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Add non square pooling and batch norm tests

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This commit is contained in:
Matúš Námešný
2024-05-20 22:16:00 +02:00
parent 6dca8ccd3c
commit 10e73638b6
7 changed files with 533 additions and 173 deletions
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147
test/layers/test_batch_norm.cu
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@@ -6,27 +6,80 @@
#include "activation.cuh"
#include "batch_norm.cuh"
TEST(BatchNormLayerTest, BatchNormSmallForwardTest) {
dim2d inputSize = {4, 4};
int nChannels = 2;
class BatchNormLayerTest : public ::testing::Test {
protected:
dim2d inputSize;
int nChannels;
std::vector<float> weights;
std::vector<float> biases;
std::vector<float> input;
std::vector<float> expected;
cudaError_t cudaStatus;
float *d_input;
float *d_output;
CUDANet::Layers::BatchNorm2d *batchNorm;
CUDANet::Layers::BatchNorm2d batchNorm(
inputSize, nChannels, 1e-5f, CUDANet::Layers::ActivationType::NONE
);
virtual void SetUp() override {
d_input = nullptr;
d_output = nullptr;
batchNorm = nullptr;
}
std::vector<float> weights = {0.63508f, 0.64903f};
std::vector<float> biases = {0.25079f, 0.66841f};
virtual void TearDown() override {
if (d_input) {
cudaFree(d_input);
}
}
batchNorm.setWeights(weights.data());
batchNorm.setBiases(biases.data());
void runTest() {
cudaError_t cudaStatus;
cudaStatus = cudaGetLastError();
EXPECT_EQ(cudaStatus, cudaSuccess);
batchNorm = new CUDANet::Layers::BatchNorm2d(
inputSize, nChannels, 1e-5f, CUDANet::Layers::ActivationType::NONE
);
batchNorm->setWeights(weights.data());
batchNorm->setBiases(biases.data());
cudaStatus = cudaGetLastError();
EXPECT_EQ(cudaStatus, cudaSuccess);
cudaStatus =
cudaMalloc((void **)&d_input, sizeof(float) * input.size());
EXPECT_EQ(cudaStatus, cudaSuccess);
cudaStatus = cudaMemcpy(
d_input, input.data(), sizeof(float) * input.size(),
cudaMemcpyHostToDevice
);
EXPECT_EQ(cudaStatus, cudaSuccess);
d_output = batchNorm->forward(d_input);
std::vector<float> output(input.size());
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * output.size(),
cudaMemcpyDeviceToHost
);
EXPECT_EQ(cudaStatus, cudaSuccess);
for (int i = 0; i < output.size(); ++i) {
EXPECT_NEAR(output[i], expected[i], 1e-5);
}
delete batchNorm;
}
};
TEST_F(BatchNormLayerTest, BatchNormSmallForwardTest) {
inputSize = {4, 4};
nChannels = 2;
weights = {0.63508f, 0.64903f};
biases = {0.25079f, 0.66841f};
// clang-format off
std::vector<float> input = {
input = {
// Channel 0
0.38899f, 0.80478f, 0.48836f, 0.97381f,
0.57508f, 0.60835f, 0.65467f, 0.00168f,
@@ -40,40 +93,44 @@ TEST(BatchNormLayerTest, BatchNormSmallForwardTest) {
};
// clang-format on
std::vector<float> output(input.size());
expected = {-0.06007f, 0.951f, 0.18157f, 1.36202f, 0.39244f, 0.47335f,
0.58598f, -1.00188f, 0.59576f, 0.79919f, -0.57001f, 0.70469f,
-0.62847f, -0.06578f, -0.43668f, 0.72952f, 0.37726f, 0.02088f,
0.35446f, 0.98092f, 1.39264f, 1.80686f, 1.67786f, 1.58318f,
-0.0269f, 0.26878f, 0.81411f, 0.09022f, 0.9126f, 0.71485f,
-0.08184f, -0.19131f};
float* d_input;
cudaStatus = cudaMalloc((void**)&d_input, sizeof(float) * input.size());
EXPECT_EQ(cudaStatus, cudaSuccess);
runTest();
}
cudaStatus = cudaMemcpy(
d_input, input.data(), sizeof(float) * input.size(),
cudaMemcpyHostToDevice
);
EXPECT_EQ(cudaStatus, cudaSuccess);
TEST_F(BatchNormLayerTest, BatchNormNonSquareInputTest) {
inputSize = {4, 6}; // Non-square input
nChannels = 2;
weights = {0.63508f, 0.64903f};
biases = {0.25079f, 0.66841f};
float* d_output = batchNorm.forward(d_input);
input = {// Channel 0
0.38899f, 0.80478f, 0.48836f, 0.97381f, 0.21567f, 0.92312f,
0.57508f, 0.60835f, 0.65467f, 0.00168f, 0.31567f, 0.71345f,
0.65869f, 0.74235f, 0.17928f, 0.70349f, 0.12856f, 0.95645f,
0.15524f, 0.38664f, 0.23411f, 0.7137f, 0.26789f, 0.83412f,
// Channel 1
0.32473f, 0.15698f, 0.314f, 0.60888f, 0.23145f, 0.78945f, 0.80268f,
0.99766f, 0.93694f, 0.89237f, 0.61234f, 0.92314f, 0.13449f,
0.27367f, 0.53036f, 0.18962f, 0.45623f, 0.14523f, 0.57672f,
0.48364f, 0.10863f, 0.0571f, 0.78934f, 0.67545f
};
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * output.size(),
cudaMemcpyDeviceToHost
);
EXPECT_EQ(cudaStatus, cudaSuccess);
expected = {-0.05598f, 0.87495f, 0.1665f, 1.2534f, -0.44404f,
1.13991f, 0.36066f, 0.43515f, 0.53886f, -0.92315f,
-0.22014f, 0.67047f, 0.54786f, 0.73517f, -0.52552f,
0.64817f, -0.63907f, 1.21453f, -0.57934f, -0.06124f,
-0.40275f, 0.67103f, -0.32712f, 0.94064f, 0.28344f,
-0.08405f, 0.25993f, 0.90592f, 0.07909f, 1.30149f,
1.33047f, 1.7576f, 1.62459f, 1.52695f, 0.9135f,
1.59436f, -0.13331f, 0.17158f, 0.73391f, -0.01254f,
0.57151f, -0.10979f, 0.83546f, 0.63156f, -0.18996f,
-0.30285f, 1.30124f, 1.05175f};
std::vector<float> expected = {-0.06007f, 0.951f, 0.18157f, 1.36202f,
0.39244f, 0.47335f, 0.58598f, -1.00188f,
0.59576f, 0.79919f, -0.57001f, 0.70469f,
-0.62847f, -0.06578f, -0.43668f, 0.72952f,
0.37726f, 0.02088f, 0.35446f, 0.98092f,
1.39264f, 1.80686f, 1.67786f, 1.58318f,
-0.0269f, 0.26878f, 0.81411f, 0.09022f,
0.9126f, 0.71485f, -0.08184f, -0.19131f};
// std::cout << "BatchNorm2d: " << std::endl;
for (int i = 0; i < output.size(); i++) {
EXPECT_NEAR(output[i], expected[i], 1e-5);
// std::cout << output[i] << " ";
}
// std::cout << std::endl;
cudaFree(d_input);
runTest();
}