dims{numSamples, channels, imgSizeH, imgSizeW};
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compare.cmpWithArg({Tensor(nullptr, dims)},
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{Tensor(nullptr, dims), Tensor(nullptr, dims)},
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{});
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}
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}
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}
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}
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}
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}
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TEST(CrossMapNormalGrad, real) {
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for (size_t numSamples : {5, 32}) {
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for (size_t channels : {1, 5, 32}) {
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for (size_t imgSizeH : {5, 33, 100}) {
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for (size_t imgSizeW : {5, 32, 96}) {
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for (size_t size : {1, 2, 3, 5, 7}) {
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VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
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<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW
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<< " size=" << size;
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FunctionCompare compare("CrossMapNormalGrad",
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FuncConfig()
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.set("size", size)
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.set("scale", (real)1.5)
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.set("pow", (real)0.5));
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Dims dims{numSamples, channels, imgSizeH, imgSizeW};
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compare.cmpWithArg({Tensor(nullptr, dims),
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Tensor(nullptr, dims),
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Tensor(nullptr, dims),
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Tensor(nullptr, dims)},
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{Tensor(nullptr, dims)},
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{});
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}
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}
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}
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}
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}
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}
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