public override void Train()
{
var graph = Config.IsImportingGraph ? ImportGraph() : BuildGraph();
var config = new ConfigProto {
AllowSoftPlacement = true
};
using (var sess = tf.Session(graph, config: config))
{
//tf.train.export_meta_graph("tfnet.meta");
//var json = JsonConvert.SerializeObject(graph._nodes_by_name.Select(x => x.Value).ToArray(), Formatting.Indented);
//File.WriteAllText($"YOLOv3/nodes-{(IsImportingGraph ? "right" : "wrong")}.txt", json);
sess.run(tf.global_variables_initializer());
print($"=> Restoring weights from: {cfg.TRAIN.INITIAL_WEIGHT} ... ");
loader.restore(sess, cfg.TRAIN.INITIAL_WEIGHT);
first_stage_epochs = 0;
foreach (var epoch in range(1, 1 + first_stage_epochs + second_stage_epochs))
{
if (epoch <= first_stage_epochs)
{
train_op = train_op_with_frozen_variables;
}
else
{
train_op = train_op_with_all_variables;
}
foreach (var train_data in trainset.Items())
{
var results = sess.run(new object[] { train_op, loss, global_step },
(input_data, train_data[0]),
(label_sbbox, train_data[1]),
public Estimator(Action model_fn, RunConfig config)
{
_config = config;
_model_dir = _config.model_dir;
_session_config = _config.session_config;
_model_fn = model_fn;
}
public string config_proto_serialized()
{
var config = new ConfigProto
{
AllowSoftPlacement = true,
};
return(config.ToByteString().ToStringUtf8());
}
public CSession(Graph graph, Status s, bool user_XLA = false)
{
lock (Locks.ProcessWide)
{
var config = new ConfigProto {
InterOpParallelismThreads = 4
};
session_ = new Session(graph, config, s);
}
}
private void InferenceEmotion(int shotsTaken, NDArray img_buffer_)
{
var imgArr = ReadTensorFromDetected(img_buffer_, img_size: 60);
ConfigProto config = new ConfigProto();
GPUOptions gpuConfig = new GPUOptions();
gpuConfig.AllowGrowth = true;
gpuConfig.PerProcessGpuMemoryFraction = 0.3;
config.GpuOptions = gpuConfig;
using (var sess = tf.Session(emotionGraph, config))
{
Tensor tensorClasses = emotionGraph.OperationByName("Identity");
Tensor imgTensor = emotionGraph.OperationByName("x");
Tensor[] outTensorArr = new Tensor[] { tensorClasses };
var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
var emotions = results[0].ToArray <float>();
//var records = new List<object>
//{
// new { Frame = shotsTaken, Results = results[0] },
//};
//csv.WriteRecord(new { Frame = shotsTaken, Results = results[0] });
//csv.Flush();
var record = new CSVRecord();
record.Neutral = (int)(Math.Round(emotions[0], 2) * 100);
record.Happy = (int)(Math.Round(emotions[1], 2) * 100);
record.Sad = (int)(Math.Round(emotions[2], 2) * 100);
record.Angry = (int)(Math.Round(emotions[3], 2) * 100);
record.Surprised = (int)(Math.Round(emotions[4], 2) * 100);
record.Date = DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss");
using (var stream = File.Open("output.csv", FileMode.Append))
using (var writer = new StreamWriter(stream))
using (var csv = new CsvWriter(writer, CultureInfo.InvariantCulture))
{
// Don't write the header again.
csv.Configuration.HasHeaderRecord = false;
csv.WriteRecord <CSVRecord>(record);
csv.NextRecord();
}
Console.WriteLine($"Results: {results[0].ToString()}");
//PreProcessEmotion(img_buffer, results[0]);
}
}
public bool Run()
{
PrepareData();
var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
var config = new ConfigProto {
AllowSoftPlacement = true
};
using (var sess = tf.Session(graph, config: config))
{
Train(sess);
}
return(true);
}
private NDArray ReadTensorFromDetected(NDArray img_buffer_, int img_size = 60)
{
var graph = tf.Graph().as_default();
ConfigProto config = new ConfigProto();
GPUOptions gpuConfig = new GPUOptions();
gpuConfig.AllowGrowth = true;
gpuConfig.PerProcessGpuMemoryFraction = 0.3;
config.GpuOptions = gpuConfig;
var t3 = tf.constant(img_buffer_, dtype: TF_DataType.TF_UINT8);
//var inp = tf.reshape(t3, (height, width, 3));
var casted = tf.cast(t3, tf.float32);
var dims_expander = tf.expand_dims(casted, 0);
var resize = tf.constant(new int[] { img_size, img_size });
var bilinear = tf.image.resize_bilinear(dims_expander, resize);
using (var sess = tf.Session(graph, config))
return(sess.run(bilinear));
}
private NDArray InferenceDetector(NDArray img_buffer_)
{
var imgArr = ReadTensorFromImageFile(img_buffer_);
ConfigProto config = new ConfigProto();
GPUOptions gpuConfig = new GPUOptions();
gpuConfig.AllowGrowth = true;
gpuConfig.PerProcessGpuMemoryFraction = 0.3;
config.GpuOptions = gpuConfig;
using (var sess = tf.Session(detectorGraph, config))
{
Tensor tensorClasses = detectorGraph.OperationByName("Identity");
Tensor imgTensor = detectorGraph.OperationByName("x");
Tensor[] outTensorArr = new Tensor[] { tensorClasses };
var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
//Console.WriteLine($"Results: {results[0].ToString()}");
return(PreProcessEmotion(img_buffer_, results[0]));
}
}
//Errors with CNTK: https://github.com/Microsoft/CNTK/issues/2614
public void Train(PredictorTrainingContext ctx)
{
InitialSetup();
tf.compat.v1.disable_eager_execution();
var p = ctx.Predictor;
var nn = (NeuralNetworkSettingsEntity)p.AlgorithmSettings;
Tensor inputPlaceholder = tf.placeholder(tf.float32, new[] { -1, ctx.InputCodifications.Count }, "inputPlaceholder");
Tensor outputPlaceholder = tf.placeholder(tf.float32, new[] { -1, ctx.OutputCodifications.Count }, "outputPlaceholder");
Tensor currentTensor = inputPlaceholder;
nn.HiddenLayers.ForEach((layer, i) =>
{
currentTensor = NetworkBuilder.DenseLayer(currentTensor, layer.Size, layer.Activation, layer.Initializer, p.Settings.Seed ?? 0, "hidden" + i);
});
Tensor output = NetworkBuilder.DenseLayer(currentTensor, ctx.OutputCodifications.Count, nn.OutputActivation, nn.OutputInitializer, p.Settings.Seed ?? 0, "output");
Tensor calculatedOutput = tf.identity(output, "calculatedOutput");
Tensor loss = NetworkBuilder.GetEvalFunction(nn.LossFunction, outputPlaceholder, calculatedOutput);
Tensor accuracy = NetworkBuilder.GetEvalFunction(nn.EvalErrorFunction, outputPlaceholder, calculatedOutput);
// prepare for training
Optimizer optimizer = NetworkBuilder.GetOptimizer(nn);
Operation trainOperation = optimizer.minimize(loss);
Random rand = p.Settings.Seed == null ?
new Random() :
new Random(p.Settings.Seed.Value);
var(training, validation) = ctx.SplitTrainValidation(rand);
var minibachtSize = nn.MinibatchSize;
var numMinibatches = nn.NumMinibatches;
Stopwatch sw = Stopwatch.StartNew();
List <FinalCandidate> candidate = new List <FinalCandidate>();
var config = new ConfigProto
{
IntraOpParallelismThreads = 1,
InterOpParallelismThreads = 1,
LogDevicePlacement = true
};
ctx.ReportProgress($"Deleting Files");
var dir = PredictorDirectory(ctx.Predictor);
if (Directory.Exists(dir))
{
Directory.Delete(dir, true);
}
Directory.CreateDirectory(dir);
ctx.ReportProgress($"Starting training...");
var saver = tf.train.Saver();
using (var sess = tf.Session(config))
{
sess.run(tf.global_variables_initializer());
for (int i = 0; i < numMinibatches; i++)
{
using (HeavyProfiler.Log("MiniBatch", () => i.ToString()))
{
var trainMinibatch = 0.To(minibachtSize).Select(_ => rand.NextElement(training)).ToList();
var inputValue = CreateNDArray(ctx, trainMinibatch, ctx.InputCodifications.Count, ctx.InputCodificationsByColumn);
var outputValue = CreateNDArray(ctx, trainMinibatch, ctx.OutputCodifications.Count, ctx.OutputCodificationsByColumn);
using (HeavyProfiler.Log("TrainMinibatch", () => i.ToString()))
{
sess.run(trainOperation,
(inputPlaceholder, inputValue),
(outputPlaceholder, outputValue));
}
if (ctx.StopTraining)
{
p = ctx.Predictor = ctx.Predictor.ToLite().RetrieveAndRemember();
}
var isLast = numMinibatches - nn.BestResultFromLast <= i;
if (isLast || (i % nn.SaveProgressEvery) == 0 || ctx.StopTraining)
{
float loss_val;
float accuracy_val;
using (HeavyProfiler.Log("EvalTraining", () => i.ToString()))
{
(loss_val, accuracy_val) = sess.run((loss, accuracy),
(inputPlaceholder, inputValue),
(outputPlaceholder, outputValue));
}
var ep = new EpochProgress
{
Ellapsed = sw.ElapsedMilliseconds,
Epoch = i,
TrainingExamples = i * minibachtSize,
LossTraining = loss_val,
AccuracyTraining = accuracy_val,
LossValidation = null,
AccuracyValidation = null,
};
ctx.ReportProgress($"Training Minibatches Loss:{loss_val} / Accuracy:{accuracy_val}", (i + 1) / (decimal)numMinibatches);
ctx.Progresses.Enqueue(ep);
if (isLast || (i % nn.SaveValidationProgressEvery) == 0 || ctx.StopTraining)
{
using (HeavyProfiler.LogNoStackTrace("EvalValidation"))
{
var validateMinibatch = 0.To(minibachtSize).Select(_ => rand.NextElement(validation)).ToList();
var inputValValue = CreateNDArray(ctx, validateMinibatch, ctx.InputCodifications.Count, ctx.InputCodificationsByColumn);
var outputValValue = CreateNDArray(ctx, validateMinibatch, ctx.OutputCodifications.Count, ctx.OutputCodificationsByColumn);
(loss_val, accuracy_val) = sess.run((loss, accuracy),
(inputPlaceholder, inputValValue),
(outputPlaceholder, outputValValue));
ep.LossValidation = loss_val;
ep.AccuracyValidation = accuracy_val;
}
}
var progress = ep.SaveEntity(ctx.Predictor);
if (isLast || ctx.StopTraining)
{
Directory.CreateDirectory(TrainingModelDirectory(ctx.Predictor, i));
var save = saver.save(sess, Path.Combine(TrainingModelDirectory(ctx.Predictor, i), ModelFileName));
using (HeavyProfiler.LogNoStackTrace("FinalCandidate"))
{
candidate.Add(new FinalCandidate
{
ModelIndex = i,
ResultTraining = new PredictorMetricsEmbedded {
Accuracy = progress.AccuracyTraining, Loss = progress.LossTraining
},
ResultValidation = new PredictorMetricsEmbedded {
Accuracy = progress.AccuracyValidation, Loss = progress.LossValidation
},
});
}
}
}
if (ctx.StopTraining)
{
break;
}
}
}
}
var best = candidate.WithMin(a => a.ResultValidation.Loss !.Value);
p.ResultTraining = best.ResultTraining;
p.ResultValidation = best.ResultValidation;
var files = Directory.GetFiles(TrainingModelDirectory(ctx.Predictor, best.ModelIndex));
p.Files.AddRange(files.Select(p => new Entities.Files.FilePathEmbedded(PredictorFileType.PredictorFile, p)));
using (OperationLogic.AllowSave <PredictorEntity>())
p.Save();
}
/// <summary>
/// Default method to build a fully-connected graph of the specified structure
/// </summary>
/// <param name="layerNodes">The number of nodes to have at each layer. Index [0] is the number of inputs, and [lenth - 1] is the number of outputs.</param>
/// <returns></returns>
public virtual Graph BuildFullyConnectedGraphInt(int[] layerNodes, float learningRate = 0.01f)
{
tf.enable_eager_execution();
var g = new Graph().as_default();
tf_with(tf.name_scope("Input"), delegate
{
Input = tf.placeholder(tf.float32, shape: new TensorShape(-1, layerNodes[0]));
YTrue = tf.placeholder(tf.int32, shape: new TensorShape(-1, layerNodes[layerNodes.Length - 1]));
});
tf_with(tf.variable_scope("FullyConnected"), delegate
{
Tensor x = Input;
Tensor y = null;
for (int i = 1; i < (layerNodes.Length - 1); i++)
{
var w = tf.get_variable("w" + i, shape: new TensorShape(layerNodes[i - 1], layerNodes[i]), initializer: tf.random_normal_initializer(stddev: 0.1f));
var b = tf.get_variable("b" + i, shape: new TensorShape(layerNodes[i]), initializer: tf.constant_initializer(0.1));
Prediction = tf.matmul(x, w) + b;
y = tf.nn.relu(Prediction);
x = y;
}
var w2 = tf.get_variable("w_out", shape: new TensorShape(layerNodes[layerNodes.Length - 2], layerNodes[layerNodes.Length - 1]), initializer: tf.random_normal_initializer(stddev: 0.1f));
var b2 = tf.get_variable("b_out", shape: new TensorShape(layerNodes[layerNodes.Length - 1]), initializer: tf.constant_initializer(0.1));
Prediction = tf.matmul(y, w2) + b2;
});
tf_with(tf.variable_scope("Loss"), delegate
{
var losses = tf.nn.sigmoid_cross_entropy_with_logits(tf.cast(YTrue, tf.float32), Prediction);
LossFunc = tf.reduce_mean(losses);
});
tf_with(tf.variable_scope("Accuracy"), delegate
{
var y_pred = tf.cast(Prediction > 0, tf.int32);
Accuracy = tf.reduce_mean(tf.cast(tf.equal(y_pred, YTrue), tf.float32));
});
// We add the training operation, ...
var adam = tf.train.AdamOptimizer(learningRate); // Set the learning rate here
TrainOp = adam.minimize(LossFunc, name: "train_op");
// Create the new session
var config = new ConfigProto
{
InterOpParallelismThreads = 1,
IntraOpParallelismThreads = 1,
GpuOptions = new GPUOptions {
},
LogDevicePlacement = true
};
Sess = tf.Session(config);
return(g);
}
/// <summary>
/// Default method to build a fully-connected graph of the specified structure
/// </summary>
/// <param name="layerNodes">The number of nodes to have at each layer. Index [0] is the number of inputs, and [lenth - 1] is the number of outputs.</param>
/// <returns></returns>
public virtual Graph BuildFullyConnectedGraphFloat(int[] layerNodes, float learningRate = 0.01f)
{
tf.enable_eager_execution();
var g = tf.get_default_graph();
tf_with(tf.name_scope("Input"), delegate
{
Input = tf.placeholder(tf.float32, shape: new TensorShape(-1, layerNodes[0]));
YTrue = tf.placeholder(tf.float32, shape: new TensorShape(-1, layerNodes[layerNodes.Length - 1]));
});
tf_with(tf.variable_scope("FullyConnected"), delegate
{
Tensor x = Input;
Tensor y = null;
for (int i = 1; i < (layerNodes.Length - 1); i++)
{
var w = tf.get_variable("w" + i, shape: new TensorShape(layerNodes[i - 1], layerNodes[i]), initializer: tf.random_normal_initializer(stddev: 0.1f));
var b = tf.get_variable("b" + i, shape: new TensorShape(layerNodes[i]), initializer: tf.constant_initializer(0.1));
Prediction = tf.matmul(x, w) + b;
y = tf.nn.relu(Prediction);
x = y;
}
var w2 = tf.get_variable("w_out", shape: new TensorShape(layerNodes[layerNodes.Length - 2], layerNodes[layerNodes.Length - 1]), initializer: tf.random_normal_initializer(stddev: 0.1f));
var b2 = tf.get_variable("b_out", shape: new TensorShape(layerNodes[layerNodes.Length - 1]), initializer: tf.constant_initializer(0.1));
Prediction = tf.matmul(y, w2) + b2;
});
tf_with(tf.variable_scope("Loss"), delegate
{
var lossVal = (Prediction - YTrue);
var scaler = (YTrue - Prediction);
var correct = tf.cast(tf.logical_and((YTrue < 10), (Prediction < 10)), TF_DataType.TF_FLOAT);
//LossFunc = tf.reduce_mean((lossVal * 0.01 * correct) + (lossVal * tf.maximum((10 - Prediction), 1) * (1 - correct)));
var mean = tf.reduce_mean(lossVal);
var std = tf.sqrt(tf.reduce_mean(tf.square(Prediction - mean)));
//LossFunc = (tf.reduce_mean(scaler));
//LossFunc = tf.reduce_mean(Prediction + tf.maximum(YTrue - Prediction, 0));
//LossFunc = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(YTrue, Prediction));
LossFunc = tf.reduce_mean(tf.abs(Prediction - YTrue));
});
tf_with(tf.variable_scope("Accuracy"), delegate
{
Accuracy = tf.reduce_mean(tf.abs(tf.sub(Prediction, YTrue)));
});
// We add the training operation, ...
var adam = tf.train.AdamOptimizer(learningRate); // Set the learning rate here
TrainOp = adam.minimize(LossFunc, name: "train_op");
// Create the new session
var config = new ConfigProto
{
InterOpParallelismThreads = 1,
IntraOpParallelismThreads = 1,
LogDevicePlacement = true
};
Sess = tf.Session(g, config);
return(g);
}
public Estimator(RunConfig config)
{
_config = config;
_model_dir = _config.model_dir;
_session_config = _config.session_config;
}