diff --git a/unit_tests/Test_AvgPoolingImpl.cpp b/unit_tests/Test_AvgPoolingImpl.cpp
index d270fbc9c72f5d057c4617bd1ba28704bf0ce6ee..d4d39db555e9d12c7e5135d1eb3db6ffc8f459c3 100644
--- a/unit_tests/Test_AvgPoolingImpl.cpp
+++ b/unit_tests/Test_AvgPoolingImpl.cpp
@@ -160,140 +160,4 @@ TEST_CASE("[gpu/operator] AvgPooling(forward)", "[AvgPooling][GPU]") {
delete[] computedOutput;
}
-
- int number_of_operation{0};
- SECTION("Random Input") {
- constexpr std::uint16_t NBTRIALS = 10;
- std::size_t kernel = 2;
- std::size_t stride = 2;
- // Create a random number generator
- std::random_device rd;
- std::mt19937 gen(rd());
- std::uniform_real_distribution<float> valueDist(
- 0.1f, 1.1f); // Random float distribution between 0 and 1
- std::uniform_int_distribution<std::size_t> dimSizeDist(std::size_t(2),
- std::size_t(10));
-
- std::uniform_int_distribution<std::size_t> nbDimsDist(std::size_t(4), std::size_t(4));
-
- // Create AveragePooling Operator
- std::shared_ptr<Node> myAvgPool = AvgPooling({kernel,kernel}, "myAvgPool", {stride,stride});
- auto op = std::static_pointer_cast<OperatorTensor>(myAvgPool -> getOperator());
- op->setDataType(DataType::Float32);
- op->setBackend("cpu");
-
- // Create the input Tensor
- std::shared_ptr<Tensor> T0 = std::make_shared<Tensor>();
- op->associateInput(0, T0);
- T0->setDataType(DataType::Float32);
- T0->setBackend("cpu");
-
- // To measure execution time of 'AveragePooling_Op::forward()'
- std::chrono::time_point<std::chrono::system_clock> start;
- std::chrono::time_point<std::chrono::system_clock> end;
- std::chrono::duration<double, std::micro> duration{};
- std::size_t number_of_operation = 0;
-
- SECTION("OutDims") {
- for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
- // generate a random Tensor
- const std::size_t nbDims = nbDimsDist(gen);
- std::vector<std::size_t> dims;
- for (std::size_t i = 0; i < nbDims; ++i) {
- dims.push_back(dimSizeDist(gen));
- }
-
- const std::size_t nb_elements = std::accumulate(dims.cbegin(), dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
- number_of_operation += nb_elements;
-
- // Fill input tensor
- float* array0 = new float[nb_elements];
- for (std::size_t i = 0; i < nb_elements; ++i) {
- array0[i] = valueDist(gen);
- }
- T0->resize(dims);
- T0 -> getImpl() -> setRawPtr(array0, nb_elements);
-
- // Run inference
- op->computeOutputDims();
- start = std::chrono::system_clock::now();
- myAvgPool->forward();
- end = std::chrono::system_clock::now();
- duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
-
- // Verify output dimensions
- REQUIRE(op->getOutput(0)->nbDims() == dims.size());
- for (size_t i = 0; i < op->getOutput(0)->nbDims(); ++i) {
- if(i == 2 || i == 3)
- REQUIRE(op->getOutput(0)->dims()[i] == (1 + static_cast<DimSize_t>(std::floor(static_cast<float>(dims[i] - kernel) / static_cast<float>(stride)))));
- else
- REQUIRE(op->getOutput(0)->dims()[i] == dims[i]);
- }
-
- delete[] array0;
- }
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
- }
-
- SECTION("Values") {
- for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
- // generate a random Tensor
- const std::size_t nbDims = nbDimsDist(gen);
- std::vector<std::size_t> dims;
- for (std::size_t i = 0; i < nbDims; ++i) {
- dims.push_back(dimSizeDist(gen));
- }
-
- const std::size_t nb_elements = std::accumulate(dims.cbegin(), dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
- number_of_operation += nb_elements;
-
- // Fill input tensor
- float* array0 = new float[nb_elements];
- for (std::size_t i = 0; i < nb_elements; ++i) {
- array0[i] = valueDist(gen);
- }
- T0->resize(dims);
- T0 -> getImpl() -> setRawPtr(array0, nb_elements);
-
- // Fill expected output
- std::vector<float> result;
- std::size_t rows = dims[2], cols = dims[3], nbMat = dims[0] * dims[1], matSize = rows*cols;
- for (size_t i = 0; i < nbMat; i++)
- {
- for(size_t r=0; r< rows; r += stride){
- for(size_t c=0; c< cols; c += stride){
- float sum = 0.0f;
- for (size_t m = 0; m < kernel; m++)
- {
- for (size_t n = 0; n < kernel; n++)
- {
- sum += array0[i * matSize + (r + m) * cols + c + n];
- }
- }
- result.push_back(sum/(kernel*kernel));
- }
- }
- }
-
- // Run inference
- op->computeOutputDims();
- start = std::chrono::system_clock::now();
- myAvgPool->forward();
- end = std::chrono::system_clock::now();
- duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
-
- op->getOutput(0)->print();
- float* computedOutput = static_cast<float*>(op->getOutput(0)->getImpl()->rawPtr());
- for (size_t i = 0; i < op->getOutput(0)->size(); i++)
- {
- REQUIRE(abs(computedOutput[i] - result[i]) < 1e-6);
- }
-
- delete[] array0;
- }
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
- }
- }
}
\ No newline at end of file