/* * MIT License * * Copyright (c) 2019 Alexey Edelev * * This file is part of NeuralNetwork project https://git.semlanik.org/semlanik/NeuralNetwork * * Permission is hereby granted, free of charge, to any person obtaining a copy of this * software and associated documentation files (the "Software"), to deal in the Software * without restriction, including without limitation the rights to use, copy, modify, * merge, publish, distribute, sublicense, and/or sell copies of the Software, and * to permit persons to whom the Software is furnished to do so, subject to the following * conditions: * * The above copyright notice and this permission notice shall be included in all copies * or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE * FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER * DEALINGS IN THE SOFTWARE. */ #include "visualizermodel.h" #include #include #include "dense.h" using namespace remotecontrol; using namespace QtProtobuf; VisualizerModel::VisualizerModel(std::shared_ptr &client, QObject *parent) : QObject(parent) , m_client(client) { m_client->getConfiguration({}, this, [this](QGrpcAsyncReply *reply) { qDeleteAll(m_layers); m_networkConfig = reply->read(); for(int i = 0; i < m_networkConfig.sizes().size(); i++) { m_layers.append(new NetworkLayerState); m_layers.last()->m_activations.setDimentions(m_networkConfig.sizes()[i], 1); QList data; for (int k = 0; k < m_networkConfig.sizes()[i]; k++) { data.append(new ValueIndicator); } m_layers.last()->m_activations.setData(data); if (i != 0) { int tolalItems = m_networkConfig.sizes()[i]*m_networkConfig.sizes()[i - 1]; m_layers.last()->m_weights.setDimentions(m_networkConfig.sizes()[i], m_networkConfig.sizes()[i - 1]); data.clear(); for (int k = 0; k < tolalItems; k++) { data.append(new ValueIndicator); } m_layers.last()->m_weights.setData(data); } } sizesChanged(); }); QObject::connect(client.get(), &remotecontrol::RemoteControlClient::ActivationsUpdated, [this](const remotecontrol::LayerMatrix &activations) { if (m_layers.isEmpty()) { return; } Dense dense(activations.matrix().matrix()); m_layers[activations.layer()]->m_activations.updateValues(Dense(activations.matrix().matrix())); // qDebug() << "ActivationsUpdated:" << dense.rows() << dense.columns() << activations.layer(); }); QObject::connect(client.get(), &remotecontrol::RemoteControlClient::BiasesUpdated, [this](const remotecontrol::LayerMatrix &biases) { if (m_layers.isEmpty()) { return; } Dense dense(biases.matrix().matrix()); // qDebug() << "BiasesUpdated:" << dense.rows() << dense.columns(); }); QObject::connect(client.get(), &remotecontrol::RemoteControlClient::WeightsUpdated, [this](const remotecontrol::LayerMatrix &weights) { if (m_layers.isEmpty()) { return; } m_layers[weights.layer()]->m_weights.updateValues(Dense(weights.matrix().matrix())); }); client->subscribeActivationsUpdates({}); client->subscribeBiasesUpdates({}); client->subscribeWeightsUpdates({}); } ValueIndicator *VisualizerModel::activation(int layer, int row) { ValueIndicator* indicator = m_layers[layer]->m_activations.value(row, 0); QQmlEngine::setObjectOwnership(indicator, QQmlEngine::CppOwnership); return indicator; } ValueIndicator *VisualizerModel::weight(int layer, int row, int column) { ValueIndicator* indicator = m_layers[layer]->m_weights.value(row, column); QQmlEngine::setObjectOwnership(indicator, QQmlEngine::CppOwnership); return indicator; }