Mathis/CUDANet
1
0
Fork 0
mirror of https://github.com/lordmathis/CUDANet.git synced 2025-11-05 17:34:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

Cleanup and refactor

Browse Source
This commit is contained in:
Matúš Námešný
2024-03-11 20:39:44 +01:00
parent f3112311da
commit e0178e2d5c
7 changed files with 108 additions and 108 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
test/layers/test_conv2d.cu
View File

@@ -16,7 +16,7 @@ class Conv2dTest : public ::testing::Test {
int numFilters,
Activation activation,
std::vector<float>& input,
std::vector<float>& kernels,
float* kernels,
float*& d_input,
float*& d_output
) {
@@ -26,7 +26,7 @@ class Conv2dTest : public ::testing::Test {
activation
);
conv2d.setKernels(kernels);
conv2d.setWeights(kernels);
// Allocate device memory
cudaStatus = cudaMalloc(
@@ -84,7 +84,7 @@ TEST_F(Conv2dTest, SimpleTest) {
Layers::Conv2d conv2d = commonTestSetup(
inputSize, inputChannels, kernelSize, stride, padding, numFilters,
activation, input, kernels, d_input, d_output
activation, input, kernels.data(), d_input, d_output
);
int outputSize = (inputSize - kernelSize) / stride + 1;
@@ -173,7 +173,7 @@ TEST_F(Conv2dTest, ComplexTest) {
Layers::Conv2d conv2d = commonTestSetup(
inputSize, inputChannels, kernelSize, stride, padding, numFilters,
activation, input, kernels, d_input, d_output
activation, input, kernels.data(), d_input, d_output
);
EXPECT_EQ(inputSize, conv2d.outputSize);

97
test/layers/test_dense.cu
View File

@@ -6,23 +6,20 @@
#include "activations.cuh"
#include "dense.cuh"
class DenseLayerTest : public::testing::Test {
class DenseLayerTest : public ::testing::Test {
protected:
Layers::Dense commonTestSetup(
int inputSize,
int outputSize,
std::vector<float>& input,
std::vector<std::vector<float>>& weights,
std::vector<float>& biases,
float*& d_input,
float*& d_output,
Activation activation
int inputSize,
int outputSize,
std::vector<float>& input,
float* weights,
float* biases,
float*& d_input,
float*& d_output,
Activation activation
) {
// Create Dense layer
Layers::Dense denseLayer(
inputSize, outputSize, activation
);
Layers::Dense denseLayer(inputSize, outputSize, activation);
// Set weights and biases
denseLayer.setWeights(weights);
@@ -37,11 +34,11 @@ class DenseLayerTest : public::testing::Test {
// Copy input to device
cudaStatus = cudaMemcpy(
d_input, input.data(), sizeof(float) * input.size(), cudaMemcpyHostToDevice
d_input, input.data(), sizeof(float) * input.size(),
cudaMemcpyHostToDevice
);
EXPECT_EQ(cudaStatus, cudaSuccess);
return denseLayer;
}
@@ -51,7 +48,7 @@ class DenseLayerTest : public::testing::Test {
cudaFree(d_output);
}
cudaError_t cudaStatus;
cudaError_t cudaStatus;
};
TEST_F(DenseLayerTest, Init) {
@@ -60,9 +57,7 @@ TEST_F(DenseLayerTest, Init) {
int inputSize = i;
int outputSize = j;
Layers::Dense denseLayer(
inputSize, outputSize, SIGMOID
);
Layers::Dense denseLayer(inputSize, outputSize, SIGMOID);
}
}
}
@@ -71,17 +66,19 @@ TEST_F(DenseLayerTest, setWeights) {
int inputSize = 4;
int outputSize = 5;
std::vector<std::vector<float>> weights = {
{0.5f, 1.0f, 0.2f, 0.8f},
{1.2f, 0.3f, 1.5f, 0.4f},
{0.7f, 1.8f, 0.9f, 0.1f},
{0.4f, 2.0f, 0.6f, 1.1f},
{1.3f, 0.5f, 0.0f, 1.7f}
// clang-format off
std::vector<float> weights = {
0.5f, 1.0f, 0.2f, 0.8f,
1.2f, 0.3f, 1.5f, 0.4f,
0.7f, 1.8f, 0.9f, 0.1f,
0.4f, 2.0f, 0.6f, 1.1f,
1.3f, 0.5f, 0.0f, 1.7f
};
// clang-format on
Layers::Dense denseLayer(inputSize, outputSize, SIGMOID);
denseLayer.setWeights(weights);
denseLayer.setWeights(weights.data());
}
TEST_F(DenseLayerTest, ForwardUnitWeightMatrixLinear) {
@@ -90,13 +87,11 @@ TEST_F(DenseLayerTest, ForwardUnitWeightMatrixLinear) {
std::vector<float> input = {1.0f, 2.0f, 3.0f};
std::vector<std::vector<float>> weights(
inputSize, std::vector<float>(outputSize, 0.0f)
);
std::vector<float> weights(outputSize * inputSize, 0.0f);
for (int i = 0; i < inputSize; ++i) {
for (int j = 0; j < outputSize; ++j) {
if (i == j) {
weights[i][j] = 1.0f;
weights[i * outputSize + j] = 1.0f;
}
}
}
@@ -106,13 +101,15 @@ TEST_F(DenseLayerTest, ForwardUnitWeightMatrixLinear) {
float* d_output;
Layers::Dense denseLayer = commonTestSetup(
inputSize, outputSize, input, weights, biases, d_input, d_output, LINEAR
inputSize, outputSize, input, weights.data(), biases.data(), d_input,
d_output, LINEAR
);
denseLayer.forward(d_input, d_output);
std::vector<float> output(outputSize);
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * outputSize, cudaMemcpyDeviceToHost
output.data(), d_output, sizeof(float) * outputSize,
cudaMemcpyDeviceToHost
);
EXPECT_EQ(cudaStatus, cudaSuccess);
@@ -130,26 +127,30 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixRelu) {
std::vector<float> input = {1.0f, 2.0f, 3.0f, 4.0f, -5.0f};
std::vector<std::vector<float>> weights = {
{0.5f, 1.2f, 0.7f, 0.4f, 1.3f},
{1.0f, 0.3f, 1.8f, 2.0f, 0.5f},
{0.2f, 1.5f, 0.9f, 0.6f, 0.0f},
{0.8f, 0.4f, 0.1f, 1.1f, 1.7f}
// clang-format off
std::vector<float> weights = {
0.5f, 1.2f, 0.7f, 0.4f,
1.3f, 1.0f, 0.3f, 1.8f,
2.0f, 0.5f, 0.2f, 1.5f,
0.9f, 0.6f, 0.0f, 0.8f,
0.4f, 0.1f, 1.1f, 1.7f
};
std::vector<float> biases = {0.2f, 0.5f, 0.7f, -1.1f};
// clang-format on
float* d_input;
float* d_output;
Layers::Dense denseLayer = commonTestSetup(
inputSize, outputSize, input, weights, biases, d_input, d_output, RELU
inputSize, outputSize, input, weights.data(), biases.data(), d_input, d_output, RELU
);
denseLayer.forward(d_input, d_output);
std::vector<float> output(outputSize);
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * outputSize, cudaMemcpyDeviceToHost
output.data(), d_output, sizeof(float) * outputSize,
cudaMemcpyDeviceToHost
);
EXPECT_EQ(cudaStatus, cudaSuccess);
@@ -170,21 +171,22 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixSigmoid) {
int inputSize = 5;
int outputSize = 4;
// clang-format off
std::vector<float> input = {0.1f, 0.2f, 0.3f, 0.4f, 0.5f};
std::vector<std::vector<float>> weights = {
{0.8f, 0.7f, 0.7f, 0.3f, 0.8f},
{0.1f, 0.4f, 0.8f, 0.0f, 0.2f},
{0.2f, 0.5f, 0.7f, 0.3f, 0.0f},
{0.1f, 0.7f, 0.6f, 1.0f, 0.4f}
std::vector<float> weights = {
0.8f, 0.7f, 0.7f, 0.3f, 0.8f,
0.1f, 0.4f, 0.8f, 0.0f, 0.2f,
0.2f, 0.5f, 0.7f, 0.3f, 0.0f,
0.1f, 0.7f, 0.6f, 1.0f, 0.4f
};
std::vector<float> biases = {0.1f, 0.2f, 0.3f, 0.4f};
// clang-format on
float* d_input;
float* d_output;
Layers::Dense denseLayer = commonTestSetup(
inputSize, outputSize, input, weights, biases, d_input, d_output,
inputSize, outputSize, input, weights.data(), biases.data(), d_input, d_output,
SIGMOID
);
@@ -192,7 +194,8 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixSigmoid) {
std::vector<float> output(outputSize);
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * outputSize, cudaMemcpyDeviceToHost
output.data(), d_output, sizeof(float) * outputSize,
cudaMemcpyDeviceToHost
);
EXPECT_EQ(cudaStatus, cudaSuccess);