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naive implementation of 3x3 and 5x5 reduction

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froejdh_e
2025-06-27 16:36:21 +02:00
parent e3f4b34b72
commit 5a9c3b717e
4 changed files with 136 additions and 0 deletions
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104
include/aare/Cluster.hpp
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@ -74,6 +74,110 @@ struct Cluster {
}
};
template<typename T>
Cluster<T, 2, 2, uint16_t> reduce_3x3_to_2x2(const Cluster<T, 3, 3, uint16_t> &c) {
Cluster<T, 2, 2, uint16_t> result;
auto [s, i] = c.max_sum_2x2();
switch (i) {
case 0:
result.x = c.x-1;
result.y = c.y+1;
result.data = {c.data[0], c.data[1], c.data[3], c.data[4]};
break;
case 1:
result.x = c.x;
result.y = c.y + 1;
result.data = {c.data[1], c.data[2], c.data[4], c.data[5]};
break;
case 2:
result.x = c.x -1;
result.y = c.y;
result.data = {c.data[3], c.data[4], c.data[6], c.data[7]};
break;
case 3:
result.x = c.x;
result.y = c.y;
result.data = {c.data[4], c.data[5], c.data[7], c.data[8]};
break;
}
// do some stuff
return result;
}
template<typename T>
Cluster<T, 3, 3, uint16_t> reduce_5x5_to_3x3(const Cluster<T, 5, 5, uint16_t> &c) {
Cluster<T, 3, 3, uint16_t> result;
// Reduce the 5x5 cluster to a 3x3 cluster by selecting the 3x3 block with the highest sum
std::array<T, 9> sum_3x3_subclusters;
//Write out the sums in the hope that the compiler can optimize this
sum_3x3_subclusters[0] = c.data[0] + c.data[1] + c.data[2] + c.data[5] + c.data[6] + c.data[7] + c.data[10] + c.data[11] + c.data[12];
sum_3x3_subclusters[1] = c.data[1] + c.data[2] + c.data[3] + c.data[6] + c.data[7] + c.data[8] + c.data[11] + c.data[12] + c.data[13];
sum_3x3_subclusters[2] = c.data[2] + c.data[3] + c.data[4] + c.data[7] + c.data[8] + c.data[9] + c.data[12] + c.data[13] + c.data[14];
sum_3x3_subclusters[3] = c.data[5] + c.data[6] + c.data[7] + c.data[10] + c.data[11] + c.data[12] + c.data[15] + c.data[16] + c.data[17];
sum_3x3_subclusters[4] = c.data[6] + c.data[7] + c.data[8] + c.data[11] + c.data[12] + c.data[13] + c.data[16] + c.data[17] + c.data[18];
sum_3x3_subclusters[5] = c.data[7] + c.data[8] + c.data[9] + c.data[12] + c.data[13] + c.data[14] + c.data[17] + c.data[18] + c.data[19];
sum_3x3_subclusters[6] = c.data[10] + c.data[11] + c.data[12] + c.data[15] + c.data[16] + c.data[17] + c.data[20] + c.data[21] + c.data[22];
sum_3x3_subclusters[7] = c.data[11] + c.data[12] + c.data[13] + c.data[16] + c.data[17] + c.data[18] + c.data[21] + c.data[22] + c.data[23];
sum_3x3_subclusters[8] = c.data[12] + c.data[13] + c.data[14] + c.data[17] + c.data[18] + c.data[19] + c.data[22] + c.data[23] + c.data[24];
auto index = std::max_element(sum_3x3_subclusters.begin(), sum_3x3_subclusters.end()) - sum_3x3_subclusters.begin();
switch (index) {
case 0:
result.x = c.x - 1;
result.y = c.y + 1;
result.data = {c.data[0], c.data[1], c.data[2], c.data[5], c.data[6], c.data[7], c.data[10], c.data[11], c.data[12]};
break;
case 1:
result.x = c.x;
result.y = c.y + 1;
result.data = {c.data[1], c.data[2], c.data[3], c.data[6], c.data[7], c.data[8], c.data[11], c.data[12], c.data[13]};
break;
case 2:
result.x = c.x + 1;
result.y = c.y + 1;
result.data = {c.data[2], c.data[3], c.data[4], c.data[7], c.data[8], c.data[9], c.data[12], c.data[13], c.data[14]};
break;
case 3:
result.x = c.x - 1;
result.y = c.y;
result.data = {c.data[5], c.data[6], c.data[7], c.data[10], c.data[11], c.data[12], c.data[15], c.data[16], c.data[17]};
break;
case 4:
result.x = c.x + 1;
result.y = c.y;
result.data = {c.data[6], c.data[7], c.data[8], c.data[11], c.data[12], c.data[13], c.data[16], c.data[17], c.data[18]};
break;
case 5:
result.x = c.x + 1;
result.y = c.y;
result.data = {c.data[7], c.data[8], c.data[9], c.data[12], c.data[13], c.data[14], c.data[17], c.data[18], c.data[19]};
break;
case 6:
result.x = c.x + 1;
result.y = c.y -1;
result.data = {c.data[10], c.data[11], c.data[12], c.data[15], c.data[16], c.data[17], c.data[20], c.data[21], c.data[22]};
break;
case 7:
result.x = c.x + 1;
result.y = c.y-1;
result.data = {c.data[11], c.data[12], c.data[13], c.data[16], c.data[17], c.data[18], c.data[21], c.data[22], c.data[23]};
break;
case 8:
result.x = c.x + 1;
result.y = c.y-1;
result.data = {c.data[12], c.data[13], c.data[14], c.data[17], c.data[18], c.data[19], c.data[22], c.data[23], c.data[24]};
break;
}
// do some stuff
return result;
}
// Type Traits for is_cluster_type
template <typename T>
struct is_cluster : std::false_type {}; // Default case: Not a Cluster

18
include/aare/ClusterVector.hpp
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@ -167,4 +167,22 @@ class ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>> {
}
};
template<typename T>
ClusterVector<Cluster<T, 2, 2, uint16_t>> reduce_3x3_to_2x2(const ClusterVector<Cluster<T, 3, 3, uint16_t>> &cv) {
ClusterVector<Cluster<T, 2, 2, uint16_t>> result;
for (const auto &c : cv) {
result.push_back(reduce_3x3_to_2x2(c));
}
return result;
}
template<typename T>
ClusterVector<Cluster<T, 3, 3, uint16_t>> reduce_5x5_to_3x3(const ClusterVector<Cluster<T, 5, 5, uint16_t>> &cv) {
ClusterVector<Cluster<T, 3, 3, uint16_t>> result;
for (const auto &c : cv) {
result.push_back(reduce_5x5_to_3x3(c));
}
return result;
}
} // namespace aare

10
python/src/bind_ClusterVector.hpp
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@ -104,4 +104,14 @@ void define_ClusterVector(py::module &m, const std::string &typestr) {
});
}
void define_reduction(py::module &m) {
m.def("reduce_3x3_to_2x2", [](const ClusterVector<Cluster<int, 3, 3, uint16_t>> &cv) {
return new ClusterVector<Cluster<int, 2, 2, uint16_t>>(reduce_3x3_to_2x2(cv));
// return new ClusterVector<Cluster<int, 3, 3>>();
})
.def("reduce_5x5_to_3x3", [](const ClusterVector<Cluster<int, 5, 5, uint16_t>> &cv) {
return new ClusterVector<Cluster<int, 3, 3, uint16_t>>(reduce_5x5_to_3x3(cv));
});
}
#pragma GCC diagnostic pop

4
python/src/module.cpp
View File

@ -81,4 +81,8 @@ PYBIND11_MODULE(_aare, m) {
DEFINE_CLUSTER_BINDINGS(int, 9, 9, uint16_t, i);
DEFINE_CLUSTER_BINDINGS(double, 9, 9, uint16_t, d);
DEFINE_CLUSTER_BINDINGS(float, 9, 9, uint16_t, f);
define_reduction(m);
}