private async Task <int> LoadModelFromJob(string job)
{
var getResultRequest = new GetResultsRequest(job);
getResultRequest.RunRequestSync();
var responseHash = getResultRequest.GetResponse().resultAddress;
while (responseHash == "")
{
Debug.Log(string.Format("Could not load job {0}. Trying again in 1 seconds.", job));
await Task.Delay(1000);
// run the request again
getResultRequest = new GetResultsRequest(job);
getResultRequest.RunRequestSync();
responseHash = getResultRequest.GetResponse().resultAddress;
}
// load the model into memory
var response = Ipfs.Get <IpfsJob>(responseHash);
var modelDefinition = response.Model;
var model = this.CreateSequential(modelDefinition);
return(model.Id);
}
private async Task <bool> CheckModelFromJob(string job)
{
var getResultRequest = new GetResultsRequest(job);
getResultRequest.RunRequestSync();
var responseHash = getResultRequest.GetResponse().resultAddress;
return(responseHash != "");
}
public string processMessage(string json_message, MonoBehaviour owner)
{
//Debug.LogFormat("<color=green>SyftController.processMessage {0}</color>", json_message);
Command msgObj = JsonUtility.FromJson <Command> (json_message);
try
{
switch (msgObj.objectType)
{
case "Optimizer":
{
if (msgObj.functionCall == "create")
{
string optimizer_type = msgObj.tensorIndexParams[0];
// Extract parameters
List <int> p = new List <int>();
for (int i = 1; i < msgObj.tensorIndexParams.Length; i++)
{
p.Add(int.Parse(msgObj.tensorIndexParams[i]));
}
List <float> hp = new List <float>();
for (int i = 0; i < msgObj.hyperParams.Length; i++)
{
hp.Add(float.Parse(msgObj.hyperParams[i]));
}
Optimizer optim = null;
if (optimizer_type == "sgd")
{
optim = new SGD(this, p, hp[0], hp[1], hp[2]);
}
else if (optimizer_type == "rmsprop")
{
optim = new RMSProp(this, p, hp[0], hp[1], hp[2], hp[3]);
}
else if (optimizer_type == "adam")
{
optim = new Adam(this, p, hp[0], hp[1], hp[2], hp[3], hp[4]);
}
return(optim.Id.ToString());
}
else
{
Optimizer optim = this.getOptimizer(msgObj.objectIndex);
return(optim.ProcessMessage(msgObj, this));
}
}
case "FloatTensor":
{
if (msgObj.objectIndex == 0 && msgObj.functionCall == "create")
{
FloatTensor tensor = floatTensorFactory.Create(_shape: msgObj.shape, _data: msgObj.data, _shader: this.Shader);
return(tensor.Id.ToString());
}
else
{
FloatTensor tensor = floatTensorFactory.Get(msgObj.objectIndex);
// Process message's function
return(tensor.ProcessMessage(msgObj, this));
}
}
case "IntTensor":
{
if (msgObj.objectIndex == 0 && msgObj.functionCall == "create")
{
int[] data = new int[msgObj.data.Length];
for (int i = 0; i < msgObj.data.Length; i++)
{
data[i] = (int)msgObj.data[i];
}
IntTensor tensor = intTensorFactory.Create(_shape: msgObj.shape, _data: data, _shader: this.Shader);
return(tensor.Id.ToString());
}
else
{
IntTensor tensor = intTensorFactory.Get(msgObj.objectIndex);
// Process message's function
return(tensor.ProcessMessage(msgObj, this));
}
}
case "agent":
{
if (msgObj.functionCall == "create")
{
Layer model = (Layer)getModel(int.Parse(msgObj.tensorIndexParams[0]));
Optimizer optimizer = optimizers[int.Parse(msgObj.tensorIndexParams[1])];
return(new Syft.NN.RL.Agent(this, model, optimizer).Id.ToString());
}
//Debug.Log("Getting Model:" + msgObj.objectIndex);
Syft.NN.RL.Agent agent = this.getAgent(msgObj.objectIndex);
return(agent.ProcessMessageLocal(msgObj, this));
}
case "model":
{
if (msgObj.functionCall == "create")
{
string model_type = msgObj.tensorIndexParams[0];
Debug.LogFormat("<color=magenta>createModel:</color> {0}", model_type);
if (model_type == "linear")
{
return(this.BuildLinear(msgObj.tensorIndexParams).Id.ToString());
}
else if (model_type == "relu")
{
return(this.BuildReLU().Id.ToString());
}
else if (model_type == "log")
{
return(this.BuildLog().Id.ToString());
}
else if (model_type == "dropout")
{
return(this.BuildDropout(msgObj.tensorIndexParams).Id.ToString());
}
else if (model_type == "sigmoid")
{
return(this.BuildSigmoid().Id.ToString());
}
else if (model_type == "sequential")
{
return(this.BuildSequential().Id.ToString());
}
else if (model_type == "softmax")
{
return(this.BuildSoftmax(msgObj.tensorIndexParams).Id.ToString());
}
else if (model_type == "logsoftmax")
{
return(this.BuildLogSoftmax(msgObj.tensorIndexParams).Id.ToString());
}
else if (model_type == "tanh")
{
return(new Tanh(this).Id.ToString());
}
else if (model_type == "crossentropyloss")
{
return(new CrossEntropyLoss(this, int.Parse(msgObj.tensorIndexParams[1])).Id.ToString());
}
else if (model_type == "categorical_crossentropy")
{
return(new CategoricalCrossEntropyLoss(this).Id.ToString());
}
else if (model_type == "nllloss")
{
return(new NLLLoss(this).Id.ToString());
}
else if (model_type == "mseloss")
{
return(new MSELoss(this).Id.ToString());
}
else if (model_type == "embedding")
{
return(new Embedding(this, int.Parse(msgObj.tensorIndexParams[1]), int.Parse(msgObj.tensorIndexParams[2])).Id.ToString());
}
else
{
Debug.LogFormat("<color=red>Model Type Not Found:</color> {0}", model_type);
}
}
else
{
//Debug.Log("Getting Model:" + msgObj.objectIndex);
Model model = this.getModel(msgObj.objectIndex);
return(model.ProcessMessage(msgObj, this));
}
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
case "controller":
{
if (msgObj.functionCall == "num_tensors")
{
return(floatTensorFactory.Count() + "");
}
else if (msgObj.functionCall == "num_models")
{
return(models.Count + "");
}
else if (msgObj.functionCall == "new_tensors_allowed")
{
Debug.LogFormat("New Tensors Allowed:{0}", msgObj.tensorIndexParams[0]);
if (msgObj.tensorIndexParams[0] == "True")
{
allow_new_tensors = true;
}
else if (msgObj.tensorIndexParams[0] == "False")
{
allow_new_tensors = false;
}
else
{
throw new Exception("Invalid parameter for new_tensors_allowed. Did you mean true or false?");
}
return(allow_new_tensors + "");
}
else if (msgObj.functionCall == "load_floattensor")
{
FloatTensor tensor = floatTensorFactory.Create(filepath: msgObj.tensorIndexParams[0], _shader: this.Shader);
return(tensor.Id.ToString());
}
else if (msgObj.functionCall == "set_seed")
{
Random.InitState(int.Parse(msgObj.tensorIndexParams[0]));
return("Random seed set!");
}
else if (msgObj.functionCall == "concatenate")
{
List <int> tensor_ids = new List <int>();
for (int i = 1; i < msgObj.tensorIndexParams.Length; i++)
{
tensor_ids.Add(int.Parse(msgObj.tensorIndexParams[i]));
}
FloatTensor result = Functional.Concatenate(floatTensorFactory, tensor_ids, int.Parse(msgObj.tensorIndexParams[0]));
return(result.Id.ToString());
}
else if (msgObj.functionCall == "ones")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Ones(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "randn")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Randn(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "random")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Random(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "zeros")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Zeros(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "model_from_json")
{
Debug.Log("Loading Model from JSON:");
var json_str = msgObj.tensorIndexParams[0];
var config = JObject.Parse(json_str);
Sequential model;
if ((string)config["class_name"] == "Sequential")
{
model = this.BuildSequential();
}
else
{
return("Unity Error: SyftController.processMessage: while Loading model, Class :" + config["class_name"] + " is not implemented");
}
for (int i = 0; i < config["config"].ToList().Count; i++)
{
var layer_desc = config["config"][i];
var layer_config_desc = layer_desc["config"];
if ((string)layer_desc["class_name"] == "Linear")
{
int previous_output_dim;
if (i == 0)
{
previous_output_dim = (int)layer_config_desc["batch_input_shape"][layer_config_desc["batch_input_shape"].ToList().Count - 1];
}
else
{
previous_output_dim = (int)layer_config_desc["units"];
}
string[] parameters = new string[] { "linear", previous_output_dim.ToString(), layer_config_desc["units"].ToString(), "Xavier" };
Layer layer = this.BuildLinear(parameters);
model.AddLayer(layer);
string activation_name = layer_config_desc["activation"].ToString();
if (activation_name != "linear")
{
Layer activation;
if (activation_name == "softmax")
{
parameters = new string[] { activation_name, "1" };
activation = this.BuildSoftmax(parameters);
}
else if (activation_name == "relu")
{
activation = this.BuildReLU();
}
else
{
return("Unity Error: SyftController.processMessage: while Loading activations, Activation :" + activation_name + " is not implemented");
}
model.AddLayer(activation);
}
}
else
{
return("Unity Error: SyftController.processMessage: while Loading layers, Layer :" + layer_desc["class_name"] + " is not implemented");
}
}
return(model.Id.ToString());
}
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
case "Grid":
if (msgObj.functionCall == "learn")
{
var inputId = int.Parse(msgObj.tensorIndexParams[0]);
var targetId = int.Parse(msgObj.tensorIndexParams[1]);
return(this.grid.Run(inputId, targetId, msgObj.configurations, owner));
}
if (msgObj.functionCall == "getResults")
{
// TODO -- This will be converted to poll blockchain
// It is written to poll IPFS right now so it
// appears to be working client side.
var experiment = Ipfs.Get <IpfsExperiment>(msgObj.experimentId);
var results = experiment.jobs.Select((job) =>
{
var getResultRequest = new GetResultsRequest(job);
getResultRequest.RunRequestSync();
var responseHash = getResultRequest.GetResponse().resultAddress;
// load the model into memory
var modelDefinition = Ipfs.Get <IpfsJob>(responseHash).Model;
var model = this.grid.CreateSequential(modelDefinition);
return(model.Id);
});
var modelIdsString = JsonConvert.SerializeObject(results.ToArray());
return(modelIdsString);
}
break;
default:
break;
}
}
catch (Exception e)
{
Debug.LogFormat("<color=red>{0}</color>", e.ToString());
return("Unity Error: " + e.ToString());
}
// If not executing createTensor or tensor function, return default error.
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
