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https://github.com/lordmathis/CUDANet.git
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Test model weights loading
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@@ -32,15 +32,7 @@ class ModelTest : public ::testing::Test {
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);
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if (setWeights) {
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std::vector<float> conv2dWeights = {
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0.18313f, 0.53363f, 0.39527f, 0.27575f, 0.3433f, 0.41746f,
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0.16831f, 0.61693f, 0.54599f, 0.99692f, 0.77127f, 0.25146f,
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0.4206f, 0.16291f, 0.93484f, 0.79765f, 0.74982f, 0.78336f,
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0.6386f, 0.87744f, 0.33587f, 0.9691f, 0.68437f, 0.65098f,
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0.48153f, 0.97546f, 0.8026f, 0.36689f, 0.98152f, 0.37351f,
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0.68407f, 0.2684f, 0.2855f, 0.76195f, 0.67828f, 0.603f
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};
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conv2d->setWeights(conv2dWeights.data());
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conv2d->setWeights(getConv1Weights().data());
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}
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model->addLayer("conv1", conv2d);
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@@ -58,19 +50,33 @@ class ModelTest : public ::testing::Test {
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);
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if (setWeights) {
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std::vector<float> denseWeights = {
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0.36032f, 0.33115f, 0.02948f, 0.09802f, 0.45072f, 0.56266f,
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0.43514f, 0.80946f, 0.43439f, 0.90916f, 0.08605f, 0.07473f,
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0.94788f, 0.66168f, 0.34927f, 0.09464f, 0.61963f, 0.73775f,
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0.51559f, 0.81916f, 0.64915f, 0.03934f, 0.87608f, 0.68364f,
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};
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dense->setWeights(denseWeights.data());
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dense->setWeights(getDenseWeights().data());
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}
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model->addLayer("linear", dense);
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return model;
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}
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std::vector<float> getConv1Weights() {
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return std::vector<float>{
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0.18313f, 0.53363f, 0.39527f, 0.27575f, 0.3433f, 0.41746f,
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0.16831f, 0.61693f, 0.54599f, 0.99692f, 0.77127f, 0.25146f,
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0.4206f, 0.16291f, 0.93484f, 0.79765f, 0.74982f, 0.78336f,
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0.6386f, 0.87744f, 0.33587f, 0.9691f, 0.68437f, 0.65098f,
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0.48153f, 0.97546f, 0.8026f, 0.36689f, 0.98152f, 0.37351f,
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0.68407f, 0.2684f, 0.2855f, 0.76195f, 0.67828f, 0.603f
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};
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}
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std::vector<float> getDenseWeights() {
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return std::vector<float>{
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0.36032f, 0.33115f, 0.02948f, 0.09802f, 0.45072f, 0.56266f,
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0.43514f, 0.80946f, 0.43439f, 0.90916f, 0.08605f, 0.07473f,
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0.94788f, 0.66168f, 0.34927f, 0.09464f, 0.61963f, 0.73775f,
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0.51559f, 0.81916f, 0.64915f, 0.03934f, 0.87608f, 0.68364f,
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};
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}
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void commonTestTeardown(CUDANet::Model *model) {
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delete model;
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}
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@@ -175,38 +181,33 @@ TEST_F(ModelTest, TestModelPredictMultiple) {
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}
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TEST_F(ModelTest, TestLoadWeights) {
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int outputSize = 3;
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CUDANet::Model *model = commonTestSetup();
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CUDANet::Model *model = commonTestSetup();
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model->loadWeights("../test/resources/model.bin");
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std::vector<float> input = {
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0.12762f, 0.99056f, 0.77565f, 0.29058f, 0.29787f, 0.58415f, 0.20484f,
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0.05415f, 0.60593f, 0.3162f, 0.08198f, 0.92749f, 0.72392f, 0.91786f,
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0.65266f, 0.80908f, 0.53389f, 0.36069f, 0.18614f, 0.52381f, 0.08525f,
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0.43054f, 0.3355f, 0.96587f, 0.98194f, 0.71336f, 0.78392f, 0.50648f,
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0.40355f, 0.31863f, 0.54686f, 0.1836f, 0.77171f, 0.01262f, 0.41108f,
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0.53467f, 0.3553f, 0.42808f, 0.45798f, 0.29958f, 0.3923f, 0.98277f,
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0.02033f, 0.99868f, 0.90584f, 0.57554f, 0.15957f, 0.91273f, 0.38901f,
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0.27097f, 0.64788f, 0.84272f, 0.42984f, 0.07466f, 0.53658f, 0.83388f,
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0.28232f, 0.48046f, 0.85626f, 0.04721f, 0.36139f, 0.6123f, 0.56991f,
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0.84854f, 0.61415f, 0.2466f, 0.20017f, 0.78952f, 0.93797f, 0.27884f,
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0.30514f, 0.23521f
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};
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CUDANet::Layers::WeightedLayer *convLayer =
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dynamic_cast<CUDANet::Layers::WeightedLayer *>(model->getLayer("conv1")
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);
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EXPECT_NE(convLayer, nullptr);
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std::vector<float> expected = {2e-05f, 0.00021f, 0.99977f};
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std::vector<float> convWeights = convLayer->getWeights();
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std::vector<float> convExpected = getConv1Weights();
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// predict
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const float *output = model->predict(input.data());
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float sum = 0.0f;
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for (int i = 0; i < outputSize; ++i) {
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EXPECT_NEAR(expected[i], output[i], 1e-5f);
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sum += output[i];
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for (int i = 0; i < convExpected.size(); ++i) {
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EXPECT_FLOAT_EQ(convExpected[i], convWeights[i]);
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}
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EXPECT_NEAR(sum, 1.0f, 1e-5f);
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CUDANet::Layers::WeightedLayer *denseLayer =
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dynamic_cast<CUDANet::Layers::WeightedLayer *>(model->getLayer("linear")
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);
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EXPECT_NE(denseLayer, nullptr);
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std::vector<float> denseWeights = denseLayer->getWeights();
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std::vector<float> denseExpected = getDenseWeights();
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for (int i = 0; i < denseExpected.size(); ++i) {
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EXPECT_FLOAT_EQ(denseExpected[i], denseWeights[i]);
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}
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commonTestTeardown(model);
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}
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