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https://github.com/lordmathis/CUDANet.git
synced 2025-11-05 17:34:21 +00:00
Implement test multiple inference
This commit is contained in:
@@ -6,77 +6,75 @@
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#include "model.hpp"
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class ModelTest : public ::testing::Test {
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protected:
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CUDANet::Model* commonTestSetup(
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int inputSize = 6,
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int inputChannels = 2,
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int outputSize = 3,
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protected:
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CUDANet::Model *commonTestSetup(
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int inputSize = 6,
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int inputChannels = 2,
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int outputSize = 3,
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int kernelSize = 3,
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int stride = 1,
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int numFilters = 2,
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int kernelSize = 3,
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int stride = 1,
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int numFilters = 2,
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int poolingSize = 2,
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int poolingStride = 2
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) {
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int poolingSize = 2,
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int poolingStride = 2
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) {
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CUDANet::Model *model =
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new CUDANet::Model(inputSize, inputChannels, outputSize);
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CUDANet::Model *model = new CUDANet::Model(inputSize, inputChannels, outputSize);
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// Conv2d
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CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
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inputSize, inputChannels, kernelSize, stride, numFilters,
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CUDANet::Layers::Padding::VALID,
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CUDANet::Layers::ActivationType::NONE
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);
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// weights 6*6*2*2
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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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model->addLayer("conv2d", conv2d);
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// Conv2d
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CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
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inputSize, inputChannels, kernelSize, stride, numFilters, CUDANet::Layers::Padding::VALID,
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CUDANet::Layers::ActivationType::NONE
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// maxpool2d
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CUDANet::Layers::MaxPooling2D *maxpool2d =
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new CUDANet::Layers::MaxPooling2D(
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inputSize - kernelSize + 1, numFilters, poolingSize,
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poolingStride, CUDANet::Layers::ActivationType::RELU
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);
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// weights 6*6*2*2
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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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model->addLayer("conv2d", conv2d);
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model->addLayer("maxpool2d", maxpool2d);
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// maxpool2d
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CUDANet::Layers::MaxPooling2D *maxpool2d = new CUDANet::Layers::MaxPooling2D(
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inputSize - kernelSize + 1, numFilters, poolingSize, poolingStride, CUDANet::Layers::ActivationType::RELU
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);
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model->addLayer("maxpool2d", maxpool2d);
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// dense
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CUDANet::Layers::Dense *dense = new CUDANet::Layers::Dense(
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8, 3, CUDANet::Layers::ActivationType::SOFTMAX
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);
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// dense weights 18*6
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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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model->addLayer("dense", dense);
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// dense
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CUDANet::Layers::Dense *dense = new CUDANet::Layers::Dense(
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8, 3, CUDANet::Layers::ActivationType::SOFTMAX
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);
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// dense weights 18*6
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std::vector<float> denseWeights = {
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0.36032f, 0.33115f, 0.02948f,
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0.09802f, 0.45072f, 0.56266f,
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0.43514f, 0.80946f, 0.43439f,
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0.90916f, 0.08605f, 0.07473f,
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0.94788f, 0.66168f, 0.34927f,
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0.09464f, 0.61963f, 0.73775f,
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0.51559f, 0.81916f, 0.64915f,
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0.03934f, 0.87608f, 0.68364f,
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};
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dense->setWeights(denseWeights.data());
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model->addLayer("dense", dense);
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return model;
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}
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return model;
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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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void commonTestTeardown(float* d_input) {
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cudaDeviceReset();
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}
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cudaError_t cudaStatus;
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cudaError_t cudaStatus;
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};
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TEST_F(ModelTest, TestModelPredict) {
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int outputSize = 3;
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CUDANet::Model *model = commonTestSetup();
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int outputSize = 3;
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CUDANet::Model *model = commonTestSetup();
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// input 6*6*2
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std::vector<float> input = {
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@@ -96,7 +94,7 @@ TEST_F(ModelTest, TestModelPredict) {
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std::vector<float> expected = {2e-05f, 0.00021f, 0.99977f};
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// predict
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const float* output = model->predict(input.data());
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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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@@ -105,4 +103,63 @@ TEST_F(ModelTest, TestModelPredict) {
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}
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EXPECT_NEAR(sum, 1.0f, 1e-5f);
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}
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TEST_F(ModelTest, TestModelPredictMultiple) {
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int outputSize = 3;
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CUDANet::Model *model = commonTestSetup();
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std::vector<float> input_1 = {
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0.81247f, 0.03579f, 0.26577f, 0.80374f, 0.64584f, 0.19658f, 0.04817f,
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0.50769f, 0.33502f, 0.01739f, 0.32263f, 0.69625f, 0.07433f, 0.98283f,
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0.21217f, 0.48437f, 0.58012f, 0.86991f, 0.81879f, 0.63589f, 0.30264f,
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0.90318f, 0.12978f, 0.35972f, 0.95847f, 0.58633f, 0.55025f, 0.68302f,
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0.61422f, 0.79524f, 0.7205f, 0.72481f, 0.51553f, 0.83032f, 0.23561f,
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0.80631f, 0.23548f, 0.84634f, 0.05839f, 0.76526f, 0.39654f, 0.95635f,
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0.75422f, 0.75341f, 0.82431f, 0.79371f, 0.72413f, 0.88557f, 0.33594f,
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0.56363f, 0.12415f, 0.05635f, 0.15952f, 0.27887f, 0.05417f, 0.58474f,
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0.75129f, 0.1788f, 0.88958f, 0.49793f, 0.85386f, 0.86262f, 0.05568f,
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0.16811f, 0.72188f, 0.08683f, 0.66985f, 0.62707f, 0.4035f, 0.51822f,
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0.46545f, 0.88722f
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};
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std::vector<float> expected_1 = {5e-05f, 0.00033f, 0.99962f};
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// predict
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const float *output_1 = model->predict(input_1.data());
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float sum_1 = 0.0f;
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for (int i = 0; i < outputSize; ++i) {
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EXPECT_NEAR(expected_1[i], output_1[i], 1e-5f);
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sum_1 += output_1[i];
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}
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EXPECT_NEAR(sum_1, 1.0f, 1e-5f);
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std::vector<float> input_2 = {
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0.83573f, 0.19191f, 0.16004f, 0.27137f, 0.64768f, 0.38417f, 0.02167f,
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0.28834f, 0.21401f, 0.16624f, 0.12037f, 0.12706f, 0.3588f, 0.10685f,
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0.49224f, 0.71267f, 0.17677f, 0.29276f, 0.92467f, 0.76689f, 0.8209f,
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0.82226f, 0.11581f, 0.6698f, 0.01109f, 0.47085f, 0.44912f, 0.45936f,
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0.83645f, 0.83272f, 0.81693f, 0.97726f, 0.60649f, 0.9f, 0.37f,
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0.20517f, 0.81921f, 0.83573f, 0.00271f, 0.30453f, 0.78925f, 0.8453f,
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0.80416f, 0.09041f, 0.0802f, 0.98408f, 0.19746f, 0.25598f, 0.09437f,
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0.27681f, 0.92053f, 0.35385f, 0.17389f, 0.14293f, 0.60151f, 0.12338f,
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0.81858f, 0.56294f, 0.97378f, 0.93272f, 0.36075f, 0.64944f, 0.2433f,
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0.66075f, 0.64496f, 0.1191f, 0.66261f, 0.63431f, 0.7137f, 0.14851f,
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0.84456f, 0.44482f
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};
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std::vector<float> expected_2 = {5e-05f, 0.0001f, 0.99985f};
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// predict
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const float *output_2 = model->predict(input_2.data());
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float sum_2 = 0.0f;
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for (int i = 0; i < outputSize; ++i) {
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EXPECT_NEAR(expected_2[i], output_2[i], 1e-5f);
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sum_2 += output_2[i];
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}
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EXPECT_NEAR(sum_2, 1.0f, 1e-5f);
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}
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@@ -84,5 +84,40 @@ if __name__ == "__main__":
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# input = torch.rand(2, 6, 6)
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print("Single test output:")
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out = model(input)
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utils.print_cpp_vector(out)
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print("Multiple predict test output 1:")
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input = torch.tensor([
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0.81247, 0.03579, 0.26577, 0.80374, 0.64584, 0.19658, 0.04817,
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0.50769, 0.33502, 0.01739, 0.32263, 0.69625, 0.07433, 0.98283,
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0.21217, 0.48437, 0.58012, 0.86991, 0.81879, 0.63589, 0.30264,
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0.90318, 0.12978, 0.35972, 0.95847, 0.58633, 0.55025, 0.68302,
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0.61422, 0.79524, 0.7205, 0.72481, 0.51553, 0.83032, 0.23561,
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0.80631, 0.23548, 0.84634, 0.05839, 0.76526, 0.39654, 0.95635,
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0.75422, 0.75341, 0.82431, 0.79371, 0.72413, 0.88557, 0.33594,
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0.56363, 0.12415, 0.05635, 0.15952, 0.27887, 0.05417, 0.58474,
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0.75129, 0.1788, 0.88958, 0.49793, 0.85386, 0.86262, 0.05568,
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0.16811, 0.72188, 0.08683, 0.66985, 0.62707, 0.4035, 0.51822,
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0.46545, 0.88722
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]).reshape(2, 6, 6)
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out = model(input)
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utils.print_cpp_vector(out)
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print("Multiple predict test output 2:")
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input = torch.tensor([
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0.83573, 0.19191, 0.16004, 0.27137, 0.64768, 0.38417, 0.02167,
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0.28834, 0.21401, 0.16624, 0.12037, 0.12706, 0.3588, 0.10685,
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0.49224, 0.71267, 0.17677, 0.29276, 0.92467, 0.76689, 0.8209,
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0.82226, 0.11581, 0.6698, 0.01109, 0.47085, 0.44912, 0.45936,
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0.83645, 0.83272, 0.81693, 0.97726, 0.60649, 0.9, 0.37,
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0.20517, 0.81921, 0.83573, 0.00271, 0.30453, 0.78925, 0.8453,
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0.80416, 0.09041, 0.0802, 0.98408, 0.19746, 0.25598, 0.09437,
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0.27681, 0.92053, 0.35385, 0.17389, 0.14293, 0.60151, 0.12338,
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0.81858, 0.56294, 0.97378, 0.93272, 0.36075, 0.64944, 0.2433,
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0.66075, 0.64496, 0.1191, 0.66261, 0.63431, 0.7137, 0.14851,
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0.84456, 0.44482
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]).reshape(2, 6, 6)
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out = model(input)
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utils.print_cpp_vector(out)
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