Mathis/CUDANet
1
0
Fork 0
mirror of https://github.com/lordmathis/CUDANet.git synced 2025-11-06 01:34:22 +00:00
Code Issues Packages Projects Releases Wiki Activity

Implement model test

Browse Source
This commit is contained in:
Matúš Námešný
2024-04-13 13:31:29 +02:00
parent 18522c2dea
commit 457e0f669f
2 changed files with 108 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files

61
test/model/test_model.cu
View File

@@ -8,7 +8,7 @@
TEST(Model, TestModelPredict) {
int inputSize = 6;
int inputChannels = 2;
int outputSize = 6;
int outputSize = 3;
int kernelSize = 3;
int stride = 1;
@@ -26,27 +26,12 @@ TEST(Model, TestModelPredict) {
);
// 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,
0.57567f, 0.6752f, 0.83416f, 0.62217f, 0.92441f, 0.96507f, 0.11171f,
0.52438f, 0.90435f, 0.46854f, 0.59987f, 0.21747f, 0.82223f, 0.2709f,
0.69207f, 0.16794f, 0.26679f, 0.49572f, 0.42392f, 0.49808f, 0.33058f,
0.54071f, 0.83304f, 0.03446f, 0.65041f, 0.58601f, 0.7208f, 0.49659f,
0.60447f, 0.70867f, 0.33336f, 0.0199f, 0.53188f, 0.15774f, 0.31791f,
0.2611f, 0.66174f, 0.22588f, 0.95612f, 0.01209f, 0.2239f, 0.51731f,
0.80405f, 0.09126f, 0.85215f, 0.01911f, 0.7448f, 0.61376f, 0.22161f,
0.71483f, 0.36953f, 0.67528f, 0.77609f, 0.5933f, 0.66035f, 0.79205f,
0.04973f, 0.78845f, 0.4023f, 0.55086f, 0.03391f, 0.06616f, 0.45673f,
0.24497f, 0.87024f, 0.43097f, 0.24168f, 0.66931f, 0.03734f, 0.31513f,
0.46383f, 0.74909f, 0.57525f, 0.5295f, 0.48876f, 0.89053f, 0.31964f,
0.87242f, 0.08605f, 0.30465f, 0.8018f, 0.53419f, 0.73341f, 0.12307f,
0.70645f, 0.40608f, 0.89397f, 0.97853f, 0.67084f, 0.47644f, 0.39974f,
0.97879f, 0.86642f, 0.20244f, 0.66219f, 0.11623f, 0.18979f, 0.52886f,
0.44583f, 0.41313f, 0.19766f, 0.47676f, 0.48318f, 0.02079f, 0.83777f,
0.41167f, 0.57684f, 0.79578f, 0.17775f
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);
@@ -59,26 +44,18 @@ TEST(Model, TestModelPredict) {
// dense
CUDANet::Layers::Dense dense(
18, 6, CUDANet::Layers::ActivationType::SOFTMAX
8, 3, CUDANet::Layers::ActivationType::SOFTMAX
);
// dense weights 18*6
std::vector<float> denseWeights = {
0.36032f, 0.33115f, 0.02948f, 0.56265f, 0.23524f, 0.96589f, 0.09802f,
0.45072f, 0.56266f, 0.5246f, 0.86663f, 0.30252f, 0.43514f, 0.80946f,
0.43439f, 0.95206f, 0.5658f, 0.16344f, 0.90916f, 0.08605f, 0.07473f,
0.95572f, 0.9127f, 0.96697f, 0.94788f, 0.66168f, 0.34927f, 0.86927f,
0.10111f, 0.67001f, 0.09464f, 0.61963f, 0.73775f, 0.15255f, 0.37537f,
0.72831f, 0.51559f, 0.81916f, 0.64915f, 0.23607f, 0.88699f, 0.39844f,
0.03934f, 0.87608f, 0.68364f, 0.03633f, 0.11632f, 0.99925f, 0.86102f,
0.6659f, 0.11022f, 0.47878f, 0.92411f, 0.38027f, 0.06771f, 0.99645f,
0.47783f, 0.54653f, 0.41552f, 0.61055f, 0.50326f, 0.79817f, 0.20008f,
0.32929f, 0.23562f, 0.0033f, 0.46628f, 0.04958f, 0.05235f, 0.28102f,
0.45705f, 0.78327f, 0.91427f, 0.41122f, 0.08883f, 0.43558f, 0.14724f,
0.74515f, 0.98215f, 0.50503f, 0.02887f, 0.25426f, 0.3463f, 0.81567f,
0.84608f, 0.15469f, 0.6069f, 0.54311f, 0.77967f, 0.50657f, 0.18208f,
0.7969f, 0.48401f, 0.36097f, 0.7563f, 0.50316f, 0.1134f, 0.98089f,
0.97041f, 0.4832f, 0.79216f, 0.06572f, 0.09688f, 0.51555f, 0.1652f,
0.73933f, 0.44365f, 0.66949f
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);
@@ -98,12 +75,14 @@ TEST(Model, TestModelPredict) {
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;
// float sum = 0.0f;
float sum = 0.0f;
for (int i = 0; i < outputSize; ++i) {
EXPECT_NEAR(expected[i], output[i], 1e-5f);
sum += output[i];
}