Implement max pooling layer

This commit is contained in:
2024-03-19 22:04:58 +01:00
parent 364715ff70
commit a0fc1b00ae
9 changed files with 245 additions and 21 deletions

92
src/kernels/pooling.cu Normal file
View File

@@ -0,0 +1,92 @@
#include "pooling.cuh"
#include "cuda_helper.cuh"
using namespace CUDANet;
__global__ void Kernels::max_pooling(
const float* __restrict__ d_input,
float* __restrict__ d_output,
const int inputSize,
const int nChannels,
const int poolingSize,
const int stride,
const int paddingSize
) {
int tid = blockDim.x * blockIdx.x + threadIdx.x;
if (tid >= inputSize * inputSize * nChannels) {
return;
}
// Get output index
int c = tid / (inputSize * inputSize);
int i = tid % (inputSize * inputSize) / inputSize;
int j = tid % inputSize;
float max = 0.0f;
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);
if (d_input[inputIndex] > max) {
max = d_input[inputIndex];
}
}
}
d_output[tid] = max;
}
__global__ void Kernels::avg_pooling(
const float* __restrict__ d_input,
float* __restrict__ d_output,
const int inputSize,
const int nChannels,
const int poolingSize,
const int stride,
const int paddingSize
) {
int tid = blockDim.x * blockIdx.x + threadIdx.x;
if (tid >= inputSize * inputSize * nChannels) {
return;
}
// Get output index
int c = tid / (inputSize * inputSize);
int i = tid % (inputSize * inputSize) / inputSize;
int j = tid % inputSize;
float sum = 0.0f;
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);
sum += d_input[inputIndex];
}
}
d_output[tid] = sum / (poolingSize * poolingSize);
}