public FlexNet3D(string modelDir, int3 boxDimensions, int gpuID = 0, int nThreads = 1, bool forTraining = true, int batchSize = 128, int bottleneckWidth = 2, int layerWidth = 64, int nlayers = 4)
{
BoxDimensions = boxDimensions;
ForTraining = forTraining;
BatchSize = batchSize;
BottleneckWidth = bottleneckWidth;
NWeights0 = layerWidth;
NLayers = nlayers;
ModelDir = modelDir;
MaxThreads = nThreads;
TFSessionOptions SessionOptions = TFHelper.CreateOptions();
TFSession Dummy = new TFSession(new TFGraph(), SessionOptions);
Session = TFHelper.FromSavedModel(SessionOptions, null, ModelDir, new[] { forTraining ? "train" : "serve" }, new TFGraph(), $"/device:GPU:{gpuID}");
Graph = Session.Graph;
NodeInputSource = Graph["volume_source"][0];
NodeInputTarget = Graph["volume_target"][0];
NodeInputWeightSource = Graph["volume_weight_source"][0];
NodeInputWeightTarget = Graph["volume_weight_target"][0];
NodeDropoutRate = Graph["training_dropout_rate"][0];
if (forTraining)
{
NodeLearningRate = Graph["training_learning_rate"][0];
NodeOrthogonalityRate = Graph["training_orthogonality"][0];
NodeOpTrain = Graph["train_momentum"][0];
NodeOutputLoss = Graph["l2_loss"][0];
NodeOutputLossKL = Graph["kl_loss"][0];
NodeBottleneck = Graph["bottleneck"][0];
}
NodeCode = Graph["volume_code"][0];
NodeOutputPredicted = Graph["volume_predict"][0];
NodeWeights0 = Graph["encoder_0/weights_0"][0];
NodeWeights1 = Graph[$"decoder_{nlayers - 1}/weights_{nlayers - 1}"][0];
if (forTraining)
{
NodeWeights0Assign = Graph["encoder_0/assign_layer0"][0];
NodeWeights0Input = Graph["encoder_0/assign_layer0_values"][0];
NodeWeights1Assign = Graph[$"decoder_{nlayers - 1}/assign_layer0"][0];
NodeWeights1Input = Graph[$"decoder_{nlayers - 1}/assign_layer0_values"][0];
}
TensorSource = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, (BoxDimensions.X / 2 + 1), BoxDimensions.Y, BoxDimensions.Z, 2),
new float[BatchSize * BoxDimensions.ElementsFFT() * 2],
0,
BatchSize * (int)BoxDimensions.ElementsFFT() * 2),
nThreads);
TensorTarget = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, (BoxDimensions.X / 2 + 1), BoxDimensions.Y, BoxDimensions.Z, 2),
new float[BatchSize * BoxDimensions.ElementsFFT() * 2],
0,
BatchSize * (int)BoxDimensions.ElementsFFT() * 2),
nThreads);
TensorWeightSource = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, (BoxDimensions.X / 2 + 1), BoxDimensions.Y, BoxDimensions.Z, 1),
new float[BatchSize * BoxDimensions.ElementsFFT()],
0,
BatchSize * (int)BoxDimensions.ElementsFFT()),
nThreads);
TensorWeightTarget = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, (BoxDimensions.X / 2 + 1), BoxDimensions.Y, BoxDimensions.Z, 1),
new float[BatchSize * BoxDimensions.ElementsFFT()],
0,
BatchSize * (int)BoxDimensions.ElementsFFT()),
nThreads);
TensorCode = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, BottleneckWidth),
new float[BatchSize * BottleneckWidth],
0,
BatchSize * BottleneckWidth),
nThreads);
TensorLearningRate = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(1),
new float[1],
0,
1),
nThreads);
TensorDropoutRate = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(1),
new float[1],
0,
1),
nThreads);
TensorOrthogonalityRate = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(1),
new float[1],
0,
1),
nThreads);
ResultPredicted = Helper.ArrayOfFunction(i => new float[BatchSize * BoxDimensions.ElementsFFT() * 2], nThreads);
ResultBottleneck = Helper.ArrayOfFunction(i => new float[BatchSize * BottleneckWidth], nThreads);
ResultLoss = Helper.ArrayOfFunction(i => new float[1], nThreads);
ResultLossKL = Helper.ArrayOfFunction(i => new float[1], nThreads);
RetrievedWeights = new float[boxDimensions.ElementsFFT() * 2 * NWeights0];
//if (!ForTraining)
RunnerPrediction = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeCode, TensorCode[i]).
AddInput(NodeDropoutRate, TensorDropoutRate[i]).
Fetch(NodeOutputPredicted),
nThreads);
//else
RunnerTraining = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeInputSource, TensorSource[i]).
AddInput(NodeInputTarget, TensorTarget[i]).
AddInput(NodeInputWeightSource, TensorWeightSource[i]).
AddInput(NodeInputWeightTarget, TensorWeightTarget[i]).
AddInput(NodeDropoutRate, TensorDropoutRate[i]).
AddInput(NodeLearningRate, TensorLearningRate[i]).
AddInput(NodeOrthogonalityRate, TensorOrthogonalityRate[i]).
Fetch(NodeOutputPredicted, NodeOutputLoss, NodeOutputLossKL, NodeBottleneck, NodeOpTrain),
nThreads);
RunnerEncode = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeInputSource, TensorSource[i]).
AddInput(NodeInputWeightSource, TensorWeightSource[i]).
AddInput(NodeDropoutRate, TensorDropoutRate[i]).
Fetch(NodeBottleneck),
nThreads);
RunnerRetrieveWeights0 = Session.GetRunner().Fetch(NodeWeights0);
RunnerRetrieveWeights1 = Session.GetRunner().Fetch(NodeWeights1);
if (ForTraining)
{
TensorWeights0 = TFTensor.FromBuffer(new TFShape(NWeights0, BoxDimensions.ElementsFFT() * 2),
new float[BoxDimensions.ElementsFFT() * 2 * NWeights0],
0,
(int)BoxDimensions.ElementsFFT() * 2 * NWeights0);
RunnerAssignWeights0 = Session.GetRunner().AddInput(NodeWeights0Input, TensorWeights0).
Fetch(NodeWeights0Assign);
RunnerAssignWeights1 = Session.GetRunner().AddInput(NodeWeights1Input, TensorWeights0).
Fetch(NodeWeights1Assign);
}
// Run prediction or training for one batch to claim all the memory needed
float[] InitDecoded;
float[] InitBottleneck;
float[] InitLoss, InitLossKL;
if (!ForTraining)
{
RandomNormal RandN = new RandomNormal(123);
Predict(Helper.ArrayOfFunction(i => RandN.NextSingle(0, 1), BottleneckWidth * BatchSize),
0,
out InitDecoded);
}
else
{
RandomNormal RandN = new RandomNormal();
Encode(Helper.ArrayOfFunction(i => RandN.NextSingle(0, 1), BatchSize * (int)BoxDimensions.ElementsFFT() * 2),
Helper.ArrayOfFunction(i => 1f, BatchSize * (int)BoxDimensions.ElementsFFT()),
0,
out InitBottleneck);
Train(Helper.ArrayOfFunction(i => RandN.NextSingle(0, 1), BatchSize * (int)BoxDimensions.ElementsFFT() * 2),
Helper.ArrayOfFunction(i => RandN.NextSingle(0, 1), BatchSize * (int)BoxDimensions.ElementsFFT() * 2),
Helper.ArrayOfFunction(i => 1f, BatchSize * (int)BoxDimensions.ElementsFFT()),
Helper.ArrayOfFunction(i => 1f, BatchSize * (int)BoxDimensions.ElementsFFT()),
0.5f,
1e-10f,
1e-5f,
0,
out InitDecoded,
out InitBottleneck,
out InitLoss,
out InitLossKL);
}
}
public CubeNet(string modelDir, int deviceID = 0, int nThreads = 1, int batchSize = 1, int nClasses = 2, bool forTraining = false)
{
lock (TFHelper.DeviceSync[deviceID])
{
DeviceID = deviceID;
ForTraining = forTraining;
ModelDir = modelDir;
MaxThreads = nThreads;
BatchSize = batchSize;
NClasses = nClasses;
TFSessionOptions SessionOptions = TFHelper.CreateOptions();
TFSession Dummy = new TFSession(new TFGraph(), SessionOptions);
Session = TFHelper.FromSavedModel(SessionOptions, null, ModelDir, new[] { forTraining ? "train" : "serve" }, new TFGraph(), $"/device:GPU:{deviceID}");
Graph = Session.Graph;
if (forTraining)
{
NodeInputMicTile = Graph["images"][0];
NodeInputLabels = Graph["image_classes"][0];
NodeInputWeights = Graph["image_weights"][0];
NodeLearningRate = Graph["training_learning_rate"][0];
NodeOpTrain = Graph["train_momentum"][0];
NodeOutputLoss = Graph["cross_entropy"][0];
}
else
{
NodeInputMicTilePredict = Graph["images_predict"][0];
}
NodeOutputArgMax = Graph["argmax_tensor"][0];
NodeOutputSoftMax = Graph["softmax_tensor"][0];
if (forTraining)
{
TensorMicTile = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, BoxDimensionsTrain.X, BoxDimensionsTrain.Y, BoxDimensionsTrain.Z, 1),
new float[BatchSize * BoxDimensionsTrain.Elements()],
0,
BatchSize * (int)BoxDimensionsTrain.Elements()),
nThreads);
TensorTrainingLabels = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, BoxDimensionsTrain.X, BoxDimensionsTrain.Y, BoxDimensionsTrain.Z, NClasses),
new float[BatchSize * BoxDimensionsTrain.Elements() * NClasses],
0,
BatchSize * (int)BoxDimensionsTrain.Elements() * NClasses),
nThreads);
TensorTrainingWeights = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, BoxDimensionsTrain.X, BoxDimensionsTrain.Y, BoxDimensionsTrain.Z, 1),
new float[BatchSize * BoxDimensionsTrain.Elements()],
0,
BatchSize * (int)BoxDimensionsTrain.Elements()),
nThreads);
TensorLearningRate = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(1),
new float[1],
0,
1),
nThreads);
}
else
{
TensorMicTilePredict = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, BoxDimensionsPredict.X, BoxDimensionsPredict.Y, BoxDimensionsPredict.Z, 1),
new float[BatchSize * BoxDimensionsPredict.Elements()],
0,
BatchSize * (int)BoxDimensionsPredict.Elements()),
nThreads);
}
if (forTraining)
{
ResultArgMax = Helper.ArrayOfFunction(i => new long[BatchSize * (int)BoxDimensionsTrain.Elements()], nThreads);
ResultSoftMax = Helper.ArrayOfFunction(i => new float[BatchSize * (int)BoxDimensionsTrain.Elements() * NClasses], nThreads);
ResultLoss = Helper.ArrayOfFunction(i => new float[BatchSize], nThreads);
}
else
{
ResultArgMax = Helper.ArrayOfFunction(i => new long[BatchSize * (int)BoxDimensionsPredict.Elements()], nThreads);
ResultSoftMax = Helper.ArrayOfFunction(i => new float[BatchSize * (int)BoxDimensionsPredict.Elements() * NClasses], nThreads);
}
if (!ForTraining)
{
RunnerPrediction = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeInputMicTilePredict, TensorMicTilePredict[i]).
Fetch(NodeOutputArgMax, NodeOutputSoftMax),
nThreads);
}
if (ForTraining)
{
RunnerTraining = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeInputMicTile, TensorMicTile[i]).
AddInput(NodeInputLabels, TensorTrainingLabels[i]).
AddInput(NodeInputWeights, TensorTrainingWeights[i]).
AddInput(NodeLearningRate, TensorLearningRate[i]).
Fetch(NodeOpTrain, NodeOutputArgMax, NodeOutputSoftMax, NodeOutputLoss),
nThreads);
}
}
// Run prediction or training for one batch to claim all the memory needed
long[] InitArgMax;
float[] InitProb;
if (!ForTraining)
{
Predict(new float[BoxDimensionsPredict.Elements() * BatchSize],
0,
out InitArgMax,
out InitProb);
}
if (ForTraining)
{
RandomNormal RandN = new RandomNormal();
Train(Helper.ArrayOfFunction(i => RandN.NextSingle(0, 1), BatchSize * (int)BoxDimensionsTrain.Elements()),
Helper.ArrayOfConstant(0.0f, BatchSize * (int)BoxDimensionsTrain.Elements() * NClasses),
Helper.ArrayOfConstant(0.0f, BatchSize * (int)BoxDimensionsTrain.Elements()),
1e-6f,
0,
out InitArgMax,
out InitProb);
}
}
public NoiseNet3D(string modelDir, int3 boxDimensions, int nThreads = 1, int batchSize = 8, bool forTraining = true, int deviceID = 0)
{
lock (TFHelper.DeviceSync[deviceID])
{
DeviceID = deviceID;
BoxDimensions = boxDimensions;
ForTraining = forTraining;
ModelDir = modelDir;
MaxThreads = nThreads;
BatchSize = batchSize;
TFSessionOptions SessionOptions = TFHelper.CreateOptions();
TFSession Dummy = new TFSession(new TFGraph(), SessionOptions);
Session = TFHelper.FromSavedModel(SessionOptions, null, ModelDir, new[] { forTraining ? "train" : "serve" }, new TFGraph(), $"/device:GPU:{deviceID}");
Graph = Session.Graph;
NodeInputSource = Graph["volume_source"][0];
if (forTraining)
{
NodeInputTarget = Graph["volume_target"][0];
NodeLearningRate = Graph["training_learning_rate"][0];
NodeOpTrain = Graph["train_momentum"][0];
NodeOutputLoss = Graph["l2_loss"][0];
}
NodeOutputPredicted = Graph["volume_predict"][0];
TensorSource = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, BoxDimensions.X, BoxDimensions.Y, boxDimensions.Z, 1),
new float[BatchSize * BoxDimensions.Elements()],
0,
BatchSize * (int)BoxDimensions.Elements()),
nThreads);
if (ForTraining)
{
TensorTarget = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, BoxDimensions.X, BoxDimensions.Y, boxDimensions.Z, 1),
new float[BatchSize * BoxDimensions.Elements()],
0,
BatchSize * (int)BoxDimensions.Elements()),
nThreads);
TensorLearningRate = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(1),
new float[1],
0,
1),
nThreads);
}
ResultPredicted = Helper.ArrayOfFunction(i => new float[BatchSize * BoxDimensions.Elements()], nThreads);
ResultLoss = Helper.ArrayOfFunction(i => new float[1], nThreads);
//if (!ForTraining)
RunnerPrediction = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeInputSource, TensorSource[i]).
Fetch(NodeOutputPredicted),
nThreads);
if (ForTraining)
{
RunnerTraining = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeInputSource, TensorSource[i]).
AddInput(NodeInputTarget, TensorTarget[i]).
AddInput(NodeLearningRate, TensorLearningRate[i]).
Fetch(NodeOutputPredicted, NodeOutputLoss, NodeOpTrain),
nThreads);
}
}
// Run prediction or training for one batch to claim all the memory needed
float[] InitDecoded;
float[] InitLoss;
//if (!ForTraining)
{
Predict(new float[BoxDimensions.Elements() * BatchSize],
0,
out InitDecoded);
}
if (ForTraining)
{
RandomNormal RandN = new RandomNormal();
Train(Helper.ArrayOfFunction(i => RandN.NextSingle(0, 1), BatchSize * (int)BoxDimensions.Elements()),
Helper.ArrayOfFunction(i => RandN.NextSingle(0, 1), BatchSize * (int)BoxDimensions.Elements()),
1e-10f,
0,
out InitDecoded,
out InitLoss);
}
}
public BoxNet(string modelDir, int gpuID = 0, int nThreads = 1, int batchSize = 128, bool forTraining = false)
{
ForTraining = forTraining;
BatchSize = batchSize;
ModelDir = modelDir;
MaxThreads = nThreads;
TFSessionOptions SessionOptions = TFHelper.CreateOptions();
TFSession Dummy = new TFSession(new TFGraph(), SessionOptions);
Session = TFHelper.FromSavedModel(SessionOptions, null, ModelDir, new[] { forTraining ? "train" : "serve" }, new TFGraph(), $"/device:GPU:{gpuID}");
Graph = Session.Graph;
NodeInputMicTile = Graph["mic_tiles"][0];
if (forTraining)
{
NodeInputLabels = Graph["training_labels"][0];
NodeLearningRate = Graph["training_learning_rate"][0];
NodeOpTrain = Graph["train_momentum"][0];
}
NodeOutputArgMax = Graph["ArgMax"][0];
NodeOutputSoftMax = Graph["softmax_tensor"][0];
TensorMicTile = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, 1, BoxDimensions.Y, BoxDimensions.X),
new float[BatchSize * BoxDimensions.Elements()],
0,
BatchSize * (int)BoxDimensions.Elements()),
nThreads);
TensorTrainingLabels = Helper.ArrayOfFunction(i => TFTensor.FromBuffer(new TFShape(BatchSize, 2),
new float[BatchSize * 2],
0,
BatchSize * 2),
nThreads);
TensorLearningRate = Helper.ArrayOfFunction(i => new TFTensor(0.0f),
nThreads);
ResultArgMax = Helper.ArrayOfFunction(i => new long[BatchSize], nThreads);
ResultSoftMax = Helper.ArrayOfFunction(i => new float[BatchSize * 2], nThreads);
if (!ForTraining)
{
RunnerPrediction = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeInputMicTile, TensorMicTile[i]).
Fetch(NodeOutputArgMax, NodeOutputSoftMax),
nThreads);
}
else
{
RunnerTraining = Helper.ArrayOfFunction(i => Session.GetRunner().
AddInput(NodeInputMicTile, TensorMicTile[i]).
AddInput(NodeInputLabels, TensorTrainingLabels[i]).
AddInput(NodeLearningRate, TensorLearningRate[i]).
Fetch(NodeOutputArgMax, NodeOutputSoftMax, NodeOpTrain),
nThreads);
}
// Run prediction or training for one batch to claim all the memory needed
long[] InitArgMax;
float[] InitProb;
if (!ForTraining)
{
Predict(new float[BoxDimensions.Elements() * BatchSize],
0,
out InitArgMax,
out InitProb);
}
else
{
RandomNormal RandN = new RandomNormal();
Train(Helper.ArrayOfFunction(i => RandN.NextSingle(0, 1), BatchSize * (int)BoxDimensions.Elements()),
Helper.Combine(Helper.ArrayOfFunction(i => new[] { 1.0f, 0.0f }, 128)),
1e-6f,
0,
out InitArgMax,
out InitProb);
}
}
