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Add non square test to conv2d

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This commit is contained in:
Matúš Námešný
2024-05-23 21:50:39 +02:00
parent 37c29d6734
commit 78a0fd0baf
2 changed files with 272 additions and 40 deletions
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180
test/layers/test_conv2d.cu
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@@ -6,25 +6,25 @@
#include "conv2d.cuh"
class Conv2dTest : public ::testing::Test {
protected:
dim2d inputSize;
int inputChannels;
dim2d kernelSize;
dim2d stride;
int numFilters;
dim2d paddingSize;
protected:
dim2d inputSize;
int inputChannels;
dim2d kernelSize;
dim2d stride;
int numFilters;
dim2d paddingSize;
CUDANet::Layers::ActivationType activationType;
std::vector<float> input;
std::vector<float> kernels;
std::vector<float> expected;
std::vector<float> input;
std::vector<float> kernels;
std::vector<float> expected;
float *d_input;
float *d_output;
float *d_input;
float *d_output;
CUDANet::Layers::Conv2d *conv2dLayer;
virtual void SetUp() override {
d_input = nullptr;
d_output = nullptr;
d_input = nullptr;
d_output = nullptr;
conv2dLayer = nullptr;
}
@@ -45,7 +45,8 @@ protected:
conv2dLayer->setWeights(kernels.data());
cudaStatus = cudaMalloc((void**)&d_input, sizeof(float) * input.size());
cudaStatus =
cudaMalloc((void **)&d_input, sizeof(float) * input.size());
EXPECT_EQ(cudaStatus, cudaSuccess);
cudaStatus = cudaMemcpy(
@@ -56,8 +57,14 @@ protected:
d_output = conv2dLayer->forward(d_input);
int outputHeight = (inputSize.first - kernelSize.first + 2 * paddingSize.first) / stride.first + 1;
int outputWidth = (inputSize.second - kernelSize.second + 2 * paddingSize.second) / stride.second + 1;
int outputHeight =
(inputSize.first - kernelSize.first + 2 * paddingSize.first) /
stride.first +
1;
int outputWidth =
(inputSize.second - kernelSize.second + 2 * paddingSize.second) /
stride.second +
1;
int outputSize = outputHeight * outputWidth * numFilters;
EXPECT_EQ(outputSize, conv2dLayer->getOutputSize());
@@ -75,12 +82,12 @@ protected:
};
TEST_F(Conv2dTest, SimpleTest) {
inputSize = {4, 4};
inputChannels = 1;
kernelSize = {2, 2};
stride = {1, 1};
numFilters = 1;
paddingSize = {0, 0};
inputSize = {4, 4};
inputChannels = 1;
kernelSize = {2, 2};
stride = {1, 1};
numFilters = 1;
paddingSize = {0, 0};
activationType = CUDANet::Layers::ActivationType::NONE;
input = {
@@ -97,7 +104,8 @@ TEST_F(Conv2dTest, SimpleTest) {
3.0f, 4.0f
// clang-format on
};
expected = {44.0f, 54.0f, 64.0f, 84.0f, 94.0f, 104.0f, 124.0f, 134.0f, 144.0f};
expected = {44.0f, 54.0f, 64.0f, 84.0f, 94.0f,
104.0f, 124.0f, 134.0f, 144.0f};
runTest();
}
@@ -116,8 +124,7 @@ TEST_F(Conv2dTest, PaddedTest) {
);
paddingSize = {paddingFirst, paddingSecond};
activationType =
CUDANet::Layers::ActivationType::NONE;
activationType = CUDANet::Layers::ActivationType::NONE;
// clang-format off
input = {
@@ -182,11 +189,11 @@ TEST_F(Conv2dTest, PaddedTest) {
2.78625f, 5.33763f, 5.80899f, 5.89785f, 5.51095f, 3.74287f, 2.64053f,
4.05895f, 3.96482f, 4.30177f, 1.94269f
};
runTest();
}
TEST_F(Conv2dTest, StridedPaddedConvolution) {
TEST_F(Conv2dTest, StridedPaddedTest) {
inputSize = {5, 5};
inputChannels = 2;
kernelSize = {3, 3};
@@ -200,8 +207,7 @@ TEST_F(Conv2dTest, StridedPaddedConvolution) {
);
paddingSize = {paddingFirst, paddingSecond};
activationType =
CUDANet::Layers::ActivationType::RELU;
activationType = CUDANet::Layers::ActivationType::RELU;
// clang-format off
input = {
@@ -238,16 +244,112 @@ TEST_F(Conv2dTest, StridedPaddedConvolution) {
};
// clang-format on
expected = {
// Channel 1
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.59803f, 2.84444f, 1.6201f, 0.0f,
0.0f, 2.38937f, 3.80762f, 3.39679f, 0.0f, 0.0f, 1.13102f, 2.33335f,
1.98488f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
// Channel 2
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.57732f, 3.55543f, 2.24675f, 0.0f,
0.0f, 3.36842f, 3.41373f, 3.14804f, 0.0f, 0.0f, 1.17963f, 2.55005f,
1.63218f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
expected = {// Channel 1
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.59803f, 2.84444f, 1.6201f,
0.0f, 0.0f, 2.38937f, 3.80762f, 3.39679f, 0.0f, 0.0f, 1.13102f,
2.33335f, 1.98488f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
// Channel 2
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.57732f, 3.55543f,
2.24675f, 0.0f, 0.0f, 3.36842f, 3.41373f, 3.14804f, 0.0f, 0.0f,
1.17963f, 2.55005f, 1.63218f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
};
runTest();
}
TEST_F(Conv2dTest, NonSquareInputTest) {
inputSize = {4, 6}; // Non-square input
inputChannels = 1;
kernelSize = {2, 2};
stride = {1, 1};
numFilters = 1;
paddingSize = {0, 0};
activationType = CUDANet::Layers::ActivationType::NONE;
input = {
// clang-format off
0.946f, 0.879f, 0.382f, 0.542f, 0.453f, 0.128f,
0.128f, 0.860f, 0.778f, 0.049f, 0.974f, 0.400f,
0.400f, 0.874f, 0.161f, 0.271f, 0.580f, 0.373f,
0.078f, 0.366f, 0.396f, 0.181f, 0.246f, 0.112f
// clang-format on
};
kernels = {0.744f, 0.745f, 0.164f, 0.157f};
expected = {1.51469f, 1.20175f, 0.82328f, 0.90169f, 0.65493f,
0.93875f, 1.38806f, 0.68429f, 0.89759f, 1.17634f,
1.01898f, 0.8924f, 0.41504f, 0.70203f, 0.76733f};
runTest();
}
TEST_F(Conv2dTest, NonSquareKernelTest) {
inputSize = {4, 4};
inputChannels = 1;
kernelSize = {1, 3}; // Non-square kernel
stride = {1, 1};
numFilters = 1;
paddingSize = {0, 0};
activationType = CUDANet::Layers::ActivationType::NONE;
input = {
// clang-format off
0.946f, 0.879f, 0.382f, 0.542f,
0.128f, 0.860f, 0.778f, 0.049f,
0.400f, 0.874f, 0.161f, 0.271f,
0.078f, 0.366f, 0.396f, 0.181f
// clang-format on
};
kernels = {0.744f, 0.745f, 0.164f};
expected = {1.42133f, 1.02745f, 0.86352f, 1.22749f,
0.97513f, 0.81465f, 0.39565f, 0.59701f};
runTest();
}
TEST_F(Conv2dTest, NonSquareStrideTest) {
inputSize = {4, 4};
inputChannels = 1;
kernelSize = {2, 2};
stride = {1, 2}; // Non-square stride
numFilters = 1;
paddingSize = {0, 0};
activationType = CUDANet::Layers::ActivationType::NONE;
input = {
// clang-format off
0.946f, 0.879f, 0.382f, 0.542f,
0.128f, 0.860f, 0.778f, 0.049f,
0.400f, 0.874f, 0.161f, 0.271f,
0.078f, 0.366f, 0.396f, 0.181f
// clang-format on
};
kernels = {0.144f, 0.745f, 0.964f, 0.164f};
expected = {1.05551f, 1.21683f, 1.18807f, 0.34818f, 0.84395f, 0.63651f};
runTest();
}
TEST_F(Conv2dTest, NonSquarePaddingTest) {
inputSize = {4, 4};
inputChannels = 1;
kernelSize = {2, 2};
stride = {1, 1};
numFilters = 1;
paddingSize = {1, 2}; // Non-square padding
activationType = CUDANet::Layers::ActivationType::NONE;
input = {
// clang-format off
0.946f, 0.879f, 0.382f, 0.542f,
0.128f, 0.860f, 0.778f, 0.049f,
0.400f, 0.874f, 0.161f, 0.271f,
0.078f, 0.366f, 0.396f, 0.181f
// clang-format on
};
kernels = {0.144f, 0.745f, 0.964f, 0.164f};
expected = {0.0f, 0.15514f, 1.0561f, 0.91f, 0.45714f, 0.52249f, 0.0f,
0.0f, 0.72576f, 1.05551f, 1.3678f, 1.21683f, 0.12528f, 0.0f,
0.0f, 0.16096f, 1.18807f, 1.57239f, 0.34818f, 0.2683f, 0.0f,
0.0f, 0.31079f, 0.84395f, 0.66357f, 0.63651f, 0.21351f, 0.0f,
0.0f, 0.05811f, 0.2839f, 0.34772f, 0.19187f, 0.02606f, 0.0f};
}