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Implement simple test for host conv2d

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This commit is contained in:
Matúš Námešný
2024-03-08 23:12:04 +01:00
parent 69ccba2dad
commit 4b6fcbc191
3 changed files with 39 additions and 12 deletions
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7
include/layers/conv2d.cuh
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@@ -25,6 +25,9 @@ class Conv2d {
int outputSize; int outputSize;
void forward(const float* d_input, float* d_output); void forward(const float* d_input, float* d_output);
void setKernels(const std::vector<float>& kernels_input);
void host_conv(const float* input, float* output);
private: private:
// Inputs // Inputs
@@ -49,10 +52,6 @@ class Conv2d {
void initializeKernels(); void initializeKernels();
void toCuda(); void toCuda();
void setKernels(const std::vector<float>& kernels_input);
void host_conv(const float* input, float* output);
}; };
} // namespace Layers } // namespace Layers

11
src/layers/conv2d.cu
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@@ -1,4 +1,5 @@
#include <string> #include <string>
#include <iostream>
#include "activations.cuh" #include "activations.cuh"
#include "conv2d.cuh" #include "conv2d.cuh"
@@ -100,21 +101,25 @@ void Layers::Conv2d::host_conv(const float* input, float* output) {
float sum = 0.0f; float sum = 0.0f;
// std::cout << "f: " << f << ", i: " << i << ", j: " << j << std::endl;
// Iterate over kernel and input matrix // Iterate over kernel and input matrix
for (int k = 0; k < kernelSize; k++) { for (int k = 0; k < kernelSize; k++) {
for (int l = 0; l < kernelSize; l++) { for (int l = 0; l < kernelSize; l++) {
for (int c = 0; c < inputChannels; c++) { for (int c = 0; c < inputChannels; c++) {
// For now stride = 1
int kernelIndex = k * (kernelSize * inputChannels * numFilters) + l * (inputChannels * numFilters) + c * (numFilters) + f; int kernelIndex = k * (kernelSize * inputChannels * numFilters) + l * (inputChannels * numFilters) + c * (numFilters) + f;
int inputIndex = (i * stride + k) * (inputSize * inputChannels) + (j + stride + l) * (inputChannels) + c; int inputIndex = (i * stride + k) * (inputSize * inputChannels) + (j * stride + l) * (inputChannels) + c;
// std::cout << "kernelIndex: " << kernelIndex << ", kernel value: " << kernels[kernelIndex] << ", inputIndex: " << inputIndex << ", input value: " << input[inputIndex] << std::endl;
sum += kernels[kernelIndex] * input[inputIndex]; sum += kernels[kernelIndex] * input[inputIndex];
} }
} }
} }
// std::cout << "sum: " << sum << std::endl;
output[i * (outputSize * numFilters) + j * (numFilters) + f] = sum; output[i * (outputSize * numFilters) + j * (numFilters) + f] = sum;
} }
} }

33
test/layers/test_conv2d.cu
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@@ -5,11 +5,11 @@
#include "conv2d.cuh" #include "conv2d.cuh"
TEST(Conv2dTest, ValidPadding) { TEST(Conv2dTest, SimpleExample) {
int inputSize = 3; int inputSize = 4;
int inputChannels = 1; int inputChannels = 1;
int kernelSize = 3; int kernelSize = 2;
int stride = 1; int stride = 1;
std::string padding = "VALID"; std::string padding = "VALID";
int numFilters = 1; int numFilters = 1;
@@ -28,8 +28,31 @@ TEST(Conv2dTest, ValidPadding) {
int outputSize = (inputSize - kernelSize) / stride + 1; int outputSize = (inputSize - kernelSize) / stride + 1;
EXPECT_EQ(outputSize, conv2d.outputSize); EXPECT_EQ(outputSize, conv2d.outputSize);
std::vector<float> input(inputSize * inputSize * inputChannels); std::vector<float> input = {
1.0f, 2.0f, 3.0f, 4.0f,
5.0f, 6.0f, 7.0f, 8.0f,
9.0f, 10.0f, 11.0f, 12.0f,
13.0f, 14.0f, 15.0f, 16.0f
};
std::vector<float> kernels = {
1.0f, 2.0f, 3.0f, 4.0f,
};
conv2d.setKernels(kernels);
std::vector<float> output(outputSize * outputSize * numFilters); std::vector<float> output(outputSize * outputSize * numFilters);
std::vector<float> kernels(kernelSize * kernelSize * inputChannels * numFilters);
conv2d.host_conv(input.data(), output.data());
std::vector<float> expected = {
44.0f, 54.0f, 64.0f,
84.0f, 94.0f, 104.0f,
124.0f, 134.0f, 144.0f
};
for (int i = 0; i < output.size(); ++i) {
EXPECT_FLOAT_EQ(expected[i], output[i]);
}
} }