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Implement test multiple inference

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This commit is contained in:
Matúš Námešný
2024-04-14 00:19:29 +02:00
parent b20ade27d8
commit 13b455e4ba
2 changed files with 152 additions and 60 deletions
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177
test/model/test_model.cu
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@@ -6,77 +6,75 @@
#include "model.hpp"
class ModelTest : public ::testing::Test {
protected:
CUDANet::Model* commonTestSetup(
int inputSize = 6,
int inputChannels = 2,
int outputSize = 3,
protected:
CUDANet::Model *commonTestSetup(
int inputSize = 6,
int inputChannels = 2,
int outputSize = 3,
int kernelSize = 3,
int stride = 1,
int numFilters = 2,
int kernelSize = 3,
int stride = 1,
int numFilters = 2,
int poolingSize = 2,
int poolingStride = 2
) {
int poolingSize = 2,
int poolingStride = 2
) {
CUDANet::Model *model =
new CUDANet::Model(inputSize, inputChannels, outputSize);
CUDANet::Model *model = new CUDANet::Model(inputSize, inputChannels, outputSize);
// Conv2d
CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
inputSize, inputChannels, kernelSize, stride, numFilters,
CUDANet::Layers::Padding::VALID,
CUDANet::Layers::ActivationType::NONE
);
// weights 6*6*2*2
std::vector<float> conv2dWeights = {
0.18313f, 0.53363f, 0.39527f, 0.27575f, 0.3433f, 0.41746f,
0.16831f, 0.61693f, 0.54599f, 0.99692f, 0.77127f, 0.25146f,
0.4206f, 0.16291f, 0.93484f, 0.79765f, 0.74982f, 0.78336f,
0.6386f, 0.87744f, 0.33587f, 0.9691f, 0.68437f, 0.65098f,
0.48153f, 0.97546f, 0.8026f, 0.36689f, 0.98152f, 0.37351f,
0.68407f, 0.2684f, 0.2855f, 0.76195f, 0.67828f, 0.603f
};
conv2d->setWeights(conv2dWeights.data());
model->addLayer("conv2d", conv2d);
// Conv2d
CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
inputSize, inputChannels, kernelSize, stride, numFilters, CUDANet::Layers::Padding::VALID,
CUDANet::Layers::ActivationType::NONE
// maxpool2d
CUDANet::Layers::MaxPooling2D *maxpool2d =
new CUDANet::Layers::MaxPooling2D(
inputSize - kernelSize + 1, numFilters, poolingSize,
poolingStride, CUDANet::Layers::ActivationType::RELU
);
// weights 6*6*2*2
std::vector<float> conv2dWeights = {
0.18313f, 0.53363f, 0.39527f, 0.27575f, 0.3433f, 0.41746f,
0.16831f, 0.61693f, 0.54599f, 0.99692f, 0.77127f, 0.25146f,
0.4206f, 0.16291f, 0.93484f, 0.79765f, 0.74982f, 0.78336f,
0.6386f, 0.87744f, 0.33587f, 0.9691f, 0.68437f, 0.65098f,
0.48153f, 0.97546f, 0.8026f, 0.36689f, 0.98152f, 0.37351f,
0.68407f, 0.2684f, 0.2855f, 0.76195f, 0.67828f, 0.603f
};
conv2d->setWeights(conv2dWeights.data());
model->addLayer("conv2d", conv2d);
model->addLayer("maxpool2d", maxpool2d);
// maxpool2d
CUDANet::Layers::MaxPooling2D *maxpool2d = new CUDANet::Layers::MaxPooling2D(
inputSize - kernelSize + 1, numFilters, poolingSize, poolingStride, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("maxpool2d", maxpool2d);
// dense
CUDANet::Layers::Dense *dense = new CUDANet::Layers::Dense(
8, 3, CUDANet::Layers::ActivationType::SOFTMAX
);
// dense weights 18*6
std::vector<float> denseWeights = {
0.36032f, 0.33115f, 0.02948f, 0.09802f, 0.45072f, 0.56266f,
0.43514f, 0.80946f, 0.43439f, 0.90916f, 0.08605f, 0.07473f,
0.94788f, 0.66168f, 0.34927f, 0.09464f, 0.61963f, 0.73775f,
0.51559f, 0.81916f, 0.64915f, 0.03934f, 0.87608f, 0.68364f,
};
dense->setWeights(denseWeights.data());
model->addLayer("dense", dense);
// dense
CUDANet::Layers::Dense *dense = new CUDANet::Layers::Dense(
8, 3, CUDANet::Layers::ActivationType::SOFTMAX
);
// dense weights 18*6
std::vector<float> denseWeights = {
0.36032f, 0.33115f, 0.02948f,
0.09802f, 0.45072f, 0.56266f,
0.43514f, 0.80946f, 0.43439f,
0.90916f, 0.08605f, 0.07473f,
0.94788f, 0.66168f, 0.34927f,
0.09464f, 0.61963f, 0.73775f,
0.51559f, 0.81916f, 0.64915f,
0.03934f, 0.87608f, 0.68364f,
};
dense->setWeights(denseWeights.data());
model->addLayer("dense", dense);
return model;
}
return model;
}
void commonTestTeardown(CUDANet::Model *model) {
delete model;
}
void commonTestTeardown(float* d_input) {
cudaDeviceReset();
}
cudaError_t cudaStatus;
cudaError_t cudaStatus;
};
TEST_F(ModelTest, TestModelPredict) {
int outputSize = 3;
CUDANet::Model *model = commonTestSetup();
int outputSize = 3;
CUDANet::Model *model = commonTestSetup();
// input 6*6*2
std::vector<float> input = {
@@ -96,7 +94,7 @@ TEST_F(ModelTest, TestModelPredict) {
std::vector<float> expected = {2e-05f, 0.00021f, 0.99977f};
// predict
const float* output = model->predict(input.data());
const float *output = model->predict(input.data());
float sum = 0.0f;
for (int i = 0; i < outputSize; ++i) {
@@ -105,4 +103,63 @@ TEST_F(ModelTest, TestModelPredict) {
}
EXPECT_NEAR(sum, 1.0f, 1e-5f);
}
TEST_F(ModelTest, TestModelPredictMultiple) {
int outputSize = 3;
CUDANet::Model *model = commonTestSetup();
std::vector<float> input_1 = {
0.81247f, 0.03579f, 0.26577f, 0.80374f, 0.64584f, 0.19658f, 0.04817f,
0.50769f, 0.33502f, 0.01739f, 0.32263f, 0.69625f, 0.07433f, 0.98283f,
0.21217f, 0.48437f, 0.58012f, 0.86991f, 0.81879f, 0.63589f, 0.30264f,
0.90318f, 0.12978f, 0.35972f, 0.95847f, 0.58633f, 0.55025f, 0.68302f,
0.61422f, 0.79524f, 0.7205f, 0.72481f, 0.51553f, 0.83032f, 0.23561f,
0.80631f, 0.23548f, 0.84634f, 0.05839f, 0.76526f, 0.39654f, 0.95635f,
0.75422f, 0.75341f, 0.82431f, 0.79371f, 0.72413f, 0.88557f, 0.33594f,
0.56363f, 0.12415f, 0.05635f, 0.15952f, 0.27887f, 0.05417f, 0.58474f,
0.75129f, 0.1788f, 0.88958f, 0.49793f, 0.85386f, 0.86262f, 0.05568f,
0.16811f, 0.72188f, 0.08683f, 0.66985f, 0.62707f, 0.4035f, 0.51822f,
0.46545f, 0.88722f
};
std::vector<float> expected_1 = {5e-05f, 0.00033f, 0.99962f};
// predict
const float *output_1 = model->predict(input_1.data());
float sum_1 = 0.0f;
for (int i = 0; i < outputSize; ++i) {
EXPECT_NEAR(expected_1[i], output_1[i], 1e-5f);
sum_1 += output_1[i];
}
EXPECT_NEAR(sum_1, 1.0f, 1e-5f);
std::vector<float> input_2 = {
0.83573f, 0.19191f, 0.16004f, 0.27137f, 0.64768f, 0.38417f, 0.02167f,
0.28834f, 0.21401f, 0.16624f, 0.12037f, 0.12706f, 0.3588f, 0.10685f,
0.49224f, 0.71267f, 0.17677f, 0.29276f, 0.92467f, 0.76689f, 0.8209f,
0.82226f, 0.11581f, 0.6698f, 0.01109f, 0.47085f, 0.44912f, 0.45936f,
0.83645f, 0.83272f, 0.81693f, 0.97726f, 0.60649f, 0.9f, 0.37f,
0.20517f, 0.81921f, 0.83573f, 0.00271f, 0.30453f, 0.78925f, 0.8453f,
0.80416f, 0.09041f, 0.0802f, 0.98408f, 0.19746f, 0.25598f, 0.09437f,
0.27681f, 0.92053f, 0.35385f, 0.17389f, 0.14293f, 0.60151f, 0.12338f,
0.81858f, 0.56294f, 0.97378f, 0.93272f, 0.36075f, 0.64944f, 0.2433f,
0.66075f, 0.64496f, 0.1191f, 0.66261f, 0.63431f, 0.7137f, 0.14851f,
0.84456f, 0.44482f
};
std::vector<float> expected_2 = {5e-05f, 0.0001f, 0.99985f};
// predict
const float *output_2 = model->predict(input_2.data());
float sum_2 = 0.0f;
for (int i = 0; i < outputSize; ++i) {
EXPECT_NEAR(expected_2[i], output_2[i], 1e-5f);
sum_2 += output_2[i];
}
EXPECT_NEAR(sum_2, 1.0f, 1e-5f);
}