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提交 f70fc4a4 编写于 作者: H hedaoyuan

move some test from test_matrixCompare.cpp to test_BaseMatrix.cpp and test_Matrix.cpp

上级 1873945d
......@@ -1578,11 +1578,6 @@ void BaseMatrixT<real>::minRows(BaseMatrixT& b) {
applyRow(aggregate::min(), b);
}
template<>
void BaseMatrixT<real>::sumCols(BaseMatrixT& b) {
applyCol(aggregate::sum(), b);
}
template<>
void BaseMatrixT<real>::maxCols(BaseMatrixT& b) {
applyCol(aggregate::max(), b);
......
......@@ -1007,8 +1007,6 @@ public:
/// calculate the minimum value of each row of the matrix b.
void minRows(BaseMatrixT& b);
/// calculate the sum of each column of the matrix b.
void sumCols(BaseMatrixT& b);
/// calculate the maximum value of each column of the matrix b.
void maxCols(BaseMatrixT& b);
/// calculate the minimum value of each column of the matrix b.
......
......@@ -110,4 +110,10 @@ void TensorCheck(AssertEq compare, real args1, real args2) {
<< ", args2 = " << args2;
}
template <typename AssertEq>
void TensorCheck(AssertEq compare, size_t args1, size_t args2) {
EXPECT_EQ(args1, args2) << "[Test error] args1 = " << args1
<< ", args2 = " << args2;
}
} // namespace autotest
......@@ -65,15 +65,24 @@ public:
// construct a argument
template <typename T>
T construct(int height, int width);
template <>
float construct(int height, int width) {
return 0.0;
}
template <>
size_t construct(int height, int width) {
size_t offset = std::rand() % (height < width ? height : width);
return offset;
}
template <>
CpuMatrix construct(int height, int width) {
CpuMatrix a(height, width);
return a;
}
template <>
GpuMatrix construct(int height, int width) {
GpuMatrix a(height, width);
......@@ -83,14 +92,22 @@ GpuMatrix construct(int height, int width) {
// init a argument
template <typename T>
void init(T& v);
template <>
void init(float& v) {
v = 0.5;
}
template <>
void init(size_t& v) {
return;
}
template <>
void init(CpuMatrix& v) {
v.randomizeUniform();
}
template <>
void init(GpuMatrix& v) {
v.randomizeUniform();
......@@ -111,10 +128,17 @@ template <std::size_t I = 0, typename... Args>
// copy a argument, copy src to dest
template <typename T1, typename T2>
void copy(T1& dest, T2& src);
template <>
void copy(float& dest, float& src) {
dest = src;
}
template <>
void copy(size_t& dest, size_t& src) {
dest = src;
}
template <>
void copy(GpuMatrix& dest, CpuMatrix& src) {
dest.copyFrom(src);
......@@ -165,8 +189,8 @@ R call(C& obj, R (FC::*f)(FArgs...), Args&&... args) {
return (obj.*f)(args...);
}
template <bool ApplyRow,
bool ApplyCol,
template <bool AsRowVector,
bool AsColVector,
std::size_t... I,
typename C,
typename R,
......@@ -177,8 +201,8 @@ void BaseMatrixCompare(R (C::*f)(Args...),
bool checkArgs = false) {
for (auto height : {1, 11, 73, 128, 200, 330}) {
for (auto width : {1, 3, 32, 100, 512, 1000}) {
CpuMatrix obj1(ApplyCol ? 1 : height, ApplyRow ? 1 : width);
GpuMatrix obj2(ApplyCol ? 1 : height, ApplyRow ? 1 : width);
CpuMatrix obj1(AsRowVector ? 1 : height, AsColVector ? 1 : width);
GpuMatrix obj2(AsRowVector ? 1 : height, AsColVector ? 1 : width);
init(obj1);
copy(obj2, obj1);
......@@ -227,7 +251,7 @@ void BaseMatrixCompare(R (C::*f)(Args...), bool checkArgs = false) {
}
template <std::size_t... I, typename C, typename R, typename... Args>
void BaseMatrixApplyRow(R (C::*f)(Args...)) {
void BaseMatrixAsColVector(R (C::*f)(Args...)) {
static_assert(sizeof...(I) == sizeof...(Args),
"size of parameter packs are not equal");
......@@ -237,11 +261,11 @@ void BaseMatrixApplyRow(R (C::*f)(Args...)) {
autotest::AssertEqual compare(1e-8);
#endif
autotest::BaseMatrixCompare<true, false, I...>(f, compare);
autotest::BaseMatrixCompare<false, true, I...>(f, compare);
}
template <std::size_t... I, typename C, typename R, typename... Args>
void BaseMatrixApplyCol(R (C::*f)(Args...)) {
void BaseMatrixAsRowVector(R (C::*f)(Args...)) {
static_assert(sizeof...(I) == sizeof...(Args),
"size of parameter packs are not equal");
......@@ -250,5 +274,5 @@ void BaseMatrixApplyCol(R (C::*f)(Args...)) {
#else
autotest::AssertEqual compare(1e-8);
#endif
autotest::BaseMatrixCompare<false, true, I...>(f, compare);
autotest::BaseMatrixCompare<true, false, I...>(f, compare);
}
......@@ -24,7 +24,6 @@ limitations under the License. */
#include "TestUtils.h"
using namespace paddle; // NOLINT
using namespace std; // NOLINT
/**
* Test member functions which prototype is
......@@ -32,8 +31,8 @@ using namespace std; // NOLINT
*/
TEST(BaseMatrix, void) {
typedef void (BaseMatrix::*FunctionProto)();
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare(static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare(static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXCOMPARE(neg);
BASEMATRIXCOMPARE(exp);
......@@ -46,7 +45,7 @@ TEST(BaseMatrix, void) {
BASEMATRIXCOMPARE(zero);
BASEMATRIXCOMPARE(one);
#undef BASEMATRIXCOMPARE
#undef BASEMATRIXCOMPARE
}
/**
......@@ -55,8 +54,8 @@ TEST(BaseMatrix, void) {
*/
TEST(BaseMatrix, real) {
typedef void (BaseMatrix::*FunctionProto)(real);
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0>(static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0>(static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXCOMPARE(pow);
BASEMATRIXCOMPARE(subScalar);
......@@ -67,7 +66,7 @@ TEST(BaseMatrix, real) {
BASEMATRIXCOMPARE(biggerThanScalar);
BASEMATRIXCOMPARE(downClip);
#undef BASEMATRIXCOMPARE
#undef BASEMATRIXCOMPARE
}
/**
......@@ -76,13 +75,13 @@ TEST(BaseMatrix, real) {
*/
TEST(BaseMatrix, real_real) {
typedef void (BaseMatrix::*FunctionProto)(real, real);
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0, 1>(static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0, 1>(static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXCOMPARE(add);
BASEMATRIXCOMPARE(clip);
#undef BASEMATRIXCOMPARE
#undef BASEMATRIXCOMPARE
}
/**
......@@ -91,8 +90,8 @@ TEST(BaseMatrix, real_real) {
*/
TEST(BaseMatrix, BaseMatrix) {
typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&);
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0>(static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0>(static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXCOMPARE(assign);
BASEMATRIXCOMPARE(add);
......@@ -129,7 +128,7 @@ TEST(BaseMatrix, BaseMatrix) {
BASEMATRIXCOMPARE(addP2P);
BASEMATRIXCOMPARE(invSqrt);
#undef BASEMATRIXCOMPARE
#undef BASEMATRIXCOMPARE
}
/**
......@@ -138,8 +137,8 @@ TEST(BaseMatrix, BaseMatrix) {
*/
TEST(BaseMatrix, BaseMatrix_real) {
typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, real);
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0, 1>(static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0, 1>(static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXCOMPARE(addBias);
BASEMATRIXCOMPARE(add);
......@@ -154,7 +153,7 @@ TEST(BaseMatrix, BaseMatrix_real) {
BASEMATRIXCOMPARE(isEqualTo);
#undef BASEMATRIXCOMPARE
#undef BASEMATRIXCOMPARE
}
/**
......@@ -163,8 +162,8 @@ TEST(BaseMatrix, BaseMatrix_real) {
*/
TEST(BaseMatrix, BaseMatrix_BaseMatrix) {
typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, BaseMatrix&);
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0, 1>(static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXCOMPARE(function) \
BaseMatrixCompare<0, 1>(static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXCOMPARE(softCrossEntropy);
BASEMATRIXCOMPARE(softCrossEntropyBp);
......@@ -181,69 +180,25 @@ TEST(BaseMatrix, BaseMatrix_BaseMatrix) {
BASEMATRIXCOMPARE(dotMulSquare);
BASEMATRIXCOMPARE(dotSquareSquare);
#undef BASEMATRIXCOMPARE
#undef BASEMATRIXCOMPARE
}
/**
* Test aggregate member functions which prototype is
* void (BaseMatrix::*)(BaseMatrix&).
*/
TEST(Aggregate, BaseMatrix) {
typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&);
#define BASEMATRIXAPPLYROW(function) \
BaseMatrixApplyRow<0>(static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXAPPLYCOL(function) \
BaseMatrixApplyCol<0>(static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXAPPLYROW(maxRows);
BASEMATRIXAPPLYROW(minRows);
BASEMATRIXAPPLYCOL(sumCols);
BASEMATRIXAPPLYCOL(maxCols);
BASEMATRIXAPPLYCOL(minCols);
#undef BASEMATRIXAPPLYROW
#undef BASEMATRIXAPPLYCOL
// member function without overloaded
TEST(BaseMatrix, Other) {
BaseMatrixCompare<0, 1, 2>(&BaseMatrix::rowScale);
BaseMatrixCompare<0, 1, 2>(&BaseMatrix::rowDotMul);
BaseMatrixCompare<0, 1, 2, 3>(&BaseMatrix::binaryClassificationError);
}
/**
* Test aggregate member functions which prototype is
* void (BaseMatrix::*)(BaseMatrix&, BaseMatrix&).
*/
TEST(Aggregate, BaseMatrix_BaseMatrix) {
typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, BaseMatrix&);
#define BASEMATRIXAPPLYROW(function) \
BaseMatrixApplyRow<0, 1>(static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXAPPLYCOL(function) \
BaseMatrixApplyCol<0, 1>(static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXAPPLYCOL(addDotMulVMM);
#undef BASEMATRIXAPPLYROW
#undef BASEMATRIXAPPLYCOL
}
/**
* Test aggregate member functions which prototype is
* void (BaseMatrix::*)(BaseMatrix&, real, real).
*/
TEST(Aggregate, BaseMatrix_real_real) {
typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, real, real);
#define BASEMATRIXAPPLYROW(function) \
BaseMatrixApplyRow<0, 1, 2>(\
static_cast<FunctionProto>(&BaseMatrix::function));
#define BASEMATRIXAPPLYCOL(function) \
BaseMatrixApplyCol<0, 1, 2>(\
static_cast<FunctionProto>(&BaseMatrix::function));
BASEMATRIXAPPLYROW(sumRows);
BASEMATRIXAPPLYCOL(sumCols);
TEST(BaseMatrix, Aggregate) {
BaseMatrixAsColVector<0>(&BaseMatrix::maxRows);
BaseMatrixAsColVector<0>(&BaseMatrix::minRows);
BaseMatrixAsColVector<0, 1, 2>(&BaseMatrix::sumRows);
#undef BASEMATRIXAPPLYROW
#undef BASEMATRIXAPPLYCOL
BaseMatrixAsRowVector<0>(&BaseMatrix::maxCols);
BaseMatrixAsRowVector<0>(&BaseMatrix::minCols);
BaseMatrixAsRowVector<0, 1>(&BaseMatrix::addDotMulVMM);
BaseMatrixAsRowVector<0, 1, 2>(&BaseMatrix::sumCols);
}
int main(int argc, char** argv) {
......
......@@ -19,25 +19,20 @@ limitations under the License. */
*/
#include <gtest/gtest.h>
#include "paddle/utils/Util.h"
#include "paddle/math/BaseMatrix.h"
#include "TestUtils.h"
using namespace paddle; // NOLINT
using namespace std; // NOLINT
/**
* Test member functions which prototype is
* void (Matrix::*)(Matrix&).
*/
TEST(BaseMatrix, real) {
typedef void (Matrix::*FunctionProto)(Matrix&);
#define MATRIXCOMPARE(function) \
BaseMatrixCompare<0>(static_cast<FunctionProto>(&Matrix::function), true);
TEST(Matrix, Matrix) {
BaseMatrixCompare<0>(&Matrix::softmax, true);
BaseMatrixCompare<0, 1>(&Matrix::sumOfSquaresBp);
}
MATRIXCOMPARE(softmax);
TEST(Matrix, Aggregate) {
BaseMatrixAsRowVector<0, 1>(
static_cast<void (Matrix::*)(Matrix&, real)>(&Matrix::collectBias));
#undef MATRIXCOMPARE
BaseMatrixAsColVector<0, 1>(&Matrix::sumOfSquares);
}
int main(int argc, char** argv) {
......
......@@ -448,60 +448,6 @@ void testMatrixZeroAtOffset(int height, int width) {
MatrixCheckEqual(*cpuA, *cpuTest);
}
void testMatrixSumOfSquaresBp(int height, int width) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuC = std::make_shared<CpuMatrix>(height, width);
MatrixPtr gpuA = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuB = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuC = std::make_shared<GpuMatrix>(height, width);
cpuA->randomizeUniform();
cpuB->randomizeUniform();
cpuC->randomizeUniform();
gpuA->copyFrom(*cpuA);
gpuB->copyFrom(*cpuB);
gpuC->copyFrom(*cpuC);
cpuA->sumOfSquaresBp(*cpuB, *cpuC);
gpuA->sumOfSquaresBp(*gpuB, *gpuC);
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, width);
outputCheck->copyFrom(*gpuA);
MatrixCheckErr(*cpuA, *outputCheck);
}
void testMatrixBinaryRowScale(int height, int width) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(height, 1);
MatrixPtr gpuA = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuB = std::make_shared<GpuMatrix>(height, 1);
MatrixPtr cpuA1 = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB1 = std::make_shared<CpuMatrix>(height, 1);
MatrixPtr gpuA1 = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuB1 = std::make_shared<GpuMatrix>(height, 1);
cpuA->randomizeUniform();
cpuB->randomizeUniform();
gpuA->copyFrom(*cpuA);
gpuB->copyFrom(*cpuB);
cpuA1->copyFrom(*cpuA);
cpuB1->copyFrom(*cpuB);
gpuA1->copyFrom(*cpuA);
gpuB1->copyFrom(*cpuB);
cpuA->addColVector(*cpuB);
gpuA->addColVector(*gpuB);
cpuA1->addColumnVector(*cpuB1);
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, width);
outputCheck->copyFrom(*gpuA);
MatrixCheckEqual(*cpuA, *outputCheck);
MatrixCheckEqual(*cpuA, *cpuA1);
}
void testMatrixAddBias(int height, int width, real scale) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(1, width);
......@@ -521,76 +467,6 @@ void testMatrixAddBias(int height, int width, real scale) {
MatrixCheckErr(*cpuA, *outputCheck);
}
void testMatrixTernaryRowScale(int height, int width) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuC = std::make_shared<CpuMatrix>(height, width);
MatrixPtr gpuA = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuB = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuC = std::make_shared<GpuMatrix>(height, width);
MatrixPtr cpuA1 = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB1 = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuC1 = std::make_shared<CpuMatrix>(height, width);
cpuA->randomizeUniform();
cpuB->randomizeUniform();
cpuC->randomizeUniform();
gpuA->copyFrom(*cpuA);
gpuB->copyFrom(*cpuB);
gpuC->copyFrom(*cpuC);
cpuA1->copyFrom(*cpuA);
cpuB1->copyFrom(*cpuB);
cpuC1->copyFrom(*cpuC);
int columnOffset = rand() % width; // NOLINT
cpuA->rowScale(columnOffset, *cpuB, *cpuC);
gpuA->rowScale(columnOffset, *gpuB, *gpuC);
cpuA1->rowScale2(columnOffset, *cpuB1, *cpuC1);
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, width);
outputCheck->copyFrom(*gpuA);
MatrixCheckEqual(*cpuA, *outputCheck);
MatrixCheckEqual(*cpuA, *cpuA1);
}
void testMatrixTernaryRowDotMul(int height, int width) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuC = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuA1 = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB1 = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuC1 = std::make_shared<CpuMatrix>(height, width);
MatrixPtr gpuA = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuB = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuC = std::make_shared<GpuMatrix>(height, width);
cpuA->randomizeUniform();
cpuB->randomizeUniform();
cpuC->randomizeUniform();
cpuA1->copyFrom(*cpuA);
cpuB1->copyFrom(*cpuB);
cpuC1->copyFrom(*cpuC);
gpuA->copyFrom(*cpuA);
gpuB->copyFrom(*cpuB);
gpuC->copyFrom(*cpuC);
int columnOffset = rand() % width; // NOLINT
cpuA->rowDotMul(columnOffset, *cpuB, *cpuC);
gpuA->rowDotMul(columnOffset, *gpuB, *gpuC);
cpuA1->rowDotMul2(columnOffset, *cpuB1, *cpuC1);
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, width);
outputCheck->copyFrom(*gpuA);
MatrixCheckErr(*cpuA, *cpuA1);
MatrixCheckErr(*cpuA, *outputCheck);
}
void testMatrixAddDotMulMMV(int height, int width) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(height, width);
......@@ -670,18 +546,11 @@ TEST(Matrix, unary) {
for (auto width : {1, 3, 32, 100, 512, 1000, 3210}) {
VLOG(3) << " height=" << height << " width=" << width;
// applyTernary
testMatrixSumOfSquaresBp(height, width);
// asRowVector
testMatrixAddBias(height, width, 1.0);
testMatrixAddBias(height, width, 3.5);
testMatrixAddDotMulMMV(height, width);
// asColVector
testMatrixTernaryRowScale(height, width);
testMatrixBinaryRowScale(height, width);
// sum
testMatrixGetSum(height, width);
......@@ -782,119 +651,6 @@ TEST(Matrix, softmax) {
}
}
void testMatrixCollectBias(int height, int width) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(1, width);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(height, width);
MatrixPtr gpuA = std::make_shared<GpuMatrix>(1, width);
MatrixPtr gpuB = std::make_shared<GpuMatrix>(height, width);
cpuA->randomizeUniform();
cpuB->randomizeUniform();
gpuA->copyFrom(*cpuA);
gpuB->copyFrom(*cpuB);
real scale = 1.0f / (rand() % 10); // NOLINT
cpuA->collectBias(*cpuB, scale);
gpuA->collectBias(*gpuB, scale);
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(1, width);
outputCheck->copyFrom(*gpuA);
MatrixCheckErr(*cpuA, *outputCheck);
}
void testMatrixSumOfSquares(int height, int width, int endCol = 0) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(height, 1);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuC = std::make_shared<CpuMatrix>(height, width);
MatrixPtr gpuA = std::make_shared<GpuMatrix>(height, 1);
MatrixPtr gpuB = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuC = std::make_shared<GpuMatrix>(height, width);
cpuA->randomizeUniform();
cpuB->randomizeUniform();
cpuC->randomizeUniform();
gpuA->copyFrom(*cpuA);
gpuB->copyFrom(*cpuB);
gpuC->copyFrom(*cpuC);
if (!endCol) {
cpuA->sumOfSquares(*cpuB, *cpuC);
gpuA->sumOfSquares(*gpuB, *gpuC);
} else {
MatrixPtr subCpuB = cpuB->subColMatrix(0, endCol);
MatrixPtr subCpuC = cpuC->subColMatrix(0, endCol);
MatrixPtr subGpuB = gpuB->subColMatrix(0, endCol);
MatrixPtr subGpuC = gpuC->subColMatrix(0, endCol);
cpuA->sumOfSquares(*subCpuB, *subCpuC);
gpuA->sumOfSquares(*subGpuB, *subGpuC);
}
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, 1);
outputCheck->copyFrom(*gpuA);
MatrixCheckErr(*cpuA, *outputCheck);
}
void testMatrixBinaryClassificationError(int height, int width) {
MatrixPtr cpuA = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuC = std::make_shared<CpuMatrix>(height, width);
MatrixPtr gpuA = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuB = std::make_shared<GpuMatrix>(height, width);
MatrixPtr gpuC = std::make_shared<GpuMatrix>(height, width);
MatrixPtr cpuA2 = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuB2 = std::make_shared<CpuMatrix>(height, width);
MatrixPtr cpuC2 = std::make_shared<CpuMatrix>(height, width);
cpuA->randomizeUniform();
cpuB->randomizeUniform();
cpuC->randomizeUniform();
gpuA->copyFrom(*cpuA);
gpuB->copyFrom(*cpuB);
gpuC->copyFrom(*cpuC);
cpuA2->copyFrom(*cpuA);
cpuB2->copyFrom(*cpuB);
cpuC2->copyFrom(*cpuC);
real scale = 0.5;
int columnOffset = rand() % width; // NOLINT
cpuA->binaryClassificationError(columnOffset, *cpuB, *cpuC, scale);
gpuA->binaryClassificationError(columnOffset, *gpuB, *gpuC, scale);
cpuA2->binaryClassificationError2(columnOffset, *cpuB2, *cpuC2, scale);
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, width);
outputCheck->copyFrom(*gpuA);
MatrixCheckErr(*cpuA, *outputCheck);
MatrixCheckErr(*cpuA, *cpuA2);
}
TEST(Matrix, aggregate) {
for (auto height : {1, 11, 16, 32, 64, 73, 128, 200, 1024, 2345}) {
for (auto width : {1, 9, 16, 32, 64, 100, 512, 1000, 1024, 2453}) {
VLOG(3) << " height=" << height << " width=" << width;
testMatrixCollectBias(height, width);
testMatrixTernaryRowDotMul(height, width);
testMatrixSumOfSquares(height, width);
testMatrixBinaryClassificationError(height, width);
}
}
}
TEST(Matrix, aggregate2) {
for (auto height : {16, 32, 128, 512, 1024}) {
for (auto width :
{16, 32, 64, 128, 256, 512, 768, 1024, 2048, 3072, 4096}) {
VLOG(3) << " height=" << height << " width=" << width;
int endCol = rand() % width; // NOLINT
testMatrixSumOfSquares(height, width, endCol);
}
}
}
void testMatrixAddAtOffset(int height, int width1, int width2) {
MatrixPtr cpuInput = std::make_shared<CpuMatrix>(height, width1);
MatrixPtr cpuOutput = std::make_shared<CpuMatrix>(height, width2);
......
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