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Add padding to avg pooling

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This commit is contained in:
Matúš Námešný
2024-05-26 18:54:12 +02:00
parent d1fc45d9e0
commit 4a67b708f0
6 changed files with 60 additions and 11 deletions
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3
include/kernels/pooling.cuh
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@@ -23,7 +23,8 @@ __global__ void avg_pooling(
const dim2d outputSize,
const int nChannels,
const dim2d poolingSize,
const dim2d stride
const dim2d stride,
const dim2d padding
);
} // namespace CUDANet::Kernels

2
include/layers/avg_pooling.cuh
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@@ -13,6 +13,7 @@ class AvgPooling2d : public SequentialLayer, public TwoDLayer {
int nChannels,
dim2d poolingSize,
dim2d stride,
dim2d padding,
ActivationType activationType
);
~AvgPooling2d();
@@ -40,6 +41,7 @@ class AvgPooling2d : public SequentialLayer, public TwoDLayer {
int nChannels;
dim2d poolingSize;
dim2d stride;
dim2d padding;
dim2d outputSize;

14
src/kernels/pooling.cu
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@@ -47,7 +47,8 @@ __global__ void Kernels::avg_pooling(
const dim2d outputSize,
const int nChannels,
const dim2d poolingSize,
const dim2d stride
const dim2d stride,
const dim2d padding
) {
int j = blockDim.x * blockIdx.x + threadIdx.x;
int i = blockDim.y * blockIdx.y + threadIdx.y;
@@ -61,13 +62,18 @@ __global__ void Kernels::avg_pooling(
for (int k = 0; k < poolingSize.first; k++) {
for (int l = 0; l < poolingSize.second; l++) {
int inputIndex = c * inputSize.first * inputSize.second +
(i * stride.first + k) * inputSize.second +
(j * stride.second + l);
int inputRow = i * stride.first + k - padding.first;
int inputCol = j * stride.second + l - padding.second;
if (inputRow >= 0 && inputRow < inputSize.first &&
inputCol >= 0 && inputCol < inputSize.second) {
int inputIndex = c * inputSize.first * inputSize.second +
inputRow * inputSize.second + inputCol;
sum += d_input[inputIndex];
}
}
}
d_output
[c * outputSize.first * outputSize.second + i * outputSize.second + j] =

11
src/layers/avg_pooling.cu
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@@ -9,15 +9,17 @@ AvgPooling2d::AvgPooling2d(
int nChannels,
dim2d poolingSize,
dim2d stride,
dim2d padding,
ActivationType activationType
)
: inputSize(inputSize),
nChannels(nChannels),
poolingSize(poolingSize),
stride(stride) {
stride(stride),
padding(padding) {
outputSize = {
(inputSize.first - poolingSize.first) / stride.first + 1,
(inputSize.second - poolingSize.second) / stride.second + 1
(inputSize.first + 2 * padding.first - poolingSize.first) / stride.first + 1,
(inputSize.second + 2 * padding.second - poolingSize.second) / stride.second + 1
};
activation = new Activation(
@@ -45,7 +47,8 @@ float* AvgPooling2d::forward(const float* d_input) {
);
Kernels::avg_pooling<<<grid, block>>>(
d_input, d_output, inputSize, outputSize, nChannels, poolingSize, stride
d_input, d_output, inputSize, outputSize, nChannels, poolingSize,
stride, padding
);
CUDA_CHECK(cudaGetLastError());

28
test/layers/test_avg_pooling.cu
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@@ -11,6 +11,7 @@ class AvgPoolingLayerTest : public ::testing::Test {
int nChannels;
dim2d poolingSize;
dim2d stride;
dim2d padding;
std::vector<float> input;
std::vector<float> expected;
@@ -34,7 +35,7 @@ class AvgPoolingLayerTest : public ::testing::Test {
cudaError_t cudaStatus;
avgPoolingLayer = new CUDANet::Layers::AvgPooling2d(
inputSize, nChannels, poolingSize, stride,
inputSize, nChannels, poolingSize, stride, padding,
CUDANet::Layers::ActivationType::NONE
);
@@ -75,6 +76,7 @@ TEST_F(AvgPoolingLayerTest, AvgPoolForwardTest) {
nChannels = 2;
poolingSize = {2, 2};
stride = {2, 2};
padding = {0, 0};
input = {
// clang-format off
@@ -102,6 +104,7 @@ TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquareInputTest) {
nChannels = 2;
poolingSize = {2, 2};
stride = {2, 2};
padding = {0, 0};
input = {// Channel 0
0.573f, 0.619f, 0.732f, 0.055f, 0.123f, 0.234f, 0.243f, 0.316f,
@@ -124,6 +127,7 @@ TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquarePoolingTest) {
nChannels = 2;
poolingSize = {2, 3}; // Non-square pooling
stride = {2, 2};
padding = {0, 0};
input = {// Channel 0
0.573f, 0.619f, 0.732f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f,
@@ -143,6 +147,7 @@ TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquareStrideTest) {
nChannels = 2;
poolingSize = {2, 2};
stride = {1, 2}; // Non-square stride
padding = {0, 0};
input = {// Channel 0
0.573f, 0.619f, 0.732f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f,
@@ -157,3 +162,24 @@ TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquareStrideTest) {
runTest();
}
TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquarePaddingTest) {
inputSize = {4, 4};
nChannels = 2;
poolingSize = {2, 2};
stride = {2, 2};
padding = {1, 0}; // Non-square padding
input = {// Channel 0
0.573f, 0.619f, 0.732f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f,
0.712f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f, 0.742f, 0.055f,
// Channel 1
0.473f, 0.919f, 0.107f, 0.073f, 0.073f, 0.362f, 0.973f, 0.059f,
0.473f, 0.455f, 0.283f, 0.416f, 0.532f, 0.819f, 0.732f, 0.850f
};
expected = {0.298f, 0.19675f, 0.3315f, 0.43775f, 0.298f, 0.19925f,
0.348f, 0.045f, 0.34075f, 0.43275f, 0.33775f, 0.3955f};
runTest();
}

11
tools/pooling_test.py
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@@ -103,6 +103,15 @@ def gen_avg_pool_non_square_stride_test_result():
print_cpp_vector(output)
def gen_avg_pool_non_square_padding_test_result():
input = _get_pool_input()
output = torch.nn.AvgPool2d(kernel_size=2, stride=2, padding=(1, 0))(input)
output = torch.flatten(output)
print_cpp_vector(output)
if __name__ == "__main__":
print("Generating test results...")
@@ -125,3 +134,5 @@ if __name__ == "__main__":
gen_avg_non_square_pool_test_result()
print("Avg pool non square stride test:")
gen_avg_pool_non_square_stride_test_result()
print("Avg pool non square padding test:")
gen_avg_pool_non_square_padding_test_result()