Mathis/CUDANet
1
0
Fork 0
mirror of https://github.com/lordmathis/CUDANet.git synced 2025-11-06 17:54:27 +00:00
Code Issues Packages Projects Releases Wiki Activity

Refactor model test

Browse Source
This commit is contained in:
Matúš Námešný
2024-04-14 00:05:32 +02:00
parent 457e0f669f
commit 53c976733b
2 changed files with 67 additions and 54 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
src/layers/conv2d.cu
View File

@@ -1,6 +1,3 @@
#include <iostream>
#include <string>
#include "activation.cuh"
#include "conv2d.cuh"
#include "convolution.cuh"

58
test/model/test_model.cu
View File

@@ -5,22 +5,25 @@
#include "max_pooling.cuh"
#include "model.hpp"
TEST(Model, TestModelPredict) {
int inputSize = 6;
int inputChannels = 2;
int outputSize = 3;
class ModelTest : public ::testing::Test {
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(inputSize, inputChannels, outputSize);
CUDANet::Model *model = new CUDANet::Model(inputSize, inputChannels, outputSize);
// Conv2d
CUDANet::Layers::Conv2d conv2d(
CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
inputSize, inputChannels, kernelSize, stride, numFilters, CUDANet::Layers::Padding::VALID,
CUDANet::Layers::ActivationType::NONE
);
@@ -33,17 +36,17 @@ TEST(Model, TestModelPredict) {
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->setWeights(conv2dWeights.data());
model->addLayer("conv2d", conv2d);
// maxpool2d
CUDANet::Layers::MaxPooling2D maxpool2d(
CUDANet::Layers::MaxPooling2D *maxpool2d = new CUDANet::Layers::MaxPooling2D(
inputSize - kernelSize + 1, numFilters, poolingSize, poolingStride, CUDANet::Layers::ActivationType::RELU
);
model.addLayer("maxpool2d", &maxpool2d);
model->addLayer("maxpool2d", maxpool2d);
// dense
CUDANet::Layers::Dense dense(
CUDANet::Layers::Dense *dense = new CUDANet::Layers::Dense(
8, 3, CUDANet::Layers::ActivationType::SOFTMAX
);
// dense weights 18*6
@@ -57,8 +60,23 @@ TEST(Model, TestModelPredict) {
0.51559f, 0.81916f, 0.64915f,
0.03934f, 0.87608f, 0.68364f,
};
dense.setWeights(denseWeights.data());
model.addLayer("dense", &dense);
dense->setWeights(denseWeights.data());
model->addLayer("dense", dense);
return model;
}
void commonTestTeardown(float* d_input) {
cudaDeviceReset();
}
cudaError_t cudaStatus;
};
TEST_F(ModelTest, TestModelPredict) {
int outputSize = 3;
CUDANet::Model *model = commonTestSetup();
// input 6*6*2
std::vector<float> input = {
@@ -78,7 +96,7 @@ TEST(Model, 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) {
@@ -87,6 +105,4 @@ TEST(Model, TestModelPredict) {
}
EXPECT_NEAR(sum, 1.0f, 1e-5f);
cudaDeviceReset();
}