Modify FunctionCompare to Compare2Function to support comparison of two CPU functions.

gangliao-patch-1
hedaoyuan 8 years ago
parent 1846d9e172
commit 1879332a30

@ -28,7 +28,7 @@ void testMatrixProjectionForward(int context_start,
std::max(0, (int)(context_start + context_length - 1)); std::max(0, (int)(context_start + context_length - 1));
if (pad == 0) is_padding = false; if (pad == 0) is_padding = false;
FunctionCompare test( CpuGpuFuncCompare test(
"ContextProjectionForward", "ContextProjectionForward",
FuncConfig() FuncConfig()
.set("context_length", context_length) .set("context_length", context_length)
@ -60,7 +60,7 @@ void testMatrixProjectionBackward(int context_start,
std::max(0, (int)(context_start + context_length - 1)); std::max(0, (int)(context_start + context_length - 1));
if (pad == 0) is_padding = false; if (pad == 0) is_padding = false;
FunctionCompare test( CpuGpuFuncCompare test(
"ContextProjectionBackward", "ContextProjectionBackward",
FuncConfig() FuncConfig()
.set("context_length", context_length) .set("context_length", context_length)

@ -22,7 +22,7 @@ void testCosSimForward(size_t height_x,
size_t height_y, size_t height_y,
size_t width, size_t width,
real scale) { real scale) {
FunctionCompare test("CosSimForward", FuncConfig().set("scale", scale)); CpuGpuFuncCompare test("CosSimForward", FuncConfig().set("scale", scale));
// prepare input arguments // prepare input arguments
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, width})); test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, width}));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_y, width})); test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_y, width}));
@ -36,7 +36,7 @@ void testCosSimBackward(size_t height_x,
size_t height_y, size_t height_y,
size_t width, size_t width,
real scale) { real scale) {
FunctionCompare test("CosSimBackward", FuncConfig().set("scale", scale)); CpuGpuFuncCompare test("CosSimBackward", FuncConfig().set("scale", scale));
// prepare input arguments // prepare input arguments
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, 1})); test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, 1}));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, 1})); test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, 1}));

@ -28,7 +28,7 @@ TEST(CrossMapNormal, real) {
<< " size=" << size; << " size=" << size;
// init Test object // init Test object
FunctionCompare test("CrossMapNormal", CpuGpuFuncCompare test("CrossMapNormal",
FuncConfig() FuncConfig()
.set("size", size) .set("size", size)
.set("scale", (real)1.5) .set("scale", (real)1.5)
@ -57,7 +57,7 @@ TEST(CrossMapNormalGrad, real) {
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW
<< " size=" << size; << " size=" << size;
FunctionCompare test("CrossMapNormalGrad", CpuGpuFuncCompare test("CrossMapNormalGrad",
FuncConfig() FuncConfig()
.set("size", size) .set("size", size)
.set("scale", (real)1.5) .set("scale", (real)1.5)

File diff suppressed because it is too large Load Diff

@ -35,7 +35,7 @@ void testFuncDDDMatrix(
size_t heightC = dimM; size_t heightC = dimM;
size_t widthC = dimN; size_t widthC = dimN;
// init Test object // init Test object
FunctionCompare test( CpuGpuFuncCompare test(
"MulOp", FuncConfig().set("aTrans", transa).set("bTrans", transb)); "MulOp", FuncConfig().set("aTrans", transa).set("bTrans", transb));
// prepare input arguments // prepare input arguments
/// matrix A : HA * WA /// matrix A : HA * WA
@ -81,8 +81,8 @@ void testFuncDSparseDMatrix(
size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) {
real scaleT = 1.0; real scaleT = 1.0;
// init Test object // init Test object
FunctionCompare test("MulOp", CpuGpuFuncCompare test(
FuncConfig().set("aTrans", false).set("bTrans", false)); "MulOp", FuncConfig().set("aTrans", false).set("bTrans", false));
// prepare input arguments // prepare input arguments
/// sparse matrix A : M * K /// sparse matrix A : M * K
test.addInputs(SparseMatrixArg( test.addInputs(SparseMatrixArg(
@ -126,8 +126,8 @@ void testFuncDDSparseMatrix(
size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) {
real scaleT = 1.0; real scaleT = 1.0;
// init Test object // init Test object
FunctionCompare test("MulOp", CpuGpuFuncCompare test(
FuncConfig().set("aTrans", false).set("bTrans", false)); "MulOp", FuncConfig().set("aTrans", false).set("bTrans", false));
// prepare input arguments // prepare input arguments
/// matrix A : M * K /// matrix A : M * K
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK})); test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK}));
@ -172,8 +172,8 @@ void testFuncSparseDDMatrix(
size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) {
real scaleT = 1.0; real scaleT = 1.0;
// init Test object // init Test object
FunctionCompare test("MulOp", CpuGpuFuncCompare test(
FuncConfig().set("aTrans", false).set("bTrans", false)); "MulOp", FuncConfig().set("aTrans", false).set("bTrans", false));
// prepare input arguments // prepare input arguments
/// matrix A : M * K /// matrix A : M * K
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK})); test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK}));

@ -25,7 +25,7 @@ TEST(Pad, real) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
for (bool test_grad : {false, true}) { for (bool test_grad : {false, true}) {
FunctionCompare compare( CpuGpuFuncCompare compare(
test_grad ? "PadGrad" : "Pad", test_grad ? "PadGrad" : "Pad",
FuncConfig() FuncConfig()
.set<std::vector<uint32_t>>("channel", {2, 3}) .set<std::vector<uint32_t>>("channel", {2, 3})

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