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Initial cpu conv implementation

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This commit is contained in:
Matúš Námešný
2024-03-07 21:24:59 +01:00
parent 07f231a30b
commit fc2c1616b4
2 changed files with 58 additions and 14 deletions
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4
include/layers/conv2d.cuh
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@@ -49,6 +49,10 @@ 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

70
src/layers/conv2d.cu
View File

@@ -6,13 +6,13 @@
#include "padding.cuh" #include "padding.cuh"
Layers::Conv2d::Conv2d( Layers::Conv2d::Conv2d(
int inputSize, int inputSize,
int inputChannels, int inputChannels,
int kernelSize, int kernelSize,
int stride, int stride,
std::string padding, std::string padding,
int numFilters, int numFilters,
Activation activation Activation activation
) )
: inputSize(inputSize), : inputSize(inputSize),
inputChannels(inputChannels), inputChannels(inputChannels),
@@ -43,11 +43,10 @@ Layers::Conv2d::Conv2d(
d_padded = nullptr; d_padded = nullptr;
if (paddingSize > 0) { if (paddingSize > 0) {
CUDA_CHECK( CUDA_CHECK(cudaMalloc(
cudaMalloc((void**)&d_padded, (void**)&d_padded, sizeof(float) * (inputSize + 2 * paddingSize) *
sizeof(float) * (inputSize + 2 * paddingSize) * (inputSize + 2 * paddingSize) * inputChannels
(inputSize + 2 * paddingSize) * inputChannels) ));
);
} }
} }
@@ -60,6 +59,11 @@ void Layers::Conv2d::initializeKernels() {
std::fill(kernels.begin(), kernels.end(), 0.0f); std::fill(kernels.begin(), kernels.end(), 0.0f);
} }
void Layers::Conv2d::setKernels(const std::vector<float>& kernels_input) {
std::copy(kernels_input.begin(), kernels_input.end(), kernels.begin());
toCuda();
}
void Layers::Conv2d::toCuda() { void Layers::Conv2d::toCuda() {
CUDA_CHECK(cudaMemcpy( CUDA_CHECK(cudaMemcpy(
d_kernels, kernels.data(), sizeof(float) * kernelSize * kernelSize, d_kernels, kernels.data(), sizeof(float) * kernelSize * kernelSize,
@@ -68,15 +72,51 @@ void Layers::Conv2d::toCuda() {
} }
void Layers::Conv2d::forward(const float* d_input, float* d_output) { void Layers::Conv2d::forward(const float* d_input, float* d_output) {
// Padd input // Padd input
int THREADS_PER_BLOCK = 256; int THREADS_PER_BLOCK = 256;
int BLOCKS = (outputSize * outputSize * inputChannels) / THREADS_PER_BLOCK + 1; int BLOCKS =
(outputSize * outputSize * inputChannels) / THREADS_PER_BLOCK + 1;
pad_matrix_kernel<<<BLOCKS, THREADS_PER_BLOCK>>>( pad_matrix_kernel<<<BLOCKS, THREADS_PER_BLOCK>>>(
d_input, d_padded, inputSize, inputSize, inputChannels, paddingSize d_input, d_padded, inputSize, inputSize, inputChannels, paddingSize
); );
// TODO: Implement 2D convolution // TODO: Implement 2D convolution
}
/*
Convolves input vector with kernel and stores result in output
input: matrix (inputSize + paddingSize) x (inputSize + paddingSize) x
inputChannels represented as a vector output: output matrix outputSize x
outputSize x numFilters
*/
void Layers::Conv2d::host_conv(const float* input, float* output) {
// Iterate over output matrix
for (int f = 0; f < numFilters; f++) {
for (int i = 0; i < outputSize; i++) {
for (int j = 0; j < outputSize; j++) {
float sum = 0.0f;
// Iterate over kernel and input matrix
for (int k = 0; k < kernelSize; k++) {
for (int l = 0; l < kernelSize; l++) {
for (int c = 0; c < inputChannels; c++) {
// For now stride = 1
int kernelIndex = k * (kernelSize * inputChannels * numFilters) + l * (inputChannels * numFilters) + c * (numFilters) + f;
int inputIndex = i * (inputSize * inputChannels) + j * (inputChannels) + c;
sum += kernels[kernelIndex] * input[inputIndex];
}
}
}
output[i * (outputSize * numFilters) + j * (numFilters) + f] = sum;
}
}
}
} }