TEST(Crop, real) {
for (size_t numSamples : {5, 32}) {
for (size_t channels : {5, 5, 32}) {
for (size_t imgSizeH : {5, 33, 100}) {
for (size_t imgSizeW : {5, 32, 96}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
for (bool test_grad : {false, true}) {
CpuGpuFuncCompare compare(
test_grad ? "CropGrad" : "Crop",
FuncConfig()
.set<std::vector<uint32_t>>("crop_corner", {0, 1, 1, 1})
.set<std::vector<uint32_t>>("crop_shape", {0, 2, 3, 3}));
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{numSamples, 2, 3, 3};
compare.addInputs(
BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims));
compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT,
test_grad ? inDims : outDims,
test_grad ? ADD_TO : ASSIGN_TO),
test_grad ? ADD_TO : ASSIGN_TO);
compare.run();
}
}
}
}
}
}
TEST(CrossMapNormalGrad, real) {
for (size_t numSamples : {5}) {
for (size_t channels : {1, 5}) {
for (size_t imgSizeH : {5, 33}) {
for (size_t imgSizeW : {5, 32}) {
for (size_t size : {1, 3}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW
<< " size=" << size;
CpuGpuFuncCompare test("CrossMapNormalGrad",
FuncConfig()
.set("size", size)
.set("scale", (real)1.5)
.set("pow", (real)0.5));
TensorShape shape{numSamples, channels, imgSizeH, imgSizeW};
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape));
// run Function
test.run();
}
}
}
}
}
}
TEST(BlockExpandBackward, real) {
for (size_t batchSize : {5}) {
for (size_t channels : {1, 5}) {
for (size_t inputHeight : {5, 33}) {
for (size_t inputWidth : {5, 32}) {
for (size_t block : {1, 3, 5}) {
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
// init Test object
std::vector<size_t> strides = {stride, stride};
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> blocks = {block, block};
CpuGpuFuncCompare test("BlockExpandGrad",
FuncConfig()
.set("strides", strides)
.set("paddings", paddings)
.set("blocks", blocks));
size_t outputHeight =
1 +
(inputHeight + 2 * padding - block + stride - 1) / stride;
size_t outputWidth =
1 +
(inputWidth + 2 * padding - block + stride - 1) / stride;
TensorShape inputShape =
TensorShape({batchSize, channels, inputHeight, inputWidth});
TensorShape outputShape =
TensorShape({batchSize,
outputHeight * outputWidth,
channels * block * block});
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, outputShape));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, inputShape),
ADD_TO);
// run Function
test.run();
}
}
}
}
}
}
}
}
