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research-project/src/visualisations.cpp

182 lines
6.0 KiB
C++

#include <algorithm>
#include <numeric>
#include "visualisations.hpp"
#include "DummyLayerVisualisation.hpp"
#include "MultiImageVisualisation.hpp"
#include "FlatLayerVisualisation.hpp"
#include "Range.hpp"
#include "ActivityAnimation.hpp"
using namespace fmri;
using namespace std;
// Maximum number of interactions shown
static constexpr size_t INTERACTION_LIMIT = 10000;
typedef vector<pair<float, pair<size_t, size_t>>> EntryList;
/**
* Normalizer for node positions.
*
* Since not every neuron in a layer may get a node in the visualisation,
* this function maps those neurons back to a node number that does.
*
* Usage: node / getNodeNormalizer(layer).
*
* @param layer Layer to compute normalization for
* @return Number to divide node numbers by.
*/
static inline int getNodeNormalizer(const LayerData& layer) {
const auto& shape = layer.shape();
switch(shape.size()) {
case 2:
return 1;
case 4:
return shape[2] * shape[3];
default:
CHECK(false) << "Unsupported shape " << shape.size() << endl;
exit(EINVAL);
}
}
/**
* Deduplicate interaction entries.
*
* For duplicate interactions, the interaction strengths are summed.
*
* @param entries
* @return the deduplicated entries.
*/
static EntryList deduplicate(const EntryList& entries)
{
map<pair<size_t, size_t>, float> combiner;
for (auto entry : entries) {
combiner[entry.second] += entry.first;
}
EntryList result;
transform(combiner.begin(), combiner.end(), back_inserter(result), [](const auto& item) {
return make_pair(item.second, item.first);
});
return result;
}
fmri::LayerVisualisation *fmri::getVisualisationForLayer(const fmri::LayerData &layer)
{
switch (layer.shape().size()) {
case 2:
return new FlatLayerVisualisation(layer, FlatLayerVisualisation::Ordering::SQUARE);
case 4:
return new MultiImageVisualisation(layer);
default:
return new DummyLayerVisualisation();
}
}
static Animation *getFullyConnectedAnimation(const fmri::LayerData &prevState, const fmri::LayerInfo &layer,
const vector<float> &prevPositions, const vector<float> &curPositions)
{
LOG(INFO) << "Computing top interactions for " << layer.name() << endl;
auto data = prevState.data();
CHECK_GE(layer.parameters().size(), 1) << "Layer should have correct parameters";
const auto shape = layer.parameters()[0]->shape();
auto weights = layer.parameters()[0]->cpu_data();
const auto numEntries = accumulate(shape.begin(), shape.end(), static_cast<size_t>(1), multiplies<void>());
vector<float> interactions(numEntries);
for (auto i : Range(numEntries)) {
interactions[i] = weights[i] * data[i % shape[0]];
}
const auto desiredSize = min(INTERACTION_LIMIT, numEntries);
auto idx = arg_nth_element(interactions.begin(), interactions.begin() + desiredSize, interactions.end(), [](auto a, auto b) {
return abs(a) > abs(b);
});
EntryList result;
result.reserve(desiredSize);
const auto normalizer = getNodeNormalizer(prevState);
for (auto i : idx) {
result.emplace_back(interactions[i], make_pair(i / shape[0] / normalizer, i % shape[0]));
}
return new ActivityAnimation(result, prevPositions.data(), curPositions.data(), -10);
}
static Animation *getDropOutAnimation(const fmri::LayerData &prevState,
const fmri::LayerData &curState,
const vector<float> &prevPositions,
const vector<float> &curPositions) {
const auto sourceNormalize = getNodeNormalizer(prevState);
const auto sinkNormalize = getNodeNormalizer(curState);
auto data = curState.data();
EntryList results;
results.reserve(curState.numEntries());
for (auto i : Range(curState.numEntries())) {
if (data[i] != 0) {
results.emplace_back(data[i], make_pair(i / sourceNormalize, i / sinkNormalize));
}
}
results = deduplicate(results);
return new ActivityAnimation(results, prevPositions.data(), curPositions.data(), -10);
}
static Animation *getReLUAnimation(const fmri::LayerData &prevState,
const fmri::LayerData &curState,
const vector<float> &prevPositions,
const vector<float> &curPositions) {
CHECK_EQ(curState.numEntries(), prevState.numEntries()) << "Layers should be of same size!";
const auto prevData = prevState.data(), curData = curState.data();
const auto sourceNormalize = getNodeNormalizer(prevState);
const auto sinkNormalize = getNodeNormalizer(curState);
EntryList results;
for (auto i : Range(curState.numEntries())) {
results.emplace_back(curData[i] - prevData[i], make_pair(i / sourceNormalize, i / sinkNormalize));
}
results = deduplicate(results);
return new ActivityAnimation(results, prevPositions.data(), curPositions.data(), -10);
}
Animation * fmri::getActivityAnimation(const fmri::LayerData &prevState, const fmri::LayerData &curState,
const fmri::LayerInfo &layer, const vector<float> &prevPositions,
const vector<float> &curPositions)
{
if (prevPositions.empty() || curPositions.empty()) {
// Not all positions known, no visualisation possible.
return nullptr;
}
switch (layer.type()) {
case LayerInfo::Type::InnerProduct:
return getFullyConnectedAnimation(prevState, layer,
prevPositions, curPositions);
case LayerInfo::Type::DropOut:
return getDropOutAnimation(prevState, curState, prevPositions, curPositions);
case LayerInfo::Type::ReLU:
return getReLUAnimation(prevState, curState, prevPositions, curPositions);
default:
return nullptr;
}
}