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Implement avg pooling

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This commit is contained in:
Matúš Námešný
2024-03-19 22:33:43 +01:00
parent a0fc1b00ae
commit ef63cbd9f1
6 changed files with 92 additions and 45 deletions
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6
include/kernels/pooling.cuh
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@@ -11,8 +11,7 @@ __global__ void max_pooling(
const int inputSize,
const int nChannels,
const int poolingSize,
const int stride,
const int paddingSize
const int stride
);
__global__ void avg_pooling(
@@ -21,8 +20,7 @@ __global__ void avg_pooling(
const int inputSize,
const int nChannels,
const int poolingSize,
const int stride,
const int paddingSize
const int stride
);
} // namespace CUDANet::Kernels

38
include/layers/avg_pooling.cuh Normal file
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@@ -0,0 +1,38 @@
#ifndef CUDANET_AVG_POOLING_H
#define CUDANET_AVG_POOLING_H
#include "activation.cuh"
#include "layer.cuh"
namespace CUDANet::Layers {
class AvgPooling2D : public SequentialLayer {
public:
AvgPooling2D(
int inputSize,
int nChannels,
int poolingSize,
int stride,
ActivationType activationType
);
~AvgPooling2D();
float* forward(const float* d_input);
private:
int inputSize;
int nChannels;
int poolingSize;
int stride;
int outputSize;
int gridSize;
float* d_output;
Activation activation;
};
} // namespace CUDANet::Layers
#endif // CUDANET_AVG_POOLING_H

2
include/layers/max_pooling.cuh
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@@ -15,7 +15,6 @@ class MaxPooling2D : public SequentialLayer {
int nChannels,
int poolingSize,
int stride,
Padding padding,
ActivationType activationType
);
~MaxPooling2D();
@@ -27,7 +26,6 @@ class MaxPooling2D : public SequentialLayer {
int nChannels;
int poolingSize;
int stride;
int paddingSize;
int outputSize;
int gridSize;

29
src/kernels/pooling.cu
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@@ -10,8 +10,7 @@ __global__ void Kernels::max_pooling(
const int inputSize,
const int nChannels,
const int poolingSize,
const int stride,
const int paddingSize
const int stride
) {
int tid = blockDim.x * blockIdx.x + threadIdx.x;
if (tid >= inputSize * inputSize * nChannels) {
@@ -28,17 +27,9 @@ __global__ void Kernels::max_pooling(
for (int k = 0; k < poolingSize; k++) {
for (int l = 0; l < poolingSize; l++) {
if (i * stride + k < paddingSize ||
i * stride + k >= (inputSize + paddingSize) ||
j * stride + l < paddingSize ||
j * stride + l >= (inputSize + paddingSize)) {
continue;
}
int inputIndex = c * inputSize * inputSize +
(i * stride + k - paddingSize) * inputSize +
(j * stride + l - paddingSize);
(i * stride + k) * inputSize +
(j * stride + l);
if (d_input[inputIndex] > max) {
max = d_input[inputIndex];
@@ -55,8 +46,7 @@ __global__ void Kernels::avg_pooling(
const int inputSize,
const int nChannels,
const int poolingSize,
const int stride,
const int paddingSize
const int stride
) {
int tid = blockDim.x * blockIdx.x + threadIdx.x;
if (tid >= inputSize * inputSize * nChannels) {
@@ -73,16 +63,9 @@ __global__ void Kernels::avg_pooling(
for (int k = 0; k < poolingSize; k++) {
for (int l = 0; l < poolingSize; l++) {
if (i * stride + k < paddingSize ||
i * stride + k >= (inputSize + paddingSize) ||
j * stride + l < paddingSize ||
j * stride + l >= (inputSize + paddingSize)) {
continue;
}
int inputIndex = c * inputSize * inputSize +
(i * stride + k - paddingSize) * inputSize +
(j * stride + l - paddingSize);
(i * stride + k) * inputSize +
(j * stride + l);
sum += d_input[inputIndex];
}

44
src/layers/avg_pooling.cu Normal file
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@@ -0,0 +1,44 @@
#include "avg_pooling.cuh"
#include "cuda_helper.cuh"
#include "pooling.cuh"
using namespace CUDANet::Layers;
AvgPooling2D::AvgPooling2D(
int inputSize,
int nChannels,
int poolingSize,
int stride,
ActivationType activationType
)
: inputSize(inputSize), nChannels(nChannels), poolingSize(poolingSize), stride(stride) {
outputSize = (inputSize - poolingSize) / stride + 1;
activation = Activation(
activationType, outputSize * outputSize * nChannels
);
d_output = nullptr;
CUDA_CHECK(cudaMalloc(
(void**)&d_output, sizeof(float) * outputSize * outputSize * nChannels
));
gridSize = (outputSize * outputSize * nChannels + BLOCK_SIZE - 1) / BLOCK_SIZE;
}
AvgPooling2D::~AvgPooling2D() {
cudaFree(d_output);
}
float* AvgPooling2D::forward(const float* d_input) {
Kernels::avg_pooling<<<gridSize, BLOCK_SIZE>>>(
d_input, d_output, inputSize, nChannels, poolingSize, stride
);
return d_output;
}

18
src/layers/max_pooling.cu
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@@ -10,26 +10,12 @@ MaxPooling2D::MaxPooling2D(
int nChannels,
int poolingSize,
int stride,
Padding padding,
ActivationType activationType
)
: inputSize(inputSize), nChannels(nChannels), poolingSize(poolingSize), stride(stride) {
switch (padding) {
case SAME:
outputSize = inputSize;
paddingSize = ((stride - 1) * inputSize - stride + poolingSize) / 2;
break;
case VALID:
paddingSize = 0;
outputSize = (inputSize - poolingSize) / stride + 1;
break;
default:
break;
}
outputSize = (inputSize - poolingSize) / stride + 1;
activation = Activation(
activationType, outputSize * outputSize * nChannels
@@ -52,7 +38,7 @@ MaxPooling2D::~MaxPooling2D() {
float* MaxPooling2D::forward(const float* d_input) {
Kernels::max_pooling<<<gridSize, BLOCK_SIZE>>>(
d_input, d_output, inputSize, nChannels, poolingSize, stride, paddingSize
d_input, d_output, inputSize, nChannels, poolingSize, stride
);
return d_output;