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TestCustomModelsFromONNX.cxx
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#include <numeric>
#include "Linear_16_FromONNX.hxx"
#include "input_models/references/Linear_16.ref.hxx"
#include "Linear_32_FromONNX.hxx"
#include "input_models/references/Linear_32.ref.hxx"
#include "Linear_64_FromONNX.hxx"
#include "input_models/references/Linear_64.ref.hxx"
#include "LinearWithSelu_FromONNX.hxx"
#include "input_models/references/LinearWithSelu.ref.hxx"
#include "Sub_FromONNX.hxx"
#include "input_models/references/Sub.ref.hxx"
#include "Add_FromONNX.hxx"
#include "input_models/references/Add.ref.hxx"
#include "Mul_FromONNX.hxx"
#include "input_models/references/Mul.ref.hxx"
#include "Div_FromONNX.hxx"
#include "input_models/references/Div.ref.hxx"
#include "Cast_FromONNX.hxx"
#include "input_models/references/Cast.ref.hxx"
#include "CastLike_FromONNX.hxx"
#include "input_models/references/CastLike.ref.hxx"
#include "ReduceMean_FromONNX.hxx"
#include "input_models/references/ReduceMean.ref.hxx"
#include "ReduceProd_FromONNX.hxx"
#include "input_models/references/ReduceProd.ref.hxx"
// hardcode reference
#include "ReduceSum_FromONNX.hxx"
#include "ReduceSumSquare_FromONNX.hxx"
#include "Shape_FromONNX.hxx"
#include "input_models/references/Shape.ref.hxx"
#include "Constant_FromONNX.hxx"
#include "input_models/references/Constant.ref.hxx"
#include "TopK_FromONNX.hxx"
#include "input_models/references/TopK.ref.hxx"
#include "ComplexTopK_FromONNX.hxx"
#include "input_models/references/ComplexTopK.ref.hxx"
#include "LinearWithLeakyRelu_FromONNX.hxx"
#include "input_models/references/LinearWithLeakyRelu.ref.hxx"
#include "Tanh_FromONNX.hxx"
#include "input_models/references/Tanh.ref.hxx"
#include "Erf_FromONNX.hxx"
#include "input_models/references/Erf.ref.hxx"
#include "LinearWithSigmoid_FromONNX.hxx"
#include "input_models/references/LinearWithSigmoid.ref.hxx"
#include "ConvWithPadding_FromONNX.hxx"
#include "input_models/references/ConvWithPadding.ref.hxx"
#include "ConvWithoutPadding_FromONNX.hxx"
#include "input_models/references/ConvWithoutPadding.ref.hxx"
#include "ConvWithAutopadSameLower_FromONNX.hxx"
#include "input_models/references/ConvWithAutopadSameLower.ref.hxx"
#include "ConvWithStridesPadding_FromONNX.hxx"
#include "input_models/references/ConvWithStridesPadding.ref.hxx"
#include "ConvWithStridesNoPadding_FromONNX.hxx"
#include "input_models/references/ConvWithStridesNoPadding.ref.hxx"
#include "ConvWithAsymmetricPadding_FromONNX.hxx"
#include "input_models/references/ConvWithAsymmetricPadding.ref.hxx"
#include "MaxPool1d_FromONNX.hxx"
#include "input_models/references/MaxPool1d.ref.hxx"
#include "MaxPool2d_FromONNX.hxx"
#include "input_models/references/MaxPool2d.ref.hxx"
#include "MaxPool3d_FromONNX.hxx"
#include "input_models/references/MaxPool3d.ref.hxx"
#include "Max_FromONNX.hxx"
#include "input_models/references/Max.ref.hxx"
#include "MaxMultidirectionalBroadcast_FromONNX.hxx"
#include "input_models/references/MaxMultidirectionalBroadcast.ref.hxx"
#include "MinMultidirectionalBroadcast_FromONNX.hxx"
#include "input_models/references/MinMultidirectionalBroadcast.ref.hxx"
#include "MeanMultidirectionalBroadcast_FromONNX.hxx"
#include "input_models/references/MeanMultidirectionalBroadcast.ref.hxx"
#include "SumMultidirectionalBroadcast_FromONNX.hxx"
#include "input_models/references/SumMultidirectionalBroadcast.ref.hxx"
#include "AvgPool_FromONNX.hxx"
#include "input_models/references/AvgPool.ref.hxx"
#include "Pow_FromONNX.hxx"
#include "input_models/references/Pow.ref.hxx"
#include "Pow_broadcast_FromONNX.hxx"
#include "input_models/references/Pow_broadcast.ref.hxx"
#include "RNNBatchwise_FromONNX.hxx"
#include "input_models/references/RNNBatchwise.ref.hxx"
#include "RNNBidirectional_FromONNX.hxx"
#include "input_models/references/RNNBidirectional.ref.hxx"
#include "RNNBidirectionalBatchwise_FromONNX.hxx"
#include "input_models/references/RNNBidirectionalBatchwise.ref.hxx"
#include "RNNDefaults_FromONNX.hxx"
#include "input_models/references/RNNDefaults.ref.hxx"
#include "RNNSeqLength_FromONNX.hxx"
#include "input_models/references/RNNSeqLength.ref.hxx"
#include "RNNSequence_FromONNX.hxx"
#include "input_models/references/RNNSequence.ref.hxx"
#include "RNNSequenceBatchwise_FromONNX.hxx"
#include "input_models/references/RNNSequenceBatchwise.ref.hxx"
#include "LSTMBatchwise_FromONNX.hxx"
#include "input_models/references/LSTMBatchwise.ref.hxx"
#include "LSTMBidirectional_FromONNX.hxx"
#include "input_models/references/LSTMBidirectional.ref.hxx"
#include "LSTMDefaults_FromONNX.hxx"
#include "input_models/references/LSTMDefaults.ref.hxx"
#include "LSTMInitialBias_FromONNX.hxx"
#include "input_models/references/LSTMInitialBias.ref.hxx"
#include "LSTMPeepholes_FromONNX.hxx"
#include "input_models/references/LSTMPeepholes.ref.hxx"
#include "GRUBatchwise_FromONNX.hxx"
#include "input_models/references/GRUBatchwise.ref.hxx"
#include "GRUBidirectional_FromONNX.hxx"
#include "input_models/references/GRUBidirectional.ref.hxx"
#include "GRUDefaults_FromONNX.hxx"
#include "input_models/references/GRUDefaults.ref.hxx"
#include "GRUInitialBias_FromONNX.hxx"
#include "input_models/references/GRUInitialBias.ref.hxx"
#include "GRUSeqLength_FromONNX.hxx"
#include "input_models/references/GRUSeqLength.ref.hxx"
#include "Softmax1d_FromONNX.hxx"
#include "input_models/references/Softmax1d.ref.hxx"
#include "Softmax2d_FromONNX.hxx"
#include "input_models/references/Softmax2d.ref.hxx"
#include "Softmax3d_FromONNX.hxx"
#include "input_models/references/Softmax3d.ref.hxx"
#include "Softmax4d_FromONNX.hxx"
#include "input_models/references/Softmax4d.ref.hxx"
#include "ConvTranspose1d_FromONNX.hxx"
#include "input_models/references/ConvTranspose1d.ref.hxx"
#include "ConvTranspose2d_FromONNX.hxx"
#include "input_models/references/ConvTranspose2d.ref.hxx"
//#include "ConvTranspose3d_FromONNX.hxx"
//#include "input_models/references/ConvTranspose3d.ref.hxx"
#include "ConvTransposeBias2d_FromONNX.hxx"
#include "input_models/references/ConvTransposeBias2d.ref.hxx"
#include "ConvTransposeBias2dBatched_FromONNX.hxx"
#include "input_models/references/ConvTransposeBias2dBatched.ref.hxx"
#include "Sqrt_FromONNX.hxx"
#include "input_models/references/Sqrt.ref.hxx"
#include "Reciprocal_FromONNX.hxx"
#include "input_models/references/Reciprocal.ref.hxx"
#include "Neg_FromONNX.hxx"
#include "input_models/references/Neg.ref.hxx"
#include "Exp_FromONNX.hxx"
#include "input_models/references/Exp.ref.hxx"
#include "AddBroadcast1_FromONNX.hxx"
#include "input_models/references/AddBroadcast1.ref.hxx"
#include "AddBroadcast2_FromONNX.hxx"
#include "input_models/references/AddBroadcast2.ref.hxx"
#include "AddBroadcast3_FromONNX.hxx"
#include "input_models/references/AddBroadcast3.ref.hxx"
#include "AddBroadcast4_FromONNX.hxx"
#include "input_models/references/AddBroadcast4.ref.hxx"
#include "AddBroadcast5_FromONNX.hxx"
#include "input_models/references/AddBroadcast5.ref.hxx"
#include "AddBroadcast6_FromONNX.hxx"
#include "input_models/references/AddBroadcast6.ref.hxx"
#include "AddBroadcast7_FromONNX.hxx"
#include "input_models/references/AddBroadcast7.ref.hxx"
#include "Concat_0D_FromONNX.hxx"
#include "LayerNormalization2d_FromONNX.hxx"
#include "input_models/references/LayerNormalization2d.hxx"
#include "LayerNormalization4d_FromONNX.hxx"
#include "input_models/references/LayerNormalization4d.hxx"
#include "ExpandSameSize_FromONNX.hxx"
#include "input_models/references/ExpandSameSize.ref.hxx"
#include "ExpandDiffSize_FromONNX.hxx"
#include "input_models/references/ExpandDiffSize.ref.hxx"
#include "GatherAxis0_FromONNX.hxx"
#include "input_models/references/GatherAxis0.ref.hxx"
#include "GatherAxis1_FromONNX.hxx"
#include "input_models/references/GatherAxis1.ref.hxx"
#include "GatherAxis2_FromONNX.hxx"
#include "input_models/references/GatherAxis2.ref.hxx"
#include "GatherAxis3_FromONNX.hxx"
#include "input_models/references/GatherAxis3.ref.hxx"
#include "Gather2d_FromONNX.hxx"
#include "input_models/references/Gather2d.ref.hxx"
#include "GatherNegativeIndices_FromONNX.hxx"
#include "input_models/references/GatherNegativeIndices.ref.hxx"
#include "Slice_FromONNX.hxx"
#include "input_models/references/Slice.ref.hxx"
#include "Slice_Default_Axis_FromONNX.hxx"
#include "input_models/references/Slice_Default_Axis.ref.hxx"
#include "Slice_Default_Steps_FromONNX.hxx"
#include "input_models/references/Slice_Default_Steps.ref.hxx"
#include "Slice_Neg_FromONNX.hxx"
#include "input_models/references/Slice_Neg.ref.hxx"
#include "Log_FromONNX.hxx"
#include "input_models/references/Log.ref.hxx"
#include "Round_FromONNX.hxx"
#include "input_models/references/Round.ref.hxx"
#include "Elu_FromONNX.hxx"
#include "input_models/references/Elu.ref.hxx"
#include "Equal_FromONNX.hxx"
#include "input_models/references/Equal.ref.hxx"
#include "LessOrEqual_FromONNX.hxx"
#include "input_models/references/LessOrEqual.ref.hxx"
#include "GreaterOrEqual_FromONNX.hxx"
#include "input_models/references/GreaterOrEqual.ref.hxx"
#include "Less_FromONNX.hxx"
#include "input_models/references/Less.ref.hxx"
#include "Greater_FromONNX.hxx"
#include "input_models/references/Greater.ref.hxx"
#include "EyeLike_FromONNX.hxx"
#include "input_models/references/EyeLike.ref.hxx"
#include "RangeFloat_FromONNX.hxx"
#include "input_models/references/RangeFloat.ref.hxx"
#include "RangeInt_FromONNX.hxx"
#include "input_models/references/RangeInt.ref.hxx"
#include "Tile5D_FromONNX.hxx"
#include "input_models/references/Tile5D.ref.hxx"
#include "Pad_FromONNX.hxx"
#include "Where_FromONNX.hxx"
#include "Sin_FromONNX.hxx"
#include "Cos_FromONNX.hxx"
#include "Abs_FromONNX.hxx"
#include "Einsum_matmul_FromONNX.hxx"
#include "Einsum_dotprod_FromONNX.hxx"
#include "Einsum_3_FromONNX.hxx"
#include "Einsum_4_FromONNX.hxx"
#include "RandomUniform_FromONNX.hxx"
#include "RandomNormal_FromONNX.hxx"
#include "Split_0_FromONNX.hxx"
#include "Split_1_FromONNX.hxx"
#include "Split_2_FromONNX.hxx"
#include "ScatterElements_FromONNX.hxx"
#include "Not_FromONNX.hxx"
#include "input_models/references/Not.ref.hxx"
#include "gtest/gtest.h"
constexpr float DEFAULT_TOLERANCE = 1e-3f;
TEST(ONNX, Linear16)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(1600);
std::fill_n(input.data(), input.size(), 1.0f);
TMVA_SOFIE_Linear_16::Session s("Linear_16_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Linear_16_ExpectedOutput::all_ones) / sizeof(float));
float *correct = Linear_16_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
/*TEST(ONNX, Linear32RootFeature)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(3200);
std::fill_n(input.data(), input.size(), 1.0f);
TMVA_SOFIE_Linear32RootFeacture::Session s("Linear_32_FromONNX.root");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Linear_32_ExpectedOutput::all_ones) / sizeof(float));
float *correct = Linear_32_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}*/
TEST(ONNX, Linear32)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(3200);
std::fill_n(input.data(), input.size(), 1.0f);
TMVA_SOFIE_Linear_32::Session s("Linear_32_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Linear_32_ExpectedOutput::all_ones) / sizeof(float));
float *correct = Linear_32_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Sub)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<float> input1({
1, 2
});
std::vector<float> input2({
0, 1
});
TMVA_SOFIE_Sub::Session s("Sub_FromONNX.dat");
std::vector<float> output = s.infer(input1.data(),input2.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Sub_ExpectedOutput::outputs) / sizeof(float));
float *correct = Sub_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Add)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<float> input1({
1, 2
});
std::vector<float> input2({
0, 1
});
TMVA_SOFIE_Add::Session s("Add_FromONNX.dat");
std::vector<float> output = s.infer(input1.data(),input2.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Add_ExpectedOutput::outputs) / sizeof(float));
float *correct = Add_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Mul)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<float> input1({
1, 2
});
std::vector<float> input2({
0, 1
});
TMVA_SOFIE_Mul::Session s("Mul_FromONNX.dat");
std::vector<float> output = s.infer(input1.data(),input2.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Mul_ExpectedOutput::outputs) / sizeof(float));
float *correct = Mul_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Div)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<float> input1({
4, 2
});
std::vector<float> input2({
2, 2
});
TMVA_SOFIE_Div::Session s("Div_FromONNX.dat");
std::vector<float> output = s.infer(input1.data(),input2.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Div_ExpectedOutput::outputs) / sizeof(float));
float *correct = Div_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Neg)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<float> input({
-1.9100, 1.8811, -1.7269, -0.1094, -0.0145, 0.2509, 0.5893, -2.2733,
-0.7077, 1.0645, -0.8607, 0.2085
});
TMVA_SOFIE_Neg::Session s("Neg_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Neg_ExpectedOutput::outputs) / sizeof(float));
float *correct = Neg_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Not)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<float> input({-0.7077, 1.0645, -0.8607, 0.2085, 4.5335, -3.4592});
TMVA_SOFIE_Not::Session s("Not_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Not_ExpectedOutput::outputs) / sizeof(float));
float *correct = Not_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Elu)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<float> input({
1.0, -2.0, 3.0, 0.5, -1.0, 2.0
});
TMVA_SOFIE_Elu::Session s("Elu_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Elu_ExpectedOutput::outputs) / sizeof(float));
float *correct = Elu_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Constant)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input (none for Constant Op)
TMVA_SOFIE_Constant::Session s("Constant_FromONNX.dat");
auto output = s.infer();
// Checking output size
EXPECT_EQ(output.size(), sizeof(Constant_ExpectedOutput::outputs) / sizeof(float));
float *correct = Constant_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, ComplexTopK)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input({
9.0000, 8.0000, 4.5000, 1.7000, 2.9000, 3.2000, 4.0000, 2.6000, 7.4000,
3.5000, 5.6000, 7.1000, 9.8000, 1.1000, 3.3000, 6.2000, 8.4000, 0.7000,
2.2000, 3.3000, 4.4000, 5.5000, 6.6000, 7.7000, 8.8000, 9.9000, 1.0000,
1.0000, 2.0000, 3.0000, 4.0000, 5.0000, 6.0000, 7.0000, 8.0000, 9.0000,
9.0000, 8.0000, 7.0000, 6.0000, 5.0000, 4.0000, 3.0000, 2.0000, 1.0000,
5.0000, 4.0000, 3.0000, 2.0000, 1.0000, 6.0000, 7.0000, 8.0000, 9.0000 });
TMVA_SOFIE_ComplexTopK::Session s("ComplexTopK_FromONNX.dat");
auto output = s.infer(input.data());
std::vector<float> values = std::get<0>(output);
std::vector<int64_t> indexes = std::get<1>(output);
// Checking output size..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
std::cout<<values.size()<<" "<<sizeof(ComplexTopK_ExpectedOutput::values) / sizeof(float)<<std::endl;
EXPECT_EQ(values.size(), sizeof(ComplexTopK_ExpectedOutput::values) / sizeof(float));
float *correct_values = ComplexTopK_ExpectedOutput::values;
// Checking every output value, one by one
for (size_t i = 0; i < values.size(); ++i) {
std::cout << "Output[" << i << "]: " << values[i] << ", Correct[" << i << "]: " << correct_values[i] << std::endl;
EXPECT_LE(std::abs(values[i] - correct_values[i]), TOLERANCE);
}
// Checking output size
EXPECT_EQ(indexes.size(), sizeof(ComplexTopK_ExpectedOutput::indexes) / sizeof(float));
float *correct_indexes = ComplexTopK_ExpectedOutput::indexes;
// Checking every output value, one by one
for (size_t i = 0; i < indexes.size(); ++i) {
std::cout << "Indexes[" << i << "]: " << indexes[i] << ", Correct[" << i << "]: " << correct_indexes[i] << std::endl;
EXPECT_LE(std::abs(indexes[i] - correct_indexes[i]), TOLERANCE);
}
}
TEST(ONNX, TopK)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input({9.0, 8.0, 4.5, 1.7, 2.9, 3.2, 4, 2.6, 7.4});
TMVA_SOFIE_TopK::Session s("TopK_FromONNX.dat");
auto output = s.infer(input.data());
std::vector<float> values = std::get<0>(output);
std::vector<int64_t> indexes = std::get<1>(output);
// Checking output size
std::cout<<values.size()<<" "<<sizeof(TopK_ExpectedOutput::values) / sizeof(float)<<std::endl;
EXPECT_EQ(values.size(), sizeof(TopK_ExpectedOutput::values) / sizeof(float));
float *correct_values = TopK_ExpectedOutput::values;
// Checking every output value, one by one
for (size_t i = 0; i < values.size(); ++i) {
std::cout << "Output[" << i << "]: " << values[i] << ", Correct[" << i << "]: " << correct_values[i] << std::endl;
EXPECT_LE(std::abs(values[i] - correct_values[i]), TOLERANCE);
}
// Checking output size
EXPECT_EQ(indexes.size(), sizeof(TopK_ExpectedOutput::indexes) / sizeof(float));
float *correct_indexes= TopK_ExpectedOutput::indexes;
// Checking every output value, one by one
for (size_t i = 0; i < indexes.size(); ++i) {
std::cout << "Indexes[" << i << "]: " << indexes[i] << ", Correct[" << i << "]: " << correct_indexes[i] << std::endl;
EXPECT_LE(std::abs(indexes[i] - correct_indexes[i]), TOLERANCE);
}
}
TEST(ONNX, EyeLike)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<float> input({
0.0, 0.0, 0.0,
0.0, 0.0, 0.0,
0.0, 0.0, 0.0
});
TMVA_SOFIE_EyeLike::Session s("EyeLike_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(EyeLike_ExpectedOutput::output) / sizeof(float));
float *correct = EyeLike_ExpectedOutput::output;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Cast)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<int64_t> input({
1,2,3,4,5,6
});
TMVA_SOFIE_Cast::Session s("Cast_FromONNX.dat");
auto output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Cast_ExpectedOutput::outputs) / sizeof(float));
float *correct = Cast_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, CastLike)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard input
std::vector<int64_t> input_1({3, 23, -8, 1});
TMVA_SOFIE_CastLike::Session s("CastLike_FromONNX.dat");
auto output = s.infer(input_1.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(CastLike_ExpectedOutput::outputs) / sizeof(float));
float *correct = CastLike_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Linear64)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(6400);
std::fill_n(input.data(), input.size(), 1.0f);
TMVA_SOFIE_Linear_64::Session s("Linear_64_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Linear_64_ExpectedOutput::all_ones) / sizeof(float));
float *correct = Linear_64_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, LinearWithSelu)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(48);
std::fill_n(input.data(), input.size(), 1.0f);
TMVA_SOFIE_LinearWithSelu::Session s("LinearWithSelu_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(LinearWithSelu_ExpectedOutput::all_ones) / sizeof(float));
float *correct = LinearWithSelu_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Tanh)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the random input
std::vector<float> input({
-0.3896, -0.3521, 0.0363, 1.0962, 0.5085, -0.8523, -0.6766, 0.2421,
1.5971, 1.3873, -0.2112, -0.6895, -0.5069, -2.1395, -0.7087, 1.1658,
1.3493, 0.8132, 1.7156, -0.8637, -0.1971, 0.0411, -0.5662, -0.2516
});
TMVA_SOFIE_Tanh::Session s("Tanh_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Tanh_ExpectedOutput::outputs) / sizeof(float));
float *correct = Tanh_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Erf)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the random input
std::vector<float> input({
-1.0412, 0.1918, 0.9985, -0.5959, 0.6842, -2.4718, 0.1804, 0.6851,
1.5646, -1.4981, 0.4248, -0.8504
});
TMVA_SOFIE_Erf::Session s("Erf_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Erf_ExpectedOutput::outputs) / sizeof(float));
float *correct = Erf_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Log)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the random input
std::vector<float> input({
1, 2, 3, 4
});
TMVA_SOFIE_Log::Session s("Log_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Log_ExpectedOutput::outputs) / sizeof(float));
float *correct = Log_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, Round)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the random input
std::vector<float> input({1.3, -4.5, 7.9, -2.6});
TMVA_SOFIE_Round::Session s("Round_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(Round_ExpectedOutput::outputs) / sizeof(float));
float *correct = Round_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, LinearWithLeakyRelu)
{
constexpr float TOLERANCE = 1;
// Preparing the standard all-ones input
std::vector<float> input({
0.4369, -0.6882, 1.0309, -1.0263, -0.1519, 1.2237, -0.7054, -0.1762,
-0.6811, -2.2597, 1.0388, -0.7993, 0.1468, 1.3257, -0.4714, -0.0958,
0.7057, -0.3749, -0.3310, 0.0986, -0.1370, 0.0832, -1.6465, -0.2793
});
TMVA_SOFIE_LinearWithLeakyRelu::Session s("LinearWithLeakyRelu_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(LinearWithLeakyRelu_ExpectedOutput::outputs) / sizeof(float));
float *correct = LinearWithLeakyRelu_ExpectedOutput::outputs;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, LinearWithSigmoid)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(48);
std::fill_n(input.data(), input.size(), 1.0f);
TMVA_SOFIE_LinearWithSigmoid::Session s("LinearWithSigmoid_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(LinearWithSigmoid_ExpectedOutput::all_ones) / sizeof(float));
float *correct = LinearWithSigmoid_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, ConvWithPadding)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(25);
std::iota(input.begin(), input.end(), 0.0f);
TMVA_SOFIE_ConvWithPadding::Session s("ConvWithPadding_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(ConvWithPadding_ExpectedOutput::all_ones) / sizeof(float));
float *correct = ConvWithPadding_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, ConvWithoutPadding)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(25);
std::iota(input.begin(), input.end(), 0.0f);
TMVA_SOFIE_ConvWithoutPadding::Session s("ConvWithoutPadding_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(ConvWithoutPadding_ExpectedOutput::all_ones) / sizeof(float));
float *correct = ConvWithoutPadding_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, ConvWithAutopadSameLower)
{
constexpr float TOLERANCE = DEFAULT_TOLERANCE;
// Preparing the standard all-ones input
std::vector<float> input(25);
std::iota(input.begin(), input.end(), 0.0f);
TMVA_SOFIE_ConvWithAutopadSameLower::Session s("ConvWithAutopadSameLower_FromONNX.dat");
std::vector<float> output = s.infer(input.data());
// Checking output size
EXPECT_EQ(output.size(), sizeof(ConvWithAutopadSameLower_ExpectedOutput::all_ones) / sizeof(float));
float *correct = ConvWithAutopadSameLower_ExpectedOutput::all_ones;
// Checking every output value, one by one
for (size_t i = 0; i < output.size(); ++i) {
EXPECT_LE(std::abs(output[i] - correct[i]), TOLERANCE);
}
}
TEST(ONNX, ConvWithStridesPadding)