diff --git a/unit_tests/operator/Test_ConstantOfShapeImpl.cpp b/unit_tests/operator/Test_ConstantOfShapeImpl.cpp
index 42505d385fde7e72e09531f1607287ffc6978f75..8ec1669b92a5116999413cf55a8c5113363ef330 100644
--- a/unit_tests/operator/Test_ConstantOfShapeImpl.cpp
+++ b/unit_tests/operator/Test_ConstantOfShapeImpl.cpp
@@ -9,32 +9,27 @@
*
********************************************************************************/
-#include <algorithm>
-#include <chrono>
-#include <cmath>
-#include <cstddef> // std::size_t
-#include <cstdint> // std::uint16_t
-#include <iostream>
+#include <chrono> // std::micro, std::chrono::time_point,
+ // std::chrono::system_clock
+#include <cstddef> // std::size_t
+#include <cstdint> // std::int64_t, std::uint16_t
#include <memory>
-#include <numeric> // std::accumulate
-#include <ostream>
-#include <random> // std::random_device, std::mt19937, std::uniform_real_distribution
+#include <random> // std::random_device, std::mt19937
+ // std::uniform_int_distribution, std::uniform_real_distribution
+#include <vector>
-#include "catch2/internal/catch_compiler_capabilities.hpp"
-#include "catch2/internal/catch_enforce.hpp"
#include <catch2/catch_test_macros.hpp>
#include <catch2/generators/catch_generators_random.hpp>
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/backend/cpu/operator/ConstantOfShapeImpl.hpp"
+#include "aidge/data/Data.hpp"
#include "aidge/data/Tensor.hpp"
+#include "aidge/filler/Filler.hpp"
#include "aidge/operator/ConstantOfShape.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/TensorUtils.hpp"
-#include <aidge/data/Data.hpp>
-#include <aidge/data/half.hpp>
-#include <aidge/filler/Filler.hpp>
-#include <aidge/operator/OperatorTensor.hpp>
-#include <aidge/operator/Reshape.hpp>
-#include <aidge/utils/TensorUtils.hpp>
-#include <aidge/utils/Types.h>
+#include "aidge/utils/Types.h"
namespace Aidge {
TEST_CASE("[cpu/operator] ConstantOfShape", "[ConstantOfShape][CPU]") {
@@ -62,7 +57,7 @@ TEST_CASE("[cpu/operator] ConstantOfShape", "[ConstantOfShape][CPU]") {
result->setDataType(DataType::Int64);
result->setBackend("cpu");
for (DimSize_t i = 0; i < result->size(); ++i) {
- result->set<int64_t>(i, input_tensor_values_dist(gen));
+ result->set<std::int64_t>(i, input_tensor_values_dist(gen));
}
return result;
};
diff --git a/unit_tests/operator/Test_DivImpl.cpp b/unit_tests/operator/Test_DivImpl.cpp
index 5d7dfdf12032d4c444e38cda6d2a4298fc552b14..b03fe4aa91e96299f2a748026ee8ca5e5d57fb5c 100644
--- a/unit_tests/operator/Test_DivImpl.cpp
+++ b/unit_tests/operator/Test_DivImpl.cpp
@@ -9,17 +9,26 @@
*
********************************************************************************/
-#include <catch2/catch_test_macros.hpp>
-#include <cstddef> // std::size_t
-#include <cstdint> // std::uint16_t
-#include <chrono>
-#include <iostream>
+#include <chrono> // std::micro, std::chrono::time_point,
+ // std::chrono::system_clock
+#include <cstddef> // std::size_t
+#include <cstdint> // std::uint16_t
+#include <functional> // std::multiplies
#include <memory>
-#include <numeric> // std::accumulate
-#include <random> // std::random_device, std::mt19937, std::uniform_real_distribution
+#include <numeric> // std::accumulate
+#include <random> // std::random_device, std::mt19937
+ // std::uniform_int_distribution, std::uniform_real_distribution
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <fmt/core.h>
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/backend/cpu/operator/DivImpl.hpp"
+#include "aidge/data/Data.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/operator/Div.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/TensorUtils.hpp"
namespace Aidge {
@@ -117,8 +126,8 @@ TEST_CASE("[cpu/operator] Div", "[Div][CPU]") {
// with broadcasting
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
SECTION("+1-D Tensor / +1-D Tensor - broadcasting") {
@@ -212,8 +221,8 @@ TEST_CASE("[cpu/operator] Div", "[Div][CPU]") {
const std::size_t nb_elements = std::accumulate(dimsOut.cbegin(), dimsOut.cend(), std::size_t(1), std::multiplies<std::size_t>());
number_of_operation += nb_elements;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
SECTION("+1-D Tensor / 1-D Tensor") {
std::size_t number_of_operation = 0;
@@ -308,8 +317,8 @@ TEST_CASE("[cpu/operator] Div", "[Div][CPU]") {
number_of_operation += nb_elements;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
}
}
diff --git a/unit_tests/operator/Test_MatMulImpl.cpp b/unit_tests/operator/Test_MatMulImpl.cpp
index d6e934b4dc8d84e8a595eb74d1af9d2c68c892d1..daef47b32ffcca880a1bf2438e9ee9c35adbb2c8 100644
--- a/unit_tests/operator/Test_MatMulImpl.cpp
+++ b/unit_tests/operator/Test_MatMulImpl.cpp
@@ -9,21 +9,26 @@
*
********************************************************************************/
-#include <catch2/catch_test_macros.hpp>
-#include <cstddef> // std::size_t
-#include <cstdint> // std::uint16_t
-#include <chrono>
-#include <iostream>
+#include <chrono> // std::micro, std::chrono::time_point,
+ // std::chrono::system_clock, std::chrono::duration
+#include <cstddef> // std::size_t
+#include <cstdint> // std::uint16_t
#include <memory>
-#include <random> // std::random_device, std::mt19937, std::uniform_real_distribution
+#include <random> // std::random_device, std::mt19937
+ // std::uniform_int_distribution, std::uniform_real_distribution
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <fmt/core.h>
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/backend/cpu/operator/MatMulImpl.hpp"
+#include "aidge/data/Data.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/operator/MatMul.hpp"
#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/TensorUtils.hpp"
-#include "aidge/backend/cpu/operator/MatMulImpl.hpp"
-
namespace Aidge {
TEST_CASE("[cpu/operator] MatMul(forward)", "[MatMul][CPU]") {
@@ -106,8 +111,8 @@ TEST_CASE("[cpu/operator] MatMul(forward)", "[MatMul][CPU]") {
delete[] bigArray2;
delete[] res;
}
- std::cout << "multiplications over time spent: " << totalComputation/duration.count() << std::endl;
- std::cout << "total time: " << duration.count() << std::endl;
+ fmt::print("INFO: number of multiplications over time spent: {}\n", (totalComputation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
SECTION("3-D Tensors") {
@@ -174,8 +179,8 @@ TEST_CASE("[cpu/operator] MatMul(forward)", "[MatMul][CPU]") {
delete[] bigArray2;
delete[] res;
}
- std::cout << "multiplications over time spent: " << totalComputation/duration.count() << std::endl;
- std::cout << "total time: " << duration.count() << std::endl;
+ fmt::print("INFO: number of multiplications over time spent: {}\n", (totalComputation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
SECTION("4-D Tensors") {
@@ -244,8 +249,8 @@ TEST_CASE("[cpu/operator] MatMul(forward)", "[MatMul][CPU]") {
delete[] bigArray2;
delete[] res;
}
- std::cout << "multiplications over time spent: " << totalComputation/duration.count() << std::endl;
- std::cout << "total time: " << duration.count() << std::endl;
+ fmt::print("INFO: number of multiplications over time spent: {}\n", (totalComputation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
SECTION("+2-D / 1-D") {
diff --git a/unit_tests/operator/Test_MulImpl.cpp b/unit_tests/operator/Test_MulImpl.cpp
index 3378861d0d3d7e74e7867c2765a0b09069fa8caf..925b9f2059518d434b74a0e2fd0cde79b334c54e 100644
--- a/unit_tests/operator/Test_MulImpl.cpp
+++ b/unit_tests/operator/Test_MulImpl.cpp
@@ -9,351 +9,338 @@
*
********************************************************************************/
-#include <catch2/catch_test_macros.hpp>
-#include <cstddef> // std::size_t
-#include <cstdint> // std::uint16_t
-#include <chrono>
-#include <iostream>
+#include <chrono> // std::micro, std::chrono::time_point,
+ // std::chrono::system_clock,
+#include <cstddef> // std::size_t
+#include <cstdint> // std::uint16_t
+#include <functional> // std::multiplies
#include <memory>
-#include <numeric> // std::accumulate
-#include <random> // std::random_device, std::mt19937, std::uniform_real_distribution
+#include <numeric> // std::accumulate
+#include <random> // std::random_device, std::mt19937
+ // std::uniform_int_distribution, std::uniform_real_distribution
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <fmt/core.h>
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/backend/cpu/operator/MulImpl.hpp"
+#include "aidge/data/Data.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/operator/Mul.hpp"
+#include "aidge/utils/ArrayHelpers.hpp"
+#include "aidge/utils/Log.hpp"
#include "aidge/utils/TensorUtils.hpp"
namespace Aidge {
- TEST_CASE("[CPU/Operator] Mul Backward", "[Mul][CPU][Backward]")
- {
- std::shared_ptr<Node> myMul = Mul();
- auto op = std::static_pointer_cast<OperatorTensor>(myMul->getOperator());
- op->setDataType(DataType::Float32);
- op->setBackend("cpu");
+TEST_CASE("[CPU/Operator] Mul Backward", "[Mul][CPU][Backward]")
+{
+ using aif32 = cpptype_t<DataType::Float32>;
+ std::shared_ptr<Mul_Op> op = std::make_shared<Mul_Op>();
+ op->setDataType(DataType::Float32);
+ op->setBackend("cpu");
- SECTION("Case 1: 2D and 1D tensors") {
- const auto T0 = std::make_shared<Tensor>(Array2D<float,2,3>(
+ SECTION("Case 1: 2D and 1D tensors") {
+ const auto T0 = std::make_shared<Tensor>(Array2D<aif32,2,3>(
+ {
{
- {
- {1,2,3},{4,5,6}
- }
+ {1,2,3},{4,5,6}
}
- ));
-
- const auto T1 = std::make_shared<Tensor>(Array1D<float,3>(
- {0.1,0.2,0.3}
- ));
+ }
+ ));
- T0->setDataType(DataType::Float32);
- T0->setBackend("cpu");
- T1->setDataType(DataType::Float32);
- T1->setBackend("cpu");
+ const auto T1 = std::make_shared<Tensor>(Array1D<aif32,3>(
+ {0.1,0.2,0.3}
+ ));
- op->getOutput(0)->setGrad(std::make_shared<Tensor>(Array2D<float,2,3>({{{1.0,1.0,1.0},{1.0,1.0,1.0}}})));
+ op->getOutput(0)->setGrad(std::make_shared<Tensor>(Array2D<aif32,2,3>({{{1.0,1.0,1.0},{1.0,1.0,1.0}}})));
- op->associateInput(0,T0);
- op->associateInput(1,T1);
- op->forwardDims();
+ op->associateInput(0,T0);
+ op->associateInput(1,T1);
+ op->forwardDims();
- myMul->forward();
- myMul->backward();
+ op->forward();
+ op->backward();
- auto T0Grad = std::make_shared<Tensor>(Array2D<float, 2,3>({{{0.1,0.2,0.3},{0.1, 0.2, 0.3}}}));
- auto T1Grad = std::make_shared<Tensor>(Array1D<float, 3>({5,7,9}));
+ const Tensor T0Grad = Array2D<aif32, 2, 3>({{{0.1,0.2,0.3},{0.1, 0.2, 0.3}}});
+ const Tensor T1Grad = Array1D<aif32, 3>({5,7,9});
- REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *T0Grad));
- REQUIRE(approxEq<float>(*(op->getInput(1)->grad()), *T1Grad));
- }
+ REQUIRE(approxEq<aif32>(*(op->getInput(0)->grad()), T0Grad));
+ REQUIRE(approxEq<aif32>(*(op->getInput(1)->grad()), T1Grad));
+ }
- SECTION("Case 2: 3D and 1D tensors") {
- const auto T0 = std::make_shared<Tensor>(Array3D<float,2,2,3>(
+ SECTION("Case 2: 3D and 1D tensors") {
+ const auto T0 = std::make_shared<Tensor>(Array3D<aif32,2,2,3>(
+ {
{
{
- {
- {1.0, 2.0, 3.0},
- {4.0, 5.0, 6.0}
- },
- {
- {7.0, 8.0, 9.0},
- {10.0, 11.0, 12.0}
- }
+ {1.0, 2.0, 3.0},
+ {4.0, 5.0, 6.0}
+ },
+ {
+ {7.0, 8.0, 9.0},
+ {10.0, 11.0, 12.0}
}
}
- ));
-
- const auto T1 = std::make_shared<Tensor>(Array1D<float, 3>({0.3,0.2,0.1}));
+ }
+ ));
- const auto newGrad = std::make_shared<Tensor>(Array3D<float,2,2,3>(
- {
- {
- {
- {1, 1, 1},
- {1, 1, 1}
- },
- {
- {1, 1, 1},
- {1, 1, 1}
- }
- }
- }
- ));
+ const auto T1 = std::make_shared<Tensor>(Array1D<aif32, 3>({0.3,0.2,0.1}));
- const auto expectedGrad0 = std::make_shared<Tensor>(Array3D<float,2,2,3>(
+ const auto newGrad = std::make_shared<Tensor>(Array3D<aif32,2,2,3>(
{
{
{
- {0.3, 0.2, 0.1},
- {0.3, 0.2, 0.1}
+ {1, 1, 1},
+ {1, 1, 1}
},
{
- {0.3, 0.2, 0.1},
- {0.3, 0.2, 0.1}
+ {1, 1, 1},
+ {1, 1, 1}
}
}
}
));
- const auto expectedGrad1 = std::make_shared<Tensor>(Array1D<float,3>(
- {22.0, 26.0, 30.0}
- ));
-
- for(auto T: {T0, T1, newGrad, expectedGrad0, expectedGrad1})
+ const Tensor expectedGrad0 = Array3D<aif32,2,2,3>(
{
- T->setBackend("cpu") ;
- T->setDataType(DataType::Float32);
+ {
+ {
+ {0.3, 0.2, 0.1},
+ {0.3, 0.2, 0.1}
+ },
+ {
+ {0.3, 0.2, 0.1},
+ {0.3, 0.2, 0.1}
+ }
+ }
}
+ );
- op->associateInput(0, T0);
- op->associateInput(1, T1);
- op->getOutput(0)->setGrad(newGrad);
- op->forwardDims();
+ const Tensor expectedGrad1 = Array1D<aif32,3>(
+ {22.0, 26.0, 30.0}
+ );
- myMul->backward();
+ op->associateInput(0, T0);
+ op->associateInput(1, T1);
+ op->getOutput(0)->setGrad(newGrad);
+ op->forwardDims();
- REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *expectedGrad0));
- REQUIRE(approxEq<float>(*(op->getInput(1)->grad()), *expectedGrad1));
- }
+ op->backward();
+
+ REQUIRE(approxEq<aif32>(*(op->getInput(0)->grad()), expectedGrad0));
+ REQUIRE(approxEq<aif32>(*(op->getInput(1)->grad()), expectedGrad1));
+ }
- SECTION("Case 3: 4D and 2D tensors") {
- const auto T0 = std::make_shared<Tensor>(Array4D<float,2, 2, 3, 3>(
+ SECTION("Case 3: 4D and 2D tensors") {
+ const auto T0 = std::make_shared<Tensor>(Array4D<aif32,2, 2, 3, 3>(
+ {
{
{
{
- {
- {1.0, 2.0, 3.0},
- {4.0, 5.0, 6.0},
- {7.0, 8.0, 9.0}
- },
- {
- {10.0, 11.0, 12.0},
- {13.0, 14.0, 15.0},
- {16.0, 17.0, 18.0}
- }
+ {1.0, 2.0, 3.0},
+ {4.0, 5.0, 6.0},
+ {7.0, 8.0, 9.0}
},
{
- {
- {19.0, 20.0, 21.0},
- {22.0, 23.0, 24.0},
- {25.0, 26.0, 27.0}
- },
- {
- {28.0, 29.0, 30.0},
- {31.0, 32.0, 33.0},
- {34.0, 35.0, 36.0}
- }
+ {10.0, 11.0, 12.0},
+ {13.0, 14.0, 15.0},
+ {16.0, 17.0, 18.0}
+ }
+ },
+ {
+ {
+ {19.0, 20.0, 21.0},
+ {22.0, 23.0, 24.0},
+ {25.0, 26.0, 27.0}
+ },
+ {
+ {28.0, 29.0, 30.0},
+ {31.0, 32.0, 33.0},
+ {34.0, 35.0, 36.0}
}
}
}
- ));
+ }
+ ));
- const auto T1 = std::make_shared<Tensor>(Array2D<float, 3,3>(
+ const auto T1 = std::make_shared<Tensor>(Array2D<aif32, 3,3>(
+ {
{
- {
- {0.5,0.3,0.1},
- {0.4,0.2,0.6},
- {0.7,0.8,0.9}
- }
+ {0.5,0.3,0.1},
+ {0.4,0.2,0.6},
+ {0.7,0.8,0.9}
}
- ));
+ }
+ ));
- const auto newGrad = std::make_shared<Tensor>(Array4D<float,2, 2, 3, 3>(
+ const auto newGrad = std::make_shared<Tensor>(Array4D<aif32,2, 2, 3, 3>(
+ {
{
{
{
- {
- {1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0}
- },
- {
- {1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0}
- }
+ {1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0}
},
{
- {
- {1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0}
- },
- {
- {1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0}
- }
+ {1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0}
}
- }
- }
- ));
-
- const auto expectedGrad0 = std::make_shared<Tensor>(Array4D<float,2,2,3,3>(
- {
+ },
{
{
- {
- {0.5, 0.3, 0.1},
- {0.4, 0.2, 0.6},
- {0.7, 0.8, 0.9}
- },
- {
- {0.5, 0.3, 0.1},
- {0.4, 0.2, 0.6},
- {0.7, 0.8, 0.9}
- }
+ {1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0}
},
{
- {
- {0.5, 0.3, 0.1},
- {0.4, 0.2, 0.6},
- {0.7, 0.8, 0.9}
- },
- {
- {0.5, 0.3, 0.1},
- {0.4, 0.2, 0.6},
- {0.7, 0.8, 0.9}
- }
+ {1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0}
}
}
}
- ));
+ }
+ ));
- const auto expectedGrad1 = std::make_shared<Tensor>(Array2D<float,3, 3>(
+ const Tensor expectedGrad0 = Array4D<aif32,2,2,3,3>(
+ {
{
{
- {58.0, 62.0, 66.0},
- {70.0, 74.0, 78.0},
- {82.0, 86.0, 90.0}
+ {
+ {0.5, 0.3, 0.1},
+ {0.4, 0.2, 0.6},
+ {0.7, 0.8, 0.9}
+ },
+ {
+ {0.5, 0.3, 0.1},
+ {0.4, 0.2, 0.6},
+ {0.7, 0.8, 0.9}
+ }
+ },
+ {
+ {
+ {0.5, 0.3, 0.1},
+ {0.4, 0.2, 0.6},
+ {0.7, 0.8, 0.9}
+ },
+ {
+ {0.5, 0.3, 0.1},
+ {0.4, 0.2, 0.6},
+ {0.7, 0.8, 0.9}
+ }
}
}
- ));
+ }
+ );
- for(const auto T: {T0, T1, newGrad, expectedGrad0, expectedGrad1})
+ const Tensor expectedGrad1 = Array2D<aif32,3, 3>(
{
- T->setBackend("cpu") ;
- T->setDataType(DataType::Float32);
+ {
+ {58.0, 62.0, 66.0},
+ {70.0, 74.0, 78.0},
+ {82.0, 86.0, 90.0}
+ }
}
+ );
- op->associateInput(0, T0);
- op->associateInput(1, T1);
- op->getOutput(0)->setGrad(newGrad);
- op->forwardDims();
+ op->associateInput(0, T0);
+ op->associateInput(1, T1);
+ op->getOutput(0)->setGrad(newGrad);
+ op->forwardDims();
- myMul->backward();
+ op->backward();
- REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *expectedGrad0));
- REQUIRE(approxEq<float>(*(op->getInput(1)->grad()), *expectedGrad1));
- }
+ REQUIRE(approxEq<aif32>(*(op->getInput(0)->grad()), expectedGrad0));
+ REQUIRE(approxEq<aif32>(*(op->getInput(1)->grad()), expectedGrad1));
+ }
- SECTION("Case 4: 3D and 2D tensors") {
- const auto T0 = std::make_shared<Tensor>(Array3D<float, 2, 3, 4>(
+ SECTION("Case 4: 3D and 2D tensors") {
+ const auto T0 = std::make_shared<Tensor>(Array3D<aif32, 2, 3, 4>(
+ {
{
{
- {
- {1.0, 2.0, 3.0, 4.0},
- {5.0, 6.0, 7.0, 8.0},
- {9.0, 10.0, 11.0, 12.0},
- },
- {
- {13.0, 14.0, 15.0, 16.0},
- {17.0, 18.0, 19.0, 20.0},
- {21.0, 22.0, 23.0, 24.0},
- }
- }
- }
- ));
-
- const auto T1 = std::make_shared<Tensor>(Array2D<float, 3, 4>(
- {
+ {1.0, 2.0, 3.0, 4.0},
+ {5.0, 6.0, 7.0, 8.0},
+ {9.0, 10.0, 11.0, 12.0},
+ },
{
- {0.1, 0.2, 0.3, 0.4},
- {0.5, 0.6, 0.7, 0.8},
- {0.9, 1.0, 1.1, 1.2}
+ {13.0, 14.0, 15.0, 16.0},
+ {17.0, 18.0, 19.0, 20.0},
+ {21.0, 22.0, 23.0, 24.0},
}
}
- ));
+ }
+ ));
- const auto newGrad = std::make_shared<Tensor>(Array3D<float, 2,3,4>(
+ const auto T1 = std::make_shared<Tensor>(Array2D<aif32, 3, 4>(
+ {
{
- {
- {
- {1.0, 1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0, 1.0},
- },
- {
- {1.0, 1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0, 1.0},
- {1.0, 1.0, 1.0, 1.0},
- }
- }
+ {0.1, 0.2, 0.3, 0.4},
+ {0.5, 0.6, 0.7, 0.8},
+ {0.9, 1.0, 1.1, 1.2}
}
- ));
+ }
+ ));
- const auto expectedGrad0 = std::make_shared<Tensor>(Array3D<float,2,3,4>(
+ const auto newGrad = std::make_shared<Tensor>(Array3D<aif32, 2,3,4>(
+ {
{
{
- {
- {0.1, 0.2, 0.3, 0.4},
- {0.5, 0.6, 0.7, 0.8},
- {0.9, 1.0, 1.1, 1.2}
- },
- {
- {0.1, 0.2, 0.3, 0.4},
- {0.5, 0.6, 0.7, 0.8},
- {0.9, 1.0, 1.1, 1.2}
- }
+ {1.0, 1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0, 1.0},
+ },
+ {
+ {1.0, 1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0, 1.0},
+ {1.0, 1.0, 1.0, 1.0},
}
}
- ));
+ }
+ ));
- const auto expectedGrad1 = std::make_shared<Tensor>(Array2D<float,3, 4>(
+ const Tensor expectedGrad0 = Array3D<aif32,2,3,4>(
+ {
{
{
- {14.0, 16.0, 18.0, 20.0},
- {22.0, 24.0, 26.0, 28.0},
- {30.0, 32.0, 34.0, 36.0}
+ {0.1, 0.2, 0.3, 0.4},
+ {0.5, 0.6, 0.7, 0.8},
+ {0.9, 1.0, 1.1, 1.2}
+ },
+ {
+ {0.1, 0.2, 0.3, 0.4},
+ {0.5, 0.6, 0.7, 0.8},
+ {0.9, 1.0, 1.1, 1.2}
}
}
- ));
+ }
+ );
- for(const auto T: {T0, T1, newGrad, expectedGrad0, expectedGrad1})
+ const Tensor expectedGrad1 = Array2D<aif32,3,4>(
{
- T->setBackend("cpu") ;
- T->setDataType(DataType::Float32);
+ {
+ {14.0, 16.0, 18.0, 20.0},
+ {22.0, 24.0, 26.0, 28.0},
+ {30.0, 32.0, 34.0, 36.0}
+ }
}
+ );
- op->associateInput(0, T0);
- op->associateInput(1, T1);
- op->getOutput(0)->setGrad(newGrad);
- op->forwardDims();
+ op->associateInput(0, T0);
+ op->associateInput(1, T1);
+ op->getOutput(0)->setGrad(newGrad);
+ op->forwardDims();
- myMul->backward();
+ op->backward();
- REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *expectedGrad0));
- REQUIRE(approxEq<float>(*(op->getInput(1)->grad()), *expectedGrad1));
- }
+ REQUIRE(approxEq<aif32>(*(op->getInput(0)->grad()), expectedGrad0));
+ REQUIRE(approxEq<aif32>(*(op->getInput(1)->grad()), expectedGrad1));
}
+}
TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
constexpr std::uint16_t NBTRIALS = 10;
@@ -366,8 +353,7 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
std::uniform_int_distribution<int> boolDist(0,1);
// Create MatMul Operator
- std::shared_ptr<Node> myMul = Mul();
- auto op = std::static_pointer_cast<OperatorTensor>(myMul-> getOperator());
+ std::shared_ptr<Mul_Op> op = std::make_shared<Mul_Op>();
op->setDataType(DataType::Float32);
op->setBackend("cpu");
@@ -441,7 +427,7 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
op->forwardDims();
start = std::chrono::system_clock::now();
- myMul->forward();
+ op->forward();
end = std::chrono::system_clock::now();
duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
@@ -451,8 +437,8 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
delete[] array1;
delete[] result;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
@@ -568,7 +554,7 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
// compute result
op->forwardDims();
start = std::chrono::system_clock::now();
- myMul->forward();
+ op->forward();
end = std::chrono::system_clock::now();
duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
@@ -582,8 +568,8 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
const std::size_t nb_elements = std::accumulate(dimsOut.cbegin(), dimsOut.cend(), std::size_t(1), std::multiplies<std::size_t>());
number_of_operation += nb_elements;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
SECTION("+1-D Tensor / 1-D Tensor") {
std::size_t number_of_operation = 0;
@@ -663,7 +649,7 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
// compute result
op->forwardDims();
start = std::chrono::system_clock::now();
- myMul->forward();
+ op->forward();
end = std::chrono::system_clock::now();
duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
@@ -678,8 +664,8 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
number_of_operation += nb_elements;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
}
}
diff --git a/unit_tests/operator/Test_PowImpl.cpp b/unit_tests/operator/Test_PowImpl.cpp
index cb5d8872c9c7242bb4aa4efca388d53b578417f9..8238da3970740f4b8d6095d7a28c000319ea004e 100644
--- a/unit_tests/operator/Test_PowImpl.cpp
+++ b/unit_tests/operator/Test_PowImpl.cpp
@@ -9,18 +9,26 @@
*
********************************************************************************/
-#include <catch2/catch_test_macros.hpp>
-#include <cmath>
-#include <cstddef> // std::size_t
-#include <cstdint> // std::uint16_t
-#include <chrono>
-#include <iostream>
+#include <chrono> // std::micro, std::chrono::time_point,
+ // std::chrono::system_clock, std::chrono::duration
+#include <cstddef> // std::size_t
+#include <cstdint> // std::uint16_t
+#include <functional> // std::multiplies
#include <memory>
-#include <numeric> // std::accumulate
-#include <random> // std::random_device, std::mt19937, std::uniform_real_distribution
+#include <numeric> // std::accumulate
+#include <random> // std::random_device, std::mt19937
+ // std::uniform_int_distribution, std::uniform_real_distribution
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <fmt/core.h>
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/backend/cpu/operator/PowImpl.hpp"
+#include "aidge/data/Data.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/operator/Pow.hpp"
+#include "aidge/utils/ArrayHelpers.hpp"
#include "aidge/utils/TensorUtils.hpp"
namespace Aidge {
@@ -118,8 +126,8 @@ TEST_CASE("[cpu/operator] Pow", "[Pow][CPU]") {
// with broadcasting
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
SECTION("+1-D Tensor / +1-D Tensor - broadcasting") {
@@ -213,8 +221,8 @@ TEST_CASE("[cpu/operator] Pow", "[Pow][CPU]") {
const std::size_t nb_elements = std::accumulate(dimsOut.cbegin(), dimsOut.cend(), std::size_t(1), std::multiplies<std::size_t>());
number_of_operation += nb_elements;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
SECTION("+1-D Tensor / 1-D Tensor") {
std::size_t number_of_operation = 0;
@@ -309,8 +317,8 @@ TEST_CASE("[cpu/operator] Pow", "[Pow][CPU]") {
number_of_operation += nb_elements;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
}
@@ -440,7 +448,7 @@ TEST_CASE("[cpu/operator] Pow", "[Pow][CPU]") {
}
}
));
- const auto expectedGrad0 = std::make_shared<Tensor>(Array3D<float, 2, 2, 3>(
+ const Tensor expectedGrad0 = Array3D<float, 2, 2, 3>(
{
{
{
@@ -453,18 +461,13 @@ TEST_CASE("[cpu/operator] Pow", "[Pow][CPU]") {
}
}
}
- ));
- const auto expectedGrad1 = std::make_shared<Tensor>(Array1D<float, 3>(
+ );
+ const Tensor expectedGrad1 = Array1D<float, 3>(
{
{14.14779854, 22.99299049, 33.56402588}
}
- ));
+ );
- for(const auto T: {input0, input1, gradOut, expectedGrad0, expectedGrad1})
- {
- T->setBackend("cpu") ;
- T->setDataType(DataType::Float32);
- }
std::shared_ptr<Node> powOp = Pow();
auto opr = std::static_pointer_cast<OperatorTensor>(powOp-> getOperator());
opr->setDataType(DataType::Float32);
@@ -475,8 +478,8 @@ TEST_CASE("[cpu/operator] Pow", "[Pow][CPU]") {
powOp->forward();
powOp->backward();
- REQUIRE(approxEq<float>(*(opr->getInput(0)->grad()), *expectedGrad0));
- REQUIRE(approxEq<float>(*(opr->getInput(1)->grad()), *expectedGrad1));
+ REQUIRE(approxEq<float>(*(opr->getInput(0)->grad()), expectedGrad0));
+ REQUIRE(approxEq<float>(*(opr->getInput(1)->grad()), expectedGrad1));
}
}
}
diff --git a/unit_tests/operator/Test_RoundImpl.cpp b/unit_tests/operator/Test_RoundImpl.cpp
index b4cf9ffbedc18b35b42ebbc05971f86e0fa584e3..8b5dd53a79242a38063f178807d5b6b40f2c0e96 100644
--- a/unit_tests/operator/Test_RoundImpl.cpp
+++ b/unit_tests/operator/Test_RoundImpl.cpp
@@ -9,15 +9,23 @@
*
********************************************************************************/
-#include <catch2/catch_test_macros.hpp>
-#include <cstddef> // std::size_t
-#include <cstdint> // std::uint16_t
-#include <chrono>
-#include <iostream>
+#include <chrono> // std::micro, std::chrono::time_point,
+ // std::chrono::system_clock, std::chrono::duration
+#include <cstddef> // std::size_t
+#include <cstdint> // std::uint16_t
+#include <functional> // std::multiplies
#include <memory>
-#include <numeric>
-#include <random> // std::random_device, std::mt19937, std::uniform_real_distribution
-#include <iomanip>
+#include <numeric> // std::accumulate
+#include <random> // std::random_device, std::mt19937
+ // std::uniform_int_distribution, std::uniform_real_distribution
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <fmt/core.h>
+
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/backend/cpu/operator/RoundImpl.hpp"
+#include "aidge/data/Data.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/operator/Round.hpp"
#include "aidge/utils/TensorUtils.hpp"
@@ -29,7 +37,7 @@ TEST_CASE("[cpu/operator] Round_Test", "[Round][CPU]") {
// Create a random number generator
std::random_device rd;
std::mt19937 gen(rd());
- std::uniform_real_distribution<float> valueDist(-15, 15);
+ std::uniform_real_distribution<float> valueDist(-15, 15);
std::uniform_int_distribution<std::size_t> dimSizeDist(std::size_t(2), std::size_t(5));
std::uniform_int_distribution<std::size_t> nbDimsDist(std::size_t(1), std::size_t(3));
@@ -59,7 +67,7 @@ TEST_CASE("[cpu/operator] Round_Test", "[Round][CPU]") {
std::size_t number_of_operation = 0;
for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
-
+
// generate 2 random Tensors
const std::size_t nbDims = nbDimsDist(gen);
std::vector<std::size_t> dims;
@@ -72,7 +80,7 @@ TEST_CASE("[cpu/operator] Round_Test", "[Round][CPU]") {
// without broadcasting
float* array0 = new float[nb_elements];
float* result = new float[nb_elements];
-
+
for (std::size_t i = 0; i < nb_elements; ++i) {
array0[i] = valueDist(gen);
result[i] = std::nearbyint(array0[i]);
@@ -86,29 +94,22 @@ TEST_CASE("[cpu/operator] Round_Test", "[Round][CPU]") {
// results
Tres->resize(dims);
Tres -> getImpl() -> setRawPtr(result, nb_elements);
-
+
op->forwardDims();
start = std::chrono::system_clock::now();
myRound->forward();
end = std::chrono::system_clock::now();
duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
- bool is_eq = approxEq<float>(*(op->getOutput(0)), *Tres);
-
- auto Output = *(op->getOutput(0));
-
- auto prt = Output.getImpl()->rawPtr();
-
- REQUIRE(is_eq);
-
+ REQUIRE(approxEq<float>(*(op->getOutput(0)), *Tres));
delete[] array0;
delete[] result;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {} μs\n", duration.count());
}
}
} // namespace Aidge
diff --git a/unit_tests/operator/Test_SubImpl.cpp b/unit_tests/operator/Test_SubImpl.cpp
index 44666ae631152c8898e24f7003b0c2ede8c67b84..471ae560a35b480945d7e5c85fb93bbbc8d459f6 100644
--- a/unit_tests/operator/Test_SubImpl.cpp
+++ b/unit_tests/operator/Test_SubImpl.cpp
@@ -9,17 +9,26 @@
*
********************************************************************************/
-#include <catch2/catch_test_macros.hpp>
-#include <cstddef> // std::size_t
-#include <cstdint> // std::uint16_t
-#include <chrono>
-#include <iostream>
+#include <chrono> // std::micro, std::chrono::time_point,
+ // std::chrono::system_clock
+#include <cstddef> // std::size_t
+#include <cstdint> // std::uint16_t
+#include <functional> // std::multiplies
#include <memory>
-#include <numeric> // std::accumulate
-#include <random> // std::random_device, std::mt19937, std::uniform_real_distribution
+#include <numeric> // std::accumulate
+#include <random> // std::random_device, std::mt19937
+ // std::uniform_int_distribution, std::uniform_real_distribution
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <fmt/core.h>
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/backend/cpu/operator/SubImpl.hpp"
+#include "aidge/data/Data.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/operator/Sub.hpp"
+#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/TensorUtils.hpp"
namespace Aidge {
@@ -117,8 +126,8 @@ TEST_CASE("[cpu/operator] Sub", "[Sub][CPU]") {
// with broadcasting
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {}μs\n", duration.count());
}
SECTION("+1-D Tensor / +1-D Tensor - broadcasting") {
@@ -212,8 +221,8 @@ TEST_CASE("[cpu/operator] Sub", "[Sub][CPU]") {
const std::size_t nb_elements = std::accumulate(dimsOut.cbegin(), dimsOut.cend(), std::size_t(1), std::multiplies<std::size_t>());
number_of_operation += nb_elements;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {}μs\n", duration.count());
}
SECTION("+1-D Tensor / 1-D Tensor") {
std::size_t number_of_operation = 0;
@@ -308,8 +317,8 @@ TEST_CASE("[cpu/operator] Sub", "[Sub][CPU]") {
number_of_operation += nb_elements;
}
- std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
- std::cout << "total time: " << duration.count() << "μs" << std::endl;
+ fmt::print("INFO: number of elements over time spent: {}\n", (number_of_operation / duration.count()));
+ fmt::print("INFO: total time: {}μs\n", duration.count());
}
}
}