void Start()
{
m_DataSet = LoadDataSet();
var batch = m_DataSet.Item1[0].batch;
m_SirenBuilder = new SirenModel(batch, false, 256, 3,
8, 2, 6,
"vector_observation", "continuous_actions", false);
m_model = m_SirenBuilder.model;
m_parameters = m_SirenBuilder.parameters;
if (loadModelFromOnnx)
{
m_model = ModelLoader.Load(model, false);
}
m_worker = WorkerFactory.CreateWorker(ms_workerType, m_model, false);
m_input = m_DataSet.Item1[9];
m_target = m_DataSet.Item2[9];
m_lr = new Tensor(1, 1, new[] { learningRate });
InitPlot();
var t = m_target.Reshape(new TensorShape(1, 100, 200, 1));
t.ToRenderTexture(targetRT, batch: 0, fromChannel: 0);
m_lastUpdateTime = Time.realtimeSinceStartup;
StartCoroutine(TrainingLoop());
}
void Start()
{
if (optimizerText)
{
optimizerText.SetText(useAdam ? "ADAM" : "SGD");
}
var batch = targetImage.height * targetImage.width;
var sirenBuilder = new SirenModel(batch, biasOutputAndTarget, useAdam: useAdam, trainableBias: learnBias);
m_model = sirenBuilder.model;
m_parameters = sirenBuilder.parameters;
if (loadModelFromOnnx)
{
m_model = ModelLoader.Load(model, false);
}
m_worker = WorkerFactory.CreateWorker(ms_workerType, m_model, false);
m_input = new Tensor(batch, 2);
InitGrid(ref m_input, targetImage.height, targetImage.width);
m_target = new Tensor(targetImage, channels: 1);
m_target = m_target.Reshape(new TensorShape(batch, 1));
m_lr = new Tensor(1, 1, new[] { learningRate });
m_beta1 = new Tensor(1, 1, new[] { beta1 });
m_beta2 = new Tensor(1, 1, new[] { beta2 });
m_epsilon = new Tensor(1, 1, new[] { epsilon });
m_worker.SetInput("input", m_input);
m_worker.SetInput("target", m_target);
m_worker.SetInput("lr", m_lr);
m_worker.SetInput("beta1", m_beta1);
m_worker.SetInput("beta2", m_beta2);
m_worker.SetInput("epsilon", m_epsilon);
InitPlot();
m_lastUpdateTime = Time.realtimeSinceStartup;
}
