Bert Peters
d0c888024d
Now every place uses the globally defined colors rather than specific colors whenever needed. Next: make those colors configurable.
263 lines
9.4 KiB
C++
263 lines
9.4 KiB
C++
#include <algorithm>
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#include <numeric>
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#include <caffe/util/math_functions.hpp>
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#include <valarray>
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#include "visualisations.hpp"
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#include "DummyLayerVisualisation.hpp"
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#include "MultiImageVisualisation.hpp"
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#include "FlatLayerVisualisation.hpp"
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#include "Range.hpp"
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#include "ActivityAnimation.hpp"
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#include "InputLayerVisualisation.hpp"
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#include "PoolingLayerAnimation.hpp"
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#include "ImageInteractionAnimation.hpp"
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using namespace fmri;
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using namespace std;
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std::size_t fmri::INTERACTION_LIMIT = 10000;
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typedef vector<pair<float, pair<size_t, size_t>>> EntryList;
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/**
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* Normalizer for node positions.
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*
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* Since not every neuron in a layer may get a node in the visualisation,
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* this function maps those neurons back to a node number that does.
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*
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* Usage: node / getNodeNormalizer(layer).
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*
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* @param layer Layer to compute normalization for
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* @return Number to divide node numbers by.
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*/
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static inline int getNodeNormalizer(const LayerData& layer) {
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const auto& shape = layer.shape();
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switch(shape.size()) {
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case 2:
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return 1;
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case 4:
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return shape[2] * shape[3];
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default:
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CHECK(false) << "Unsupported shape " << shape.size() << endl;
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exit(EINVAL);
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}
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}
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/**
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* Deduplicate interaction entries.
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*
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* For duplicate interactions, the interaction strengths are summed.
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*
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* @param entries
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* @return the deduplicated entries.
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*/
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static EntryList deduplicate(const EntryList& entries)
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{
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map<pair<size_t, size_t>, float> combiner;
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for (auto entry : entries) {
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combiner[entry.second] += entry.first;
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}
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EntryList result;
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transform(combiner.begin(), combiner.end(), back_inserter(result), [](const auto& item) {
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return make_pair(item.second, item.first);
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});
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return result;
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}
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static fmri::LayerVisualisation *getAppropriateLayer(const fmri::LayerData &data, const fmri::LayerInfo &info)
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{
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switch (info.type()) {
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case LayerInfo::Type::Input:
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if (data.shape().size() == 4) {
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return new InputLayerVisualisation(data);
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} else {
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return new FlatLayerVisualisation(data, FlatLayerVisualisation::Ordering::SQUARE);
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}
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default:
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switch (data.shape().size()) {
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case 2:
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return new FlatLayerVisualisation(data, FlatLayerVisualisation::Ordering::SQUARE);
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case 4:
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return new MultiImageVisualisation(data);
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default:
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return new DummyLayerVisualisation();
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}
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}
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}
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fmri::LayerVisualisation *fmri::getVisualisationForLayer(const fmri::LayerData &data, const fmri::LayerInfo &info)
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{
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LOG(INFO) << "Loading state visualisation for " << data.name();
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auto layer = getAppropriateLayer(data, info);
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layer->setupLayerName(data.name(), info.type());
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return layer;
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}
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static Animation *getFullyConnectedAnimation(const fmri::LayerData &prevState, const fmri::LayerInfo &layer,
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const vector<float> &prevPositions, const vector<float> &curPositions)
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{
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auto data = prevState.data();
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CHECK_GE(layer.parameters().size(), 1) << "Layer should have correct parameters";
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const auto shape = layer.parameters()[0]->shape();
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auto weights = layer.parameters()[0]->cpu_data();
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const auto numEntries = accumulate(shape.begin(), shape.end(), static_cast<size_t>(1), multiplies<void>());
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vector<float> interactions(numEntries);
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const auto stepSize = shape[1];
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for (auto i : Range(numEntries / stepSize)) {
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caffe::caffe_mul(shape[1], &weights[i * stepSize], data, &interactions[i * stepSize]);
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}
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const auto desiredSize = min(INTERACTION_LIMIT, numEntries);
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auto idx = arg_partial_sort(interactions.begin(), interactions.begin() + desiredSize, interactions.end(),
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[](auto a, auto b) {
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return abs(a) > abs(b);
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});
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EntryList result;
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result.reserve(desiredSize);
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const auto normalizer = getNodeNormalizer(prevState);
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for (auto i : idx) {
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if (abs(interactions[i]) < EPSILON){
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break;
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}
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result.emplace_back(interactions[i], make_pair((i % shape[1]) / normalizer, i / shape[1]));
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}
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return new ActivityAnimation(result, prevPositions.data(), curPositions.data());
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}
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static Animation *getDropOutAnimation(const fmri::LayerData &prevState,
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const fmri::LayerData &curState,
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const vector<float> &prevPositions,
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const vector<float> &curPositions) {
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const auto sourceNormalize = getNodeNormalizer(prevState);
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const auto sinkNormalize = getNodeNormalizer(curState);
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auto data = curState.data();
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EntryList results;
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results.reserve(curState.numEntries());
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for (auto i : Range(curState.numEntries())) {
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if (data[i] != 0) {
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results.emplace_back(data[i], make_pair(i / sourceNormalize, i / sinkNormalize));
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}
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}
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results = deduplicate(results);
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return new ActivityAnimation(results, prevPositions.data(), curPositions.data());
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}
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static Animation *getReLUAnimation(const fmri::LayerData &prevState,
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const fmri::LayerData &curState,
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const vector<float> &prevPositions,
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const vector<float> &curPositions) {
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CHECK_EQ(curState.numEntries(), prevState.numEntries()) << "Layers should be of same size!";
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std::vector<float> changes(prevState.numEntries());
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caffe::caffe_sub(prevState.numEntries(), curState.data(), prevState.data(), changes.data());
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if (curState.shape().size() == 2) {
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EntryList results;
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for (auto i : Range(curState.numEntries())) {
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if (curState.data()[i] > EPSILON) {
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results.emplace_back(changes[i], make_pair(i, i));
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}
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}
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return new ActivityAnimation(results, prevPositions.data(), curPositions.data());
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} else {
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return new ImageInteractionAnimation(changes.data(), prevState.shape(), prevPositions, curPositions);
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}
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}
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static Animation *getNormalizingAnimation(const fmri::LayerData &prevState, const LayerData &curState,
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const vector<float> &prevPositions,
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const vector<float> &curPositions) {
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CHECK(prevState.shape() == curState.shape()) << "Shapes should be of equal size" << endl;
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valarray<DType> scaling(prevState.data(), prevState.numEntries());
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scaling /= valarray<DType>(curState.data(), curState.numEntries());
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// Fix divisions by zero. For those cases, pick 1 since it doesn't matter anyway.
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normalize(begin(scaling), end(scaling));
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scaling = log(scaling);
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if (prevState.shape().size() == 2) {
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scaling /= scaling.max();
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EntryList entries;
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entries.reserve(scaling.size());
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for (auto i : Range(scaling.size())) {
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if (std::abs(curState[i]) > EPSILON) {
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entries.emplace_back(scaling[i], make_pair(i, i));
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}
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}
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return new ActivityAnimation(entries, prevPositions.data(), curPositions.data());
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} else {
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return new ImageInteractionAnimation(&scaling[0], prevState.shape(), prevPositions, curPositions);
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}
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}
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static Animation *getSoftmaxAnimation(const fmri::LayerData &curState, const vector<float> &prevPositions,
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const vector<float> &curPositions)
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{
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CHECK_EQ(curState.shape().size(), 2) << "Softmax only supported for flat layers.";
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std::vector<float> intensities(curState.data(), curState.data() + curState.numEntries());
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rescale(intensities.begin(), intensities.end(), 0, 1);
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EntryList entries;
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for (auto i = 0u; i < intensities.size(); ++i) {
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entries.emplace_back(intensities[i], make_pair(i, i));
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}
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return new ActivityAnimation(entries, prevPositions.data(), curPositions.data());
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}
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Animation * fmri::getActivityAnimation(const fmri::LayerData &prevState, const fmri::LayerData &curState,
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const fmri::LayerInfo &layer, const vector<float> &prevPositions,
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const vector<float> &curPositions)
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{
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if (prevPositions.empty() || curPositions.empty()) {
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// Not all positions known, no visualisation possible.
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return nullptr;
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}
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LOG(INFO) << "Loading interaction for " << layer.name();
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switch (layer.type()) {
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case LayerInfo::Type::InnerProduct:
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return getFullyConnectedAnimation(prevState, layer,
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prevPositions, curPositions);
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case LayerInfo::Type::DropOut:
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return getDropOutAnimation(prevState, curState, prevPositions, curPositions);
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case LayerInfo::Type::ReLU:
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return getReLUAnimation(prevState, curState, prevPositions, curPositions);
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case LayerInfo::Type::Pooling:
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return new PoolingLayerAnimation(prevState, curState, prevPositions, curPositions);
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case LayerInfo::Type::LRN:
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return getNormalizingAnimation(prevState, curState, prevPositions, curPositions);
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case LayerInfo::Type::Softmax:
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return getSoftmaxAnimation(curState, prevPositions, curPositions);
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default:
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return nullptr;
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}
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}
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