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Start implementing weights import

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This commit is contained in:
Matúš Námešný
2024-04-15 22:17:48 +02:00
parent d8c50116e8
commit f4ae45f867
3 changed files with 159 additions and 31 deletions
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91
test/model/test_model.cu
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@@ -8,6 +8,8 @@
class ModelTest : public ::testing::Test {
protected:
CUDANet::Model *commonTestSetup(
bool setWeights = true,
int inputSize = 6,
int inputChannels = 2,
int outputSize = 3,
@@ -28,17 +30,19 @@ class ModelTest : public ::testing::Test {
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);
if (setWeights) {
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("conv1", conv2d);
// maxpool2d
CUDANet::Layers::MaxPooling2D *maxpool2d =
@@ -46,27 +50,29 @@ class ModelTest : public ::testing::Test {
inputSize - kernelSize + 1, numFilters, poolingSize,
poolingStride, CUDANet::Layers::ActivationType::RELU
);
model->addLayer("maxpool2d", maxpool2d);
model->addLayer("maxpool1", 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);
if (setWeights) {
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("linear", dense);
return model;
}
void commonTestTeardown(CUDANet::Model *model) {
delete model;
delete model;
}
cudaError_t cudaStatus;
@@ -103,6 +109,8 @@ TEST_F(ModelTest, TestModelPredict) {
}
EXPECT_NEAR(sum, 1.0f, 1e-5f);
commonTestTeardown(model);
}
TEST_F(ModelTest, TestModelPredictMultiple) {
@@ -162,4 +170,43 @@ TEST_F(ModelTest, TestModelPredictMultiple) {
}
EXPECT_NEAR(sum_2, 1.0f, 1e-5f);
commonTestTeardown(model);
}
TEST_F(ModelTest, TestLoadWeights) {
int outputSize = 3;
CUDANet::Model *model = commonTestSetup();
model->loadWeights("../test/resources/model.bin");
std::vector<float> input = {
0.12762f, 0.99056f, 0.77565f, 0.29058f, 0.29787f, 0.58415f, 0.20484f,
0.05415f, 0.60593f, 0.3162f, 0.08198f, 0.92749f, 0.72392f, 0.91786f,
0.65266f, 0.80908f, 0.53389f, 0.36069f, 0.18614f, 0.52381f, 0.08525f,
0.43054f, 0.3355f, 0.96587f, 0.98194f, 0.71336f, 0.78392f, 0.50648f,
0.40355f, 0.31863f, 0.54686f, 0.1836f, 0.77171f, 0.01262f, 0.41108f,
0.53467f, 0.3553f, 0.42808f, 0.45798f, 0.29958f, 0.3923f, 0.98277f,
0.02033f, 0.99868f, 0.90584f, 0.57554f, 0.15957f, 0.91273f, 0.38901f,
0.27097f, 0.64788f, 0.84272f, 0.42984f, 0.07466f, 0.53658f, 0.83388f,
0.28232f, 0.48046f, 0.85626f, 0.04721f, 0.36139f, 0.6123f, 0.56991f,
0.84854f, 0.61415f, 0.2466f, 0.20017f, 0.78952f, 0.93797f, 0.27884f,
0.30514f, 0.23521f
};
std::vector<float> expected = {2e-05f, 0.00021f, 0.99977f};
// predict
const float *output = model->predict(input.data());
float sum = 0.0f;
for (int i = 0; i < outputSize; ++i) {
EXPECT_NEAR(expected[i], output[i], 1e-5f);
sum += output[i];
}
EXPECT_NEAR(sum, 1.0f, 1e-5f);
commonTestTeardown(model);
}